Background: Neuroendocrine tumors (NET) are a rare group of epithelial neoplasm present in gastrointestinal tract (GI) (67.5%), bronchopulmonary tree (25.3-30%), and in 15% of cases, the primary sites cannot be identified. Although endoscopic screening, improvement in pathological techniques, and early detection have shown improvement in NET survival rates, the prognosis of advanced, metastatic, and poorly differentiated NET is very poor. In this study, we aimed to evaluate the effect of Gastrointestinal and pancreatic (GEPs) NETs grade on overall survival. Method: We searched observational studies describing the overall survival or prognostic factors of primary GEP NETs from May 2011 -May 2021 following PRISMA guidelines. Studies describing the effect of primary grade 3 GEP NETs on overall survival were included. Meta-analysis was performed, and pooled hazard ratio and their 95% confidence interval (95% CI) were obtained. The forest plots were created using random-effects models and sensitivity analysis was performed to account for the heterogeneity. Results: Seven studies with 7692 confirmed patients were included. In our meta-analysis grade 3 GEP NET were associated with higher odds of poor survival (pooled HR: 2.73; 95% CI: 1.36–5.47; p = 0.005), with 92% heterogeneity between studies (p < 0.0001). To account for heterogeneity, sensitivity analysis was performed by removing two outlying studies (Fathi et al. and Foubert et al.) on funnel plots. The results after sensitivity analysis did not change and still showed significant association of grade 3 with poor survival (pooled HR: 4.53; 95% CI: 3.54–5.78; p < 0.00001), with no heterogeneity between studies (p = 0.72; I2 = 0%). Conclusion: Our meta-analysis found that grade 3 GEP NETs are associated with poor survival and additional future studies are needed to identify other risk factors associated with poor survival in GEP NETs to improve mortality.

Accuracy and computational cost are the main challenges of deep neural networks in image recognition. This paper proposes an efficient ranking reduction to binary classification approach using a new feed-forward network and feature selection based on ranking the image pixels. Preference net (PN) is a novel deep ranking learning approach based on Preference Neural Network (PNN), which uses new ranking objective function and positive smooth staircase (PSS) activation function to accelerate the image pixels’ ranking. PN has a new type of weighted kernel based on spearman ranking correlation instead of convolution to build the features matrix. The PN employs multiple kernels that have different sizes to partial rank image pixels’ in order to find the best features sequence. PN consists of multiple PNNs’ have shared output layer. Each ranker kernel has a separate PNN. The output results are converted to classification accuracy using the score function. PN has promising results comparing to the latest deep learning (DL) networks using the weighted average ensemble of each PN models for each kernel on CFAR-10 and Mnist-Fashion datasets in terms of accuracy and less computational cost.

Real-world time series data sets contain a combination of linear and nonlinear patterns, making the time series forecasting problem more challenging. In this paper, a new hybrid methodology is introduced for forecasting univariate time series data sets using a multiplicative error modeling approach. An autoregressive integrated moving average (ARIMA) model is combined with an autoregressive neural network (ARNN) for improving the predictions of individual forecast models. The proposed multiplicative ARIMA-ARNN model glorifies the chances of capturing the different combinations of linear and nonlinear patterns in time series. The model shows outstanding performance on six standard time-series data sets compared to other widely used single and hybrid forecasting models.

Teeth segmentation plays a pivotal role in dental diagnosis, treatment, planning, and the development of computer-aided dental systems. It enables precise identification and analysis of dental structures, aiding in detecting dental abnormalities, measuring tooth dimensions, and assessing oral health conditions. Accurate teeth segmentation also facilitates the automation of dental workflows, leading to improved efficiency and reduced human error. Artificial Intelligence (AI) has witnessed rapid advancements, with various approaches developed or enhanced. While Convolutional Neural Networks (CNNs) have been widely used in medical image segmentation, the U-Net architecture has emerged as a standout performer due to its exceptional segmentation capabilities. This paper presents a proof of concept for the Attention U-Net architecture, as previously used in [1], applied to teeth segmentation. The study demonstrates the superior performance of this network in accurately segmenting teeth using a newly available benchmark dataset called Tufts Dental X-Ray Dataset. When trained and tested on 10-fold cross-validation, the model achieved an average dice coefficient of 95.01%, intersection over union of 90.6%, and pixel accuracy of 98.82%. These scores surpass those of all other networks implemented on the same dataset. By leveraging the Attention U-Net architecture, our research showcases the potential of advanced AI techniques in dental radiography. The findings contribute to the ongoing efforts to develop automated systems to assist dental professionals in their clinical practice.

This research introduces a pioneering solution to the challenges posed by gastrointestinal tract (GI) cancer in radiation therapy, focusing on the imperative task of precise organ segmentation for minimizing radiation-induced damage. GI imaging has historically used manual demarcation, which is laborious and uncomfortable for patients. We address this by introducing the ResECA-U-Net deep learning model, a novel combination of the UNet and ResNet34 architectures. Furthermore, we further augment its functionality by incorporating the Efficient Channel Attention (ECA-Net) methodology. By utilizing data from the UW-Madison Carbone Cancer Centre, we carefully investigate several image processing techniques designed to capture critical local characteristics. With its foundation in computer vision concepts, the ResECA-U-Net model is excellent at extracting fine details from GI images. Sophisticated metrics such as intersection over union (IoU) and the dice coefficient are used to evaluate performance. Our study's outcomes demonstrate the effectiveness of the suggested method, yielding an impressive 96.27% Dice coefficient and 91.48% IoU. These results highlight the significant contribution that our strategy has made to the advancement of cancer therapy. Beyond its scientific merits, this work has the potential to significantly enhance cancer patients' quality of life and provide better long-term outcomes. Our work is a significant step towards automating and optimizing the segmentation process, which can potentially change how GI cancer is treated completely.

Energy market price volatility and an upward trajectory of prices per unit of electricity have sent all industrial sectors and many economies to the brink of recession. Alongside the urgent need for decarbonisation of all industries, achieving a higher level of energy independence across all sectors seems imperative. A multi-disciplinary approach with a proposed system of CO₂ emissions reduction and capture technologies has the potential for short-term emissions reduction to near-zero in absolute terms. The findings of this research showed CO₂ emissions reduction of ~30% from 977t of CO_2e to 684t in one single blast furnace production cycle, by switching the energy provider generating energy exclusively from renewable sources. Replacing coal with biomass (BECCS), resulted in an additional reduction of ~30%, to CO_2e to 479t CO_2e. Installing solar PV panels for energy generation on site resulted in a further ~30% reduction, to 335 tonnes CO_2e. Finally, the installation of CO₂ filters reduced the CO₂ emissions to near-zero. Simultaneously, energy-saving and process improvement measures implementation (up to 60% efficiency increase), excess heat recovery (&lt;30% of energy savings), and retrofitting renewable energy technology resulted in an energy independence of 88%. Short-term engineering solutions, partly subsidised in the UK, are readily available.

The impact of the agriculture net carbon effect is crucial for climate change mitigation, yet research on this topic in China is currently insufficient. In this study, the carbon balance ratio (CBR) is used to characterise the net carbon effect of agriculture, and the spatial Durbin model and threshold regression model were used to empirically analyse the CBR with a sample of 30 provincial-level regions in China from 2004 to 2019. The aim was to investigate the mechanism of the influence of the land management scale (SCALE) of farming on the CBR. The results of the study indicate that national farming generally has a net sink effect, with large changes in interannual fluctuations; the expansion of SCALE has an obvious role in increasing sinks and reducing emissions, and has a positive spatial spillover effect on CBR, with significant spatial heterogeneity. Furthermore, as the SCALE expands, the direction of the influence of rural residents’ income and education level on CBR changes in the opposite direction, showing a significant non-linear relationship characteristic. However, in most areas, the SCALE threshold has not yet been reached, suggesting that the improvement in education levels can still offset the negative impact of economic income growth in the short term. The study recommends paying attention to the role of farmers’ human capital, changing production methods, and implementing carbon peaking and carbon neutrality goals to enhance the net sink effect of the farming industry, which would be achieved by increasing land management scale.

Iron is a critical yet limited nutrient for microbial growth. To scavenge iron, most microbes produce siderophores—diverse small molecules with high iron affinities. Different siderophores are specifically recognized and uptaken by corresponding recognizers, enabling targeted interventions and intriguing cheater-producer dynamics. We propose constructing a comprehensive iron interaction network, or "iron-net," across the microbial world. Such a network offers the potential for precise manipulation of the microbiota, with conceivable applications in medicine, agriculture, and industry, as well as advancing microbial ecology and evolution theories. Previously, our successful construction of an iron-net in the Pseudomonas genus demonstrated the feasibility of co-evolution-inspired digital siderophore typing. Enhanced by machine learning techniques and expanding sequencing data, forging such an iron-net calls for multidisciplinary collaborations, and holds significant promise in addressing critical challenges in microbial communities.

In recent years, video anomaly detection technology , which can intelligently analyze massive video and quickly find abnormal phenomena, has attracted extensive attention with the wide application of video surveillance technology . To address the complex and diverse problem of abnormal human behavior detection in surveillance videos, a surveillance video abnormal behavior detection and localization supervised method based on the deep network model and the traditional method is proposed. Specifically, we combined AGMM and YOLACT methods to obtain more accurate foreground information by fusing the foreground maps extracted by each technique. To further improve the accuracy , we use the PWC-Net technique to extract features of the foreground images and input them into an anomaly classification model for classification. The proposed method effectively detects and locates the abnormal behavior in the monitoring scene. In addition to the aforementioned methods, this paper also employs YOLOV5 and DeepSORT networks for object detection and tracking in the video, which allows us to track the detected objects for better understanding of the scene in the video. Experiments on the UCSD benchmark dataset and the comparison with state-of-the-art schemes prove the advantages of our method.

What is the ideal soil-grass combination for maximum photosynthesis? In this study, we investigated how soil and grassland types affect photosynthesis in the grasslands of Gannan, China. We divided the grasslands of Gannan into 166 study sites, each with a unique soil-grass combination by intersecting the soil and ecoregion maps using ArcGIS. We obtained 19 years of data on the Net Photosynthesis (PsnNet) of grasslands in the area from 2000 to 2018 and then divided them into “growing season” (June to September) and “non-growing season” (October top May). Between 2000 and 2018, PsnNet of grasslands showed a gradually increasing trend. The effect of soil type on PsnNet was not significant during the growing season. However, it was highly significant during the non-growing season. Among the soil types, grasses that grew in Mollic, Gelic, and Haplic soils had the highest rate of photosynthesis. The difference in PsnNet among the various grass types was highly significant during both seasons. However, Tropical and Subtropical succulent evergreen broad-leaf shrubs and Temperate meadows had the highest rate of photosynthesis. Additionally, there was a highly significant difference in PsnNet among the various soil-grass interactions. In the growing season, TStEgBLS growing Eutric soils had the highest PsnNet. However, SaDBLS growing in Gelic soils had the the highest.

We address the problem of finding a unique graph embedding that best describes a graph's "topology". This issue is of particular interest in the chemistry of materials. Graphs that admit a tiling in 3-dimensional Euclidean space are termed tessellate, those that do not decussate. We give examples of decussate and tessellate graphs that are finite and 3-periodic. We conjecture that a graph has at most one tessellate embedding. We give reasons for considering this the default "topology" of periodic graphs.

Sea ice extraction and segmentation of remote sensing images is the basis for sea ice monitoring. Machine learning-based image segmentation methods rely on manual sampling and require complex feature extraction. Deep-learning semantic segmentation methods have the advantages of high efficiency, intelligence, and automation. Sea ice segmentation using deep learning methods faces the following problems: in terms of datasets, the high cost of sea ice image label production leads to fewer datasets for sea ice segmentation; in terms of image quality, remote sensing image noise and Severe weather conditions affects image quality, which affects the ac-curacy of sea ice extraction. To address the quantity and quality of the dataset, this study used multiple data augmentation methods for data expansion. To improve the semantic segmentation accuracy, the SC-U2-Net network was constructed using multi-scale inflation convolution and a multi-layer Convolutional Block Attention Module (CBAM) attention mechanism for the U2-Net network. The experiments showed that (1) data augmentation solved the problem of an insuffi-cient number of training samples to a certain extent and improved the accuracy of image seg-mentation. (2) This study designed a multilevel Gaussian noise data augmentation scheme to improve the network's ability to resist noise interference and achieve a more accurate segmenta-tion of images with different degrees of noise pollution. (3) The inclusion of a multi-scale inflation perceptron and multi-layer CBAM attention mechanism improved the ability of U2-Net network feature extraction and enhanced the model accuracy and generalization ability.

Fractures of the talar neck and body are associated with spine fractures and scoliosis deformity, which affect cosmetic appearance and cause difficulty in ambulation. The implant design for talus surgery is thriving as a functional alternative in case of severe talar destruction, focusing on segmentation and reconstruction of the talus’s shape. However, manual segmentation of the talus is time-consuming and subjective. In this study we exploited the automatic segmentation framework to efficiently train a deep learning-based model to accurately segment the talus based on computed tomography imaging. We developed three model configurations with nnU-Net and investigated their Dice similarity coefficients (DSC) and 95% Hausdorff distances (HD95) for talus segmentation on a CT image dataset. The three configurations performed well (DSC > 0.95, HD95 < 0.6). When tested on the same samples, one of the configurations was more efficient while ensuring higher accuracy. We propose to focus on talus anatomic variations with increasing age based on this framework and apply it to clinical trials at the next stage.

A common spinal condition, spondylolisthesis is the presence of a relative back or forth displacement between the upper and lower vertebra due to one vertebra being oriented away from the smooth curvature of a normal spine. Aging-related illnesses such as degenerative spondylolisthesis are especially burdensome on social welfare and health-care systems in an aging society, especially radiologists and clinical physicians. Therefore, we proposed a computer aided diagnosis algorithm, named LumbarNet, for vertebral slippage detection on clinical X-ray images. Collaborating with i) a P-grade, ii) a piecewise slope detection scheme, and iii) a dynamic shift detection routine, LumbarNet was thus specialized for analyzing complex structural patterns in lumbar spine X-ray images and outcompeted other U-Net based methods. Extensive experiments on lumbar spine X-ray images in standard clinical practices showed that LumbarNet achieved a mean intersection over union value of 0.88 in vertebral region detection and an accuracy of 88.83% in vertebral slippage detection.

The Dark Web is known as a place triggering a variety of criminal activities. Anonymization techniques enable illegal operations, leading to the loss of confidential information and its further use as bait, a trade product or even a crime tool. Despite technical progress, there is still not enough awareness of the Dark Web and its secret activity. In this study, we introduced the Dark Web Enhanced Analysis (DWEA), in order to analyze and gather information about the content accessed on the Dark Net based on data characteristics. The research was performed to identify how the Dark Web has been influenced by recent global events, such as the COVID-19 epidemic. The research included the usage of a crawler, which scans the network and collects data for further analysis with machine learning. The result of this work determines the influence of the COVID-19 epidemic on the Dark Net.

Best horticulture management has been practiced to reduce the amount of inorganic fertilizer in connection with net (mosquito net) for higher production of Indian spinach. Excessive use of chemical fertilizers in agriculture promoted negative effect on the environment and human health in Bangladesh. The experiment was conducted at the Horticulture Farm of Bangladesh Agricultural University (BAU), Mymensingh during the period February to May 2016. Two factor experiments were conducted on nettings: control (N₀), Netting (N₁) and different fertilizers: control (F₀), Vermicompost 10 t/ha (F₁), Vermicompost 15 t/ha (F₂), 2/3^rdof F₂+ 1/3^rdof F₄ (F₃), Inorganic fertilizer (F₄). Combination of mosquito net and combined of organic and inorganic fertilizers (N₁F₃) gave the significantly higher growth and yield of Indian spinach compared to other treatment combination. The maximum vine length (77.71 cm), after 45 days after sowing was found from N₁F₃ which was 72% higher compared to control. The highest yield of Indian spinach was 38.67 t/ha from N₁F₃ which was 80% higher compared to control. The treatment combination of N₁F₃ provided maximum leaf numbers per plant, leaf length.

The Sentinel-1 SAR Extra Wide swath mode with its 40 meters pixel size allows accurate lead mapping in Arctic sea ice areas. We present an improved winter lead detection algorithm based on a modified U-Net convolutional neural network. We introduce a preprocessing procedure that balances normalized radar cross section (NRCS) values between sub-swaths. This results in more consistent Sentinel-1 SAR images as input for the classifier, which is especially important for the cross-polarization HV channel. In turn, image consistency is essential for automatic image semantic segmentation. We develop a new lead detection algorithm and compare it with a previously published version. We produce pan-Arctic, 40 meters resolution lead maps and present pan-Arctic lead area fraction on a 12 km grid for January 2019. Within different Arctic regions, the lead area fraction is stable and varies only within the expected natural variability during the one month studied here. The average Arctic-wide lead area fraction is 2.44 % with 0.25 % standard deviation during January 2019. The improved lead detection algorithm leads to a better lead discrimination and extends the applicability area to the entire Arctic.

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

Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often could not accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherently could capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

Osteoarthritis (OA) is the most common degenerative joint disease that causes chronic pain and disability. Different innate immune components, including macrophages, T cells, and neutrophils, participate in osteoarthritis pathophysiology. Neutrophils are the most abundant circulating leukocytes with multiple specialized functions contributing to innate and adaptive immune functions. Although neutrophils produce proinflammatory cytokines and chemokines, reactive oxygen species (ROS), matrix-degrading enzymes, and neutrophil extracellular traps (NET) that promote joint degradation as the first recruit cells in an inflamed joint, these cells also play an important role in joint repair by regulating the immune response, releasing anti-inflammatory factors, and activating some protective genes. In this review, various aspects of neutrophil biology, their role in inflammation and its association with osteoarthritis, and possible therapeutic approaches to target neutrophils for the treatment of osteoarthritis are described. Understanding neutrophil heterogeneity and their mechanisms of action in joint inflammation, provides a potential strategy for OA management.

Atmospheric CO2 concentration, temperature and precipitation escalate and it is critical to investigate the agricultural practices best suited to face such new conditions. An important aspect is soil management, which alters soil organic matter content, nutrients and structure, and thus soil/atmosphere interactions and ultimately production. This in vitro study addresses greenhouse gas (GHG) emissions from two long-term soil management experiments in France. One set of soil columns were managed with either grain legume + cover crops or no grain legumes + bare fallow. The other with either plowed or reduced tillage soils. GHG exchange (CO2, N2O) measurements was conducted under different environmental conditions, before and during growth of barley and eventually with three levels of barley biomass cuts (everything removed, cut halfway, intact). The gas exchange was influenced by time and environmental settings, and to a smaller degree management history, with highly variable emissions of N2O. The environmental treatments induced different patterns of effect depending on the management history of the soils. The gas exchange from soil with grain legume was more responsive to the environmental treatments than the rotation without grain legumes. Depending on the history of tillage, the gas exchange responded likewise differently to the induced environment.

Neutrophils play an important role as the first line of innate immune defense. One function of neutrophils, called neutrophil extracellular traps (NETs), has been discovered recently. NETs are extensive fibrous structures released extracellularly from activated neutrophils in response to infection. They are composed of cytosolic protein assembled on a scaffold of released chromatin. These structures suppress the dissemination of micro-organisms in blood by trapping them mechanically, and by exploiting coagulant function to segregate them within the circulation. In addition, NET components (DNA, histone, and granule proteins) also contribute to the triggering of an inflammatory process. NET function, however, can be regarded as a double-edged sword. On one hand, NET formation is an efficient strategy for neutralizing invading micro-organisms. On the other hand, NET can be harmful to the host, as its exposed by-products that are toxic to endothelial cells and parenchymal tissue. We present here the analogous biological and physiological features of the harmful positive amplification loop between inflammation and tissue damage induced by NETosis dysregulation and Coronavirus Disease-2019 (COVID-19) pathogenesis. Considering the rapid evolution of this disease symptoms and its lethality, we hypothesize that COVID-19 progresses under an amplifier loop, leading to an massive, uncontrolled inflammation process. We also describe the correlations of COVID-19 symptoms and biological features with those consecutive to uncontrolled NET formation causing various sterile or infectious diseases. General clinical conditions, and numerous pathological and biological features, are analogous with NETs deleterious effects. We postulate that Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV2) induces a disproportionate virus-induced NET release, and that this plays a key role in COVID-19 pathogenesis. While neutrophils are the principal starting point for extracellular and circulating DNA release, targeting NETs rather than neutrophils themselves may stand for an effective strategy. This paper offers an in-depth review of NET formation, function and pathogenic dysregulation, as well as of current and future therapies to control NET unbalance. As such, it enables us also to suggest new therapeutic strategies to fight COVID-19. In combination with or independent of the latest tested approaches, we propose that, in the short term, deoxyribonuclease I (DNase-1) treatment should be evaluated; we also advocate a significant increase in research on the development of toll-like receptors (TLR) and C-type Lectin like receptors (CLEC) inhibitors, and on anti-IL26 therapies.

Understanding the dynamics of Organic Carbon mineralization is fundamental in forecasting biosphere to atmosphere Net Carbon Ecosystem Exchange (NEE). With this perspective, we developed 3D-CMCC-PSM, a new version of the hybrid Process Based Model 3D‐CMCC FEM where also heterotrophic respiration (R_h) is explicitly simulated. The aim was to quantify NEE as a forward problem, by subtracting Ecosystem Respiration (R_eco) to Gross Primary Productivity (GPP). To do so, we developed a simplification of the Soil Carbon dynamics routine proposed in DNDC [1]. The method calculates decomposition as a function of soil moisture, temperature, state of the organic compartments, and relative abundance of microbial pools. Given the pulse dynamics of soil respiration, we introduced modifications in some of the principal constitutive relations involved in phenology and littering sub-routines. We quantified the model structure related uncertainty in NEE, by running our training simulations over 1000 random parameter-sets extracted from parameters distributions expected from literature. 3D-CMCC-PSM predictability was tested on independent time series for 6 Fluxnet sites. The model resulted in daily and monthly estimations highly consistent with the observed time series. It showed lower predictability in Mediterranean ecosystems, suggesting that it may need further improvements in addressing evapotranspiration and water dynamics.

Background: Handheld cameras may increase access to teleretinal diabetic retinopathy screenings (TDRS), as they are more economical and nimbler than traditional desktop cameras. However, their use in safety-net clinical settings is less understood. Methods: The Los Angeles County Department of Public Health collaborated with the Los Angeles County Department of Health Services (DHS) to pilot the use of handheld cameras in DHS’s TDRS program. This study assessed screening metrics and interviewed TDRS coordinators and photographers about their experiences using these cameras. Results: Handheld cameras are harder to operate and performed less optimally on key screening metrics (e.g., image quality) compared to desktop cameras. However, this challenge appeared to be linked to the selected model, rather than to all handheld cameras. The adoption of handheld cameras in DHS was aided by their integration into an existing agency infrastructure that already supports the use of desktop cameras; these operational supports include an established workflow, a data platform, and experienced photographers. Conclusions: The use of handheld cameras for TDRS is a promising practice for assuring equitable access to screenings. Handheld cameras are portable, smaller in size, and lower in cost than desktop cameras. Future projects should invest in and assess the performance of higher-quality models of these cameras, especially their use in safety-net clinical settings.

Cutting and packing problems have been studied for decades and are of concern for a wide variety of industries to reduce costs and material utilization. This paper addresses the one-dimensional cutting stock problem, focusing on minimizing total stock usage. Procedures that address this problem include linear programming methods and metaheuristics. However, linear programming methods are limited to low-complexity instances, while most metaheuristics require extensive parameter tuning. In this paper, we develop a Petri-net model to construct cutting patterns. Using the reachability tree of the Petri net, the filtered beam search algorithm is implemented to find the best solution. Our algorithm is compared with the Least Lost Algorithm and the Generate & Solve algorithm over five datasets of instances. These algorithms share some characteristics with ours and have proven to be effective and efficient. Experimental results demonstrate that our algorithm effectively finds optimal solutions for both low and high-complexity instances. These findings confirm that Petri nets are suitable for modeling and solving the one-dimensional cutting stock problem.

The giant octopus caught off the east coast of Korea is an important fishery resource with the largest haul and is mostly caught using net pots. While giant octopus pots have different shapes, drum, rectangular, and cylinder pots are mostly used. However, these pots have not been thoroughly evaluated. Here, we evaluated the fishing performance of three giant octopus pots, their fluid resistance, and underwater movement. Six sea trials were conducted near the Hupo region in Gyeongsangbuk-do. In total, 163 individuals (62,724 g), 190 individuals (49,913 g), and 154 individuals (52,364 g) were caught using drum, rectangular, and cylinder pots, respectively. Drum pots showed significant differences in the amount of catch by weight (ANOVA test, p&lt;0.05). Moreover, the percentage of minimum conservation size was the lowest in cylinder pots (6.5%). Drum pots showed the highest stability in the underwater stability test, while underwater fluid resistance at 2.5 kt fluid speed was in the following order: drum pots (7.66 kgf) &gt; rectangular pots (7.16 kgf) &gt; cylinder pots (7.04 kgf). Therefore, drum pots showed excellent fishing performance. This study provides foundational data for developing managing systems for giant octopus resources and establishing the extent of fishing gear usage.

To enable net zero sustainable thermal building energy this study develops an open-source thermal house model to couple solar photovoltaic (PV) and heat pumps (HP) for grid-connected residential housing. The calculation of both space heating and cooling thermal loads and the selection of HP is accomplished with a validated Python model for air-source heat pumps. The capacity of PV required to supply the HPs is calculated using an System Advisor Model integrated Python model. Self-sufficiency and self-consumption of PV and the energy imported/exported to the grid for a case study are provided, which shows that simulations based on the monthly load profile have a significant reduction of 43% for energy sent to/from the grid compared to the detailed hourly simulation and an increase from 30% to 60% for self-consumption and self-sufficiency. These results show the importance of more granular modeling and also indicate mismatches of PV generation and HP load based on hourly simulation datasets. The back-calculation PV sizing algorithm combined with HP and thermal loads presented in this study exhibited robust performance. The results indicate this approach can be used to accelerate the solar electrification of heating and cooling to offset the use of fossil fuels in northern climates.

Color image binarization plays a pivotal role in image preprocessing work, significantly impacting subsequent tasks, particularly in text recognition. This paper concentrates on Document Image Binarization (DIB), aiming to separate a image into foreground (text) and background (non-text content). Through a thorough analysis of conventional and deep learning-based approaches, we conclude that prevailing DIB methods leverage deep learning technology. Furthermore, we explore the receptive fields pre- and post-network training to underscore the Transformer model's advantages. Subsequently, we introduce a lightweight model based on the U-Net structure, enhanced with the Mobile ViT module to better capture global information features in document images. Given its adeptness at learning both local and global features, our proposed model exhibits superior performance on two standard datasets (DIBCO2012 and DIBCO2017) compared to state-of-the-art methods. Notably, our proposed DIB method presents a straightforward end-to-end model devoid of additional image preprocessing or post-processing. Moreover, its parameter count is less than a quarter of the HIP'23 model, which achives best results on three datasets(DIBCO2012, DIBCO2017 and DIBCO2018). Finally, two sets of ablation experiments were conducted to verify the effectiveness of the proposed binarization model.

Lignocellulosic biomass has demonstrated great potential as feedstock for pellet production, notwithstanding the fact that the industrial production of pellets is faced with some economic challenges. This study presents a technoeconomic analysis of six case scenarios to develop a process model for pellet production from sawdust and oat straw that employs torrefaction and steam explosion pretreatment prior to pelletization. SuperPro Designer was used to carry out this evaluation. The pellet plants were designed to have a capacity of 9.09 t/h of sawdust and oat straw each. The pellet yield ranged from 59 kt– 72 kt/year. Scenarios analyzed included variations of steam explosion and torrefaction. In some scenarios materials were lost in the form of liquid and gas due to the pretreatment process. The breakdown of equipment purchase cost showed that the torrefaction reactor is the most expensive unit with about 51% of the purchase cost. Facility-dependent and feedstock costs were the major significant contributors to the pellet production cost. The profitability of pellet production as determined by the net present value (NPV), internal rate of return (IRR), and payback period and was found to be sensitive to variations in feedstock cost.

Global warming causes an alarming increase in temperature which negatively affects growth, physiology, and yield of rice. To gain some insights into the impacts of booting HT (at 42°C, 3 h for 7 days), we investigated its effect in three rice genotypes, namely, N22, KDML105 and IR64. Booting HT caused an extended phenology and lower photosynthesis and plant growth rate but an increase in chalkiness. Although, prolonged phenology from dough to physiological maturity led to longer duration of grain filling, the negative effects of its was significantly lower yield components, yield and harvest index in all rice. Among cultivars, N22 presented adapted ability to maintain leaf gas exchange and compensated vegetative part by an increase in tiller numbers resulting in less affected growth rate. It prolonged phenology that caused extended grain filling. Consequence, N22 was the lowest reduction in number of seed panicle^–1, number of filled seeds hill^–1, yield, HI as well as the lowest percentage chalkiness. KDML105 adapted to booting HT by maintaining leaf gas exchange, increasing specific leaf area and prolonging phenology. The longest extended phenology together with the maintenance of high photosynthesis during dough grain were associated with moderate yield reduction and chalkiness. However, IR64 showed dramatic reductions in photosynthesis, growth rate, and yield but highest percentage grain chalkiness. Therefore, in response to booting HT, N22, KDML105 and IR64 could be indicated as being heat tolerant, moderately heat sensitive, and heat sensitive, respectively. This can be applied for crop modelling and rice heat tolerance in breeding program.

Two monophyletic Daphnia species (Daphnia magna and D. similis) were exposed to a sub-lethal concentration of Pb (50 µg/L) for nine generations under two food regimes (usual and restricted) and analysed for acetylcholinesterase (AChE) activity, hatching delay and lifespan, and Net Reproductive Rate (R0) at subcellular, individual and population levels, respectively. At the sixth generation, Pb acclimated neonates were moved to a clean media for three more generations to check for recovery. D. magna showed no Pb effect on Net Reproductive Rate (R0). However, Pb stimulated reproduction, reduced lifespan and decreased AChE activity. Hatching delay and lifespan did not improve during the recovery process, indicating genetic adaptation. Food restriction reduced R0, lifespan, delayed hatching, and increased AChE activity; opposite outcomes were shown for D. similis. Full recovery shown by R0 suggests a physiological acclimation of D. similis. Under food restriction, the animals exhibited a reduction of R0 and lifespan, delayed hatching and increased AChE activity; however, with no Pb effect. The recovery process under food restriction showed that D. similis might not cope with Pb exposure, indicating failed recovery. Such outcomes indicate that a model species' sensitivity may not represent another's sensitivity.

Studying concurrent systems is to sort the read/write and control events in the concurrent system according to the order, or in partial order, or in strict order. Because of the concurrency of events and the timeliness of writing events, there will be overlap between events, which requires interval order analysis. A single start and end sequence to represent the entire hierarchical order structure as well as all equivalent spaced order observations.a single sequence of beginnings and endings to represent the entire stratified order structures as well as all equivalent interval order observations. Mazurkiewicz traces and Comtracks are hierarchical traces, but they cannot describe interval traces. In a few sentences of this summary, we can tell the problems (petri interval traces with data), the previous work (Mazurkiewicz traces, Comtracks), the reasons that cannot be solved, and our own solutions (DPN: Petri nets with data, interval traces). This paper focuses on a BE (Beginnings and Endings) sequence representing an equivalent class of runs, or targets. Our goal is to have a BE sequence to represent the entire hierarchical order structure, that is, all equivalent observable interval orders.

This study reports an empirical analysis of an all-electric, Net Zero Energy Housing (NZEH) development located in a mixed-humid climate zone (4A, Virginia, USA). Circuit-level energy monitors were used to measure energy consumption and energy production data (solar photovoltaic) at 1-hr intervals in six identical apartments over 24 months. The study employs a multi-step case study methodology to a) empirically evaluate energy consumption and production data, b) identify the temporal variability of energy consumption and production data at different time scales, c) understand the impact(s) of weather and human-building interaction on energy consumption and production, and d) synthesize the study’s “lessons learned” toward data-driven recommendations for future NZEH researchers and practitioners. The study found that the development’s net zero energy goal was achieved in three of six case units and that NZEH housing performance was more influenced by human-building interaction than weather variability. The analysis also found the solar photovoltaic (PV) performance to be reliable across the sampled units over the periods of measurement, suggesting that solar PV could be oversized as an approach to overcome verifiability in HBI and achieve NZEH performance goals.

The aim of this paper is to discuss the relations between various notions of sequential completeness and the corresponding notions of completeness by nets or by filters in the setting of quasi-metric spaces. We propose a new definition of right $K$-Cauchy net in a quasi-metric space for which the corresponding completeness is equivalent to the sequential completeness. In this way we complete some results of R.~A. Stoltenberg, Proc. London Math. Soc. \textbf{17} (1967), 226--240, and V.~Gregori and J.~Ferrer, Proc. Lond. Math. Soc., III Ser., \textbf{49} (1984), 36. A discussion on nets defined over ordered or pre-ordered directed sets is also included.

Rice-Tartary buckwheat is a special type of Tartary buckwheat with easy hulling thin shell. The local rice-Tartary buckwheat ‘cv. Xiaomiqiao’ (XMQ) was used to explore the characteristics of photosynthesis, starch synthesis, yield and quality, as compared to Tartary buckwheat ‘cv. Jinqiao 2’(JQ2). XMQ showed significantly lower values of net photosynthetic rate, ADPGase activity and starch synthase activity at one or more periods during grain filling, as compared to that of JQ2. The effective branch number and grain number per plant of XMQ were similar to that of JQ2 regardless of different years, but the 1000-grain weight was significantly lighter that resulted in the significantly lower yield. XMQ accumulated similar contents of starch (amylose, amylopectin) and protein (glutelin, albumin, prolamin, globulin) to that of JQ2, but exhibited significantly lower flavonoid content. The values of peak viscosity and hot paste viscosity in XMQ were similar to that of JQ2, but the values of cool paste viscosity, breakdown and setback were significantly lower. In conclusion, XMQ showed low yield due to the insufficient of photosynthesis and starch synthesis. XMQ exhibited high nutritional quality as well as normal Tartary buckwheat and stored 18.88mg g-1 of flavonoid, that can be consumed as daily diet. The present results will provide a basis for food processing and breeding of rice-Tartary buckwheat.

Animal behavior analysis is a crucial tasks for the industrial farming. In an indoor farm setting, extracting Key joints of animal is essential for tracking the animal for longer period of time. In this paper, we proposed a deep network that exploit transfer learning to trained the network for the pig skeleton extraction in an end to end fashion. The backbone of the architecture is based on hourglass stacked dense-net. In order to train the network, key frames are selected from the test data using K-mean sampler. In total, 9 Keypoints are annotated that gives a brief detailed behavior analysis in the farm setting. Extensive experiments are conducted and the quantitative results show that the network has the potential of increasing the tracking performance by a substantial margin.

Pedestrian detection is the core of driver assistance system, which collects the road conditions through the radars or cameras on the vehicle, judges whether there is a pedestrian in front of the vehicle, supports decisions such as raising the alarm, automatically slowing down or emergency stopping to keep pedestrians safe, and improves the security when the vehicle is moving. Suffered from weather, lighting, clothing, large pose variations and occlusion, the current pedestrian detection still has a certain distance from the practical applications. In recent years, deep networks have shown excellent performance for image detection, recognition and classification. Some researchers employed deep network for pedestrian detection and achieve great progress, but deep networks need huge computational resources which make it difficult to put into practical applications. In real scenarios of autonomous vehicle, the computation ability is limited. Thus, the shallow networks such as UDN (Unified Deep Networks) is a better choice since it performs well on consuming less computation resources. Base on UDN, this paper proposes a new deep network model named as two-stream UDN, which augments another branch for solving traditional UDN’s indistinction of the difference between trees / telegraph poles and pedestrians. The new branch accepts the upper third part of the pedestrian image as input, and the partial image has less deformation, stable features and more distinguished characters from other objects. For the proposed two-stream UDN, multi-input features including HOG feature, Sobel feature, color feature and foreground regions extracted by GrabCut segmentation algorithms are fed. Compared with the original input of UDN, the multi-input features are more conducive for pedestrian detection since the fused HOG features and significant objects are more significant for pedestrian detection. Two-stream UDN is trained through two steps: First, the two sub-networks are trained until converge; then we fuse results of the two subnets as the final result and feed it back to the two subnets to fine tune network parameters synchronously. To improve the performance, Softplus is adopted as activation function to obtain faster training speed, and positive samples are mirrored and rotated with small angle to make positive and negative samples more balanced.

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed applying approximate and numerical approaches, the multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. The self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared.

(1) Background: Learning and training in variable conditions favors adapting to unstable or changing environments. The aim of the study was to test the effect of variable practice on the accuracy of the forehand net approach shot in tennis.; (2) Methods: Thirty (n=30) first-class national players (12.9 ± 1.1 years old) participated, divided into three groups: i) induced variability training (n=10) (varying court surfaces and balls), ii) specific training (n=10) and iii) usual training -control group- (n=10). All groups trained for a month: 12 sessions of 20 minutes -3 per week-. The accuracy of the shots were analyzed through a 2D capture and digitization process of the ball bounce on the court; (3) Results: The variability group presented better accuracy values after the period without practice than the stable training group (p = 0.041; ES= 0.51).; (4) Conclusions: the application of variability in the game conditions during tennis training seems to have a favorable effect on the retention of accuracy in the forehand down the line approach to the net.

Owing to the growing concerns due to dwindling fossil fuel reserves, increasing energy demand and climate emergency, it is imperative to develop and deploy sustainable energy technologies to ensure future energy supply as well as transition to the net zero world. In this context, there is great potential in the biorefinery concept for supplying drop in biofuels in the form of biodiesel. Biodiesel as a fuel can certainly bridge the gap where electrification or the use of hydrogen is not feasible, for instance heavy vehicles, farm and marine transportation sector. However, biodiesel industry also leads to formation of large amount of crude glycerol as the byproduct. Crude glycerol due to presence of several impurities may not be a suitable feedstock for all high value products derived from glycerol but fits well with glycerol esterification for producing glycerol acetins, which have numerous applications. This review critically looks at the processes using nanostructured solid/liquid acid catalysts for glycerol esterification including the economic viability of the scale up.

Rapid screening of botanical extracts for the discovery of bioactive natural products was performed using a fractionation approach in conjunction with flow-injection high-resolution mass spectrometry for obtaining chemical fingerprints of each fraction, enabling the correlation of the relative abundance of molecular features (representing individual phytochemicals) with the read-outs of bioassays. We applied this strategy for discovering and identifying constituents of Centella asiatica (C. asiatica) that protect against Aβ cytotoxicity in vitro. C. asiatica has been associated with improving mental health and cognitive function, with potential use in Alzheimer’s disease. Human neuroblastoma MC65 cells were exposed to subfractions of an aqueous extract of C. asiatica to evaluate the protective benefit derived from these subfractions against amyloid β-cytotoxicity. The % viability score of the cells exposed to each subfraction was used in conjunction with the intensity of the molecular features in two computational models, namely Elastic Net and selectivity ratio, to determine the relationship of the peak intensity of molecular features and % viability. Finally, the correlation of mass spectral features with MC65 protection and their abundance in different sub-fractions were visualized using GNPS molecular networking. Both computational methods unequivocally identified dicaffeoylquinic acids as providing strong protection against Aβ-toxicity in MC65 cells, in agreement with the protective effects observed for these compounds in previous preclinical model studies.

Crack inspection in railway sleepers is crucial for ensuring rail safety and avoiding deadly accidents. Traditional methods for detecting cracks on railway sleepers are very time-consuming and lack efficiency. Therefore nowadays, researchers are paying attention to the vision-based algorithm, especially Deep Learning algorithms. In this work, we adopted the U-net for the first time for detecting cracks on a railway sleeper and proposed a modified U-net architecture named Dense U-net for segmenting the cracks. In the Dense U-net structure, we established several short connections between the encoder and decoder blocks, which enabled the architecture to obtain better pixel information flow. Thus, the model extracted the necessary information in more detail to predict the cracks. We collected images from railway sleepers, processed them in a dataset, and finally trained the model with the images. The model achieved an overall F1-score, precision, Recall, and IoU of 86.5% 88.53%, 84.63%, and 76.31% respectively. We compared our suggested model with the original U-net, and the results demonstrate that our model outperformed the U-net in both quantitative and qualitative results. Moreover, we considered the necessity of crack severity analysis and measured a few parameters of the cracks (e.g., length, maximum width, area, density). The engineers must know the severity of the cracks to have an idea about the most severe locations and take the necessary steps to repair the badly affected sleepers.

Abstract: This study observes the characteristics and influencing factors of carbon fluxes of the Momoge salt marsh ecosystem over four years, which behaves as a CO2 sink. The daily, seasonal, and interannual variations of CO2 fluxes in the Momoge salt marshes were observed using the eddy covariance method and compared with various environmental factors. An overall daily “U” shaped distribution was observed, with uptake during the day (negative values) and release at night (positive values). Annually, the carbon fluxes in the study area roughly exhibit a “V” shape. Carbon fluxes during the non-growing season predominantly showed positive values, indicating the release of CO2 into the atmosphere. Photosynthetically active radiation was the primary influencing factor affecting hourly and daytime variations in net ecosystem exchange (NEE) during the growing season, while temperature was the main factor influencing nighttime NEE dynamics. Air temperature, soil temperature, photosynthetically active radiation, precipitation, and water level all had significant impacts on daily net CO2 exchange. At the monthly scale, larger values of soil temperature, air temperature, photosynthetically active radiation, and aboveground biomass correspond to a stronger carbon absorption capacity of the ecosystem. Overall, temperature remains the primary factor for carbon fluxes in the Momoge wetlands.

The operating theatre represents a key contributor to healthcare-derived emissions. Here, we outline the carbon footprint of a lumbar microdiscectomy at our regional unit in Greater Manchester, with the use of AI technology. In doing so, we aim to define the extent of the environmental impact and identify carbon hotspots for sustainable change. Our unit of analysis covered the start of the patient journey into hospital through to the final outpatient appointment. Publicly available and locally sourced data was used. An inventory analysis of two lumbar microdiscectomies was conducted. Each item was categorised reconciled against our AI-powered ‘CO2 analysis’ database. Kilograms of carbon dioxide equivalent (kg CO2e) was chosen as the primary measure of impact. An average lumbar microdiscectomy at our trust generates 477.73kg CO2e. The operation (173.89kg CO2e, 36.40%) and the inpatient stay (144.67kg CO2e, 30.28%) constituted the biggest contributors. Single-use intraoperative equipment contributed to over three-quarters of the total emissions from the operation. By defining the environmental burden of elective spinal surgery, we have identified carbon hotspots to target for sustainable change. Substituting polluting elements for carbon-friendly alternatives and challenging the single-use culture, modern healthcare systems can help pave the way to a ‘net zero’ healthcare system.

The implementation of residential distributed generation (DG) in South America has shown significant growth in the last decade, driven by increased research on photovoltaics and renewable energy sources. Regulatory policies and business models have been crucial in fostering the adoption of DG in the region, with residential self-supply being the most receptive sector for implementing the concept. This article presents a literature review on DG as applied to residential self-supply in South America by comprehensively analyzing documents published between 2013 and 2023, applying the PRISMA methodology. In total, 37 literature resources have been systematically selected and reviewed to contribute to the identification of trends and developments in the field of DG in the residential sector, as well as highlighting areas that require further research and attention from stakeholders, including policymakers, investors, technology providers and consumers in the South American context.

Bone Scan Index (BSI) is an image biomarker for quantification on bone metastasis of cancers. To compute BSI, not only the hotspots (metastasis) but also the bones have to be segmented. Most related researches focus on binary classification on bone scintigraphy: having metastasis or none. Rare studies focus on pixelwise segmentation. In this study we compare three advanced convolutional neural network (CNN) based models to explore the bone segmentation on dataset in house. The best model is Mask R-CNN, which reaches the precision, sensitivity and F1-score: 0.93, 0.87, 0.90 for prostate cancer patients and 0.92, 0.86, 0.88 for breast cancer patients, respectively. The results are the average of 10-fold cross-validation, which reveals the reliability for clinical use on bone segmentation.

A real-time monitoring system for the operational status of fishing vessels is an essential element for the modernization of the fishing industry. The operational status of fishing vessels can be identified by using onboard cameras to detect the deck crew and the use of fishing nets. Due to the typically limited processing capacity of shipboard equipment and the significant memory consumption of detection models, however, general target detection models are unable to perform real-time image detection to identify the operational status of fishing vessels. In this paper, we propose a lightweight real-time deck crew and the use of fishing nets detection method, YOLOv5s-SGC. It is based on the YOLOv5s model, which uses surveillance cameras to obtain video of fishing vessels operating at sea and enhances the dataset. YOLOv5s-SGC replaces YOLOv5s’s backbone and the ordinary convolutional blocks in the feature fusion network with ShufflNetV2 and Ghost module.

In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Despite a rapid rise in NBS development in recent years, the methods for evaluating NBS still have certain gaps. We propose an approach based on a combination of remote sensing data and meteorological variables to reconstruct the spatio-temporal variation of net ecosystem exchange from eddy-covariance stations. Lagrangian particle dispersion model was used for upscaling of satellite images and flux towers. We trained data-driven models based on kernel methods separately for each selected land cover class. The results suggest that the proposed approach to quantifying carbon exchange on a medium-to-large scale by blending eddy covariance flux data with moderate resolution satellite and weather data provides a set of key advantages over previously deployed methods: (1) scalability, achieved via the validation design based on a separate set of eddy covariance stations; (2) high spatial and temporal resolution due to use of the Landsat imagery; (3) robust and accurate predictions due to improved data quality control, advanced machine learning techniques, and rigorous validation. The machine learning models yielded high cross-validation results. Overall we present here globally scaled technology for the land sector based on high resolution remote sensing imagery, meteorological variables, and direct carbon flux measurements of eddy covariance flux stations.

Agricultural mechanization is an important factor to improve the green total factor productivity of planting industry, which is the key way to realize the sustainable development and high-quality development of agriculture. Based on the panel data of 30 provinces in China from 2001 to 2019, this paper uses the stochastic frontier analysis method of output oriented distance function to measure the green total factor productivity of China’s planting industry based on net carbon sink, and empirically studies the impact of agricultural mechanization on the green total factor productivity in China’s planting industry. The empirical analysis finds that mechanization can significantly promote the planting green total factor productivity, and this basic conclusion is still robust after using instrumental variables, sub sample regression. Further research found that the path of mechanization on planting green total factor productivity is mainly reflected in technology progress and spatial spillover. The mechanism of operation scale expansion, factor allocation optimization and technical efficiency change is not significant. Given these findings, the paper adds considerable value to the empirical literature and also provides various policy- and practical implications.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

Modern portfolio theory claims that diversification into non-correlated or negatively correlated activities reduces the overall risk of a portfolio. Considering the total income of a bank as a portfolio of interest income and non-interest income, this paper investigates how the variability of interest income and non- interest income, and covariance between interest income and non-interest income influence the various risk factors of banks. We set out a study in the Indian context. We have extracted data for the period 2005-2017 and employed an extended version of Ridge, Lasso and Elastics Net regression to take care of multi-collinearly in our data. We have considered 10-fold cross-validation techniques to get optimal values of tuning parameters for Ridge, Lasso, and Elastics Net regression (which is a convex combination of ridge and the LASSO). We have compared different regression techniques by comparing RMSE and R2. We observe that non-interest income is positively correlated with interest income in the Indian context, but it does stabilize variance, idiosyncratic risk & market risk (Beta) of Indian Banks.

Neuroendocrine neoplasms (NENs) comprise a heterogeneous group of rare malignancies mainly originated from hormones secreting cells, which are widespread in human tissues. The identification of mutations in ATRX/DAXX genes in sporadic NENs, as well as the high burden of mutations scattered throughout MEN-1 gene in both sporadic and inherited syndromes, provided new insights into the molecular biology of tumour development. Other molecular mechanisms, such as the NOTCH signaling pathway, have shown to play an important role in the pathogenesis of NENs. NOTCH receptors are expressed on neuroendocrine cells and generally, act as tumour suppressor proteins, but in some contexts can function as oncogenes. The biological heterogeneity of NENs suggests that to fully understand the roles and the potential therapeutic implications of gene mutations and NOTCH signaling in NENs, a comprehensive analysis of genetic alterations, NOTCH expression patterns and their potential roles across all NEN subtypes is required.

Climate variability influences plant growth. With the variation in atmospheric CO2 concentration leading to global warming, it is urgent to monitor the performance of plant ecosystems for optimal carbon sequestration. Net primary productivity (NPP) is the perfect measurement tool as it measures the net carbon flux between the atmosphere and green plants and the factors that affect it. This study applied remote sensing techniques, specifically one of the radiation use efficiency models; the CASA model (Carnegie-Ames-Stanford approach) to assess the spatio-temporal dy-namics of NPP in Togo from 1987 to 2022 and the climatic parameters that influence it. The annual average NPP over the 36 years of the study is 4565.31 Kg C ha⁻¹. Variability is observed over the years with extremes in 2017 (6312.26 Kg C ha⁻¹) and 1996 (3394.29 Kg C ha⁻¹). Natural formations identified as high-production areas saw their productivity increase between 2000 and 2022. The interaction between climate change and land use changes negatively influences the variation of Total Production (PT) between 2000 and 2022, while individually, these parameters positively influence the variation of NPP (58.28% and 188.63%). The correlation result is positive and higher between NPP and light use efficiency (LUE) (r² = 0.75). Actual evapotranspiration also shows a positive correlation with NPP (r² = 0.43). A positive but weak correlation is observed between NPP and precipitation, potential evapotranspiration (r² = 0.20; 0.10 respectively). Temperatures have almost no correlation with NPP (r² = 0.5). Climatic parameters as a whole under the LUE banner influence NPP more. This study helps understand ecosystem performance in the context of To-go's commitments to reduce greenhouse gas emissions and combat climate change.

Rapidly and accurately extracting tobacco plant information can facilitate tobacco planting man-agement, precise fertilization and yield prediction. In karst plateau of southern China, tobacco plant identification is affected by large ground undulations, fragmented planting areas, complex and diverse habitats and uneven plant growth. This study took a tobacco planting area in Gui-zhou Province as the research object and used DJI UAVs to collect UAV visible light images. Considering plot fragmentation, plant size, presence of weeds and shadow masking, this area was classified into eight habitats. The datasets of different habitats were constructed to train the U-Net model. The results show that (1) the overall precision, recall, F1-score and IOU of tobacco plant information extraction were 0.68, 0.85, 0.75 and 0.60, respectively. (2) The precision was the highest for the subsurface-fragmented and weed-free habitat and the lowest for the smooth-tectonics and weed-infested habitat. (3) The weed-infested habitat with smaller tobacco plants can cause blurred images, reducing the plant identification accuracy. This study verified the feasibility of the U-Net model for tobacco single-plant identification in complex habitats. Decomposing complex habitats to establish the sample set method is a new attempt to improve crop identification in complex habitats in karst mountainous areas.

Rising demand and urbanization have led to the global increase and intensification of livestock production. Intensive production has harmful effects on the environment, public health (due to the emergence of zoonotic diseases), and animal welfare. In order to better understand how livestock production intensifies as countries develop and incomes rise, we examine the relationship between income per capita and intensification of poultry and cattle, using the states of India as a study region. We also replicate this analysis at the district level to test whether the relationship holds at different spatial scales. We identify outlying states and districts to see if there are policies that can keep agriculture production largely extensive, even as economic growth occurs. The results support the relationship of income being positively correlated with the proportion of livestock in intensive conditions. However, there are notable outlier states which have grown wealthy while still maintaining largely extensive production, indicating that there may be some cases where economic growth can occur without intensification.

It is time-consuming for acquiring complete data by fully phase encoding in two orthogonal directions along with one frequency encoding direction. Undersampling in the 3D k-space is promising in accelerating such 3D MRI process. Though 3D undersampling can be conducted according to predefined probability density, the density based method is not optimal. Because of the large amount of 3D data and computational cost, it is challenging to perform data-driven and learning-based 3D undersampling and subsequent 3D reconstruction. To tackle this challenge, this paper proposes a deep neural network called EEUR-Net, realized by optimizing specific undersampling patterns for the fully-sampled 3D k-space data. Innovatively, our undersampling algorithm employs an end-to-end deep learning approach to optimize phase encoding patterns and uses a 3D U-Net for image reconstruction of undersampled data. Through end-to-end training, we obtain an optimized 3D undersampling pattern, which significantly enhances the quality of the reconstructed image under the same acceleration factor. A series of experiments on a knee MRI dataset demonstrated that, in comparison to standard random uniform, radial, Poisson and equispaced Cartesian undersampling schemes, our end-to-end learned undersampling pattern considerably improves the reconstruction quality of undersampled MRI images.

Long-lasting insecticidal nets (LLINs) have been the main contributor to the reduction of malaria in the past two decades in sub-Saharan Africa. Development of pyrethroid insecticide resistance threatens the effectiveness of these LLINs, especially when nets become holed and the insecticide decays. Three classes of dual active ingredient (AI) LLINs, have been assessed for their physical durability as follows: 1) Royal Guard®, combining pyriproxyfen, which is known to disrupt female reproduction and fertility of eggs, and a pyrethroid alpha-cypermethrin; 2) Interceptor® G2, two adulticides with differing modes of action; chlorfenapyr and alpha-cypermethrin; 3) OlysetTM Plus incorporates permethrin (pyrethroid) and a synergist, piperonyl butoxide, to enhance the potency of pyrethroid insecticides; all nets were compared to standard pyrethroid only net (Interceptor®). About 40,000 nets of each type were distributed in February 2019 to different villages in Misungwi. A total of 3072 LLINs were followed at 6, 12, 24, 30 and 36 months to assess survivorship and fabric integrity in a community setting. The median functional survival were less than three years with Interceptor®, Interceptor® G2 and Royal Guard® having 1.9 year each and 0.9 years for OlysetTM Plus . After 36 months, 90% of OlysetTM Plus and Royal Guard® and 87% of Interceptor® G2 were no longer present (thrown away) in the households due to wear and tear, compared to 79% for standard Interceptor®. Short life spans of all assessed LLINs were driven by material of the net, rather than social economic status and housing material. All dual AI LLINs have a poor textile durability with OlysetTM Plus being the worst of the three.

In Holter monitoring, the precise detection of standard heartbeats and Ventricular Premature Contractions (PVCs) is paramount for accurate cardiac rhythm assessment. This study introduces a novel application of the 1D U-Net neural network architecture aimed at enhancing PVC detection in Holter recordings. Training data comprised the Icentia11k, INCART DB, and our custom dataset. The model’s efficacy was subsequently validated against traditional Holter analysis methodologies across multiple databases, including AHA DB, MIT 11 DB, NST, and another custom dataset encompassing challenging real-world examples. The results underscored the 1D U-Net model’s prowess in QRS complex detection, achieving near-perfect balanced accuracy scores across all databases. PVC detection exhibited variability, with balanced accuracy scores ranging from 0.909 to 0.986. Despite some databases, like the AHA DB, showcasing lower sensitivity metrics, their robust balanced accuracy accentuates the model’s equitable performance in discerning both false positives and negatives. In conclusion, while the 1D U-Net architecture is a formidable tool for QRS detection, there’s a clear avenue for further refinement in its PVC detection capability, given the inherent complexities and noise challenges in real-world PVC occurrences.

Quantification of the trade-offs among greenhouse gas (GHG) emissions, yield and farmers’ incomes is essential for proposing economic and environment nitrogen (N) management strategies for optimizing agricultural production. A four-year (2017–2020) field experiment (including four treatments: control experiment (CK), suitable utilization of fertilization (SU), emission reduction treatment (ER), and high fertilization (HF)) was conducted on maize (Zea mays L.) in the North China Plain. The Life Cycle Assessment (LCA) method was used in this study to quantify the GHG emissions and farmers’ incomes during the whole maize production process. The total GHG emissions of CK, SU, ER and HF treatments in the process of maize production are 10,755.2, 12,908.7, 11,950.1, and 14,274.5 kg CO2-eq ha−1 respectively, of which the direct emissions account for 84.8%, 76.8%, 74.9%, and 71.0%, respectively. Adding inhibitor significantly reduced direct GHG emissions, and the N2O and CO2 emissions from the maize fields in the ER treatment decreased by 30.0% and 7.9% compared to those in the SU treatment. Insignificant differences in yield were found between the SU and ER treatments, indicating that adding fertilizer inhibitors did not affect farmers’ incomes while reducing GHG emissions. The yield for SU, ER and HF treatments all significantly increased by 12.9%–24.0%, 10.0%–20.7% and 2.1%–17.4% compared to CK, respectively. In comparison with CK, both SU and ER significantly promoted agricultural net profit (ANP) by 16.6% and 12.2% with the mean ANP values of 3,101.0 USD ha-1 and 2,980.0 USD ha-1, respectively. Due to the high agricultural inputs, the ANP values in the HF treatment was 11.2%, 16.6% and 12.4% lower than those in the SU treatment in 2018–2020. In conclusion, the combination of N fertilizer and inhibitors was proved to be an environmentally friendly, high-profit and low-emissions production technology while sustaining or even increasing maize yields in the North China Plain, which was conducive to achieving agricultural sustainability.

Vein segmentation and projection correction constitute the core algorithms of auxiliary venipuncture device, responding to the accurate venous positioning to assist puncture and reduce the number of punctures and pain of patients. This paper proposes an improved U-Net for segmenting vein and a coaxial correction for image alignment in the self-build vein projection system. The proposed U-Net is embedded by Gabor convolution kernels in the shallow layers to enhance segmentation accuracy. Additionally, to mitigate the semantic information loss caused by channel reduction, the network model is lightweighted by the mean of replacing conventional convolutions with inverted residual blocks. During the visualization process, a method that combining coaxial correction and homography matrix is proposed to address the non-planarity of the dorsal hand in this paper. First, use a hot mirror to adjust the light paths of both projector and camera to be coaxial, and then align the projected image with dorsal hand using a homography matrix. Using this approach, the device requires only a single calibration before use. With the implementation of the improved segmentation method, an accuracy rate of 95.12% is achieved by the dataset. The intersection over union ratio between the segmented and original images is reached at 90.07%. The entire segmentation process is completed in a 0.09 second, and the largest distance error of vein projection onto dorsal hand is 0.53mm. Experiments show that the device has reached practical accuracy and has the value of research and application.

We investigated whether cigarette smoking and dietary acid load (DAL) are associated with the risk of chronic obstructive pulmonary disease (COPD) in middle-aged healthy Korean men. Healthy men without diagnosed chronic disease (40-64 yrs) from the KNHANES-VI (2013–2015) included in the analysis (n=774) were subdivided by smoking status and DAL levels as estimated by the net endogenous acid production (NEAP) quartile. Current smokers tended to have a higher risk of COPD than non-smokers before and after the adjustment. When divided by the DAL quartile, the Q4 tended to have a higher risk of COPD than the Q1. Additionally, current smokers with lower (Q2), modest (Q3), and highest NEAP scores (Q4) showed more than 4-fold higher risks of COPD than the non-smokers with the lowest NEAP scores (Q1). Ex–smokers with higher NEAP scores (Q3 and Q4) showed more than 4-fold higher risks of COPD than the Q1. Interestingly, the risk of COPD was also more than 6-fold higher in non-smokers with the highest NEAP scores compared to the Q1. NEAP scores and smoking status synergistically increased the risk of COPD in middle-aged healthy Korean men. It suggests that DAL levels are an important factor for the prevention and management of COPD.

Compressed Sensing (CS) MRI has shown great potential in enhancing time efficiency. Deep learning techniques, specifically Generative Adversarial Networks (GANs), have emerged as potent tools for speedy CS-MRI reconstruction. Yet, as the complexity of deep learning recon-struction models increases, this can lead to prolonged reconstruction time and challenges in achieving convergence. In this study we present a novel GAN-based model that delivers superior performance without escalating model complexity. Our generator module, built on the U-net architecture, incorporates dilated residual (DR) networks, thus expanding the network's receptive field without increasing parameters or computational load. At every step of the downsampling path, this revamped generator module includes a DR network, with the dilation rates adjusted according to the depth of the network layer. Moreover, we have introduced a channel attention mechanism (CAM) to distinguish between channels and reduce background noise, thereby fo-cusing on key information. This mechanism adeptly combines global maximum and average pooling approaches to refine channel attention. We conducted comprehensive experiments with the designed model using public domain MRI datasets of the human brain. Ablation studies af-firmed the efficacy of the modified modules within the network. Compared to other relevant models, our proposed model exhibits exceptional performance, achieving not only excellent sta-bility but also outperforming other networks in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The model presents a promising pathway for enhancing the efficiency and quality of CS-MRI reconstruction.

Transitioning towards net zero emissions is critical in the chemical and process industries to combat climate change and ensure environmental sustainability. Key focus areas of this review include the reduction of carbon emissions, the efficient utilization of energy resources, and the adoption of sustainable practices. Cutting-edge technologies such as biomass utilization, biotechnology applications, and waste management strategies are crucial in achieving this transition. Industries, including cement production, encounter unique challenges in their quest for sustainability and must actively seek innovative solutions to mitigate their carbon footprint effectively. The role of hydrogen as a clean fuel and its potential to revolutionize the chemical and process sectors is also discussed. The European Green Deal and Sustainable Development Goals (SDGs) are significant for the chemical industry. They provide a clear roadmap and framework for promoting sustainability, driving innovation, and reducing the industry's environmental impact. By aligning with these initiatives, the chemical industry can enhance its competitiveness, contribute to societal well-being, and foster collaboration across sectors to achieve shared sustainability objectives.

Abstract: Establishing carbon neutrality (or Net Zero) in cities appears to be an ambitious challenge, which drives urban transformation efforts around the world to enhance people-centric living. Cities launch corresponding strategies focusing mainly on the United Nations 2030 goals to form their climate-neutral future. This paper provides practical guidance on city digital transformation to achieve carbon net zero. These guidelines illustrate actionable recommendations to reduce their carbon footprint using digital technologies. Following a multi-method research approach, findings from a bibliometric literature and a standards’ gap analysis were combined with practical evidence and circulated with focus group experts to conclude to these guidelines, which are the foundation for further standardization. The outcomes provide concrete guidelines to policymakers, planners, and other stakeholders for city digital transformation to establish carbon neutrality, via emphasizing the need for digital technologies to enhance efficiency in several areas and via leveraging green ICTs for city economic growth while also transforming citizen behavior.

In neuroendocrine neoplasms (NENs), the use of new radiopharmaceuticals has improved the accuracy of diagnosis and staging, refined surveillance approaches, and introduced specific and personalized radiation therapies. Nuclear medicine has therefore acquired a crucial role in the management of NENs patients by improving their risk stratification and personalized therapies. Artificial intelligence (AI) and radiomics can enable physicians to further improve the overall efficiency and accuracy of the use of these tools in both di-agnostic and therapeutic settings by improving the prediction of tumor grade, differential diagnosis from other malignancies, assessment of tumor behavior and aggressiveness, and prediction of treatment response. This systematic review aims to describe the state-of-the-art on AI and radiomics applications in molecular imaging of NENs.

The relation between CO2 emissions and atmospheric CO2 concentration has traditionally been treated with more or less complex models with several boxes. Our approach is motivated by the question of how much CO2 must necessarily be absorbed by sinks. Observations lead to the model assumption, that carbon sinks like oceans or biosphere are linearly dependent on CO2 concentration on a decadal scale. In particular this implies the falsifiable hypothesis that oceanic and biological CO2 buffers have not significantly changed in the past 50 years and are not saturated in the forseeable future. The simple model with 2 parameters explains very well the CO2 emission and historical CO2 concentration data. The model gives estimates of the natural emissions, the pre-industrial CO2 equilibrium concentration levels, the half-life time of an emission pulse, and the future CO2 concentration level from a given emission scenario. This is validated by an ex-post forecast of the last 20 years. The important result is that with the stated polices scenario of the IEA future CO2 concentrations will not rise above 475 ppm. The model is compared with the Carbon modul of the Bern model, mapping their complex IRFs to a single time variant parameter.

Many corporations and governments aspire to become Net Zero Carbon Dioxide by 2030-2050. Achieving this goal requires understanding where energy is produced and consumed, the magnitude of CO₂ generation, and the Carbon Cycle. Many prior proposed solutions focus on reducing future CO₂ emissions from continued use of fossil fuels. Examination of these technologies exposes their limitations and shows that none offer a complete solution. For example, bioethanol is shown to be both carbon and energy inefficient. Direct Air Capture technologies are needed to reduce CO₂ already in the air. The most natural form of Direct Air Capture involves letting nature do the work of creating biomass via photosynthesis. However, it is necessary to break the Carbon Cycle by permanently sequestering that biomass carbon in “landfills” modified to discourage decomposition to CO₂ and methane. Tree leaves and biomass grown on-purpose, such as high yield switchgrass, are proposed as good biomass sources for this purpose. Left unsequestered, leaves decompose with a short Carbon Cycle time constant releasing CO₂ back to the atmosphere. While in any given year, leaves represent a small fraction of a tree’s above ground biomass, leaves can represent a substantial fraction of the total biomass generated by a tree when integrated over a tree’s lifetime. Understanding the chemistry of the distinct phases landfills undergo is the key to minimizing or eliminating decomposition. First, the compact cross-linked structure of cellulose and keeping water out will make it difficult for initial depolymerization to release sugars. Air ingress should be minimized to minimize Phase I aerobic decomposition. pH manipulation can discourage acid formation during Phase II. Lignocellulose is low in nutrients needed for anaerobic decomposition. Inhibitors can be added if needed. The goal is to move quickly to the dormant phase where decomposition stops. The cost for Carbon Capture and Storage (CCS) for growing and sequestering high yield switchgrass is estimated to be lower than CCS for steam reforming of methane hydrogen plants (SRM) and supercritical or combined cycle coal power plants. Thus, sequestration of biomass is a natural, carbon efficient, and low-cost method of Direct Capture. Biomass sequestration can provide CO₂ removal on giga tonnes per year scale and can be implemented in the needed timeframe (2030-2050).

We have an unprecedented ability to analyze and map the Earth’s surface, as deep learning technologies are applied to an abundance of Earth observation systems collecting images of the planet daily. In order to realize the potential of these data to improve conservation outcomes, simple, free, and effective methods are needed to enable a wide variety of stakeholders to derive actionable insights from these tools. In this paper we demonstrate simple methods and workflows using free, open computing resources to train well-studied convolutional neural networks and use these to delineate objects of interest in publicly available Earth observation images. With limited training datasets (<1000 observations), we used Google Earth Engine and Tensorflow to process Sentinel-2 and National Agricultural Imaging Program data, and use these to train U-Net and DeepLab models that delineate ground mounted solar arrays and parking lots in satellite imagery. The trained models achieved 81.5% intersection over union between predictions and ground-truth observations in validation images. These images were generated at different times and from different places from those upon which they were trained, indicating the ability of models to generalize outside of data on which they were trained. The two case studies we present illustrate how these methods can be used to inform and improve the development of renewable energy in a manner that is consistent with wildlife conservation.

The continuous and precise mapping of glacier calving fronts is essential for monitoring and understanding rapid glacier changes in Antarctica and Greenland, which have the potential for significant sea level rise within the current century. This effort has been mostly restricted to the slow and painstaking manual digitalization of the calving front positions in thousands of satellite imagery products. Here, we have developed a machine learning toolkit to robustly and automatically detect glacier calving front margins in satellite imagery. The toolkit is based on semantic image segmentation using Convolutional Neural Networks (CNN) with a modified U-Net architecture to isolate the calving fronts from satellite images after having been trained with a dataset of images and their corresponding manually-determined calving fronts. As a case study we train our neural network on a varied set Landsat images with lowered resolutions from Jakobshavn, Sverdrup, and Kangerlussuaq glaciers, Greenland and test the results on novel images from Helheim glacier, Greenland to evaluate the performance of the approach. The neural network is able to identify the calving front in new images with a mean deviation of 96.3 m from the true fronts, equivalent to 1.97 pixels on average, while the corresponding error for manually-determined fronts on the same resolution images is 92.5 m. We find that the trained neural network significantly outperforms common edge detection techniques, and can be used to continuously map out calving-ice fronts with a variety of data products.

This work addresses the analysis and characterization of deadlocks in discrete-event systems modeled by labeled Petri nets (LPNs) with undistinguishable and unobservable transitions. To provide a solution for the notorious problem, it is essential to present an effective characterization such that deadlock control and synthesis are technically and methodologically possible. To this end, we introduce the notion of dangerous implicit vectors (DIVs) which implicitly threaten the system deadlock-freedom. The set of dead markings is divided into two subsets: dead basis markings (DBMs) and dangerous implicit markings (DIMs). An algorithm is designed to compute the sets of DIVs and DIMs at a given basis state of a system. Moreover, by virtue of linear algebraic equations, we formulate sufficient conditions to identify the existence of blocking markings in an LPN. Finally, an algorithm is developed to construct an observed graph that is a compact representation of the reachability graph of a Petri net with respect to the existence of dead reaches. At the end of this paper, experiment results that illustrate the correctness and effectiveness of the reported solution are presented.

The rising occurrence of chronic kidney disease represents a significant health concern, impacting over 10% of individuals. Magnetic Resonance Imaging (MRI) biomarkers identify pathological alterations, are non-invasive, and can help lessen biopsy requirements and complications in chronic kidney disease patients. Total volume is the most assessed metric in autosomal dominant polycystic kidney disease patients, assisting in tracking chronic kidney disease progression. Kidney segmentation is essential for evaluating renal volume. Nevertheless, it often relies on manual segmentation, which is time-consuming and highly subjective. However, Deep Learning (DL) techniques have led to creating algorithms that provide accurate, cost-effective, and user-independent outcomes. Therefore, this study explores DL-based strategies for kidney segmentation, particularly U-Net and Attention U-Net. Both architectures were assessed with the standard cross-entropy loss function and a focal cross-entropy loss function. Ultimately, the proposed methods were used for both 2-class and 3-class segmentation to improve the segmentation of border regions. The highest Dice coefficient achieved on the testing set was 0.966.

The growing emphasis on carbon considerations and the pursuit of net-zero emissions have brought about a paradigm shift in project management. To successfully facilitate the transition towards net-zero emissions, major projects must not only adapt existing systems but also embed carbon targets into their core strategies. While several studies have investigated carbon integration during the procurement phase, limited attention has been given to the construction project manager (PM) perspective. This study aims to bridge this research gap by exploring the challenges and barriers faced by construction PMs when integrating carbon targets and metrics into major Architectural, Engineering and Construction (AEC) projects, as well as evaluating the readiness of project teams to deliver on these. The study deployed a qualitative exploratory research design, where semi-structured interviews were conducted with 17 AEC project professionals actively engaged in the planning and execution of major projects in the UK. Thematic analysis of the data revealed a range of challenges and barriers faced by PM teams delivering these projects. The research findings contribute to the field of construction PM by enhancing the understanding of the challenges faced by project managers and project teams when integrating carbon requirements into the delivery of major projects. It uncovers a series of challenges and prevalent practices in major projects that have the potential to impede progress towards the net-zero transition.

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

The energy efficiency of existing buildings is a crucial factor in addressing Europe’s energy con-sumption challenges, accounting for nearly 40% of the total energy usage. The ongoing energy crisis, driven by the reliance on fossil fuels and increasing prices, has underscored the urgency of adopting sustainable and energy efficient practices especially to the existing building stock. Cyprus is par-ticularly vulnerable due to its heavy dependence on imported fuels for electricity generation and faces significant challenges in transforming its existing building stock into energy-efficient and sustainable structures. Extensive focus has been made by the government on the energy-efficient retrofit of non-residential public buildings. This study examines an existing cultural hall. The case study of the Cultural Hall represents the building stock before 2010 which is lacking thermal in-sulation, double glazing and any energy efficient element. It then offers an ideal case study to ex-plore sustainable solutions which can be easily adopted from the construction industry and from the building owners. The primary objective of this research is twofold: first, to develop a comprehensive retrofit plan to transform the cultural hall into a net-zero energy building; and second, to explore how this transformation can give value back to the local community. After running two simulation scenarios and providing two energy performance certificates, one with at the existing state and one with the intended retrofit measures, it is clear that the building can be a net zero energy consump-tion building. Some interesting results derived, present also how this building can provide value back to the community i.e. through solar carports for EV charging.

Partial differential equations (PDEs) are used to describe a wide range of phenomena, such as heat transfer, fluid dynamics, and quantum mechanics. By solving PDEs, we can ob- tain insights into the behavior of the system and make predictions about its future evolution. Conventional numerical methods for obtaining the approximate solutions of PDEs may re- quire extensive computational resources and time, especially for complex PDEs and large- scale problems. The recently developed physics-informed neural network (PINN) has shown promise in a variety of scientific and engineering fields by incorporating physical laws into the loss functions of the neural network (NN). In addition, the training of PINN does not require ground truth data, but it has poor generalization ability to unseen domains. On the other hand, a convolutional neural network (CNN) has fast inference and better generalization ability, but it requires a large amount of training data. Taking the advantages of PINN and CNN by using Legendre multiwavelets (LMWs) as basis functions, we introduce a new method to approach the PDEs in this paper, namely Physics-Informed Legendre Multiwavelets CNN (PiLMWs-CNN), in order to continuously approximate a grid-based state representation that can be handled by a CNN. PiLMWs-CNN enable us to train our models using only physics-informed loss functions without any pre- computed training data, simultaneously providing fast and continuous solutions that gener- alize to previously unknown domains. In particular, the LMWs can simultaneously possess compact support, orthogonality, symmetry, high smoothness, and high approximation order. Compared to orthonormal polynomial (OP) bases, the approximation accuracy can be greatly increased and computation costs can be significantly reduced by using LMWs. We applied PiLMWs-CNN to approximate the damped wave equation, incompressible Navier-Stokes (N- S) equation, and two-dimensional heat conduction equation. The experimental results show that this method provides more accurate, efficient, and fast convergence with better stability when approximating the solution of PDEs.

The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.

The automatic diagnosis of cardiovascular diseases has received much attention in the deep learning field. In this context, the segmentation of the left ventricle endocardium constitutes a major task in diagnosing heart conditions such as health failure and hypertrophic cardiomyopathy. The objective of this paper is to propose a "deep convolution network" for segmenting the internal cavity of the left ventricle (endocardium) using MRI images. In particular, we design an improved UNET model which handles additional inception modules for efficiently segmenting the internal cavity of the left ventricle. Our approach has been validated on the Sunnybrook Cardiac Data (SCD) dataset and has showed promising results in terms of precision. More specifically, the improved UNET largely outperforms the baseline UNET model and many existing state-of-the-art methods.

Using the World Bank Global Findex Database for 91 countries in 2014, 2017, and 2021, we examine whether fintech levels influence bank performance and whether fintech’s interaction with GDP per capita causes differential effects on bank performance globally. Since fintech levels were already very high for rich countries when the World Bank started providing fintech development statistics in 2014, we estimate AbFintech by regressing fintech levels on GDP per capita by year. AbFintech is the difference between the fintech level and its fitted values. Then, using multiple regression analyses, we investigate the impact of AbFintech on bank performance worldwide, focusing on the differential effects of AbFintech and GDP levels on bank performance. We find AbFintech significantly increases bank performance, primarily in less developed countries. Specifically, AbFintech increases banks’ ROA in the least developed countries and net interest margin in the 75th percentile countries. Also, AbFintech decreases the cost-to-income ratio in the 75th percentile countries, while it increases the ratio in the most developed countries. AbFintech does not affect the ratio of noninterest income to total income, regardless of the level of economic development.

Rooftop solar (RTS) projects have the potential to contribute to climate change mitigation and sustainable development objectives. In India, grid-connected RTS capacity has been targeted at 40 GW by the end of the year 2022 however, the installed capacity is limited to 8.2 GW only. As a result, the country missed its 2022 target of installing 100 GW of solar power capacity, largely due to inadequate uptake of RTS. The prevailing practices of RTS implementation is observed to be complex, repetitive, tedious, and time-consuming for potential consumers and there is a lack of coordination among implementing agencies. Therefore, even with vast potential, supportive measures, dedicated policies, and regulations the penetration of RTS systems is far below their respective potential. The introduction of information technology (IT) intervention in the RTS sector would integrate key stakeholders at a single platform so as to reduce the administrative burden of implementing agencies, optimization of processing time, handle multiple consumers simultaneously, and connect consumers with authentic vendors, etc. This would help them to understand and perform their shared roles and responsibilities and enhance the RTS implementation across the state. In this study, the scope for IT interventions in the existing RTS implementation process of the Odisha Renewable Energy Development Agency (OREDA) has been assessed and a simplistic approach has been developed for the execution of the RTS projects in relatively lesser time with the least human interface as well as resources. This approach has been successfully demonstrated with OREDA and a cumulative capacity of around 5 MWp (individual project of 1-2 kWp) residential grid-connected RTS projects implemented through the web platform. Currently, other state nodal agencies are also adopting the model for RTS execution and consumer proliferation through IT interventions.

In order to explore the grassland ecosystem productivity and landscape ecological patterns of main pastoral grasslands in China, it provides a theoretical basis for the efficient implementation of ecological engineering and rational management of grassland resources in the region. This study analyzed the changes in grassland area, landscape index (LSI), and net primary productivity (NPP) in seven major pastoral areas in China in 1985, 1995, 2005, and 2015. Results showed that (1) the sizes of the grassland study area in 1985, 1995, 2005, and 2015 were 248.34, 243.93, 245.80, and 244.660 km2 respectively. (2) The dominance of grassland in the landscape pattern increased from 2005 to 2015 as compared with that in 1985–1995 and 1995–2005. The degrees of spatial heterogeneity were reduced. (3) The grassland NPP showed spatial and temporal differences. The average NPP of grassland increased by 21.30%, 16.47%, and 36.17% during 1985–1995, 2005–2015, and 1985–2015, respectively. From 1995 to 2005, the average NPP decreased by 5.05 gC/m2, which is equivalent to -3.61% of the average NPP in 1995. The total amount of grassland NPP in the study area was the greatest in 2015, showing increments of 36.37% and 16.61% compared with those in 1985 and 2005, respectively.

With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational cost. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system.

The energy transition towards Net Zero Emission by 2060 hinges on the renewable energy power plants in Indonesia. Good practices in several countries suggest a peer-to-peer (P2P) energy trading system using blockchain technology, supported by renewable energy (solar panels), an innovation to provide equal access to sustainable electricity while reducing the impact of climate change. The P2P energy trading concept has a higher social potential than the conventional electricity buying and selling approach, such as that of PLN (the grid management concept), but does not have a sharing element. This model implements a solar-powered mini-grid system and produces a Smart Contract that facilitates electricity network users to buy, sell, and trade electricity in rural areas via smartphones. This study aims to analyze the stakeholders' perceptions of the peer-to-peer (P2P) energy trading model using blockchain technology in Gumelar District, Banyumas Regency, Central Java Province, Indonesia. The stakeholders include households, the Ministry of Energy and Mineral Resources (ESDM), the State Electricity Company (PLN), the Institute for Essential Services Reform (IESR), Awina Company, and IPB University. The strength factors of this model are the freedom to generate and sell electricity (0.1) and transparency in electricity usage and sales by disclosing the amount, duration, and price of electricity (0.1). Regarding weakness factors, this model is primarily suitable for the lower middle class residing in rural or remote areas (0.02). Additionally, the intermittent electricity production to meet the substantial demand is a weakness (0.03). The opportunity factor for the model is the increase in public awareness and cooperation in using environmentally friendly, pollution-free electricity (0.12). Furthermore, there is an opportunity for additional income (0.12) and new business investment (0.12). Regarding threat factors, the model faces significant threats from the implementation aspect involving multiple parties (0.04). There is a potential threat in the form of losses for state utilities such as PLN or individuals who have already established electricity infrastructure if this program is implemented on a large scale (0.04). This model is located at the coordinate point (-0.40; 0.31), placing it in quadrant II. This indicates that the model should apply the Stability Strategy and focus on the Selective Maintenance Strategy for improvement. The stability strategy goal is to maintain the current situation by leveraging opportunities and addressing weaknesses. For effectiveness and efficiency, the strategy emphasizes fostering collaboration with PLN, which holds the legal authority as the sole utility company in Indonesia with an extensive electricity network, as well as advocating for regulations that support the independent purchase and sale of solar-powered electricity, particularly in areas without PLN network coverage.

Public health suffers from noxious gas of beached Sargassum algae. Floating barrages containing them at sea will be useful provided efficiently catch them and resist to hydrodynamic pressure. Nowadays design and installation of algae barrages are empiric. Structural break, anchor and mooring chain drifts can arise. We provide a mechanical model to evaluate stresses and loads on a structure made of nets and moorings. Hydrodynamic uncertainties occur through catches, fouling, and a range of sea currents appearing in lagoons or sheltered bays. We consider a nonlinear finite-element model of elastic membrane in large displacements and small strains. We study some forcings fixed a priori on a standard section of barrage. A section 50 m long, 1 m high, with two net-faces is examined at low and moderate current velocities, 0.05 and 0.35 m.s-1, while assuming specific vertical and horizontal catch pressures. A barrage installed in lagoon at Le François on Martinique Island and being observable by satellite imagery could benefit of the computed net and mooring tensions.

BACKGROUND: Few studies have examined the use of immunoassay urine drug testing of cancer patients in palliative care clinics. OBJECTIVES: We examined the frequency of immunoassay urine drug test (UDT) abnormalities and the factors associated with aberrancy at a safety-net hospital palliative medicine clinic. METHODS: A retrospective review of the electronic medical records of consecutive eligible patients seen at the outpatient palliative medicine clinic in a resource-limited safety net hospital system was conducted between September 1, 2015 and December 31, 2020. We collected longitudinal data on patient demographics, UDT findings, and potential predictors of aberrant results. RESULTS: Of the 913 patients in the study, 500 (55%) underwent UDT testing, with 455 (50%) having the testing within the first three visits. Among those tested within the first three visits, 125 (27%) had aberrant UDT results; 44 (35%) of these 125 patients were positive for cocaine. In a multivariable regression model analysis of predictors for aberrant UDT within the first 3 visits, non-Hispanic White race (odds ratio [OR]=2.13; 95% confidence interval [CI]: 1.03-4.38; p=0.04), history of illicit drug use (OR=3.57; CI: 1.78-7.13; p< 0.001), and history of marijuana use (OR=7.05; CI: 3.85-12.91; p< 0.001) were independent predictors of an aberrant UDT finding. CONCLUSION: Despite limitations of immunoassay UDT, it was able to detect aberrant drug taking behaviors in a significant number of patients seen at a safety net hospital palliative care clinic, including cocaine use. These findings support universal UDT monitoring and utility of immunoassay-based UDT in resource limited settings.

The recent happenings in the world such as flood and wild forest fire were as a result of climate effect as being envisaged by scientists. It is urgent now to adopt a source of energy that will eliminate this effect on our universe. Solar energy is the major energy means that is abundant which could be utilized. In this review, the prospects of solar energy exploration were studied in Nigeria which include assessments, economic viability and hybrid systems. Findings show higher potential in the North as compared to the Southern region. Additionally, potential of offshore solar energy system was simulated by considering 2002, 2003 and 2004 data sets from Era5-land base. Their monthly mean, seasonal changes and annual mean value were estimated. The algebraic annual solar radiance for 2002, 2003 and 2004 were 34,914.732 kWh/m2, 33,898.316 kWh/m2 and 34,338.324 kWh/m2. Suggestions were made due to the present status of solar energy utilization that will enhance its maximum usage and development. One of these is the establishment of a functioning financial scheme and the database for all renewable energy systems. When all these are put in place, the energy supply will increase, climate effect will be reduced, and the economy will be boosted.

Bone metastasis detection and quantification on bone scintigraphy is challenging and clinical important for treatment and patient’s life quality. To develop a CNN based diagnostic system for automated segmentation on bone metastasis regions is non-trivial, especially for a small dataset. A dataset in house comprising 100 breast cancer patients and 100 prostate cancer patients is utilized for this research. The Double U-Net model is adapted through the integration of background removal, adding negative samples, and transfer learning methods for bone metastasis detection. The performance is investigated via 10-fold cross-validation and computed in pixel-wise scale. The best model we achieved has precision of 63.08%, sensitivity of 70.82%, and F1-score of 66.72%. The developed system has the potential to provide pre-diagnostic reports for physicians in final decisions and the calculation of the bone scan index (BSI) with the combination with bone skeleton segmentation.

Over 130 countries have committed to reaching net-zero CO2 or GHG emissions by 2050, yet this ambition is rarely underpinned by robust policies. By applying a detailed integrated assessment modelling approach for Brazil, we assess, for the first time, the extent to which the existing and planned local policies could put Brazil on the path to its net zero pledge. This includes quantifying the role of nature-based solutions, such as protection and restoration, and engineered solutions, such as bioenergy with carbon capture and storage (BECCS). We show protection is the single most important climate mitigation measure at relatively low costs, whereas relying heavily on engineered solutions would jeopardise Brazil’s chances of achieving its net zero pledge. We also show that the mismatch between Brazil's short- and long-term climate targets reflects current weak environmental governance. Our analysis reinforces the urgent need for Brazil to eliminate illegal deforestation and go beyond to help fight climate change whilst curbing biodiversity loss.

With global warming, arable land in boreal regions is tending to expand into high latitude regions in the northern hemisphere. This entails certain risks; such that inappropriate management could result in previously stable carbon sinks becoming sources. Agroecological models are an important tool for assessing the sustainability of long-term management, yet applications of such models in boreal zones are scarce. We collated eddy-covariance, soil climate and biomass data to evaluate the simulation of GHG emissions from grassland in eastern Finland using the process-based model DNDC. We simulated gross primary production (GPP), net ecosystem exchange (NEE) and ecosystem respiration (Reco) with fair performance. Soil climate, soil temperature and soil moisture at 5 cm were excellent, and soil moisture at 20 cm was good. However, the model overestimated NEE and Reco following crop termination and tillage events. These results indicate that DNDC can satisfactorily simulate GHG fluxes in a boreal grassland setting, but further work is needed, particularly in simulated second biomass cuts, the (>20 cm) soil layers and model response to management transitions between crop types, cultivation, and land use change.

Process (or business process) management systems fulfill defining, executing, monitoring and managing process models deployed on process-aware enterprises. Accordingly, the functional formation of the systems is made up of three subsystems such as modeling subsystem, enacting subsystem and mining subsystem. In recent times, the mining subsystem has been becoming an essential subsystem. Many enterprises have successfully completed the introduction and application of the process automation technology through the modeling subsystem and the enacting subsystem. According as the time has come to the phase of redesigning and reengineering the deployed process models, from now on it is important for the mining subsystem to cooperate with the analyzing subsystem; the essential cooperation capability is to provide seamless integrations between the designing works with the modeling subsystem and the redesigning work with the mining subsystem. In other words, we need to seamlessly integrate the discovery functionality of the mining subsystem and the analyzing functionality of the modeling subsystem. This integrated approach might be suitable very well when those deployed process models discovered by the mining subsystem are complex and very large-scaled, in particular. In this paper, we propose an integrated approach for seamlessly as well as effectively providing the mining and the analyzing functionalities to the redesigning work on very large-scale and massively parallel process models that are discovered from their enactment event logs. The integrated approach especially aims at analyzing not only their structural complexity and correctness but also their animation-based behavioral properness, and becomes concretized to a sophisticated analyzer. The core function of the analyzer is to discover a very large-scale and massively parallel process model from a process log dataset and to validate the structural complexity and the syntactical and behavioral properness of the discovered process model. Finally, this paper writes up the detailed description of the system architecture with its functional integration of process mining and process analyzing. And more precisely, we excogitate a series of functional algorithms for extracting the structural constructs as well as for visualizing the behavioral properness on those discovered very large-scale and massively parallel process models. As experimental validation, we apply the proposed approach and analyzer to a couple of process enactment event log datasets available on the website of the 4TU.Centre for Research Data.

Scheduling of real time tasks are very important aspect in systems as processes should complete its task at a specific time. There is a need of high energy efficiency and low response time in large data stream so for this energy efficient resources and optimized frameworks are needed. Both hard real time and mixed critically systems are targeted. Soft deadline can be handled while hard deadlines are difficult to cater. Different algorithms are used to schedule tasks like rate monotonic, earliest deadline first, deadline monotonic etc.

Chile faces a serious crisis on housing affordability given that most of the population is unable to secure a house. While housing prices between 2008 increased by 70.96%, wages only increased by 20.43%. This article presents the analysis of the housing prices configuration for the main district in the country: Santiago Centro. The assessment focuses on verticalised housing buildings constructed between 2015 and 2019. The article develops an exploratory study on the price of housing in the city of Santiago de Chile, to generate a diagnosis to identify the role played by expectations of profitability when configuring that price. Based on the information generated, we seek to contribute to the discussion on public policies that advance towards the development of affordable housing for households in central communes with high urban value, as is the case of Santiago Centro. We hypothesize that profitability expectations of real estate developers play a key role in the housing prices and an adjustment in the profit ratios may increase the affordability keeping the housing market over profitable rates. The research is relevant due to the lack of data transparency in the Chilean housing market, where access to investment costs, land values, yields and house price formation are not part of the public discussion, even though this implies that many households are facing severe difficulties in paying for access to decent housing.

Normally, the Nernst voltage calculated from the concentration of the reaction gas in the flow channel is considered to be the ideal voltage (reversible voltage) of the oxyhydrogen fuel cell, but actually it will cause a concentration gradient when the reaction gas flows from the flow channel through the gas diffusion layer to the catalyst layer. The Nernst voltage loss in fuel cells in most of the current literature is thought to be due to the difference in concentration of reaction gas in the flow channel and concentration of reaction gas on the catalyst layer at the time when the high net current density is generated. Based on the Butler-Volmer equation in oxyhydrogen fuel cell, this paper demonstrates that the Nernst voltage loss is caused by the concentration difference of reaction gas in flow channel and on the catalytic layer at the time when equilibrium potential (Galvanic potential) of each electrode is generated.

Supplementing essential amino acids (EAA), without considering non-EAA (NEAA) and energy contents in reduced crude protein (CP) diets may alter essential-to-total AA or true protein (E:T) and energy-to-protein ratios, potentially compromising growth. This study aimed to evaluate the effects of CP, E:T and net energy (NE) on broiler performance. Treatments were: T1— reduced CP (16%, RP), low NE (9.9 MJ/kg, LNE), low E:T (0.56, LE:T); T2—RP, LNE, high E:T (0.60, HE:T), with imbalanced EAA (excess Met and deficient Thr); T3—RP, high NE (10.4 MJ/kg, HNE), LE:T; T4—RP, HNE, HE:T; T5— normal CP (18%, NP), LNE, LE:T; T6—NP, LNE, HE:T; T7—NP, HNE, LE:T; and T8—NP, HNE, HE:T. The study employed as-hatched Cobb 500 broilers in two experiments. Exp.1 studied performance from d19 to 35, with 8 replicates per treatment and 16 birds per replicate (n=1024). Exp.2 measured NE values in respiration chambers from d25 to 28, with 6 replicates per treatment, and 2 birds (a male and a female) per replicate (n=96). The measured NE values were used to calculate NE intake (NEi) in Exp.1. The results showed that T4 improved (P &lt; 0.001) weight gain (WG), feed conversion ratio (FCR), and NEi relative to T1, T2 and T3, and protein efficiency (WG/CP intake) relative to all treatments. The live performance (feed intake, WG, FCR) and NEi of birds fed T4 reached a level equal to those of birds fed NP-diets (T5 to T8). These results suggest that a dietary E:T ratio of 0.60 is necessary to maximize nutrient utilization and to restore growth rate in broilers fed RP-diets.

In order to achieve global climate goals, it is necessary to estimate greenhouse gases (GHG) fluxes from ecosystems. To obtain a comprehensive assessment of CO2, СН4, N2O natural fluxes for the Russian Federation we used the "bottom-up" method according to [1]. We updated estimates for forest ecosystems based on State Forest Inventory data and satellite monitoring of forest disturbances. For grassland ecosystems, it was based on the correct distribution of areas between steppe and non-steppe zones. The estimated net uptake of natural ecosystems of Russia was 1.1 ± 1.8 billion tons of CO2-eq./year. The study shows that if only CO2 is taken into account, the net absorption of terrestrial ecosystems in Russia corresponds to more than -2.5 billion tons of CO2 (35% of forests’ contribution). However, given the emissions of non-CO2 GHGs, total net absorption in Russia’s natural ecosystems is reduced to about -1 billion tons of CO2-eq (with the forests’ contribution increasing to 80%). With regard to anthropogenic fluxes, the overall balance of GHGs in Russia corresponds to net-emissions of 1 billion tons of CO2-eq/year into the atmosphere. To improve reporting under the Paris Agreement, countries should aim to include only anthropogenic ("manageable") GHG fluxes on managed land.

Agarwood, a highly valuable fragrant wood of Aquilaria spp. has been widely used in perfumes, traditional medicine, religious rites and cultural activities. Due to the presence of aromatic and medicinal properties, the Agarwood has gained its economic importance in recent years. It has been providing livelihood and employment facilities to many people who are cultivating those. Owing to high demand and profitability, the people of Assam has started to cultivate Agarwood in a commercial manner in recent days. This study was conducted to examine the economic viability of Agarwood cultivation in Assam and to understand the problems of the cultivators to consider Agarwood cultivation as the primary source of income. The primary data were collected during the months of May and June, 2022 from 210 households using structured questionnaire. The cost-benefit analysis revealed that INR 100 of initial investment yields a net benefit of INR 623 which indicates that the return from the Agarwood cultivation among the cultivators are very high. Thus, the present study argues that the government should encourage the cultivators by providing the necessary support in the form of a legalized market, credit requirement, creating the awareness about the demand of Agarwood and making provision of training.

AK is a common precancerous skin condition that requires effective detection and treatment monitoring. To improve the monitoring of AK burden in clinical settings with enhanced automation and precision, the present study evaluates the application of semantic segmentation based on the U-Net architecture (AKU-Net). AKU-Net employs transfer learning to compensate for the relatively small dataset of annotated images and integrates a recurrent process based on convLSTM to exploit contextual information and address the challenges related to low contrast and ambiguous boundaries of AK-affected skin regions. We used an annotated dataset of 569 clinical photographs from 115 patients with actinic keratosis to train and evaluate the model. From each photograph, patches of 512x512 pixels were extracted using translation lesion boxes that encompass lesions in different positions and capture different contexts of perilesional skin. In total, 16488 translation augmented crops were used for training the model, and 403 lesion center crops were used for testing. To demonstrate the improvements in AK detection, AKU-net was compared with plain U-Net and U-Net++ architectures. The experimental results highlighted the effectiveness of AKU-Net, improving both automation and precision over existing approaches, paving the way for more effective and reliable evaluation of actinic keratosis in clinical settings.

Organophosphate pesticides (OPs) are toxic substances that contaminate aquatic environments, interfere with the development of the nervous system, and induce Neurodevelopmental Toxicity (NDT) in animals and humans. The canonical mechanism of OP neurotoxicity involves the inhibi-tion of acetylcholinesterase (AChE), but other mechanisms non-AChE are also involved and not fully understood. We used network toxicology and molecular docking to identify molecular targets and toxicity mechanisms common to OPs. Targets related to diazinon-oxon, chlorpyrifos oxon, and paraoxon OPs were predicted using the Swiss Target Prediction and PharmMapper databases. Targets related to NDT were compiled from GeneCards and OMIM databases. In order to construct the protein-protein interaction (PPI) network, the common targets between OPs and NDT were imported into the STRING. Networks topological analyses identified EGFR, MET, HSP90AA1, and SRC as hub nodes common to the three OPs. Using Reactome pathway and gene ontology we found that signal transduction, axon guidance, cellular responses to stress, and glutamatergic signaling activation play key roles in OP-induced NDT.

CO₂ emissions have been identified as the main driver for climate change, with devastating consequences for the global natural environment. The steel industry is responsible for ca. 8%-11% of global CO₂ emissions, due to high fossil-fuel and energy consumption. The onus is therefore on industry to remedy the environmental damage caused and to decarbonise production. This desk research report follows a two-tiered route: 1) exploring the Bio Steel Cycle; 2) proposing a seven-step strategy to overcome the challenges within the iron and steel industry. The true levels of CO₂ emissions in the steelmaking process, during the blast-furnace/basic-oxygen-furnace operation, will be detailed first, at 4.61t of CO₂ emissions/t of steel produced. The manuscript proposes a solution for reduced carbon emissions steelmaking with the Bio Steel Cycle components and detail on how to reach net-zero carbon emissions steel production. The 7-step-implementation-strategy as a potential solution to achieve net and true zero carbon emissions steel production in the short to medium-term. The findings of this are pointing towards the conclusion that CO₂ emissions seem to have been under-reported and under-estimated in the past, but the emission can be addressed if the correct scenarios for net-zero steel manufacturing are implemented by 2050.

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in tropical and subtropical areas. Changes to hydrology and salinity linked to water infrastructure development may create conditions favorable to the aquatic vegetation that is suitable habitat for the intermediate snail hosts of schistosome parasites. With thousands of small and large water reservoirs, irrigation canals, and dams developed or under construction in Africa, it is crucial to accurately assess the spatial distribution of high-risk environments that are habitat for freshwater snail intermediate hosts of schistosomiasis in rapidly changing ecosystems. Yet, standard techniques for monitoring snails are labor-intensive, time-consuming, and provide information limited to the small areas that can be manually sampled. Consequently, in low-income countries where schistosomiasis control is most needed, there are formidable challenges to identify potential transmission hotspots for targeted medical and environmental interventions. In this study, we developed a new framework to map the spatial distribution of suitable snail habitat across large spatial scales in the Senegal River Basin by integrating satellite data, high-definition, low-cost drone imagery, and an artificial intelligence (AI)-powered computer vision technique called semantic segmentation. A deep learning model (U-Net) was built to automatically analyze high-resolution satellite imagery to produce segmentation maps of aquatic vegetation, with a fast and robust generalized prediction that proved more accurate than a more commonly used random forest approach. Accurate and up-to-date knowledge of areas at highest risk for disease transmission can increase the effectiveness of control interventions by targeting habitat of disease-carrying snails. With the deployment of this new framework, local governments or health actors might better target environmental interventions to where and when they are most needed in an integrated effort to reach the goal of schistosomiasis elimination.