This research was to examine the impacts of long-term rice-crayfish coculture systems(RS) on soil nutrients, carbon pools, and rice yields in paddy fields , aiming to establish a scientific basis for the sustainable development of RS in the northern region of Zhejiang. The results showed that the change from rice monoculture (CK) to RS resulted in a 24.99% increase in the 5-year average of soil ammonium nitrogen(AN), while the soil nitrate nitrogen(NN), available potassium(AK), and available phosphorus content(AP) decreased by 28.02%, 16.05%, and 28.76% respectively. Moreover, the total organic carbon(TOC), easily oxidizable organic carbon(EOC), dissolved organic carbon(DOC), and microbial biomass carbon(MBC) decreased by 2.45%, 8.82%, 35.31%, and 65.84% respectively. Correlation analysis showed that there was a significant positive correlation between NN, EOC and MBC in the RS. In terms of rice yield, 5-year average of rice yield in RS has decreased by 8.4% compared to CK.The average yield of planting early-maturing varieties of rice was reduced by 13.16%, while the average yield of planting late-maturing varieties of rice was reduced by 6.00%. In conclusion, enhancing water and nutrient management and cultivating late maturing rice varieties are essential strategies for achieving the sustainable development of RS in northern Zhejiang.

A recent study showed the potential of DA Perten 7200 NIR Spectrometer in detecting chlorpyrifos-methyl pesticide residue in rough, brown, and milled rice. However, this instrument is still lab-based and generally suited for point-of-sale testing in many countries. To provide a field deployable version of this technique, an existing light emitting diode (LED)-based instrument which provide discrete NIR wavelength illumination and reflectance spectra over the range of 850-1550 nm was tested. Spectra were collected from rough, brown, and milled rice at different pesticide concentrations and analysed for quantitative and qualitative measurement using partial least squares regression (PLS) and discriminant analysis (DA). Simulations for LED-based instruments were also evaluated using segments of spectra from the DA7200 to represent LED illumination. For the simulation of the existing LED-based instrument (LEDPrototype1) using their wavelengths range yielded 70.4% to 100% correct classification. Simulation of a second LED instrument, LEDPrototype2, with spectral segments selected based on significant wavelength regions from PLS regressions coefficients obtained from the DA7200 showed improved performance with R2 of 0.59 to 0.82 and correct classifications of 71.3 to 100%. An actual LED based instrument with this capability could provide a quick screening tool to determine if MRLs are exceeded.

Brown rice is a familiar whole grain in Japan. We examined national trends in brown rice consumption among Japanese adults aged ≥ 20 years old, using individual-level data from the National Health and Nutrition Surveys conducted between 2012 and 2019. We employed multivariable logistic regression to identify factors associated with brown rice consumption. The 95th percentile of daily brown rice intake remained at 0.0 grams throughout the study period. The percentage of brown rice consumers increased from 1.8% (95% confidence interval: 1.6–2.1) in 2012 to 2.3% (95% confidence interval: 1.8–3.0) in 2019. Compared with individuals who consumed only white rice, brown rice consumers had significantly higher mean intake levels of macronutrients, legumes, vegetables, fruits, and nuts. Brown rice consumption was positively associated with certain sociodemographic characteristics (being female, older age, residing in a major city, living without very young children, and having higher education levels) and health behaviors (lower body mass index, engaging in regular exercise, and being a former or never smoker). Despite its potential nutritional benefits in balanced diets, only a small fraction of adults in Japan consume brown rice, indicating a need for further promotion, particularly among individuals with characteristics associated with brown rice consumption.

The study aimed to compare the cardiovascular benefits of Omega-3 Polyunsaturated Fatty Acids from fish oil versus krill oil in different forms and dosages. Following PRISMA 2015, data analysis with R software, and databases were searched from 1993 to 2023, (ID PROSPERO: CRD42024502338). Sixty high-quality studies were analyzed, covering various forms of fish oil and krill oil at different dosages (fish oil (ethyl ester-EE, triglyceride-TG, re-esterified triglycerides-rTG, capsules, emulsion-EM); krill oil (phospholipid/free fatty acid-PL/FFA, high concentration phospholipid-HPL, low concentration phospholipid-LPL)). Meta-analysis revealed that krill oil with phospholipid/free fatty acid at doses 2000-2900 mg effectively lowered triglycerides, while fish oil in ethyl ester form at doses above 3000 mg significantly reduced total cholesterol. Fish oil in triglyceride form at doses 2000-2900 mg and other variations positively impacted HDL-C levels. Fish oil in EE form above 3000 mg and triglyceride form below 2000 mg notably reduced LDL-C. However, there was limited effectiveness observed for other interventions. Analysis for LDL-C exhibited publication bias, while other parameters demonstrated reliability (Egger's test). In conclusion, krill oil - PL/FFA, Fish oil - EE above 3000 mg, Fish oil - TG 2000-3000 mg, and fish oil - rTAG 300-1900 mg effectively in reducing heart risk patients.

Fish species diversity in the Black Sea is affected by the introduction of non-indigenous species, particularly through the process of "mediterranization" and the expansion of the ranges of invasive species. In August 2023, we documented an increasing quantity of four non- indigenous sparid species, namely Spicara smaris (Linnaeus, 1758), Diplodus puntazzo (Walbaum, 1792), Diplodus annularis (Linnaeus, 1758), and Lithognathus mormyrus (Linnaeus, 1758), in the coastal Bulgarian waters of the Black Sea, based on citizen science data obtained from small-scale fisheries and pelagic trawlers. However, in the autumn of 2022, only small quantities of S. smaris were collected periodically, indicating significant fluctuations in abundance and occasional presence. The other three species were recorded in August 2023 in the central coastal region near the Fandakliyska River, at depths of less than 10 m. This brief communication presents information on the identified species, including sampling locations and biological data, and examines the sparid species records in the Black Sea. In addition, we reviewed recent studies on the distribution and biological traits of the identified species in their natural Mediterranean habitats.

In the context of the Lubumbashi Coal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and fragmentation observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges.

In areas like commercial and policy decisions—it's important to know how different treatments affect things and how well they work. However, treatments affect things on average—but now—with fancy computer methods and big sets of data, interest lies in figuring out how treatments might work differently for different people. For instance, let's say instead of just looking at what works for most people, researchers want to know which treatment is best for each person. This would help doctors to decide the right treatment for each patient. This idea isn't just for healthcare—it can help in other areas too, like deciding how to govern employees or making government policies. There are some ways people have been trying to do this already, but they struggle when there are lots of different factors to consider. New computer methods, though, are good at handling lots of factors, but they need a ton of data to work well. Therefore, this study looks at three computer methods that are—Virtual Twin Random Forest (VTRF), Causal Forest (CF), and Causal Net (CN) using real-world data about encouraging people to do manual labour. One problem this study is trying to solve is something called the "winner's curse", where a treatment might seem better than it really is because of how it's predicted by the computer. We are also testing some ideas to see if we can fix this problem and make the predictions more accurate.

Dengue, a perilous fever-type illness transmitted by mosquitoes, remains a significant global health concern. The incidence of dengue outbreaks is primarily influenced by climate factors, contributing to fluctuating dengue cases. Therefore, this study aims to develop predictive models for dengue outbreaks employing Machine Learning (ML) techniques. Four distinct ML models, namely K-Nearest Neighbor (KNN), Random Forest (RF), Gradient Boosting Regressor (GBR), and Support Vector Regressor (SVR), were employed in this study. Prior to model analysis, thorough data preprocessing was conducted. In some instances, certain attributes exhibiting sparse correlations with dengue occurrence were eliminated based on correlation analysis. Upon finalizing the models, performance evaluation was executed through comparison based on Mean Absolute Error (MAE) metrics. The findings of this comparative analysis revealed that the KNN model exhibited significantly superior performance compared to the other three models. This outcome underscores the potential of KNN in enhancing the accuracy of dengue outbreak predictions.

In the Vietnamese Mekong delta, soil quality and crop yield are steadily declining under rice monocultures with three crops per year. The objective of this study was to evaluate the medium-term effects of rotating rice with upland crops and adding organic amendments on rice yield and relate it to soil quality. A field trial with split-plot design including two factors and three replicates was carried out from 2017 to 2020, over the course of nine consecutive cropping seasons. Crop rotations and organic amendments were applied as main-plot and subplot factors, respectively. The rotations were: 1) rice–rice–rice (R–R–R), 2) soybean–rice–rice (So–R–R), and 3) sesame–rice–rice (Se–R–R), while organic amendment treatments included: i) no amendment (NO-AM); ii) compost of rice straw and cow manure (RS+CM), and iii) sugarcane compost (SGC); the composts were applied at a rate of 2.0 t ha-1. The rotation cycle started with the so-called spring–summer (SS), followed by summer–autumn (SA) and ended with the winter–spring (WS) season. Rice yield significantly (P < 0.05) increased by organic amendments after nine growing seasons (2019–2020 WS), with an increment of 5.1% for RS+CM (7.07 ton/ha) and 6.1% for SGC (7.14 ton/ha). Contrary to our expectation, rotations with upland crops did not significantly increase rice yield. Rice yield significantly and positively correlated with an integrated soil quality index SQI (r = 0.86) for the topsoil (0–15 cm), but not for the subsoil (15–30 cm). The increased availability of soil nutrients (Si and marginally also P) and improved soil physical properties probably induced by organic amendments, along with other soil properties under study, cumulatively attributed to enhanced rice yield. Use of a SQI involving several soil quality indicators enables to overall quantify the importance of soil fertility for rice yield versus other factors and it provides an effective means of quantifying the integrated effect of improved management. Moreover, integrating a wide range of soil quality indicators in a SQI ensures its applicability across diverse settings, including different crop rotations and various soil types.

Soil secondary salinization has seriously affected the greenhouse vegetable production in China. To improve the secondary salinization greenhouse soil and enhance the soil physical-chemical properties in an eco-environmental way, different organic amendments (straw, straw biochar, Trichoderma bio-organic manure, commercial organic manure) were applied using pot experiment for 60 day to comprehensively screen the optimal remediation method. In this study, soil nutrient condition and salt movement were assessed, and predictive models using multi-linear regression (MLR) and Random Forest (RF) were developed to estimate soil salinization parameters. The findings indicated a significant decrease in soil salt content following the application of organic materials compared to the control treatment. Specifically, the addition of straw at a rate of 250g/kg resulted in a 59.38% reduction in soil salt levels after 60 days (P < 0.05). Furthermore, the main salt ions exhibited dynamic changes over the course of the experiment, with reductions observed in Na+, Ca2+, Cl-, and NO3- content under the 250 g/kg straw treatment by 83.63%, 74.67%, 63.26%, 59.07%, respectively, compared to CK (P < 0.05). In addition, the SO42- content under 125 g/kg commercial organic manure amendment reduced by 48.94%, respectively (P < 0.05). Contrary to expectations, the addition of organic materials significantly increased the levels of total nutrients (N/P/K) and available potassium (AK) and phosphorus (AP) in the soil. Specifically, the addition of straw at a rate of 250 g/kg resulted in increases of 18.02%-87.38% in total potassium (TK), 66.67%-200% in total phosphorus (TP), and 142.87%-367.8% in AK after a period of sixty days (P < 0.05). Ultimately, the treatment involving the addition of 250 g/kg of straw demonstrated the most pronounced impact on the physical and chemical properties of the soil. The random forest method shows promise in accurately predicting soil salt and soil sodium adsorption ratio (SAR)indicators, thereby providing a valuable tool for estimating soil properties.

The silver fir (Abies alba Mill.) is among the most valuable conifers in Europe for ecological and economic reasons. In the course of history, primarily in the 20th century, its share in stands has been declining due to ill-suited management practices, especially clear-cut management, air pollution, and wildlife-induced damage. Based on recent knowledge of fir ecology and population dynamics, small-scale shelterwood and selection management have been introduced in fir stands, which have also stabilized them. Fir is an essential species for maintaining high stability and biodiversity, especially on planosols and in waterlogged habitats. Due to its shade tolerance and environmental flexibility, particularly at higher altitudes, it can coexist very well with many tree species in mixtures, which can increase the productive potential of stands. It can form stands of heterogeneous structure, ranging from single to multi-layer to selection. For its successful natural regeneration, it is essential to reduce cloven-hoofed game and thus prevent bud browsing damage. On the other hand, fir is a species relatively resistant to bark stripping and the spread of secondary rot compared to Norway spruce (Picea abies [L.] Karst.). During global climate change, fir is expected to shift to higher elevations with sufficient precipitation, while in the southern part of its natural range or at lower elevations, outside water-influenced habitats, it is likely to decline. This paper essentially reviews the description and distribution of the species, its ecological requirements, threats and diseases, habitat and stand conditions, and close-to-nature forest management practices with emphasis on ongoing climate change.

We investigated the response of leaf photosynthetic capacity to climate warming and its variation among provenances of Larix gmelinii (Dahurian larch). Seedlings of 11 L. gmelinii provenances were transplanted into two common gardens with different climate conditions (control and warming climate). We measured the leaf photosynthetic capacity and explored its influencing factors. The warming treatment significantly increased the maximum net photosynthetic rate (Pmax-a), photosynthetic nitrogen use efficiency (PNUE), maximum carboxylation rate (Vcmax), maximum electron transport rate (Jmax), triose phosphate utilization rate (TPU), mesophyll conductance (gm), leaf nitrogen content (Narea), and chlorophyll content (Chlm). The Pmax-a was significantly positively associated with Vcmax, Jmax, TPU, gm, and Narea, and the slope of the correlations between Pmax-a and Vcmax, Jmax, and TPU was steeper in the warming treatment. The responses of Pmax-a, PNUE, Vcmax, Jmax, TPU, Narea, and Chlm to warming differed among provenances. The effects of warming on Pmax-a, Vcmax, Jmax, and TPU increased and then decreased as the aridity index of the original site increased. Overall, the warming treatment improved the photosynthetic capacity of L. gmelinii, but the extent of the improvement varied among provenances. These findings provide insights into the mechanisms underlying the responses of L. gmelinii to climate warming.

Fraudulent transactions pose a significant threat to financial institutions and e-commerce platforms.Machine learning models, trained on historical labeled data (fraudulent vs. legitimate transactions), are often employed to identify and prevent fraud. However, real-world datasets frequently exhibit class imbalance, where fraudulent transactions (minority class) are significantly outnumbered by legitimate transactions (majority class). Machine learning models may perform poorly as a result of this imbalance, underestimating fraud and favouring the majority class. This paper proposes a novel approach to address class imbalance and improve fraud detection accuracy. We explore the implementation of FROST (Feature space RObust Synthetic saTuration) oversampling, a technique specifically designed to generate synthetic samples for the minority class. The FROST function leverages the k-nearest neighbors (KNN) algorithm and a user-defined amplification factor (m) to create synthetic data points that closely resemble existing minority class instances. We integrate the FROST-enhanced oversampling technique into the machine learning pipeline for fraud detection. The paper evaluates the effectiveness of this approach compared to traditional oversampling methods and analyzes its impact on classification accuracy metrics.

In order to address the issue of nutrient deficiency in coconut coir-based nursery substrates, three materials with nutrient supplementation effects, namely pig manure, composted rice husks, and agricultural humic acid, were added in equal volumes to the coir-based substrate. The newly formulated substrates were then tested for their seedling growth effects on cucumber and rice. Results indicate that the substrate formulation consisting of "10% pig manure + 30% peat + 50% coconut coir + 10% vermiculite" was capable of satisfying the growth requirements of both cucumber and rice seedlings, with the highest acceptance among farmers.

The substitution of chemical fertilizers by organic manure plays a critical role in sustainable crop production. Nevertheless, organic amendments promote the global warming potential (GWP) in rice paddies due to increased methane (CH4) emissions. Increasing evidence shows that lime application can reduce CH4 emissions from acidic paddy soils, while it is still not clear whether liming can reduce the GWP in rice fields under organic manure substitution. A two-year field experiment was conducted to investigate the impacts of pig manure substitution and lime application on grain yield, CH4 and nitrous oxide (N2O) emissions in a subtropical double-cropped rice field in China. The experiment consisted of three treatments: chemical fertilization (CF, 100% N from urea), 50% of chemical N substituted by pig manure (1/2N + M), and the 1/2N + M treatment plus lime amendment (1/2N + M + L). On average, 1/2N + M reduced rice yield by 5.65% compared to CF, while lime application had no effect on rice yield. Mean cumulative CH4 emissions were 218.8% higher in 1/2N + M than in CF, whereas 1/2N + M + L reduced CH4 emissions by 36.6% compared to 1/2N + M. Neither pig manure substitution nor lime application affected N2O emissions. Consequently, 1/2N + M increased the GWP and greenhouse gas intensity (GHGI) by 214.6% and 228.3%, respectively, compared to CF. In contrast, 1/2N + M + L reduced the GWP and GHGI by 36.4% and 36.5% compared to 1/2N + M. Lime application can mitigate CH4 emissions and GWP induced by pig manure amendment in double-cropped rice fields.

Cancer is a disease caused by the abnormal growth of cells in different parts of body is one of the top causes of death globally. Microarray gene expression data plays a critical role in the identification and classification of cancer tissues. Due to recent advancements in Machine Learning (ML) techniques, researchers are analyzing gene expression data using a variety of such techniques to model the progression rate &amp; treatment of cancer patients with great effect. But high dimensionality alongside the presence of highly correlated columns in gene expression datasets leads to computational difficulties. This paper aims to propose the use of ML classification techniques- Linear Discriminant Analysis (LDA) &amp; Random Forest (RF) for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. Six distinct categorization performance measures were used to evaluate these approaches; RF method provided us with higher accuracy than LDA method. The method and results of this article should be helpful to researchers who are dealing with many genes in microarray data.

Within the context of sustainable material development for the construction sector, this study explores the viability of panels composed of recycled newspaper-derived cellulose (9%), rice husk (14%), borax (15%), and polyvinyl acetate-based glue (62%). The methods employed involve water absorption and density tests, following ASTM C-1763 and C-303 standards. The fire resistance test conforming to the European standard UNE 23-725-90, and a mold growth test using AOAC 2002.11. The composite is prone to absorbing a significant amount of water when exposed to humid conditions. However, the incorporation of rice husk and borax can provide benefits such as improved moisture resistance compared to other cellulitic insulators, which may be advantageous in specific climates. Also, the developed composite has an intermediate density compared to other polymeric materials. This characteristic presents advantages in applications that demand a balance between strength and lightness. During the fire resistance tests, the samples with borax presented less fire propagation, and superficial damage compared with the specimens without borax. This is important, considering the fireproofing property, required in building materials. The presence of borax in the composite has an inhibitory effect on the growth of fungi and yeasts. These findings are consistent with previous research. Nevertheless, further research and testing are imperative to assess its viability and performance for commercial applications.

Amylose content (AC) stands as a pivotal determinant of rice grain quality, primarily governed by the Waxy gene (Wx), with its allelic diversity significantly influencing AC variation. In the current culinary landscape, soft rice varieties, known for their low AC and superior palatability, are particularly sought after by consumers. Notably, many soft japonica rice varieties cultivated in southern China, including the Nangeng series, are characterized by the presence of the Wxmp allele, derived from the ancestral Wxmq allele. While both alleles are associated with reduced AC, there remains a paucity of detailed understanding regarding the interplay between specific functional single nucleotide polymorphisms (SNPs) within these alleles and the overarching rice grain quality. Employing site-directed mutagenesis, we engineered three distinct transgenic rice lines, each harboring either the Wxmp, Wxmq, or Wxb-5c alleles, against the background of the glutinous rice cultivar Nip(wx). This suite of transgenic rice showcased a gradation ingrain transparency inversely correlated to AC, delineating a sequence from Wxmq, signifying the lowest, through Wxmp, to Wxb-5c, indicating the highest. Consequently, this gradation in AC precipitated variations in other rice grain physicochemical attributes, such as the taste value of cooked rice, gel consistency (GC), and starch pasting properties. Furthermore, analyses of gene expression and enzyme activity revealed that the functional SNPs, Ex4-53G/A and Ex5-53T/C, lead to a decline in the GBSSI enzyme activity without affecting expression levels. These findings underscore the potential of harnessing these SNPs in breeding programs aimed at developing soft rice varieties endowed with enhanced culinary qualities.

The domestic ducks (Anas platyrhynchos domesticus) are resistant to most of the highly pathogenic avian influenza virus (HPAIV) infections. In this study, we characterized the lung proteome and phosphoproteome of ducks infected with HPAI H5N1 virus (A/duck/India/02CA10/2011/Agartala) at 12 hr, 48 hr and 5 days post infection condition. Total 2082 proteins were differentially expressed and 320 phosphorylation sites mapping to 199 phosphopeptides corresponding to 129 proteins were identified. Functional annotation of proteome data analysis reveals activation of RIG-I-like receptor and Jak-STAT signaling pathways, that lead to induction of interferon stimulated genes (ISGs) expression. The pathway analysis of phosphoproteome datasets also confirms the activation of RIG I, Jak-STAT signaling, NF-kappa B signaling and MAPK signaling pathways in the lung tissues. The induction of ISGs proteins (STAT1, STAT3, STAT5B, STAT6, IFIT5 and PKR) establishes protective antiviral immune response in duck lung tissue. Further, the protein-protein interaction network analysis identified proteins like AKT1, STAT3, JAK2, RAC1, STAT1, PTPN11, RPS27A, NFKB1 and MAPK1 as main hub proteins that might play important roles in disease progression in ducks. Together, functional annotation of proteome and phosphoproteome datasets reveals the molecular basis of the disease progression and disease resistance mechanism in ducks infected with HPAI H5N1 virus.

This paper explores the role of agroforestry in mitigating climate change by sequestering atmospheric carbon in the tropics and subtropics, specifically in the Madhupur Sal forest of Bangladesh. Agroforestry, combining trees with crops on agricultural lands, is recognized for its potential to act as carbon sinks and enhance productivity. The study assesses various agroforestry practices, including Acacia-Pineapple-Turmeric-Papaya, Acacia-Pineapple-Zinger-Banana, and Sal-Pineapple-Aroid combinations. The research reveals improved farm productivity in these agroforestry systems, with different tree species sequestering varying amounts of carbon. Acacia species, ranging from 12 to 25ft in height, sequestered an average of 23.35 lbs/year, while Sal species (Shorea robusta), with trees 45 to 61ft tall, sequestered 49.80 lbs/year on average. Factors such as tree height, diameter at breast height (DBH), number of leaves, and branches influence carbon sequestration. The paper suggests integrating the carbon trading system of the Clean Development Mechanism (CDM) with Carbon Sequestration (CS) potential to leverage agroforestry for greenhouse gas emission reduction in Bangladesh. By emphasizing the profitability of these practices alongside carbon sequestration, the study encourages the adoption of agroforestry as a sustainable and economically viable strategy for climate change mitigation.

This paper explores the capability of various machine learning algorithms, including Random Forests and advanced gradient boosting techniques such as XGBoost, LightGBM, and CatBoost, to predict customer churn in the telecommunications sector. For this analysis, a dataset available to the public was employed. The performance of these algorithms was assessed using recognized metrics, including Accuracy, Precision, Recall, F1-score, and the Receiver Operating Characteristic Area Under Curve (ROC AUC). These metrics were evaluated at different phases: subsequent to data preprocessing and feature selection; following the application of SMOTE and ADASYN sampling methods; and after conducting hyperparameter tuning on the data that had been adjusted by SMOTE and ADASYN. The outcomes underscore the notable efficiency of upsampling techniques such as SMOTE and ADASYN in addressing the imbalance inherent in customer churn prediction. Notably, the application of random grid search for hyperparameter optimization did not significantly alter the results, which remained comparatively unchanged. The algorithms' performance post- ADASYN application marginally surpassed that observed after SMOTE application. Remarkably, LightGBM achieved an exceptional F1-score of 89% and an ROC AUC of 95% subsequent to the ADASYN sampling technique. This underlines the effectiveness of advanced boosting algorithms and upsampling methods like SMOTE and ADASYN in navigating the complexities of imbalanced datasets and intricate feature interdependencies.

The development of analog rice, apart from being an effort to diversify food, also has the potential to be develop as functional food in fulfilling the nutrients needed by a community. Katuk leaf is known for its ability to accelerate breast milk production for lactating women which is inseparable from sterol as bioactive content. This study aimed to determine the best formulation of analog rice made from cassava flour, banana flour, Katuk leaf powder and soy lecithin that was sensory acceptable, in a shape resembling rice, and able to fulfill the nutritional needs of lactating women. Analog rice was produced using extruder machine before the physical and sensory properties analyses were carried out, followed by the chemical properties analysis. Formulation C (80% cassava flour, 20% banana flour, 3% Katuk leaf powder and 0.5% soy lecithin) was obtained as the best or most preferred formulation based on sensory analysis which the grain shaped oval round resembling original rice and had green-brownish color, fluffy texture, distinct aroma and taste derived from the raw material used. Therefore, this research is expected to support the development of analog rice for providing the main staple food for fulfilling the lactating women’s nutrition.

Understanding the optimal strategy for a real estate investment and how performance changes based on characteristics is crucial for optimising the achievable return. This is prominent in tourist areas such as Paphos where there is no clear distinction as to whether short or long-term approaches are optimal. The study aimed to develop a model for predicting the optimal rental strategy whilst assessing which model performed best and which property attributes impacted its return the greatest. Short-term data was collected from AirDNA and long-term data was manually collected from real estate agents websites. Furthermore, Random Forest, K-Nearest Neighbour and Multiple Linear Regression models were created to predict the highest and best use for each property. Model accuracy varied between data sets with the best performing model for short-term properties being Random Forest (R-Squared: 0.843), and Distance-Based Multiple Linear Regression for long-term approach (R-Squared: 0.843). The study demonstrated that accurate models could be created to predict the optimal rental strategy with the number of bedrooms being the main driver for rental income, followed by luxury finishes and the presence of a pool. It found that locational characteristics didn’t impact the returns significantly assuming that the property was located within a tourist area.

Quaking aspen (Populus tremuloides Michx.) is an important deciduous species in western U.S. forests. Existing maps of aspen distribution are based on Landsat imagery and often miss small stands (&lt;0.09 ha or 30 m²), which rapidly regrow when managed or following disturbance. In this study, we present methods for deriving a new regional map of aspen forests using one year of Sentinel-1 (S1) and Sentinel-2 (S2) imagery in Google Earth Engine. We assessed the annual phenology of aspen in the Southern Rockies and leveraged the frequent temporal resolution of S1 and S2 to create ecologically relevant seasonal composites. Additionally, we derived spectral indices and radar textural features targeting the canopy structure, moisture, and chlorophyll content. Using spatial block cross-validation and Random Forests, we assessed the accuracy of different scenarios and selected the best performing set of features for classification. Comparisons were then made with existing landcover products across the study region. The resulting map improves on existing landcover products in both accuracy (average F1-score = 93.1%) and detection of smaller forest patches. These methods enable accurate mapping at spatial and temporal scales relevant to forest management for one of the most widely distributed tree species in North America.

Environmental pollution is one of today's main ecological and social concerns. Among these pollutants are the heavy metals present in the burning of fossil fuels, mining, smelting of metallic minerals, fertilizers and pesticides. Billions of tons of heavy metals are emitted every year from industrial chimneys and sewers, contaminating the hydrosphere and polluting rivers, lakes and seas. In this context, freshwater fish are the most affected due to their proximity to the source of contamination and are therefore used as bioindicators in assessing environmental impact following exposure to metals. However, our understanding of the effects of heavy metals on detoxification organs - such as the liver and gills - in freshwater fish in situ is still limited. Therefore, we conducted a systematic review to determine the main changes and mechanisms involved in morphological modifications in the liver and gills of cichlid fish following exposure to heavy metals in situ. Based on a structured search, 36 original articles were selected from the PubMed and SCOPUS databases. The selection of studies and the identification of duplicates were identified by Rayyan, and the study selection process was presented in the PRISMA statement diagram. Bias analysis was carried out using the Software Review Mananger (RevMan) 5.3, from the Cochrane Collaboration. Thirty-six studies were selected which showed negative effects attributed to environmental contamination by heavy metals, ranging from bioaccumulation to morphophysiological alterations. Almost all the heavy metals analyzed were found in the liver and gill tissues, especially cadmium, copper, lead, zinc, iron and manganese. In view of the continuous morphophysiological changes identified in the liver and gill studies, it is possible to state that bioaccumulation analyses, combined with these configurations, are emerging as crucial tools for assessing and guiding environmental monitoring. This approach aims not only to preserve but also to strengthen the integrity of the aquatic ecosystem and the ichthyofauna.

The development of reliable predictive algorithm for house price as the housing market is a stand-out among the most involved regarding valuing the price and continues to fluctuate, is constantly a need for socio-economic advancement and welfare of citizen. In this paper, we develop machine learning algorithms for forecasting UK housing Price, and find an optimal algorithm that forecasts housing price accurately on the premises of the presence of many features or covariates. After applying correlation analysis to remove correlated variables in order to avoid multicollinearity, thereby increasing the statistical power, a novel method of using regression analysis to first of all understand and select statistically significant features for the various regions in England based on North South divide is adopted. These features are then used in the machine learning algorithm to further increase the statistical power of the algorithm, increase the level of accuracy for each of them and ultimately increase the predictive values for the algorithms. The model construction involves 3 stages: 1- correlation analysis to identify and remove correlated variables thereby avoiding multicollinearity and increasing the statistical power of the linear regression, 2 - using linear regression to determine variables that are statistically significant and 3 - building the machine learning algorithms based on the variables that are statistically significant from the linear regression. A comprehensive dataset of UK Paid housing Price from 2010 to 2019 was linked to a number of other datasets to generate a total 21 variables or features used for the models. Catboost, Gradient Boosting, Bagging, Random Forest, Extra Tree all achieved the excellent models performance result in all the regions considered. The comparison of the seven models showed that Extra Tree algorithm consistently achieved the best performance in term of level of accuracy in all the regions. K-Nearest Neighbours (KNN) is the only algorithm with less than 50% level of accuracy. Noticeably, the regions considered had varying or differing insignificant variables, implying that although many variables are common (statistically significant) to all the regions, there are regional differences and impact when modelling or predicting housing prices. This study validates the practicability of developing a machine learning methodology for the prediction of housing price. This research offers a reference for future house price prediction based on machine learning.

Human activities (e.g., exploitation, utilization, and conservation) exert substantial impact on forest ecological functions (FEF). The human influence on FEF significantly varies across developmental stages, which are attributed to the temporal and spatial in nature. This study evaluates some notable FEF indices (FEFI) in the 448 counties within the Yellow River basin (YRB) using panel data from the Seventh to Ninth National Forest Inventory. Data analysis involves employing the residual trends, geographical and temporal weighted regression. Results indicate that, firstly, the overall forest ecological function in the YRB is moderate to inferior, with superior FEF in counties endowed with more natural forest resources. Afforestation demonstrates a short-term improvement, but its effectiveness diminishes over time. Secondly, the positive spatial correlation is stronger among counties, characterized by both high-high and low-low agglomeration effects. Scattered planting, simple stand structure, and concentrated harvesting of forests at the identical stand ages hinder the formation of complex and large-scale FEF agglomerations. Thirdly, significant spatial-temporal differences exist in the impact of human activities on FEF. In the upper reaches of the basin, increased vegetation coverage through agricultural and forestry production benefited FEF, however, some over-exploitation of forests and grasslands is observed there. In the middle reaches, the economic development, expanding population and the greening activities help to improve FEF effectively, but excessive water using in agricultural production brought more difficulties to FEF improvement. In the lower reaches of the basin, improved wastelands and forest protection positively influence FEF. This study recommends that county governments should prioritize forest management with multi-species and multi-layered complex forests. Each county should define its position in regional development and ecological protection, considering the potential impacts on neighboring regions. This holistic approach promotes more effective integrated regional management.

Piscine lactococcosis is a disease caused by Lactococcus garvieae, L. petauri and L. formosensis. It is considered to be an emerging disease and poses a risk to global aquaculture production. Studies of the genetic diversity of Lactococcus spp. strains have epidemiological importance since they provide information on the genetic relationships between isolates and indicate geographic and temporal distribution. The aim of this study was to evaluate the genetic diversity of Brazilian Lactococcus spp. isolates obtained from native fish species using PCR-based DNA fingerprinting techniques. For this, 36 isolates were selected and submitted to gyrB sequencing, molecular serotyping, REP-, BOX-, and RAPD-PCR approaches. The discriminatory power and the congruence between the typing methods were calculated. After the correct taxonomic discrimination of the isolate, a total of 14, 5 and 17 isolates were reclassified as L. garvieae, L. formosensis and L. petauri, respectively. L. garvieae and L. petauri are related to serotype I, while L. formosensis strains were classified as serotype II. The typing methods tested are useful for evaluating the genetic diversity of isolates and a comprehensive indication of the diversity was found. Heterogeneous and homogeneous populations were observed for L. garvieae and L. petauri, respectively. RAPD-PCR demonstrated a greater discriminatory power for L. formosensis and L. petauri, while REP-PCR showed better results for L. garvieae strains, thus these methodologies are recommended for genotyping these bacterial species. In conclusion, Brazilian isolates of Lactococcus spp. obtained from native fish species constitute a genetically diverse population and the techniques used in this study allowed a better genetic characterization of the isolates.

Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

This research presents a comprehensive framework for efficiently generating forest fire datasets from Google Earth Engine data sources. The primary contribution of this work lies in providing a methodology to swiftly extract forest fire factors without the need for permissions or access to private datasets, rendering the dataset openly accessible and shared without barriers. Furthermore, given that the remote sensing data used is a global dataset, it can be applied in any region without restrictions. In this study, Peninsular Malaysia is chosen as a case study to demonstrate the framework's effectiveness. The generated dataset includes essential variables including the climate and environment, landcover, topography, and anthropogenic factors facilitating the analysis of fire occurrences. The methodology empowers data scientists, enabling them to leverage their analytical skills on the extracted dataset without requiring specialized remote sensing knowledge. Additionally, this study also showcases the adoption of large language models, specifically GPT-4 with the Noteable plugin, as a tool for conducting preliminary analyses on the generated dataset. Sample analyses reveal that several key features, including the KBDI, LST, PDSI, climate water deficit, and precipitation, significantly impact forest fire occurrences in Peninsular Malaysia. Despite the successful application of the GPT-4 with Noteable plugin, certain limitations and challenges are identified, highlighting the necessity for further validation of the tool's applicability and limitations. This study encourages future research to (1) adopt the proposed framework in other regions, (2) explore more detailed analyses encompassing all variables, and (3) leverage machine learning for advanced forecasting.

Rice blast and brown planthopper (BPH) are two of the most destructive diseases and pests in rice (Oryza sativa L.) production in China. The development of multi-resistant varieties is widely recognized as the most efficient and environmentally friendly approach to controlling crop diseases and pests. Functional molecular markers (FMMs) have been developed from functional variants in the genic region associated with trait variation, greatly enhancing the efficiency of identifying and pyramiding valuable genes in crop breeding. In this study, two FMMs and a multiplex PCR amplification system were developed for two major broad-spectrum BPH resistance genes, Bph6 and Bph9. By assistance of FMMs in multi-resistant rice restorer line development pipeline, two lines (Huahui7713 and Huahui3006) with blast and BPH resistance were developed by pyramiding three resistance genes Pigm, Bph6 and Bph9. Huahui7713 and Huahui3006 as male lines, two new hybrid rice varieties (WeiLiangYou7713 and Xuanliangyou3006) were certificated and commercial released in China. WeiLiangYou7713 and Xuanliangyou3006 showed improved blast and BPH resistance, and no penalty on yield and grain-quality. Annual promotion area of WeiLiangYou7713 has exceeded 53.3 thousand hectares, indicating its widespread adoption and success in the market. Huahui7713 and Huahui3006 could have great potential for utilization in blast and BPH resistance rice breeding.

The fish supply chain (FSC) industry faces a significant challenge in terms of maintaining fish quality and authenticity throughout the chain. The main goal of modern FSC systems is tracking and tracing the fish product at each stage, from harvester to end customer, as well as assessing its quality and authenticity. Integrating FSC with new technologies, such as Blockchain (BC), Big Data, Artificial intelligence (AI), and Internet of Things (IoT), enhances fish product traceability, authenticity, visibility, and security. We propose a BC-based FSC framework that follows a layered architecture consisting of a SC layer, an IoT layer, and a BC layer. We integrate a new AI-based hand-held device, a Quality, Adulteration & Traceability (QAT) device, that can identify fish species and assess fish quality. We leverage the key features of BC and smart contracts, deployed over the Ethereum platform for the proposed framework integrated with QAT technology. Extensive validation of smart contracts is conducted to prove its practical feasibility.

Forests are crucial natural resources in Indonesia, storing significant biodiversity and providing essential benefits for both the environment and people's livelihoods. They contribute significantly to the country's economy, support local communities, and play a vital role in mitigating climate change through carbon sequestration. This study examines the carbon storage valuation in tree forests of Telaga Warna National Park, Indonesia, highlighting the importance of preserving forests for carbon sequestration and climate resilience. The data was collected within a subplot measuring 10x10 meters, and for trees within a plot measuring 20x20 meters. The study focuses on pole and tree species. Based on the data obtained, it is found that the dominant vegetation in Telaga Warna National Park is Riung Anak / Casitanopsis javanica, with a potential carbon value of US$ 2.96 per Ha for pole species and US$ 98.49 for tree species.

Following the adoption of the Kunming-Montreal Global Biodiversity Framework (KM-GBF), with the Convention on Biological Diversity serving as its guiding treaty, the European Union (EU) has just reached an agreement on an EU Nature Restoration Regulation. This study carries out a systematic cross-walk between the restoration regulation and the KM-GBF, focusing on their implications for forest ecosystems. This paper identifies areas of alignment, divergence, and potential gaps related to habitat restoration, species protection, climate resilience, and the sustainable use of natural resources. The methodology adopts a grounded approach, starting with the 23 targets outlined in the KM-GBF and proceeding to the 28 articles set out in the restoration regulation. The results highlight the need for better alignment between the KM-GBF, the restoration regulation, and other forest-relevant EU policy instruments. The study stresses the need for a coherent and integrated EU policy approach that can address the diverse challenges and policy objectives facing forests. It concludes that amendments to the restoration regulation have significantly diluted its potential impact, limiting the EU Members States accountability and ability to meet KM-GBF goals and targets. It further stresses the need for strategies that can reconcile divergent EU policy pathways, support forest management and restoration efforts, while aligning with global biodiversity objectives.

Genetic variability in rice breeding program plays the very crucial role. It provides outstanding pool of superior alleles governing better agronomic and quality characters through association mapping. For the understanding of population structure and genetic relationship among the different rice lines is indispensable prior to setting of correlation among dynamic alleles and traits. In the present investigation, genetic diversity and population structure of 116 rice accessions by using 64 polymorphic SSR markers was targeted for the evaluation of the genetic relatedness and diversity. Genotyping assessment based on SSR markers revealed a total of 225 alleles, with an average PIC value of 0.755. The germplasm lines were classified into three distinct subgroups through population structure analysis, utilizing both model and distance-based approaches. AMOVA analysis showed that 11% of the total variation could be attributed to differences between groups, while the remaining 89% of the variation was likely due to differences within groups. The study suggests that the population structure and genetic relatedness should be considered when working with the core collection of 116 rice germplasm lines for association mapping, aiming to establish marker-trait associations.

This study was to identify clinical cancer biomarkers for recurrent gastric cancer survivors using artificial intel-ligence. From a hospital-based cancer registry database in Taiwan, the datasets of the incidence of recurrence and clinical risk features were included in 2476 gastric cancer survivors. We benchmarked Random forest with MLP, C4.5, AdaBoost, and Bagging algorithms on metrics and leveraged synthetic minority oversampling technique (SMOTE) for imbalanced dataset issues, cost-sensitive learning for risk assessment, and SHapley Additive ex-Planations (SHAP) for feature importance analysis in this study. Our proposed Random forest outperformed the other models with an accuracy of 87.9%, a recall rate of 90.5%, an accuracy rate of 86% and F1 of 88.2% on re-current category by a 10-fold cross-validation in a balanced dataset. We identified clinical features of recurrent gastric cancer, which are the top five features, stage, number of regional lymph node involvement, Helicobacter pylori, BMI(body mass index), and gender, which significantly affect the prediction model's output, are worth paying attention to in the following causal effect analysis. Using an artificial intelligence model, the risk factors for recurrent gastric cancer could be identified and cost-effectively ranked according to their feature importance. In addition, they should be crucial clinical features to provide physicians with the knowledge to screen high-risk patients in gastric cancer survivors as well.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Background/objectives: The health benefits of resistant starch are well known, but there have not been many studies available documenting the effect of basmati rice products containg resistant starch upon blood glucose level and lipid profile in vivo efficacy. Methods: Common cooking methods were used to prepare basmati rice products including two boiling methods viz. boiling 1 (boiling by absorption), boiling 2 (boiling in extra amount of water), frying and pressure cooking. The cooked rice were held at various temperatures and times: it was made fresh (T1), kept at room temperature (20–22°C) for 24 h (T2), kept at 4°C for 24 h (T3), and then reheated after being kept at 4°C for 24 h(T4). Next, the products' proximate composition, total dietary fibre (soluble and insoluble), resistant starch (RS), and In vitro starch digestion rate (rapidly and slowly digested starch) were examined. The effect of RS on blood glucose and lipid profile were measured in humans and rats. Histopathological study of liver and pancreas in rats were also observed. Results: The basmati rice prepared by boiling 1 and stored with T3 was found more effective to control blood glucose and lipid profile. Similarly in rats, the blood glucose level, cholesterol, triglycerides and LDL were reduced about 29.7, 37.9, 31.3 and 30.5% respectively, after consumption of basmsti rice prepared by boiling 1 and stored at T3. Conclusion: Rice products stored at 4⁰C have a high amount of resistant starh, insoluble dietary fibre, slowly digestible starch and low glycemic index. Thus helps to inhibit glycogen and lipid synthesis.

In the dynamic landscape of the telecommunications industry, understanding and mitigating customer churn is paramount for maintaining competitive advantage and sustaining revenue streams. This comprehensive literature review delves into the intricacies of customer churn prediction, a crucial aspect in fortifying the bond between telecom businesses and their customers. The review methodically analyzes 11 articles published between 2000 and March 2024, spotlighting the strategic role of Customer Relationship Management and the prevalence of high churn rates that necessitate a shift from customer acquisition to retention strategies. Furthermore, the review navigates through the challenges inherent in churn prediction, including the complexities of managing large, imbalanced, and noisy datasets and the intricacies involved in model selection and optimization. Despite these challenges, the paper highlights the superior capability of machine learning techniques in handling complex datasets to provide accurate predictions, albeit acknowledging their practical complexities. The main aim of the study is to provide an overview, serving as a knowledge base for researchers interested in machine learning techniques within the realm of telecommunications.

Studies of Phytophthora impacts in forests generally focus on individual species without recogni-tion that Phytophthora occur in multispecies communities. We investigated community structure of Phytophthora species in the rhizosphere of Agathis australis (kauri) in Te Wao Nui o Tiriwa / Waitākere Ranges, New Zealand, in the context of kauri dieback disease expression. Soil sampling and tree health monitoring was conducted on 767 randomly selected mature kauri trees. Phy-tophthora species were detected using both soil baiting and DNA metabarcoding of eDNA. Four species were detected with soil baiting (P. agathidicida, P. cinnamomi, P. multivora, and P. pseudo-cryptogea) and an additional three species with metabarcoding (P. kernoviae, P. cactorum/P. aleatoria and an unknown clade 7 species). Phytophthora cinnamomi was the most abundant species and was distributed throughout the forest. Both P. multivora and P. agathidicida were limited to forest edges, suggesting more recent arrivals. P. agathidicida presence was strongly correlated with declining canopy health, confirming its role as the main river of kauri dieback. The limited distribution of P. agathidicida and infrequent detections (11.0% samples) suggests that that this species is spreading as an introduced invasive pathogen and provide hope that with strategic management uninfected areas of the forest can be protected. The frequent detections of P.cinnamomi and P. multivora, from symptomatic trees in the absence of P. agathidicida suggest more research is needed to understand their roles in kauri forest health.

The Australian tropical montane cloud forest (TMCF) is home to approximately 18 percent of the nation’s total vascular plant species. Over the past century, human activity and industrial development have caused global climate changes, posing a severe and irreversible danger to the entire land-based ecosystem, and WTWHA is no exception. The current average annual temperature of WTWHA in FNQ is 24 °C. However, in the coming years (by 2030), the average annual temperature increase is estimated to be between 0.5 and 1.4 °C compared to the climate observed between 1986 and 2005. Looking further ahead to 2070, the anticipated temperature rise is projected to be between 1.0 and 3.2 °C, with the exact range depending on future emissions. We identified 84 plant species endemic to the WTWHA in FNQ, which are already experiencing climate change threats. Few of these plants are used in herbal medicines. This study comprehensively reviewed metabolomics studies conducted on these 84 plant species until now toward understanding their physiological and metabolomics responses to global climate change. This review also discusses: i) recent developments in plant metabolomics studies that can be applied to study and understand the interactions of wet tropical plants with climatic stress, ii) medicinal plants and isolated phytochemicals with structural diversity, and iii) reported biological activities of crude extracts and isolated compounds.

Artificial Intelligence (AI) and Machine Learning (ML) approaches that could learn from large data sources have been identified as useful tools to support clinicians in their decisional process; AI and ML implementations have had a rapid acceleration during the recent COVID-19 pandemic. However, many ML classifiers are “black box” to the final user, since their underlying reasoning process is often obscure. Additionally, the performance of such models suffer from poor generalization ability in presence of dataset shift. Here, we present a comparison between an explainable-by-design (“white box”) model (Bayesian Network (BN)) versus a black-box model (Random Forest), both studied with the aim to support clinicians of Policlinico San Matteo University Hospital in Pavia (Italy) during the triage of COVID-19 patients. Our aim is to evaluate whether the BN predictive performances are comparable with those of a widely used but less explainable ML model such as Random Forest and to test the generalization ability of the ML models across different waves of the pandemic.

Abstract Global marine fish harvest has reached a plateau over the last decade. Efforts to increase aquaculture tend to face limitations in water resources and contamination problems. Of the current fish harvest at least 50% is discarded as waste. The chemical and microbiological contaminations limit the utilization of harvested fish. There is a need to improve fish preservation to minimize spoilage and process them into more appealing products. Instead of resorting to individual food processing methods, the efficiency of processing could best be increased by a combination of conventional and modern processing methods, or combinations of modern processing methods. Fish waste is a rich source of oils containing essential fatty acids, polypeptides, and amino polysaccharides that could be utilized through upscaling of the current scientifically proven methods, to new processing technologies. Separation of collagens, gelatins, bioactive peptides, edible fish oils, and chitosan form the primary stages in the utilization of fish waste. The products need purification to meet food quality and safety standards, and desirable industrial characteristics. The diversity of information and products generated through new methods requires advanced data handling and prediction systems, such as artificial intelligence, to address food safety and get the best out of fish processing and utilization.

The global increase in population has placed significant pressure on food security, leading to the emergence of aquaculture as a vital source of aquatic foods. However, rising costs and the limited availability of fishmeal in aquafeeds have spurred the search for alternative protein sources. While plant-based ingredients have been integrated into commercial aquafeeds, they come with challenges, including low protein content, palatability issues, and the presence of antinutritional factors. In this context, fish silage, derived from fish waste and discarded fish, stands out as a promising alternative technology due to its cost-effectiveness and sustainability attributes. The production of fish silage involves the addition of organic/inorganic acids or lactic acid bacteria to homogenized fish waste. This process yields a valuable mixture rich in peptides, free amino acids, and partially hydrolyzed proteins, offering significant nutritional benefits for animal diets. Beyond its nutritional advantages, fish silage contributes to environmental sustainability by reducing the impact of waste disposal and enhancing the value of fish waste. The objective of this review is to promote sustainable practices in the aquaculture industry in response to the increasing demand for high-quality feed nutrients. To achieve this, it includes an analysis of research results related to silage technology, an appraisal of the advantages and disadvantages of using fish silage as a feed additive, and a focus on emerging trends in this field.

Software project estimating (SPE) is a powerful but necessary step in the production of projects for software. Software project estimating includes several components, estimating software time, software resources, software costs, and software effort estimation (SEE). SEE seeks to estimate the number of "hours-person" and "months-person" of work required for developing or managing a software program. During the initial phase of the development of software, future efforts are impossible to predict. To estimate effort, many deep learning (DL) and machine learning (ML) models were previously created. For estimate, single-model techniques and ensemble methods were investigated in this research. Ensemble methods involve a combination of multiple separate models. Bagging, boosting, and voting were the ensemble methods looked into for estimation. Datasets considered for estimation were COCOMO81, COCOMONasa I, COCOMONasa II, Albrecht, Maxwell, China, Desharnais, Desharnais_1_1, Kitchenham, Boehm, and Belady. The evaluation criteria used MAE, RAE, RMSE, RRSE, and CC. The result from previous studies is that the total size of the set of data utilized is small in predicting the effort, as the largest size of the set of data used is the China dataset containing only 499 projects. So, in this study, the dataset has been merged to increase the total size of the set of data and increase the accuracy of prediction. The results indicated that the suggested merged dataset increased effort estimation accuracy.

Annually, the oak forests of the Zagros Mountain chains, in western Iran and northeastern Iraq, face recurring challenges posed by forest fires, particularly in the Kurdo-Zagrosian forests situated in western Iran and northeastern Iraq. Assessing fire susceptibility relies significantly on vegetation condition. Integrating in situ data, Remote Sensing (RS) data, alongside the integration of Geographical Information Systems (GIS), presents a cost-effective and precise approach to capturing environmental conditions before, during, and after fire events, minimizing the need for extensive fieldwork. This study refines and applies the Zagros Grass Index (ZGI), a local vegetation index tailored to discern between grass-covered surfaces from tree canopies in Zagros forests, identifying the grass masses as the most flammable fuel type. Utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as input, from 2013 to 2022, the ZGI aims to mitigate the influence of tree canopies, isolating NDVI values solely attributable to grass cover. By incorporating phenological characteristics of forest trees and grass species, the ZGI outperforms NDVI in mapping grass-covered areas that are crucial for fire susceptibility assessment in the study region. Results demonstrate a strong overlap between ZGI-based maps and recorded fire occurrences, validating the efficacy of the index in fire susceptibility estimation.

The forest regeneration phase in Sweden commonly involves mechanical soil preparation followed by planting of Scots pine or Norway spruce seedlings. The prepared soil offers planting positions with different properties, including decreased damage by pine weevil (Hylobius abietis L.). Nitrogen fertilization can be applied at the time of planting to aid establishment of the seedlings. In this study we compared the effect of organic nitrogen-fertilization for different planting positions, and seedling sizes on early survival and growth of Scots pine and Norway spruce seedlings. The main planting positions were capped mound, hinge, and mineral soil. Seedlings planted close to organic material were categorized as “low quality positions”, since proximity to organic material increase pine weevil attraction. Higher mortality rates related to pine weevil damage were recorded for seedlings planted in the low-quality positions, regardless of seedling size or N-fertilization. Pine weevil attack rates increased with increasing seedling size. Growth was in general lowest in mineral soil positions. The effect of organic N-fertilization on growth was positive for spruce regardless of planting positions and seedling size, while depending on planting position and seedling size for pine, indicating that effects of organic N-fertilization depend on seedling species, -size and planting position.

Accurate detection of forest restoration sites using deep learning is crucial for effective ecosystem management, optimizing reforestation efforts, and ensuring environmental sustainability. While various deep learning-based systems have been developed for this purpose, the influence of different neural network architectures on model performance during the identification of forest restoration sites remains underexplored. This study aimed to address this gap by exploring the optimal methodology for extracting and classifying candidate sites for forest restoration based on Convolutional Neural Networks (CNNs), specialized for image recognition. Four categories—arable land, road and barren, quarry, and forest—were defined as candidate sites for forest restoration. A dataset comprising 17,043 samples was divided into training (11,929) and validation (5,114) sets at a ratio of 7:3 for model learning. Model accuracy was evaluated using pixel accuracy (PA) and mean intersection over union (mean IoU). The Resnet152-U-Net model demonstrated a pixel accuracy of 95.2% and a mean IoU of 61.3%, indicating excellent performance in extracting candidate sites for forest restoration. This approach offers spatial and temporal advantages over conventional field surveys or aerial image-based assessments, potentially serving as valuable data for selecting future forest restoration sites.

The outstanding occurrences of red wood ant nests (RWA; Formica rufa-group) at two study sites in the Oberpfalz, NE Bavaria, Germany, could be confirmed by the 2023 re-inventory (a total of 5,393 nests and an increase by ≈8 %). RWA nests preferred mature and medium mature pine-dominated forests. Standing and downed dead wood do not negatively influence nests. Nest counts with woodpecker cavities and the diversity of the herb layer increased. The highly clustered nests in NW-SE direction suggests a strong interaction between nests distributions and the tectonic environment. Our developed (re-)inventory approach in combination with the comprehensive photo database is a valuable tool for a) monitoring the entire forest habitat including woodpecker cavities and dead wood in, at and around nests, and b) identifying GeoBio-Interactions. Such a comprehensive approach leads to more accurate and realistic counts of RWA nests during (re-)inventories and provides scientific evidence of the status quo of RWA occurrence. Our results disproved the unfounded and scientifically unsubstantiated blanket statement by laymen of the ASW Landesverband Bayern e.V. that RWA nest numbers in the Oberpfalz or even in the entire Bavaria are in sharp decline. We suggest seven to eight years as a suitable time interval for re-inventories.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

Modern forestry systems are based upon typologies of forest types (FT). Many proposals were developed in Argentina, but without following unified criteria. The objective was to compare different approaches based on (i) functional forests (phenoclusters), and (ii) forest canopy-cover composition by tree species. We employed data of National Forest Inventories, forest models and climate data, comparing the proposals using univariate analyses. We test the performance of phenoclusters to differentiate the variability of native forests (proxy: forest structure), biodiversity (proxy: indicator species) and environment (proxies: soil carbon stock, elevation, climate). We proposed a simple forest type classification methodology based on species composition, considering basal area of tree species. Finally, we compared the performance of both proposals. In regions where monospecific forests are predominant, the classifications based on forest canopy-cover composition are feasible, but phenoclusters allowing to increase the complexity at landscape level. In those regions were predominant multi-specific stands, the classifications based on forest canopy-cover composition are useless, and phenoclusters allowing to decrease the complexity at landscape level. These results allowing to harmonize national FT classifications by using criteria and indicators to achieve sustainable forest management and conservation proposals.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with &gt; 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by &gt;1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

Aquaculture is rapidly becoming one of the pivotal sectors in the farm economy, driven by the increasing demand for high-quality animal protein at an affordable cost, especially with the escalating human population. However, the expansion of high-density fish populations also brings forth a challenge – the rapid transmission and spread of infectious disease agents among them. To combat this, vaccination is emerging as a reliable and standardized method for providing immunity against viral and bacterial outbreaks. The ideal vaccine is expected to be safe, effective, economical, and easily administered. The fish vaccination industry continually publishes new information on fish immunology and vaccinology, contributing to the improvement of vaccine formulation and efficacy. This review aims to offer insights into the current status of bacterial, viral, and parasitic diseases, discuss existing vaccinations, and address potential industry-threatening diseases like infectious edwardsiellosis, motile aeromonas septicemia, TiLV, salmon anemia, vibriosis, and white spot disease. Technological advancements have played a crucial role in enhancing our understanding of fish immunological mechanisms, leading to improved vaccine administration and the development of recombinant live attenuated, subunit, DNA, and RNA vaccines. However, challenges such as oral tolerance, vaccine degradation, and stressful environments persist, impacting vaccine efficacy. Addressing these challenges and gaining a deeper understanding of the fish immune system and host-pathogen interactions will be pivotal for future improvements, contributing to the sustainability of aquaculture and enhancing global food security.

Plants significantly shape root-associated microbiota, making rhizosphere microbes useful environmental indicator organisms for safety assessment. Here, we report the pyrosequencing of the bacterial 16S ribosomal RNA in rhizosphere soil samples collected from transgenic cry1Ab/cry1Ac Bt rice Huahui No. 1 (GM crop) and its parental counterpart, Minghui63. We identified a total of 2,579 quantifiable bacterial operational taxonomic units (OTUs). Many treatment-enriched microbial OTUs were identified, including 14 NonGM-enriched OTUs and 10 GM-enriched OTUs. The rhizosphere microbiota communities were influenced more strongly by growth in the field than by rice varieties. Furthermore, Huahui No. 1 transgenic Bt rice had a greater impact on the rhizosphere bacterial communities than Minghui63. Early developmental stages of the transgenic Bt rice had a significant impact on many Bacillaceae communities. While soil chemical properties were not significantly altered by the presence of transgenic Bt rice, we found peak expression level of Bt proteins during the seedling stage, which may be an intriguing factor for bacterial diversity variations. Based on these findings, we conclude that transgenic Bt rice has a significant impact on root-associated bacteria. This information may be leveraged in future environmental safety assessments of transgenic Bt rice varieties.

Eco-agricultural technology is crucial to alleviating agricultural resource scarcity and environ-mental pressures. However, financial constraint affects its successful promotion. Digital finance has a significant impact on farmers, but existing research lacks an exploration of the impact of digital finance on farmers' adoption of eco-agricultural technology. This study focuses on Rice–Crayfish Co-cultivation Technology as an example. It utilizes survey data from 1,063 households in China. An Endogenous Switching Probit model is employed to solve self-selection bias. The results reveal several key findings. First, the average treatment effect on the treated is 51.5%. This indicates that if farmers who use digital finance were to stop using it, the probability of adopting rice–crayfish co-cultivation technology would decrease by 51.5%. Therefore, digital finance is beneficial for farmers in adopting this technology. Second, heterogeneity analysis reveals that digital finance has a greater promoting effect on older farmers, those with lower education levels, and higher proportions of agricultural income. This suggests a greater reliance on digital financial services among vulnerable groups. Third, digital finance promotes farmers' adoption of rice–crayfish co-cultivation technology by alleviating financial constraints, expanding information channels, and increasing social capital accumulation. Overall, the findings offer valuable insights for for-mulating supportive policies.

In light of the critical and emerging global challenge posed by the increasing frequency and severity of heatwaves in recent decades, this study demonstrates a practical and robust workflow that bridges the gap in understanding how the effective integration of remote sensing data with ground-based observations can facilitate a comprehensive assessment of spatiotemporal heatwave patterns and trends in the central region of Thailand. This research transcends traditional methods, utilizing air temperature (Tair) and satellite-derived land surface temperature (LST) data from 1981 to 2019. Results exhibit that an analysis of Tair indicates increasing daytime heatwaves in peri-urban areas, with significant trends in heatwave number (HWN), heatwave frequency (HWF), and heatwave duration (HWD), heatwave amplitude (HWA) rises in urban settings while nighttime heatwaves significantly intensify in rural locales. Notably, LST trends reveal varied patterns, with peri-urban areas showing marked daytime increases in heatwave magnitude (HWM), HWA, and HWF. Correlation analysis (p=0.05) highlights strong daytime associations between Tair and LST in rural (HWN, HWF, HWD, r&gt;0.90) and peri-urban (HWM, HWA, r&gt;0.65) regions. Overall, this study advances approaches to the adaptable measurement and spatial-temporal pattern of heatwave-related risk areas, providing insights for decision-makers to develop sustainable practices and strategies for climate change adaptation.

Forage fish are crucial to human nutrition, especially in developing countries. However, in these countries, most forage fish catches are used to make aquafeeds for aquaculture, which leaves only a small quantity for direct human consumption. This practice increases fish prices in local markets and undermines people's nutrition. Overfishing forage fish for industrial purposes harms the ecosystem, reduces the catch of larger fish, and marginalizes local fishermen. Although previous discussions have touched qualitatively on the ethics and injustice of this practice, this article provides quantitative scales to measure the impact of the practice on the finances and forage fish consumption of low-income consumers using reasonable assumptions and literature values. Increasing the annual supply of forage fish by 25% to the 5 million MT of fresh fish markets globally could save consumers several billion US dollars annually. Families could consume a higher percentage of nutritious fish for less money. Furthermore, leaving forage fish in the sea could benefit wildlife and increase the capture of larger fish, such as mackerel, which are in demand in local markets and fished by local fishermen. The unintentional and involuntary contributions of low-income societies to enable the growth of high-value fish and shrimp for affluent societies seem unjust and warrant further investigation.

Sustainable continuous cover forestry is defined and analyzed in several ways. The differential equation representing growth of the basal areas of individual trees, motivated by fundamental biological production theory by Lohmander (2017a), is analyzed and extended in different directions. From the solution of the differential equation, the basal area and the tree diameter are obtained as explicit functions of time. The diameter is a strictly increasing function of time. In the absence of competitors, the diameter increment is shown to be a strictly decreasing function of time. Hence, the diameter increment can also be interpreted as a strictly decreasing function of the diameter. Alternative forms of adjustment of the differential equation, with consideration of competition, are defined. If the competition is strong, with large trees in the vicinity of a particular tree, then the basal area increment, and the diameter increment, are reduced. The growth of a large tree is less sensitive than the growth of a small tree, to competition from other trees. Under strong competition, the basal area increment, and the diameter increment, are strictly concave functions of the size of the tree. The unique maximum of the diameter increment occurs at a higher diameter, if the competition increases. In dynamic equilibrium, the tree size frequency distribution is stationary. If natural tree mortality can be avoided via the harvest strategy, the tree size frequency distribution is a function of the size and competition dependent growth function, and the harvest strategy. Empirical tree size frequency data are used to simultaneously estimate parameters of a size and competition dependent growth function and the applied harvest strategy, via nonlinear optimization. The properties of the estimated growth function are consistent with the corresponding properties of the production theoretically motivated hypothetical function, and the properties of the estimated harvest strategy confirm the corresponding hypotheses. The R2 of the nonlinear regression exceeds 0.97. With access to an empirically estimated equilibrium tree size distribution, it is possible to: 1. Estimate size frequency relevant parameters of tree size and competition dependent growth functions for individual trees. 2. Estimate the applied harvest strategy. 3. Explain and reproduce the empirically estimated tree size equilibrium distribution.

Garlic, as an important economic crop in China, still has room for improvement in terms of identification using remote sensing technology, Among them, high-precision classification of garlic has become an important subject. The Erhai Lake is an important freshwater lake in China. Under the influence of technology and policies, significant changes have occurred in the cultivation of garlic crops. this study constructed multidimensional features for crop classification suitable for Google Earth Engine, and Propose a method for identifying garlic crops using sample and feature datasets under limited conditions. The results indicate that: 1) In the land-use classification of the Erhai Lake Basin, the importance ranking of characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. 2) The Random Forest method based on feature selection demonstrates high classification accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reaches 95.79%, with a Kappa coefficient of 0.9481. 3) The expansion direction of garlic cultivation in the Erhai Lake Basin initially strengthened and then weakened from 1999 to 2023. The vertical development of garlic cultivation reached saturation, showing a slow trend towards horizontal expansion between 2005 and 2018. The planting distribution in various townships in the Erhai Lake Basin gradually shifted from a relatively uniform distribution to an upstream development in the basin. This study utilizes the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Simultaneously, it accurately, rapidly, and efficiently extracts planting information, demonstrating significant potential for practical applications.

Precise individual tree or sample-based inventories derived from 3D point cloud data of mobile laser scanning can improve our comprehensive understanding of the structure, function, resilience, biodiversity, and ecosystem services of urban forests. This study assessed the performance of a hand-held laser scanning system for the extraction of tree position, diameter at breast height (DBH) and tree height (H) in an urban area. A total of 2083 trees of 13 species from 34 plots were analyzed. The results showed that the registration of tree positions using ground control points (GCPs) demonstrated high accuracy, with errors consistently below 0.4 m, except for a few instances. The extraction accuracy of DBH for all trees and individual species remained consistently high, with a total root mean square error (RMSE) of 2.06 cm (6.89%) and a bias of 0.62 cm (2.07%). Notably, broad-leaved trees outperformed coniferous trees, with RMSE and bias values of 1.86 cm (6%) and 0.76 cm (2.46%) compared to 2.54 cm (9.46%) and 0.23 cm (0.84%). Tree height extraction accuracy varied significantly among different species, with R2 values ranging from 0.65 to 0.92. Generally, both DBH and height were underestimated. Linear mixed effects models (LME) were applied to evaluate factors affecting the performance of HLS with the plot as a random factor. LME analysis revealed that plant type and terrain significantly influenced the accuracy of DBH and H derived from HLS data. Other fixed factors of plot area, tree density and trajectory length didn’t have significance. With a large sample size, we concluded that the HLS demonstrated sufficient accuracy in extracting individual tree parameters in urban forests.

Abstract: Community forests are being promoted in Togo as an alternative to the degradation of protected areas and as a means of restoring forest landscapes. The present study is carried out within Kouampante Community Forest (KCF) in Ogaro (Togo, West Africa). It intends to contribute to the sustainable management of KCF. Specially, it aims at: (i) mapping forest ecosystems and analysing their dynamic and (ii) characterising the floristic diversity of the KCF. Forest ecosytems are mapped and their dynamic evaluated based on Google Earth images of 2010 and 2020. The floristic and silvicultural analyses are based on inventories carried out in 13 plots of 1,000 m² each. The KCF is made up mainly by tree/shrub savannahs (58.73%) and croplands/fallow (41.27%) in 2010. These two land use types have undergone changes over the period. In 2020, the KCF had two land use types: tree/shrub savannahs (84.30%) and croplands/fallows (15.70%). The flora census consisted of 59 species grouped in 6 families. The most important plant species are Detarium microcarpum and Vitellaria paradoxa. The Fabaceae were the most abundant and diversified families, followed by the Combretaceae. The demographic structure showed a high abundance of individuals with small diameter and height. This indicates the possibility of a rapid restoration of the native vegetation. Protecting the forest from anthropogenic threats could be crucial for achieving the desired objective.

The TROPOMI XCH4 data product requires rigorous cloud filtering to achieve a product accuracy of &lt;1%. To this end, the operational XCH4 data processing has been based on SUOMI-NPP VIIRS cloud observations. However, SUOMI-NPP is nearing at the end of its operational life and has encountered malfunctions in 2022 and 2023. In this study, we introduce a novel machine learning cloud clearing approach based on a random forest classifier (RFC). The RFC is trained on collocated TROPOMI and SUOMI-NPP VIIRS data to emulate VIIRS-like cloud clearing. After training, cloud masking requires only TROPOMI data, and so becomes operationally independent of SUOMI-NPP. We demonstrate the RFC approach by applying cloud clearing to operational TROPOMI XCH4 data for August 2022, a period in which VIIRS was not operational. For validation, we analyze the TROPOMI XCH4 data at 12 TCCON stations. Comparison of cloud clearing using the RFC and the original VIIRS method reveals excellent agreement with a similar station-to-station bias (-7.4 ppb versus -5.6 ppb), a similar standard deviation of the station-to-station bias (11.6 ppb versus 12 ppb), and the same Pearson correlation coefficient of 0.9. Remarkably, the RFC cloud clearing provides a slightly higher volume of data (2182 versus 2035 daily means) and appears to have fewer outliers. Since November 21, 2023, the RFC approach is part of ESA’s operational processing chain. For now the default practice is to utilize SNPP-VIIRS when accessible. Only in cases where VIIRS data is unavailable do we resort to the RFC cloud mask.

Quantifying different stocking rates effects in three study areas placed at Mediterranean forest (Cuenca, Spain) was conducted by applying a multiparametric soil quality index, SQI, developed in undisturbed forest soils (> 40 years). The main objective is to advance in the development and application of multiparametric indexes which allow for soil condition assessment. For it, is analyse the effectiveness of a multiparametric soil quality index (SQI) as an indicator livestock impacts on the soil in mediterranean forests. Control areas without stocking rates were compared in forest stands with different age (Thicket forest stand, < 30 years; High polewood forest stand, 30-60 years; Old growth forest stand, > 60 years); with various intensity grazing areas (permanent livestock passage: inactive sheepfold for 2-3 years, and active sheepfold; intermittent livestock passage: bare soil, pine stand, scrubland). The multiparametric soil quality index (SQI) applied sensitive to changes in forest ecosystems depending of different stocking rates. However, for even more preci-sion to assess cattle alteration, multiparameter index recalibrated, creating a new one; Soil Status Index by Livestock (SSIL). Correlation between quality ranges obtained with both indexes in dif-ferent study areas suggest that SSIL can be considered a livestock impact reference indicator in Mediterranean forest soils.

Emerald ash borer (EAB), Agrilus planipennis, an invasive phloem-boring beetle, was detected in the Luhansk Region of Ukraine in 2019. In the following years, it expanded to a large part of the Kharkiv region and the parks of Kyiv. Previous research proved that the climate in Luhansk and neighboring regions is favorable for EAB, and the lack of a host plant (Fraxinus sp.) cannot prevent pest expansion in Ukraine. Considering the necessity to detect inhabited trees in time, our current research aimed to identify the most attractive EAB forest subcompartments considering forest site conditions and stand structure. MaxEnt model predicted the potential distribution of EAB with high accuracy (AUC = 0.842). The most significant five variables provided 82.9% to the model, particularly «Age of trees, years», «Area of forest subcompartment, ha», «Mean height of trees, m», «The proportion of Fraxinus excelsior in the stand composition, %», and «Hygrotope index (humidity level), point». A very high probability of EAB expansion in the Luhansk region (&gt;90 %) is predicted at 20 % of the forest area with Fraxinus sp. in the stand composition. The probability of EAB spread above 30% is predicted for 75 % of such area. To improve the forecasting accuracy, it could be necessary to consider the data on road localization along which the pest can spread passively, as well as dominate wind speed.

In this study, we evaluated the antibacterial effect against Staphylococcus aureus and Salmonella Typhimurium in fried fish paste using floating electrode-dielectric barrier discharge (FE-DBD) plasma (1.1 kV, 43 kHz, N2 1.5 m/s, 1-60 min). In addition, quality evaluation (pH, VBN) of fried fish paste was conducted after FE-DBD plasma treatment. When FE-DBD plasma was treated for 1, 5, 10, 20, 30 and 60 min, S. aureus decreased by 0.16-1.13 log10 CFU/g, and S. Typhimurium decreased 0.25-1.13 log10 CFU/g. Both decreased &gt; log10 CFU/g at 60 min. The D-value was 58.92 and R2 was 0.97 for S. aureus using first-order kinetics, and the D-value was 43.60 and R2 was 0.97 for S. Typhimurium using the Weibull model. There was no significant difference in pH after FE-DBD plasma treatment (p &gt; 0.05). However, volatile basic nitrogen (VBN) significantly decreased as the treatment time increased (p &lt; 0.05), and it was the lowest at 60 min to 3.46. Therefore, FE-DBD plasma treatment could be considered as a technology for preserving the quality of processed foods.

The foraging behavior of ruminating ungulates significantly impacts forest ecosystems due to their nutritional requirements. This study focuses on inter-specific comparisons of bark browsing (stripping) between Douglas fir (Pseudotsuga menziesii (Mirb.) Franco) and silver fir (Abies alba Mill.). Field measurements were conducted at the previously established research-demonstration site "Husárik" in the Javorníky Mountains, north-western Slovakia. We didn’t only measure the sizes of wounds on stems but also assessed the dimensions of unrecovered parts of wounds, subsequently calculating areas of recovered bark patches. Then, the total areas of wounds, recovered patches, and unrecovered parts were determined. Additionally, the percentages of wound areas, recovered areas, and unrecovered areas were expressed as their proportions to the total bark surface. Influencing factors such as the position of wounds along the vertical stem profile and stem diameter class were analyzed using two- and three-way ANOVA followed by Fisher's LSD test. The results demonstrate the susceptibility of both tree species to bark browsing by ruminating ungulates, primarily red deer (Cervus elaphus L.) in our study site. Douglas fir exhibited slightly less intense browsing compared to silver fir, while silver fir had a slightly higher recovery rate. Specifically, 17.0% of the bark surface in Douglas fir and 21.5% in silver fir were browsed. The proportions of recovered areas on wounds were 62.5% and 69.6% in Douglas fir and silver fir, respectively. Regarding the vertical stem profile, the most intense browsing occurred at a height of 101-125 cm from the ground level, with rates of 40.7% in Douglas fir and 47.0% in silver fir. Browsing intensity showed negligible variation among diameter classes, which were classified as up to 35 mm, 35-50 mm, and over 50 mm. Our findings suggest that, as an introduced species, Douglas fir is not any suitable substitute for other commercially significant tree species in European countries where ruminating ungulates are overabundant.

Adaptations to climate change rely on understanding the dynamics of plant biomass stocks on the planet. The high levels of deforestation in Cerrado have transformed this biome into the second-largest Brazilian source of carbon emissions. The objective of this study was to develop a method to accurately estimate aboveground and total biomass values among shrublands, savannas, and forests located in the Cerrado biome, using an allometric equation adjusted from canopy height, obtained through optical and laser sensors. The results show similarity between the estimates employed by our method and the data found in the literature review for different phytophysiognomies in the Cerrado biome. Shrubland formations showed higher biomass estimation uncertainties due to the discontinuity of isolated trees and the lower canopy height when compared to more clustered tree canopies in savannas and taller canopies in forests. Aboveground biomass estimates were related to expansion factors, and specific maps were developed for each compartment by root, litter, and necromass. The sum of these compartments is presented in the aboveground and below forest biomass map. This study presents, for the first time, the mapping of total biomass in 10 m pixels of all regions of the Cerrado biome.

The arguments on the origin and evolution of the evergreen broad-leaved forest in eastern Asia, especially in the evolutionary process, exist generally, even contrary in some cases. The origin and evolution of the flora of eastern Asia, the formation time of Asian monsoon, and the implications from phylogenetic and molecular biogeographic studies in some important taxa, as well as in palaeobotanical evidences, are debatable. Most researches from different disciplines suggested that the monsoon in Miocene is a key to diversification of eastern Asian flora and its evergreen broad-leaved forest. The common view is that the evergreen broad-leaved forest of East Asia is closely related to the monsoon intensity and developments, which are caused by the uplift of Himalaya-Tibet, during or after the mid-Miocene. The analysis on the floristic elements show that the present subtropical evergreen broad-leaved forest in eastern Asia could have an early or ancient tropical origin and a tropical Asian affinity, but their species are dominated by the Chinese endemic or eastern Asian distribution, many of which are the tropical sister ones. The time of Himalayan uplift and intensity of monsoon climate is believed the key for the formation of the evergreen broad-leaved forest in eastern Asia. Combined existing palaeobotanical findings, the uplift of Himalayas and the formation of monsoon climate, as well as floristic elements of the subtropical evergreen broad-leaved forest, we believe that it was evolved from a Asian tropical rain forest after mid-Miocene in southeastern region of eastern Asia, while the ancient subtropical evergreen broad-leaved forests in southwestern region continuously evolved into the present subtropical ones.

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in seafood was performed, with the objective of appraising the effectiveness of the control strategies placed along the food chain. Whereas all the 13 QRA models retrieved were of short scope, covering at most from end of processing to consumption; the majority (85%) focused on smoked or gravad fish. Since the modelled pathways commenced with the packaged product, none of the QRA models addressed cross-contamination events. Many models agreed on the fact that keeping the product’s temperature at 4.0-4.5 ºC leads to greater reductions in the final risk of listeriosis than reducing the shelf life by one week; and that the effectiveness of both measures can be surpassed by reducing the initial occurrence of L. monocytogenes in the product (at the end of processing). It is therefore necessary that future QRA models for RTE seafood contains a processing module that can provide insight into intervention strategies that can retard L. monocytogenes growth, such as the use of bacteriocins, ad-hoc starter cultures and/or organic acids, and other strategies seeking to reduce cross-contamination at the facilities, such as stringent controls for sanitation procedures. Since risk estimates were shown to be moderately driven by growth kinetic parameters, namely, the exponential growth rate, the minimum temperature for growth, and the maximum population density, further work is needed to reduce uncertainties about them.

Soil, geochemical, microbiological and archaeological studies were conducted at eight settlements dating from the Paleolithic to Late Medieval and New Times near the Southern Trans-Urals mountains, Russia. The forest-steppe landscapes, rivers and abundance of mineral resources have attracted people to region since ancient times. Properties of cultural layer (CLs): close to neutral pH, well structured, absence of salinity, enrichment with exchangeable calcium and anthropogenic phosphorus (0.2–0.4%). The majority of CLs start from 3–25 cm and last up to 40–60 cm and contain 6–10% organic carbon (Corg), in a layer of 0–20 cm because modern processes are developing here, at Ishkulovo site (around 14th century) Corg decreases to 1.3%, because CL is below 80 cm and in the absence of fresh organic material it is mineralized. The content of microbial biomass of the 0–10 cm layer is 520–680 μg С/g of soil, microbes cause the emission of 0.2–1.0 μg C–CO2/g soil per h. The proximity of the studied sites to deposits of copper, chromium, zinc, manganese and Magnitogorsk Metallurgical and Uchaly processing plants creates geochemical anomalies of soils. Pollutants are also released into the CLs from bronze products, ore, ceramic fragments and raw materials from their manufacture. It leads to the concentration of pollutants of the 1st (arsenic, lead, zinc) and 2nd (cobalt, copper, nickel, chromium) hazard classes in the studied CLs, often in quantities higher than the clarke and less often – more than the allowable content for human health.

The modifications in planting structure and continuous land use adjustments have significant implications for the structure, diversity, and functioning of arthropod communities in farmland landscapes, particularly regarding the biological control of pest insects by natural enemy species. Therefore, there is an urgent need to comprehensively and accurately assess insect community diversity in croplands. In this study conducted between 2022-2023, we utilized Malaise traps and sweep netting to collect insects from three different double-cropping paddy fields. We extracted DNA from preservative ethanol of Malaise traps and tissue samples of selected parasitoid wasps, followed by amplification using DNA barcoding primers to prepare high-throughput sequencing libraries. As a result, we annotated a total of 4956 operational taxonomic units (OTUs) belonging to 174 genera and 32 families of parasitoid wasps. Our findings demonstrate that the ethanol filter method efficiently captures a wide range of information diversity. However, it demonstrates a lower resolution and may result in a reduction of species abundance. Furthermore, we have identified additional insect species in the parasitoid samples. This demonstrates that high throughput sequencing from adult parasitoid wasps can effectively detect host specie, enabling a comprehensive understanding of host species and providing novel insights into food web construction.

The use of hyperthermia is one of the effective methods of treating cancer. In this case, the problem arises of constructing and predicting the distribution of the thermal field inside tissues depending on their type. This paper discusses the use of linear and polynomial regression methods, as well as regression algorithms based on decision tree, random forest and K nearest neighbors methods to recover missing temperature values. The influence of the size of the training sample when performing regression on the quality of the reconstructed values is investigated. Experimental recommendations on sample size are given for the selected decision tree and random forest methods. The possibility of a significant (10 times or more) reduction in the initial sample size, without leading to a decrease in the R² metric by more than 0.05 for various tissues, has been shown.

This study aimed to identify changes in visitor behavior and visitor interest in healing forests before and after the COVID-19 pandemic. The study used text mining analysis techniques to identify changes in visitation behavior over time, divided into three periods: pre-COVID-19 (January 1 to December 31, 2019), during the COVID-19 pandemic (November 1, 2020 to October 31, 2022), and post-COVID-19 (November 1, 2022 to October 31, 2023 ). After the COVID-19 outbreak, healing forest use behavior did not regress to pre-COVID-19 levels. Activity-based keywords such as "hiking," "trekking," and "walking" stood out as the main drivers of this change in behavior. Therefore, related authorities must examine the scalability of the functions, services, and programs of healing forests from a general healing space to a space for leisure and tourism. These findings will contribute to the development of future marketing strategies and programs for healing forests.

Aging, a natural process occurring in both normal and degenerative conditions, is closely associated with collagen degradation, impacting various bodily systems such as the skin. The continuous aging of the skin, influenced by both intrinsic and extrinsic factors, underscores the importance of collagen in dermatological and cosmetic contexts. Notably, collagen supplements enriched with essential amino acids like proline and glycine along with marine fish collagen have become popular for their safety and effectiveness in mitigating the aging process. To compile relevant literature on the anti-aging applications of marine collagen, a systematic search and analysis of peer-reviewed papers was conducted using reputable databases including PubMed, Cochrane Library, Web of Science, and Embase, covering publications from 1956 to 2023. From in vitro to in vivo experiments, the reviewed studies elucidate the anti-aging benefits of marine collagen, emphasizing its role in combating skin aging and promoting overall skin health. Many bioactive marine peptides exhibit diverse anti-aging properties, including free radical scavenging, apoptosis inhibition, lifespan extension in various organisms, and protective effects in aging humans. Furthermore, the peptide production of hyaluronic acid is discussed as a mechanism to fortify collagen and enhance skin moisture, contributing to the anti-aging effects of collagen supplementation. The integration of bio-tissue engineering in marine collagen applications is also explored, highlighting its proven utility in addressing skin and bone damage. Insights from this review illuminate the diverse biomedical applications of marine collagen alongside keystone molecular editing tools such as CRISPR, positioning it as a versatile resource in anti-aging interventions. However, limitations to the scope of its application exist. Thus, by delving into these nuanced considerations, this review contributes to a comprehensive understanding of the potential and challenges associated with marine collagen in the realm of anti-aging applications.

(1) Background: There is a risk of Aflatoxin B1(AFB1) pollution in yellow rice wine[1],threatening human life and health, the risk is a major focus of government attention and social concern.The current detection technology cannot meet the needs of large-scale testing due to the disadvantages of high false positive, cumbersome operating procedures or high cost.Therefore, it is urgent to study and establish a high-sensitivity and high-fast detection method for AFB1 to provide technical support for yellow rice wine quality supervision and risk assessment.; (2) Methods: In this study, three-factor and three-level orthogonal experiments were carried out for oscillation time, reaction temperature and tomography time to determine the best reaction conditions for pretreatment.AFB1 detection was carried out by aflatoxin time-resolved fluorescence immunochromatography technology，Through the ratio of the T signal value of the detection line to the C signal value of the quality control line and the natural logarithmic value of the standard solution concentration[2], a rapid detection technology of time-resolution fluorescence immunochromatography is established.The technology was methodically assessed, the actual yellow rice wine samples were tested, and the results were compared with the HPLC method; (3) Results: The results show that the optimal reaction conditions for pretreatment are: the sample oscillation time is 20min, the sample reaction temperature is 37°C, and the sample chromatography time is 6min. In the actual sample detection of yellow rice wine, The time-resolved fluorescence immunochromatography detection technology established in this study detects AFB1. The detection limit is 0.3 μg·kg-1,The linear range is 0.8-12.0μg·kg-1,The linear equation of the corresponding standard curve y=-0.1913x+0.5206 (R2=0.9948),The standard deviation is 0.029.The results of the in-batch accuracy and precision of this method show that the addition and recovery rate is 77.9% - 105.7%,The coefficient of variation is in 4.5% - 10.3%; Inter-batch accuracy and precision test results show that the addition recovery rate is75.9% - 85.8%,the coefficient of variation is in 8.1% - 13.9%,explain that the detection technology has good accuracy and precision.Compared with HPLC, the relative error is less than 10%,It shows that aflatoxin time-resolution fluorescence immunochromatography technology is in good agreement with the test results of national standard methods; (4) Conclusions:he rapid detection technology of time-resolution fluorescence immunochromatography of AFB1 in yellow rice wine is easy to operate with fast detection speed, high sensitivity, good repeatability and stability, and has broad application prospects.

In-depth insights into the profound impacts of climate change and human activities on water resources are garnered through the dynamic changes in surface water, a crucial aspect of effective water resource management and the preservation of aquatic ecosystems. This paper introduces an innovative approach employing the random forest algorithm for the systematic extraction and monitoring of surface water at large regional or national scales. This method integrates spectral bands, spectral indices, and digital elevation model data, offering a novel perspective on this critical task. A data filling model is proposed to mitigate the impact of missing data due to cloud cover. Leveraging the capabilities of the Google Earth Engine (GEE), detailed information on surface water dynamics during the rainy and dry seasons in the Yangtze River Basin (YRB) from 1991 to 2021 is extracted using Landsat time series imagery. The analysis encompasses spatial-temporal variation characteristics and trends, with a specific focus on the intricate interplay between the areal extent of surface water and hydro-meteorological factors in each sub-basin of the YRB. Importantly, this includes considerations of potential groundwater contributions to surface water. Key findings from our research include: (1) Achieving a remarkable overall accuracy of 0.96 ± 0.03 in obtaining reliable surface water datasets with the support of GEE. (2) Identifying significant trends, such as a noteworthy increase in rainy season surface water bodies (+248.0 km2·yr−1) and a concerning decrease in surface ice/snow cover during both rainy and dry seasons, with change rates of −39.7 km2·yr−1 and −651.3 km2·yr−1, respectively. (3) Uncovering the driving mechanisms behind these changes, revealing positive correlations between the areal extent of rainy season surface water bodies and precipitation, as well as negative correlations between surface ice/snow cover area and average surface skin temperature. It is crucial to note that these driving factors exhibit variation among secondary river systems, underscoring the complexity of surface water dynamics. Furthermore, comparative analyses with existing surface water products are conducted, contributing to a deeper understanding of the advantages and uncertainties inherent in our proposed extraction method. The proposed method for large-scale surface water extraction not only enhances the monitoring of spatio-temporal surface water dynamics in the YRB but also provides valuable insights for the sustainable utilization and protection of water resources, considering the potential role of groundwater in supplementing surface water.

Since 2015, community forests have been promoted in Togo as an alternative to protected areas degradation and as a means of contributing to forest landscape restoration. The study focuses on the Nakpadjouak Community Forest (NCF) in Tami (Togo, West Africa) which contributes to community forests sustainable management. It aims in (i) mapping forest ecosystems and analysing their dynamic and (ii) characterizing the floristic diversity of the NCF. The ecosystems were mapped and their dynamic evaluated based on Google Earth images of 2014 and 2020. Floristic and forestry inventories were carried out using the transect technique in a sample of 20 plots of 50 m x 20 m. The NCF was made up mainly by wooded/shrub savannahs (95.37%) and croplands/fallow (4.63%) in 2014. These two land use types undergone changes over the 6 years prior to 2020. By 2020, the NCF had 3 land use types: wooded/shrub savannahs (77.59%), open forest/wooded savannahs (22.23%), and croplands/fallows (0.18%). A total of 89 plant species belonging to 70 genera and 28 families were recorded within the NCF. The dominant species are: Hetopogon contortus (L.) P.Beauv. and Combretum collinum Fresen. followed by Pteleopsis suberosa Engl. & Diels, Annona senegalensis Pers. The most common species are: Lannea acida A.Rich. s.l., A. senegalensis, Vitellaria paradoxa C.F.Gaertner subsp. paradoxa, C. collinum and Acacia dudgeonii Craib ex Holland. Due to its small area of just 40 hectares and its diverse plant life, this community forest of Savannahs Region is a significant biodiversity hotspot and warrants conservation efforts.

Abstract: In the Western of Mekong Delta belong to Vietnam a amount of alluvium sediments flows from upstream China to Vietnam, they are going from the river branches to in the Mekong Delta, then get out the Western Sea of Vietnam. This sediments accumulated gradually elevation the new land. The increase elevation of the land creates a fertile coastal plain where mangrove forests with a rich ecosystem of plants and animals. Over time, these forests change, with different plant species succeeding each other. This aims of this study to finding plant species in the forest, classification of forest types based on ecological regions, assessement the biodiversity of tree species, and compare the abundance communities, measuring the growth of the forest in these regions. In 2023, a comprehensive survey was conducted by using a systematic approach. The number of survey plots have done depend on the density of the forest, Base on the width of the forest range, the number of survey plots from 500m to 1,000m set up 1-2 plots, sub region set up from 10 to 15 plots. In total, 68 plots have done established in the erea, the area of plot is 100 square meters (10m x 10m). Using the statistical software in forestry to survey and analysis data. The result provided a complete picture of the tree species composition, distribution, and community structure characteristics in each ecological sub-region. The result of survey showed in the sub-region 1 have seven species as Sonneracia ovate, Rhzophora apiculata, Excoecaria agallochha, Avicennia officinalis, Avicennia alba, Agricerasvcoculatum, Bruguiera gymnorhiza. In the sub region 2 have eleven species as Sonneracia caseolaris, Annona reticulata, Lumnitzera littorea, Lumnizera racemosa, Rhizophora apiculata, Excoecaria agallocha, Avicennia officinalis, Avicennia alba, Hibiscus tillacerus, Bruguiera gymorhiza, Aericeras comiculatum. In the sub region 3 have eight as Sonratia caseolaris, Rhizophora mucronata, Rhizophora apiculata, Excoecaria agallocha, Avicennia marine, Avicennia alba, Bruguiera gymorhiza. In the region 4 have ten species as Sonneracia caseolaris, Sonneracia ovate, Lumnizera racemosa, Rhizophoa apiculata, Excoecaria agallocha, Avicennia marina, Avicennia officinalis, Aviecnnia alba, Bruguiera gymorhiza, Bruguiera cylindrica The total species of the mangrove forest in the Western Mekong Delta have 16 species from 11 plant families have been identified. Among these species have 6 dominant species include Avicennia oficinali),Avicennia alba, Rhizophora apiculata, Excoecaria agallocha, Someratia caseolaris, and Bruguiera yipamoriza. Two species easy grow on the new land are Avicennia officinalis Avicennia alba this findings show they can develope on the original new land for the shore of the Western Mekong Delta. The surveyed results also calculated the average of the height, diameter (D1.3), canopy, health of the nature mangrove tree for each sub region and total region. From these results showed the division of foresty structure, the structure of height, diameter (D1.3), canopy, vitality of the sub region and total region in the Western Mekong Delta.

In recent decades, large-scale forest disturbances such as windthrow and bark beetle infestations have significantly impacted the carbon balance of Earth and forest ecosystems, mainly by altering soil respiration (SR) in addition to decreased gross primary productivity. SR is considered the second highest flux on Earth, emitting 78-98 Gt C yr-1 to the atmosphere, thus contributing to global warming. Post-disturbance development is marked by changes in the size of SR. To investigate the impact of bark beetle (Ips typographus) infestations in mountain spruce forests on SR, we measured SR at infested sites by bark beetles and adjacent undisturbed stands in the High Tatra Mountains (Slovakia). The measurements were conducted during the vegetation period (May-September) in 2016 and 2017, five to six years after the initial attack by bark beetles, at an altitudinal gradient (1100-1400 m a.s.l.). SR varied throughout different months and altitudes, and we observed that SR at infested sites reached higher values (in most cases) than at uninfested stands. Average monthly and altitude SR rates during both vegetation periods showed higher values at infested sites, but not all of them were statistically significant. We observed an insignificant change in SR rate between 2016 and 2017 at both infested and uninfested sites. The highest rates of SR were observed in 2016 from July to August at both infested and undisturbed sites. On the other hand, in 2017 the highest SR rates were observed in June and July at undisturbed sites and from June to August at infested sites. However, yearly SR from May-September at infested sites showed significantly higher rates than uninfested ones in both years. SR showed a decreasing pattern with elevation gain in 2016 at infested sites. However, this pattern was not observed in 2017. Based on our observation and results we postulate that SR rates at infested sites are boosted by needle fall and debris from dead and dying trees, which increases heterotrophic SR and compensates the decrease of autotrophic SR from tree roots. This study provides important insights into the impact of bark beetle infestations on SR and highlights the need for further research on the long-term effects of forest disturbances on carbon cycling.

The chloroplast is important organelle responsible for photosynthesis and various bio- synthetic functions essential to green plants. However, the regulatory mechanism of chloroplast development remains elusive. Here, we characterized a mutant with defective chloroplasts in rice (Oryza sativa L.) named it albino and lethal 13(osal13), which exhibited a distinct albino phenotype in leaves, eventually leading to seedling lethality. Molecular cloning revealed that OsAL13 encodes a novel protein in rice for which no homologous gene or known conserved domain was found. Analysis of the biological characteristics showed that the RNAi2 leaf was completely albino phenotype, the root and shoot lengths were significantly inhibited compared with wild type. Transmission electron microscopy indicated that RNAi2 were defective in chloroplast development. Overexpression of OsAL13 resulted the photosynthetic rate increase, So the total grain number per panicle also would be significantly increase. Besides, OsAL13 was located in chloroplast and constitutively expressed in various tissues, including green and non-green tissues. In addition, disruption of OsAL13 resulted in altered expression of plastid-encoded polymerases and nuclear-encoded chloroplast genes, OsAL13 also affected hormone levels in plant tissues. Therefore, our results provide new insights into the role of OsAL13 in regulating chloroplast development in rice.

Wildfires are important natural drivers of forest stands dynamics, strongly influencing on their natural regeneration and ecosystem services. This paper presents a comprehensive analysis of spatiotemporal burnt area (BA) patterns over the period 2000–2022 in the Middle Volga region of the Russian Federation on the base of remote sensing time series, considering the impact of cli-matic factors on forest fires. The temporal trends were assessed with the Mann-Kendall nonpara-metric statistical test and Theil-Sen’s slope estimator using the LandTrendr algorithm on the Google Earth Platform (GEE). The accuracy assessment indicated a high overall accuracy (> 84%) and F-score value (> 82%) for forest burnt area detection as evaluated against 581 test sites of ref-erence data. The results revealed that the fire occurrences in the region were mainly irregular with the highest frequency of 7.3 over a 22-year period. The total forest BA was about 280 thousand ha, which equals to 1.7% of the land surface area or 4.0% of the total forested area under study in the Middle Volga region. The coniferous forest stands are the most fire-prone ecosystems accounting for 59.0 % of the total BA; deciduous stands accounts for 25.1%; and insignificant fire occurrences were registered in young forests and shrub lands. On a seasonal scale, temperature generally has a greater impact on the BA than precipitation and wind speed.

Natural vegetation restoration has become an effective and rapid way for eco-logical restoration in fragile areas. However, the response of soil microorgan-isms remains unclear. Using high-throughput sequencing methods, we evalu-ated the dynamics of soil bacterial and fungal communities during forest suc-cession (shrubland, secondary forest, and primary forest) in the karst region of Southwest China. Secondary forest had significantly higher bacterial α-diversity than the shrubland and primary forest. Soil bacterial community in the primary forest was close to the shrubland but diverged from the secondary forest, and that the fungal community could be discriminated along forest suc-cession. Also, the microbial co-occurrence network indicated that soil fungi had less but more intense relationships than bacteria in the karst forests. Further-more, soil properties (pH, SOC, TN, moisture, and AK), soil microbial biomass (MBP, and MBN), and plant factors (Shannon index of woody plants) drove the dynamics of the soil bacterial community, while soil properties (i.e., pH) mostly explained the variation of the soil fungal community along karst forest succession. Different responses of soil bacteria and fungi to forest succession in a karst region give hints for ecological restoration along forest succession in the karst region.

Little is known about how the degree of mixing of forest-forming species affects forest floor hydrology. We evaluated the water storage capacity of the resulting litter layer by mixing litterfall of Scots pine and sessile oak and studying their decomposition time. We prepared 90 artificial samples containing pure pine litter, pure oak litter, and mixed pine-oak litter with varying shares of pine needles. These samples were subjected to 15 months of decomposition in soil. After every three months of decay, some samples were removed from the soil, and their water storage capacity, bulk density, and C:N ratio were evaluated. Our findings indicate that samples with the greatest water storage capacity had a low C:N ratio and a predominant share of oak leaves. Conversely, samples with a high C:N ratio and a predominant share of pine needles had the lowest water storage capacity. After 12 and 15 months of decomposition, the water storage capacity increased by more than 52% compared to the initial water capacity of the samples. The highest increase in water storage capacity (>40%) was observed in samples with a predominant share of oak leaves, while the lowest (approximately 28%) was recorded in samples with 80 and 100% of pine needles.

Salt stress is one of the most severe environmental stresses limiting the productivity of crops, including rice. However, there is a lack of information on how salt-stress sensitivity varies across different developmental stages in rice. In view of this, a comparative evaluation of contrasting rice varieties CSR36 (salt tolerant) and Jaya (salt sensitive) was conducted, wherein NaCl stress (50 mM) was independently given either at seedling (S-stage), tillering (T-stage), flowering (F-stage), seed-setting (SS-stage) or throughout the plant growth, from seedling till maturity. Except for S-stage, CSR36 exhibited improved NaCl stress tolerance than Jaya, at all other tested stages. Principal component analysis (PCA) revealed that the improved NaCl stress tolerance in CSR36 coincided with enhanced activities/levels of enzymatic/non-enzymatic antioxidants (root ascorbate peroxidase for T- and S+T stages and root catalase for F-, S+T and S+T+F-stages) and higher accumulation of osmolytes (shoot proline for F-stage and S+T+F-stage), indicating better antioxidant capacitance and osmotic adjustment, respectively. In contrast, higher shoot accumulation of Na+ and consequent increase in Na+/K+, Na+/Ca+2 and Na+/Mg+2 ratio in root and shoot, were identified as major variables associated with NaCl stress induced growth reduction in Jaya. In addition, CSR36 exhibited higher levels of Fe3+, Mn2+ and Co3+ and lower Cl- and SO42-, suggesting its potential to discriminate essential and non-essential nutrients, which might contribute to NaCl stress tolerance. Taken together, the findings provided the frame-work for stage-specific salinity responses in rice, which will facilitate crop-improvement programs for specific ecological niches including the coastal regions. Rice, salt stress, stage-specific salinity, antioxidant defense, ionic homeostasis, salt tolerance

Rice is an important cereal crop worldwide, the growth of which is affected by rice blast disease, caused by the fungal pathogen Magnaporthe oryzae. As climate change increases the diversity of pathogens, the disease-resistance genes (R genes) in plants must be identified. The major blast-resistance genes have been identified in indica rice varieties; therefore, japonica rice varieties with R genes now need to be identified. Because leucine-rich repeat (LRR) domain proteins possess R-gene properties, we used bioinformatics analysis to identify the rice candidate LRR domain receptor-like proteins (OsLRR-RLPs). OsLRR-RLP2, which contains six LRR domains, showed differences in the DNA sequence, containing 43 single-nucleotide polymorphisms (SNPs) in indica and japonica subpopulations. The results of M. oryzae inoculation analysis indicated that indica varieties with partial deletion of OsLRR-RLP2 showed susceptibility, whereas japonica varieties with intact OsLRR-RLP2 showed resistance. oslrr-rlp2 mutant generated using clustered regularly interspaced palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) showed increased pathogen susceptibility, whereas plants overexpressing this gene showed pathogen resistance. These results indicate that OsLRR-RLP2 confers resistance to rice, and OsLRR-RLP2 may be useful for breeding resistant cultivars.

The utilization of smart IoT devices, commonly referred to as digital twins, is aimed at the digitalization of human knowledge within aquaculture processes. This involves the incorporation of cutting-edge technologies, including information-based management with big data and modeling, to automate machinery and gain comprehensive insights into the aquaculture environment and fish farm conditions. The ultimate objective is to empower farmers to make informed decisions, furnishing them with objective data to enhance their capacity in monitoring and controlling the various factors impacting fish production. As a result, farming decisions can be fine-tuned to enhance fish health and optimize farm output. In the context of large and modern aquaculture farms, technological innovation becomes imperative to automate processes, minimize labor requirements, and streamline fish feeding operations. Remarkably, the literature currently offers limited discussions on the digital transformation of aquaculture through the application of digital twin methodologies. A prior study underscores the critical influence of factors such as market prices and fish survival rates on the profitability of offshore caging culture. In this study, we embark on an analysis of the prerequisites for establishing a digital twin infrastructure tailored to intelligent fish feeding management. This infrastructure is designed to facilitate the integration of technology and data-driven decision-making, ultimately enhancing the efficiency of fish feeding processes. The proposed architecture for the fish feeding digital twin encompasses various digital twin components, encompassing water quality forecasting, fish population assessment, fish metrics estimation, fish feed prediction, and evaluation of fish feeding intensity. Furthermore, we optimize the daily fish feeding process through reinforcement learning algorithms. Finally, we implement a cloud-based AIoT system that provides the runtime environment for executing digital twins and controlling our intelligent fish feeding machinery. Experimental findings underscore the efficacy of the proposed digital twin system in significantly improving traditional fish feeding processes, notably in terms of reducing food costs and labor requirements.

Background: Atypical teratoid/rhabdoid tumor (AT/RT) is an uncommon and aggressive pediatric central nervous system neoplasm. However, a universal clinical consensus or reliable prognostic evaluation system for this malignancy is lacking. Our study aimed to develop a risk model based on comprehensive clinical data to assist in clinical decision-making. Methods: We conducted a retrospective study by examining data from the Surveillance, Epidemiology, and End Results (SEER) repository, spanning 2000 to 2019. The external validation cohort was sourced from the Children's Hospital Affiliated to Chongqing Medical University, China. To discern independent factors affecting overall survival (OS) and cancer-specific survival (CSS), we applied Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest (RF) regression analyses. Based on these factors, we structured nomogram survival predictions and initiated a dynamic online risk-evaluation system. To contrast survival outcomes among diverse treatments, we used propensity score matching (PSM) methodology. Molecular data with the most common mutations in AT/RT were extracted from the Catalogue of Somatic Mutations in Cancer (COSMIC) database. Results: The annual incidence of AT/RT showed an increasing trend (APC, 2.86%; 95% CI:0.75-5.01). Our prognostic study included 316 SEER database participants and 27 external validation patients. The entire group had a median OS of 18 months (range 11.5 to 24 months) and median CSS of 21 months (range 11.7 to 29.2). Evaluations involving C-statistics, DCA, and ROC analysis underscored the distinctive capabilities of our prediction model. An analysis via PSM highlighted that individuals undergoing triple therapy (integrating surgery, radiotherapy, and chemotherapy) had discernibly enhanced OS and CSS. The most common mutations of AT/RT identified in the COSMIC database were SMARCB1, BRAF, SMARCA4, NF2, and NRAS. Conclusions: In this study, we devised a predictive model that effectively gauges the prognosis of AT/RT and briefly analyzed its genomic features, which might offer a valuable tool to address existing clinical challenges.

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation, maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from imputation methods due to potential bias. Drought quantification involved the computation of the Aridity Index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann-Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting geographical location and elevation does not strongly influence observed dryness trends.

Stresses due to drought and high soil salt content are the most severe threats to global rice pro-duction, causing a significant decline in rice yield. Therefore, this study examined the effects of treatment with various plant extracts on the stresses on rice plants associated with drought and high salinity. Additionally, this study examined various physiological and biochemical parameters such as growth, photosynthetic activity, chlorophyll content, and lipid peroxidation in rice plants after treatment with selected plant extracts under drought and salt stress conditions. Out of the eleven extracts tested, four extracts, namely soybean leaf, soybean stem, Chinese chive, and onion extracts, were found to effectively reduce drought stress. A reduction of only 24-37% in shoot fresh weight was observed in rice plants under drought stress that were treated with these ex-tracts compared to the 78% reduction observed in control plants. In addition, rice plants under salt stress which were treated with soybean leaf, soybean stem, moringa (Moringa oleifera), and Undaria pinnatifida extracts were observed to have lower reductions in shoot fresh weight (3-23%) com-pared to the control plants (43%). The effectiveness varied with the concentrations of the plant ex-tracts. Water content was higher in the rice plants treated with extracts than in the control plants after 6 days of drought and salt stress, but not after 4 days of drought and salt stress. Although photosynthetic efficiency (Fv/Fm) and electron transport rate (ETR), and the content of pigments (chlorophyll and carotenoid) varied based on the types and levels of stress and the extracts that the rice plants were treated with, generally photosynthetic efficiency and pigment contents were higher in the treated rice compared to control plants than in the control plants. Reactive oxygen species (ROS) such as the superoxide radical, hydrogen peroxide (H2O2), and malondialdehyde (MDA) levels increased as the duration of the period of stress increased. ROS and MDA levels were lower in the treated rice compared to control plants. Proline and soluble sugar accumulation also increased as the duration of the stressperiod increased. However, proline and soluble sugar ac-cumulation were lower in the treated rice compared to control plants. Generally, all the parame-ters investigated in this study were similar regardless of the plant extract used for treating the rice plants. Thus, extracts found to be effective in this study can be used to alleviate the adverse effects of stress on rice crops associated with drought and high salinity soil.

This study introduces a cybernetic control and architectural framework for a robotic fish avatar operated by a human. The behavior of the robot fish is influenced by the electromyographic (EMG) signals of the human operator, triggered by stimuli from the surrounding objects and scenery. An deep artificial neural network (ANN) with perceptrons classifies EMG signals, discerning the type of muscular stimuli generated. The research unveils a fuzzy-based oscillation patterns generator (OPG) designed to emulate functions akin to a neural central pattern generator, producing coordinated fish undulations. The OPG generates swimming behavior as an oscillation function, decoupled into coordinated step signals, right and left, for a dual electromagnetic oscillator in the fish propulsion system. Furthermore, the research presents an underactuated biorobotic mechanism of the subcarangiform type, comprising a two-solenoid electromagnetic oscillator, an antagonistic musculoskeletal elastic system of tendons, and a multi-link caudal spine composed of helical springs. The biomechanics dynamic model and control for swimming, as well as the ballasting system for submersion and buoyancy, are deduced. Experimental results encompass EMG pattern recognition, OPG, and coordinated fish locomotion, with additional topics illustrated through numerical simulations.

The overwhelming evidence supports the assertion that the climate is undergoing change. Understanding the perception of locals of the magnitude of climate change and its influence on non-timber forest products (NTFPs) challenge at a global scale. Although studies on climate change, value addition, and the multiple uses of NTFPs and their impact on local livelihoods have been conducted, locals' perceptions of these factors have not been fully explored. This study aims to assess various social factors, including education, gender, and religion, and their influence on locals' perceptions of the abovementioned factors. The research focused on five regions where NTFPs are prevalent in Ghana (Western North, Bono, Ahafo, Ashanti, and Eastern regions). To investigate the impact of social factors on local perceptions of climate change, value addition, and the multiple values of NTFPs within rural communities, a total of 732 interviews with a team of interviewers across these five regions in Ghana were carried out. We applied a logistic regression model to assess the social factors and their influencing locals’ perceptions of the mentioned variables. It was revealed that age, gender, education, and occupation did not significantly influence locals' perceptions of climate change. However, religion was found to influence locals' perceptions of climate change. Furthermore, the results demonstrated that gender (P<0.000328) and education (P<0.00426) strongly influenced locals' perceptions of value addition to NTFPs. In contrast, religion and constraints (related to time, finance, and skills) did not significantly influence locals' perceptions. Additionally, it was observed that gender (0.00132), education (0.00692), and religion (8.12e-05) significantly influenced how locals perceive and use NTFPs. In conclusion, this study provides valuable insights into the intricate interplay among local perceptions, climate change, value addition, multiple uses of NTFPs, and the capacity of NTFPs to enhance the well-being of forest fringe communities. These findings underscore the importance of comprehensive, tailor-made interventions that consider local perspectives and adhere to sustainable approaches, with the aim of optimising the positive impacts of NTFPs in poverty alleviation and overall community development.

In the present work we have studied the short-term edaphic recovery of a burned pine forest of Pinus pinaster Aiton in Cerro del Castillo (Sierra de Guadarrama, Central Spain) in which a hydrological-forestry restoration was being carried out after the fire occurred in July 2022. For this purpose, variations in physicochemical properties (conductivity, relative humidity, organic matter and pH) and collembolan communities were analyzed by comparing the soils of a natural pine forest and a burned pine forest during the 24 weeks after the fire in 3 different samplings. With the data obtained, a series of diversity indices were calculated and statistical analyses such as ANOVA, hierarchical classification and principal component analysis were developed, with the aim of understanding in greater depth the recovery of collembolan populations, the changes in these physicochemical variables and the relationships between both parameters. This study shows how the fire reduced the amount of organic matter and water, and increased soil pH and conductivity, affecting edaphic fauna populations. In the short term, there was an increase in relative humidity and a decrease in electrical conductivity. The edaphic fauna communities of the burned pine forest increased their diversity and equity without reaching the values of the natural pine forest. At the specific level, however, the springtail populations matched the richness and even reached a very slightly higher Shanon-Wiener diversity index than that the natural pine forest populations in just 24 weeks. Finally, it was observed that the samples from the burned pine forest with recovered collembolan populations had the closest physicochemical parameters to those of the natural pine forest.

Having in view the sustainable management of forests, the intensity of silvotechnical interventions is analyzed and documented for young mixed beech stands regenerated naturally by taking into account the allometric relationships between some representative dendrometric elements, respectively, between the height of the trees and the corresponding diameter of their crowns. For this purpose, a functional dependence is established between the height and diameter of the tree crown from young beech stands within which silvotechnical interventions were or were not applied. A mathematical model based on cubic spline regression was developed, also taking into account the determination of the average diameter of the crown for each stand. This average diameter can be used to determine the optimal number of trees per surface unit (ha) at a given time. The research was carried out in three young beech stands, through statistical-mathematical inventories, on sample plots of 300 square meters with a tolerance of 10% and a coverage probability of 90%. The optimal diameter of the crown was obtained by applying the spline regression model, and the most appropriate functional dependence was established, resulting in a linear regression law with a positive slope.

This research centers on the Mulberry-Dyke and Fish-Pond System in Digang Village, Huzhou, exem-plifying the traditional cyclic agricultural models of China. The study's objective is to evaluate its integral value concerning agricultural production, ecological environment, and heritage conservation. Employing the Analytical Hierarchy Process (AHP) and Fuzzy Comprehensive Evaluation (FCE) methodologies, we developed a comprehensive evaluation framework comprising 8 dimensions and 29 factors, aimed at assessing the provisioning, regulating, and cultural services of this ecosystem. The findings indicate a robust performance of the ecosystem services in Digang Village's Mulberry-Dyke and Fish-Pond System, with cultural services significantly standing out. In contrast, the regulating services appeared relatively weaker, pinpointing shortcomings in mulberry land management, while the provisioning services demonstrated substantial strength. These insights are pivotal for comprehending the system's ecosystem service values, highlighting areas for managerial improvements, and guiding future assessment and management strategies. This study contributes theoretical insights and empirical experiences for future evaluations of the Mulberry-Dyke and Fish-Pond System's ecosystem services.

The 21st century has seen the launch of new space-borne sensors based on LiDAR (light detection and ranging) technology developed in the second half of the 20th century. LiDAR was initially developed to integrate laser-focused imaging with the capability to determine distances through the measurement of signal return times, utilizing suitable sensors and data acquisition electronics. Nowadays, these sensors have transformed into robust instruments, offering novel opportunities for mapping terrain, canopy heights, and estimating above-ground biomass (AGB) across local to regional scales. This work aims to analyze the scientific impact of these sensors on large-scale for-est mapping to retrieve 3D canopy information, monitor forest degradation, estimate AGB, and model key ecosystem variables such as primary productivity and biodiversity. In this way, a worldwide bibliometric analysis of this topic was carried out based on up to 412 publications in-dexed in the Scopus database during the period 2004-2022. The results showed that the number of published documents increased exponentially in the last five years, coinciding with the commis-sioning of two new LiDAR space missions: Ice, Cloud and Land Elevation Satellite (ICESat-2) and Global Ecosystem Dynamics Investigation (GEDI). These missions are providing data since 2018 and 2019, respectively. The journal that demonstrated the highest productivity in this field was "Remote Sensing," and among the leading contributors, the top five countries in terms of publica-tions were the USA, China, the UK, France, and Germany. In the realm of prominent research in-stitutions, France boasted six, the USA had four, China had three, while the UK and Canada each had one. The upward trajectory in the number of publications recorded from 2004 to 2022 catego-rizes the subject under investigation as a highly trending research topic, particularly within the context of enhancing the administration of forest resources and engaging in global climate treaty frameworks mandating the surveillance and reporting of carbon stocks in forests. The recent launch in August 2022 of the Terrestrial Ecosystem Carbon Monitoring Satellite (TECMS; China State Administration of Forestry and Grassland), along with the planned launch in the coming years of up to three new space sensors, such as the Multi-footprint Observation LiDAR and Im-ager (Japan Aerospace Exploration Agency), the BIOMASS P-band Synthetic Aperture Radar (SAR) (European Space Agency), and the LiDAR Surface Topography (LIST; NASA), will greatly contribute to expanding the ability to map and monitor forest systems at very large scales. In this context, the integration of space-borne data, including imagery, SAR, and LiDAR, is anticipated to steer the trajectory of this research in the upcoming years.

The extended cleavage specificities of two hematopoietic serine proteases originating from the ray-finned fish spotted gar (Lepisosteus oculatus) have been characterized using substrate phage display. The preference for particular amino acids at, and surrounding the cleavage site was further validated using a panel of recombinant substrates. For one of the enzymes, the gar granzyme G, a strict preference for the aromatic amino acid Tyr was observed at the cleavable P1 position. Using a set of recombinant substrates showed that the gar granzyme G had a high selectivity for Tyr, but also with lower activity cleaving after Phe but not after Trp. The second enzyme, gar DDN1, instead showed a high preference for Leu in the P1 position of substrates. This latter enzyme also showed high preference for Pro in the P2 position and Arg in both P4 and P5 positions. The selectivity for the two Arg residues in positions P4 and P5, suggest a highly specific substrate selectivity of this enzyme. Screening of the gar proteome with the consensus sequences obtained by substrate phage display for these two proteases resulted in a very diverse set of potential targets. Due to this diversity, a clear candidate for a specific immune function of these two enzymes cannot yet be identified. Antisera developed against the recombinant gar enzymes were used to study their tissue distribution. Tissue sections from juvenile fish showed expression of both proteases in cells in Peyer´s patch-like structures in the intestinal region, indicating they may be expressed in T or NK cells. However, due to the lack of antibodies to specific surface markers in the gar, it has not been possible to specify the exact cellular origin. A marked difference in abundance was observed for the two proteases where gar DDN1 was expressed at higher levels than gar granzyme G. However, both appear to be expressed in the same or similar cells, having a lymphocyte-like appearance.

Land surface temperature (LST) is a critical parameter for land surface and atmospheric interactions. However, the applicability of current land surface temperature estimates for field-level hydrological, agricultural, and ecological operations is still challenging due to their coarse spatiotemporal resolution. In the current article, we have downscaled 100 m LST to 10 m by using high-resolution Sentinel-1,2 optical-microwave data and three different models- 1) Thermal Sharpening (TsHARP); 2) Thin Plate Spline (TPS); and 3) Random Forest (RF). The extensive analysis was performed at agricultural farms in the semi-arid (IARI) region of India during the winter and summer seasons of 2020-21 and 2021-22, arid (CAZRI) and per-humid (UBKV) region (2021-2022). The calibration accuracy of the RF model was shown to better agreement with the coefficient of determination (R2) of 0.982, root means square error (RMSE) 0.181 and normalized root means square error (RMSE) 0.061 with their lower values of standard errors over three diverse agro-climatic zones. The findings demonstrate that the validation accuracy of models' varied according to the agroclimatic zones, although RF and TPS consistently outperformed as compared to the TsHARP models.

Gesture recognition technology has been quickly developed in the field of human-computer interaction. The multiple-input multiple-output (MIMO) radar has been widely adopted in gesture recognition because of its notable spatial resolution. In this work, a highly accurate MIMO radar-based hand gesture recognition algorithm with very low complexity is proposed. To make the proposed system applicable in the industry and work well even when the size of the training data is limited, we applied several low-complexity adaptive signal processing methods to extract features and reduce the noise effect. First, spectrum analysis is applied to range-Doppler maps (RDMs) and a cell-averaging constant false alarm rate (CA-CFAR) with mirror filters is applied to improve the robustness to noise. Afterward, the features related to the distance, speed, direction, and the elevation angle of the moving object are determined by the proposed adaptive signal analysis techniques and the random forest is applied for classification. The proposed system has the capability to precisely distinguish and identify eight motions, including waving, moving to the left or right, patting, pushing, pulling, and rotating clockwise or anticlockwise, with an accuracy of 95%. Experiments demonstrate the capability of the proposed hand gesture recognition system to classify different movements precisely.

This study developed Makgeolli (Korean traditional alcoholic beverage) by adding raw 20% Codium fragile (Cf), a green seaweed, to a mixed base consisting of rice and oats (1:1 ratio) (COM) and analyzed its quality characteristics and antioxidant activity. The rice based Makgeolli supplemented with raw 20% Cf was designated as control (CRM). The physicochemical (pH, acidity), microbiological (yeast, lactic acid bacteria), antioxidant (DPPH, ABTS), and sensory properties (7-henodonic scale) of CRM and COM were investigated during 7 days of fermentation. The pH/acidity levels of CRM and COM reached 3.66/0.68 and 3.69/0.95 after 7 days of fermentation, respectively. Compared to CRM, COM had lower alcohol and sugar contents (P < 0.05) (CRM: 14.5%/9.8 Brix, COM: 11.2%/7.4 Brix). However, COM contained more yeast and lactic acid bacteria (P < 0.05) (CRM: 1.30/4.28 log, COM: 6.04/6.09 log) compared to CRM. The antioxidant activity of DPPH/ABTS (P < 0.05) in COM was approximately 1.5 times that of CRM (CRM: 40.00/51.57%, COM: 56.76/70.91%). In addition, COM had an excellent sensory preference score (P < 0.05) due to its overall fresh flavor and savory taste (CRM: 4.93, COM: 6.06). Therefore, COM was shown to be Makgeolli with enhanced antioxidant function, with yeast and lactic acid bacteria, and improved flavor, suggesting the possibility of commercializing this Makgeolli.

Multispectral and 3D LiDAR remote sensing data sources are valuable tools for characterizing the 3D vegetation structure and thus understanding the relationship between forest structure, biodiversity and microclimate. This study focuses on mapping riparian forest species in the canopy strata using a fusion of Airborne LiDAR data and multispectral multi-sources and multi-resolution satellite imagery; Sentinel-2 and Pleiades at tree level. The idea is to assess the contribution of each data source in the tree species classification at the considered level. The data fusion was processed at feature-level and decision level. At feature level, LiDAR 2D attributes were derived and combined with vegetation indices. At decision level, LiDAR data was used for 3D tree crown delimitation providing unique trees or groups of trees that are used as a support for the species classification. Data augmentation techniques were used to improve the training process. Best results were obtained by the fusion of Sentinel-2 time series and LiDAR data with a Kappa of 0.66 thanks to red-edge based indices that better discriminate vegetation species and the temporal resolution of Sentinel-2 images that allows monitoring the phenological stages helping to discriminate the species.