Search Results (6,057)

Identifying the primary soil parameters, weather variables and crop management practices that influence spatial variations in crop water use is essential for strategically defining optimal agricultural management practices. In this study, soil physico-chemical, weather and crop management variables were used through random forest (RF)-based modeling to evaluate the determinants of actual evapotranspiration (ETa) in winter wheat across Slovakia. ETa was estimated using Landsat imagery and the Python implementation of the Surface Energy Balance Algorithm for Land (PySEBAL), along with information from the Land Use/Cover Area frame Survey (LUCAS) over four cropping seasons. Overall, good agreements were found between PySEBAL-derived ETa and measured values, with RMSE and R² values of 0.93 mm and 0.87, respectively. Seasonal ETa values ranged from 434.87 mm to 506.12 mm, with the highest and lowest average values found in the 2011/2012 and 2017/2018 cropping seasons, respectively. The RF model showed good performance in predicting seasonal ETa, with an RMSE of 21 mm/season for the training data and 32 mm/season for the validation data, and R² values of 0.90 and 0.72, respectively. Our analysis indicated that ETa was primarily influenced by relative humidity, wind speed, solar radiation, altitude, and pH. The study further indicated that wheat production was unsuitable above 600 m elevation, while optimal crop water use occurred below 200 m. Addressing issues such as soil erosion and acidification could improve wheat crop water use efficiency across Slovakia. This modeling approach can serve as a basis to develop a crop water use forecasting system for sustainable wheat production in the region. Full article

The increasing demand for monoclonal antibody (mAb) therapeutics has intensified the need for more efficient and consistent biomanufacturing processes. We present a data-driven, machine-learning (ML) approach to exploring and predicting upstream yield behavior. Drawing on industrial-scale batch records for a single mAb product from a contract development and manufacturing organization, we applied regression models to identify key process parameters and estimate production outcomes. Random forest regression, gradient boosting machine, and support vector regression (SVR) were evaluated to predict three yield indicators: bioreactor final weight (BFW), harvest titer (HT), and packed cell volume (PCV). SVR outperformed other models for BFW prediction (R² = 0.978), while HT and PCV were difficult to model accurately with the available data. Exploratory analysis using sequential least-squares programming suggested parameter combinations associated with improved yield estimates relative to historical data. Sensitivity analysis highlighted the most influential process parameters. While the findings demonstrate the potential of ML for predictive, data-driven yield improvement, the results should be interpreted as an exploratory proof of concept rather than a fully validated optimization framework. This study highlights the need to incorporate process constraints and control logic, along with interpretable or hybrid modeling frameworks, to enable practical deployment in regulated biomanufacturing environments. Full article

As carbon cycling and global environmental protection gain increasing attention, forest disturbance research has intensified worldwide. Constrained by limited data availability, existing frameworks often rely on extracting individual spectral bands for simple binary disturbance detection, lacking systematic approaches to visualize and classify causes of disturbance over large areas. Accurately identifying disturbance types is critical because different disturbances (e.g., fires, logging, pests) exhibit vastly different impacts on forest structure, successional pathways and, consequently, forest carbon sequestration and storage capacities. This study proposes an integrated remote sensing and deep learning (DL) method for forest disturbance type identification, enabling high-precision monitoring in Northeast China from 1992 to 2023. Leveraging the Google Earth Engine platform, we integrated Landsat time-series data (30 m resolution), Global Forest Change data, and other multi-source datasets. We extracted four key vegetation indices (NDVI, EVI, NBR, NDMI) to construct long-term forest disturbance feature series. A comparative analysis showed that the proposed convolutional neural network (CNN) model with six feature bands achieved 5.16% higher overall accuracy and a 6.92% higher Kappa coefficient than a random forest (RF) algorithm. Remarkably, even with only six features, the CNN model outperformed the RF model trained on fifteen features, achieving a 0.4% higher overall accuracy and a 0.58% higher Kappa coefficient, while utilizing 60% fewer parameters. The CNN model accurately classified forest disturbances—including fires, pests, logging, and geological disasters—achieving a 92.26% overall accuracy and an 89.04% Kappa coefficient. This surpasses the 81.4% accuracy of the Global Forest Change product. The method significantly improves the spatiotemporal accuracy of regional-scale forest monitoring, offering a robust framework for tracking ecosystem dynamics. Full article

Urban water infrastructure faces increasing deterioration, necessitating accurate, cost-effective condition assessment. Traditional inspection techniques are intrusive and inefficient, creating demand for scalable machine learning (ML) solutions. This study develops a hybrid ML meta-model to predict underground pipe conditions using a comprehensive dataset of 11,544 records. The objective is to enhance multi-class classification performance while preserving interpretability. A stacked hybrid architecture was employed, integrating Random Forest, LightGBM, and CatBoost models. Following data preprocessing, feature engineering, and correlation analysis, the neural network-based stacking meta-model achieves 96.67% accuracy, surpassing individual base learners while delivering enhanced robustness through model diversity, improved probability calibration, and consistent performance on challenging intermediate condition classes, which are essential for condition prioritization. Age emerged as the most influential feature, followed by length, material type, and diameter. ROC-AUC scores ranged from 0.894 to 0.998 across all models and classes, confirming high discriminative capability. This work demonstrates hybrid architectures for infrastructure diagnostics. Full article

Sustainable soil management requires striking a balance between productivity and soil health. While agroforestry practices are known to improve soil health and ecosystem functions, the contribution of microbial diversity to maintaining multifunctional soil processes in pecan (Carya illinoinensis) cultivation has yet to be fully elucidated. This study examined microbial diversity, soil functions, and multifunctionality across different pecan intercropping setups. We compared a monoculture pecan plantation with three agroforestry models: pecan–Paeonia suffruticosa–Hemerocallis citrina (CPH), pecan–P. suffruticosa (CPS), and pecan–P. lactiflora (CPL). We employed high-throughput sequencing (16S and ITS) to determine the soil bacterial and fungal communities and analyzed the species diversity, extracellular enzyme activities, and physicochemical properties. Soil multifunctionality (SMF) was evaluated using 20 indicators for nutrient supply, storage, cycling, and environmental regulation. Agroforestry increased soil fungal diversity and improved multifunctionality when compared to monoculture. The CPS and CPH models were the most beneficial, increasing multifunctionality by 0.74 and 0.55 units, respectively. Structural equation modeling revealed two key pathways: bacterial diversity significantly enhanced nutrient cycling and environmental regulation, whereas fungal diversity primarily promoted nutrient cycling. These pathways together delivered clear gains in multifunctionality. Random forest analysis identified key predictors (total nitrogen, total carbon, available potassium, β-1,4-N-acetylglucosaminidase, and alkaline phosphatase), highlighting the joint importance of nutrients and microbial enzymes. Our results demonstrate that selecting species in pecan agroforestry alters microbial communities and activates key functions that support soil health and long-term resilience. Hence, pecan agroforestry maintains SMF through microbial processes, with CPS showing the strongest effect. These results can inform species selection and encourage broader testing for resilient, biodiversity-based farming practices. Full article

Postpartum haemorrhage is one of the leading causes of maternal morbidity and mortality worldwide. The early identification of bleeding risk in individual women is crucial for enabling timely interventions and improving patient outcomes.This study aims to evaluate various exploratory and classification methodologies, alongside optimisation strategies, for identifying predictors of postpartum haemorrhage. K-means clustering was employed on a retrospective cohort of patients, incorporating demographic, obstetric, and laboratory variables, to delineate patient profiles and select pertinent features. Initially, a classical logistic regression model, implemented without cross-validation, facilitated the identification of six significant predictors for postpartum haemorrhage: lactate dehydrogenase, urea, platelet count, non-O blood group, gestational age, and first-degree lacerations, all of which are variables routinely available in clinical practice. Furthermore, machine learning algorithms—including stepwise logistic regression, ridge logistic regression, and random forest—were utilised, applying cross-validation to optimise predictive performance and enhance generalisability. Among these methodologies, ridge logistic regression emerged as the most effective model, achieving the following metrics: sensitivity 0.857, specificity 0.875, accuracy 0.871, F1-score 0.759, and AUC 0.907. While machine learning techniques demonstrated superior performance, the integration of classical statistical methods with machine learning approaches provides a robust framework for generating reliable predictions and fostering significant clinical insights. Full article

Puebla is Mexico’s third largest coffee-producing state, supporting more than 40,000 families in the Sierra Norte region alone. In this area, the heterogeneity of production, which ranges from traditional subsistence methods to technified models, and a significant difference in the level of dedication to production represent major challenges for the sustainability of coffee farming. This study aimed to classify coffee producers in the Tlaxcalantongo ejido, Xicotepec, Puebla, according to their level of productive dedication, using multivariate techniques such as hierarchical clustering, non-metric multidimensional scaling (NMDS), and Random Forest. Data were obtained from a structured questionnaire with 102 questions administered in person to 50 active producers. The cluster analysis found patterns and differences in the productive dedication of coffee growers that allowed them to be differentiated into two groups. Group 1 (8%) showed minimal fertilization practices and low operating expenses, reflecting significant differences in resource management. In contrast, producers in group 2 (92%) had a profile characterized by intensive fertilization practices, greater investment in inputs, and structured agronomic management. In the NMDS analysis, dimension 1 was significantly associated with the group of producers with low productive dedication and dimension 2 was significantly associated with the group with greater dedication, while the third dimension showed no clear differentiation between the groups. The variables that determined the productive dedication profiles were fertilization application, division, type, and expenditure. Full article

Phosphates were widely used in composting, but their impact on the degradation of organic matter transformation in food waste compost was not well known. In this study, Ca(H₂PO₄)₂·H₂O and K₂HPO₄ were separately added to food waste for a 30-day composting process. Chemical stoichiometry, high-throughput sequencing, and Mantel analysis were used to reveal the effect of phosphate addition on carbon conversion in composting. Results showed that soluble phosphate addition enhanced compost maturation despite inhibiting crude protein degradation. At the end of composting, the addition of Ca(H₂PO₄)₂·H₂O and K₂HPO₄ resulted in a 33.75% and 45.15% increase in GI compared to the control group. Compared with K₂HPO₄, Ca(H₂PO₄)₂·H₂O addition improved total organic carbon (degradation rate increased by 2.9%) and total volatile solids (increased by 1.13%) degradation while reducing pH (decreased by 0.52), promoting total nitrogen preservation (increased by 25.56%). Microbial co-occurrence networks showed that phosphate increased community complexity and stability, enriching core taxa (Lentilactobacillus, Paraburkholderia, Catelliglobosispora, and Pseudarthrobacter). Mantel tests linked microbial diversity to lipid decomposition and maturation. Random forest analysis revealed that additive soluble phosphate boosted organic matter and lipid degradation by stimulating Tepidisphaera and Thermobifida, while suppressing Lactiplantibacillus. Additionally, soluble phosphate enhanced crude protein degradation via Compostibacillus, Weizmannia, and Ureibacillus enrichment. At the end of composting, Tepidisphaera (14.68%) and Thermobifida (30.62%) had a higher proportion in Ca(H₂PO₄)₂·H₂O treatment, which might be an important reason why this treatment was beneficial for organic matter degradation. Overall, Ca(H₂PO₄)₂·H₂O achieved the highest maturity and nitrogen retention, proving optimal for food waste composting. Full article

The escalating environmental crisis and the threat posed by environmental crime demand more effective prevention strategies. The predictive mapping of environmental crimes can address this challenge by improving monitoring and response. This study proposes an analysis and modelling of the occurrence of environmental crimes in the Canary Islands, a territory of exceptional ecological value and strong tourism and urban sprawl pressures. Four types of illegal activity were examined: buildings and constructions, mining and tilling, solid waste dumping, and liquid waste discharging. A predictive modelling framework based on Random Forest (RF) machine learning algorithms was applied to identify spatial patterns and environmental crime potential. A colour-based environmental crime potential map was generated for each island, showing the likelihood of 0, 1, 2, 3, or all 4 types of environmental crime. Findings reveal that 43.2% of the surface area of the islands could potentially be affected by at least one crime type. Potential occurrences are lower in protected natural areas, in islands with lower population densities and in inland areas compared to coastal regions. The methodology provides a foundation for future research which could assist policymakers and environmental protectors in combating and preventing environmental crimes more effectively and contribute to the preservation of their ecosystems. Full article

Background: The automated analysis of intestinal organoids in microscopy images are essential for high-throughput morphological studies, enabling precision and scalability. Traditional manual analysis is time-consuming and subject to observer bias, whereas Machine Learning (ML) approaches have recently demonstrated superior performance. Purpose: This study aims to evaluate YOLO (You Only Look Once) for organoid segmentation and classification, comparing its standalone performance with a hybrid pipeline that integrates DL-based feature extraction and ML classifiers. Methods: The dataset, consisting of 840 light microscopy images and over 23,000 annotated intestinal organoids, was divided into training (756 images) and validation (84 images) sets. Organoids were categorized into four morphological classes: cystic non-budding organoids (Org0), early organoids (Org1), late organoids (Org3), and Spheroids (Sph). YOLO version 10 (YOLOv10) was trained as a segmenter-classifier for the detection and classification of organoids. Performance metrics for YOLOv10 as a standalone model included Average Precision (AP), mean AP at 50% overlap (mAP50), and confusion matrix evaluated on the validation set. In the hybrid pipeline, trained YOLOv10 segmented bounding boxes, and features extracted from these regions using YOLOv10 and ResNet50 were classified with ML algorithms, including Logistic Regression, Naive Bayes, K-Nearest Neighbors (KNN), Random Forest, eXtreme Gradient Boosting (XGBoost), and Multi-Layer Perceptrons (MLP). The performance of these classifiers was assessed using the Receiver Operating Characteristic (ROC) curve and its corresponding Area Under the Curve (AUC), precision, F1 score, and confusion matrix metrics. Principal Component Analysis (PCA) was applied to reduce feature dimensionality while retaining 95% of cumulative variance. To optimize the classification results, an ensemble approach based on AUC-weighted probability fusion was implemented to combine predictions across classifiers. Results: YOLOv10 as a standalone model achieved an overall mAP50 of 0.845, with high AP across all four classes (range 0.797–0.901). In the hybrid pipeline, features extracted with ResNet50 outperformed those extracted with YOLO, with multiple classifiers achieving AUC scores ranging from 0.71 to 0.98 on the validation set. Among all classifiers, Logistic Regression emerged as the best-performing model, achieving the highest AUC scores across multiple classes (range 0.93–0.98). Feature selection using PCA did not improve classification performance. The AUC-weighted ensemble method further enhanced performance, leveraging the strengths of multiple classifiers to optimize prediction, as demonstrated by improved ROC-AUC scores across all organoid classes (range 0.92–0.98). Conclusions: This study demonstrates the effectiveness of YOLOv10 as a standalone model and the robustness of hybrid pipelines combining ResNet50 feature extraction and ML classifiers. Logistic Regression emerged as the best-performing classifier, achieving the highest ROC-AUC across multiple classes. This approach ensures reproducible, automated, and precise morphological analysis, with significant potential for high-throughput organoid studies and live imaging applications. Full article

Forest managers need regular accurate assessments of forest conditions to make informed decisions associated with harvest schedules, growth projections, merchandising, investment, and overall management planning. Traditionally, this is achieved through field-based sampling (i.e., timber cruising) a subset of the trees within a desired area (e.g., 1%–2%) through stratification of the landscape to group similar vegetation structures and apply a grid within each stratum where fixed- or variable-radius sample locations (i.e., plots) are installed to gather information used to estimate trees throughout the unmeasured remainder of the area. These traditional approaches are often limited in their assessment of uncertainty until trees are harvested and processed. However, the increasing availability of airborne laser scanning datasets in commercial forestry processed into Digital Inventories^® enables the ability to non-destructively assess the accuracy of these field-based surveys, which are commonly referred to as cruises. In this study, we assess the uncertainty of common field sampling-based estimation methods by comparing them to a population of individual trees developed using established and validated methods and in operational use on the University of Idaho Experimental Forest (UIEF) and a commercial conifer plantation in Louisiana, USA (PLLP). A series of repeated sampling experiments, representing over 90 million simulations, were conducted under industry-standard cruise specifications, and the resulting estimates are compared against the population values. The analysis reveals key limitations in current sampling approaches, highlighting biases and inefficiencies inherent in certain specifications. Specifically, methods applied to handle edge plots (i.e., measurements conducted on or near the boundary of a sampling stratum), and stratum delineation contributes most significantly to systematic bias in estimates of the mean and variance around the mean. The study also shows that conventional estimators, designed for perfectly randomized experiments, are highly sensitive to plot location strategies in field settings, leading to potential inaccurate estimations of BAA and TPA. Overall, the study highlights the challenges and limitations of traditional forest sampling and impacts specific sampling design decisions can have on the reliability of key statistical estimates. Full article

Cultivated land (CL) is essential for human survival, as its coordinated utilization plays a crucial role in both food production and ecological protection. In this study, we focus on Aksu, a typical oasis in arid areas of Xinjiang, to explore how to improve the eco-efficiency of cultivated land utilization (ECLU) from the perspective of carbon emissions under different ownership structures. The goal is to provide policy support for the sustainable intensification of CL in Aksu. The super-efficiency slack-based measure (Super-SBM) model was used to calculate the ECLU, while the carbon emissions coefficient method was employed to estimate cultivated land carbon emissions (CLCE). Additionally, the random forest regression (RFR) model was utilized to analyze differences in CLCE between collective and state-owned cultivated lands. Finally, a Geo-detector analysis was conducted to identify driving factors of CLCE. The findings indicate that the overall ECLU values in Aksu initially increased and subsequently decreased over time. During the study period, Kalpin showed the highest ECLU, followed by Wensu and Wushi. The total CLCE in Aksu demonstrated an initial increase followed by a decrease, but the overall trend was growth, from 3.7 t in 2008 to 5.63 t in 2019, on average. It was observed that carbon emissions from state-owned cultivated land were greater than those from collective cultivated land, and carbon emissions from non-food crops were higher than those from food crops. Furthermore, spatial heterogeneity was evident in the CLCE. The single factor detection results showed that the Local_GDP (q = 0.763, representing the explanatory power of the Local_GDP on cultivated land carbon emissions) was identified as the main driver of CLCE in Aksu. The interactive detection results indicated that the Local_GDP and Farmer income (0.839) had stronger effects on CLCE in Aksu than any other two factors. It was also found that ownership of CL directly affects CLCE and indirectly affects the ECLU. In conclusion, it is necessary to formulate corresponding countermeasures for improving the ECLU involving government intervention, as well as cooperation with farmers and other stakeholders, to address these issues effectively within Aksu’s agricultural sector. Full article

Accurate placement and geometry of simulation plots are essential for spatially explicit modeling of forest ecosystems. This study introduces a Monte Carlo-based approach for optimizing the spatial alignment of simulation plots with their source polygons, improving their ability to represent stand-level heterogeneity. The method is implemented in GenSimPlot, an open-source Python plugin for QGIS (version 3.30) that automates the generation, placement, and refinement of simulation plots using simple geometric shapes. Monte Carlo optimization iteratively adjusts translation, rotation, and scaling parameters to maximize spatial congruence, thereby enhancing the fidelity of forest growth simulations. A built-in hyperparameter tuning module based on random search enables users to explore optimal parameter settings systematically. In addition, GenSimPlot supports the extraction of qualitative and quantitative environmental variables and terrain from raster datasets, facilitating integration with forest growth models and broader ecological simulations. The proposed approach improves plot representativeness and enables robust scenario analysis across heterogeneous landscapes. Full article

Cost is a key performance indicator for evaluating the success of architectural construction projects. While previous studies have relied on quantitative data and statistical models to evaluate cost performance, recent advancements in methods have enabled analysis using unstructured data. Unstructured data, particularly in construction supervision reports, can be considered the significant variables for performance evaluation, as they represent independent third-party monitoring of the construction project’s execution. This study aims to present a framework that supports cost performance evaluation using unstructured data and random forests (RFs), a representative method of machine learning. Specifically, association rule analysis and social network analysis were used to identify the main keywords, and an RF model was applied to these data to evaluate cost performance. The tuning of hyper-parameters in the RF was implemented by the Bayesian optimization technique with the augmentation of the original dataset. The accuracy of cost performance evaluation was 59% for the traditional logistic regression (LR), 74% for the regularization-based logistic regression (BLR) designed to prevent overfitting, and 76% for the RF model utilizing augmented data. The complementary utility of the models consisting of the proposed framework can be useful for deriving various evaluation explanations about cost performance. The applicability is expected to increase as more data become available in the future. Full article

The construction industry is a major contributor to global resource consumption and waste. This sector extracts over two billion tons of raw materials each year and contributes over 30% of all solid waste generated annually through construction and demolition debris. The movement toward circularity in the built environment aims to replace linear processes of extraction and disposal by promoting policies favoring building preservation and adaptive reuse, as well as the salvage and reuse of building materials. Few North American cities have implemented explicit policies that incentivize circularity to decouple urban growth from resource consumption, and there remain substantial hurdles to adoption. Nonetheless, existing regulatory and planning tools, such as zoning codes and historic preservation policies, may already influence redevelopment in ways that could align with circularity. This article examines spatial patterns in these indirect pathways through a case study of a college town in New York State, assessing how commonly used local planning tools shape urban redevelopment trajectories. Using a three-stage spatial analysis protocol, including exploratory analysis, Geographically Weighted Regressions (GWRs), and Geographic Random Forest (GRF) modeling, the study evaluates the impact of zoning regulations and historic preservation designations on patterns of demolition, reinvestment, and incremental change in the building stock. National historic districts were strongly associated with more building adaptation permits indicating reinvestment in existing buildings. Mixed-use zoning was positively correlated with new construction, while special overlay districts and low-density zoning were mostly negatively correlated with concentrations of building adaptation permits. A key contribution of this paper is a replicable protocol for urban building stock analysis and insights into how land use policies can support or hinder incremental urban change in moves toward the circular city. Further, we provide recommendations for data management strategies in small cities that could help strengthen analysis-driven policies. Full article