Phylogenetic trees of coronaviruses are difficult to interpret because they undergo frequent ge-nomic recombination. Here, we propose a new method, named coloured genomic bootstrap (CGB) barcodes, to highlight the polyphyletic origins of human sarbecoviruses and understand their host and geographic ori-gins. The results indicate that SARS-CoV and SARS-CoV-2 contain genomic regions of mixed an-cestry originating from horseshoe bat (Rhinolophus) viruses. First, different regions of SARS-CoV share exclusive ancestry with five Rhinolophus viruses from Southwest China (RfYNLF/31C: 17.9%; RpF46: 3.3%; RspSC2018: 2.0%; Rpe3: 1.3%; RaLYRa11: 1.0%) and 97% of its genome can be related to bat viruses from Yunnan (China), supporting its emergence in Rhinolophus species of this province. Second, different regions of SARS-Cov-2 share exclusive ancestry with eight Rhi-nolophus viruses from Yunnan (RpYN06: 5.8%; RaTG13: 4.8%; RmYN02: 3.8%), Laos (RpBA-NAL103: 3.3%; RmarBANAL236: 1.7%; RmBANAL52: 1.0%; RmBANAL247: 0.7%), and Cam-bodia (RshSTT200: 2.3%), and 98% of its genome can be related to bat viruses from northern Laos and Yunnan, supporting its emergence in Rhinolophus species of this region. Although CGB barcodes are very useful to retrace the origins of human sarbecoviruses, further investigations are needed to better apprehend the diversity of coronaviruses in bats from Cambo-dia, Laos, Myanmar, Thailand and Vietnam.

Practitioners who assess the risk associated with urban trees often factor in the presence or absence of visual tree defects when determining whether a tree may fail. While these defects are a main fixture in many tree risk assessment systems and best management practices, the research supporting their usefulness in predicting tree failure during storms is limited. When looking at past research involving populations of storm-damaged trees, there are several defects that have never predicted failure (or have been associated with reduced rates of failure). In this study, we took a closer look at four such defects: codominant branches; branch unions with included bark; multiple stems originating from the same point; and overextended branches. After Hurricane Ian, we revisited 1519 risk assessed trees where one of these four defects was identified as the primary condition of concern. Fourteen of these trees experienced branch failure during the storm (which hit the study area as a downgraded tropical storm). Upon closer inspection, none of these failures occurred at the defect of concern. Our findings indicate that none of the defects assessed appeared to increase the likelihood of tree failure in the species tested. Our results are in line with past research on these defects derived from post-storm assessments and analysis.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Responses to water stress were measured for sugar maple (Acer saccharum subsp. saccharum Marshall) sources from Oklahoma (Caddo sugar maple), Missouri, Tennessee, Ontario and a black maple (Acer saccharum subsp. nigrum F. Michx.) source from Iowa. Seedling sources were selected for differences in temperature and precipitation of their geographic origins. Seedlings were preconditioned through moist (watered daily) or dry (watered every 4-7 days) cycles and then exposed to prolonged water stress. Dry preconditioned sugar maple seedlings from Oklahoma, Missouri, and Tennessee, sources from warmer and/or drier climates with greater relative evapotranspiration potentials, declined less rapidly in net photosynthesis, stomatal conductance, and water use efficiency (WUE) as water stress increased than dry preconditioned seedlings from Ontario and Iowa having origins in cooler, moister climates. Under imposed water stress the Ontario and Iowa sourced seedlings increased their root to shoot ratios and decreased their specific leaf area, mechanisms for drought avoidance. However, no corresponding changes in these values occurred for Oklahoma, Missouri, and Tennessee sources. Results from this study suggest greater tolerance of water stress in the Oklahoma, Missouri and Tennessee ecotypes from the western and southern range of sugar maple resulted primarily with WUE rather than other water stress coping mechanisms.

Oaks are a dominant and key tree species in Mediterranean forest ecosystems. However, in recent decades, oak forests have been heavily impacted by oak decline, a worldwide phenomenon exacerbated by climate change. The charcoal disease agent Biscogniauxia mediterranea is involved in the decline of Mediterranean oak formations in a variety of contexts. Here, we investigated the impact and role of B. mediterranea in decline of oaks in Castelfidardo Forest, a relict wood of the late Holocene and a Site of Community Importance. We established five plots within which we recorded the tree positions, any symptoms and signs of decline, association of B. mediterranea to declining trees, and the deadwood and associated mycota. Of 471 oaks inspected, 7.0% showed brownish exudates on the stems, 46.9% showed epicormic shoots along the main trunk, and 24.4% showed black carbonaceous stromata on diseased branches and trunks. The decline was most severe for Quercus cerris, which comprised plots #4 and #5, at 50.0% (81/162 trees) and 29.0% (33/114), respectively; then for Quercus robur for plot #3, at 40.0% (38/95); and finally for Quercus pubescens for plots #1 and #2, at 13.7% (7/51) and 12.3% (6/49), respectively. Bark tissue was collected from trees with charcoal cankers, with microscopy examination and identification by mycological and molecular methods. This investigation revealed close association between oaks with pronounced reduction of vitality and incidence of B. mediterranea. The deadwood was equally distributed among the five plots, and was heavily colonized by Basidiomycota. The high incidence of this oak-weakening pathogen B. mediterranea appears to be related to stress conditions (e.g., water scarcity, prolonged drought). However, absence of silvicultural management, high competition among physiologically mature trees, and the geographic isolation of this residual forest might have predisposed these oaks to decline.

Flexible implementations of Monte Carlo Tree Search (MCTS), combined with domain specific knowledge and hybridization with other search algorithms, can be a very powerful for the solution of problems in complex planning. We introduce mctreesearch4j, a standard MCTS implementation written as a standard JVM library following key design principles of object oriented programming. We define key class abstractions allowing the MCTS library to flexibly adapt to any well defined Markov Decision Process or turn-based adversarial game. Furthermore, our library is designed to be modular and extensible, utilizing class inheritance and generic typing to standardize custom algorithm definitions. We demon- strate that the design of the MCTS implementation provides ease of adaptation for unique heuristics and customization across varying Markov Decision Process (MDP) domains. In addition, the implementation is reasonably performant and accurate for standard MDP’s. In addition, via the implementation of mctreesearch4j, the nuances of different types of MCTS algorithms are discussed.

Soil CO2 efflux (FCO2) plays a dominant role in the terrestrial carbon (C) cycle but interpreting constraints on local observations is impeded by challenges in disentangling belowground CO2 sources. Trees contribute most C to forest soils, so linking aboveground properties to FCO2 could open new avenues to study plant-soil feedbacks and facilitate scaling; furthermore, FCO2 responds dynamically to meteorological conditions, complicating predictions of total FCO2 and forest C balance. We tested for proximity effects of individual Acer saccharum Marsh. trees on FCO2, comparing FCO2 within 1 m of mature stems to background fluxes before and after an intense rainfall event. Wetting significantly increased background FCO2 (6.4±0.3 vs. 8.6±0.6 s.e. μmol CO2 m-2s-1), with a much larger enhancement near tree stems (6.3±0.3 vs. 10.8±0.4 μmol CO2 m-2s-1). FCO2 varied significantly among individual trees and post-rain values increased with tree diameter (with a slope of 0.058 μmol CO2 m-2s-1 cm-1). Post-wetting amplification of FCO2 (the ‘Birch effect’) in root zones often results from the improved mobility of labile carbohydrates and further metabolization of recalcitrant organic matter, which may both occur at higher densities near larger trees. Our results indicate that plant-soil feedbacks change through tree ontogeny and provide evidence for a novel link between whole-system carbon fluxes and forest structure.

This work presents updated reconstructions of watershed runoff to San Francisco Estuary from tree-ring data to AD 903, coupled with models relating runoff to freshwater flow to the estuary and salinity intrusion. We characterize pre-development freshwater flow and salinity conditions in the estuary over the past millennium and compare this characterization with contemporary conditions to better understand the magnitude and seasonality of changes over this time. This work shows that the instrumented flow record spans the range of runoff patterns over the past millennium (averaged over five, ten, twenty and one hundred years), and thus serves as a reasonable basis for planning-level evaluations of historical hydrologic conditions in the estuary. Over annual timescales we show that, although median freshwater flow to the estuary has not changed significantly, it has been more variable over the past century compared to pre-development flow conditions. We further show that the contemporary period is generally associated with greater spring salinity intrusion and lesser summer-fall salinity intrusion relative to the pre-development period. Thus, salinity intrusion in summer and fall months was a common occurrence under pre-development conditions and has been moderated in the contemporary period due to the operations of upstream reservoirs, which were designed to hold winter and spring runoff for release in summer and fall. This work also confirms a dramatic decadal-scale hydrologic shift in the watershed from very wet to very dry conditions during the late 19th and early 20th centuries; while not unprecedented, these shifts have been seen only a few times in the past millennium. This shift resulted in an increase in salinity intrusion in the first three decades of the 20th century, as documented through early records. Population growth and extensive watershed modification during this period exacerbated this underlying hydrologic shift. Putting this shift in the context of other anthropogenic drivers is important in understanding the historical response of the estuary and in setting salinity targets for estuarine restoration. By characterizing the long-term behavior of San Francisco Estuary, this work supports decision-making in the State of California related to flow and salinity management for restoration of the estuarine ecosystem.

Financial markets require a great deal of decision making from the investors and market makers. One metric that can help ease the process of decision making is investment risk which can be measured in two parts; systematic risk and idiosyncratic risk. Clear understanding of the volatilities in each risk component can be a powerful signal in recognizing the right assets to maximize the investment returns. In this paper, we focus on the idiosyncratic volatility values and pre-calculate the idiosyncratic volatility values for 31,198 members of NYSE, Amex and Nasdaq markets for the trades occurring between January 1963 and December 2019. Utilizing a subset of dataset, limited to Nasdaq100 index, we consider the application of machine learning techniques in predicting the idiosyncratic volatility values using the raw trade data to explore a data extension option for the future market trade records that have not yet occurred. We offer a deep learning based regression model and compare it with traditional tree-based methods on a small subset of our per-calculated idiosyncratic volatility dataset. Our analytical results show that the performance of the deep learning techniques is much more robust in comparison to that of the traditional tree-based baselines.

Tapping panel dryness (TPD) syndrome is a complex disease of Rubber tree (Hevea brasiliensis L.) which causes cessation of latex drainage upon tapping of rubber tree. Rubber tree virus (RTV1) was identified as a novel pathogen associated with rubber tree and a potential causal agent of TPD. RTV1 is a monopartite RNA virus that is linear, non-enveloped and has a single-stranded (ss) positive RNA genome of approximately 6081 nucleotides and is composed of two major open reading frames (ORFs), ORF1 (polyprotein), and ORF2 (movement protein. This study aimed to investigate the possibility of rubber genome encoded tree microRNAs (miRNAs) as novel therapeutic targets against RTV1 using in silico algorithms. Mature rubber tree miRNAs are retrieved from the miRBase database and are used for hybridization of RTV1 using five different five different computational algorithms including miRanda, RNA22, RNAhybrid and psRNATarget. A total of eleven common rubber tree miRNAs were identified based on consensus genomic positions. The consensus of four algorithms predicted the hybridization sites of hbr-miR396a and hbr-miR398 at common locus positions 6676, 1840 respectively. To validate the prediction, secondary structures of the consensual rubber tree miRNAs and free energy of duplex binding were calculated using the RNAfold and RNAcofold algorithms respectively. We created a plot between rubber tree miRNAs and RTV1 ORFs by using Circos algorithm. In this study, we predicted eleven consensual rubber tree miRNAs. Among these miRNAs, hbr-miR398 was identified as the most effectual miRNA that may target the ORF1 gene of the RTV1 genome. The predicted data will be important in the development of rubber trees resistant to RTV1.

To substitute of conventional manure and peat with alternatives sourcing from environmental conservation concerns, several promising alternatives has been attracting scientific parties’ interest, recently. However, among them compost perform the best, mostly and support carbon sequestration and mitigation against climate change. The article describes the made locally produced 70% in volume olive pruning branches compost (COMP) performance in two trials as an organic amendment in pepper production and an olive sapling substrate during 2019-2021 organic management in Turkey. The application of COMP to pepper trial conducted using factorial randomised block design with 4 replications and 6 treatments increased total organic matter and soil organic carbon, significantly (p<0.05) as compared to non-used plots in two locations. The olive sapling trial was conducted using a randomised plot design with 4 replications and 4 treatments. After the 12 months of growth, compost had the largest architecture rooted plants significantly different (p<0.05). Fresh volume (cm³) of COMP used saplings were obtained 35% less than 40% peat treatment, significantly (p<0.05) while 6^th month measurement was found as 40%. It is concluded that to enhance circular economy recycling and composting olive pruning branches is lucrative for the country to reduce external input usage in organic horticultural production.

At present, diversified and highly concurrent businesses in the Internet industry often require heterogeneous databases formed by multiple databases to meet the needs. This report introduces database kernel SG-ColBase we developed. After achieving read and write concurrency control, data rollback, atomic log writing, and downtime data redo to ensure complete transaction support. The parallelism of database kernel execution is extended through field level locks and snapshot reads. Use the Bloom filter, resource cache pool, memory pool, skip list, non blocking log cache, and asynchronous data writing mechanism to improve the overall execution efficiency of the system. In terms of data storage, column storage, logical key and LSM-tree are introduced. While improving the data compression ratio and reducing data gaps, all disk data operations are written in incremental order. With the characteristics of asynchronous batch operation, the data writing speed is greatly improved. Thanks to the continuous feature of vertical data brought by column storage, the disk scanning brought by vertical traversal is reduced, which is a qualitative leap in efficiency compared with traditional relational databases in the big data analysis scenario. SG-ColBase can reduce the use of heterogeneous databases in business and improve R&D efficiency.

In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.

In plants, phase change from the juvenile stage to maturity is a tightly controlled process at the physiological and genetic level, which is controlled by evolutionary highly conserved microRNAs. These phase changes are more pronounced in woody plant species, but the majority of molecular genetic studies on the regulation of this transition has been done in annual model or crop species. This process is of particular significance for the in vitro propagation of woody plant species, as individuals or tissues that have undergone the transition to vegetative maturity are recalcitrant to propagation. Development of effective methodologies for silver birch vegetative propagation are required to increase the efficiency of breeding programs. Conserved miRNAs that were differentially expressed between juvenile and mature silver birch tissues were identified using high-throughput sequencing of small RNA libraries. These differentially expressed miRNAs could potentially be utilized to develop markers indicating the juvenility or maturity of silver birch explants and in vitro cultures. In addition, the obtained results will provide an insight into the molecular mechanisms regulating vegetative phase change in silver birch and other perennial woody plant species.

Trees in residential landscapes provide many benefits, but can injure persons and damage property when they fail. In hurricane-prone regions like Florida, USA, the regular occurrence of hurricanes has provided an opportunity to assess factors that influence the likelihood of wind-induced tree failure and develop species failure profiles. We assessed open-grown trees in Naples, Florida, following the passage of Hurricane Irma in September 2017 to determine the effect of relevant factors on the degree of damage sustained by individual trees. Of 4,034 assessed individuals (n = 15 species), 74% sustained no damage, 4% sustained only minor damage (i.e., minimal corrective pruning needed), 6% sustained significant damage (i.e., major corrective pruning needed), and 15% were whole tree failures (i.e., overturned trees or trees requiring removal). The proportion of individuals in each damage category varied among species, stem diameter at 1.4 m above ground, and the presence of utility lines, which was a proxy for maintenance. We compared our results with the findings of seven previous hurricanes in the region to explore species’ resilience in hurricanes.

Delineating chilling and forcing periods is one of the challenging topics in understanding how temperatures drive the timing of budburst and bloom in fruit tree species. Here, we investigated this question on olive trees, using flowering data collected over six years on 331 cultivars in the worldwide collection of Marrakech, Morocco. Using a Partial Least Squares approach on a long-term phenology (29 years) of ‘Picholine Marocaine’ cultivar, we showed that the relevance of delineating the chilling and forcing periods depends more on the variability of inter-annual temperatures than on the long-term datasets. In fact, chilling and forcing periods are similar between those delineated by using datasets of 29 years and those of only 6 years (2014–2019). We demonstrated that the variability of inter-annual temperatures is the main factor explaining this pattern. We then used the datasets of six years to assess the chill and heat requirements of 285 cultivars. We classified Mediterranean olive cultivars into four groups according to their chill requirements. Our results, using the Kriging interpolation method, indicated that flowering dates of most of these cultivars (92%) were governed by both chilling and forcing temperatures. Our investigations provided first insights to select adapted cultivars to global warming.

Tree ordinances can be an effective means of preserving urban forests in the face of development pressures. Despite this, they also have the potential to be divisive among the public - especially when applied to privately-owned land. In this study we surveyed 1,716 Florida urban residents to understand how they value regulation and management of the urban forest. Specifically, we asked about: tree protection ordinances, incentive programs to manage or plant trees, justification for tree removal, and development. Most respondents supported tree protections, even when applied to trees on their own property or when they had the potential to limit development activities. Additionally, there was limited support for removing healthy trees for development. Respondents supported the use of funds for urban forestry efforts – particularly at the local or state level.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first identified in humans in Wuhan City at the end of December 2019. Since then, it has spread to all the countries. Therefore, global interest has been focused on discovering treatments and developing successful vaccines. This study sequenced the complete genome of the SARS-COV-2 Omicron Omicron (BA.1) and sub-variants (BA.1.1, BA.2), which were isolated from 40 individuals in Duhok, Iraq. Ninety-five different mutations were identified when the complete genome of the SARS-COV-2 virus discovered in Wuhan, China (accession number: NC 045512.2) was matched to the virus sequence using sequencing technology (Illumina, USA). Sequence analysis revealed 38 mutations in spike glycoprotein (S), 30 of which were found in ORF1a. Additionally, 11 mutations were found in ORF1b, and 7,3,2,1 mutations were found in Nucleocapsid (N), membrane protein (M), Open Reading Frames 6 (ORF6), Open Reading Frames 9 (ORF9), and Envelope (E) genes, respectively. Phylogenetic analysis and transmission further confirmed that the isolates found in Iraq had distinct infection origins and were closely related to those from other countries and states. According to the findings of this study, a new vaccine can be developed based on identifying new Omicron variant mutations and sub-variants such as BA.2, which were identified for the first time in Iraq.

Using the orbit decomposition, a new enumerative polynomial P(x) is introduced for abstract (simplicial) complexes of a given type, e.g., trees with a fixed number of vertices or triangulations of the torus with a fixed graph. The polynomial has the following three useful properties. (I) The value P(1) is equal to the total number of unlabeled complexes (of a given type). (II) The value of the derivative P'(1) is equal to the total number of nontrivial automorphisms when counted across all unlabeled complexes. (III) The integral of P(x) from 0 to 1 is equal to the total number of vertex-labeled complexes, divided by the order of the acting group. The enumerative polynomial P(x) is demonstrated for trees and then is applied to the triangulations of the torus with the vertex-labeled complete four-partite graph G = K_{2,2,2,2}, in which specific case P(x) = x^{31}. The graph G embeds in the torus as a triangulation, T(G). The automorphism group of G naturally acts on the set of triangulations of the torus with the vertex-labeled graph G. For the first time, by a combination of algebraic and symmetry techniques, all vertex-labeled triangulations of the torus (twelve in number) with the graph G are classified intelligently without using computing technology, in a uniform and systematic way. It is helpful to notice that the graph G can be converted to the Cayley graph of the quaternion group Q_8 with the three imaginary quaternions i, j, k as generators.

The proton exchange membrane fuel cell (PEMFC) is a critical and essential component of zero-emission electro-hydrogen generators. An accurate prediction of its performance is important for optimal operation management and preventive maintenance of these generators. However, the prediction is not simple because the PEMFCs have complex electrochemical reactions with multiple nonlinear relations between operating variables as inputs and voltage as output. In this paper, we propose an efficient prediction approach based on XGBRegressor and Tree-structured Parzen Estimator. In addition, to better select relevant features, Kernel Principal Component Analysis and Mutual Information are jointly used. The proposed approach allows consideration of the dynamic operating conditions of the PEMFC. To test and validate the robustness of the proposed approach, a real data-set of ten PEMFCs was considered. Furthermore, a comparison study with traditional machine learning models, such as artificial neural networks and support vector machine regression, was investigated. The obtained results confirm that our model outperforms the considered traditionel machine learning models.

Tropical forests provide habitats for diverse flora and fauna. In addition, they are essential in regulating the climate while at the same time millions of people depend on them for their livelihoods. As seen by rising political commitment and worldwide promises to reduce rates of deforestation and boost restoration of degraded forest ecosystems, forests are becoming more widely acknowledged for their role as a nature-based solution to many sustainable development concerns. Understanding tropical forest dynamics and their conservation have so become more important. This study analyzed, the forest stand structure, tree species composition, abundance and diversity and the regeneration status of Londiani Forest. Londiani Forest is divided into three blocks, namely Kedowa, Chebewor and Londiani and from each of these blocks belt transects 25 m wide and 1 km long were established 100 m from the edge of the forest to reduce edge effects. At every 200 m along the transect, 25 m × 25 m quadrats were set up in which, an inventory of all tree species was done. For tree species that could not be identified in the field, the local name was provided by para-taxonomists who participated in the data collection and the species name was later identified with the help of a taxonomist or a manual of woody tree species of Londiani Forest. The diameter at breast height (DBH) was measured at 1.3 m from the ground using a diameter tape. Tree height was measured using a Suunto angular clinometer. A nested quadrat 5 m x 5 m quadrat within the 25 m x 25 m quadrat was used to sample saplings while a 1 m x 1 m quadrat was used for sampling seedlings. Regeneration status was assessed using the number of seedlings and saplings. Stumps of trees cut were counted including identifying tree species from which the tree stump was derived. Data from the quadrats were entered into Microsoft Excel. Total stem density, species density, basal area, species basal area, relative density and species diversity were determined. A total of 1,308 individual trees belonging to 34 different species from 24 families were recorded. Kedowa had the highest (27) species richness followed by Chebewor (19) then Londiani (14). There was a statistically significant difference in species richness among the three forest blocks (p<0.05). There were no statistically significant differences in the mean DBH (F=0.560; p=0.729) and height class distribution (F= 0.821, p=.558) among the three forest blocks. There was a statistically significant difference in stem density (F=12.22; p=0.005) and woody species diversity (F=0.32; p=0.001) within the three forests blocks. There was no statistically significant difference in the basal area distribution within the three forest blocks p>0.005. The similarity index ranged from 0.34 to 0.47. In all the forest blocks, there was evidence of seedlings and saplings indicating that there was regeneration. Results clearly show that while the Londiani Forest shows variation in characteristics, there is clear evidence that it is regenerating.

Canistel (Pouteria campechiana (Kunth) Baehni) (yellow sapote, canistel). Originally from Mexico, the fruit is edible, and the tree is used as an ornamental and medicinal plant. Descriptive studies were carried out with the objective of morphologically characterizing the fruits and leaves of trees located in different regions of Mexico to select outstanding specimens and propose their massive reproduction. The trees were selected in three zones. In zone 2 from flower anthesis to harvest 11 months, fruits with 3 seeds, greater weight (180 to 330 g) and quantity of pulp (198.88 g), subglobose shape, total soluble solids 33 %. The fruits of zone 1 at harvest 8 months and zone 3 took 9 months to be harvested, in the rest of the characteristics evaluated were exceeded by the fruits of zone 2. To characterize the canistel (P. campechiana) leaf and identify accessions or cultivars, the three main variables, according to the principal component analysis were: leaf area, perimeter, and minor diameter. The three zones where canistel fruits and leaves were characterized are important and can be recommended for propagation in zones with favorable climatic conditions for fruiting in tropical and subtropical regions of Mexico.

Tree nuts show nutritional properties and human health benefits. However, they contain allergenic proteins, which make them harmful to the sensitised population. The presence of tree nuts on food labelling is mandatory and, consequently, the development of suitable analytical methodologies to detect nuts in processed foods is advisable. Real-time PCR allowed a specific and accurate amplification of allergen sequences. Some food processing methods could induce structural and/or conformational changes in proteins by altering their allergenic capacity, as well as produce the fragmentation and/or degradation of genomic DNA. In this work, we analysed by means of Real-time PCR, the influence of pressure and thermal processing through Instant Controlled Pressure Drop (DIC) on the detectability of hazelnut,pistachio and cashew allergens have been tested. The detection of targets in hazelnut, pistachio and cashew (Cor a 9, Pis v 1 and Ana o 1, respectively) is affected by the treatment, in different extent depending on the tree nut. Results are compared to those previously obtained by our group in the analysis of different treatments on the amplificability of the same targets. Reduction in amplificability is similar to that reported for some autoclave conditions. Our assays might allow detecting up to 1000 mg/kg of hazelnut, pistachio and cashew flours after being submitted to DIC treatment in food matrices.

Crapemyrtle (Lagerstroemia indica L.: Lythraceae) is the most popular summer flowering tree in the U.S. Its total value sold has almost doubled since 1998. Consumers prize crapemyrtles for their beauty and pest resistance. However, current crapemyrtle production and use is being threatened by crapemyrtle bark scale (Acanthococcus lagerstroemiae (Kuwana, 1907)) (CMBS), which has been confirmed in 12 U.S. states after its first sighting in Texas in 2004. Our survey results indicate that producers anticipate a significant decrease in the value of crapemyrtle due to CMBS, in the magnitude of 29.93% and 33.79%, in our 2018 and 2019 surveys respectively. Our findings indicate industry demand for CMBS control. We used a non-parametric test to compare the producers’ responses to several questions regarding CMBS-control, among the different producer categories included in our sample. Incorporated businesses showed the most support, followed by part-nerships, and family/individual operations were the least supportive of science-based CMBS control research. Large businesses predicted a more serious decrease in crapemyrtles’ value as compared to smaller businesses. More businesses with high volume of crapemyrtle-related business considered the benefits of CMBS-control to be higher than its cost, as compared to other businesses. We also used a relative importance index to illustrate the ranking of different attrib-utes of crapemyrtles that producers consider while making decisions about growing/purchasing the plants. Flower color was found to be the most important attribute, followed by disease re-sistance. If the issue of CMBS gets out of control, the industry might need to find potential re-placements to crapemyrtle. The most popular landscape plants that can potentially replace crapemyrtle, in the opinion of producers we surveyed, are vitex (Texas lilac) and magnolia.

The demand for explainable and transparent models increases with the continued success of reinforcement learning. In this article, we explore the potential of generating shallow decision trees (DT) as simple and transparent surrogate models for opaque deep reinforcement learning (DRL) agents. We investigate three algorithms for generating training data for axis-parallel and oblique DTs with the help of DRL agents ("oracles") and evaluate these methods on classic control problems from OpenAI Gym. The results show that one of our newly developed algorithms, the iterative training, outperforms traditional sampling algorithms, resulting in well-performing DTs that often even surpass the oracle from which they were trained. Even higher dimensional problems can be solved with surprisingly shallow DTs. We discuss the advantages and disadvantages of different sampling methods and insights into the decision-making process made possible by the transparent nature of DTs. Our work contributes to the development of not only powerful but also explainable RL agents and highlights the potential of DTs as a simple and effective alternative to complex DRL models.

Field observations of external wounds associated with two common tree injection methods compared open (plug-less) and sealed (plug) systems in green ash (Fraxinus pennsylvanica Marshall) trees . A wound from any cause within 1.37 meters above the ground was common with 28.8% of all trees. The open system had statistically fewer (p&lt;0.001) trees with at least one wound (11.6% of trees) than the sealed system (47.4% of trees). The open system had fewer (P&lt;0.001) wounds (0.17, 0.04 SE) per tree and a smaller (P&lt;0.001) total wound area (25.5 cm2, 8.7 SE) per tree, compared to the sealed system wounds (1.14, 0.13 SE) per tree and the total wound area (99.7 cm2, 16.2 SE) per tree. The incidence of a tree with a wound(s) within 1.37 m above the ground was 7.2 times more likely with trees treated though the sealed system. Wounds in the sealed system were observed to appear to have a high rate of improper application of plugs, which was associated in 77% of the cases to explain the wounds. Implications of study results are further provided to best protect ash trees, while at the same time reducing the incidence external wounding on ash trees.

Ponderosa pine is an integral part of the forested landscape in the western US; it is the dominant tree species on landscapes that provide critical ecosystem services. Moderate drought tolerance allows it to occupy the transition zone between forests and open woodlands and grasslands. Increases in stand density resulting from wildfire suppression, combined with lengthening, intensifying and more frequent droughts have resulted in reduced tree vigor and stand health in dry ponderosa pine throughout its range. To address a management need for efficient landscape-level surveys of forest health, we used Random Forests to develop an object-oriented classification of individual tree crowns (ITCs) into vigor classes using existing, agency acquired 4-band aerial imagery. Classes of tree vigor were based on quantitative physiological and morphological attributes established in a previous study. We applied our model across a landscape dominated by ponderosa pine with a variety of forest treatments to assess their impacts on tree vigor and stand health. We found that stands that were both thinned and burned had the lowest proportion of low vigor ITCs, and that stands treated before the 2014-2016 drought had lower proportions of low vigor ITCs than stands treated more recently (2016). Upland stands had significantly higher proportions of low vigor trees than lowland stands. Maps identifying the low vigor ITCs would assist managers in identifying priority stands for treatment and marking trees for harvest or retention. These maps can be created using already available imagery and GIS software.

Effective development of digital twins of real-world objects requires sophisticated data collection techniques and algorithms for automated modeling of individual objects. In City Information Modeling (CIM) systems, individual buildings can be modeled automatically at the second Level Of Detail or LOD 2. Similarly, automated solutions for 3D modeling of individual trees at different levels of details are required for Tree Information Modeling (TIM) and building Forest Digital Twins (FDT). The existing algorithms support the automated modeling of trees by generating models of the canopy and the lower part of the trunk. Our work in this paper is based on the assumption that, similar to the canopy shape, the structure of tree trunk and branches is also an important consideration. An algorithm for automated modeling of tree trunks is proposed. Aiming to generate 3D models of tree trunks, the suggested approach starts by extracting the trunk point cloud, which is then segmented into single stems. Finally, the individual stems are modeled by dividing them into slices. The geometry of the developed tree models is represented in a matrix format. This solution enables automatic comparisons of tree elements over time, which is necessary for monitoring changes in forest stands and promoting sustainable forest management. Despite the huge variation of trunk geometric forms, the proposed modeling approach can gain an accuracy of better than 4 cm in the constructed tree trunk models.

In the broad context of vulnerability detection, deep learning has achieved considerable progress but faces generalization challenges in multilingual environments . We introduce a novel approach named AST-FMVD, which leverages transfer learning and abstract syntax trees. By employing semantic similarity clustering and context-aware technology, the method constructs node mapping relationships between different languages, enabling zero-shot learning in vulnerability detection. The method was validated by applying Java's vulnerability detection model in the Python domain, successfully demonstrating that AST-FMVD retains the original model's detection capabilities in the target domain. In conclusion, the proposed method offers a promising solution to the inherent problems in multi-language vulnerability detection, signifying a potential leap in the application of deep learning, transfer learning, and abstract syntax trees for improved cross-domain performance.

Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. This study investigated the molecular evaluation of a new mango cultivar ‘Azeem Chaunsa’ using a set of the most effective 50 hyper-variable polymorphic SSR markers. Highly specific DNA fingerprints were identified in the genome of this mango cultivar, ‘Azeem Chaunsa’ compared with three standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. An agglomerative hierarchical clustering method was used to construct dendrogram based on the ‘Unweighted Pair- Group Method with Arithmetic Mean’ (UPGMA). The genetic similarity coefficients were recorded between the mangos cultivars ranged from 0.49 to 0.67. Cultivar identification (CID) evaluates association among standard cultivars and Azeem Chaunsa and further concludes significant variations. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future mango breeding programmes in the context of climate change.

Tree decline resulting from climate change results in physiological weaknesses, attack by harmful pests and pathogen, and threatens forest ecosystem stability. In the work described here, the effects of drought on wood density, tree ring width, and variations in vessel morphology were investigated in Persian oak (Quercus brantii) in the forests of the Zagros Mountains, Ilam province, western Iran. Radial discs were cut from trunks of declined and healthy trees and wood blocks cut radially from the sapwood, heartwood, and juvenile core. Observations were made on transverse sections from the blocks using microscopy. In trees with decline symptoms, wood density was greater than the healthy trees. Furthermore, declining trees had the narrowest ring width, reduced vessel diameter, vessel area, and highest number of vessels and tylose in pith towards bark. It was concluded that changes in anatomical features are associated with the weakening of the trees and are components in declining tree health.

Development and redevelopment are important drivers of tree removal and canopy loss in urban landscapes. Local ordinances are often used to curtail tree removal, but punitive regulations alone may not be enough to reduce urban tree canopy loss in land development. In Florida (US), efforts to balance trees and development have so far focused on fees and fines, but with a recent backlash against tree regulations and the fast pace of urban growth, we explored the possible role of incentives in urban tree policies. We interviewed 20 land developers across Florida to understand their perspectives on current barriers and potential incentives for tree preservation and planting. We collected data from developers, whose perspectives on tree preservation are often unknown or overlooked, despite their significant role in tree planting, removal, and retention in and around cities. Our results show that major barriers to tree preservation and planting include requirements to grade sites for stormwater management, site constraints, and monetary costs. Most developers did not know of any existing incentives beyond intrinsic motivations but said that financial incentives would be most appealing to them. Top incentive suggestions include increasing building density, reducing impact fees and tax liability, and changing tree mitigation policies. Another promising finding is that developers are willing to work with regulators to find solutions that benefit both parties. Future research should consider evaluating the level of support and viability of different incentives by gathering feedback from policymakers, land developers, and the public.

Water pollution is a common problem for dams situated within an urban or agricultural catchment. This can negatively affect the hydro ecosystem, drinking, recreational and other uses of water. In this study, the drinking water quality class of the Roodeplaat Dam, South Africa which faces pollution problems was modeled using machine learning algorisms in Python Jupyter Notebook 6.0.0. Eleven monthly water quality parameters recorded at five sampling stations from January 1981 to September 2017 were used for training and testing the model. Five machine learning classifiers: Gaussian Naïve Bayes (GNB), K-nearest neighbors (KNN), Decision Tree (DT), Support Vector Machines (SVM), and Linear Regression (LR) at a test size of 20%, 25%, 30%, and 40% were used to classify water into five classes (Excellent to Very bad). It was investigated that the dam water has only three classes good, medium, and bad. The prediction accuracies of machine learning algorithms from the highest to the lowest were 96.39%, 96.17%, 92.25%, 90.20, and 54.19% for KNN, DT, SVM, GNB, and LR, respectively. Therefore, KNN at a test size of 30% was recommended to classify the water quality of Roodeplat Dam accurately. Hence, machine learning algorithms can be used to identify the class of water quality before the water is treated and distributed for drinking use.

Fault detection in PV arrays and inverters is critical for ensuring maximum efficiency and performance. Artificial intelligence (AI) learning can be used to quickly identify issues, resulting in a sustainable environment with reduced downtime and maintenance costs. As the use of solar energy systems continues to grow, the need for reliable and efficient fault detection and diagnosis techniques becomes more critical. This paper presents a novel approach for fault detection in photovoltaic (PV) arrays and inverters, combining AI techniques. It integrates Elman Neural Network (ENN), Boosted Tree Algorithms (BTA), Multi-layer Perceptron (MLP), and Gaussian Processes Regression (GPR) for enhanced accuracy and reliability in fault diagnosis. It leverages their strengths for accuracy and reliability of fault diagnosis. Feature engineering-based sensitivity analysis was utilized for feature extraction, the fault detection and diagnosis were assessed using several statistical criteria including PBAIS, MAE, NSE, RMSE, and MAPE. Two intelligent learning scenarios are carried out, the first scenario is done for PV array fault detection with DC power (DCP) as output. The second scenario is done for inverter fault detection with AC power (ACP) as the output. The proposed technique is capable of detecting faults in PV arrays and inverters, providing a reliable solution for enhancing the performance and reliability of solar energy systems. Real-world solar energy dataset is used to evaluate the proposed technique, with results compared to existing detection techniques and obtained results show that it outperforms existing fault detection techniques, achieving higher accuracy and better performance. The GPR-M4 optimization justified reliably among all the models with MAPE=0.0393, and MAE=0.002 for inverter fault detection and MAPE=0.091, and MAE=0.000 for PV array fault detection.

Soybean with insignificant differences in appearance have large differences in their internal physical and chemical components, therefore follow-up storage, transportation and processing require targeted differential treatment. A fast and effective machine learning method based on hyperspectral data of soybean for pattern recognition of categories is designed as a non-destructive testing method in this paper. A hyperspectral-image dataset with 2299 soybean seeds in 4 categories is collected; Ten features is selected by extreme gradient boosting algorithm from 203 hyperspectral bands in range 400 to 1000 nm; A Gaussian radial basis kernel function support vector machine with optimization by the Tree-structured Parzen Estimator algorithm is built as TPE-RBF-SVM model for pattern recognition of soybean categories. The metrics of TPE-RBF-SVM are significantly improved compared with other machine learning algorithms. The accuracy is 0.9165 in the independent test dataset which is 9.786% higher for vanilla RBF-SVM model and 10.02% higher than the extreme gradient boosting model.

The paper proposes a rock classification method using the vibration signal of the rotary drilling process. This proposed method classifies four rocks with a reliability of 100 %, from the vibration signal record of a lasting 1/4 second. For the design of a suitable classification method, several attributes of the vibration signal were calculated for two different signal recording lengths. Cluster dendrogram, ANOVA test, and boxplot were used to determine useful attributes and proper signal length. The classification rule was found using a decision tree, machine learning tool. The publication gradually describes the process of creating the classification method and the results of the reliability verification of the proposed classification method. Rock disintegration by rotary drilling was carried out under standardized experimental conditions. The disintegration rocks were andesite, granite, limestone, and concrete.

We re-inventoried red wood ant nests (RWA) in 12 study sites (≈1281 ha) in the Westeifel Volcanic Field, Germany, in an area-wide and integrated ecosystem approach after 12 years. Combined with the re-identification of previously mapped nests using a photo database, this approach leads to more accurate nest counts in (re)inventories. A total increase of nests from 1144 (2009) to 1252 (2021), and a dramatic one for the Heidberg site by 52-fold (2009) and 85-fold (2021) compared to a 1984 inventory was observed, contrasting with earlier postulations of a decline in RWA. Early to medium mature (41-80 years) and mature (³81–140 years) spruce forests were the preferred habitat. Large increase in small nest sizes suggests an increase in new nest settlements also in clearings, despite climate-induced forest dieback over the past decade. A decline in biodiversity in the herbaceous layer was observed; highly proliferating blackberries had no negative impact on RWA nests. Monitoring of woodpecker cavities in RWA nests is suggested as an indirect indicator tool for evaluating populations in forests. Positive ground movement rates caused by the Eifel plume are suggested another factor favoring nest settlements. This study contributes to the urgent update of statistically valid data required to a) effectively substantiate the status-quo of RWA occurrences, b) protect RWA as ecosystem engineers, c) advance understanding of Geo-Bio-Interactions in the wake of climate change, and c) contribute to the German government's Insect Conservation Action Program.

Phytoseiidae inhabit a wide range of herbs, shrubs and trees. Aesculus hippocastanum is an important ornamental tree in Europe and is likely reservoir of these mites. We therefore assessed the species composition and the spatial and seasonal variability in the abundance of Phytoseiidae in city parks in South Bohemia, Czech Republic. Leaf samples were randomly collected from horse chestnut tree branches at eight sites, five times during the vegetation season in 2013. The mites were collected by washing technique and mounted on slides for identification. In total, 13,903 specimens of phytoseiid mites were found, and eight species were identified: Amblyseius andersoni, Euseius finlandicus, Kampimodromus aberrans, Neoseiulella tiliarum, Phytoseilus macropilis, Paraseiulus talbii, Paraseiulus triporus and Typhlodromus (Typhlodromus) pyri. Paraseiulus talbii and P. macropilis were recorded on the leaves of horse chestnut trees for the first time in the Czech Republic in this study. The predominant species was E. finlandicus (96.25%). The number of mites per compound leaf was, on average, 2.53, 10.40, 23.54, 11.59 and 9.27 on the sampling dates in each month between May and September, respectively. The mite density was significantly affected by the sampling site and date.

Defining the best conditions for the initial development of native species is one of the significant challenges to forest production. Therefore, this study aimed to evaluate andiroba's physiological behavior and growth (Carapa guianensis Aubl.) seedlings at different shading levels. In a nursery seedling, C. guianensis seedlings grew on 0, 30, 50, and 70% shading, obtained with polyethylene screens. We measured at six plants per treatment. For the physiological data we measurement four times the total chlorophyll content, chlorophyll a/b ratio, maximum photochemical efficiency of photosystem II (Fv/Fm), maximum potential photosynthesis (A), stomatal conductance (gs), transpiration (E), intrinsic water use efficiency (WUEi), and carbon use efficiency (CUE), besides the growth parameters. While for growth rates parameters we developed six evaluation for as leaf area ratio (LAF), relative growth rates (RGR), and net assimilation rate (NAR). Chlorophyll content and Fv/Fm were inversely proportional to the light intensity. The other physiological and growth parameters showed better behavior in shaded environments, emphasizing the 50% treatment, with no significant difference between the 30% shading treatment. Therefore, the C. guianensis seedlings present growth plasticity under different shading levels, and we recommended producing seedlings at 50 or 30% shading.

Through this study, we established equations for estimating the standing tree carbon stock, based on 24 tree species in multiple size classes in a case study at the Ngao Demonstration Forest (NDF) in northern Thailand. Four hundred thirty-nine wood samples from trees in mixed deciduous forest (MDF), dry dipterocarp forest (DDF), and dry evergreen forest (DEF), were collected using non-destructive methods to estimate above-ground carbon equations through statistical regression. The equations were established based on four criteria: 1) the coefficient of determination (R2), 2) Standard error of estimate (SE), 3) F-value, and 4) Significant value (p-value, α ≤ 0.05). The above-ground carbon stock (C) equations for standing trees in the MDF was C = 0.0199DBH2.1887H0.5825, for DDF was C = 0.0145DBH2.1435 H0.748, for DEF was C = 0.0167DBH2.1423H0.7070, and the general equation for all species/wood density groups was C = 0.017543DBH2.1625H0.6614, where DBH is tree diameter at breast height and H is tree total height. The above-ground carbon stock in the DDF, MDF, and DEF was 142, 53.02, and, 12 tons/ha, respectively, and the estimated above-ground carbon stock in the Mae Huad sector at the NDF was 61 tons/ha.

Paleoclimate information is key to understanding the climate system and predicting future climate. Currently, stable isotope analysis of whole molecules in tree rings is among the most advanced paleoclimate tools. Still, it only enables reconstructions of one climate parameter at a time (univariate climate reconstruction), a fundamental limitation. Here, we investigated whether this limitation can be overcome by analysing isotope variability at the level of intramolecular carbon positions in tree-ring glucose. We found that drought governs isotope variability at glucose C-1 to C-3, whereas radiation governs isotope variability at glucose C-5 to C-6. These isotope-climate relationships are statistically and mechanistically robust. According to cross-validation analysis, they can be reconstructed with high accuracy and precision. Thus, intramolecular isotope analysis enables multivariate climate reconstructions (proof of concept).

Pruning is an important part in controlling the branching, good fruit size, and prodctivity of fruit trees. and consequently, ‘Valencia’ orange trees were grown many years ago, the trees were suffering from a decline in productivity in the newly reclaimed lands after certain years of plantation. This study elucidated the effect of pruning severity on growth, yield and fruit quality of ‘Valencia’ orange trees. Four levels of pruning were applied on 35 years old ‘Valencia’ orange trees as follows: T1: Control (un-pruned), T2: Light pruning ( 25% of main branches were removed); T3: Moderate pruning (50% of main branches were removed) and T4: Heavy pruning: 75% of main branches were removed. The treatments were done in February 15th (for each season). The results indicated that pruning severity increased vegetative growth paraments such as shoot length and leaf area and decreased canopy volume of Valencia orange trees in comparison to control. Maximum yield was gained from 75% followed by 50% and 25% pruning severity treatment. The pruning treatments improve in fruit weight, size, firmness, juice content, TSS%, TSS/acid ratio and vitamin C content. In addition, pruning produced an increasing in fruit yield per tree as well as yield increasing %. Hence, it might be concluded that pruning at different severity applied in February can be used effectively on ‘Valencia’ orange trees to improve vegetative growth, fruit physical and chemical properties. Besides, increasing fruit yield by nearly 20% in comparison to control.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

Multi-level direct networks fueled by the evolving technology of active optical cables and increasing pin-bandwidth achieve reduced diameters and cost, with high-radix switches. These networks, like Dragonfly, are becoming the preferred aspirants for extreme high-performance parallel machines such as exa-scale computing. In this paper, we introduce a Hyper Z-Tree topology, which deploys the Z-Tree, a variant of fat-tree, as a computing node of a Generalized Hypercube (GHC) configuration. The resulting configuration provides higher bisection bandwidth, lower latency for some applications and higher throughput. Furthermore, the levels of the fat tree offer several path diversities across the GHC dimensions, hence conceding a more fault tolerant architecture. To profit from these path diversities, we propose two adaptive routing algorithms, which are extensions to the routing algorithm suggested in HyperX topology. These two algorithms exhibit better latencies and throughput than the HyperX.

Recent Socio-economic changes resulted to mass abandonment of agricultural lands in Central-Eastern Europe. This leads to landscapes homogenisation, especially the disappearance of semi-open habitats of the farmland-woodland interface. The information on potentials of transforming feral, abandoned landscapes to wood-pastures is limited. We studied the ecological features and silvopastoral benefits of a landscape subjected to intensive cattle grazing after ~20 years of abandonment, i.e. the mosaic of habitats, cattle’s preference for habitats and for arboreal forages. The nutritional characteristics of arboreal forage and herbaceous forage were compared. After the initial increase in the landscape’s woodland share, cattle grazing halted further woody succession and stabilised the landscape structure, with treeless grassland occupying 49% of the landscape (most preferred by cattle with regard to the time spent), followed by treed grassland (19%), pioneering birch/alder groves (13%), riparian vegetation (12%, least preferred by cattle), and close-canopy woods (6%). The consumption intensity of arboreal forages was on about 4.5 folds higher than that of herbaceous forage. Our study proved that the grazing herd of arobust cattle breed can turn a feral, post-agricultural land into a working wood pasture, consisting of interconnected open grasslands and various facets of woodland, which seldom occur in contemporary high forests. We advocate for the transition management of abandoned farmlands towards integrated silvopastoral landscapes for sustainable provision of multiple ecosystem services that cannot be provided by segregated agriculture and forestry.

This article is devoted to the results of in-depth analysis of the system of binary-oppositional structures in DNA n-plet alphabets and their algebraic-matrix representations. These results show that the molecular complementary replication of DNA strands is accompanied by the presence of an algebraic version of the principle "like begets like" in matrix representations of DNA alphabets having internal structures. This algebraic version is based on binary-oppositional structures in the genetic molecular system, which can be represented by binary numbers and corresponding matrices of DNA alphabets. The received results allow thinking that the phenomenon "like begets like" (or a complementary replication in a wide sense) is systemic in the genetic organization and is connected with algebraic features of biological organization. Correspondingly, the biological principle "like begets like" can be additionally modeled by algebraic-matrix methods and approaches. Such algebra-matrix modeling of the genetic coding system gives new ways for studying and understanding the key role of the named principle in genetic and other inherited physiological complexes. On this way, the author discovered general rules of stochastic organization of information binary sequences of genomic DNAs of eukaryotes and prokaryotes. The presented rules are connected with information dichotomies of probabilities and corresponding fractal-like trees of probabilities, which fundamentally differ from constructional dichotomies in biological bodies. The received phenomenological data and rules lead to new biological ideas.

This article presents a comparative analysis of three Machine Learning models, namely Logistic Regression, Decision Tree Classifier, and Random Forest Classifier, for prostate cancer detection. The models were trained and evaluated using clinical data, and their performance was assessed using various evaluation metrics. The results show that Logistic Regression achieved the highest accuracy (90%) among the three models, followed by Random Forest Classifier (76.67%) and Decision Tree Classifier (73.33%). Similarly, Logistic Regression demonstrated superior precision (95.65%) and F1 Score (93.62%), indicating its effectiveness in identifying true positive cases. However, the Decision Tree Classifier exhibited higher recall for the negative class (83.33%) compared to the positive class (70.83%), while Random Forest Classifier showed balanced recall for both classes (66.67% for negative and 79.17% for positive). These findings suggest that Logistic Regression outperforms the other models in terms of accuracy and precision, while the Decision Tree Classifier and Random Forest Classifier provide better recall for certain classes. The results highlight the potential of Machine Learning in prostate cancer detection and provide insights for further research and improvement of the models.

Chi-squared automatic interaction detector (CHAID) algorithm is considered to be one of the mostly used supervised learning methods as it is adaptable to solving any kind of problem at hand. We have been starkly aware of CHAID maps non-linear relationships quite well, and it can empower predictive models with stability. But we don’t know how high its accuracy is precisely. To find out the perfect scope CHAID algorithm fits into, this paper presents the analysis of the accuracy of the CHAID algorithm. We introduce the causes, applicable conditions and application scope of CHAID algorithm at first, and then highlight the difference of branching principal between the CHAID algorithm and several other common decision tree algorithms, which is our first step towards basic analysis on CHAID algorithm. We next employee an actual branching case to help us understand CHAID algorithm better. Specifically, we use vehicle customer satisfaction data to compare multiple decision tree algorithms, and cite some factors that affecting the accuracy and some corresponding countermeasures which are more conducive to us to obtain accurate results. The results show that CHAID can analyze the data very well, and detect the significantly correlated factors surely. In this paper, we learn the clear information required to understand CHAID algorithm, thereby make better choices when we need to use decision tree algorithms.

The lack of a generalizable machine learning model for predicting the safety of food for 1 human consumption is a significant challenge for policymakers and responsible authorities. This 2 study provides a step-by-step guide to predict the results of seafood product import inspections, 3 focusing on identifying and understanding the critical factors that influence these results. By compar- 4 ing the performances of an ensemble of machine learning models, this study combines the strengths 5 of multiple algorithms to improve the predictive accuracy and gain insights into the key factors 6 impacting them. The ensemble model based on the soft voting technique achieves superior perfor- 7 mance to that based on the hard voting technique in terms of the recall and area under the curve 8 (AUC) scores. The study discovered that various characteristics, such as the exporting country ratio, 9 major product category, overseas manufacturer ratio, importer ratio, and seasonal variation, had a 10 substantial influence on the models’ decisions. This research guide for predicting seafood product 11 import inspection results could pave the path for other items to follow.

Alzheimer’s disease manifests itself as a complex pathological condition with neuroinflammation, oxidative stress and cholinergic dysfunction being a few of the many pathological changes. Due to the complexity of the disease current therapeutic strategies aim at a multitargeted approach often relying on a combination of sub-stances with versatile and complementary effects. In the present study, unique combination of α-lipoic acid, citicoline, extracts of leaves from olive tree and green tea, Vitamin D3, selenium and an immune supporting complex was tested in a scopolamine-induced dementia in rats. Using behavioral and biochemical methods we assessed the effects of this combination on learning and memory, and elucidated the mechanisms under-lying its effects. Our results showed that as compared to the components, experimental combination was most efficient in improving short- and long-term memory assessed by the step-through method as well as spatial memory, assessed by T-maze and Barnes maze underlying decrease in AChE activity and LPO, in-creases in SOD activity in cortex; activities of catalase and GPx, levels of BDNF and pCREB in the hippocam-pus. No significant histopathological changes or blood parameter changes were detected, making the experi-mental combination an effective and safe candidate in a multitargeted treatment of AD.

In this study, we developed a model that can predict whether patients with cerebral arteriovenous malformation (AVM) will be cured 36 months after intervention by means of stereotactic radiosurgery (SRS), and identified the most important predictors that explain the probability of being cured. A machine learning (ML) approach was applied using decision tree (DT) and logistic regression (LR) techniques on historical data (sociodemographic, clinical, treatment, angioarchitecture, and radiosurgery procedure) of 202 patients with AVM who underwent SRS at the Instituto de Radiocirugía del Perú (IRP) between 2005 and 2018. The LR model obtained the best results for predicting AVM cure with an accuracy of 92.16%, sensitivity of 92.86%, specificity of 88.89%, precision of 97.50%, and AUC of 97.62%, which shows that ML models are suitable for use for predicting the prognosis of medical conditions such as AVM and can be a support tool for medical decision-making. In addition, several factors were identified that could explain whether patients with AMV would be cured at 36 months with the highest likelihood: the location of the AVM, the occupation of the patient, and the presence of hemorrhage.

Browsing of wild ungulates can have profound effects on the structure and composition of forests. In the Swiss National Park, the density of wild ungulates including red deer (Cervus elaphus), ibex (Capra ibex), and chamois (Rupicapra rupicapra) is exceptionally high due to strict protection and the absence of large predators. We examined count data of larch (Larix decidua), cembra pine (Pinus cembra), spruce (Picea abies), upright mountain pine (Pinus mugo subsp. uncinata), and mountain ash (Sorbus aucuparia) of four sampling years between 1991 and 2021 and modelled how different topographic and location factors affected the probability of browsing on saplings of larch, cembra pine, and spruce. Despite the high density of wild ungulates, the numbers of saplings and young trees increased over the past 30 years. The probability of browsing on saplings was highest for larch at a height of 10 – 40 cm and increased with increasing elevation. In our study area, open grasslands are mainly located above the tree line, which might explain the positive correlation between elevation and the probability of browsing. Other factors like exposition and slope, available food resources and disturbance by humans did not have clear effects on the probability of browsing.

Conservation of tropical endemic amphibians largely suffers from Wallacean shortfall, a gap to which predictive species distribution models have contributed significantly to bridge by delineating probable distribution and the underlying suitable habitat within their distributional range. However, rarely is a prediction model ground-truthed to evaluate their predictive performance. Here we present a species distribution modeling approach using maximum entropy algorithm corrected for smaller sample size, in guiding explorative surveys aimed at optimizing survey effort and discovering unrecorded populations of Zhangixalus suffry, a rhacophorid tree-frog endemic to the northeastern part of the Indian subcontinent along with the factors limiting their distribution. With only 16 established historical locality data to model for (after spatial thinning to reduce autocorrelation) and a set of environmental predictors (climatic, topographic, and landscape composition), our model prediction enabled the successful discovery of seven new population records from unreported landscapes, extending its southernmost distributional limit over a considerable distance. The final composite distribution model combining all the locality records (n=23) predicted similar core areas of suitable habitat consistent with the known geographic distribution of the species but showed poor representation under existing coverage of Protected Area (PA) network in the Region with only 7% representation of suitable habitat under protection. Habitat suitability of a site was significantly governed by factors related to precipitation (precipitation seasonality and precipitation of the warmest quarter) and topographic factors that can influence it (elevation and aspect). This corroborates with the known ecology of Rhacophorid frogs, especially concerning their seasonal explosive reproductive strategy and foam nest-building behavior.Through this study, we propose explorative surveys guided by species distribution models to expedite unknown population discovery of rare, tropical endemic amphibians and using such taxa as surrogates in identifying conservation priority zones that can be directly applied to reserve design and conservation and management planning.

Parkland contributes economic, social, and ecological benefits worldwide. However, parklands degradation is an encounter to biodiversity loss needs generating information on woody species. This study was aimed to investigate woody species composition, diversity, structure, and management of parklands along Agroecology, and farmers’ wealth status. Multistage sampling technique was employed. Highland Bede Kebele, and midland Durgi Kebele were randomly selected. A total of 60 quadrats with size of 100m x 50m were used for inventory. A total of 276 respondents were used. Species richness, Shannon, Simpson, and evenness diversity indices were subjected to one-way ANOVA, and independent t-test for comparison STATA V. 17.0. Of 31 woody species, (16 families in 26 genera) observed, 26 woody species belonging to 13 families in 22 genera from midland while 18 woody species belonging to 12 families in 16 genera from highland parklands were observed. Species richness 6.8, 3.7; Shannon 1.82, 1.68; Simpson 0.85, 0.73; evenness 0.83, 0.7 were observed in midland, and highland parklands, respectively. Poorly regenerated stands were observed along Agroecology. Pruning, pollarding, coppicing, lopping, cutting, fertilizing, watering were common management practices. In conclusion, woody species diversity, and structure were affected by Agroecology, and socioeconomic factors needing intervention to enhancing woody species conservation.

Rare study on quantitative relationship between energetic impact of debris flows on the intensity and duration of growth disturbances of tree rings was carried out, partly due to lack of feasible approaches and detailed field evidence. In this study, we firstly determine the age of a recent debris flow derived from historic landslide deposits at Qingyang mountain (QYM) on the northeastern Tibet plateau by dendrogeomorphic technique. We acquired the quantitative data of annual widths of tree rings in history and confirmed the influence of debris flow rather than other factors (e.g. climatic event and inset outbreaking) in disturbing the growth of tree rings in a specific year. Using the approach, we determined the age of the debris flow at QYM occurred in 1982, which was speculated to be triggered by high monthly precipitation of July in 1982. Subsequently, based on the boundaries of historic debris flow identified on remote sensing images before and after 1982 and depth-integrated continuum model, we reconstructed the process of 1982-debris flow and obtained the kinematic energy of debris flow impacting on the sampled trees. Based on the study, we observed that two growth disturbance patterns of tree rings influenced by the reconstructed 1982-debris flow were revealed including growth suppression and asymmetric growth. A raw logarithm relationship between duration (i.e. lasting time for the disturbed tree rings to recover the initial width) and intensity of growth disturbances (i.e. growth suppression ratio of disturbed tree rings) was obtained. We concluded that there is a negative exponential relationship between simulated kinematic energy of debris flow impacting on the disturbed trees and time to recover the initial width of corresponding tree rings.

The rapid development of LiDAR technology has promoted great changes in forest resource surveys. The airborne LiDAR point cloud can provide precise tree height and detailed vertical structure of the tree stands. Coordinating some representative ground sample plots, LiDAR can be used to estimate key forest resource indicators such as forest stock volume, diameter at breast height, and forest biomass at a large scale. By establishing relationship models between the forest parameters of sample plots and the calculated parameters of LiDAR, these developments may eventually expand the models to large-scale forest resource surveys of entire areas. In this study, eight sample plots in northeast China are used to verify and update the information using point cloud obtained by the LiDAR scanner riegl-vq-1560i. Firstly, the tree crowns are segmented using the profile-rotating algorithm, and dominant trees height are used to check and rectify the tree locations. Secondly, considering the correlation between forestry parameters and tree species, we establish models to distinguish between species using geometric characteristics of tree crowns. Thirdly, when the tree species is known, parameters such as height, crown width, diameter at breast height, biomass and stock volume can be extracted from trees. The prediction models of forestry parameters can also be verified, which can be extended to accurate large-scale forestry surveys based on LiDAR data. Finally, experiment results demonstrate that the F-score of the eight plots in the tree segmentation exceed 0.95, the accuracy of tree species correction exceeds 90%, and the R2 of tree height, east-west canopy width, north-south canopy width, diameter at breast height, above-ground biomass and stock volume are 0.893, 0.757, 0.694, 0.840, 0.896 and 0.891, respectively. The above results indicate that the LiDAR-based estimation of forestry parameters is practical and that these forestry parameter prediction models can be widely applied in forest resource monitoring.

Acute encephalopathy with biphasic seizures and reduced diffusion (AESD) is characterized by biphasic seizures following febrile viral infections and delayed reduced diffusion of the cerebral white matter on magnetic resonance imaging (MRI) diffusion-weighted imaging (DWI) (bright tree appearance, BTA). However, hypoxic encephalopathy with biphasic seizures and AESD-mimicking imaging findings has not been reported. Herein, we report an case of hypoxic encephalopathy due to suffocation with concomitant biphasic seizures and BTA, mimicking AESD. On day 1, a 5-month-old female was found face down with breathing cessation and deteriorating consciousness level. The electroencephalography (EEG) revealed periodic epileptic discharges, suggesting possible nonconvulsive status epilepticus. Despite the improvements in consciousness level and EEG abnormalities on day 2, her consciousness level deteriorated again with generalized tonic-clonic seizures on day 3, and head MRI-DWI revealed restricted diffusion predominantly in the subcortical areas, suggesting BTA. Treatment for acute encephalopathy resolved clinical sei-zures and EEG abnormalities. The persistence of abnormal EEG, reflecting abnormal excitation and accumulation of neurotoxic substances caused by hypoxia, may have contributed to the devel-opment of AESD-like findings. As hypoxic encephalopathy causes AESD-like biphasic seizures, monitoring the consciousness level, seizure occurrence, and EEG abnormalities even after acute symptoms have temporarily improved following hypoxia is essential.

Build a special identical equation, use its calculation characters to prove and search for solution of any odd converging to 1 equation through (*3+1)/2^k operation, change the operation to (*3+2^m-1)/2^k, and get a solution for this equation, give a specific example to verify. Thus prove the Collatz Conjecture is true. Furthermore, analysis the sequences produced by iteration calculation during the procedure of searching for solution, build a weight function model, prove it decrease progressively to 0, build a complement weight function model, prove it increase to its convergence state. Build a (*3+2^m-1)/2^k odd tree, prove if odd in (*3+2^m-1)/2^k long huge odd sequence can not converge, the sequence must outstep the boundary of the tree after infinite steps of (*3+2^m-1)/2^k operation.

Species diversity has been shown to be influenced by environmental factors, but the mechanism underlying their relationship remains unclear across spatial scales. Based on field investigation data collected from 3,077 sample plots in temperate forest ecosystems, we compared tree species richness, evenness and dominance at 10 km × 10 km, 30 km × 30 km and 90 km × 90 km spatial scales. Then, we detected the scale dependence of changes in tree species composition on climate, topography and forest structure using variation partitioning and quantified their contribution to tree diversity with gradient boosted models (GBMs) and fitted their relationships. The magnitude of tree richness, evenness and dominance significantly increased with spatial scale. Ecological factors jointly accounted for 24.3%, 26.5% and 38.5% of the variation in tree species composition at the three spatial scales, respectively. Annual mean temperature had a strong impact on tree richness, evenness and dominance and peaked at an intermediate scale. Tree evenness and dominance increased with the variation of temperature but had upper and lower limits. Tree richness obviously increased with annual precipitation on multiple scales and decreased with annual sunshine duration at large spatial scale. Tree richness, evenness and dominance obviously increased with the variation elevation and diameter at breast height at large scales and small scales, respectively. Tree dominance decreased with tree height in a hump at small scale. The dependence of tree diversity on ecological factors increased with spatial scales. Furthermore, different factors exert various controls on tree diversity at different spatial scales, representing a comprehensive mechanism regulating tree diversity.

The paper presents a new conception of quasi-distributed sensor for simultaneous measurement of several physical fields and the results of an experimental study of this sensor. A distinctive feature of the sensor defined by the sensitive RC elements connected in the original tree-like structure. The proposed is the structure of the sensor and measurement system as well as corresponding measurement algorithm. The high accuracy demonstrated by the sensor’s prototype gives possibility to effective use the proposed sensors in many technical and scientific applications.

Arborists commonly investigate the extent of stem decay to assess the likelihood of stem failure when conducting tree risk assessments. Studies have shown that (i) arborists can sometimes judge the extent of internal decay based on external signs; (ii) sophisticated tools can reliably illustrate the extent of internal decay; and (iii) assessing components of tree risk can be highly subjective. We recruited 18 experienced tree risk assessors who held the International Society of Arboriculture’s Tree Risk Assessment Qualification (TRAQ) to assess the likelihood of stem failure due to decay after each of 5 consecutive assessments on 30 individuals of 2 genera. Five assessment techniques, in stepwise order, were 1) visual, 2) sounding the trunk with a mallet, 3) viewing a scaled diagram of the cross-section that revealed sound and decayed wood ascertained from resistance drilling, 4) viewing sonic and electrical resistance tomograms, and 5) consulting with a peer. For each technique, assessors assigned two or more likelihood of failure ratings (LoFRs) for at least 83% of trees, which were proportionally greatest after assessors viewed tomograms; the proportions did not differ among the other four assessment techniques. Covariates that influenced the distribution of LoFRs included percent of the cross-section that was decayed, and assessors’ experience using resistance drilling devices and tomography in regular practice. Practitioners should be aware that disagreement on the likelihood of tree failure exists even among experienced arborists.

This article delves into the pivotal role that biodiversity of soil microbes plays in bolstering tree health and overall agroecosystem productivity. With the advent of regenerative agricultural practices, there has been a resurgence of interest in understanding the complex interactions between soil microbiota and plant roots, particularly through processes like rhizophagy. Rhizophagy, the digestion of living root cells by soil microorganisms, stands out as a fundamental mechanism in nutrient cycling and overall tree vitality. By examining the impact of enhanced microbial diversity on tree health, we shed light on the practical applications, such as the utilization of the BEAM Inoculant product, which has demonstrated remarkable efficacy in augmenting soil health and, consequently, tree Vigor

Global energy scenario is becoming vital for the sustainable economic development of a region or country. Current changes in energy production, mainly due to scarcity and geopolitical factors, have proven the need for changes in energy mix towards a lower energy dependence. In that sense, a considerable amount of biomass waste is generated in many regions, because of agro-industrial activities, whose management could contribute to energy production. The aim of this work was to study the determination of combustion conditions in a biomass stove by using different raw materials as fuels, such as pine, poplar, and plum tree pellets. For that purpose, power, excess air, and biomass content were optimized, among other parameters, and fumes were analyzed with a Testo 335 analyzer, recording temperatures with temperature probes. As a conclusion, high yields were found for the optimized parameters of the studied biomass (ranging from 91.1 % for poplar pellet to 92.34 % for pine pellet), making these three biomass fuels suitable for combustion in the abovementioned stove. Also, increasing biomass flow by 25 % in the stove contributed to a higher efficiency of the process, especially in the case of plum tree pellets.

This study addresses the problem of accurately predicting azimuth and elevation angles of signals impinging on an antenna array employing Machine Learning (ML). Using the information obtained at a receiving system when a transmitter’s signal hits it, a Decision Tree (DT) model is trained to estimate azimuth and elevation angles simultaneously. Simulation results demonstrate the robustness of the proposed DT-based method, showcasing its ability to predict the Direction of Arrival (DOA) in diverse conditions beyond the ones present in the training dataset, i.e., the results display the model’s generalization capability. Additionally, the comparative analysis reveals that DT-based DOA estimation outperforms the state-of-the-art MUltiple SIgnal Classification (MUSIC) algorithm, establishing DTs as competitive alternatives for DOA estimation in signal reception systems.

Education on native tree species using indigenous knowledge systems is crucial in elementary school environmental education because it sensitizes learners on the significant roles played by the native trees and their interconnections with their culture and existence. Unfortunately in Ghana, there is no pedagogical model that would aid elementary school teachers in carrying out such education.This study was aimed at developing, implementing and evaluating an indigenous knowledge inspired pedagogical model for native tree education among elementary school learners in Ghana. The convergent parallel mixed methods design that aimed at collecting quantitative and qualitative datasets on the development and evaluation of the developed pedagogical model. The results show that the pedagogical model was deemed very satisfactory in all its aspects for native tree education among elementary school learners in Ghana. The model was able to assist in developing the sustainability consciousness and competence of elementary school learners toward caring for native trees through tree planting, making of nurseries and gardens, and engaging in native tree conservation campaigns. The study recommends that the Ministry of Education should adopt, improve and widely disseminate the pedagogical model and help in its broader implementation in various elementary schools across the country.

This paper presents a limited scope dynamic probabilistic risk assessment (D-PRA) on the survivability of commercial of the shelf (COTS) drones tasked with surveilling areas with varying radiation levels after a nuclear accident. The D-PRA is based on a discrete-dynamic event tree (D-DET) approach, which couples with the OpenEPL error propagation framework to model sequences leading to Loss of Mission (LOM) scenarios due to component failures in the drone’s navigation system. Radiation effects are simulated by calculating the total ionizing dose (TID) against the permissible limit per component, and errors are propagated within the electronic hardware and software blocks to quantify navigation system availability per radiation zone. The proposed methods are integrated into the traditional event tree/fault tree approach and the most vulnerable components are radiation-hardened (RAD-HARD) to the extent specified by a predefined mission success criterion. The results demonstrate the usefulness of the proposed approach in performing trade studies for incorporating COTS components into RAD-HARD drone designs.

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree heterogeneity in tree size may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree-ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in tree radial growth rate relative to the past. These theoretical advances facilitate the understanding of changes in the extent of subalpine forests.

This paper presents a model for archival and retrieval of the videos of natural flowers. To design an efficient video retrieval system the stages namely, keyframe selection, feature extraction, feature dimensionality reduction and indexing are essential for fast browsing and accessing of videos. Three different keyframe selection approaches are proposed using clustering algorithms after segmenting flower regions from its background. Deep Convolutional Neural Network is used as a feature extractor. After keyframe selection, a video is represented with a set of keyframes. To reduce the feature dimension of a video, two feature selection methods are utilized. For an efficient archival and fast retrieval of flower videos an indexing method called KD-tree is recommended. For a given query video, similar videos are retrieved both in relative and absolute search modalities. An extensive experimentation conducted on a relatively large flower video dataset. The data set consists of 7788 videos of 30 different species of flowers. The videos are captured with three different devices in different resolutions. The comparative study reveals proposed keyframe selection approaches gives better results. It has also been observed that the videos retrieved in absolute approach with features selected from Binormal separation metric and indexing gives good results.

Lasiodiplodia theobromae, as one of the causing agents associated with Chinese hickory trunk cankers, has caused huge economic losses to the Chinese hickory industry due to its extremely strong pathogenicity. Though the biological characteristics of this pathogen and the occurrence pattern of this disease have been reported, few studies have focused on investigating the mechanisms responsible for L. theobromae survival strategies and pathogenicity. The high-quality genome data and the efficient transformation system are the basis for researching above mechanism events. In this study we sequenced and assembled L. theobromae strain LTTK16-3, and established the first protoplasmic preparation method and polyethylene glycol (PEG) -mediated genetic transformation system for L. theobromae. These genetic information and transformation methods established the foundation for the future mechanisms study of L. theobromae and set up the possibility of targeted molecular improvements for Chinese hickory canker control.

The groundbreaking Advanced Forest-Based Tagging Framework (AFTF) represents a paradigm shift in the domain of information extraction from medical texts. AFTF introduces a novel dual-binary tree structure that redefines how entity-relation triples are extracted. This innovative approach directly tackles the limitations of traditional linear and graph-based methods, effectively addressing challenges related to overlapping triples and computational efficiency. The AFTF model stands as a beacon of excellence, surpassing established baselines by significant margins in comprehensive evaluations on two pivotal medical datasets. Notably, AFTF achieves remarkable improvements in F1 scores, showcasing its prowess in accurate information extraction from complex medical narratives. Beyond its exceptional performance in the medical domain, AFTF exhibits remarkable versatility. This adaptability is vividly demonstrated through its robust performance across three diverse public datasets, further affirming its position as a versatile and reliable solution for information extraction tasks. This paper provides a comprehensive exposition of the AFTF architecture, shedding light on its innovative design principles and its efficient handling of intricate medical texts. AFTF represents a groundbreaking step forward in the realm of information extraction, promising enhanced accuracy, efficiency, and adaptability for a wide range of applications.

Cities are especially vulnerable to heavy rainfall events and the associated flooding. Urban drainage systems are not dimensioned to handle extreme heavy rainfall events, there is a need to enhance the resilience of those systems. Nature-based solutions (NBS) have the potential to retrofit existing urban drainage systems by providing, among other benefits, storage volume. This study presents an approach for including NBS with different degrees of implementation in a 1D/2D model and investigates their potential for flood mitigation in a study area in Berlin. The analyzed NBS are infiltration systems (e.g. infiltration swales) dimensioned to return periods of T = 5 and 100 years, various green roofs and tree locations (e.g. tree trenches). The NBS are represented by SWMM LID elements. The results show that overall the mitigation effect of NBS declines with increasing rainfall intensities. However, infiltration systems dimensioned to T = 100 years achieve almost three times the flood reduction compared to systems dimensioned to T = 5 years, even during extreme heavy rainfall events (100 mm), resulting in a reduced total flood volume of 15.1% to 25.8%. Overall, green roofs (excluding extensive green roofs) provide the greatest flood reduction (33.5%), while tree locations have the least effect.

Stand biomass in boreal forests has attained recognition for its crucial role in carbon cycling. In this study, we examined the effect of stand age on above- and belowground biomass allocation in downy birch (Betula pubescens Ehrh.) in three drained peatland sites in Central Finland. We used allometric equations to estimate individual-tree above-ground biomass and its various components, including stem wood, stem bark, living branches, leaves, and dead branches. The above-ground biomass ranged from 10.5 t ha-1 to 276.0 t ha-1 and foliage biomass from 0.009 t ha-1 to 0.6 t ha-1. We found that stand age had a strong correlation with above-ground biomass, but foliage biomass appeared to peak at the middle-aged (50 years) stand compared to the young (10 years) or mature (80 years) stands. We observed coarse root biomass production of 45, 250, and 110 g m-2 in the 0–50 cm peat layer for young, middle-aged, and mature stands, respectively. Most coarse root biomass occurred in the top 20-cm layer (77%). These findings improve our understanding of how biomass allocation patterns – specifically those in foliar biomass– change over time, and the impact on litter inputs into the soil.

Helicobacter pylori (H. pylori) is known to play a significant role in the development of peptic ulcer diseases and gastric cancer. Previous studies have reported the existence of polymorphism in the ureC gene of H. pylori. However, the relationship between genetic sequence variations in ureC and the pathogenesis of peptic ulcer diseases remains unclear. We aimed to investigate the associations between ureC sequence variations and the development of gastric ulcer and gastric cancer. PCR amplification and DNA sequencing to analyze ureC sequences, and conducted phylogenetic analysis using MEGA X software. Samples from Taiwanese patients with gastric ulcer and gastric cancer were included in this study. PCR was used to amplify the ureC gene from these samples, followed by DNA sequencing. The obtained sequences were then subjected to phylogenetic analysis using MEGA X software. In addition, ureC sequences from different geographical regions, including China and other countries, were included in the analysis for comparison. Analysis of the ureC se-quences from Taiwanese samples revealed that 6 out of 7 gastric ulcer samples clustered together in one group, while the sequences from 7 out of 8 gastric cancer samples were distributed across other groups. Phylogenetic analysis incorporating ureC sequences from different geographical regions showed that Taiwanese ureC sequences could be divided into two distinct groups, although the bootstrap values supporting this separation were low. Notably, the ureC sequences from China formed a distinct group with a high bootstrap value, separate from sequences from all other countries. There are two genotypes of ureC sequences present in Taiwanese samples, with one genotype showing a closer association with gastric cancer. Additionally, the ureC sequences from China appear to be unique and separate from those obtained from other countries, indicating the presence of genetic diversity in ureC sequences among H. pylori strains from different regions, potentially contributing to differences in disease outcomes. Further research is needed to elucidate the specific mechanisms by which ureC sequence variations may influence the pathogenesis of peptic ulcer diseases and gastric cancer.

Coxsackievirus A6 (CV-A6) has emerged as the predominant causative agent of hand, foot, and mouth disease (HFMD) in young children. Since the declaration of coronavirus disease 2019 (COVID-19) as a global pandemic, the incidence of infectious diseases, including HFMD, has decreased markedly. When social mitigation was relaxed during the COVID-19 pandemic in 2022, re-emergence of HFMD was observed in Gwangju, South Korea, and seasonal characteristics of the disease appeared to have changed. To investigate the molecular characteristics of enterovirus (EV) associated with HFMD during 2022, 277 specimens were collected. Children aged under 5 years accounted for the majority of affected individuals. EV detection and genotyping were performed using real-time RT-PCR and nested RT-PCR followed by sequence analysis. EV detection rate was found to be 82.3%, and the main genotype identified was CV-A6. Sixteen CV-A6 samples were selected for whole genome sequencing. According to phylogenetic analysis, all CV-A6 strains from this study belonged to the sub-genotype D3 clade based on VP1 sequences. Analysis of 3D polymerase phylogeny showed that only the recombinant RF-A group was identified. In conclusion, circulating EV types should be continuously monitored to understand pathogen emergence and evolution during the post-pandemic era.

Jatropha curcas is a potential energy crop and has been identified as a significant resource for biodiesel. Retrotransposons occupy the plant genome in a large proportion, so the specific retrotransposon characterization will help improve understanding of the Jatropha genome evolution and organization. This research aimed to characterize and classify Ty1-copia retrotransposons in J. curcas using specific gene sequence and to detect the distribution of Ty1-copia in J. curcas and J. integerrima chromosomes using florescence in situ hybridization (FISH) method. Ty1-copia sequences from J. curcas were isolated using three degenerate primer sets specific to the conserved Ty1-copia reverse transcriptase gene for polymerase chain reaction (PCR). FISH technique using the Ty1-copia probes was used to map physically on J. curcas and J. integerrima chromosomes. Altogether, 164 Ty1-copia sequences were obtained to use for the phylogenetic analysis and were classified into the four families of TAR, Angela, Ale and Bianca. Physical map using FISH method of Angela and Ale families on J. curcas and J. integerrima chromosomes exhibited biases for specific regions around the centromeres or at the chromosome terminal regions. The study indicates the high variation of the Ty1-copia elements in the genome of Jatropha. These results will enhance understanding of retrotransposon chromosomal distribution and evolution of the J. curcas genome and also promotion of retrotransposon possible utilization in J. curcas improvement projects using retrotransposon-based markers.

Background: As long-lived organisms, urban trees often encounter development and redevelopment activities in close proximity during their lifespans. These activities can damage tree roots, often through methods like root severing during trenching or excavation. Methods: In 2017, we simulated trenching damage on mature Quercus virginiana Mill. trees at three different distances from the base (3x, 6x, and 12x the stem diameter). After five years, we revisited these trees to assess root regrowth based on the cut root's cross-sectional area (CSA) and distance from the base. Results: We observed regrowth in all but 38 (6.7%) of the 557 cut roots revisited. The lack of regrowth in some roots was not associated with our original treatments, the CSA of the roots at the time of trenching, or distance between the cut root end and the trunk (minimum P-value = 0.841). On average, the observed CSA of the regrowth was 22.2% of the original root's CSA. Only our original trenching treatments significantly predicted the level of regrowth observed five years after pruning (P-value = 0.024). Discoloration due to root pruning was minimal. Conclusion: In summary, our findings indicate that root systems require many years to recover from trenching damage. Increasing the distance between trenching activities and trees may have a minor effect on root regrowth but primarily helps reduce initial stress on the tree.

The aim of this research is to investigate and use a variety of immersive multisensory media techniques in order to create convincing digital models of fossilised tree trunks for use in XR. This is made possible via the use of geospatial data derived from sources such as aerial imaging using UAS, terrestrial material using cameras and also include both the visual and audio element for better immersion, accessible and explorable in 6 Degrees of Freedom (6DoF). Immersiveness is a key factor in order to result in output that is especially engaging to the user. Both conventional and alternative methods are explored and compared, emphasising in the advantages made possible via the help of Machine Learning Computational Photography. Material is collected using both UAS and terrestrial camera devices, including a 3D-360º camera with 6 sensors, using stitched panoramas as sources for photogrammetry processing. Difficulties such as capturing large free standing objects using terrestrial means were overcome using practical solutions involving mounts and remote streaming solutions. Conclusions indicated that superior fidelity can be achieved by the help of Machine Learning Computational Photography processes and higher resolutions and technical specs of equipment not necessarily translating to superior outputs.

Urban tree classification is pivotal in urban planning and management, facilitating informed decision-making processes. In this study, we introduce a novel data presentation method termed Pseudo Tree Crown, designed to enhance the accuracy and efficiency of urban tree classification. Leveraging the latest advancements in artificial intelligence (AI), we employ a state-of-the-art classification scheme, PyTorch, to maximize the accuracy of tree classification. Our results demonstrate a robust classification accuracy of over 95% from high spatial resolution imagery from Unmanned Aerial Vehicle (UAV), underscoring our proposed approach’s effectiveness. Moreover, the adaptability of our method renders it applicable to various study areas, highlighting its versatility and potential for widespread implementation in urban planning and management initiatives.

Analyzing urban-rural spatial structures and changes are of great significance for under-standing the urban-rural relationship evolution. While previous studies mostly focused on the urban internal spatial structure evolution, less from the regional scale to explore the urban and rural evolution. Here, taking Dezhou city, a rapidly urbanization city in China as a case study, we employed the local contour tree method and nighttime light data to map urban and active rural regions’ range during 2012-2020, and further explored the respective development processes of them. This study found that different from rural regions, internal structures of urban regions are more complex, and there are often multiple hot spots inside them. The urban-rural regions’ area had increased significantly by 39.3% during 2012-2020 (p&lt;0.05). The urban and rural region rankings of the identified counties are basically consistent with the urban and rural population rankings. Particularly, different from perspectives of earlier land use (i.e., built-up land or impervious surface), this study identified urban and active rural regions in view of the scope of active human activities. This study therefore found populations were greatly responsible for affecting changes of urban and active rural region structures. These findings could be helpful for understanding urban-rural dynamics.

The process of sexual reproduction in eukaryotes starts when gametes from two different sexes encounter each other. Paramecium, a unicellular eukaryote, undergoes conjugation and uses a gametic nucleus to enter the sexual reproductive process. The molecules responsible for recognizing mating partners, hypothetically called mating-type substances, are still unclear. We have identified an O3-type mating substance polypeptide and its gene sequence using protein chemistry, molecular genetics, immunofluorescence, RNA inter-ference, and microinjection. The O3-type substance is a polypeptide found in the ciliary membranes, located from the head to the ventral side of cells. The O3-type substance has a kinase-like domain in its N-terminal part located outside the cell and four EF-hand motifs that bind calcium ions in its C-terminal part located inside the cell. RNA interference and immunofluorescence revealed that this polypeptide positively correlated with the expression of mating reactivity. Microinjection of an expression vector incorporating the O3Pc-MSP gene induced additional O3 mating type in the recipient clones of different mating types or syngen. Phylogenetic analysis indicates this gene is widely present in eukaryotes and exhibits high homology among closely related species. The O3Pc-MSP gene had nine silent mutations compared to the complementary mating type of the E3 homologue gene.

Bipolar disorder is a severe mood disorder and is one of the top 20 reasons of disability in the world. It causes a huge burden on society. In this study, the prediction models of bipolar disorder were constructed based on the concept of knowledge distillation. The input data consisted of patients of bipolar disorder and matched controls, all of which were selected from the open database MIMIC. The method of kernel density estimation (KDE) was exploited to generate probability density functions (PDF) which identify distributions of input data. The PDF values referred to as the soft labels were combined with the input data to construct the prediction models of bipolar disorder using decision tree and artificial neural network respectively. According to the evaluation results, indicators for identifying positive samples of bipolar disorder were improved. Meanwhile, the indicators for identifying negative samples have also been advanced. In addition, the branching attributes selected by the decision trees can be mapped back to specific disease diagnoses, which are all associated with bipolar disorder. In conclusion, using KDE to generate the soft label information of the input data can make knowledge distillation work and has improved the performances of prediction models for bipolar disorder.

High-precision ground point cloud data has a wide range of applications in various fields, and the separation of ground points from non-ground points is a crucial preprocessing step. Therefore, designing an efficient, accurate, and stable ground extraction algorithm is of great significance for improving the processing efficiency and analysis accuracy of point cloud data. The study area in this article was a Park in Guilin, Guangxi, China. The point cloud was obtained by utilizing the UAV platform. In order to improve the stability and accuracy of the Filter algorithm, this article proposed a triangular grid filter based on Slope Filter, found violation points by the spatial position relationship within each point in the triangulation network, improved KD-Tree-Based Euclidean Clustering, and applied it to the non-ground points extraction, this method has good accuracy, stability and achieves good results in separating ground points from non-ground points. At first, using Slope Filter to remove some non-ground points, to reduce the error of taking ground points as non-ground points; Secondly, established a triangular grid based on the triangular relationship between each point, the violation-triangle can determin through the grid, and then the corresponding violation points were found in the violation-triangle; Thirdly, according to the three-point collinear method to extract the regular points, used these points to extract the regular landmarks by KD-Tree-Based Euclidean Clustering and Convex Hull Algorithm; Finally, removed disperse points and irregular landmarks by Clustering Algorithm. In order to confirm the superiority of this algorithm, this article compared the filter effects of various algorithms on the study area and filtered the 15 sample data provided by ISPRS, and obtained an average error of 3. 46%. The results showed that the algorithm in this article have a high processing efficiency and accuracy, which can greatly improve the processing efficiency of point cloud data in practical applications.

The minimum dominating tree (MDT) problem consists of finding a minimum weight sub-graph from an undirected graph, such that each vertex not in this sub-graph is adjacent to at least one of the vertices in it, and the sub-graph is connected without any ring structures. This paper presents a Dual Neighborhoods Search (DNS) algorithm for solving the MDT problem, which integrates several distinguishing features, such as two neighborhoods collaboratively working for optimizing the objective function, a fast neighborhood evaluation method to boost the searching effectiveness, and several diversification techniques to help the searching process jump out of the local optimum trap thus obtaining better solutions. DNS improves the previous best-known results for 4 public benchmark instances while providing competitive results for the remaining ones. Several ingredients of DNS are investigated to demonstrate the importance of the proposed ideas and techniques.

Analytics have become increasingly popular among sports in recent years, providing valuable information regarding physical and mental health. Many athletes have found that analytics during training can help to improve their performance, reduce the risk of injury, and enhance their overall well-being. This paper aims to improve the results of handball players by applying a method for measuring the influence of different trials on aggregate performance calculated per each athlete in order to qualify for the Olympic Games. By separating the isolated action of each trial, the result is an additional influence caused by the interaction of factors or the simultaneous action. That might explain why the neuromuscular feedback loop is utterly necessary to perform handball motor actions. Further on, the ML analysis can help identify areas for improvement, optimize training programs, and enhance overall team performance.

Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Total genomic DNA (gDNA) was generated using CTAB method from each cultivar. The most effective 50 hyper-variable SSR markers were selected. Highly specific DNA fingerprints were identified in the candidate line ‘Azeem Chaunsa’ compared with three standard cultivars using SSR-PCR. An agglomerative hierarchical clustering method was used to construct dendrogram based on the UPGMA clustering method. Cultivar identification diagram (CID) was constructed to evaluate association among standard cultivars and Azeem Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. The genetic similarity coefficients were recorded between the cultivars of mango ranged from 0.49 to 0.67. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future.

Bitcoin has been launched for over a decade and made an increasing impact on the world’s financial order, which attracted extensive attention of researchers. Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes including reachable nodes and unreachable nodes. In this article, a detection system BNS (Bitcoin Network Sniffer) was prososed, which could collect as many Bitcoin nodes as possible. For reachable nodes, the authors designed an algorithm BRF (Bitcoin Reachable-nodes Finding) based on node activity evaluation, which reduced the nodes to be detected and greatly shortened the detection time. For unreachable nodes, the authors trained a dicision tree model BUF(Bitcoin Unreachable-nodes Finding) to identify unreachable nodes based on attribute features from massive node addresses. Experiments showed that BNS performed better than the website "Bitnodes" in total number and efficiency. Based on the experimental results, the authors analyzed the real network size, node "churn" and geographical distribution.

Rice production in the Anhui province is threatened by fungal diseases. We obtained twenty-five fungal isolates from rice and wild-rice leaves showing leaf spot disease collected along the Yangtze River. A phylogenetic analysis based on internal transcribed spacer (ITS), translation elongation factor 1 alpha (TEF1-α), and beta tubulin (TUB2) sequences revealed that one isolate (SS-2-JB-1B) grouped with Nigrospora shaerica, one (QY) with Nigrospora chinensis, twenty-two with Ngrospora oryzae, and one strain (QY-2) grouped in its own clade, related to but clearly different from N. oryzae. Nineteen tested isolates including sixteen strains from the N. oryzae clade and the three isolates of the other three clades, caused disease on detached rice leaves. The three isolates that did not belong to N. oryzae were also able to cause disease on rice seedlings, suggesting that they were rice pathogens. Isolate QY-2 differed from the other isolates in terms of colony mor-phology, cell-size, and susceptibility to fungicides, indicating that this isolate represents a new species that we named Nigrospora anhuiensis. Our analysis showed that N. sphaerica, N. chinensis, and the new species N. anhuiensis can cause rice leaf spot disease in the field. This research pro-vides new knowledge for understanding rice leaf spot disease.

Natural rubber is an important national strategic and industrial raw material. Leaf anthracnose of rubber tree caused by the Colletotrichum species is one of the important factors restricting the yield of natural rubber. In this study, we isolated and identified strain Bacillus velezensis SF334 that exhibited significant antagonistic activity against both of C. australisinense and C. siamense, the dominant species of Colletotrichum causing leaf anthracnose of rubber trees in Hainan province of China, from a pool of 223 bacterial strains. The cell supernatants of SF334 had a significant prevention effect for leaf anthracnose of rubber tree with an efficacy of 79.67% against C. siamense and 71.8% against C. australisinense, respectively. We demonstrated that SF334 can lead to the lysis of C. australisinense and C. siamense mycelium by causing mycelial expansion, resulting in mycelial rupture and subsequent death. B. velezensis SF334 also harbors some traits of plant growth-promoting rhizobacterium such as secreting siderophore, protease, cellulose, pectinase and auxin of indole-3-acetic acid (IAA), as well as has broad-spectrum antifungal activity against some important plant pathogenic fungi. The genome combined with comparative genomic analyses indicated that SF334 possesses most genes of the central metabolic and secondary metabolites of B. velezensis strains. To our knowledge, this is the first time to report a Bacillus velezensis strain as a promising biocontrol agent against leaf anthracnose of rubber tree caused by C. siamense and C. australisinense. The results suggest that B. velezensis could be a potential candidate agent for leaf anthracnose of rubber tree.

In the clock network design, the trade-off between power consumption and timing closure is an important and difficult issue. To achieve this target, a hybrid clock network architecture that combines both clock tree and clock mesh seems to be a promising solution. In this paper, we propose a novel clock mesh architecture – cross-mesh, with the average dispersion of the overall driving force, our methodology creates small non-zero skew clock trees. In addition, we integrate clock gating and register clustering techniques to further reduce dynamic power consumption. The proposed algorithms have four stages: cross-mesh planning, register clustering, mesh line connecting, and load balancing. Experimental results show that our methodology and algorithms get feasible solution effectively and improve both power consumption and clock skew simultaneously.

Machine learning models are increasingly being used in critical domains, but their complexity, lack of transparency, and poor interpretability remain problematic. Decision trees (DTs) and rule-based approaches are well-known examples of interpretable models, and numerous studies have investigated techniques for approximating tree ensembles using DTs or rule sets; however, tree ensemble approximators do not consider interpretability. These methods are known to generate three main types of rule sets: DT-based, unordered-based, and decision list-based. However, no known metric has been devised to distinguish and compare these rule sets. Therefore, the present study proposes an interpretability metric to allow comparisons of interpretability between different rule sets, such as decision list- and DT-based rule sets, and investigates the interpretability of the rules generated by the tree ensemble approximators. To provide new insights into the reasons why decision list-based and inspired classifiers do not work well for categorical datasets consisting of mainly nominal attributes, we compare objective metrics and rule sets generated by the tree ensemble approximators and the \textit{Recursive-Rule eXtraction algorithm (Re-RX) with J48graft}. The results indicated that \textit{Re-RX with J48graft} can handle categorical and numerical attributes separately, has simple rules, and achieves high interpretability, even when the number of rules is large.

The dependence of the width of annual rings of woody plants on the characteristics of the snow cover is analyzed in various natural zones of the West Siberian Plain and adjacent mountainous areas: the maximum thickness and water reserve for the entire winter period and for individual months, the dates of disappearing, establishment, and duration of the occurrence of a stable snow cover. It has been shown that the role of the thickness and water content of snow cover for the radial growth of trees is differentiated by geographical location. On the plain, it intensifies in the forest-tundra and dry steppe. The response of radial growth to snow cover in the upper and lower parts of the forest belt is often opposite. Dates of establishment of stable snow cover are more important for tree growth compared to dates of disappearance. Dates of disappearance of stable snow cover are more significant in the southern regions than in the northern ones. The value of the duration of the period with stable snow cover for tree growth is higher in the southern regions.

The socioeconomic sectors increasingly rely on prompt and affordable tools to predict climatic conditions. This study uses a decision tree classifier model to identify similar monthly ENSO 3.4 indices from March 2001 to February 2011 through monthly historical ENSO3.4 indices (December 1950 – February 2001). Historical monthly ENSO3.4 indices were used to extract available high-resolution monthly historical precipitation grids (1950-2001) for Mexico and constructed monthly ensembles (March 2001 – February 2011 hindcasts). Formerly, a precipitation categorization of monthly observed and simulated precipitation (2001-2011 hindcasts) was employed. Thereafter, global vector data and spatial map assessments were performed seasonally (winter, spring, summer, and fall). Global KSS and HSS metrics indicated the model approach has skill, especially in the spring and fall. Spatial metrics exposed large areas with hit rates > 0.60 in winter and spring, though in winter and spring (dry seasons), it was due to the precipitation categorization hindcast and observed were within the first classification. Lower hit rates, but still acceptable (> 0.40), were detected in summer and fall (during the rainy season and at the end, respectively) because more precipitation classifications were tested and prone to miss. Spatial Spearman correlations provided specific areas of significant model performances and found that high hit rates during the winter and spring are spurious. Meanwhile, it was also found that during the fall the model approach is reliable in most of Mexico, followed by the spring, and in constraint areas in summer and winter across the country. It also appears that dominant ENSO neutral events followed by balanced ENSO cooling and warming events do not prefer the model skill. Overall, the empirical method shows promise (low-cost human and computational resources if ENSO indices expose certainty in its Lead months to forecast) and has the potential to be used in other developing countries influenced by El Niño phenomena.

The redox properties of quinones underlie their unique characteristics as organic battery components that outperform the conventional inorganic ones. Furthermore, the redox properties could be precisely shaped by using different substituent groups. Machine learning and statistics, onthe other hand, have proven to be very powerful approaches for efficient in silico design of novel materials. Herein, we demonstrated the machine learning approach for the prediction of the redox activity of quinones that potentially can serve as organic battery components. For the needs of the present study, a database of small quinone-derived molecules was created. A large number of quantum chemical and chemometrics descriptors was generated for each molecule and subsequently different statistical approaches were applied to select the descriptors that most prominently characterize the relationship between structure and redox-potential. Various machine-learning methods for screening of prospective organic battery electrode materials were deployed to select the most trustworthy strategy for machine learning aided design of organic redox materials. It was found that ridge regression models perform better than regression decision tree and decision tree based ensemble algorithms.

In Tunisia, olive is the most cultivated fruit crop while in the north where rainfall exceeded 400mm.year-1, the olive growing area is in widely extension in marginal soil with high slope gradient. Thus, the inclusion of cover crops is a sustainable solution to increase the ecosystem productivity, improve the soil fertility and oil yields. But, in Tunisia a few study were investigated the effect of legumes cover crop on the olive productivity. Therefore, the aim of this study was to evaluate the short-term (two cropping seasons s 2021 and 2022) effects of different seeded cover crops and soil management practices on the cover crops and soil characteristics, as well as enzymes activities and related microbial diversity. Six intercropping were tested in rainfed olive orchards (cv.Chétoui): Spontaneous vegetation (Spont), Wheat, Vetch, Oat, Fenugreek, mixture Vetch-Oat and compared to control (3 periodic tillage/year without intercropping). During the first cropping season, cover crops were cut as animal feed and only residues were incorporated into the soil. While, during the second year, all cover crops biomass were incorporated into the soil. Results indicate that the dry biomass and carbon uptake were significantly higher in grasses species (Wheat and Oat). But, highest level of nitrogen was found in Fenugreek cover crops. As compared to control, all cover crops including spontaneous vegetation increased soil organic matter and macronutrient level, mainly phosphorus. The most activity of β-glucosidase, phosphatase and arysulphatase was found in the cover crops plot of wheat and oat. In addition, this study improve that the utilization of the carbon sources was dominated by amides, amine and amino-acids in the fenugreek plot; while it was dominated by polymers and carboxylic acids in the case of wheat and oat.

Retrospective information about the climate system and plant ecophysiology are key inputs in climate and Earth system modelling. Dendrochronology provides such information with large spatiotemporal coverage, and stable carbon isotope (¹³C/¹²C) analysis across tree-ring series is among the most advanced dendrochronological tools. For the past seventy years, this analysis was performed on whole molecules, and, to this day, ¹³C/¹²C variation is attributed to ¹³C discrimination during CO₂ diffusion into leaves and assimilation by rubisco. By contrast, ¹³C discrimination by post-rubisco processes is presumed constant. Here, recently reported results on the first dataset of intramolecular ¹³C discrimination in tree rings were synthesised by variance component analysis. The emerging picture is not consistent with the classical (DR-discrimination-centred) concepts and practices of carbon-isotope dendrochronology. Specifically, leaf and stem post-rubisco discrimination each account for more variation in the data than diffusion-rubisco discrimination, i.e., post-rubisco discrimination is not constant. Furthermore, diffusion-rubisco discrimination is used widely as proxy of leaf intrinsic water-use efficiency (iWUE), a key determinant in the responses of global biogeochemical cycles to climate change. However, since diffusion-rubisco discrimination is a small component of the total data variance, whole-molecule analysis yields confounded iWUE estimates, yet intramolecular analysis will likely offer solutions. Lastly, all currently observed ¹³C/¹²C-climate relationships are attributed to diffusion-rubisco discrimination. However, here, relationships with temperature and radiation derive from leaf-level post-rubisco discrimination while relationships with precipitation derive from stem-level post-rubisco discrimination. That said, advances in mass spectrometry may soon make intramolecular ¹³C/¹²C analysis broadly available taking carbon-isotope dendrochronology to the next level.

This study examines the impact of chainsaw chain type and tree species on the concen-tration of inhalable wood dust generated during motor-manual harvesting in forested areas. The chainsaw chain is a critical component, contributing not only to productivity but also to the oper-ator's health and safety. Wood dust creation during harvesting operations poses significant risks and necessitates careful attention due to its potential health effects. We investigated the effects by conducting real-world measurements of inhalable dust within the operator's breathing zone during forestry work. Two different chain types were evaluated: the commonly used 3/8" pitch chain (conventional chain) and the 0.325" pitch chain. Additionally, measurements were taken for three tree species: beech, oak, and pine (including both live and standing dead trees after a fire). Results showed that, overall, using the conventional 3/8" chain type yielded the highest concentration of wood dust for all three tree species. Notably, the highest wood dust concentration was observed in the burned Pinus brutia cluster, also with the 3/8" chain pitch. These findings emphasize the im-portance of understanding how chain type and tree species contribute to wood dust levels and provide valuable insights for enhancing operator health and safety during motor-manual har-vesting operations

This study provides a comprehensive assessment of Derawan Island's coral reefs over two decades (2003, 2011, and 2021) from a machine learning classification perspective. Employing non-parametric algorithms like Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART), our analysis primarily focused on spatial and temporal changes in coral habitats. RF emerged as the most accurate method, demonstrating an accuracy of 71.43% with Landsat 9, 73.68% with Sentinel 2, and 78.28% with Multispectral Aerial Photos. We found that the accuracy of our classification results was significantly influenced by the geographic resolution, as well as the quality of field and satellite/aerial image data. Through spatial clustering, the coral habitats exhibited an Nearest Neighbor Index (NNI) value of 0.8727, indicating specific patterns of distribution. The analysis revealed a decrease in coral reef extent from 2003 to 2011, shrinking to 16 hectares with varying densities, followed by a slight area increase but with more heterogeneous densities between 2011 and 2021. This study not only highlights the dynamic nature of coral reef habitats over two decades but also underscores the critical role of machine learning in environmental monitoring and conservation efforts.

Key elements of Blue-Green Infrastructure are vegetation and stormwater storage. A combination of a bioretention cell with an underlying trench (BC-T) serving as a tree pit is often used in dense urban environments. An adequate ratio of drained area to bioretention cell area is a crucial design parameter. The ratio is derived from the hydrological balance; however, input data are often difficult to obtain or uncertain. The goal is to study the sensitivity of such data (tree water uptake and water holding capacities of soil filter and trench substrate) in the BC-T design. Sensitivity analysis is performed for the setup of a BC-T used in Prague, Czech Republic. A 10-year rainfall series (1 hour resolution) is used as an input. Data that are subject to the sensitivity analysis are changed for different trench exfiltration rates, and the effect on the size of the drained area is studied. At low trench exfiltration rates (1.8 mm.h-1), both the water holding capacity of the trench substrate and potential tree water uptake have a significant influence (more than a 20% change in the size of the drained area) and cannot be neglected. At good exfiltration rates (more than 18 mm.h-1) or when the trench is equipped with an underdrain, all studied parameters can be neglected. However, it is recommended to reduce the size of the drained area by 10-20%.

Timely and accurate information on tree species is crucial for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. Multimodal remote sensing data and deep learning seem to be the current tree species classification research mainstream, whether or not. The current review on the remote sensing data and deep learning tree species classification methods perspectives to analyze the unimodal and multimodal remote sensing data and classification methods in this realm is missing. To bridge the gap, we search for major trends in the remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss the limitations.

The process of rainfall interception by tree organs is crucial in mitigating the impact of intense rainfall on urban drainage systems, particularly in the context of climate change. this study selected ten commonly found tree species in Shanghai. Main parts of trees, including leaves, branches, and bark, were collected to analyze their ability to intercept rainwater. The optimized Artificial Rainfall Simulation System (ARSS) was applied to simulate the rainfall. The time-changing process of rainwater interception in three organs was measured during a 180-minute rainfall event which under four different rainfall intensities (4, 8, 12, and 16 mm/h, respectively). Process models of rainwater interception in different organs were fitted with adsorption kinetic equations. The rainwater interception process of tree organs complied with the quasi 2nd-order adsorption kinetic equation. The rainwater interception capacity of leaves, branches, and bark of ten urban tree species ranged from 0.05 to 0.34 mm, 0.13 to 0.24 mm, and 0.29 to 1.22 mm, respectively. The rainwater interception capacity of three organs significantly differs (p&lt;0.05). It reveals that bark exhibits the highest rainwater interception ability. Coniferous tree species have a greater ability to intercept rainwater than broad-leaved tree species. There are also differences in the rainwater interception ability of trees in urban and natural areas.