Search Results (33,337)

The increasing penetration of renewable energy sources in isolated microgrids has intensified the need for Energy Management Systems (EMS) capable of reducing diesel dependence while ensuring reliable operation. This study compares three EMS strategies for a photovoltaic–diesel–battery microgrid representative of remote communities in the Brazilian Amazon: a Myopic strategy (M1), a Predictive strategy (M2), and a PPO-RL strategy (M3), evaluated under identical physical and operational constraints using real irradiance and demand data over the complete year of 2025 at a 15 min resolution. The three strategies achieved very similar diesel consumption, with a difference of less than 0.7% between the best and worst cases; M2 achieved the lowest consumption (218,863.19 L), the highest renewable penetration (52.14%), and the lowest curtailment (1.06%), with M3 performing comparably but with more frequent generator starts. An illustrative, qualitative analysis of the highest- and lowest-PV days suggested that differences among strategies become more pronounced under high photovoltaic availability, while low renewable availability drives all strategies toward similar diesel-dominated operation. These findings indicate that the main benefit of advanced EMS strategies lies not in large diesel savings, but in improved coordination and utilization of renewable and storage resources. Additionally, a simplified economic analysis of the photovoltaic–battery hybridization, based on M2’s diesel savings, showed an internal rate of return of approximately 30.7% p.a. and a payback period of approximately 3.2 years, well above the 14% p.a. benchmark, confirming the hybridization as an economically attractive investment regardless of which EMS strategy is adopted. Full article

Water quality management is a growing global priority due to increasing pressures from climate change, population growth, urbanisation, industrialisation, agricultural intensification, and emerging contaminants. These challenges threaten freshwater ecosystems, public health, economic development, and progress towards Sustainable Development Goal 6 (SDG 6). This critical review employed a PRISMA-guided systematic literature review to evaluate future directions in water quality management from sustainability, resilience, and social equity perspectives. A total of 11,485 records were identified, with 3632 duplicates removed. Following the screening of 7853 records and assessment of 1441 full-text articles, 117 studies met the inclusion criteria for qualitative synthesis. Geographical analysis showed that 88 studies (75.2%) adopted a global perspective encompassing both developed and developing countries, while 24 studies (20.5%) focused on developing countries and 5 studies (4.3%) on developed countries. The review identified notable progress in water quality monitoring, wastewater treatment, pollution control, and integrated water resources management. Emerging technologies such as smart monitoring systems, artificial intelligence, advanced treatment processes, and resource recovery approaches were consistently highlighted as promising tools for improving water quality and enhancing adaptive capacity. Despite these advances, significant barriers remain, including fragmented governance systems, climate-related risks, emerging contaminants, and persistent inequalities in access to safe water resources. The findings underscore the importance of integrating sustainability, resilience, and social equity within water governance frameworks. Achieving long-term water security will require coordinated and transformative approaches that strengthen links between science, policy, and practice while promoting inclusive, resilient, and sustainable water management. Full article

Empirical research on organizational behavior contributes significantly to the examination of workplace impacts and to a deeper understanding of the underlying drivers of employee behavior. The long-term sustainability of organizations operating in VUCA environments depends largely on flexible adaptation and effective change management. The aim of this research is to examine the associations between workplace factors related to organizational change and employees’ intention to quit and perceived job insecurity during a crisis. Special emphasis is placed on stress caused by organizational change as an appraisal-based construct that may statistically mediate the association between employees’ psychological and physical strain and intention to quit. The research was conducted on a Hungarian sample, using a questionnaire survey among employees working in an SME environment. Based on the TSM (Transactional Stress Model), JD-R (Job Demands–Resources), and COR (Conservation of Resources) frameworks, hypotheses were formulated regarding the associations among workplace strain, stress caused by organizational change, intention to quit, and job insecurity. The hypotheses were tested using mediation models estimated within a structural equation modeling (SEM) framework. The results indicate a significant positive association between psychological strain and intention to quit, together with a significant indirect effect through stress caused by organizational change, a pattern consistent with partial mediation. Physical strain was also significantly and positively associated with intention to quit, but its indirect effect through stress caused by organizational change was not statistically significant. Indifferent leadership was significantly and positively associated with job insecurity, while the hypothesized indirect effect through stress caused by organizational change was not statistically significant. Full article

Regional landslide susceptibility screening can support sustainable land management by improving the allocation of limited geological-survey and field-verification resources toward locations requiring further investigation. An uncertainty-aware framework integrating ensemble learning, spatial validation, inter-model agreement, and probability uncertainty was proposed for regional landslide investigation prioritization in the Hualong–Xunhua region of the Upper Yellow River Basin, China. Using 281 manually interpreted landslide locations, 281 pseudo-absence samples, and twelve conditioning factors, four tree-based models (Random Forest, XGBoost, LightGBM, and CatBoost) were developed, and their probability outputs were combined through arithmetic averaging. Model performance was evaluated using stratified random validation and 10 km spatial block validation, while agreement and probability divergence were incorporated to define screening priorities. Random-validation AUC values ranged from 0.885 to 0.898, and spatial-validation AUC values ranged from 0.852 to 0.873; the Mean Ensemble achieved AUC values of 0.8961 and 0.8637, respectively. NDVI showed the highest mean normalized permutation importance (0.754), although sensitivity analysis demonstrated that other factors retained useful discrimination after removing NDVI and land-cover information. The final priority screening zone covered 862.72 km² (19.04% of the valid mapped area). The framework provides a transparent decision-support approach for sustainable land management in mountainous regions by identifying investigation priorities while accounting for model consistency and uncertainty. Full article

Contemporary career sustainability increasingly depends on workers’ capacity to continuously develop their competencies and on organizations’ ability to support this process. Drawing on the sustainable career framework and Conservation of Resources theory, this study examines how career commitment translates into outcomes relevant to career sustainability and under which organizational conditions this process is strengthened. Specifically, we test the mediating role of continuous learning in the relationships between career commitment and subjective career success and work-role performance, as well as the moderating role of organizational career management. Data were collected through an online survey from 203 employees. The proposed moderated mediation model was tested through path analysis using robust maximum likelihood estimation and bootstrapping. Results showed that career commitment was both directly and indirectly associated with career success and work-role performance, with continuous learning mediating these relationships. Organizational career management moderated the relationship between career commitment and continuous learning, resulting in stronger conditional indirect effects at higher levels of organizational career management. These findings are consistent with continuous learning representing a potential developmental pathway linking individual career commitment with sustainable career outcomes and highlight organizational career management as an important contextual condition associated with the strength of this pathway. Organizations should therefore complement employees’ career motivation dynamics with formal and informal career-development practices that enable continuous learning and support sustainable career outcomes. Full article

The withdrawal of international donor funding poses significant challenges for organisations serving marginalised populations, particularly where external resources are central to service delivery. This study explored the role of USAID withdrawal in shaping social work practice with queer individuals through a case study of one organisation in Mthatha, Eastern Cape, South Africa. Guided by Resource Dependency Theory, the study examined how the withdrawal of donor funding affected organisational sustainability, service provision, and professional social work practice within a queer-focused organisation. A qualitative case-study design employing reflexive thematic analysis was used. Data were collected through semi-structured interviews with twelve participants comprising organisational managers, social workers, and queer service users. The findings show that prior USAID support had enabled psychosocial services, advocacy, outreach and capacity building, yet also shaped organisational priorities and constrained professional autonomy. Following withdrawal, participants described programme reductions, staffing losses, increased workloads, diminished access to support for service users, and organisational uncertainty. In response, they articulated aspirations for funding diversification, stronger government partnerships and greater professional autonomy. The study concludes that while external funding can expand queer-focused social work services, heavy reliance on a single international donor creates vulnerabilities that threaten continuity of care. Sustainable service delivery requires diversified funding, strategic local partnerships and enhanced state support. Full article

The transition toward a digital, circular, and human-centric (Industry 5.0) economy is as much a financial and economic transformation as a technological one. Firms that build the innovation capacity to redesign how they create and capture value depend on managers and investors who allocate scarce capital under uncertainty. Yet, whether the financial support that employees perceive actually accompanies innovative behavior, or whether the human and organizational conditions surrounding it matter more, remains underexamined at the firm level, particularly in service industries. This study examines how employees’ perceptions of their organization’s financial capability and willingness to support innovation relate to innovative work behavior (IWB) and how those perceptions operate alongside perceived organizational climate (OC), using the sports economy—a large and innovation-dependent service sector—as a test case. A quantitative cross-sectional survey was conducted among 181 coaches employed in for-profit sports organizations in Lithuania. Data were analyzed using correlation and hierarchical multiple regression with demographic controls, a test of the climate–finance interaction, and diagnostic checks for common-method bias and multicollinearity. OC was positively associated with both IWB and perceived financial support for innovation. Perceived financial capability and willingness correlated with IWB at the bivariate level but added no significant variance once OC and the controls entered the model (ΔR² = 0.013, p = 0.230). The climate–finance interaction was likewise non-significant. OC remained the strongest correlate, accounting on its own for approximately 24% of the variance in IWB and for an additional 19 percentage points beyond the demographic controls. Because all measures were self-reported at a single point in time, these results are interpreted as associations rather than causal effects, and the pattern is consistent with—though does not establish—an interpretation in which perceived financial support accompanies innovative behavior only where the organizational climate already supports it. The study contributes to research on innovative work behavior, human resource management, and the human-centric premise of Industry 5.0, suggesting to managers and funders that innovation budgets are unlikely to translate into innovative behavior unless paired with motivation, learning opportunities, leadership support, and psychological safety. Full article

Accurate precipitation information is crucial for hydrological simulation and water resources management, especially in humid mountainous regions where complex terrain and spatially heterogeneous rainfall introduce considerable uncertainties. Satellite-based precipitation products provide important data sources for hydrological applications; however, their reliability and impacts on runoff simulations remain uncertain across different spatial and temporal scales. This study presents a multi-scale validation of four precipitation products, including GSMaP, PERSIANN, GPM IMERG, and CLDAS, and evaluates their effects on hydrological simulation in the Oujiang River Basin, a typical humid mountainous basin in southeastern China. Daily precipitation estimates from 2015 to 2020 were compared with gauge observations using statistical metrics and precipitation event indicators. The hydrological applicability of each product was further assessed by driving a semi-distributed Xin’anjiang model, with evaluations conducted at the basin outlet, seasonal periods, extreme rainfall events, and internal subbasins. Results showed that CLDAS achieved the best overall agreement with gauge observations, with lower systematic bias and higher capability in detecting precipitation variability. Satellite-only products exhibited larger uncertainties, particularly during extreme rainfall events and in areas with complex terrain. These precipitation uncertainties were further propagated into runoff simulations, leading to differences in hydrological performance among products. CLDAS-driven simulations showed the highest accuracy, achieving R², NSE, and KGE values of 0.706, 0.681, and 0.815, respectively, which were comparable to simulations driven by gauge-based precipitation. Multi-scale analysis revealed that product performance varied among subbasins due to differences in topography, rainfall characteristics, and human regulation. This study demonstrates the importance of multi-scale validation for quantifying uncertainties in satellite-based precipitation products and improving their application in hydrological modeling over mountainous regions. Full article

Accurate runoff forecasting serves as a fundamental basis for the scientific management of water resources and flood and drought risk mitigation. Owing to the nonlinearity, non-stationarity, and multi-scale temporal characteristics of runoff series, existing deep learning models still exhibit notable limitations in capturing long-term trends, responding to abrupt hydrological events, and ensuring model interpretability. The original Transformer relies on global self-attention, whose computational complexity increases quadratically with sequence length; it also has limited capacity to capture short-term local temporal dependencies such as rainfall–runoff relationships, and lacks prior constraints tailored to hydrological processes. To address these challenges, this study proposes a collaborative forecasting framework that integrates a gated convolutional Transformer (GCTrans) with an improved black kite algorithm (IBKA), enabling accurate, stable, and interpretable daily-scale runoff prediction. The GCTrans model consists of three customized modules: convolution-enhanced positional encoding (CEPE), which combines learnable positional encoding with local causal convolution to strengthen the temporal association of adjacent rainfall–runoff events, thereby providing a hydrologically meaningful positional reference for the attention mechanism; gated convolutional attention (GCA), which adopts a dual-path parallel architecture comprising global self-attention and local causal convolution to adaptively fuse long-term seasonal patterns with short-term storm-induced variations, thus capturing both baseflow evolution and flood peak responses; and a temporal gated output layer (TGOL), which performs adaptive feature weighting along the temporal dimension to selectively enhance the contribution of critical driving periods associated with extreme flood events, thereby improving the flood peak prediction accuracy. In addition, an improved black kite algorithm (IBKA) was developed by incorporating Tent chaotic initialization to enhance initial population diversity and introducing cosine adaptive inertia weights to dynamically balance global exploration and local exploitation, effectively alleviating premature convergence in high-dimensional hyperparameter spaces. Validation using data from the ME-Inland snowmelt-dominated watershed and the OR-Coastal storm-driven coastal watershed in the United States demonstrated that the GCTrans model consistently outperformed benchmark models including TCN, LSTM, Transformer, and Informer. After synergistic optimization with IBKA, both prediction accuracy and stability were further improved. SHAP-based interpretability analysis revealed that the model’s feature response patterns are statistically consistent with the rainfall–runoff generation mechanisms of the study basins: temperature-related drivers dominate in the inland watershed, while precipitation plays a dominant role in the coastal watershed, confirming the hydrological plausibility of the model’s decision-making logic. The integrated framework—encompassing model architecture, optimization algorithm, and interpretability—offers a valuable methodological reference for deep learning-based runoff forecasting in complex hydrological settings. Full article

Monitoring water quality is critical for protecting freshwater ecosystems and supporting sustainable water resource management. Lake Tana, Ethiopia’s largest freshwater lake, faces increasing agricultural and urban pressures, while conventional monitoring remains costly and spatially constrained. This study developed an integrated Sentinel-2 remote sensing and machine learning framework to estimate chlorophyll-a (Chl-a), turbidity (TU), total nitrogen (TN), and total phosphorus (TP) using 858 in situ observations and Google Earth Engine. Random Forest (RF), Extreme Gradient Boosting (XGB), Artificial Neural Networks (ANN), and Support Vector Regression (SVR) were evaluated using spectral bands, band combinations, and indices. RF provided the best predictions for Chl-a (R² = 0.94 ± 0.01; RMSE = 2.11 ± 0.18 µg L⁻¹; MARE = 5%) and TP (R² = 0.91 ± 0.01; RMSE = 0.26 ± 0.01 mg L⁻¹; MARE = 8.7%), whereas XGB performed best for TU (R² = 0.93 ± 0.01; RMSE = 5.17 ± 0.43 NTU; MARE = 7%) and TN (R² = 0.94 ± 0.02; RMSE = 0.18 ± 0.02 mg L⁻¹; MARE = 9.9%). The strong predictive performance of RF and XGB across both optically active and inactive parameters demonstrates the capability of the framework to capture complex spectral water quality relationships and support spatially continuous assessment. Significant seasonal differences (p < 0.001) showed higher dry season Chl-a (137.1%) and higher wet season TP (21.7%), TU (7.5%), and TN (3.9%). Long-term paired observations further indicated increases in Chl-a (73.7%), TN (30%), and TP (14.3%) from December 2016 to December 2025 (p < 0.001). Spatial hotspot analysis revealed strong clustering of TU, TN, and TP, particularly around tributary mouths and nearshore areas, highlighting priority zones for monitoring and intervention. Overall, integrating field observations, Sentinel-2 imagery, and machine learning provides an accurate, scalable, and cost-effective approach for monitoring diverse water quality parameters. The framework offers a transferable solution for strengthening freshwater monitoring in data-scarce regions and supporting sustainable management of lakes under increasing water quality pressures. Full article

Japan’s shrinking population lowers current material demand but may accelerate retirement of inherited buildings and infrastructure. We develop a normalized two-material vintage stock–flow model that separates recovery, displacement quality, domestic retention, processing capacity, and downstream outlets. Under a stylized 95% mineral recovery target, the reference quality threshold is 0.374. Along the 2025–2070 demographic path, cumulative material-cycle emissions are 16.9% below a frozen-demography counterfactual; additional legacy release offsets 4.0% of gross scale savings. Advancing both quality and capacity lowers emissions by 6.55% relative to both-delayed preparation; a strict inflow-linked outlet reduces this difference to 2.97%. An illustrative concrete–asphalt–metal sensitivity changes the sign of the release offset in some designs. A restricted official output-composition weight combined with explicit displacement-value assumptions places constructed quality on either side of the reference threshold. Conditional cost-ratio comparisons and 0%, 1%, and 3% time weighting connect physical constraints to economic evaluation without estimating Japanese costs. We distinguish inventory emissions, cost-effectiveness conditional on package costs, and social welfare. These normalized diagnostics inform sustainable resource management. They are not national forecasts, facility validation, or investment recommendations; high recovery alone cannot ensure effective circularity. Full article

Evapotranspiration (ET) is a major component of the water and energy cycle, influencing hydrologic processes, agricultural management, groundwater recharge, and land surface–atmosphere interactions. Water balance methods for estimating ET are widely used because of their direct connection to the conservation of mass and their applicability across scales. This review examines the theoretical basis and recent developments in water balance approaches for estimating ET across different hydroclimatic regimes. It summarizes classic soil water balance methods, physically based hydrologic models, remote-sensing approaches, and integrated machine learning techniques. Major themes include uncertainty in precipitation, runoff, and storage estimates; groundwater flow; water balance closure; spatial heterogeneity; and the integration of Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat, and ground-based observations. More recently, hybrid physics-based and machine learning approaches have advanced ET estimation by combining process-based understanding with data-driven methods. Advances in computational hydrology, data assimilation, and Earth observation datasets are improving applications related to irrigation management, drought assessment, climate adaptation, and water-resource planning. Challenges remain in quantifying uncertainty, assessing model transferability, and representing groundwater and storage dynamics under changing hydroclimatic conditions. Overall, the review highlights the continued importance of water balance approaches for understanding ET dynamics and supporting sustainable water-resource management. Full article

Enhancing urban resilience is a critical pathway for sustainable development in mountainous cities facing multi-hazard scenarios and rapid urbanization pressures. From an emergency management perspective, this study develops a data-informed urban resilience assessment framework to identify key drivers of urban resistance, response, and recovery capacities. Methodologically, a multidimensional indicator system is built on the Pressure–State–Response (PSR) framework, with quantifiable metrics selected to characterize urban operational features. Data-driven screening identifies core variables, and the Random Forest algorithm quantifies indicator importance to construct a comprehensive evaluation model. A representative mountainous city in China—Lishui—is analyzed to validate the approach, drawing on a 15-year panel dataset (2010–2024) that captures pre- and post-disaster dynamics. Results show that population agglomeration and industrial activities dominate risk pressure; economic development and public services underpin system stability; and medical resource allocation and public emergency response capacity significantly boost resilience. Temporal analysis reveals three distinct stages—initial development, fluctuating adjustment, and rapid improvement—reflecting changes in infrastructure, institutional capacity, and urban development. Urban resilience emerges from multidimensional synergies among pressure, state, and response factors, highlighting the importance of integrated infrastructure planning and cascading risk mitigation. The framework provides a reference for identifying mountainous cities’ key resilience factors, while the findings may help decision-makers identify risks, formulate resilience strategies, and optimize sustainable development pathways. Full article

The growing water demand and shift in climatic conditions accentuate drought impacts on regional water resources. This study applies a comparative MCDM framework based on Entropy weighting, TOPSIS and VIKOR to evaluate monthly drought conditions using multiple climatic variables. Erzurum Province in eastern Türkiye, characterized by a high-altitude continental climate and a snow-influenced hydrological regime, was selected as the study area. Twelve monthly climatic parameters covering the 2012–2024 period were analyzed. Within the 2012–2024 record, Seasonal Mann–Kendall tests indicated significant upward tendencies in temperature, mean vapor pressure, and total vapor pressure, whereas precipitation showed no statistically significant tendency. Given the relatively short observation period, these results represent mid-term tendencies and should not be interpreted as evidence of long-term climatic change. Entropy weighting yielded the highest weights for evaporation (0.3091), precipitation (0.1813), and temperature (0.1524), representing matrix information density rather than direct physical causation. Using the adopted literature-based classification thresholds, Entropy-TOPSIS classified 97 months (67.36%) as drought-affected, compared with 70 months (48.61%) for Entropy-VIKOR. Sensitivity analysis showed that these absolute drought frequencies varied with the selected threshold values. However, their underlying continuous scores exhibited strong inverse rank alignment (Spearman’s ρ = −0.917, p < 0.001), achieving an 81.25% categorical agreement (Cohen’s κ = 0.629). Sensitivity testing confirmed rank structure stability even upon excluding evaporation, though TOPSIS displayed greater parameter sensitivity than VIKOR. Standardized indices (SPI and SPEI) showed weak overall correspondence, aligning notably only at the 1-month scale. The results show that TOPSIS and VIKOR can rank monthly conditions similarly, but drought class assignments are sensitive to methodology and thresholds. In high-altitude and snow-affected environments like Erzurum, the combined consideration of multiple climate variables can contribute to a more cautious assessment of drought periods and support the sustainable management of water resources. Full article

With the rapid advancement of quantum computing technology, IoT edge devices widely deployed in industrial settings are facing severe threats to their quantum-resistant security. This work focuses on the challenges of adapting Quantum Key Distribution (QKD) and Post-Quantum Cryptography (PQC) for resource-constrained environments. This work systematically identifies four critical obstacles: difficulties in physical integration due to device heterogeneity; constraints imposed by the limited computing power and storage resources of edge nodes on PQC algorithm execution; compatibility gaps between QKD/PQC and existing industrial protocols (such as Modbus and Profinet); and the engineering cost pressures associated with large-scale deployment. To address these issues, the paper proposes three synergistic lightweight countermeasures: (1) a tiered deployment architecture for quantum-safe security that accommodates device heterogeneity, enabling dynamic trade-offs between security strength and resource overhead; (2) middleware for quantum-classical hybrid network protocol coordination, ensuring seamless integration between QKD/PQC and industrial control protocols; and (3) a three-tier distributed key management mechanism (edge node, network proxy, and central system) that offloads computation- and storage-intensive tasks to high-resource nodes, thereby alleviating bottlenecks at the edge. Experimental validation within a smart manufacturing factory’s sensor network demonstrates that the proposed scheme maintains post-quantum security capabilities (with QKD integration validated at the proof-of-concept level) while reducing per-node deployment costs by approximately 92.4% compared to pure QKD solutions and by about 32% compared to pure PQC solutions, all while facilitating seamless, non-disruptive upgrades. This study indicates that lightweight algorithm design, hardware-software co-optimization, and tiered architectural deployment constitute a viable pathway for advancing IoT edge security into the quantum-safe era. Full article