Search Results (9,066)

Understanding how ecological dominance aligns with culturally and economically valued plant use is critical for participatory forest management. This study integrated vegetation structure and ethnobotanical valuation to assess angiosperm importance across three forest strata (Mixed Deciduous Forest (MDF), Dry Dipterocarp Forest site 1 (DDF1), and Dry Dipterocarp Forest site 2 (DDF2)) within the Ngao Model Forest, Northern Thailand. Fifteen 10 × 10 m vegetation plots (five per forest stratum) were surveyed to calculate the Importance Value Index (IVI), and 198 semi-structured interviews were conducted to derive the Use Value Index (UVI) and a standardized Socio-Economic Value Index (SEVI). A total of 112 angiosperm species were recorded across forest types, with strong structural dominance by dipterocarps in DDF sites and greater compositional heterogeneity in MDF. Spearman rank correlation analysis supported the working hypothesis that ecological dominance is only weakly associated with cultural and socio-economic importance. IVI showed weak but significant positive correlations with UVI (ρ = 0.288, p < 0.05) and SEVI (ρ = 0.300, p < 0.05), indicating partial but limited alignment between structural abundance and livelihood value. Several species with moderate or low IVI exhibited disproportionately high UVI and SEVI scores, reflecting their importance in food, medicinal, and commercial use categories. Conversely, certain canopy dominants showed limited ethnobotanical significance. These findings demonstrate that ecological abundance alone is an insufficient proxy for community-defined species value. Integrating structural, cultural, and socio-economic indices provides a more comprehensive framework for identifying priority species in community-managed forest systems. The IVI–UVI–SEVI comparative approach offers practical insights for model forest governance by distinguishing ecological dominants, multipurpose livelihood species, and culturally significant taxa occurring outside forest interiors. This multidimensional valuation framework strengthens participatory forest management and biodiversity prioritization in heterogeneous tropical landscapes. Full article

Traffic incident impact prediction remains challenging for intelligent transportation systems due to complex spatiotemporal dependencies. This study analyzes 38,430 real-world traffic incidents from Istanbul (2022–2024) to predict normalized traffic deviation $\Delta Traffic(\%)$ using machine learning with rigorous temporal validation. Three models—Random Forest (RF), XGBoost, and LightGBM—were evaluated using rolling-origin cross-validation (2022 training, 2023 testing; 2022–2023 training, 2024 testing) to prevent temporal leakage, employing a strictly operational 13-feature set that excludes information unavailable at incident onset (t₀). LightGBM achieved MAE = 26.81 ± 1.94% and R² = 0.506 ± 0.042 (mean ± std across folds) with 95% bootstrap confidence intervals of [27.54%, 28.81%] for MAE on the 2024 test set, significantly outperforming historical baselines (R² = 0.100 ± 0.054, p < 0.001, Bonferroni-corrected). Feature ablation studies revealed that temporal features contribute 65.2% of predictive power, while incident type contributes only 1.3%. Distributional robustness analysis confirms conclusions are stable across distributional treatments (log, winsorised, quantile), with feature importance rank correlations ρ = 1.000 between all treatment pairs. This work provides empirical evidence for context-aware traffic management systems and demonstrates the importance of proper temporal validation in transportation forecasting. Full article

Credit risk prediction is essential for financial institutions to effectively assess the likelihood of borrower defaults and manage associated risks. This study presents a comparative analysis of deep learning architectures and traditional machine learning models on imbalanced credit risk datasets. To address class imbalance, we employ three resampling techniques: Synthetic Minority Over-sampling Technique (SMOTE), Edited Nearest Neighbors (ENN), and the hybrid SMOTE-ENN. We evaluate the performance of various models, including multilayer perceptron (MLP), convolutional neural network (CNN), long short-term memory (LSTM), gated recurrent unit (GRU), logistic regression, decision tree, support vector machine (SVM), random forest, adaptive boosting, and extreme gradient boosting. The analysis reveals that SMOTE-ENN combined with MLP achieves the highest F1-score of 0.928 (accuracy 95.4%) on the German dataset, while SMOTE-ENN with random forest attains the best F1-score of 0.789 (accuracy 82.1%) on the Taiwanese dataset. SHapley Additive exPlanations (SHAP) are employed to enhance model interpretability, identifying key drivers of credit default. These findings provide actionable guidance for developing transparent, high-performing, and robust credit risk assessment systems. Full article

Background: Agriculture degrades soils, affects the delivery of ecosystem services, and contributes to climate change. Methods: This research examined nitrogen and sulfur recycling in soils under cropland expansion in Ghana at (a) reconnaissance scale in northern Guinea savannah (NGS), southern Guinea savannah (SGS), forest–savannah transition (FST), and semi-deciduous forest (SDF) agro-ecological zones (AEZs), and (b) farm level in rain Forest and the FST AEZs based on “duration of cultivation”. Fresh soils (20 cm depth) were incubated for 28 days at 28 °C, followed by the determination of mineralized nitrogen and sulfur at 14 and 28 days using standard methods. Results: Low nitrogen and sulfur contents led to predominant nitrogen and minor sulfur immobilizations, particularly in FST and savannah AEZs. Microbial biomass and pedogenic Fe controlled much of the nitrogen immobilization. At the farm level, dithionite Al and soil pH controlled nitrogen immobilization, particularly in relatively older farms, being pronounced in forest-related AEZs. Conclusions: Although the study is laboratory-based, it highlights the severe nature of soil degradation (SD) under cropland expansion in regions prone to poor nutrient budgets. Therefore, it calls for drastic measures to halt SD by adopting ecozone- and climate-driven sustainable soil management and agricultural systems. Full article

Background and Objectives: Diabetes mellitus is a chronic disease prevalent worldwide, carrying significant health and economic burdens. Early diagnosis is critical for effective disease management; however, the multidimensional and complex nature of diabetes makes accurate prediction challenging. In this study, an original feature selection approach based on the Apriori algorithm, traditionally used in market basket analysis, is proposed to identify symptom patterns associated with diabetes. The literature emphasizes that feature selection remains an active research problem. Materials and Methods: A real-world dataset consisting of 16 categorical symptom variables from 520 individuals, obtained from the UCI Machine Learning Repository, was used. Variable encoding, missing data checks, and continuous variable transformations were performed during the preprocessing stages. The basic symptoms frequently associated with diabetes were identified through association analysis using the Apriori algorithm, and these features were used for classification with four different machine learning algorithms (K-Nearest Neighbors, Support Vector Machines, Artificial Neural Networks, and Random Forests). Accuracy, precision, sensitivity, specificity, and F1 score metrics were considered in evaluations conducted on both full and reduced datasets. Results: Feature selection was found to significantly improve model performance, with the SVM algorithm achieving the highest success rate at 97% accuracy and an F1 score of 0.961. KNN stood out in identifying positive cases with 0.975 sensitivity. Conclusions: These findings reveal that Apriori-based feature selection is an effective and explainable method for symptom-based diabetes prediction. This method can contribute to the development of low-cost, symptom-based decision support systems, especially in areas with limited resources. Full article

Due to climate change, seedling damage caused by drought stress is expected to increase in both afforestation sites and nurseries. Therefore, to ensure stable seedling production under high-temperature conditions and to cultivate seedlings with enhanced drought tolerance through hardening treatments, the development of an effective irrigation system is required. Conventional physiological methods for non-destructive drought detection, such as chlorophyll fluorescence and leaf temperature measurements, require expensive and manual operation, thereby limiting their real-time applicability in forest nurseries. This study evaluated the applicability of using image-based leaf angle measurements for drought stress detection in Quercus acutissima Carruth. seedlings. One-year-old seedlings were grown under two water regimes—well-watered (CT: control) and unwatered (DT: drought)—through Day 8. Statistical analyses (RMANOVA) revealed that changes in the leaf angle parameter PMD–MD (the difference between the previous and current measurement days) showed treatment effects similar to those of the physiological responses ΦNO (quantum yield of non-regulated energy dissipation) and qL (fraction of open PSII reaction centers) to drought on Day 6. Leaf angle reflected drought stress but did not precede physiological changes, indicating its role as a complementary rather than an early indicator. Multiple regression models identified AT (air temperature), SM (soil moisture), Fm′ (maximum fluorescence in the light-adapted state), and VPD (vapor pressure deficit) as the main factors influencing leaf angle variation. Although leaf angle was affected by combined environmental stresses such as high temperature, it was less sensitive to heat stress than physiological responses based on RMANOVA results. These results indicate the potential of image-based leaf angle measurements for drought stress detection. To establish plant-based smart irrigation systems, future studies should validate and refine this approach using larger datasets. Full article

Synthetic promoters are crucial for precise gene expression in transgenic plants, but their rational design is hindered by the difficulty in identifying functional cis-regulatory elements (CREs). In this study, we aimed to develop a systematic approach for discovering tissue-specific cis-regulatory modules (CRMs) and generating functional synthetic promoters in poplar. We performed extensive transcriptomic analysis across various poplar tissues to obtain categorical labels and detected motifs in all gene promoters using known transcription factor binding site (TFBS) position weight matrices. Informative, tissue-specific TFBSs were predicted using a random forest model. Applying this to a root-specific gene, PopRTS1, we identified putative root-specific CRMs. These CRMs were then used to construct synthetic promoters, which were experimentally validated through rapid infiltration and GUS staining assays across different tissues. We successfully identified a root-specific synthetic promoter, PRTS1. Our findings demonstrate that machine learning can effectively decipher regulatory codes from omics data to predict functional CRMs. This work provides a feasible and systematic method for screening and designing tissue-specific synthetic promoters, offering significant potential for advancing targeted gene expression systems in plant biotechnology. Full article

Endocrine-Disrupting Chemicals (EDCs) disrupt thyroid hormone (TH) synthesis, transport, metabolism, and action, thereby perturbing systemic endocrine homeostasis. Transthyretin (TTR) is a key TH transport protein that regulates circulating hormone distribution and tissue availability, particularly during critical developmental windows. Chemical interference with TTR-binding may alter TH bioavailability and represent a transport-mediated molecular initiating event within thyroid-axis perturbation. Despite widespread exposure, many thyroidal EDCs remain unidentified, and their health effects are difficult to assess due to multiple simultaneous exposures. To support endocrine hazard identification and chemical prioritization within risk assessment frameworks, we developed machine learning-based QSAR models during the Tox24 challenge, using a dataset of 1512 chemicals to predict TTR-binding affinity. Of these, 67% were used for training, 13% for testing, and 20% for validation. Molecular descriptors were selected by first removing highly correlated features and then ranking the remaining descriptors using mutual information regression. The leverage approach was applied to define the models’ applicability domain (AD). Five machine learning algorithms, including gradient boosting regressor (GBR), Random Forest, Lasso Regression, Support Vector Machine (SVM), and regularized SVM models, were developed. The GBR model demonstrated the best overall performance. This model achieved an R² of 0.89 on the training set, 0.58 on the test set, and 0.55 on the validation set. The molecular descriptor analysis highlights hydrophobicity, steric effects, branching, connectivity, and ionization/electronic effects as the mechanistic basis for TTR disruption and stabilization, providing structural insight into features associated with thyroid hormone displacement. The AD analysis indicated that 97.5% of the test set and 96.0% of the validation set fell within the reliable descriptor space. Importantly, these predictions extend beyond model benchmarking by informing weight-of-evidence evaluations of thyroid-axis perturbation and supporting the prioritization of chemicals for targeted testing within non-animal new approach methodologies. Overall, this work highlights the application of in silico approaches for screening EDCs, supporting the prioritization and identification of potentially harmful chemicals. Full article

The rapid growth of Internet-of-Things (IoT) deployments has substantially expanded the attack surface of modern cyber–physical systems, making accurate and computationally feasible malware detection essential for enterprise and industrial environments. This study presents a large-scale, systematic comparison of 27 machine learning (ML) and 18 deep learning (DL) models for IoT malware detection across eight major malware categories: Trojan, Botnet, Ransomware, Rootkit, Worm, Spyware, Keylogger, and Virus. A realistic dataset was constructed using 50,000 executable samples collected from the Any.Run platform, including 8000 malware instances (1000 per class) and 42,000 benign samples. Each sample was executed in a sandbox to extract detailed static and behavioral telemetry. A targeted feature-selection pipeline reduced the feature space to 47 diagnostic features spanning static properties, behavioral indicators, process/file/registry activity, debug signals, and network telemetry, yielding a compact representation suitable for malware detection in IoT settings. Experimental results demonstrate that ensemble tree-based ML models consistently dominate performance on the engineered tabular feature set as 7 of the top 10 models are ML, with CatBoost and LightGBM achieving near-ceiling accuracy and low false-positive rates. Per-malware analysis further shows that optimal model choice depends on malware behavior. CatBoost is best for Trojan/Spyware, LightGBM for Botnet, XGBoost for Worm, Extra Trees for Rootkit, and Random Forest for Keylogger, while DL models are competitive only for specific categories, with TabNet performing best for Ransomware and FT-Transformer for Virus. In addition, an end-to-end computational time analysis across all 45 models reveals a clear efficiency advantage for boosted tree ensembles relative to most DL architectures, supporting deployment feasibility on commodity CPU hardware. Overall, the study provides actionable guidance for designing adaptive IoT malware detection frameworks, recommending gradient-boosted ensemble ML models as the primary deployment choice, with selective DL models only when category-specific gains justify additional computational cost. Full article

This paper provides a feasibility study of a non-invasive microwave-based glucose-sensing system based on a small printed slot antenna with etched step-impedance resonators (SIRs) on an FR4 substrate in the ground plane at approximately 5.7 GHz. The sensor proposed takes advantage of the effect of the antenna resonant frequency and reflection coefficient (S11) perturbation due to the dielectric loading of a human finger placed in the antenna near field. Instead of declaring direct glucose specificity, this paper is dedicated to understand whether the measures of RF can be translated to the invasive glucose values under the condition of controlled positioning. A vector network analyzer was used to measure the experimental values where resonant frequency and S11 magnitude were obtained at the point of peak sensitivity due to fixed finger placement at the point. These RF properties were associated with invasively measured glucose values using three modeling methods: a simple analytical linear formula, a second-degree Polynomial Ridge regression model, and a Random Forest machine learning model. The comparative analysis has established that nonlinear data-driven models outperform the analytical formulations significantly with the highest predictive accuracy being the Random Forest model (R² = 0.72, RMSE = 10.57 mg/dL, MAE = 5.16 mg/dL). The findings affirm that the impacts of antenna loading control the raw measurements, but the trend related to glucose can be extracted upon machine learning calibration under controlled conditions. The research provides a methodological framework of RF-based non-invasive glucose sensing and the need to employ various phantom-based validation, sub-subject-based modeling, or clinically based evaluation metrics in future studies. Full article

The increasing emphasis on sustainability in digital printing requires quantitative methods for optimizing key performance indicators (KPIs) under technical and operational constraints. The term digital twin is used here in a methodological and analytical sense, as a simulation framework for analyzing interdependence, prediction, and multi-criteria optimization of KPIs, rather than as a direct virtual replica of a specific physical production system. This paper proposes a hybrid simulation–prediction model based on a digital twin framework for optimization of KPIs in sustainable digital printing, with particular emphasis on overall equipment effectiveness (OEE). Due to the limited availability of structured industrial data, the model is developed using a synthetically generated dataset constructed in accordance with industry-reported operating ranges and technically realistic digital printing process variables. Random Forest and XGBoost algorithms are applied to model nonlinear relationships between process parameters and KPIs, including material waste, energy consumption, machine downtime, and OEE. Based on these predictive models, a constrained multi-objective optimization procedure is performed to identify Pareto-efficient configurations that reduce material waste and energy consumption while maintaining acceptable downtime and OEE levels. The results characterize structural trade-offs among environmental and operational KPIs within a formally defined decision space. Full article

Aboveground biomass (AGB) is a key indicator of vegetation productivity and terrestrial carbon stocks; therefore, robust AGB estimation is critical for assessing ecosystem services and carbon cycle research. Previous studies have largely focused on forest and cropland ecosystems. In contrast, roadside vegetation along highways and other linear transport corridors remains comparatively underexplored despite its potentially important role as a carbon sink. Here, we integrate field-measured AGB samples with GF-2 high-resolution satellite imagery to evaluate the suitability of multiple remote-sensing predictors and machine-learning algorithms for estimating AGB in highway roadside vegetation. Six remote-sensing variables were used as predictors, including four vegetation indices (Normalized Difference Vegetation Index (NDVI), Perpendicular Vegetation Index (PVI), Enhanced Vegetation Index (EVI), and Modified Soil-Adjusted Vegetation Index (MSAVI) and two-band ratios (B342 and B12/34). Five regression models—multiple linear regression (MLR), partial least squares regression (PLSR), random forest (RF), support vector regression (SVR), and extreme gradient boosting (XGBoost)—were developed and systematically compared under both single-variable and multi-variable scenarios. Model performance was evaluated using five-fold cross-validation, with the coefficient of determination (R²) and the root mean square error (RMSE) as metrics of evaluation. The results indicate that the RF model under the multi-variable scenario achieved the best overall performance, with a training R² of 0.83 and a testing RMSE of 0.84 kg·m⁻², substantially outperforming the other linear and non-linear models. The optimal RF model was further applied to GF-2 imagery to produce a spatially explicit AGB map for a 32 km highway segment and a 30 m roadside buffer on both sides, yielding an estimated total aboveground biomass of 566.97 t for the corridor. These findings demonstrate that combining high-resolution remote sensing with machine-learning approaches can effectively improve AGB estimation for linear roadside vegetation systems, providing technical support for ecological monitoring, roadside greening management, and carbon accounting for transport infrastructure. Full article

While understanding the drivers of river water quality is crucial, the dependence on ground observations hinders the accurate quantification of driver thresholds, as well as the scale-dependent effects of buffer zones. By transcending the limitations of ground observations, satellite remote sensing provides the spatially continuous data required to define effective buffer zones and determine the threshold intervals for natural and anthropogenic drivers, effectively promoting sustainable watershed management. Herein, we determined the total nitrogen (TN), total phosphorus (TP), permanganate index (COD_Mn), and turbidity in the Minjiang River of Fujian Province by synergizing Sentinel-2 imagery and in situ data (2021–2024). Subsequently, we further employed generalized additive models (GAMs) considering scale-dependent (50 m to 20 km) characteristics to screen and evaluate the natural–anthropogenic factors influencing the water quality indicators. The GAMs revealed that TN exhibited multiphasic responses to forest cover and water area, characterized by alternating positive and negative effects across their range. TP was found to be predominantly driven by agricultural and urban land use, showing clear scale–threshold effects. This study provides an integrated framework that moves beyond retrieval to quantitatively assess the impact of multi-scale natural–anthropogenic factors, offering actionable insights for precise watershed zoning and science-based management for the sustainable development of river systems. Full article

Armillaria solidipes, the causal agent of Armillaria root rot, poses a severe and persistent threat to poplar forest plantations. This study evaluated the biocontrol efficacy of the endophytic bacterium Bacillus velezensis BY6 against this pathogen and elucidated its multimodal mechanisms of action. BY6 application significantly reduced disease severity by 37.19% at 30 days post-treatment. 16S rRNA (V3–V4) microbiome analysis revealed that BY6 reshaped both the rhizosphere and phyllosphere bacterial communities, consistently enriching beneficial taxa, including Pantoea ananatis and members of Acidobacteria, while suppressing opportunistic groups. Concurrently, BY6 activated systemic defenses in poplar, evidenced by enhanced activities of key enzymes PAL and POD, and the upregulated expression of SA/JA pathway marker genes (PR1, JAZ, and COI1), coupled with the downregulation of the auxin transporter gene AUX1. These data indicate that the biocontrol efficacy of B. velezensis BY6 was mediated by a dual mechanism: the modulation of both rhizospheric and phyllospheric bacterial communities, direct elicitation of systemic defense pathways in poplar, which synergistically enhanced resistance against A. solidipes. Full article

To address the critical need for accurate human thermal comfort prediction in winter heating environments, this study established a comprehensive thermal comfort dataset containing 2089 valid samples through experiments. On this basis, thermal comfort prediction models were constructed using three multi-class machine learning algorithms: Support Vector Classification, K-Nearest Neighbors, and Random Forest. The predictive performance of 63 different feature combinations was systematically evaluated. The results indicate that the feature subset comprising indoor air temperature, forehead temperature, cheek temperature, dorsal hand temperature, heart rate, and systolic blood pressure yields the optimal prediction performance. Among the evaluated models, the Random Forest model demonstrated superior overall performance, achieving an accuracy exceeding 90% and an AUC ranging from 96% to 99%, significantly outperforming the SVC and KNN models. Compared with the traditional Predicted Mean Vote (PMV) model, the machine learning models developed in this study showed a substantial improvement in prediction accuracy under identical conditions; notably, the Random Forest model improved accuracy by approximately 40% over the PMV model. Based on these findings, a smart heating system framework integrating environmental sensors, wearable devices, and intelligent control valves is proposed, providing a theoretical basis and technical approach for realizing personalized and energy-efficient heating control. Full article