Search Results (27,672)

Flood damage assessment remains challenging, as conventional flood risk management mainly relies on hydraulic hazard maps that do not explicitly reproduce observed damage patterns. Recent advances in remote sensing and machine learning (ML) enable the integration of environmental and socio-economic data with historical impact information to improve flood damage modeling. This study proposes an explainable machine learning framework for flood damage susceptibility mapping, using observed institutional damage records from the 2011 and 2013 flood events combined with 17 geospatial flood risk factors (FRFs) representing hazard, exposure, and vulnerability. This approach enables the capture of non-linear relationships between flood damage and FRFs. For comparison purposes, the same framework was also applied using hydraulically modeled flood extents corresponding to return periods of 30, 200, and 500 years. The framework was tested along the Basilicata Ionian coast in southern Italy, a Mediterranean region characterized by complex geomorphology, intense rainfall events, and recurrent flood impacts. An eXtreme Gradient Boosting (XGBoost) model was trained using 17 FRFs related to hazard, exposure, and vulnerability at a spatial resolution of 20 m. The model achieved high performance with an accuracy of 0.988, an F1-score for the minority class of 0.860, and an ROC-AUC (test) of 0.996. High to very high flood damage probability was predicted in approximately 4.1% of the study area, mainly in low-lying floodplains near river corridors and infrastructure. SHAP-based explainability analysis revealed that damage susceptibility was predominantly driven by hazard and exposure factors: Drainage density (17.10%), Railway distance (16.33%), and Elevation (15.42%), extreme precipitation (Max rainfall, 10.66%) and Street distance (7.51%), with socio-economic vulnerability contributing less than 4%. The observed damage target exhibited clear threshold-like patterns (e.g., sharp risk increases below ~25/35 m elevation or within ~150/200 m of road infrastructure), contrasting with the smoother, continuous gradients produced by hydraulic scenarios. This analysis identified the most influential predictors and their response ranges. The proposed framework complements hydraulic hazard mapping by explicitly modeling observed flood damage, supporting flood risk assessment in flood-prone coastal regions. Full article

Accurate prediction of food consumption is essential for strengthening regional food security planning, particularly in areas experiencing increasing food demand and environmental uncertainty. This study aims to predict food consumption patterns in Sukabumi Regency, West Java, Indonesia, using an integrated artificial intelligence approach. The research combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) for forecasting food consumption trends with three machine learning classification algorithms—Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR)—to classify food consumption levels. Historical rice consumption data from 2014 to 2024 were used to train the forecasting model and generate projections up to 2030. The ANFIS training process was conducted with 100 epochs and an error tolerance of 0, resulting in a training error value of 0.182, indicating strong model learning capability. The comparison between predicted and actual consumption values showed a prediction accuracy of 95.2%, demonstrating the reliability of the model in capturing consumption patterns. Furthermore, food consumption levels were classified into three categories: low, medium, and high. The classification results revealed that Random Forest achieved the most consistent performance across cross-validation folds, while SVM and Logistic Regression experienced misclassification in the medium consumption category. In several evaluation scenarios, machine learning models achieved accuracy levels up to 99.75%, precision 99.76%, recall 99.75%, and F1-score 99.75%. The integration of ANFIS forecasting and machine learning classification provides a robust analytical framework for understanding food consumption dynamics and supports data-driven policy formulation aimed at strengthening regional food security in Sukabumi Regency. Full article

Background: Cephalosporins, widely used β-lactam antibiotics, are becoming significant environmental pollutants, primarily due to their high use and persistence. They are released into the environment mainly through wastewater treatment plants, agricultural runoff, and hospital discharge, with particularly high concentrations recorded in effluents. Conventional wastewater treatment methods have inadequate removal efficiency, while advanced treatments, such as ozonation, activated carbon adsorption, and advanced oxidation processes, although more efficient, may produce toxic by-products. Recent studies emphasize the importance of improved detection and monitoring techniques and advocate for stricter effluent regulations. Despite growing research attention, important knowledge gaps remain, including limited long-term field monitoring, insufficient data on environmentally realistic exposure scenarios, and incomplete assessment of transformation-product toxicity. Methods: The search strategy used the SCOPUS and PUBMED databases with the keywords “cephalosporin” AND “aquatic environment”, resulting in 341 records. After applying predefined inclusion and exclusion criteria, 110 peer-reviewed English-language studies meeting predefined thematic inclusion criteria and relevant to the occurrence, environmental fate, ecotoxicological effects, antimicrobial resistance, and removal of cephalosporins in aquatic environments were included in the narrative synthesis. Results: The literature on cephalosporins in aquatic environments has expanded significantly from 1978 to 2025, prompted by concerns about pharmaceutical contamination and antibiotic resistance. Studies from 2016 to 2025 used advanced and multidisciplinary monitoring techniques, revealed key pollution sources such as wastewater treatment plants and hospitals, and correlated antibiotic residues with resistance genes, highlighting the need for continued monitoring and mitigation efforts. Ecotoxicological and fate studies further indicate that transformation processes may generate products with altered or increased toxicity, complicating environmental risk assessment. Conclusions: The literature shows increasing attention to cephalosporins in aquatic environments, reporting associations with antimicrobial resistance and adverse effects on aquatic organisms, including potential toxicity from transformation products. This review highlights the need for integrated monitoring, standardized toxicity assessment, and improved treatment strategies within a One Health framework. Full article

Stone heritage deteriorates through physical, chemical, and biological processes driven by water, climate, and microbial colonization. Multifunctional polymeric coatings combining hydrophobic and antimicrobial moieties have emerged as a promising conservation strategy, yet a substantial gap remains between laboratory innovation and real-world performance. This review critically examines advances from 2021 to 2026, covering wetting theory, antimicrobial mechanisms, and material architectures, including molecularly integrated systems, Sol–Gel hybrids, nanocomposites, and layered systems. Long-term studies on the Aurelian Walls in Rome and stone in Reims show that biocidal efficacy typically declines within one to two years despite the chemical persistence of the coatings. In parallel, hydrophobic performance often deteriorates over time due to UV exposure, particulate deposition, and surface chemical changes, leading to increased wettability and reduced protective efficiency. Substrate porosity governs durability and visual compatibility (ΔE* < 5 threshold), while treatments can reshape microbial communities, favoring stress-tolerant meristematic fungi. Regulatory pressure on fluorinated compounds drives the development of more sustainable alternatives. Emerging directions include stimuli-responsive systems, self-healing materials, slippery interfaces, and precision polymer architectures. However, future progress will depend on tailoring formulations to major lithotypes, improving compatibility with porous substrates, and validating performance through standardized accelerated aging and multi-year field trials. Bridging laboratory design with environmental exposure data and conservation practice will be essential for achieving durable and culturally acceptable protection strategies. Full article

This study investigates the influence of blockchain-enabled supply chain traceability (BESCT) on sustainable supply chain practices (SSCP) in the context of small and medium-sized enterprises (SMEs) in the Turkish manufacturing sector. Grounded in the Resource-Based View (RBV), the research further examines the mediating roles of perceived information transparency (PIT) and supply chain collaboration (SCC) and the moderating effect of environmental orientation (EO). The study employs a quantitative research design using data collected from 652 managers representing various manufacturing SMEs. Structural equation modeling via SmartPLS 4.0 is applied to test a moderated mediation model and assess the relationships among the constructs. The results indicate that BESCT is positively associated with SSCP both directly and through PIT and SCC as mediating mechanisms. PIT is linked to improved visibility and information integrity, while SCC is associated with joint sustainability efforts across supply chain partners. Moreover, EO strengthens the positive associations between BESCT and PIT with SSCP, while its effect on collaboration is more nuanced. Given the cross-sectional design, these findings should be interpreted as associative rather than causal. In addition, the use of a non-probability convenience sampling approach may limit generalizability, and the results should be interpreted with caution. This study contributes to the RBV literature by conceptualizing blockchain as a traceability-enabled dynamic capability that supports sustainability-oriented practices in SMEs. Full article

The world’s growing need for energy, fueled by industrial expansion and a rising population, continues to be a challenge for the scientific community. The heavy reliance on fossil fuels that contribute to environmental degradation and public health concerns, is shifting toward sustainable alternatives, with hydrogen production via advanced catalysts as an energy source emerging as a promising solution. This transition addresses the challenges posed by harmful combustion emissions. In this study, we developed an innovative PANI@Cu-NA-MOF nanocomposite catalyst through a sol–gel synthesis approach that strategically integrates conducting polymers with metal–organic frameworks. The catalyst was characterized using different sets of techniques. Surface morphology and elemental composition were investigated using SEM-EDX, while structural analysis was carried out with FTIR that helped to identify the chemical bonds and functional groups, and UV-Vis spectroscopy provided information on its light absorption properties. In addition, TGA was used to evaluate thermal behavior, and XPS offered detailed surface chemical analysis. It was observed by morphology that PANI@Cu-NA-MOF is a noncapsular-like structure. It is thermally highly stable; a TGA study showed that up to 550 °C, almost 2.5% of weight was lost. The single peak in UV-Vis is the preparation of a successful composite. XPS and FTIR reveal the required peaks of functional groups and elements. The PANI@Cu-NA-MOF composite turned out to be quite effective for water electrolysis, requiring an overpotential of just 0.47 V to drive the reaction. When tested against the reversible hydrogen electrode, we observed onset potentials of 1.6 V/RHE for the oxygen evolution reaction and 0.2 V/RHE for the hydrogen evolution reaction. What makes this particularly interesting is that such performance significantly cuts down on the energy needed for electrolysis, which could make hydrogen production much more practical. Since hydrogen burns cleanly and offers a real alternative to fossil fuels, having an efficient catalyst like this brings us one step closer to sustainable energy. Full article

While Environmental, Social, and Governance (ESG) rating divergence poses a barrier to accurate sustainability measurement and sustainable investment, how internal managerial cognition addresses this external market misalignment remains underexplored. To address the research question of how executive focus shapes market consensus on corporate sustainability, this study integrates the Attention-Based View and Signaling Theory to examine the potential mitigating role of Top Management Team (TMT) environmental attention on ESG rating divergence. Utilizing high-dimensional fixed-effects regressions and textual analysis, we analyze a sample of Chinese A-share non-financial listed firms from 2015 to 2023. Empirical results indicate that a transparent and forthcoming managerial environmental focus helps reduce rating divergence, thereby partially aligning informational baselines. This cognitive alignment can act as an information calibrator, particularly when environmental issues match the firm’s core industry materiality, and this association appears more pronounced in regions with stringent environmental regulations. Robustness checks support the notion that substantive, quantitative sustainability disclosures driven by executive attention assist in alleviating informational misalignment among external rating agencies. These findings offer socio-economic and policy insights for advancing sustainable development, suggesting that regulators could consider encouraging structured sustainability reporting to support the role of executive cognition in standardizing ESG measurements. Full article

This study proposes and analyzes an integrated framework for monitoring potable water quality and laboratory effluent management in universities, with emphasis on its practical application in a Brazilian public institution. Adopting a qualitative and documentary approach, the research was based on high-impact scientific publications, institutional reports, and environmental databases. The results demonstrate that effective water and effluent governance depends on the interaction of three core dimensions: regulatory compliance, technological innovation, and institutional governance. These elements operate synergistically to ensure transparency, risk prevention, and environmental accountability. The proposed University Laboratory Water Monitoring Framework (UL-WMF) illustrates how universities can transform water control into a managerial and educational tool aligned with sustainability goals. The illustrative institutional application revealed potential for integrating Internet of Things (IoT) and Laboratory Information Management System (LIMS) technologies into environmental management routines, reinforcing universities’ strategic role in achieving global sustainability objectives. Despite relying on secondary data, this study provides a scalable foundation for decision support systems and future empirical validation. The novelty of the University Laboratory Water Management Framework (UL-WMF) lies in its integration of potable water monitoring and laboratory effluent governance into a single operational framework, addressing a gap in the existing literature and offering a model specifically tailored to the context of universities in developing countries. The applied component of the study consists of an illustrative institutional case constructed exclusively from publicly available environmental and governance reports. This illustration serves to demonstrate the operational relevance of the proposed framework, without implying field measurements or primary data collection. Full article

Wildfires are among the key drivers of transformation in boreal ecosystems; however, the mechanisms of postfire recovery in the arid regions of Eurasia remain insufficiently understood. The aim of this study was to identify the role of burn severity and associated edaphic and hydrological factors in shaping the natural regeneration trajectories of Scots pine forests in the Kostanay region of northern Kazakhstan. This study is based on the integration of field data on seedling regeneration and soil conditions with the analysis of long-term satellite-derived indices (NDVI, NDMI, and NBR). Sample plots were grouped according to fixed burn severity classes, which enabled a consistent statistical comparison and reduced the interpretative ambiguity that has characterized previous studies in the region. The results indicate that pine forest regeneration is most successful under low and moderate burn severity, where seed sources are preserved and favourable moisture conditions are maintained. In contrast, high burn severity leads to a reduction in regenerative potential and a shift in recovery trajectories toward deciduous or sparsely vegetated communities. The spectral indices derived from the remote sensing data strongly agreed with the field-based indicators, confirming their suitability for assessing postfire vegetation dynamics. Soil properties act as important modifying factors of recovery processes, particularly under conditions of limited water availability. These findings enhance the current understanding of postfire recovery mechanisms in the arid part of the boreal zone and highlight the need for differentiated management of postfire landscapes. The integration of field observations with remote sensing data provides a robust framework for monitoring and forecasting recovery processes under an increasingly intensified fire regime. Full article

Hepatocellular carcinoma (HCC) represents a major global health challenge and the third leading cause of cancer-related mortality worldwide. Its epidemiological burden is rapidly increasing, largely driven by the rising prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD), which is now recognized as the most common chronic liver disease globally. Notably, MASLD frequently coexists with type 2 diabetes mellitus (T2DM), sharing several features, including the interplay of common genetic, metabolic, and environmental factors, thus contributing to a complex multifactorial pathogenesis. Relevantly, patients affected by both conditions represent a subgroup at particularly high risk of liver disease progression and hepatocarcinogenesis. In this population, metabolic and inflammatory disturbances act synergistically to create a pro-tumorigenic hepatic environment where insulin resistance (IR) plays a crucial role, by driving hepatic lipotoxicity, mitochondrial dysfunction, and inflammatory signaling with oxidative stress, thereby establishing a permissive environment for worsening steatosis and malignant transformation. Increasing evidence supports the concept of MASLD as a multisystem disorder reflecting the systemic nature of metabolic dysfunction. Within this framework, beyond IR, extrahepatic factors have also emerged as important contributors to steatosis progression, worsening of T2DM, and modulation of HCC risk. In particular, the gut–liver axis has gained recognition as a key regulator of hepatic homeostasis, integrating signals from the intestinal microbiota, immune responses, and metabolic pathways. Dysregulation of this crosstalk promotes systemic inflammation and metabolic imbalance, exacerbating IR and fostering a pro-oncogenic hepatic environment. This review examines the interconnected metabolic and immune mechanisms linking IR and gut–liver axis dysfunction to HCC development in patients with MASLD and T2DM, highlighting their implications for risk stratification and precision-based therapeutic strategies. Full article

The psychological benefits of waterfront public spaces have become an important topic in environmental design and architectural research. However, existing studies have primarily focused on the direct relationship between physical environmental attributes and user satisfaction, with limited attention to the psychological mechanisms through which architectural design influences residents’ well-being. This study examines waterfront concert halls as a type of cultural architectural space and develops a theoretical model integrating environmental restoration theory and place attachment theory. In this model, waterfront design perception is conceptualized as a multidimensional construct including water visibility, water accessibility, water harmony, and water interactivity, while perceived restorativeness and place attachment are treated as mediating variables, and residents’ well-being as the outcome variable. Based on questionnaire data collected from 1345 urban residents across six Chinese cities and seven waterfront concert hall cases, and analyzed using covariance-based structural equation modeling, the results show that waterfront design perception has a significant positive effect on residents’ well-being. Perceived restorativeness and place attachment both play mediating roles and jointly form a sequential pathway through which environmental perception is translated into psychological and emotional benefits. These findings extend the understanding of waterfront design from objective spatial attributes to subjective experiential processes and provide empirical support for the design of waterfront cultural architecture aimed at enhancing the well-being of urban residents. Full article

To address trajectory-tracking degradation of unmanned surface vehicles (USVs) in constrained waters caused by model uncertainty, strong environmental disturbances, and actuator limitations, this paper proposes a robust disturbance-observer-based optimization model predictive control method. First, a nonlinear tracking error model is established for a 3-DOF USV by incorporating environmental loads, parametric perturbations, and unmodeled dynamics into the kinematic and dynamic equations. Based on this model, a prediction model suitable for model predictive control is derived through linearization and discretization. Then, to estimate complex unknown disturbances online, a robust disturbance observer integrating a radial basis function neural network (RBFNN) with an adaptive sliding-mode mechanism is developed, enabling real-time approximation and compensation of lumped disturbances in the surge and yaw channels. Furthermore, to overcome actuator saturation caused by the direct superposition of feedforward compensation and feedback control in conventional composite strategies, a dynamic constraint reconstruction mechanism is introduced. By feeding the observer-generated compensation signal back into the MPC optimizer, the feasible control region is updated online so that the total control input satisfies both magnitude and rate constraints of the propulsion system. Theoretical analysis based on Lyapunov theory proves the uniform ultimate boundedness of the observation errors and neural-network weight estimation errors, while input-to-state stability theory is employed to establish closed-loop stability. Comparative simulations under sinusoidal trajectories, time-varying curvature paths, and large-maneuver turning conditions demonstrate that the proposed method significantly improves tracking accuracy, disturbance rejection capability, and control feasibility under severe disturbances and parameter mismatch. Full article

To mitigate environmental impact, specifically the CO${}_2$ emissions associated with conventional thermal and nuclear facilities, renewable energy sources are increasingly being adopted as primary alternatives. However, integrating these renewable sources into the utility grid poses a significant challenge, primarily due to the stochastic and nonlinear nature of weather. Consequently, it is imperative that power systems operate under an intelligent control model to ensure energy output meets strict power quality standards. In this context, accurate forecasting is a cornerstone of smart power management, particularly in off-grid architectures, where predicting Power Quality Parameters (PQPs) is fundamental for system optimization and error correction. This study conducts a comprehensive comparative evaluation of nine different predictive architectures for estimating PQPs. The algorithms analyzed include $LSTM$, $GRU$, $DNN$, $CNN1D-LSTM$, $BiLSTM$, attention mechanisms, $DT$, $SVM$, and $XGBoost$. The central objective is to develop a reliable basis for the automated regulation and enhancement of electrical quality in isolated systems. The specific parameters investigated are power voltage (U), Voltage Total Harmonic Distortion ($TH{D}_u$), Current Total Harmonic Distortion ($TH{D}_i$), and short-term flicker severity ($P_{st}$). Data for this investigation were acquired from an experimental off-grid setup at VSB-Technical University of Ostrava (VSB-TUO), Czech Republic. To assess model performance, we utilized root mean square error ($RMSE$) as the primary accuracy metric, while simultaneously evaluating computational efficiency in terms of processing speed and memory consumption during testing. Full article

Nanophotonics, an interdisciplinary field merging nanotechnology and photonics, has enabled transformative advancements across diverse sectors, including green energy, biomedicine, and optical computing. This review comprehensively examines recent progress in nanophotonic principles and applications, highlighting key innovations in material design, device engineering, and system integration. In renewable energy, nanophotonics allows the use of light-trapping nanostructures and spectral control in perovskite solar cells, concentrating solar power systems, and thermophotovoltaics. This has significantly enhanced solar conversion efficiencies, approaching theoretical limits. In biosensing, nanophotonic platforms achieve unprecedented sensitivity in detecting biomolecules, pathogens, and pollutants, enabling real-time diagnostics and environmental monitoring. Medical applications leverage tailored light–matter interactions for precision photothermal therapy, image-guided surgery, and early disease detection. Furthermore, nanophotonics underpins next-generation optical neural networks and neuromorphic computing, offering ultrafast, energy-efficient alternatives to von Neumann architectures. Despite rapid growth, challenges in scalability, fabrication costs, and material stability persist. Future advancements will rely on novel materials, AI-driven design optimization, and multidisciplinary approaches to enable scalable, low-cost deployment. This review summarizes recent progress and highlights future trends, including novel material systems, multidisciplinary approaches, and enhanced computational capabilities, paving the way for transformative applications in this rapidly evolving field. Full article

The extensive application of unmanned aerial vehicles (UAVs) in power inspection, military operations and environmental monitoring demands stronger robustness and adaptability for autonomous decision-making systems. Existing methods suffer from heavy map dependence, high computational complexity and insufficient exploration and generalization. Traditional approaches based on expert rules and planning algorithms only suit fixed scenarios and degrade severely in complex dynamic environments. To address these problems, this paper proposes a complex-scene-oriented autonomous decision-making method for UAVs (CADU). It builds a closed-loop decision chain by integrating perception, strategy and execution modules, and adopts curiosity mechanism and contrastive learning to enhance exploration and adaptability. Experimental results show that the proposed CADU achieves an average reward of 0.85, a trajectory smoothness of 0.87, a flight stability of 0.85, and a cumulative collision count of $8\pm 1.2$, which significantly outperforms DDPG, PPO and SAC baselines. It provides a reliable and efficient scheme for UAV autonomous decision-making in complex scenarios. Full article