Search Results (786)

In-depth analysis of epidemic models, particularly for cholera, is crucial because they serve as significant tools for disease transmission prediction, evaluation of control strategies, and optimization of healthcare resource management. The stochastic models provide increased realism by incorporating environmental uncertainty such as variability in water quality, disparities in access to sanitation, and population mobility. The present work generalizes a deterministic two-patch cholera model to a stochastic framework. We first prove the existence and uniqueness of global solutions, then establish the extinction condition $R_0^{*}<1$ for disease eradication in the long term. A key contribution lies in proving the existence of a unique ergodic stationary distribution when $R_0^{\left(1\right)}>1$ and $R_0^{\left(2\right)}>1$. Furthermore, we derive the stochastic threshold $R_0=max\{R_0^{\left(1\right)},R_0^{\left(2\right)}\}$, which corresponds to the basic reproduction number ${\mathcal{R}}_0=max\{{\mathcal{R}}_0^{\left(1\right)},{\mathcal{R}}_0^{\left(2\right)}\}$. Lastly, numerical simulations are employed to confirm theoretical results. Full article

Grain moisture content is a key variable for safe storage, drying control, and quality management. Microwave sensing is attractive because water strongly modulates the complex relative permittivity (ε* = ε′ – jε″) of granular agricultural products, thereby shaping broadband scattering-parameter spectra. This study presents a meter-referenced feasibility evaluation of an interpretable S-parameter–permittivity–moisture chain using a vector network analyzer over 2–18 GHz. Wheat, maize, and mung bean were prepared at six moisture levels, and the moisture values were referenced to two commercial grain moisture meters (MC_ref) to represent rapid on-site benchmarking rather than absolute gravimetric moisture determination. Therefore, the reported errors should be interpreted as commercial-meter-referenced calibration indicators rather than absolute gravimetric moisture prediction accuracy. Two free-space configurations were compared on the same platform: a two-horn transmission setup under controlled packing and a metal-backed double-pass reflection setup intended to represent single-sided access under loose bulk packing. After SOLT calibration and empty-holder background normalization, ε′ and ε″ were retrieved via complex-domain nonlinear least-squares fitting of physics-based slab models to measured S21 spectra. The results show that moisture-dependent dielectric responses were grain- and configuration-dependent. In particular, ε″ generally provided a more robust moisture-sensitive feature in the free-space transmission configuration, whereas the optimal single-parameter predictor in the metal-backed configuration differed among grains. A mid-band frequency window of approximately 8–16 GHz provided more stable inversion by avoiding low-frequency coupling artefacts and high-frequency signal-to-noise degradation. The metal-backed configuration preserved moisture trends but yielded lower effective ε′ values, likely due to increased air fraction under loose packing. These results indicate that packing state, grain type, and frequency-window selection are critical factors for transferring microwave moisture calibration from laboratory measurements to practical grain-handling scenarios. Full article

Climate change and soil degradation pose a challenge to grape quality, motivating the development of integrated monitoring approaches combining soil analysis with remote sensing techniques. However, harmonized information addressing this multidisciplinary challenge remains scarce. Therefore, this systematic review synthesizes the scientific literature published since 2020 with the aim of (i) identifying key soil properties and techniques applied, (ii) evaluating remote sensing approaches and their integration with soil data, and (iii) highlighting knowledge gaps and challenges for sustainable precision viticulture. A search in Scopus yielded 197 full-text articles classified into three thematic groups and analyzed using a standardized extraction protocol. Our synthesis reveals that pH, electrical conductivity, soil organic matter, and cation exchange capacity are the most consistently reported physicochemical parameters across the reviewed studies, while next-generation sequencing and multi-omics approaches are increasingly adopted in microbiological research to characterize rhizosphere communities and their links to terroir expression. In remote sensing, multispectral UAV platforms and satellite missions (Sentinel-2, Landsat) combined with vegetation indices, principally NDVI, dominate the toolset for monitoring vine vigor and water status. Nevertheless, genuine integration of remote-sensing outputs with root-zone soil measurements remains uncommon, with most studies treating both data streams independently. The principal knowledge gaps identified concern the absence of standardized sustainability assessment frameworks, limited cross-terroir transferability of predictive models, and insufficient long-term multi-site datasets to underpin climate change adaptation in vineyard management. Full article

This study aimed to optimize vacuum frying parameters, frying temperature (80–120 °C) and frying time (10–20 min), using response surface methodology (RSM) to maximize the quality of rice crackers from black glutinous rice. Vacuum frying temperature and time had no significant (p > 0.05) effect on protein, fiber, total anthocyanin content, and total flavonoid content. An increase in frying temperature increased the expansion ratio and total phenolic content (TPC), while decreasing bulk density and DPPH. Extending frying time significantly (p ≤ 0.05) increased fat content. Increasing both frying temperature and time reduced hardness, moisture, and water activity, and significantly changed color. These trends were evaluated using regression models with R² values ranging from 0.858 to 0.999. Based on the developed models, the optimal condition was estimated at approximately 110 °C for 10 min, graphically predicting rice crackers with 23.32%db fat, hardness of 4.83 N, and TPC of 2.63 mg GAE/g. Compared with atmospheric frying (160 °C, 10 min), the optimal vacuum frying condition (110 °C, 10 min) reduced fat by 36.16%, decreased hardness by 68.65%, and increased TPC by 95.49%, suggesting that vacuum frying can produce black glutinous rice crackers with lower fat, higher antioxidant compounds, and greater crispiness under these specific parameters. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article

Accurate prediction of contaminant transport and self-purification processes in rivers remains challenging because pollutant dispersion, biochemical reactions, and hydrodynamic conditions interact across multiple spatial scales. This study aims to develop and compare mathematical models for soluble contaminant transport and biodegradable organic matter removal in channels and rivers. Unsteady advection–diffusion–reaction equations were formulated for one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) transport scenarios and solved through numerical techniques based on the transformation of partial differential equations into systems of ordinary differential or algebraic equations. In parallel, the classical Streeter–Phelps model and an extended formulation incorporating turbulent diffusion were implemented to evaluate organic load degradation and oxygen deficit dynamics. Simulations were performed using a Matlab R2019a-based computational framework under representative hydraulic and reaction conditions obtained from literature data and empirical correlations. The results showed that, under specific conditions, the 3D model reproduced trends comparable to those predicted by the 2D model, while the latter approached the behavior of the 1D formulation. The Streeter–Phelps model predicted an organic load removal efficiency of 97.74%, a purification index of 1.9564, a critical time of 18.43 h, and a critical distance of 6.93 km. These findings provide a useful framework for river water-quality assessment and support future applications involving complex hydrodynamic and pollutant-loading scenarios. Full article

This study investigates the valorization of construction and demolition (C&D) waste streams and an industrial by-product for sustainable concrete production. Recycled concrete aggregates (RCA) and recycled brick aggregates (RBA), derived from C&D wastes, together with pelletized recycled fly ash aggregates (FAA) produced from thermal power plant fly ash, were used as total replacements for natural coarse aggregates. Six concrete mixtures were prepared at a constant water-to-cement ratio of 0.50 using untreated and slag slurry–treated aggregates. A slag slurry-based two-stage mixing approach (TSMA), incorporating ground granulated blast furnace slag (GGBFS), was applied as a practical and potentially scalable treatment method to enhance aggregate quality and interfacial bonding. The results show that complete replacement of natural aggregates reduced fresh concrete unit weight by up to 17%, while meeting the minimum compressive strength requirements for structural applications. Slag slurry treatment led to statistically significant improvements in mechanical properties, reduced variability, and enhanced overall reliability. In addition, widely used code-based prediction models (TS500, ACI, Eurocode-2, NZS 3101-1:2006, and CSA A23.3-04), originally developed for conventional concrete, were evaluated for their applicability in estimating key mechanical properties of recycled and by-product aggregate concretes, and alternative regression-based models were developed to improve prediction accuracy. Overall, the findings demonstrate the potential for effective utilization of C&D wastes and industrial by-products in structural concrete, contributing to resource efficiency and reduced reliance on natural aggregates. Full article

This study presents an integrated computational framework for quantifying industrial impacts on marine ecosystems through the combined assessment of multiple environmental quality indices. The Aquatic Water Quality Index (AWQI) and four diagnostic pollution indices, namely the Heavy Metal Pollution Index (HPI), Metal Index (MI), Degree of Contamination (C_d), and Pollution Index (PI), were applied across 23 offshore sites in Mesaieed Industrial City, Qatar, to establish a high-resolution baseline for evaluating the effects of industrial effluents and brine discharge. Multivariate statistical analyses, including Principal Component Analysis (PCA) and Cluster Analysis (CA), identified Cr, Pb, Mn, Ni, and Zn as the principal drivers of water quality variability, effectively distinguishing anthropogenic influences from natural background conditions. To enable rapid and automated marine environmental assessment, three machine learning models—Artificial Neural Networks (ANN), Random Forest (RF), and Decision Trees (DT)—were developed and evaluated for predicting the investigated indices. Model performance was assessed through rigorous training–testing validation and the Diebold–Mariano test. The results demonstrated that model selection significantly influences predictive accuracy. Among the evaluated algorithms, RF achieved the highest predictive performance for AWQI (R² = 0.88) and C_d (R² = 0.92), whereas ANN performed best for HPI (R² = 0.89), and DT yielded the most accurate predictions for MI (R² = 0.82). Despite the index-specific strengths of individual models, RF emerged as the most robust and generalizable approach, consistently providing superior performance across heterogeneous environmental datasets. The proposed framework advances marine water quality assessment from conventional descriptive monitoring toward a proactive, data-driven paradigm, offering a scalable and cost-effective decision support tool for environmental management, pollution mitigation, and evidence-based coastal governance in industrialized coastal regions. Full article

Loquat (Eriobotrya japonica) is a commercially important subtropical fruit, and its internal sweetness is an important indicator of market quality. Accurate and non-destructive determination of total soluble solids content (TSSC) is therefore essential for fruit grading and quality evaluation. In this study, short-wave infrared hyperspectral imaging (1000–2400 nm) was combined with a multi-scale spectral attention adaptive convolutional neural network (MSSA-ACNN) for rapid TSSC prediction. Spectral data were preprocessed using an SG-MSC-DT strategy to reduce noise and scattering effects, while conventional models (PLSR, Ridge, and SVM) were used for comparison. The proposed model combines multi-scale feature extraction with a dual-path attention mechanism, enabling adaptive enhancement of informative chemical wavebands while suppressing irrelevant variations. Experimental results, rigorously validated through a 5-fold cross-validation strategy, demonstrated that the proposed approach achieved the best predictive performance, with an R_p² of 0.942, RMSEP of 0.505, and RPD of 3.091, outperforming traditional methods. In addition, attention weight analysis revealed that the model mainly focused on spectral regions associated with water and carbohydrate absorption, indicating consistency between the learned features and known chemical information. These results suggest that the proposed method provides an effective and interpretable approach for non-destructive evaluation of loquat quality and shows potential for application in intelligent fruit grading systems. Full article

To support refined water quality evaluation and short-term early warning in multi-section river systems, this study developed three percentile-based composite indices: the Water Quality Index (WQI), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and the Entropy Weight Method (EWM). Monthly multi-parameter monitoring data from 2019 to 2025 were used, covering ten river sections (P1–P5, M1–M5). The three indices were compared in terms of statistical distribution, methodological consistency, and anomaly response. An integrated assessment–prediction framework was further established. Within this framework, a one-step prediction scheme was applied to evaluate four models: Long Short-Term Memory networks (LSTM), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost). The results show that WQI scores were generally high and fluctuated within a narrow range. A clear “ceiling effect” was observed in the moderate-to-high grade intervals. WQI also showed weak consistency with TOPSIS and EWM (r ≈ 0.29–0.32). In contrast, TOPSIS and EWM were more sensitive to water quality fluctuations and extreme risks, and were moderately correlated with each other (r ≈ 0.53). Using TOPSIS < 50 as the threshold, 49 severe anomalous events were identified. These events were mainly clustered in February–April 2020, April–July 2023, and June–September 2025, with sections P4, M1, and M2 acting as high-incidence sites. In several typical events, WQI values remained high, indicating that reliance on WQI alone may delay early warning. Prediction results further reveal that the choice of index strongly affects sequence predictability. Taking XGBoost as the reference, the median validation R² followed a stable gradient: WQI (0.807) > TOPSIS (0.723) > EWM (0.594). XGBoost yielded positive R² values across all indices and sections. It also achieved the most robust overall performance and the strongest cross-site, cross-index generalization capability. Full article

Expanded snack extrusion is governed by tightly coupled interactions among raw material composition, moisture, barrel temperature, screw speed, feed rate, screw configuration, die geometry, and energy input. These variables affect not only final responses such as expansion ratio, bulk density, hardness, crispness, and water absorption or solubility indices, but also intermediate state variables including specific mechanical energy (SME), melt temperature, die pressure, melt viscosity, and bubble growth dynamics. As a result, modelling has become essential for product design, process optimisation, and scale-up. This review critically evaluates the major classes of models used to describe process–structure–quality relationships in the extrusion of expanded snacks. The literature shows that empirical regression and response surface methodology (RSM) remain the most widely applied tools because they are experimentally efficient and easy to interpret. However, mixture-process designs are more appropriate when formulation and operating variables are changed simultaneously, while phenomenological and mechanistic approaches provide better physical insight into expansion and structure development. More recently, machine-learning and interpretable artificial intelligence approaches have demonstrated strong predictive capability when large, well-curated datasets are available. Across model families, a consistent theme is that operating variables act on final product quality through intermediate process state variables rather than independently. On that basis, this review proposes a practical hybrid framework for expanded snack extrusion: a mixture-process quadratic model augmented with SME, die pressure, melt temperature and shear-related state variables, and structured in three levels linking (i) controllable inputs to state variables, (ii) state variables to measurable quality attributes, and (iii) quality attributes to a gold-standard product target or sensory-control criterion. Such a model offers a realistic balance between predictive performance, physical interpretability, experimental burden, and industrial usefulness, while also providing a clear pathway toward future digital twin and machine-learning-enabled optimisation. Full article

A predictive gate-to-gate life cycle assessment (LCA) of plasma-assisted ammonia synthesis at TRL 4 is presented according to ISO 14040/44 standards. General plasma-assisted synthesis was evaluated through a mini-review‚ sensitivity analysis‚ and predictive LCA. The specific DBD needle-to-plate configuration LCA is performed using previously published experimental data. Two distinct scenarios were investigated. In the literature-based baseline scenario derived from sensitivity analysis, electricity consumption was 533 kWh/kg NH₃, giving a carbon footprint of 26.65–639.60 kg CO₂-eq/kg NH₃; electricity contributed 98.5% of total emissions, and impacts remained about 2.05 times higher than conventional Haber–Bosch. In contrast, the experimental DBD case study required 63,450 kWh/kg NH₃, showing reactor efficiency as the dominant driver of environmental performance. The BCS (≈1.39 kWh/kg NH₃) suggests that optimized plasma systems could potentially surpass conventional ammonia synthesis in energy efficiency. The environmental performance of plasma-assisted ammonia synthesis is affected by NH₃, NO_x, N₂O, and hydrogen emissions due to impacts on climate, air quality, water systems, and biodiversity. Future improvements may come from reactor and electrode optimization, catalyst integration, alternative plasma sources, and better process and heat integration, although deployment will likely depend on major efficiency gains and may be limited to niche decentralized applications. Full article

Multivariate time-series anomaly detection on physical sensor networks faces three challenges that generic deep learning models inadequately addressed: heterogeneous sensor reliability, context-dependent anomaly scoring, and inactionable binary outputs lacking per sensor attribution. We propose an uncertainty-aware Temporal Convolutional Network (TCN) framework built on two tightly integrated uncertainty-driven components: (i) an Adaptive Uncertainty-Aware Attention (AUAA) mechanism that gates temporal attention weights by per sensor predictive uncertainty obtained from Monte Carlo dropout; and (ii) a Dynamic Weight Adapter that learns context-sensitive blending of reconstruction error and uncertainty via a GRU over weight history. The architecture also includes an exploratory per sensor attribution head, which we audit rather than claim: a controlled-perturbation test shows it is not yet causally faithful. We complement the empirical architecture with two distribution-free theoretical results: a Chebyshev-type false-positive bound on the hybrid anomaly score, and a Monte Carlo posterior moment convergence result at rate $O\left(M^{-1/2}\right)$. Evaluated on four-month indoor air quality sensor data, the Full Enhanced model achieves $R^2=0.9988$ and MSE $1.65\times {10}^{-4}$, a $25.2\%$ MSE reduction over the Base TCN ($R^2=0.9984$, MSE $2.20\times {10}^{-4}$). Because the IAQ stream is unlabeled, the primary quantitative detection evaluation uses the labeled Skoltech Anomaly Benchmark (SKAB), a publicly available industrial water-circulation corpus disjoint from the IAQ training distribution; it yields an 8.8 × F1 advantage ($0.477$ vs. $0.054$) and a 14.4 × recall advantage ($0.418$ vs. $0.029$) for the proposed model configuration over the Base TCN at a validation-calibrated threshold applied without retuning. Against twelve established detectors under a unified protocol, the proposed model attains the best F1 and recall, while the strongest reconstruction baselines retain higher precision and a marginally higher ROC-AUC, a recall-driven trade-off. Ablation isolates each component’s contribution, the detector degrades gracefully under channel masking and noise, and the distribution-free false-positive bound is empirically respected. The framework retains a low inference cost ($0.16$ ms per window at $M=20$ Monte Carlo samples, including the uncertainty pass). Full article

This study investigates the development of a functional hummus enriched with black cumin seed oil (Nigella sativa) and evaluates its physicochemical properties and oxidative stability during 21 days of refrigerated storage. Additionally, the applicability of near-infrared (NIR) spectroscopy as a rapid and non-destructive analytical tool for hummus quality assessment was examined. Hummus samples were prepared by partially replacing olive oil with black cumin seed oil at levels of 4, 6, 8, and 12% (v/v). Chemical composition, peroxide value, and water activity were monitored over time, while multivariate statistical methods (Principal Component Analysis and Partial Least Squares Regression) were used to correlate NIR spectral data with reference measurements. The results showed that the incorporation of black cumin seed oil did not significantly affect the overall macronutrient composition but altered the fatty acid profile by increasing the content of polyunsaturated fatty acids. Oxidative changes were observed during storage, with peroxide values increasing after day 7, while samples with higher levels of black cumin seed oil exhibited improved oxidative stability in later stages. Water activity remained constant across all formulations. NIR spectroscopy demonstrated high predictive accuracy for fat, protein, carbohydrate, and dietary fiber content (R² > 0.99), while lower performance was observed for water activity and dry matter. The findings confirm the potential of NIR spectroscopy for rapid quality monitoring of functional plant-based spreads. This study highlights the feasibility of developing a functional hummus enriched with black cumin seed oil and supports the application of NIR spectroscopy as an efficient tool for monitoring compositional and oxidative changes during storage. Full article

Groundwater resources in the Kingdom of Saudi Arabia (KSA) are important for meeting the needs of human communities, agriculture, and industry. In Western KSA, groundwater from coastal aquifers is an essential resource that complements desalinated seawater. Therefore, ensuring the quality and contamination of groundwater has emerged as a critical priority for preserving water security. The aim of this research is to evaluate the groundwater quality and its vulnerability to contamination within the Wadi Marawani Basin. To achieve this aim, water quality indices (WQIs), the DRASTIC model, and machine learning (ML) algorithms were employed alongside a Geographic Information System (GIS). The results of the chemical analysis of 64 water samples were used in these assessments. Furthermore, several input parameters were evaluated using the DRASTIC model to estimate the DRASTIC index (DI) and generate a groundwater vulnerability map. Three ML algorithms—specifically, a Multilayer Perceptron (MLP), a Random Forest (RF), and a Decision Tree (DT)—were utilized to forecast WQIs such as the total dissolved solids (TDS) and sodium adsorption ratio (SAR), in addition to the DRASTIC index (DI). The results revealed that around 36% of the samples were classified as fresh water (<1000 mg/L). The SAR ranged from 1.10 to 32.50, indicating that most samples were suitable for irrigation. Approximately 22% of the basin was classified as demonstrating high vulnerability, whereas about 78% demonstrated low-to-moderate vulnerability. Assessment of the ML models showed high predictive accuracy for the TDS, SAR, and DI. The MLP-Vul. model attained an R² value of 1.00 and RMSE value of 0.01, the RF-Vul. model achieved an R² of 0.94 and RMSE of 3.17, and the DT-Vul. model attained an R² of 0.92 and RMSE of 3.57. Although there was a minor increase in RMSE across all models during the testing phase, their predictive performance remained clear. Full article