Search Results (42,900)

This study investigates machine learning techniques for predicting the behavior of proton exchange membrane electrolyzers, which are vital for sustainable hydrogen production. This work addresses these challenges by integrating artificial neural networks to develop predictive models capable of capturing the performance of proton exchange membrane electrolyzers with high accuracy. This research utilizes a multi-layer perceptron network architecture, optimized through rigorous data preprocessing, parameter tuning, and error minimization strategies. The dataset used was based on published PEME numerical simulation datasets and encompasses key performance indicators, including stack voltage, water transport, and electrochemical reactions. The trained artificial neural networks models achieved mean squared error values of 3.66 × 10⁻⁵ and 9.75 × 10⁻⁶, with correlation coefficients of 0.99996 and 0.99958, demonstrating near-perfect predictive accuracy. A comparative benchmarking study against alternative regression algorithms revealed that the proposed MLP models significantly outperformed Gradient Boosting and Random Forest by several orders of magnitude, thereby establishing a higher level of persuasiveness and reliability for the developed framework. Average deviation rates of 0.11% and −0.01% further validated model reliability. The novelty of this work lies in its comprehensive approach, which goes beyond isolated metrics by addressing interactions across system parameters. This integrated framework enables enhanced prediction, control, and optimization of proton exchange membrane electrolyzer’s performance, setting a new benchmark for leveraging machine learning in hydrogen energy systems. These findings pave the way for scalable, cost-effective solutions to improve proton exchange membrane electrolyzers’ efficiency and operational reliability. Full article

Background/Objectives: Drug resistance is a major challenge in cancer therapy, and mitochondria contribute to this process by controlling both metabolic adaptability and cell survival signaling. Mitophagy, the selective lysosomal removal of dysfunctional mitochondria, has been implicated in therapy resistance, yet its role in sunitinib-resistant renal cancer remains poorly defined. Methods: In this study, acquired sunitinib resistance was established in ACHN renal cancer cells through eight months of stepwise dose escalation. Initial selection conditions were determined using CCK-8 viability and crystal violet colony assays in parental ACHN cells, whereas sustained proliferation under continuous sunitinib exposure was used as the operational criterion for the resistant phenotype. Resistant and parental sensitive cells were treated with 25 µM and 50 µM succinic acid, alone or in combination with sunitinib. Gene expression of BNIP3, NIX, FUNDC1, LC3, PINK1, Parkin, PGAM5, SRC, LONP1, and ATP5F1A was measured by RT-qPCR, and BNIP3 and NIX protein levels were assessed by ELISA. Results: Resistant cells showed significant upregulation of receptor-mediated mitophagy components BNIP3, NIX and FUNDC1 (p < 0.05), with no significant change in LC3, alongside suppression of PINK1, Parkin, and mitochondrial homeostasis-associated genes LONP1, PGAM5, and ATP5F1A (p < 0.05). Succinic acid predominantly reduced BNIP3 and NIX protein levels in both cell lines and suppressed BNIP3, NIX, and LC3 mRNA expression in resistant cells. In contrast, the sunitinib + 50 µM succinic acid combination selectively increased PARKIN, PGAM5, LONP1, and ATP5F1A expression in resistant cells (2.49- to 5.98-fold; p < 0.005), a pattern not observed in parental cells. Conclusions: These findings indicate that sunitinib resistance in ACHN cells is associated with upregulated transcription of receptor-mediated mitophagy components and downregulated transcription of PINK1/Parkin pathway genes, and that exogenous succinic acid selectively upregulates PARKIN and other mitochondrial homeostasis-related gene expression in resistant, but not parental, cells. Full article

Flood disaster risk assessment serves as an important foundation for formulating regional sustainable development strategies. This study establishes a risk assessment model for flood disasters in small and medium-sized rivers based on a theoretical framework integrating Geographic Information Systems (GIS), the Delphi method, and Bayesian networks (Delphi–BNs). An indicator system for the assessment was developed from three dimensions: hazard, vulnerability, and exposure. Hazard is represented by flood inundation area and depth; vulnerability is indicated by population distribution and economic layout; and exposure is reflected by road accessibility. By constructing a GIS-based Bayesian network and employing the Delphi method to create a probabilistic and spatially explicit model, this approach quantifies various sources of uncertainty in the assessment process, enabling a probabilistic expression of risk. Based on the risk assessment results, a stratified, phased flood emergency rescue and personnel transfer plan was established, designating extremely high-risk areas as the core zones for the first phase of personnel transfer, high-risk areas as the second-phase rescue zones, and medium-risk areas as the third-phase rescue zones, thereby providing clear operational guidance for flood emergency response in the basin. The Delphi–BN assessment framework developed in this study focuses on the core elements of flood disaster risk formation, organically integrates expert experience with spatial big data, and effectively overcomes the limitations of traditional assessment methods, such as strong subjectivity, insufficient accuracy, and poor quantification. It achieves a refined and quantitative assessment of flood risk in small and medium-sized river basins in semi-arid regions. The outcomes of this research contribute to a clearer understanding of both the driving mechanisms and the spatial patterns of regional flood risk. Furthermore, they establish a scientifically credible and operationally relevant foundation for key disaster-response decisions, encompassing timely emergency actions, phased population transfers, and the optimized deployment of limited emergency resources. Full article

Larval fish assemblages provide critical insights into reproductive dynamics, biodiversity baselines, and ecosystem resilience, yet remain poorly documented in the Colombian Pacific. This study analyzed ichthyoplankton diversity and spatial turnover across six neritic localities sampled between 2008 and 2022. Standardized plankton tows yielded 43,277 larvae representing 291 morphospecies, 150 genera, 66 families, and 38 orders. The richness observed accounts for ~34% of fish species listed for the Eastern Tropical Pacific and 15% of adult bony fishes reported for the Colombian Pacific, exceeding previous records. Diversity patterns revealed pronounced spatial heterogeneity: Cupica emerged as a biodiversity hotspot, Pizarro and Tortugas were dominated by few taxa, and Sanquianga exhibited high evenness despite moderate richness. Beta diversity analyses indicated turnover exceeding 60% and dissimilarity surpassing 70%, underscoring strong locality identity shaped by oceanographic gradients, geomorphology, and estuarine dynamics. Dominance patterns highlighted contrasting ecological strategies, with cosmopolitan taxa such as Cetengraulis mysticetus coexisting alongside habitat-restricted species of Labrisomus. These findings establish the Colombian Pacific neritic zone as a reservoir of genetic and functional diversity, with high richness and turnover reinforcing its role in sustaining ecosystem functioning and fisheries productivity. Methodological handicaps—including reliance on morphology-based identification, formalin preservation limiting molecular analyses, and uneven temporal coverage—constrain taxonomic resolution, yet the ecological patterns observed provide a robust foundation for conservation planning. Future research integrating molecular tools, trait-based approaches, and long-term monitoring will be essential to refine diversity estimates and strengthen adaptive management under scenarios of climate variability and increasing anthropogenic pressures in the Eastern Tropical Pacific. Full article

Open-pit coal mining in arid and desert regions causes extensive and persistent surface disturbance, yet long-term monitoring of disturbance and backfilling processes remains challenging. Existing time-series change detection approaches can identify spectral changes but provide limited information on mining disturbance types and their evolution pathways. To address this issue, an automated surface disturbance detection method (Auto-SD) was developed for open-pit coal mines in arid and desert environments. This method integrates disturbance-type identification and temporal information extraction using the tasseled cap brightness (TCB) component to characterize changes associated with surface material exposure and accumulation. Using Landsat imagery from 1990 to 2023, Auto-SD was applied to 89 open-pit coal mines in China’s arid and desert regions, achieving an overall classification accuracy of 0.84. The cumulative disturbed area reached 423.10 km², while the internal dumping area reached 94.25 km², indicating limited backfilling recovery. Disturbance intensified after 2006, whereas backfilling lagged behind, forming a trajectory of rapid expansion, delayed recovery, and gradual stabilization. Spatially, mining areas exhibited a progressive transition from external dumping to internal dumping and backfilling. Furthermore, cumulative pit area generally followed an S-shaped growth pattern with mining duration. These findings provide new insights into long-term mining landscape evolution and support ecological restoration assessment and sustainable resource management in arid mining regions. Full article

High-altitude wetlands in the Caucasus Anatolia transition zone remain among the least-documented avian habitats in the Western Palearctic. Standardised avian biodiversity assessment in such systems provides essential evidence for sustainable wetland management, supporting the conservation planning and long-term ecological monitoring needed to safeguard these ecosystems under increasing anthropogenic pressure. We report on the avifauna of Çalı Lake (2237 m a.s.l.; 391 ha; Kars Province, Türkiye), a nationally designated wetland located on the Central Asian Flyway, based on five systematic survey periods conducted from March 2024 to Spring 2026 using line transects and point counts, combined with a 25 × 25 m grid-based GIS analysis encompassing 498 cells. Approximately 31 ha of the core open-water and marsh perimeter within the 391 ha designated boundary is covered; upland steppe and pasture zones beyond the active survey perimeter were excluded. A total of 154 species belonging to 18 orders and 41 families were recorded, representing approximately 30.5% of Turkey’s national checklist. IUCN status assessment identified two Endangered species, Neophron percnopterus and Oxyura leucocephala, two Vulnerable, five Near Threatened, and 145 Least Concern species. Grid-level species richness averaged 1.47 ± 1.20 per cell per period; cumulative richness per grid reached 7.62 ± 2.73 across all five survey periods. Spearman rank correlation between per-grid richness (S) and abundance (N) was consistently strong across all five periods (ρ = 0.52–0.60; all p < 0.001). A Friedman test indicated significant overall variation across periods (χ²(4) = 127.73, p < 0.001, Kendall’s W = 0.064, a negligible effect size by conventional benchmarks, indicating that the statistically significant variation reflects trivially small per-cell richness differences at this block size). Bonferroni-corrected post hoc Wilcoxon tests revealed that all significant contrasts involved the 2024 Spring–Summer period or the 2026 partial Spring window, while the four fully comparable 2024 Autumn–2025 periods showed no significant differences. A Lorenz concentration curve yielded a Gini coefficient of 0.351, with the top 10% of grid cells concentrating 24.0% of all individual detections in the central and south-western lake zones. Collectively, these findings document Çalı Lake as a species-rich high-altitude wetland with significant conservation value, and establish a reproducible spatial and temporal baseline for long-term ornithological monitoring. These results demonstrate the value of standardised biodiversity assessment as a practical tool for sustainable wetland governance and align with international sustainability frameworks, including the UN Sustainable Development Goals on life on land and clean water and sanitation. Full article

Aesthetic perception of rural streetscapes reflects individuals’ cognitive responses to their surroundings and is central to understanding how landscape preferences are formed. Existing studies using Scenic Beauty Estimation (SBE) are constrained by incomplete indicator systems and overreliance on linear approaches. This study proposes an interpretable machine learning framework that integrates multi-source data to examine the nonlinear influences of streetscape features on SBE. Using Sanguan Village, a water-networked settlement in Jiangsu, we developed a 24-indicator system spanning color, spatial, natural, artificial, and cultural dimensions. Based on 523 panoramic images and aesthetic ratings from 1175 respondents, we compared OLS, DT, MLP, SVR, RF, and XGBoost models. The best-performing XGBoost, combined with SHAP analysis, revealed threshold effects and interaction patterns among variables. Green visibility, architectural aesthetics, building visibility, sky visibility, environmental coordination, and water are the top six feature variables most strongly associated with rural streetscape aesthetic perception, and each exhibits threshold effects. The saturation threshold for green visibility is 0.153, and architectural aesthetics can only make a positive contribution when its score exceeds 3.815. The appropriate range for building visibility is below 0.452, while the optimal value for sky visibility is approximately 0.194. We also explored the context-dependence of these threshold effects across urban and rural settings. This study proposes streetscape optimization strategies focusing on screening key factors, controlling their thresholds, and coordinating the allocation of streetscape features. The interpretable analytical framework for rural scenic beauty established in this research can facilitate evidence-based landscape optimization and provide scientific support for sustainable rural development and tourism in this case. Full article

Climate risks drive dynamic adjustments to global climate policies, creating significant climate policy uncertainty (CPU). This uncertainty profoundly affects enterprises’ survival and sustainable development. A key question emerges: how do enterprises’ perception of CPU influence their resilience? To answer this, this study adopts a capability perspective and empirically examines the impact of climate policy uncertainty perception (CPUP) on enterprise resilience (RESI) and the underlying mechanism. Using panel data on Chinese A-share listed companies on the Shanghai and Shenzhen Stock Exchanges from 2009 to 2023, the study defines CPUP as the interaction between a news-based provincial CPU index and the frequency of climate risk words in annual report texts, and measures RESI with the entropy weight method across four dimensions (business volatility, long-term growth, short-term performance, and enterprise survival). Panel regression with fixed effects indicates that CPUP significantly enhances RESI. A one-standard-deviation increase in CPUP raises RESI by approximately 0.0019 index units, equivalent to about 2.2% of the standard deviation of RESI. This effect is more pronounced for enterprises in the eastern and central regions and in high-carbon industries. Mechanism tests confirm that CPUP boosts RESI by optimizing management capabilities and strengthening development capabilities, revealing a capability-driven path between CPUP and RESI. This study enriches the theoretical understanding of CPU’s economic consequences and RESI antecedents from a capability perspective. It also provides empirical references for enterprises to build resilience amid policy fluctuations and for policymakers to formulate regionally differentiated climate policies. Full article

The rapid transition toward electrified mobility and climate neutrality has prioritized the structural and environmental optimization of battery electric vehicle (BEV) subsystems. While vehicle lightweighting enhances operational efficiency, the production phase of structural enclosures and battery cells frequently introduces severe environmental and economic impacts and supply chain vulnerabilities. This study presents a comprehensive cradle-to-gate environmental life cycle assessment (LCA), life cycle costing (LCC), and semi-quantitative social assessment of alternative battery housing materials and battery cell architectures. To achieve a functionally accurate comparison, alternative materials, including a novel recyclable thermoplastic acrylic sheet molding compound (SMC), commercial thermoset SMCs, aluminum (AlMg3), and stainless steel, are evaluated using an analytical stiffness- and strength-equivalent methodology across three real-world geometric demonstrators. Simultaneously, lithium iron phosphate (LFP) liquid electrolyte prismatic cells and solid-state polymer pouch cells are assessed. Material-level results indicate that, while aluminum minimizes the structural mass, primary aluminum manufacturing exhibits the highest global warming potential and processing costs. Conversely, Polytec SMC and Elium SMC achieve the lowest environmental impacts alongside competitive total production costs. At the cell level, prismatic LFP architectures display superior environmental performance compared to solid-state pouch cells, which suffer from energy-intensive processing and lower volumetric capacity normalization. Demonstrator-level aggregation reveals that the electrochemical cells heavily dominate the environmental and economic footprint of the complete assembly, with the housing accounting for less than 5% of the total global warming potential (GWP) and 1% of the total costs. The social assessment reveals moderate and comparable performance across all systems, with slight advantages for thermoplastic composite-based configurations in terms of circularity potential and innovation perception. Overall, the study highlights the critical importance of the cell architecture and manufacturing processes in determining battery system sustainability, while demonstrating the relevance of lightweight composite housings in reducing the structural mass with a minimal environmental penalty. Full article

The rapid growth of rural e-commerce is reshaping rural development, yet its implications for territorial sustainability remain unclear. Using panel data from 1503 Chinese counties from 2014 to 2022, the effects of Taobao Village development and the mechanism of territorial sustainability are examined through causal, spatial, and mechanism analyses. The results show the following: (1) Taobao Village development significantly improves territorial sustainability, with stronger effects observed in central and northeastern China, while the impacts vary considerably across regions due to differences in economic foundations, digital infrastructure, and land-use conditions. (2) Spatial analysis reveals that the sustainability-enhancing effects of Taobao Villages are mainly localized, with no significant spillover effects to neighboring counties, indicating the constraints of existing administrative and spatial governance systems. (3) The ecological land-use change induced by Taobao Village development is characterized by quantity reduction with potential quality upgrading. The development of Taobao Village has reduced the ecological land area, but it does not mean a decline in ecological functions. The converted land may be composed of low-quality ecological plots, and the fiscal benefits driven by e-commerce and China’s land use compensation policy may maintain or enhance the overall ecological function. These findings highlight the importance of integrating digital rural development with ecological sustainability and territorial spatial governance. The study provides policy implications for promoting resilient and sustainable rural transformation through differentiated land-use strategies. Full article

This study investigates the effect of Arundo donax L.-derived lignin on the rheological behaviour, mechanical performance and oxidative ageing resistance of bitumen. Arundo donax is a fast-growing invasive grass with high lignin content, representing a promising sustainable biomass source for bitumen modification. Lignin was extracted via the Acid Detergent Lignin method, yielding a fine powder (50–300 μm). The incorporation of 6 wt% lignin into a 35/50 paving-grade bitumen induced significant changes in binder behaviour. Infrared spectroscopy (FTIR) confirmed the polyaromatic and oxygenated nature of lignin and indicated that its interaction with bitumen is primarily physical, involving polar intermolecular interactions rather than chemical bonding. Lignin modification significantly increased stiffness, elasticity, and rutting resistance, as evidenced by higher softening point, complex modulus, and recovery after creep loading. Furthermore, FTIR analysis confirmed a reduced susceptibility to oxidative ageing, demonstrated by lower increases in carbonyl and sulfoxide indexes after ageing. This suggests distinct antioxidant activity associated with the phenolic structures of lignin. Despite these benefits, severe long-term ageing led to a marked reduction in fatigue life, ductility, low-temperature cracking resistance, and adhesive properties. Overall, these results demonstrate that Arundo donax-derived lignin is a promising sustainable modifier for bitumen, though optimisation of the dosage and blending conditions is necessary to balance durability against long-term fracture performance. Full article

This study employs a qualitative research design to examine cultural values concerning marriage and family through Turkish proverbs and to analyze how these values intersect with theological meanings. Situated at the intersection of folklore and Islamic ethics, it explores how oral tradition articulates, transmits, and regulates the values, norms, and moral boundaries associated with marriage and family life. According to the findings, proverbs portray the family as a foundational institution imbued with sacred significance, while marriage is construed as a normative form of life that protects the individual from moral, social, and emotional fragmentation. This discourse accords with Qur’anic and Prophetic principles that encourage marriage and associate family life with a consciousness of servitude to God. The study further indicates that, in Turkish proverbs, women are imagined as central figures who sustain domestic harmony, contribute to family well-being, and shape the moral fabric of the household. One of the study’s most significant findings is that polygyny, although recognized in Islamic law as a conditional legal dispensation, is treated in Turkish proverbs through a reserved and often critical cultural discourse. Recent empirical findings indicate that traditional attitudes retain a notable degree of continuity under modern social conditions. Within this framework, the study examines cultural understandings of marriage and family reflected in Turkish proverbs in relation to religious norms and Islamic family ethics, with particular attention to the relationship between oral culture and religious and moral frameworks of meaning. Full article

We examined the relationships between self-directed learning skills, university belonging, and student engagement in open online and distance learning environments within the framework of Self-Determination Theory. The research was conducted using a correlational survey model. Data were collected from 696 university students enrolled at a large-scale open, online and distance learning institution in Türkiye. We used three instruments measuring self-directed learning skills, university belonging, and student engagement. The data were analyzed using partial least squares structural equation modeling. The findings indicate that university belonging significantly and positively predicts all dimensions of student engagement. While self-control skills, learning skills, and sustaining the desire to learn significantly explain university belonging, metacognitive awareness and ability to identify sources were found to have no significant effect. Furthermore, it was found that university belonging acts as a significant mechanism in the relationships between self-control skills, learning skills, and sustaining the desire to learn, and student engagement. In this open and distance learning context, student engagement was not explained by individual learning skills alone. University belonging may therefore be one mechanism that partly accounts for the association between self-directed learning and academic participation. The study contributes by testing self-directed learning at the level of its dimensions, by identifying university belonging as a mechanism that partly accounts for its association with engagement, and by situating this model in a large-scale open and distance learning setting. Full article

Climate change is becoming an increasingly important source of economic and health vulnerability in developing countries. This study examines the dynamic relationship between climate change and poverty across 22 countries in the Middle East and North Africa (MENA) region from 2000 to 2023. Poverty is captured through two indicators: household consumption expenditure, the monetary dimension, and infant mortality, the non-monetary dimension. Methodologically, the analysis relies on a panel autoregressive distributed lag (panel ARDL) model, estimated using the Pooled Mean Group (PMG) and Mean Group (MG) approaches. The results reveal a long-run relationship among climatic variables, macroeconomic factors, and poverty-related indicators. In the long run, precipitation is associated with a decline in household consumption expenditure, while temperature is associated with higher infant mortality, indicating a deterioration in both monetary and health-related well-being under changing climatic conditions. In the short run, rising temperatures are also associated with lower household consumption expenditure, revealing the immediate vulnerability of living standards to climate shocks. In addition, GDP per capita is associated with higher household consumption and lower infant mortality, while education is associated with lower health-related poverty. Inflation appears to exacerbate poverty, whereas the positive association between health expenditure and infant mortality suggests reverse causality or inefficiencies in the allocation of health resources. These findings highlight the need to articulate climate adaptation strategy, macroeconomic stability, education investment, and improved efficiency of health spending in order to achieve sustainable reduction in poverty in the MENA region. Full article

Tourism destinations increasingly require data-driven tools to interpret competitiveness, capacity use, housing-related pressure, and post-shock change. This study develops a machine-learning-based destination marketing intelligence framework for a non-probability analytical sample of 29 European destinations observed annually between 2015 and 2024. Destinations were retained when sufficiently comparable information was available across the common study window for the six raw indicators required to construct the performance-pressure framework. Tourism demand, accommodation capacity, labor, investment intensity, and housing-cost pressure are transformed into normalized indicators and analyzed using principal component analysis, k-means clustering, classification trees, random forests, and robustness checks. The first three principal components explain 84.2% of total variance. Although silhouette favors three clusters, the four-cluster solution provides stronger Calinski–Harabasz separation and leave-one-destination-out stability. The retained solution identifies four relative destination-state configurations: lower performance with near-average pressure; high rotation, moderate performance, and lower pressure; high performance with lower pressure; and extreme housing pressure. Under leave-one-destination-out validation, random forests achieve 86.6% accuracy and a Cohen’s kappa of 76.9%. The configurations are pressure-sensitive marketing-intelligence categories rather than comprehensive sustainability classifications or permanent country typologies. Full article