Search Results (922)

The present contribution provides a systematic review of the international scientific literature on the relationship between contaminated sites and real estate market dynamics. The objective is to investigate whether and to what extent the presence of environmental risk sources—both active or decommissioned—affects the value of surrounding residential properties. In particular, the review is focused on an examination of the methods commonly used in relevant studies to measure, interpret, and represent this impact across different geographical contexts, identifying the main magnitude ranges found in the selected contributions. Several studies consistently confirm a statistically significant negative relationship between proximity to polluting sites and real estate values, although the relevance of this effect varies considerably across case studies. Other records highlight non-notable impacts or even positive effects following remediation and redevelopment interventions. The evidence suggests that this relationship is complex and influenced by factors such as site type, contamination severity, specificities of the local urban context and community perception. Moreover, the findings underscore regional variations in the extent and nature of price impacts, reflecting diverse regulatory frameworks and remediation efforts. The outcomes of the literature review provide a robust foundation for developing more effective evaluation tools able to support decision-making processes, enabling policymakers, planners, and investors to promote sustainable urban regeneration, improve environmental justice, and reduce spatial inequalities. Ultimately, this study highlights the critical need for integrating environmental, social, and economic dimensions to fully capture the multifaceted effects of contaminated sites on property markets, thereby orienting more informed and equitable urban development strategies worldwide. Full article

The move to sustainable energy systems has increased the requirement for comprehensive decision support frameworks that are uncertainty-aware to guide the selection of solar power plant sites. The rapid growth of investments in solar energy has increased the demand for systematic and accurate decision-support tools to choose the best sites for photovoltaic (PV) power facilities. The selection of solar power plant sites is a complicated multi-criteria decision-making (MCDM) problem that involves technical, economic, environmental, social, and technological aspects. The process is typically associated with ambiguity and incomplete knowledge of experts. To overcome these problems, this paper offers an interval-valued neutrosophic fuzzy TOPSIS (IVN-TOPSIS) method, which extends the standard TOPSIS methodology by including truth, indeterminacy, and falsity membership degrees as interval values. The methodology is utilized in a real case study in the Mediterranean region of Türkiye, comprising three provinces with great potential: Antalya, Mersin, and Adana. An assessment of a complete set of environmental, economic, social, and technological criteria is performed using expert judgments stated in interval-valued neutrosophic language assessments. They were incorporated into a Geographic Information System (GIS) to produce a suitability map indicating the most suitable sites for the facility. The suggested approach is different from the traditional crisp or fuzzy MCDM techniques since it clearly models the degrees of truth, indeterminacy, and falsehood, thus providing a more detailed representation of the expert evaluations. According to the data, Mersin is the most ideal site for the construction of a solar power plant, followed by Antalya, and the least suitable site is Adana. The results suggest that sustainable solar energy planning must go beyond technical resource potential and include integrated and uncertainty-aware assessments. The suggested IVN-TOPSIS framework can serve as a powerful decision-support tool to policymakers, planners, and investors that wish to encourage regionally balanced and sustainable renewable energy development. Full article

Early-stage residential building design in dense urban environments involves complex interactions among zoning regulations, geometric configuration, environmental performance, and economic feasibility. Conventional CAD–spreadsheet workflows and parametric BIM-based approaches remain limited in systematically resolving these interdependent trade-offs and typically rely on heuristic iteration and post hoc regulatory verification. To address this limitation, this study proposes REGEN, a regulation-aware BIM-enabled multi-objective optimization framework for sustainable residential building design. The framework formalizes planning and building-control regulations as explicit algebraic constraints embedded within a parametric BIM environment and integrates them with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to generate regulation-compliant design alternatives with respect to the encoded planning and building-control regulations. REGEN simultaneously optimizes five competing objectives: maximizing project profit, green-area provision, and building efficiency while minimizing geometric shape factor and building footprint area. A real condominium feasibility case in Bangkok, Thailand, is used to benchmark the proposed framework against conventional practice and parametric BIM-based design under identical site and regulatory conditions. The results reveal a non-convex Pareto front that exposes complex trade-offs among environmental, geometric, and economic objectives. The selected closest-to-utopia solution achieves 65.50% building efficiency, 606 m² of green area, a shape factor of 0.399, and a building footprint area of 1078 m² while maintaining a competitive project profit of 104.55 million THB without maximizing FAR utilization. The findings suggest that regulation-aware generative optimization has the potential to serve as an explainable and decision-oriented approach for sustainable construction and early-stage residential development planning. Full article

Background/Objectives: First-generation CDK4/6 inhibitors (palbociclib, ribociclib, abemaciclib) target the conserved ATP-binding pocket of CDK4 and, despite clinical success, are limited by acquired resistance and insufficient exploration of alternative regulatory sites. This study aimed to identify a putative allosteric small-molecule candidate at the CDK4 αE-helix–Cyclin D1 α1-helix protein–protein interaction (PPI) interface within the CDK4/Cyclin D1/p21 ternary complex using RapidFunnel-AI, a decision-interpretable virtual-screening pipeline. Methods: Starting from 50,000 ChEMBL 33 molecules, the pipeline sequentially applied a Q-Fold/RapidFunnel topological Tanimoto scan based on clinical CDK4/6 inhibitor motifs, fragment-level electronic-property enrichment, ADMET/PAINS filtering, dry Vina-GPU docking, hydration-mediated AutoDock-GPU (Version 1.6) docking, explicit-solvent molecular dynamics, contact-retention analysis, and MM-GBSA energy decomposition. The Q-Fold Thermo-Core surrogate model provided fragment-level enrichment, predicting the HOMO–LUMO gap (R² = 0.93) and isotropic polarizability (R² = 0.98) on QM9. Candidate selection did not rely on the lowest docking or MM-GBSA score alone, but on pose persistence, contact continuity, and energy-component consistency. Results: The workflow reduced the initial library to 43 topologically prioritized candidates, 25 ADMET/PAINS-filtered ligands, and 9 docking-derived complexes for MD validation. Ligand_020 emerged as the only candidate that preserved a persistent binding mode at Site 2 during a 500 ns simulation—an interface engagement reproduced across three independent 500 ns replicates with no full dissociation in any replicate—with a protein Cα RMSD of 2.88 ± 0.32 Å, a ligand heavy-atom RMSD of 3.56 ± 0.28 Å, and a van der Waals-dominated MM-GBSA profile (ΔGbind = −28.23 ± 3.57 kcal/mol). In contrast, palbociclib and ribociclib, forcibly placed at Site 2 as negative controls, lost most initial contacts within 5 ns and tended to detach despite more favorable MM-GBSA values. Conclusions: These results suggest that single-score docking or MM-GBSA ranking can generate false positives at shallow PPI interfaces. By integrating AI-assisted prioritization, multipocket docking, explicit-solvent MD, contact-retention analysis, and energy-component consistency, RapidFunnel-AI nominated Ligand_020 as an experimentally testable putative allosteric hit targeting the CDK4/Cyclin D1 interface, offering a reusable platform for PPI-focused oncological drug discovery. Full article

Forest carbon assessment requires methods that capture the combined effects of stand structure, site conditions, carbon pools, operational emissions, and circular-economy processes. This study aimed to develop and optimize hybrid machine learning models for predicting the gross CO₂e (carbon dioxide equivalent) balance of Polish forest stands using measurable stand- and site-related variables. The research was based on a primary dataset describing forest management in major Polish macroregions in 2020–2024. After data cleaning and preprocessing, multiple machine learning algorithms, including ensemble, boosting, neural, and hybrid models, were trained, validated, and tested. Model performance was assessed using standard regression metrics, overfitting diagnostics, learning curves, and SHAP (Shapley Additive Explanations). Most models achieved high predictive accuracy, with six of ten algorithms reaching R² values above 0.90 on the test set. The reduction in strongly correlated variables helped limit multicollinearity and excessive overlap between predictors and the target variable, supporting a more reliable interpretation of model performance. The CatBoost algorithm achieved the highest predictive performance on the test set (R² = 0.948), while also recording the lowest root mean squared error (RMSE = 152.242). However, the Decision Tree demonstrated the weakest generalization performance (R² = 0.806) on the test set. SHAP analysis identified tree height as the most influential predictor, followed by tree age, number of trees, species composition, and selected habitat features. The novelty of the study lies in integrating hybrid machine learning, interpretable modelling, and circular-economy-related carbon balance components into a single framework for rapid and operational forest carbon assessment in Polish forest stands. Full article

Rootstock selection is a key component of sustainable vineyard planning, as it strongly influences vine adaptation to soil and environmental conditions. Despite its importance, this decision is often based on empirical knowledge rather than on structured, site-specific approaches. This study presents SR-Vitis, a decision-support module developed within the Vitis system, designed to support rootstock selection through a rule-based framework integrating pedological, climatic, and agronomic variables. The model translates site-specific characteristics into suitability criteria for a set of widely used European rootstocks. The system was applied to four vineyards located in two contrasting Italian winegrowing regions (Chianti Classico and Alta Murgia) to assess the coherence of the model outputs under different pedoclimatic conditions. The comparison with existing tools and current grower choices showed a general agreement in most cases, while also identifying situations where alternative rootstocks may better match site constraints. These results suggest that SR-Vitis can effectively support a more structured and transparent decision-making process. Although not intended as a predictive validation study, this work provides a first operational assessment of the model and highlights its potential as a practical tool for vineyard planning. By integrating expert knowledge and soil-based criteria into an accessible digital framework, SR-Vitis contributes to bridging the gap between empirical practices and data-supported approaches, supporting viticultural adaptation under increasing environmental variability. Full article

Background and Clinical Significance: Peristomal pyoderma gangrenosum (PPG) is a rare subtype of pyoderma gangrenosum, most commonly associated with inflammatory bowel disease or haematologic disorders. Its occurrence in patients with solid malignancies is uncommon. PPG in an oncologic setting poses diagnostic and therapeutic challenges because systemic immunosuppressive therapy, wound care, and ongoing chemotherapy must be carefully balanced; Case Presentation: We report the case of a Japanese man in his 50s with stage IVB rectal adenocarcinoma who developed rapidly progressive peristomal ulceration clinically consistent with PPG around a colostomy 12 weeks after initiation of panitumumab-containing systemic chemotherapy. The diagnosis was made on clinical grounds and was strongly supported by the clinical morphology, exclusion of major mimickers, and response to systemic corticosteroid therapy, although histopathological confirmation was not obtained. Because existing diagnostic criteria for pyoderma gangrenosum are not specifically designed for peristomal disease, they were used as supportive rather than definitive diagnostic tools. Skin biopsy was avoided due to the risk of pathergy at the peristomal site. Superficial cultures were not obtained because frequent cleansing and faecal contamination were likely to compromise diagnostic accuracy. To minimise mechanical pathergy, the stoma appliance was changed from a one-piece soft convex system to a two-piece flat system. Multidisciplinary management, including systemic corticosteroids, meticulous stoma care, and selective ultrasonic debridement, resulted in complete epithelialisation by Week 26. Chemotherapy was temporarily withheld during the active inflammatory phase and later resumed. Despite successful control of the peristomal ulceration, the patient died from progressive malignancy at Week 34; Conclusions: This case highlights the clinical challenge of balancing immunosuppressive therapy for clinically suspected PPG with ongoing oncologic treatment. Mechanical pathergy related to stoma appliance use was considered a more likely precipitating factor than chemotherapy alone, although panitumumab may have contributed to impaired cutaneous repair. Close collaboration among dermatologists, oncologists, surgeons, WOC nurses, and family caregivers is essential for multidisciplinary decision-making in complex oncologic settings. Full article

This study investigates the comparative effectiveness of Lead Rubber Bearing (LRB) and Friction Pendulum System (FPS) isolation units under varying seismic hazard levels and soil classes, within the framework of the Turkish Building Earthquake Code (TBEC 2018). The assessment was conducted in two stages. First, keeping the site class constant, multiple locations characterized by different seismic hazard levels are examined. Second, a fixed geographical location is considered to evaluate the influence of different site classes on isolator response. The performance of the isolation systems is evaluated in terms of displacement demand, base shear ratio, and code-based verification criteria. Additional sensitivity checks were performed using selected limit values to better understand the response trends under changing hazard and soil parameters. The findings highlight how soil amplification effects and seismic intensity levels influence the relative advantages of LRB and FPSs. The results provide practical insight for the selection of seismic isolation systems in hazard-prone regions, contributing to improved performance-based decision-making in earthquake-resistant design. The isolator parameter choices were set based on average catalogue values provided by manufacturers to make this research an example. As a result of the analysis of the isolators’ performance, it was concluded that the FPS-type isolator performed better as acceleration values increased. Full article

Introduction: Studying the structure and dynamics of living communities is essential from both ecological and wildlife management perspectives. Objective: The main objective of this study was to analyze the fish community structure inhabiting different river sections across several basins in the Mediterranean area. The data collected here contributed to: (i) creating a regional and national reference inventory to assess ichthyological biodiversity; (ii) generating digital cartographic information on species distribution and potential habitats; and (iii) providing scientific data to update national legal protection for governments. Methodology: Fish assemblages were monitored using electrofishing, which ensures reproducible data and long-term comparability. The study period extended until autumn 2025, with intensive sampling at 30 sites across major water bodies in the Valencian Community and selected rivers in Mijares, Turia, Jucar and Palancia basins. Results: The results reveal notable ichthyological richness in the studied basins (Turia, Júcar, Palancia, Mijares), with 12 native species identified. Cyprinidae and Leuciscidae were the most representative families, both in species number and spatial distribution, consistent with their dominance in Mediterranean river systems. Areas with the highest species richness corresponded to the middle and lower river sections and to ecologically valuable coastal wetlands. However, the study also detected 10 invasive alien species, representing 45% of the total fish fauna recorded. This high proportion reflects the significant ecological alteration affecting rivers and wetlands in these basins and underscores the urgent need for management actions to limit the spread of invasive species and reduce their impact on native biodiversity. The most widespread IAS were the bleak (A. alburnus), mainly in the Júcar basin, and the mosquitofish (G. holbrooki), predominantly in coastal wetlands. Conclusions: This study contributes directly to updating the Atlas of Ichthyofauna of the Valencian Community, providing a robust and current information base to support environmental decision-making at regional and national levels. The findings highlight the importance of strengthening proactive conservation measures, particularly in areas where biodiversity is most vulnerable. Full article

Alternate wetting and drying (AWD) irrigation reduces CH₄ emissions from flooded rice but amplifies N₂O pulses; identifying candidate phenological windows for the remote screening of greenhouse gas (GHG) fluxes remains challenging with small datasets. In a single-site, single-season exploratory study at INIA Vista Florida (Lambayeque, Peru), eight UAV flights were paired with eight PlanetScope SuperDove scenes ($|\Delta |\le 1$ d) and closed-chamber CH₄, N₂O and CO₂ fluxes under four water regimes (CF, AWD₅, AWD₁₀, AWD₂₀; 96 sub-plot × date observations). Multivariate explanatory power was assessed by bootstrap Ridge regression on each sensor’s native predictors (VI + GLCM + $T_{\mathrm{mean}}$ for the UAV, VI for PlanetScope). Maximum tillering (79 DAS) emerged as a candidate UAV window, ranking in the top three for all gases through GLCM textures, whereas PlanetScope peaked at Mid-boot and Late-boot (103–107 DAS), with median ${\widetilde{R^2}}_{\mathrm{UAV}}$ at $0.34$–$0.71$ and ${\widetilde{R^2}}_{\mathrm{Planet}}$ at $0.20$–$0.60$. Nested Leave-One-Plot-Out (LOPO) validation gave $R_{\mathrm{CV}}^2$ between $+0.57$ and $+0.69$ for four of six platform × gas combinations (UAV-CH₄ and Planet-N₂O stayed weak), and $T_{\mathrm{mean}}$ was decisive for N₂O on the UAV ($\Delta R^2=+0.48$). Repeating the stage selection inside every LOPO fold preserved the leading combinations and their ranking. These exploratory windows and sensor-native descriptors need multi-site, multi-season validation before operational use. Full article

Accurate forecasting of groundwater level (GWL) fluctuations in drought-prone and data-limited regions remains a major challenge for sustainable groundwater management. The complexity of nonlinear and dynamic groundwater systems, influenced by spatiotemporal variability and limited observational data, further complicates the development of reliable predictive models. Groundwater is a critical resource for irrigation and domestic use in drought-prone northwestern Bangladesh, requiring accurate forecasting of GWL dynamics for sustainable management. To address this challenge, the present study evaluates seven deep learning (DL) approaches: GRU, LSTM, hybrid LSTM–GRU, and their Genetic Algorithm (GA)- and Particle Swarm Optimization (PSO)-variants, using time-series data from nine observation wells. The developed models were benchmarked against the widely used univariate time-series forecasting model, ARIMA. Model performance varied spatially. The GA-LSTM model performed best at Bagha–Arani (R = 0.879, IOA = 0.906, NRMSE = 0.149), while the standalone LSTM achieved superior results at Bagmara–Auchpara (R = 0.940, IOA = 0.958, NRMSE = 0.155). All DL models outperformed the benchmark ARIMA model across all locations. Overall, the best models achieved R = 0.724–0.940, IOA = 0.707–0.958, NRMSE = 0.149–0.285, and MAD = 0.369–1.369 m, indicating strong predictive skill. Optimization (GA, PSO) improved accuracy, particularly for GRU-based models, though LSTM remained competitive in several sites. Hybrid and optimized models required higher computational cost due to iterative tuning but often yielded improved accuracy. A CRITIC–EDAS multi-criteria decision-making framework, based on six statistical metrics, identified no universally superior model; instead, optimal choices varied by location. Selected models successfully forecasted future GWL trends, capturing temporal variability. The integrated modelling–ranking framework provides a robust, scalable approach for groundwater management in data-limited, drought-affected regions. Full article

Frequent heavy rainstorm disasters have led to the need for temporary storage of large quantities of heterogeneous disaster-related solid waste within a short period, making temporary storage an important issue in the construction and optimization of the urban comprehensive urban emergency management systems. This study takes the “23·7” catastrophic rainstorm event in Mentougou District, an area prone to rainstorm disasters in Beijing, as a case study and develops an auxiliary decision-making model for site selection that integrates estimates of construction waste and household goods waste, an “initial selection—screening—optimization” suitability evaluation, and the optimization of spatial layout optimization. By combining the spatial analysis method of the Geographic Information System (GIS), an evaluation index system covering natural geography, ecological environment, and socio-economic factors was constructed. An integrated AHP–EWM model was constructed, merging the expert-driven, subjective weighting of the Analytic Hierarchy Process with the objective, data-derived weighting of the Entropy Weight Method to determine indicator weights. The suitability distribution for site selection was studied by combining the multi-factor weighted overlay model, and the area most suitable for construction of Temporary Disaster Waste Storage Sites (TDWSSs), accounting for 4.51% of the total area, was identified. Subsequently, multiple constraints—including ecological protection redlines and minimum area requirements—were superimposed to exclude non-compliant areas. Ultimately, a combined optimization model integrating the minimum facility location model, maximum coverage model, and minimum impedance model was constructed, and the optimal site selection scheme was determined via ArcGIS. The results show that, when seven TDWSSs are considered, the coverage rate of administrative villages within the 20 km transportation service range reaches 97.38%. The results also indicate that, when the number of TDWSSs exceeds eight, the increase in the coverage rate tends to be moderate and the optimization space is limited, indicating that the layout scheme with seven TDWSSs is close to the regional optimal solution. This framework provides crucial guidance for post-rainstorm TDWSS planning and layout optimization. Full article

Offshore renewable energy is widely recognised as a critical pathway for decarbonising electricity systems, but the integration of floating offshore wind turbines with wave energy converters remains technically challenging. This paper presents a structured literature review of hybrid wave–wind offshore energy platforms, drawing on 114 reviewed sources published between 2000 and 2026. The review classifies hybrid concepts using a three-axis framework based on floating platform type, wave energy converter (WEC) integration approach, and energy-dominance category. It then compares representative configurations, including point absorbers, oscillating water columns, flap-type devices, and heaving torus concepts, with emphasis on hydrodynamic response, energy contribution, structural complexity, mooring implications, validation status, and optimization suitability. The findings show that no single hybrid configuration can be ranked as universally superior because reported performance depends strongly on platform geometry, WEC scale, site wave climate, modelling assumptions, and validation maturity. Point absorber systems offer modularity and lower integration complexity, oscillating water column (OWC)-based systems provide protected power take-off (PTO) integration and moderate hydrodynamic interaction, flap-type systems can provide stronger motion-control potential but impose higher structural and mooring demands, and spar–torus concepts remain geometrically compatible with spar platforms but are generally wind-dominated. The review further shows that optimization method selection should depend on problem class: gradient-based methods are most suitable for local PTO tuning, evolutionary methods for non-convex multi-objective layout problems, surrogate-based methods for high-cost coupled simulations, and data-driven methods for adaptive control. The paper concludes that future progress requires standardized benchmark models, transparent evidence-level reporting, multi-physics co-optimization, techno-economic assessment, and systematic experimental or field validation before definitive concept ranking or commercial-readiness claims can be made. For decision-makers, industry stakeholders, and policymakers, the framework supports early-stage concept screening, identification of technology-specific risk factors, prioritisation of validation and investment pathways, and alignment of hybrid-platform development with site conditions, infrastructure constraints, and policy objectives. 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

CT perfusion (CTP) is widely used in acute ischemic stroke imaging, particularly for treatment selection beyond conventional time windows. However, automated perfusion maps are not direct measurements of irreversible tissue injury, but estimates shaped by deconvolution strategy, temporal correction, dispersion handling, and software-specific thresholds. This review provides a clinically oriented explanation of how CTP algorithms influence the estimation of ischemic core and hypoperfused tissue. Particular attention is given to singular value decomposition (SVD) methods, Bayesian approaches, and timing parameters, including time to maximum (Tmax), Delay, time to peak (TTP), and mean transit time (MTT). Differences in residue function estimation and threshold definition may generate variable outputs across software platforms, even from the same source dataset. Perfusion thresholds should therefore not be treated as universally interchangeable. CTP findings should be integrated with clinical status, non-contrast CT, CT angiography (CTA), collateral status, occlusion site, and imaging-to-treatment context, serving as decision-support tools rather than isolated measures of tissue viability. Full article