Search Results (851)

Oil spill simulation models are essential for predicting the oil spill behavior and movement in marine environments. In this study, we comprehensively reviewed a large and diverse body of peer-reviewed literature obtained from Scopus and Web of Science. Our initial analysis phase focused on examining trends in scientific publications, utilizing the complete dataset derived after systematic screening and database integration. In the second phase, we applied elements of a systematic review to identify and evaluate the most influential contributions in the scientific field of oil spill simulations. Our analysis revealed a steady and accelerating growth of research activity over the past five decades, with a particularly notable expansion in the last two. The field has also experienced a marked increase in collaborative practices, including a rise in international co-authorship and multi-authored contributions, reflecting a more global and interdisciplinary research landscape. We cataloged the key modeling frameworks that have shaped the field from established systems such as OSCAR, OIL-MAP/SIMAP, and GNOME to emerging hybrid and Lagrangian approaches. Hydrodynamic models were consistently central, often integrated with biogeochemical, wave, atmospheric, and oil-spill-specific modules. Environmental variables such as wind, ocean currents, and temperature were frequently used to drive model behavior. Geographically, research has concentrated on ecologically and economically sensitive coastal and marine regions. We conclude that future progress will rely on the real-time integration of high-resolution environmental data streams, the development of machine-learning-based surrogate models to accelerate computations, and the incorporation of advanced biodegradation and weathering mechanisms supported by experimental data. These advancements are expected to enhance the accuracy, responsiveness, and operational value of oil spill modeling tools, supporting environmental monitoring and emergency response. Full article

Landslides are among the most hazardous natural phenomena affecting Greater Abidjan, causing significant economic and social damage. Strategic planning supported by geographic information systems (GIS) can help mitigate potential losses and enhance disaster resilience. This study evaluates landslide susceptibility using logistic regression and frequency ratio models. The analysis is based on a dataset comprising 54 mapped landslide scarps collected from June 2015 to July 2023, along with 16 thematic predictor variables, including altitude, slope, aspect, profile curvature, plan curvature, drainage area, distance to the drainage network, normalized difference vegetation index (NDVI), and an urban-related layer. A high-resolution (5-m) digital elevation model (DEM), derived from multiple data sources, supports the spatial analysis. The landslide inventory was randomly divided into two subsets: 80% for model calibration and 20% for validation. After optimization and statistical testing, the selected thematic layers were integrated to produce a susceptibility map. The results indicate that 6.3% (0.7 km²) of the study area is classified as very highly susceptible. The proportion of the sample (61.2%) in this class had a frequency ratio estimated to be 20.2. Among the predictive indicators, altitude, slope, SE, S, NW, and NDVI were found to have a positive impact on landslide occurrence. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), demonstrating strong predictive capability. These findings can support informed land-use planning and risk reduction strategies in urban areas. Furthermore, the prediction model should be communicated to and understood by local authorities to facilitate disaster management. The cost function was adopted as a novel approach to delineate hazardous zones. Considering the landslide inventory period, the increasing hazard due to climate change, and the intensification of human activities, a reasoned choice of sample size was made. This informed decision enabled the production of an updated prediction map. Optimal thresholds were then derived to classify areas into high- and low-susceptibility categories. The prediction map will be useful to planners in helping them make decisions and implement protective measures. Full article

In recent years, recovering precious and base metals such as silver and copper from end-of-life products has become a fundamental factor in the sustainable development of many countries. This not only supports environmental goals but is also a profitable economic activity. Therefore, in this study, we investigate the recovery of silver and copper from an end-of-life photovoltaic panel powder using an alternative leaching system containing sulfuric acid and ferric sulfate instead of nitric acid-based leaching systems, which are susceptible to producing hazardous gases such as NO_x. To obtain this goal, a series of experiments were designed with the Central Composite Design (CCD) approach using Response Surface Methodology (RSM) to evaluate the effect of reagent concentrations on the leaching rate. The leaching results showed that high recovery rates of silver (>85%) and copper (>96%) were achieved at room temperature using a solution containing only 0.2 M sulfuric acid and 0.15 M ferric sulfate. Analysis of variance was applied to the leaching data for silver and copper recovery, resulting in two statistical models that predict the leaching efficiency based on reagent concentrations. Results indicate that the models are statistically significant due to their high R² (0.9988 and 0.9911 for Ag and Cu, respectively) and the low p-value of 0.0043 and 0.0003 for Ag and Cu, respectively. The models were optimized to maximize the dissolution of silver and copper using Design Expert software. Full article

Background/Objectives: In the last few decades, the dengue virus, a prevalent flavivirus, has demonstrated various epidemiological, economic, and health impacts around the world. Dengue virus serotype 2 (DENV2) plays a vital role in dengue-associated mortality. The RNA-dependent RNA polymerase (RdRp) of DENV2 is a charming druggable target owing to its crucial function in viral reproduction. In recent years, streptomycetes natural products (NPs) have attracted considerable attention as a potential source of antiviral drugs. Methods: Seeking prospective inhibitors that inhibit the DENV2 RdRp allosteric site, in silico mining of the Streptome database was executed. AutoDock4.2.6 software performance in predicting docking poses of the inspected inhibitors was initially conducted according to existing experimental data. Upon the assessed docking parameters, the Streptome database was virtually screened against DENV2 RdRp allosteric site. The streptomycetes NPs with docking scores less than the positive control (68T; calc. −35.6 kJ.mol⁻¹) were advanced for molecular dynamics simulations (MDS), and their binding affinities were computed by employing the MM/GBSA approach. Results: SDB9818 and SDB4806 unveiled superior inhibitor activities against DENV2 RdRp upon MM/GBSA//300 ns MDS than 68T with ΔG_binding values of −246.4, −242.3, and −150.6 kJ.mol⁻¹, respectively. A great consistency was found in both the energetic and structural analyses of the identified inhibitors within the DENV2 RdRp allosteric site. Furthermore, the physicochemical characteristics of the identified inhibitors demonstrated good oral bioavailability. Eventually, quantum mechanical computations were carried out to evaluate the chemical reactivity of the identified inhibitors. Conclusions: As determined by in silico computations, the identified streptomycetes NPs may act as DENV2 RdRp allosteric inhibitors and mandate further experimental assays. Full article

To address the challenges posed by the instability of renewable energy output and load fluctuations on grid operations and to support the low-carbon sustainable development of the energy system, this paper integrates artificial intelligence technology to establish an economic stability dispatch framework for distributed resource aggregators. A phased multi-contract collaborative scheduling model oriented toward sustainable development is proposed. Through intelligent algorithms, the model dynamically optimises decisions across the day-ahead and intraday phases: During the day-ahead scheduling phase, intelligent algorithms predict load demand and energy output, and combine with elastic performance-based response contracts to construct a user-side electricity consumption behaviour intelligent control model. Under the premise of ensuring user comfort, the model generates a 24 h scheduling plan with the objectives of minimising operational costs and efficiently integrating renewable energy. In the intraday scheduling phase, a rolling optimisation mechanism is used to activate energy storage capacity contracts and dynamic frequency stability contracts in real time based on day-ahead prediction deviations. This efficiently coordinates the intelligent frequency regulation strategies of energy storage devices and electric vehicle aggregators to quickly mitigate power fluctuations and achieve coordinated control of primary and secondary frequency regulation. Case study results indicate that the intelligent optimisation-driven multi-contract scheduling model significantly improves system operational efficiency and stability, reduces system operational costs by 30.49%, and decreases power purchase fluctuations by 12.41%, providing a feasible path for constructing a low-carbon, resilient grid under high renewable energy penetration. Full article

Background/Objectives: Traumatic brain injury (TBI) is a global public health concern, affecting over 60 million people annually. It is associated with high rates of mortality and disability, particularly among young and economically active individuals, and remains the leading cause of death in people under 40 years of age. Although computed tomography (CT) is the standard method for excluding intracranial bleeding (ICB), it is frequently unavailable in resource-limited settings where the burden of TBI is greatest. The InfraScanner 2000 is a near-infrared spectroscopy (NIRS) device designed to detect ICB and may serve as a triage tool in environments without access to CT imaging. This study aimed to evaluate the diagnostic performance of the InfraScanner 2000 for detecting ICB in the emergency department (ED) of a trauma center in a cohort of Colombian patients with TBI. Methods: This prospective study was conducted in Cali, Colombia, between December 2019 and February 2021. Adult patients presenting to the ED with blunt TBI were enrolled. InfraScanner assessments were performed according to a standardized protocol, and all participants underwent head CT within 6 h of injury. Results: A total of 140 patients were included. Of these, 66% were male and 34% were female. Most patients (63.57%) were between 18 and 39 years old, with a median age of 39 years (IQR: 18–86). The InfraScanner demonstrated a sensitivity of 60.0% (95% CI: 32.5–84.8), specificity of 78.4% (95% CI: 71.2–85.6), positive predictive value (PPV) of 25.0%, and negative predictive value (NPV) of 94.2% for detecting ICB. Conclusions: The InfraScanner 2000 showed good specificity and high NPV in identifying ICB among Colombian patients with TBI. These findings suggest it could serve as a useful triage tool to support decision-making in emergency settings with limited access to CT imaging. Full article

The discharge of synthetic dyes in industrial wastewaters poses a serious environmental threat as they are difficult to degrade naturally and are harmful to aquatic organisms. This study aimed to evaluate the feasibility of using clean untreated rice husk (CRH) as a sustainable and low-cost adsorbent for the removal of methylene blue (MB) from synthetic wastewater. This approach effectively avoids the energy-intensive grinding process by directly using whole unprocessed rice husk, highlighting its potential as a sustainable and cost-effective alternative to activated carbon. A series of batch adsorption experiments were conducted to evaluate the effects of key operating parameters such as initial dye concentration, contact time, pH, ionic strength, and temperature on the adsorption performance. Adsorption kinetics, isotherm models, and thermodynamic analysis were applied to elucidate the adsorption mechanism and behavior. The results showed that the maximum adsorption capacity of CRH for MB was 5.72 mg/g. The adsorption capacity was stable and efficient between pH 4 and 10, and reached the highest value at pH 12. The presence of sodium ions (Na⁺) and calcium ions (Ca²⁺) inhibited the adsorption efficiency, with calcium ions having a more significant effect. Kinetic analysis confirmed that the adsorption process mainly followed a pseudo-second-order model, suggesting the involvement of a chemisorption mechanism; notably, in the presence of ions, the Elovich model provided better predictions of the data. Thermodynamic evaluation showed that the adsorption was endothermic (ΔH° > 0) and spontaneous (ΔG° < 0), accompanied by an increase in the disorder of the solid–liquid interface (ΔS° > 0). The calculated activation energy (Ea) was 17.42 kJ/mol, further supporting the involvement of chemisorption. The equilibrium adsorption data were well matched to the Langmuir model at high concentrations (monolayer adsorption), while they were accurately described by the Freundlich model at lower concentrations (surface heterogeneity). The dimensionless separation factor (RL) confirmed that the adsorption process was favorable at all initial MB concentrations. The results of this study provide insights into the application of agricultural waste in environmental remediation and highlight the potential of untreated whole rice husk as a sustainable and economically viable alternative to activated carbon, which can help promote resource recovery and pollution control. Full article

The accurate early-stage estimation of environmental load (EL) and construction cost (CC) in road infrastructure projects remains a significant challenge, constrained by limited data and the complexity of construction activities. To address this, our study proposes a machine learning-based predictive framework utilizing artificial neural networks (ANNs) and deep neural networks (DNNs), enhanced by autoencoder-driven feature selection. A structured dataset of 150 completed national road projects in South Korea was compiled, covering both planning and design phases. The database focused on 19 high-impact sub-work types to reduce noise and improve prediction precision. A hybrid imputation approach—combining mean substitution with random forest regression—was applied to handle 4.47% missing data in the design-phase inputs, reducing variance by up to 5% and improving data stability. Dimensionality reduction via autoencoder retained 16 core variables, preserving 97% of explanatory power while minimizing redundancy. ANN models benefited from cross-validation and hyperparameter tuning, achieving consistent performance across training and validation sets without overfitting (MSE = 0.06, RMSE = 0.24). The optimal ANN yielded average error rates of 29.8% for EL and 21.0% for CC at the design stage. DNN models, with their deeper architectures and dropout regularization, further improved performance—achieving 27.1% (EL) and 17.0% (CC) average error rates at the planning stage and 24.0% (EL) and 14.6% (CC) at the design stage. These results met all predefined accuracy thresholds, underscoring the DNN’s advantage in handling complex, high-variance data while the ANN excelled in structured cost prediction. Overall, the synergy between deep learning and autoencoder-based feature selection offers a scalable and data-informed approach for enhancing early-stage environmental and economic assessments in road infrastructure planning—supporting more sustainable and efficient project management. Full article

With rapid socio-economic development, excessive anthropogenic consumption and the exploitation of natural resources have impaired the self-healing, supply, and carrying capacities of ecosystems. The assessment and prediction of ecosystem service values (ESVs) are crucial for the coordinated development of ecology and economy. This research examines the Xiangjiang River Basin and combines land use data from 1995 to 2020, Landsat images, meteorological data, and socio-economic data. These data are incorporated into the PLUS model to simulate land use patterns in 2035 under the following five scenarios: natural development, economic development, farmland protection, ecological protection, and coordinated development. Additionally, this research analyzes the dynamics of land use and changes in ESVs in the Xiangjiang River Basin. The results show that between 1995 and 2020 in the Xiangjiang River Basin, urbanization accelerated, human activities intensified, and the construction land area expanded significantly, while the areas of forest, farmland, and grassland decreased continuously. Based on multi-scenario simulations, the ESV showed the largest and smallest declines under economic development and ecological protection scenarios, respectively. This results from the economic development scenario inducing a rapid expansion in construction land. In contrast, construction land expansion was restricted under the ecological protection scenario, because the ecological functions of forests and water bodies were prioritized. This research proposes land use strategies to coordinate ecological protection and economic development to provide a basis for sustainable development in the Xiangjiang River Basin and constructing a national ecological security barrier, as well as offer Chinese experience and local cases for global ecological environment governance. Full article

Sports stadiums significantly influence urban morphology; however, empirical quantification of these effects remains limited. This study quantitatively examines the spatiotemporal relationship between sports architecture and urban functional evolution using Jiangwan Stadium in Shanghai—China’s first Western-style sports facility—as a case study. Employing Point of Interest (POI) data, ArcGIS spatial analyses, chi-square tests, and linear regression-based predictive modeling, we illustrate how the stadium has catalyzed urban regeneration and functional diversification over nearly a century. Our findings demonstrate a transition from sparse distributions to concentrated commercial and service clusters within a 1000 m radius around the stadium, notably in food and beverage, shopping, finance, insurance, and transportation sectors, significantly boosting local economic vitality. The area achieved peak functional diversity in 2016, showcasing a balanced integration of residential, commercial, and service activities. This research provides actionable insights for urban planners and policymakers on leveraging sports facilities to foster sustainable urban regeneration. Full article

Wild-growing edible mushrooms are known to bioaccumulate radionuclides from their environment, particularly the natural isotope potassium-40 (⁴⁰K) and anthropogenic cesium-137 (¹³⁷Cs). However, region-specific data for commercially relevant species in north-eastern Poland remain limited, despite the cultural and economic importance of mushroom foraging and export. This study aimed to assess the radiological safety of wild mushrooms intended for human consumption, with particular attention to regulatory compliance and potential exposure levels. In this study, 230 mushroom samples representing 19 wild edible species were analyzed using gamma spectrometry, alongside composite soil samples collected from corresponding foraging sites. The activity concentration of ¹³⁷Cs in mushrooms ranged from 0.94 to 159.0 Bq/kg fresh mass (f.m.), and that of ⁴⁰K from 64.4 to 150.2 Bq/kg f.m. None of the samples exceeded the regulatory limit of 1250 Bq/kg f.m. for ¹³⁷Cs. The highest estimated annual effective dose was 2.32 µSv from ¹³⁷Cs and 0.93 µSv from ⁴⁰K, with no exceedance of regulatory limits observed in any sample. A strong positive correlation was observed between ¹³⁷Cs activity in soil and mushroom dry mass (Spearman’s Rho = 0.81, p = 0.042), supporting predictable transfer patterns. Additionally, the implications of mushroom drying were assessed considering Council Regulation (Euratom) 2016/52, which mandates radionuclide levels in dried products be evaluated based on their reconstituted form. After such adjustment, even the most contaminated dried samples were found to comply with food safety limits. These findings confirm the radiological safety of wild mushrooms from north-eastern Poland and contribute novel data for a region with limited prior monitoring, in the context of current food safety regulations. Full article

Maximizing the power output of wind farms is critical for improving the economic viability and grid integration of renewable energy. Active wake control (AWC) strategies, such as yaw-based wake steering, offer significant potential for power generation increase but require predictive models that are both accurate and computationally efficient for real-time implementation. This paper proposes a data-driven observer to rapidly estimate the potential power gain achievable through AWC as a function of the ambient wind direction. The approach is rooted in Koopman operator theory, which allows a linear representation of nonlinear dynamics. Specifically, a model is developed using an Input–Output Extended Dynamic Mode Decomposition framework combined with Sparse Identification (IOEDMDSINDy). This method lifts the low-dimensional wind direction input into a high-dimensional space of observable functions and then employs iterative sparse regression to identify a minimal, interpretable linear model in this lifted space. By training on offline simulation data, the resulting observer serves as an ultra-fast surrogate model, capable of providing instantaneous predictions to inform online control decisions. The methodology is demonstrated and its performance is validated using two case studies: a 9-turbine and a 20-turbine wind farm. The results show that the observer accurately captures the complex, nonlinear relationship between wind direction and power gain, significantly outperforming simpler models. This work provides a key enabling technology for advanced, real-time wind farm control systems. Full article

Driven by dual carbon targets, the scale of wind power integration has surged dramatically. However, its strong volatility causes insufficient short-term prediction accuracy, severely constraining grid security and economic dispatch. To address three key challenges in extracting temporal characteristics of strong volatility, adaptive fusion of multi-source features, and enhancing model interpretability, this paper proposes a Time-Domain Dual-Channel Adaptive Learning Model (TDDCALM). The model employs dual-channel feature decoupling: one Transformer encoder layer captures global dependencies while the raw state layer preserves local temporal features. After TCN-based feature compression, an adaptive weighted early fusion mechanism dynamically optimizes channel weights. The ACON adaptive activation function autonomously learns optimal activation patterns, with fused features visualized through visualization techniques. Validation on two wind farm datasets (A/B) demonstrates that the proposed method reduces RMSE by at least 8.89% compared to the best deep learning baseline, exhibits low sensitivity to time window sizes, and establishes a novel paradigm for forecasting highly volatile renewable energy power. Full article

As digital transformation becomes an increasingly central focus of national and regional policy agendas, parallel efforts are intensifying to stimulate innovation as a critical driver of firm competitiveness and high-quality economic growth. However, regional disparities in innovation capacity persist. This study proposes an integrated framework in which regionally tracked digital economy indicators are leveraged to predict firm-level innovation performance, measured through patent activity, across China. Drawing on a comprehensive dataset covering 13 digital economic indicators from 2013 to 2022, this study spans core, broad, and narrow dimensions of digital development. Spatial dependencies among these indicators are assessed using global and local spatial autocorrelation measures, including Moran’s I and Geary’s C, to provide actionable insights for constructing innovation-conducive environments. To model the predictive relationship between digital metrics and innovation output, this study employs a suite of supervised machine learning techniques—Random Forest, Extreme Learning Machine (ELM), Support Vector Machine (SVM), XGBoost, and stacked ensemble approaches. Our findings demonstrate the potential of digital infrastructure metrics to serve as early indicators of regional innovation capacity, offering a data-driven foundation for targeted policymaking, strategic resource allocation, and the design of adaptive digital innovation ecosystems. Full article

Background: The accumulation of agri-food waste is a major environmental and economic challenge and converting these by-products into bioactive compounds fits within the circular bioeconomy. This study aimed to evaluate the antimicrobial potential of extracts derived from Cannabis sativa L. leaves (CSE), Crocus sativus tepals (CST), and Prunus avium L. cherry waste (VCE) against four key bacterial species (Staphylococcus aureus, Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa). Methods: Minimum inhibitory concentration (MIC) assays were performed to assess antibacterial activity, while a bioinformatic pipeline was implemented to explore possible molecular targets. Full-proteome multiple sequence alignments across the bacterial strains were used to identify conserved, strain-specific proteins, and molecular docking simulations were applied to predict binding interactions between the most abundant compounds in the extracts and their targets. Results: CSE and CST demonstrated bacteriostatic activity against S. aureus and B. subtilis (MIC = 15.6 mg/mL), while VCE showed selective activity against B. subtilis (MIC = 31.5 mg/mL). CodY was identified as a putative molecular target for CSE and CST, and ChaA for VCE. Docking results supported the possibility of spontaneous binding between abundant extract constituents and the predicted targets, with high binding affinities triggering a strong interaction network with target sensing residues. Conclusions: This study demonstrates the antimicrobial activity of these agri-food wastes and introduces a comprehensive in vitro and in silico workflow to support the bioactivity of these agri-food wastes and repurpose them for innovative, eco-sustainable applications in the biotechnology field and beyond. Full article