Search Results (939)

Air pollution forecasting is particularly challenging in basins with frequent winter seasons and temperature inversions. In this study, we developed and rigorously evaluated deep learning models to forecast PM₁₀ and the Air Quality Index (AQI) in Igdır, Türkiye, using a five-year, hourly dataset (2020–2024) from the Igdır/Central station (PM₁₀, NO₂, O₃, SO₂; meteorology: pressure, temperature, wind speed, relative humidity, precipitation, cloud cover). Using linear interpolation and Z-score normalization, sine/cosine features (hour, month) were used to encode temporal periodicity, and a 72-h lookback → 24-h look-ahead design was employed. LSTM, GRU, BiLSTM, and CNN-LSTM models were compared under a three-stage ablation (meteorology only; +cyclic encoders; +lagged targets), and their hyperparameters were tuned via Bayesian optimization. The deep learning results were further contextualized against a Multiple Linear Regression (MLR) baseline serving as a snapshot persistence model to evaluate the specific advantage of LSTM’s temporal memory in short-horizon forecasting. Multi-output forecasting is central to the proposed design, featuring a multi-task learning (MTL) framework based on a single shared temporal encoder with two task-specific regression heads that simultaneously predict PM₁₀ and AQI. Compared with separate single-task models, the multi-output setup exploits cross-target covariance (AQI’s dependence on pollutant loads under meteorology), improves data efficiency and generalization through shared representations, and promotes coherent, horizon-stable forecasts across targets, which is particularly valuable when winter stagnation regimes couple PM₁₀ and AQI dynamics. Moreover, this study introduces a structured ablation design to explicitly evaluate the added value of multi-output forecasting under inversion-dominated basin conditions. The results show stepwise gains from cyclic encoders and, most strongly, from lagged target histories. Under the optimized 24-h setting, LSTM performs best (R²_{PM₁₀} = 0.7989, RMSE = 48.74 µg/m³; R²_{AQI} = 0.6626, RMSE = 37.81), marginally surpassing GRU and clearly outperforming BiLSTM and CNN-LSTM. Horizon sensitivity confirms the benefit of nowcasting: when retrained for shorter horizons, LSTM attains R² = 0.9991 for PM₁₀ (MAE = 2.44; RMSE = 3.30 µg/m³) and 0.9535 for AQI (MAE = 4.87; RMSE = 14.03) at 1 h, and R² = 0.9792 (PM₁₀; MAE = 9.70; RMSE = 15.67) and 0.8849 (AQI; MAE = 11.19; RMSE = 22.08) at 6 h. Residual diagnostics reveal heteroskedastic, regime-dependent errors peaking near 0 °C and low winds, as well as a conservative bias that underpredicts extremes. Collectively, the findings show that multi-output, temporally aware deep models enable accurate operational forecasting in Igdır. The proposed framework provides real-time air quality alerts and daily planning, providing decision support for sustainable air quality management, public health protection, and evidence-based urban policy and is transferable to similar continental basin environments. Full article

Accurate source apportionment of soil heavy metals (HMs) is critical for targeted pollution mitigation and ecological remediation. This review systematically synthesizes and compares five mainstream source apportionment approaches—receptor models (positive matrix factorization, PMF; absolute principal component score-multiple linear regression, APCS-MLR; UNMIX model), stable isotope tracing, and random forest (RF)-based machine learning—to provide researchers with a comprehensive methodological framework. The methodology includes a systematic literature review, comparative analysis of methodological principles, and synthesis of representative case studies from diverse geographical contexts. The core principles, evolutionary paths, typical use cases (e.g., industrial zones, agricultural fields, regional surveys), and inherent limitations are synthesized for each method. A practical decision framework linking research contexts (study objectives, spatial scales, data availability) to optimal method selection, along with guidelines for multi-method integration, is proposed. This review provides actionable guidance for researchers and practitioners in selecting appropriate methods for specific pollution scenarios, ultimately supporting more effective environmental management and policy development. Full article

Accurate prediction of TBM penetration rate (ROP) is of critical importance for the planning of tunneling operations and performance assessment. In this study, both classical multiple linear regression (MLR) and machine learning approaches—namely Random Forest, Bagged Trees, Support Vector Machine, and LSBoost—were employed to investigate the contributions of BI, UCS, DPW, α, and BTS parameters to ROP prediction. Univariate and MLR analyses exhibited limited explanatory power (R² = 0.365), confirming that ROP is governed by complex, multivariate, and nonlinear interactions. Comparative machine learning analyses revealed that LSBoost provides the most reliable predictions, achieving the highest accuracy (R² = 0.9565) and the lowest error metrics (RMSE = 0.1794; MAPE = 5.63%) for both original and normalized datasets. While Random Forest and Bagged Trees demonstrated comparable performance, SVM showed limited predictive capability on the original dataset (R² = 0.452; RMSE = 0.637; MAPE = 18.60). However, its performance improved substantially following data normalization, approaching that of LSBoost (R² = 0.936; RMSE = 0.218; MAPE = 4.87). Feature importance analyses based on PDP-driven Jacobian sensitivity and SHAP methods indicate that UCS, BI, and DPW are the dominant factors governing TBM penetration performance, while also demonstrating that model outputs remain interpretable in an interaction-aware manner. These findings highlight that machine learning-based approaches can deliver both reliable prediction and interpretability even with small and heterogeneous datasets, and suggest that future research should focus on integrating larger datasets, hybrid modeling strategies, and advanced explainability techniques. Full article

Urban morphology influences the distribution and variation of land surface temperature (LST) by altering surface cover type. However, the coupling effects of the daily LST cycle and the multidimensional morphological driving mechanisms remain insufficiently explored in existing studies. This study, based on ECOSTRESS diurnal LST data, focuses on Harbin, a representative city in China’s cold climate regions. By integrating land cover data, urban morphology vector data, and interpretable machine learning models, it investigates the intricate relationship between urban morphology indicators and LST over a 24 h cycle under cold climate conditions. LST prediction was carried out using Gradient Boosting Decision Trees (XGBoost), Random Forests (RF), Support Vector Machines (SVM), and Multiple Linear Regression (MLR), with an evaluation of the prediction accuracy of each model. The findings indicated the following: (1) The influence of 2D and 3D urban morphological indicators on LST exhibits significant seasonal variation, with Building Otherness (BO), Mean Building Height (BH), and the Normalized Difference Vegetation Index (NDVI) exerting notable impacts on LST in both winter and summer. (2) Significant interactions exist between certain urban morphological indicators that can effectively reduce LST, when the Patch Land Area Proportion (PLAND) exceeds 40%, increasing the Largest Patch Index (LPI) contributes to lowering LST in summer. (3) Among the evaluated machine learning algorithms, XGBoost demonstrates the highest prediction accuracy. This study provides scientific insights for urban planning and policy development, aiding in the optimization of urban morphological designs to effectively regulate LST. Full article

This study develops and validates heating energy consumption models for school buildings in Osijek-Baranja County in Croatia, integrating an overview of the legal framework, a review of heating energy consumption in schools, and approaches to predicting consumption. Three models, Multiple linear regression (MLR), Artificial neural networks (ANN), and Random forest (RF), were tested using training and validation datasets. Although ANN and RF achieved higher accuracy during training, their complexity and computational demands reduce their suitability for everyday use in schools. MLR, despite slightly lower accuracy (R² = 0.897 on validation), proved to be the most practical due to its simplicity, transparency, and minimal resource requirements. Additional testing on schools in eastern Croatia confirmed its strong performance, with high R² and low MAPE values, reflecting the uniformity of heating systems that predominantly rely on gas or district heating. In contrast, prediction accuracy decreases in coastal regions where diverse fuels such as electricity or heating oil are used, indicating the need for region-specific models. Overall, the findings show that MLR is the most applicable model for widespread implementation, while ANN and RF offer potential for specialized cases or future enhancement. Full article

This work investigates how ultrasonic vibration can enhance friction stir welding (FSW) of an AA6082-T6 aluminium alloy and develops a data-driven tool to predict joint performance from process settings. A custom ultrasonic transducer and horn were designed and tuned using finite element modal and harmonic analyses, confirming a strong longitudinal resonance near 27.9 kHz with a tip amplitude of about 46 µm. A 27-run factorial experiment varied tool rotation (600–900 rpm), welding speed (45–55 mm/min), and plunge depth (0.10–0.25 mm). Welded joints were assessed using tensile strength and Vickers hardness. Four predictive models, support vector regression (SVR), Gaussian process regression (GPR), artificial neural networks (ANNs), and multiple linear regression (MLR) were trained and compared under five-fold cross-validation. The best joint quality was obtained at 900 rpm, 55 mm/min, and a 0.25 mm plunge depth, yielding a tensile strength of 188.7 MPa and a hardness of 102 HV. Overall, MLR provided the strongest predictive performance while remaining interpretable (UTS R² = 0.81, RMSE = 11.84 MPa; hardness R² = 0.67, RMSE = 2.36 HV), matching the ANN for UTS prediction and outperforming the ANN, GPR, and SVR for hardness. A coupling physics-based ultrasonic design with an interpretable predictive model offers a practical route to reduce trial and error, improve parameter selection, and accelerate the process development for ultrasonic vibration-assisted FSW of aluminium alloys; however, modest models can outperform complex ones when the dataset is limited. Full article

Melt-blown thermoplastic polyurethane (TPU) non-woven fabrics are increasingly valued in biomedical and industrial applications due to their elasticity, breathability, and skin compatibility. However, the relationship between their microscale fibrous network architecture and macroscopic properties remains insufficiently understood. This study investigates the influence of fiber diameter, porosity, and grammage on the mechanical properties of TPU non-woven fabrics through experimental characterization and finite element modeling (FEM). TPU pellets were melt-blown into fabrics with controlled structural variations, and their properties were analyzed using scanning electron microscopy (SEM), tensile testing, and air permeability measurements. Multiple linear regression (MLR) revealed that increased grammage predominantly enhances tensile strength (transverse β = 0.764, longitudinal β = 0.899), while fiber diameter and porosity affect transverse and longitudinal elongation in a different way. FEM simulations based on three-dimensional fiber networks further validated these relationships, demonstrating that increased porosity (e.g., from 85% to 92.5%) reduces tensile stress by over 50%, whereas larger fiber diameters (e.g., from 2 μm to 14 μm) decrease elongation at break by approximately 70%. By integrating experimental and computational approaches, this research provides valuable insights for optimizing TPU non-woven fabrics to meet specific performance requirements in wound dressings and other advanced applications. Full article

Accurate prediction of production dynamics in chemical composite cold production (CCCP) for heavy oil reservoirs remains challenging due to complex multi-phase fluid interactions and nonlinear flow regime transitions. Traditional numerical simulations are computationally expensive and rely heavily on detailed geological characterization. To address these limitations, a data-driven predictive framework integrating physical mechanisms with machine learning is proposed. A dual-driven feature selection strategy combining Spearman rank correlation and the Entropy Weight Method (EWM) was applied to quantify nonlinear parameter correlations and data informativeness, identifying injection-production balance and development and maximum adsorption capacity as dominant factors controlling oil production fluctuations. Latin Hypercube Sampling (LHS) was used to construct a representative parameter space, followed by weighted standardization. A Multiple Linear Regression (MLR) model was then trained to jointly predict key production indicators. Field validation shows strong predictive capability, with a coefficient of determination above 0.94 and relative fitting error below 5%. The method reduces computational time by over two orders of magnitude while maintaining high precision. Full article

Lightweight concrete incorporating expanded polystyrene (EPS) remains an active area of research due to its potential to produce more sustainable resource-efficient construction materials. However, identifying the optimal mix design for EPS-infused concrete typically requires extensive experimental trials, resulting in significant time, cost, and material consumption. To address this challenge, this study proposes an artificial neural network (ANN) predictive model with 5-fold cross-validation to estimate compressive strength performance and to develop mix design recommendations based on actual and predicted results. A total of 55 experimental samples were prepared and grouped into 11 batches, with the EPS volume replacement levels ranging from 0% to 50% at 5% increments. Model performance was evaluated using mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), coefficient of determination (R²), and scatter index (SI), with graphical representations like predicted vs. actual plots, response plots, and residual plots, and the results were benchmarked against a multiple linear regression (MLR) model. Among the tested configurations, the 4-5-1 ANN model demonstrated the highest predictive accuracy. Furthermore, a Shapley (SHAP) analysis was conducted to interpret the model behavior and determine the relative importance of the input variables. The findings reveal that EPS content had the greatest influence on compressive strength prediction, followed by slump value, then gravel content, and finally concrete density. Full article

Decarbonizing energy production is critical to slowing the effects of climate change and furthering global sustainability. Progress is often gauged via carbon dioxide (CO₂) emissions; however, sources of CO₂ vary beyond combustion, presenting a significant challenge to accurate tracking due to these various sources and sinks and the ubiquitous nature of CO₂ in the atmosphere. Nitrogen oxide (NO_X) emissions have previously been proposed as a surrogate for tracking sustainability, as they are primarily released from combustion processes. Facility-level data from Canada’s National Pollutant Release Inventory and Greenhouse Gas Reporting Program over a six-year period is used to assess the correlation between NO_X and CO₂ emissions from integrated facilities across Canada. Combustion-related CO₂ emissions accounting for approximately 94% of Canadian industrial emissions are examined, targeting eleven industries which together encompass over 90% of combustion emissions. Multiple linear regressions (MLRs) on each industry correlating NO_X, CO₂, and the inventory methods used (i.e., emission factors (EFs), source monitoring, mass balance, engineering estimates, and speciation) show R² values ranging from 0.81 to 0.96 for all but one industry. Several industries indicate that the methods used to calculate emissions influence the correlation of CO₂ to NO_X, highlighting issues in the current inventory techniques. The NO_X-to-CO₂ ratios calculated for the integrated facilities are similar to the ratios of the published main process-level EFs for NO_X to CO₂ (where available). These MLR models on NO_X could be used to predict CO₂ emissions with relative ease and accuracy in other jurisdictions, thereby simplifying large-scale emission inventory compilation while tracking sustainability. Full article

Robust quantification of greenhouse gas (GHG) balances in tea plantations is critical for evaluating their contribution to agricultural carbon neutrality. This study aimed to develop data-driven models to quantify soil organic carbon (SOC) sequestration and N₂O emissions in Chinese tea plantations, evaluate their net GHG balance at the national scale, and assess the mitigation potential under alternative nitrogen management scenarios. Using a comprehensive national dataset, we compared multiple machine learning (ML) approaches with a conventional multiple linear regression (MLR) model to simulate N₂O emissions and SOC changes in Chinese tea plantations. All ML models substantially outperformed the MLR model, with the Random Forest (RF) algorithm achieving the highest predictive accuracy. The RF models yielded R² values of 0.68 for N₂O emissions and 0.67 for SOC changes, with no significant prediction bias. Variable importance and marginal effect analyses revealed strong non-linear controls. Mineral N fertilizer input was the dominant driver of N₂O emissions, followed by organic N input, soil clay content, and SOC. In contrast, SOC dynamics were primarily regulated by organic carbon inputs, tea plantation age, climate variables, and soil pH. National-scale simulations indicated an average N₂O emission intensity of 9.03 kg N₂O ha⁻¹ yr⁻¹ and a mean SOC sequestration rate of 0.88 t C ha⁻¹ yr⁻¹. Overall, SOC sequestration offset N₂O emissions, rendering Chinese tea plantations a net GHG sink (−2525 Gg CO₂-eq yr⁻¹). Scenario analyses showed that mineral N reduction increased net GHG uptake by 1804 Gg CO₂-eq, while organic fertilizer substitution achieved a substantially larger mitigation potential of 5961 Gg CO₂-eq. By integrating SOC sequestration and N₂O emissions within a unified modeling framework and applying machine-learning-based national-scale simulations, this study provides a more comprehensive and data-driven quantification of GHG balances in tea ecosystems, offering a scientific basis for evaluating their role in agricultural carbon neutrality strategies. Full article

Atmospheric aerosols modulate Earth’s radiation balance through direct effects and through their role as cloud condensation nuclei (CCN), contributing to variability in near-surface temperature (NST). Galactic cosmic rays (GCRs) further influence aerosol–cloud interactions by enhancing particle formation and growth, but combined aerosol optical depth (AOD)–GCR effects on NST remain poorly constrained across climates. Using satellite and reanalysis data, we examine joint influences on NST anomalies at three neutron-monitoring stations, Oulu, Newark, and Hermanus, during 2000–2022. The sites share similar geomagnetic cutoffs but contrasting climates, enabling separation of ionization from geomagnetic shielding. Multiple linear regression (MLR) captures AOD effects and their modulation by GCR flux. Adding an interaction term (AOD × GCR) improves fit, raising adjusted R² from 0.22→0.31 (Oulu), 0.37→0.52 (Newark), and 0.69→0.78 (Hermanus). ECMWF reanalysis shows hydrophilic organic matter aerosol (OMA) dominates (0.19, 0.29, 0.41 µg kg⁻¹ at Oulu, Newark and Hermanus), with sulphate elevated at Oulu/Newark and coarse sea salt at Hermanus. Elevated OMA and sulphate at Oulu/Newark imply GCR-enhanced fine CCN and cooling, whereas humid, sea-salt-rich Hermanus favors ion-mediated growth of larger hygroscopic particles that increase longwave trapping and warming. Findings provide site-specific evidence that GCR ionization modulates aerosol processes and contributes to regional NST variability, informing improved parameterizations in climate models. Full article

Reliable estimation of extreme rainfall is essential for hydraulic design and flood risk mitigation, particularly in arid regions where rainfall exhibits strong temporal and spatial variability. This study presents a statistical framework for developing rainfall intensity-duration-frequency (IDF) curves, complemented by a machine learning-based assessment of model bias and performance. The analysis was conducted using data from ten rainfall stations located within or near the Wadi Al-Rummah Basin. Annual maximum series (AMS) from 1969 to 2024 were first reconstructed to address missing years using a modified normal ratio method (NRM) combined with nearest-station selection, ensuring spatial consistency while preserving station-specific rainfall characteristics. Six probability distributions (Weibull, Gumbel, gamma, lognormal, generalized extreme value (GEV), and generalized Pareto) were fitted to each station, and the best-fit distribution was identified using multiple goodness-of-fit (GOF) criteria, including the Kolmogorov–Smirnov (K-S) test, Anderson–Darling (A-D) test, root mean square error (RMSE), chi-square (χ²) statistic, Akaike information criterion (AIC), Bayesian information criterion (BIC), and the coefficient of determination (R²). Statistical IDF curves were then developed for durations ranging from 5 to 1440 min and return periods from 2 to 1000 years. To evaluate the robustness of the statistically derived IDF curves, three machine learning (ML) models, multiple linear regression (MLR), regression random forest (RRF), and multilayer feed-forward neural network (MFFNN), were trained as surrogate models using duration, return period, and station geographic attributes as predictor variables. Model performance was evaluated using RMSE, MAE, and mean bias metrics across stations and return periods. The lognormal distribution emerged as the best-fit model for four stations, while the Gumbel and gamma distributions were selected for two stations each. Overall, no single probability distribution consistently outperformed others, indicating station-dependent behavior. Among the machine learning models, the MFFNN achieved the closest agreement with statistical IDF estimates ($RMSE\approx 0.97$, $MAE\approx 0.65$, $bias\approx -0.02$), followed by RRF and MLR based on global average performance across all stations and return periods. The proposed framework offers a reliable approach for rainfall IDF development and evaluation in arid region watersheds. Full article

Background/Objectives: Serotonin transporter (SERT) inhibition represents a central pharmacological strategy in the treatment of major depressive disorder. In this study, an integrated computational framework combining quantitative structure–activity relationship (QSAR) modeling, molecular docking analysis, and in silico ADMET profiling was applied to identify and prioritize novel candidate structures. Methods: Conformation-independent QSAR models were developed using local molecular graph invariants and SMILES-based descriptors optimized through a Monte Carlo learning procedure, while a genetic algorithm–multiple linear regression (GA–MLR) was employed to derive statistically robust predictive models from a large descriptor pool. Model quality, robustness, and external predictivity were rigorously evaluated using multiple statistical validation criteria. In parallel, a field-based contribution analysis was applied to construct a three-dimensional QSAR model, enabling spatial interpretation of structure–activity relationships. Fragment-level contributions associated with activity enhancement or attenuation were subsequently identified and used to design new candidate inhibitor structures. Results: The designed compounds were further evaluated by molecular docking, InducedFit Docking and Binding Pose MetaDynamics (BPMD) into the SERT binding site, providing a structure-based assessment consistent with the trends observed in QSAR modeling. In addition, in silico ADMET analysis was performed to assess key pharmacokinetic and safety-related properties relevant to central nervous system drug development. Conclusions: The proposed workflow demonstrates the utility of combining data-driven QSAR modeling with structure-based and pharmacokinetic considerations to rationalize and prioritize novel serotonin transporter-focused scaffold optimization, offering a transferable strategy for early-stage antidepressant drug discovery. Full article

Precise timing of rice harvesting is critical for ensuring grain yield and quality. Traditional manual evaluation methods are highly subjective and time-consuming, highlighting the critical demand for rapid, non-destructive approaches to estimate rice maturity. This study focused on cold-region japonica rice grown in Heilongjiang Province, aiming to develop and validate dual-scale (pot and field) maturity estimation models. For model development, canopy spectral data were collected using two complementary acquisition tools: a ground-based active sensor (CGMD402) and UAV-borne multispectral imagery. Four modeling algorithms—Linear Regression (LR), Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM)—were utilized, with input variables comprising single spectral indices (Normalized Difference Vegetation Index, NDVI; Ratio Vegetation Index, RVI) and composite spectral indices (Normalized Difference Maturity Ratio Vegetation Index, NDMRVI; Normalized Difference Pigment Ratio Vegetation Index, NDPRVI). At the pot scale, composite spectral indices showed stronger correlations with rice maturity than single indices. Among the four algorithms, the DT model with combined NDVI + RVI input yielded the optimal comprehensive performance, with a coefficient of determination (R²) of 0.957, a root mean square error (RMSE) of 0.064, and a relative error (RE) of 4.8% in the test set. At the field scale, NDVI and RVI both exhibited strong negative correlations with maturity (Spearman’s correlation coefficients of −0.76 and −0.79, respectively). While the RF model performed best in the training set (R² = 0.752), it was prone to overfitting; in contrast, Multiple Linear Regression (MLR, Ridge Regression) with NDVI + RVI combination demonstrated greater stability in the test set (R² = 0.515, RMSE = 0.116). Notably, composite spectral indices consistently outperformed single indices across all modeling algorithms, but their accuracy was comparable to the optimal single index combination model. To tackle the challenge of scaling models from pot to field conditions, this research developed a “modeling–validation–evaluation–scaling” framework and a four-indicator combined judgment criterion (ΔR²–ΔRMSE–ΔRE–SF). Quantitative analysis showed that the optimal pot-scale model suffered significant accuracy loss during cross-scale transfer: ΔR² = 0.447, ΔRMSE = 0.120, ΔRE = 22.84%, and Scale Transfer Factor (SF) = 2.875. A “regional calibration + residual correction” scheme was proposed, which is expected to reduce the transferred RMSE to below 0.12 and SF to 1.8–2.0. Overall, this research offers a reliable technical method for large-scale, non-destructive monitoring of rice maturity, which can facilitate data-driven precision harvesting decisions. Full article