Search Results (62)

The urban heat island (UHI) effect in Andean cities is a critical yet understudied phenomenon, where complex topography and rapid urbanization uniquely alter local climates. This research analyzes the spatiotemporal evolution of the surface UHI and its linkage to urban morphology in Ayacucho, Peru, through a 40-year multi-temporal analysis (1986–2016) using Landsat images. We developed a synthetic Urban Heat Island Index (UHII) through Principal Component Analysis (PCA), integrating land surface temperature (LST), spectral indices, and urban morphological parameters. Our results identify a critical transition in 2006, with the emergence of persistent heat spots driven by unplanned expansion. The surface UHI intensity reached urban-rural differences of 4.31 °C (day) and 5.82 °C (night), showing a positive trend. Urban morphology was a key determinant, with high-density blocks exhibiting a minimum nocturnal LST 3.53 °C higher than low-density areas. Statistical trend tests confirmed a significant intensification, while a strong negative correlation with vegetation indices (R² = 0.97) underscored the vital mitigation role of green infrastructure. This study provides academics with a robust methodological framework for UHI analysis in complex terrains. For public and private urban managers, it offers spatially explicit evidence to prioritize actionable strategies, such as integrating green infrastructure and regulating urban form, to enhance climate resilience in Andean cities. Full article

The skin of vertebrates serves as a crucial interface with the external environment. In fish, it performs various functions, mainly offering protection against pathogens through the action of specialized cells. Cyclostomes, such as hagfish, lack scales and rely heavily on mucus for defense. These jawless vertebrates possess specialized glands that produce a unique mucous exudate when threatened, forming a thick slime that can clog the gills of predators. This substance, composed of mucus and filamentous proteins, offers hagfish a distinct evolutionary advantage and may explain their survival among extinct agnates. These proteins are produced in the cytoplasm of epidermal thread cells, which are unique to hagfish and contain coiled, intermediate filaments. Despite extensive research on thread cell morphology, their roles remain poorly understood. This study investigates the putative defense function of epidermal thread cells in three hagfish species, Eptatretus cirrhatus (J. R. Forster, 1801), Eptatretus stoutii (Lockington, 1878), and Myxine glutinosa (Linnaeus, 1758), using immunohistochemistry, confocal microscopy, and bioinformatics techniques to better understand their contribution to hagfish immunity and ecological resilience. Full article

Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems. Full article

We present an enhanced Extreme Value Analysis (EVA) framework designed to improve the forecasting of extremely low-production events in photovoltaic (PV) systems and to reveal the key inter-variable relationships governing performance under extreme conditions. The proposed Extreme Value Dynamic Benchmarking Method (EVDBM) extends classical EVA by integrating the Dynamic Identification of Significant Correlation (DISC)-thresholding algorithm and explainable AI (XAI) mechanisms, enabling dynamic identification and quantification of correlation shifts during extreme scenarios. Through a combination of grid and Bayesian optimization, EVDBM adaptively fine-tunes variable weights to improve fit, interpretability, and benchmarking consistency. By transforming return values predicted via EVA into dynamic benchmarking scores, EVDBM evolves static tail modeling into a data-driven, explainable benchmarking system capable of identifying critical vulnerabilities and resilience patterns in real time. Applied to real PV production datasets, EVDBM achieved an average improvement of 13.2% in correlation-based $R_{corr}^2$ and demonstrated statistically significant reductions in residual error ($p_t<0.01$) in the João dataset, confirming its robustness and generalizability. Quantile-to-quantile analyses further showed improved alignment between modeled and empirical extremes, validating the method’s stability across distributional tails. Ablation studies revealed cumulative gains in interpretability and predictive stability in the EVA → EVDBM → EVDBM + XAI progression, while computational complexity remained near-linear with respect to input dimensionality. Overall, EVDBM delivers a transparent, statistically validated, and operationally interpretable framework for extreme event modeling. Its explainable benchmarking structure supports actionable insights for risk management, infrastructure resilience, and strategic energy planning, establishing EVDBM as a generalizable approach for understanding and managing extremes across diverse application domains. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Amid intensifying geopolitical tensions and global uncertainties, regional economies face mounting pressures that threaten both stability and sustainability. Against this backdrop, building resilient regional systems has become a central issue in sustainable urban development. As a key driver of resilience, innovation has been central to China’s development agenda. Continuous and large-scale R&D investment has redirected focus from input expansion to efficiency improvement, positioning R&D efficiency at the heart of resilience-building. Under external shocks and uncertainty, can improvements in R&D efficiency enhance regional economic resilience? If so, which additional factors embedded in sustainable urban development planning can further amplify this effect? To address these questions, this study employs provincial panel data from 2000 to 2021 and integrates the SBM-DEA approach with an entropy-weighted resilience index for regression analysis. The results indicate that (1) R&D efficiency exerts a positive but limited impact on resilience, with an average increase of only 0.188 units, indicating that efficiency alone cannot generate resilient economies without institutional coordination; (2) human capital agglomeration and financial density strengthen this relationship, highlighting the need to integrate talent and financial strategies; (3) the positive effect is observed in eastern provinces but remains insignificant in central and western regions, revealing pronounced structural disparities that risk widening the resilience gap across regions rather than fostering balanced development; and (4) targeted government intervention effectively converts innovation efficiency into resilience gains, fostering coordinated and sustainable development. This study empirically demonstrates that improving R&D efficiency significantly enhances regional resilience in China and based on this evidence introduces the ICT Synergy Framework as a novel analytical lens for understanding how innovation, capital, and talent jointly drive resilience and sustainable development. The findings further suggest that targeted government intervention in R&D resource allocation can reinforce resilience, offering broader lessons for other developing economies. By integrating innovation outcomes with spatial and institutional planning, the study provides actionable insights for advancing sustainable urban development and coordinated regional growth. Full article

In the subtropical regions of southern China, springtime is often characterized by persistently high humidity, leading to frequent condensation on building envelopes and interior surfaces. Top-floor rooms are particularly vulnerable due to their direct exposure to outdoor conditions through walls and the roof, making condensation prevention a critical concern. This study is grounded in the residential habits and spatial preferences of southern Chinese residents and evaluates three roof configurations—standard, thickened, and green roofs—using EnergyPlus (v22.1.0) simulation software to analyze their effects on indoor relative humidity levels in top-floor spaces. The results demonstrate that green roof systems significantly reduce indoor relative humidity, especially in high-rise residential buildings. Taking a 30-story residential building as an example, with a conventional roof, the indoor relative humidity remains at 100% for extended periods during high-risk condensation intervals, resulting in surface condensation. In contrast, when a green roof with a soil depth of 70 cm and daylilies at a height of 100 cm is used, the peak indoor maximum relative humidity is reduced by 10–40%, and the duration of condensation decreases to zero. Among the factors involved in green roofs, including plant height, soil depth, and leaf area index (LAI), soil depth shows a significant negative correlation with the maximum indoor relative humidity (correlation coefficient r = −0.987, p < 0.01), while the LAI exhibits a positive correlation with the maximum indoor relative humidity (r = 0.180, p < 0.05). Selecting plant species with a low LAI and increasing soil depth are effective passive strategies for humidity control and condensation prevention. These findings establish a basis for optimizing building environmental models and propose passive design strategies to enhance overall performance. In addition, the study highlights how roof greening contributes to global sustainability goals, particularly SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by improving indoor comfort, enhancing resilience, and reducing climate-related risks. Full article

Sport performance results from the interaction of tactical, technical, physiological and psychological factors, but psychological aspects are often minimized or analyzed in a decontextualized manner. This exploratory pilot study aimed to contribute to the development of a diagnostic framework that links individual behaviors during football attack–defense transition moments (ADT) with psychological attributes. Twenty male U14 players were assessed across five official matches regarding their ADT performance indicators. The Athletic Coping Skills Inventory (ACSI-28) and the Resilience Scale (RS) were applied during the competition. Statistical analyses included correlation tests and Bayesian analysis. Players showed a significant tendency to sustain ball recovery behaviors after possession loss (p = 0.004). Psychological resilience and athletic coping skills varied substantially between individuals without positional differences, as well as RS scores were significantly below the high-resilience threshold (147; p = 0.013). A moderate positive correlation emerged between RS Factor 1 and the ACSI-28 subscale “Coping with Adversity” (r = 0.574, p = 0.008). Posterior distributions provide exploratory signals suggesting possible positive associations for two psychological constructs considering ADT individual behaviors: “Concentration” in relation to the maintenance of recovery actions (Mode = 0.439; 95% CI [0.030, 0.721]) and “Goal Setting” in relation to the rapid initiation of recovery actions (Mode = 0.465; 95% CI [0.059, 0.734]). Nevertheless, Bayes Factors favored the null model overall, indicating that these signals are weak and require replication. By contrast, most psychological constructs, including resilience, showed no reliable evidence of correlation with recovery-related actions. The findings highlight the need to further research the integration of psychological assessment into football performance diagnostics, while also indicating that psychological factors alone are insufficient to fully explain youth players’ individual ADT behaviors. Full article

This study addresses the growing importance of integrating innovation into corporate sustainability strategies by examining the financial and environmental performance of ten firms listed on the Borsa Istanbul Sustainability Index over a five-year period. The main objective is to develop and test a novel, data-driven analytical framework that reduces reliance on subjective expert judgments while providing actionable insights for sustainability-oriented decision-making. Within this framework, the entropy method from the Multi-Criteria Decision Making (MCDM) approach is first applied to calculate the objective weights of sustainability criteria, ensuring that the analysis is grounded in real performance data. Building on these weights, an innovative reverse Decision-Making Trial and Evaluation Laboratory (DEMATEL) model, implemented through a custom artificial neural network-based software, is introduced to estimate direct influence matrices and reveal the causal relationships among criteria. This methodological advance makes it possible to explore how environmental and financial factors interact with R&D expenditures and to simulate their systemic interdependencies. The findings demonstrate that R&D serves as a central driver of both environmental and financial sustainability, highlighting its dual role in fostering corporate innovation and long-term resilience. By positioning R&D as both an enabler and outcome of sustainability dynamics, the proposed framework contributes a novel tool for aligning innovation with strategic sustainability goals, offering broader implications for corporate managers, policymakers, and researchers. Full article

Food production systems in Ecuador’s high Andean region are pivotal for food security, rural livelihoods, and agrobiodiversity, yet they are increasingly exposed to climate stress. We assessed four representative crops (tree tomato, quinoa, potato, and maize) across three Andean zones (North, Center, South) in 2015–2022 using monthly NASA POWER (MERRA-2) climate fields. After confirming non-normality, Spearman correlations and multiple linear regressions with leave-one-year-out validation were applied to quantify the influence of maximum/minimum temperature and precipitation on cultivated and harvested area, production, sales, and loss categories. To place monthly signals in a process context, daily extreme-event diagnostics (ETCCDI-style) were also computed: heat days ($\mathrm{TX}90$), ≥5-day dry spells, and the annual maximum consecutive dry days (${\mathrm{CDD}}_{max}$). Models explained a wide range of variability across crops and zones (approx. $R^2\sim 0.55$–0.99), with quinoa showing the most consistent fits (several outcomes $R^2>0.90$). Extremes provide an eye-catching, actionable picture: the Southern zone concentrated dryness hazards, with 1–5 dry spells $\ge 5$ days per year and ${\mathrm{CDD}}_{max}$ up to ∼8 days, while heat-day frequency showed non-significant declines across zones in 2015–2022. Reanalysis frost days were virtually zero—consistent with under-detection of local valley frosts at coarse resolution—so frost risk was interpreted via monthly signals and reported losses. Overall, the results show precipitation-driven vulnerabilities in the South and support quinoa’s role as a resilient option under increasing climate stress, offering concrete guidance for water management and climate-smart planning in mountain agroecosystems. Full article

Background/Objectives: Caring–healing modalities (CHMs), i.e., non-pharmacological, nurse-led interventions rooted in caring science, have shown promise in reducing emotional distress, while enhancing resilience. CHMs are heterogeneous, making it challenging to determine how they are formulated to build resilience, mitigate emotional distress, and explore their mechanisms of action. This scoping review mapped the literature on CHMs, including their components, targeted outcomes, and measures. Methods: This review was conceptually driven by Watson’s Theory of Human Caring, followed the JBI methodology, and reported according to the PRISMA-ScR. Experimental studies, systematic reviews, opinion pieces, and the gray literature on CHMs for emotional distress and resilience delivered to persons with cancer, written in English, were considered. No date or setting limits were applied. Eleven databases (e.g., PubMed and CINAHL Full Text), were searched. Two independent reviewers screened, selected, and extracted the data. The results were interpreted using Watson’s theory. Results: We included 16 records (2016–2025), mostly from the United States (n = 4; 25%) and China (n = 6; 37.5%). The CHMs mainly targeted persons with breast cancer. The CHMs were categorized into four groups: mindfulness-based, group-based, expressive, and educational. Common active ingredients included peer support and group discussions. Dedicated healing spaces facilitated CHMs delivery; mode of delivery and dose varied widely. Conclusions: This review provides a foundational understanding of CHMs as a caring-based, holistic approach to cancer survivorship. Findings identify CHMs’ key components, including active ingredients, mode of delivery, and dose. Future studies should ensure diversity in terms of cancer type, as most existing studies focused on breast cancer. Full article

This study investigates the fracture behavior of recycled aggregate concrete by integrating fractal theory and empirical modeling to quantify how recycled coarse aggregates (RCAs) and recycled fine aggregates (RFAs) influence crack complexity and maximum crack width under varying content and loads. The results reveal distinct scale-dependent behaviors between RCA and RFA. For RCA, moderate dosages enhance fractal complexity (a measure of surface roughness) by promoting micro-crack proliferation, while excessive RCA reduces complexity due to matrix homogenization. In contrast, RFA significantly increases both fractal complexity and crack width under equivalent loads, reflecting its susceptibility to micro-scale interfacial transition zone (ITZ) degradation. Non-linear thresholds are identified: RCA’s fractal complexity plateaus at high loads as cracks coalesce into fewer dominant paths, while RFA’s crack width growth decelerates at extreme dosages due to balancing effects like particle packing. Empirical models link aggregate dosage and load to fractal dimension and crack width with high predictive accuracy (R² > 0.85), capturing interaction effects such as RCA’s load-induced complexity reduction and RFA’s load-driven crack width amplification. Secondary analyses further demonstrate that fractal dimension correlates with crack width through non-linear relationships, emphasizing the coupled nature of micro- and macro-scale damage. These findings challenge conventional design assumptions by differentiating the impacts of RCA (macro-crack coalescence) and RFA (micro-crack proliferation), providing actionable thresholds for optimizing mix designs. The study also advances sustainable material design by offering a scientific basis for updating standards to accommodate higher recycled aggregate percentages, supporting circular economy goals through reduced carbon emissions and waste diversion, and laying the groundwork for resilient, low-carbon infrastructure. Full article

This systematic literature review pioneers the synthesis of cybersecurity challenges for automotive digital twins (DTs), a critical yet underexplored frontier in connected vehicle security. The notion of digital twins, which act as simulated counterparts to real-world systems, is revolutionizing secure system design within the automotive sector. As contemporary vehicles become more dependent on interconnected electronic systems, the likelihood of cyber threats is escalating. This comprehensive literature review seeks to analyze existing research on threat modeling and security testing in automotive digital twins, aiming to pinpoint emerging patterns, evaluate current approaches, and identify future research avenues. Guided by the PRISMA framework, we rigorously analyze 23 studies from 882 publications to address three research questions: (1) How are threats to automotive DTs identified and assessed? (2) What methodologies drive threat modeling? Lastly, (3) what techniques validate threat models and simulate attacks? The novelty of this study lies in its structured classification of digital twin types (physics based, data driven, hybrid), its inclusion of a groundbreaking threat taxonomy across architectural layers (e.g., ECU tampering, CAN-Bus spoofing), the integration of the 5C taxonomy with layered architectures for DT security testing, and its analysis of domain-specific tools such as VehicleLang and embedded intrusion detection systems. The findings expose significant deficiencies in the strength and validation of threat models, highlighting the necessity for more adaptable and comprehensive testing methods. By exposing gaps in scalability, trust, and safety, and proposing actionable solutions aligned with UNECE R155, this SLR delivers a robust framework to advance secure DT development, empowering researchers and industry to fortify vehicle resilience against evolving cyber threats. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

Achieving sustainable development in the aviation industry increasingly relies on the synergistic operation of complex product innovation ecosystems. These ecosystems not only drive technological breakthroughs, but also serve as crucial enablers of resource efficiency, ecological resilience, and long-term industrial competitiveness. This study explores how specific configurations of synergistic factors within innovation ecosystems support sustainable innovation outcomes in the aviation sector. Drawing on the innovation ecosystem theory and principles of sustainable development, we employed fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine 15 representative aviation equipment R&D cases, including AVIC Tongfei and AVIC Xifei. The analysis centers on five key dimensions: core enterprise leadership, value chain collaboration, cross-organizational innovation, technology–market feedback loops, and institutional policy support. These dimensions interact to shape multiple synergy pathways that facilitate sustainable transformation. The results reveal that no single factor alone is sufficient to ensure high innovation sustainability. Instead, three distinct synergy configurations emerge: (1) core enterprise-led model, which reduces resource redundancy through optimized value chain governance; (2) the industry chain collaboration model, which enhances environmental performance via modular design and lifecycle management; and (3) cross-organization innovation collaboration model, which improves material reuse and infrastructure sharing through collaborative mechanisms. Together, these pathways form a reinforcing cycle of innovation–efficiency–sustainability, offering a practical framework for aligning technological advancement with ecological goals. This study deepens the understanding of how innovation ecosystem mechanisms contribute to sustainable development, particularly in high-integration industries. It offers actionable insights into achieving the Sustainable Development Goals (SDGs) through collaborative innovation and systemic resource optimization. Full article