Search Results (12,239)

The state of Bahia in Brazil possesses significant, yet underexploited, iron ore reserves. To support the initial stages of mineral exploration in this vast region, cost-effective and rapid large-scale mapping methods are essential. This paper presents a workflow based on publicly available remote sensing data for a state mineral prospectivity mapping (MPM) for iron. The methodology employs a Random Forest (RF) classification model on Sentinel-2 multispectral images, trained with a randomly selected dataset in the image at varying distances defined from the location of known iron mines in the state. The Spectral Angle Mapper (SAM) algorithm was used to categorize the samples according to spectral similarity features with laboratory-confirmed ore signatures from samples collected in the mine pit area. The resulting MPM successfully delineated known iron districts and highlighted new, unexplored areas with potential. A quantitative evaluation of the model yielded an overall accuracy of 69.8%, a macro-average F1-score of 0.697, and a Cohen’s Kappa coefficient of 0.623, indicating a reasonable agreement beyond random chance. This work demonstrates a validated, low-cost, and simple approach for regional-scale MPM, offering a valuable reconnaissance tool for preliminary exploration, particularly in extensive and data-scarce regions. Full article

The low water and fertiliser utilisation efficiency and soil quality degradation caused by high water and fertiliser inputs are the primary challenges facing goji berry cultivation in arid regions. A two-year field experiment was conducted from 2021 to 2022. The experiment included three irrigation rates (I₁, I₂, I₃) of 2160, 2565, and 2970 m³·hm⁻² and three nitrogen application rates (N₁, N₂, N₃) of 165, 225, and 285 kg·hm⁻² to quantify their impacts on soil nutrients, enzyme activity, and goji berry yield in the root zone. Results indicate that the indicators of soil nutrients decrease with increasing soil depth, with depths of 0–20 cm accounting for 24.80–72.48% of total content. With fertility period progression, soil organic matter at depths of 0–80 cm exhibits a “folded-line” trend, while total nitrogen, nitrate nitrogen, and available phosphorus show an “M”-type trend. At depths of 0–40 cm, the proportions of urease, sucrase, and alkaline phosphatase activities all exceeded 70%. At I₁ irrigation rate, enzyme activities gradually increased with rising nitrogen application rates. At I₂ and I₃ irrigation rates, enzyme activities first increased, then decreased with increasing nitrogen application. The highest yields of both fresh and dried fruits were achieved at I₂N₂ treatment, increasing by 14.17% and 14.78%, respectively, compared to conventional management (CK). Analysis of the random forest model indicates that the soil-driven factors influencing yield formation include SA, UA, APA, HPA, SOM, NH₄⁺-N, and TP. Analysis of SQI and yield fitted data indicates that water–nitrogen coupling significantly influences wolfberry yield by regulating soil quality. Partial least squares (PLS-PM) showed that N application and irrigation of soil nutrients did not cause a significant indirect impact on goji berry yield, but a significant positive effect on goji berry yield occurred through enzyme activity. Full article

Background/Objectives: The identification of a decedent through skeletal analysis is dependent on accurate estimation of demographic characteristics, including biological sex. The most well-known sex estimation technique using the pubic bone is the Phenice method. In 2012, it was revised by Klales and colleagues and a logistic regression equation to predict sex was applied. Later, a program that estimates sex from Klales’ scoring with a random forest model, MorphoPASSE, was developed by Klales. Methods: Here we compare the accuracy of the original and revised methods, along with MorphoPASSE, using a contemporary sample of Northern Italians with documented sex. We further test the assertions by Phenice that his method is easy to employ for new observers and that ambiguity can be applied when characteristics do not morphologically fit into the categories of the method. Accuracy, error, bias, sensitivity, and specificity were calculated for each approach, along with McNemar’s tests for paired data, which compared documented sex and estimated sex. A linear weighted Cohen’s Kappa measured the differences in scoring between a new observer and an experienced observer. Results: Phenice’s method achieved higher accuracy (97%) than the Klales method and MorphoPASSE (86% each), as well as higher sensitivity and specificity, and lower error and bias. All McNemar’s tests conducted were not significant. The new observer demonstrated a similar accuracy (93%) to the experienced observer (97%). Furthermore, comparisons of Phenice’s scoring with ambiguity indicate its superior performance for capturing variation over the Klales method and MorphoPASSE. Conclusions: Phenice’s method is recommended in forensic anthropology and bioarchaeological contexts, particularly in Milan. Full article

Within the rapidly advancing disciplines of natural language processing (NLP) and artificial intelligence (AI), this paper introduces an innovative approach aimed at improving access to health-related information. Fueled by the growing reliance on digital platforms for health inquiries, our research unveils an intelligent chatbot designed to categorize health-related queries and deliver personalized advice. By leveraging a diverse dataset and employing advanced NLP techniques, our models, which include Support Vector Machines, Random Forest, Bagging Classifier, among others, assist in building a flexible conversational agent. The evaluation metrics demonstrate that the Bagging Classifier delivers outstanding results, reaching an accuracy of 99%. The study concludes with a comparative analysis, positioning the Bagging Classifier as a benchmark for accuracy and performance in the classification of health-related queries. Full article

As cities worldwide strive to promote healthy and sustainable non-motorized transport, the equity of dockless bike-sharing has become a central issue in urban transport planning. This study investigates the relationship between human-scale urban environmental perceptions and the equity of bike-sharing usage within 15-minute cycling catchments of metro stations. Using Shenzhen, China, as a case study, we integrated bike-share trip records from August 2021 (around 43 million trips), population grid data, and Baidu Street View images analyzed with deep learning models. The study first quantified the spatial inequality of bike-sharing usage within each metro catchment area using a per capita trip Gini coefficient. Subsequently, we assessed the correlation between these equity metrics and human-scale urban qualities quantified from street-level imagery. The findings reveal significant intra-catchment usage disparities, with some central urban station areas showing relatively equitable bike-sharing distribution (Gini as low as 0.37), while others, particularly on the urban fringe, exhibit highly inequitable patterns (Gini as high as 0.93). Spearman correlation analysis showed that catchments perceived as “livelier” and more “interesting” had significantly lower Gini coefficients, whereas other perceptual factors such as safety, beauty and wealth showed no significant linear relationship with equity. A Random Forest model further indicated that “liveliness” and “lack of boredom” are the strongest predictors of usage equity, highlighting the critical role of vibrant street environments in promoting equitable access. These findings bridge the fields of transportation equity and urban governance, suggesting that improving the human-scale environment around transit hubs, thereby making streets more engaging, safe, and pleasant, could foster more inclusive and equitable use of bike-sharing. Full article

Type 2 diabetes mellitus (T2DM) is a growing global health challenge, expected to affect over 600 million people by 2045. The discovery of new antidiabetic agents remains resource-intensive, motivating the use of machine learning (ML) for virtual screening based on molecular structure. In this study, we developed a predictive pipeline integrating two distinct descriptor types: high-dimensional numerical features from the Mordred library (>1800 2D/3D descriptors) and categorical ontological annotations from the ClassyFire and ChEBI systems. These encode hierarchical chemical classifications and functional group labels. The dataset included 45 active compounds and thousands of inactive molecules, depending on the descriptor system. To address class imbalance, we applied SMOTE and created balanced training and test sets while preserving independent validation sets. Thirteen ML models—including regression, SVM, naive Bayes, decision trees, ensemble methods, and others—were trained using stratified 12-fold cross-validation and evaluated across training, test, and validation. Ridge Regression showed the best generalization (MCC = 0.814), with Gradient Boosting following (MCC = 0.570). Feature importance analysis highlighted the complementary nature of the descriptors: Ridge Regression emphasized ClassyFire taxonomies such as CHEMONTID:0000229 and CHEBI:35622, while Mordred-based models (e.g., Random Forest) prioritized structural and electronic features like MAXsssCH and ETA_dEpsilon_D. This study is the first to systematically integrate and compare structural and ontological descriptors for antidiabetic compound prediction. The framework offers a scalable and interpretable approach to virtual screening and can be extended to other therapeutic domains to accelerate early-stage drug discovery. Full article

Agriculture significantly contributes to greenhouse gas (GHG) emissions, yet fluxes from irrigated semi-arid systems remain poorly quantified. This study investigates CO₂, CH₄, N₂O, and NH₃ fluxes in a short-term lettuce experiment under semi-arid conditions. The objective was to quantify flux variability and identify key environmental and management drivers. High-frequency soil gas flux measurements were conducted under three treatments: irrigated soil (I), irrigated soil with plants (IP), and irrigated soil with plants plus NH₄NO₃ fertilizer (IPF). Environmental factors, including solar radiation, soil temperature, water-filled pore space, and relative projected leaf area, were monitored. A Random Forest model identified main flux determinants. Fluxes varied with plant function, growth, and fertilization. IP exhibited net CO₂ uptake through photosynthesis, whereas I and IPF showed net CO₂ emissions from soil respiration and fertilizer-induced disruption of plant function, respectively. CH₄ uptake occurred across treatments but decreased with plant presence. Fertilization in IPF triggered episodic N₂O (EF = 0.1%) and NH₃ emissions (EF = 0.97%) linked to nitrogen input. Vegetated semi-arid soils can act as CO₂ sinks when nitrogen is optimally managed. Excess or poorly timed nitrogen delays CO₂ uptake and increases reactive nitrogen losses. Methanotrophic activity drives CH₄ dynamics and is influenced by plants and fertilization. Maintaining crop vigor and applying precision nitrogen management are essential to optimize productivity while mitigating GHG and NH₃ emissions in semi-arid lettuce cultivation. Full article

The accurate forecasting of surface air temperature (T2M) is crucial for climate analysis, agricultural planning, and energy management. This study proposes a novel forecasting framework grounded in structured temporal decomposition. Using the Kolmogorov–Zurbenko (KZ) filter, all predictor variables are decomposed into three physically interpretable components: long-term, seasonal, and short-term variations, forming an expanded multi-scale feature space. A central innovation of this framework lies in training a single unified model on the decomposed feature set to predict the original target variable, thereby enabling the direct learning of scale-specific driver–response relationships. We present the first comprehensive benchmarking of this architecture, demonstrating that it consistently enhances the performance of both regularized linear models (Ridge and Lasso) and tree-based ensemble methods (Random Forest and XGBoost). Under rigorous walk-forward validation, the framework substantially outperforms conventional, non-decomposed approaches—for example, XGBoost improves the coefficient of determination ($R^2$) from 0.80 to 0.91. Furthermore, temporal decomposition enhances interpretability by enabling Ridge and Lasso models to achieve performance levels comparable to complex ensembles. Despite these promising results, we acknowledge several limitations: the analysis is restricted to a single geographic location and time span, and short-term components remain challenging to predict due to their stochastic nature and the weaker relevance of predictors. Additionally, the framework’s effectiveness may depend on the optimal selection of KZ parameters and the availability of sufficiently long historical datasets for stable walk-forward validation. Future research could extend this approach to multiple geographic regions, longer time series, adaptive KZ tuning, and specialized short-term modeling strategies. Overall, the proposed framework demonstrates that temporal decomposition of predictors offers a powerful inductive bias, establishing a robust and interpretable paradigm for surface air temperature forecasting. Full article

Monitoring muscle fatigue is essential for ensuring safety and maximizing the effectiveness of resistance training. Conventional methods such as electromyography (EMG), inertial measurement units (IMU), and ratings of perceived exertion (RPE) involve complex procedures and have limited applicability, particularly in unsupervised or robotic exercise environments. This study proposes a machine learning-based approach to directly predict RPE from force–time data collected during repeated isokinetic bench press sets. Thirty-two male participants (64 limb datasets) performed seven sets at a standardized 7RM load, with load cell data and RPE scores recorded. Biomechanical features representing magnitude, variability, energy, and temporal dynamics were extracted, along with engineered features reflecting relative changes and inter-set variations. The findings indicate that RPE is more closely related to relative fatigue progression than to absolute biomechanical output. Incorporating engineered features substantially improved predictive performance, with the Random Forest model achieving the highest accuracy and more than 93% of predictions falling within ±1 RPE unit of the reported values. The proposed approach can be seamlessly integrated into intelligent resistance machines, enabling automated load adjustment and providing substantial potential for applications in both athletic training and rehabilitation contexts. Full article

Human activities have altered rivers worldwide, but how their combined effects shape fish assemblages remains unclear. We therefore surveyed fish and habitats seasonally along the middle and lower reaches of the Han River, China, during 2022, specifically in June–August (wet season) and October–November (dry season). This study analyzed the spatial distribution pattern of fish diversity, explored the effects of natural factors (e.g., hydrology, water quality) and human stressors (e.g., dam, land use) on the spatial pattern of fish diversity, and identified the key driving factors. Cluster analysis and Non-metric Multidimensional Scaling (NMDS) showed that the fish communities could be divided into three groups: the Danjiangkou reservoir area (DRA), the middle reaches (MR), and the lower reaches (LR). For α-diversity, the LR had the highest value, followed by the DRA, with the MR being the lowest. For β-diversity, the MR had the highest value, followed by the LR, with the DRA being the lowest. Random Forest model showed that fish diversity was mainly affected by natural factors; among these factors, the key drivers of α-diversity were hydrological factors such as the water level (3.56–5.97%) and river width (4.53–4.69%), while for β-diversity, the key drivers were water quality factors, including the dissolved oxygen (10.08–12.36%), total nitrogen (6.49–9.31%), and chlorophyll a (8.26–8.40%). Structural Equation Modeling further revealed that natural factors affected β-diversity mainly through direct pathways, while human stressors affected β-diversity mainly through indirect pathways. The results revealed the differential roles of natural factors and human stressors in driving the patterns of fish α-diversity and β-diversity in human-disturbed rivers, which will provide a scientific basis for the conservation of fish diversity in the Han River. Full article

Accurate precipitation data are essential for effective water resource management. This study aimed to correct precipitation values from the ERA5_Ag reanalysis dataset using observational data from 20 meteorological stations located in the Tensift basin, Morocco. Five machine learning models were evaluated: MLP, XGBoost, CatBoost, LightGBM, and Random Forest. Model performance was assessed using RMSE, MAE, R², and bias metrics, enabling the selection of the best−performing model to apply the correction. The results showed significant improvements in the accuracy of precipitation estimates, with R² ranging between 0.80 and 0.90 in most stations. The best model was subsequently used to correct and generate raster maps of corrected precipitation over 42 years, providing a spatially detailed tool of great value for water resource management. This study is particularly important in semi−arid regions such as the Tensift basin, where water scarcity demands more accurate and informed decision−making. Full article

Identifying age-appropriate books for children is a complex task that requires balancing linguistic, cognitive, and thematic factors. This study introduces an artificial intelligence–supported framework to estimate the Suitable Age Value (SAV) of Turkish children’s books targeting the 2–18-year age range. We employ repeated, stratified $5\times 5$ cross-validation and report out-of-fold (OOF) metrics with 95% confidence intervals for a dataset of 300 Turkish children’s books. As classical baselines, linear/ElasticNet, SVR, Random Forest (RF), and XGBoost are trained on the engineered features; we also evaluate a rule-based Ateşman readability baseline. For text, we use a frozen dbmdz/bert-base-turkish-uncased encoder inside two hybrid variants, Concat and Attention-gated, with fold-internal PCA and metadata selection; augmentation is applied only to the training folds. Finally, we probe a few-shot LLM pipeline (GPT-4o-mini) and a convex blend of RF and LLM predictions. A few-shot LLM markedly outperforms the zero-shot model, and zero-shot performance is unreliable. Among hybrids, Concat performs better than Attention-gated, yet both trail our best classical baseline. A convex RF + LLM blend, learned via bootstrap out-of-bag sampling, achieves a lower RMSE/MAE than either component and a slightly higher QWK. The Ateşman baseline performance is substantially weaker. Overall, the findings were as follows: feature-based RF remains a strong baseline, few-shot LLMs add semantic cues, blending consistently helps, and simple hybrid concatenation beats a lightweight attention gate under our small-N regime. Full article

Accurate wildfire prediction and identification of key environmental drivers are critical for effective wildfire management. We propose a spatiotemporally adaptive framework integrating ST-DBSCAN clustering with GTWR-RFR. In this hybrid model, Random Forest captures local nonlinear relationships, while GTWR assigns adaptive spatiotemporal weights to refine predictions. Using historical wildfire records from Hunan Province, China, we first derived wildfire occurrence probabilities via ST-DBSCAN, avoiding the need for artificial non-fire samples. We then benchmarked GTWR-RFR against seven models, finding that our approach achieved the highest accuracy (R² = 0.969; RMSE = 0.1743). The framework effectively captures spatiotemporal heterogeneity and quantifies dynamic impacts of environmental drivers. Key contributing drivers include DEM, GDP, population density, and distance to roads and water bodies. Risk maps reveal that central and southern Hunan are at high risk during winter and early spring. Our approach enhances both predictive performance and interpretability, offering a replicable methodology for data-driven wildfire risk assessment. Full article

Hyperspectral remote sensing provides a powerful tool for crop nutrient monitoring and precision fertilization, yet its application is hindered by high-dimensional redundancy and inter-band collinearity. This study aimed to improve maize nitrogen estimation by constructing three types of two-dimensional full-band spectral indices—Difference Index (DI), Simple Ratio Index (SRI), and Normalized Difference Index (NDI)—combined with spectral preprocessing methods (raw spectra (RAW), first-order derivative (FD), and second-order derivative (SD)). To optimize feature selection, three strategies were evaluated: Grey Relational Analysis (GRA), Pearson Correlation Coefficient (PCC), and Variable Importance in Projection (VIP). These indices were then integrated into machine learning models, including Backpropagation Neural Network (BP), Random Forest (RF), and Support Vector Regression (SVR). Results revealed that spectral index optimization substantially enhanced model performance. NDI consistently demonstrated robustness, achieving the highest grey relational degree (0.9077) under second-derivative preprocessing and improving BP model predictions. PCC-selected features showed superior adaptability in the RF model, yielding the highest test accuracy under raw spectral input (R² = 0.769, RMSE = 0.0018). VIP proved most effective for SVR, with the optimal SD–VIP–SVR combination attaining the best predictive performance (test R² = 0.7593, RMSE = 0.0024). Compared with full-spectrum input, spectral index optimization effectively reduced collinearity and overfitting, improving both reliability and generalization. Spectral index optimization significantly improved inversion accuracy. Among the tested pipelines, RAW-PCC-RF demonstrated robust stability across datasets, while SD-VIP-SVR achieved the highest overall validation accuracy (R² = 0.7593, RMSE = 0.0024). These results highlight the complementary roles of stability and accuracy in defining the optimal pipeline for maize nitrogen inversion. This study highlights the pivotal role of spectral index optimization in hyperspectral inversion of maize nitrogen content. The proposed framework provides a reliable methodological basis for non-destructive nitrogen monitoring, with broad implications for precision agriculture and sustainable nutrient management. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article