Search Results (35,745)

Extreme heat increasingly threatens public health and urban sustainability, creating a need for fine-scale assessments that connect spatial risk patterns with climate-resilient planning. This study assessed heat risk across 1639 blocks in Tianjin’s six central districts, China. A block-scale heat-risk index (HRI) was constructed within the Hazard–Exposure–Vulnerability–Adaptability (HEVA) framework by integrating remote-sensing, population, built-environment, socioeconomic, and public-service data using a modified CRITIC weighting method. Differences in HRI among Local Climate Zones (LCZs) were examined, and three machine-learning models were compared. Among the three evaluated models, Random Forest showed the best overall test-set performance, and supplementary five-fold spatial block validation retained the same model ranking and major feature-importance ordering despite lower absolute predictive performance. Feature importance, partial dependence plots, and SHapley Additive exPlanations were subsequently used to interpret feature contributions and nonlinear associations. HRI was lower in the resource-rich urban core and higher in peripheral transitional zones, accompanied by clear spatial differences in adaptive-resource provision. The urban core generally exhibited higher adaptive-resource provision, whereas peripheral areas showed more limited provision. Under the adopted HRI formulation, higher values of this component contributed mathematically to lower composite HRI, rather than demonstrating a realised risk-reduction effect. Built LCZs generally exhibited higher HRI than land-cover LCZs; LCZ 5 (open mid-rise) had the highest median HRI, whereas LCZ G (water) had the lowest. Higher sky view factor and vegetation cover were generally associated with lower HRI, while larger blocks, taller buildings, and greater distances from water bodies were associated with higher HRI. These findings support differentiated block-scale interventions, heat-response resource allocation, and climate-resilient urban regeneration. Full article

The scarcity of global water resources has made irrigation optimization a key challenge for sustainable agriculture. Rice cultivation, one of the most water-intensive cropping systems, offers significant opportunities for improving water-use efficiency through artificial intelligence (AI) technologies. This systematic review evaluates the application of AI techniques for irrigation optimization in rice farming within the context of Agriculture 4.0, with the aim of identifying the principal AI approaches, application domains, research trends, and future directions. The review was conducted following the PRISMA 2020 guidelines using publications retrieved from ScienceDirect, IEEE Xplore, and the ACM Digital Library published over the past five years. Fourteen studies meeting the predefined eligibility criteria were selected for qualitative synthesis and bibliometric analysis. The results indicate that machine learning and deep learning approaches, particularly Long Short-Term Memory (LSTM) networks and Random Forest (RF) models, dominate the current literature. These methods are primarily applied to irrigation scheduling, crop water requirement prediction, soil moisture estimation, and yield forecasting. The analysis also reveals a strong geographical concentration of research in Asia, which accounts for 64.3% of the selected studies. Furthermore, data availability remains limited, with half of the datasets accessible only upon request and only 7.1% publicly available, highlighting an important barrier to reproducibility and broader adoption of AI-based irrigation solutions. Emerging research directions include the integration of remote sensing, Internet of Things (IoT) technologies, and intelligent decision support systems to enable adaptive, data-driven irrigation management. Overall, this review provides a comprehensive overview of the current state of AI-enabled irrigation optimization in rice farming, identifies existing research gaps, and outlines future opportunities for developing sustainable intelligent irrigation systems. Full article

Context: The increasing adoption of Internet of Things (IoT) technologies has transformed digital marketing into a software-intensive, data-driven ecosystem requiring continuous optimization under uncertainty. Existing decision-support approaches primarily optimize engagement or cost independently and rarely integrate behavioral dynamics, sustainability constraints, and managerial preferences within a unified information systems framework. Objectives: This study develops and evaluates a fuzzy multi-objective optimization framework that supports intelligent software-based marketing decision making by simultaneously maximizing customer engagement, minimizing digital resource consumption, and reducing behavioral saturation in IoT-enabled environments. Methods: A multi-objective mathematical model was developed in which customer responsiveness is represented through probabilistic engagement parameters, while fuzzy membership functions and a Max–Min satisfaction criterion represent imprecise managerial aspiration levels across the conflicting objectives. The small-scale experiment was solved exactly in GAMS to obtain reference Pareto-optimal solutions, whereas the large-scale experiment was conducted as a simulation study using NSGA-II and MOPSO to evaluate scalability and algorithmic performance. Both experimental settings relied exclusively on synthetically generated datasets; no real-world enterprise, customer-level, or campaign-level marketing data were used. Performance was assessed through Pareto-front analysis, key performance indicators, sensitivity analysis, and scenario-based managerial evaluation. Results: The proposed framework successfully generated high-quality Pareto-optimal solutions across multiple optimization objectives. NSGA-II consistently achieved superior customer engagement, personalization efficiency, and behavioral balance, whereas MOPSO demonstrated faster execution and lower sustainability costs. Sensitivity analysis confirmed the robustness of the framework under varying behavioral parameters, while scenario analysis showed that different optimization strategies can be selected according to organizational priorities. The principal limitation is that the framework has been evaluated only in controlled synthetic environments, which limits direct empirical generalization to operational enterprise marketing settings. Future research should validate the framework using longitudinal enterprise marketing data, real-time IoT interaction streams, and field-based deployment studies. Conclusions: The proposed framework contributes to information systems research by integrating fuzzy decision support, multi-objective optimization, and behavioral modeling into a scalable software architecture for IoT-enabled marketing. The approach enables adaptive, explainable, and sustainable decision making, providing organizations with a practical decision-support system capable of balancing customer experience, operational efficiency, and digital sustainability in intelligent marketing ecosystems. Full article

Developing reliable numerical tools and analytical methodologies is essential to optimize the design phase of composite structures. The quasi-static fracture behaviour of glass-fibre reinforced polymer bonded joints was evaluated for bidirectional and quasi-isotropic sequences. The specimens were bonded with a two-component epoxy-based adhesive system. The complete mixed-mode I+II fracture envelope was obtained using double cantilever beam, three-point bending End-Notched Flexure, and mixed-mode bending configurations. A data reduction scheme based on the equivalent crack method was adapted to calculate fracture energies as a function of the compliance evolution recorded during the tests. The power law energy fracture criterion was used to describe the complete mixed-mode I+II fracture envelope. The criterion proved to be very accurate in predicting the mixed-mode fracture behaviour for both series. A cohesive zone model with a trapezoidal softening law was used to validate the experimental procedure of the three test configurations. The results obtained for the fracture envelope using the power law criterion and the local strengths were used as input for the numerical simulations. The model efficiently described the full fracture envelope of both stacking sequences, with a deviations below 3% for the B series and below 7.5% for the Q series. Full article

A double lattice Boltzmann method (LBM) based on the D2Q9 model was developed to simulate natural convection melting and heat transfer in energy systems, including nuclear systems. Fluid flow in the liquid region was solved using the multi-relaxation-time LBM (MRT-LBM), whereas the energy equation was treated with the single-relaxation-time LBM (SRT-LBM) over the entire domain. Phase change was modeled through an enthalpy–porosity formulation, with the liquid fraction and solid–liquid interface determined from local enthalpy, while explicit mushy-region resolution was avoided to improve computational efficiency. Bounce-back treatment represented solid boundaries and the evolving interface. The model was validated against gallium-melting experiments and previous finite element and finite volume computations. The predicted melt-front evolution and convection behavior agreed well with benchmark data, confirming that the proposed framework can capture coupled fluid motion and heat transfer during melting without adaptive meshes or level-set methods. Full article

Saudi Arabia’s expanding construction sector and increasing generation of construction and demolition (C&D) waste create a need to explore higher-value architectural applications for recovered materials while supporting culturally responsive and climate-adapted design. In this context, this study maps evidence on recycled C&D materials and screens selected material pathways for suitability, architectural applicability, and implementation readiness, using environmental and technical indicators to inform the future development of a circular Mashrabiya as a contemporary reinterpretation of the Saudi Rowshan. The study does not propose a detailed fabrication route or component design. It combines a PRISMA 2020 systematic literature review with an exploratory comparative assessment of primary and recycled material pathways. The review identified 1254 records, screened 1107 unique records, and included 51 studies in the final qualitative synthesis. No high-confidence study directly validates a recycled-content Mashrabiya; therefore, we interpret the material recommendations as research-readiness priorities rather than demonstrated optimal solutions. Based predominantly on moderate/transferable evidence, recycled aluminum emerges as the highest-priority pathway for future prototype testing in lightweight and geometrically complex applications. Recycled steel remains a viable alternative where greater structural contribution is required, although weight and corrosion protection remain constraints. Recycled aggregates remain relevant for C&D waste diversion and hybrid or more massive façade applications, despite more limited advantages for intricate screens. Comparable Saudi component-level fabrication and procurement cost data were insufficient to derive a defensible percentage cost saving; therefore, no cost-effectiveness advantage is claimed, and component-level life-cycle costing is identified as a priority for the subsequent prototype phase. The findings support a heritage-informed decision framework integrating environmental performance, fabrication feasibility, climatic resilience, material circularity, and Saudi implementation readiness. Full article

Intrusion Detection Systems (IDSs) are essential for securing Internet of Things (IoT) and Internet of Medical Things (IoMT) environments, yet most machine learning-based IDSs assume that training and testing data follow similar distributions. In practice, domain shifts arising from differences in device characteristics, communication protocols, and traffic patterns can substantially increase false positive rates (FPRs), reducing operational reliability. This study proposes DEA-IDS (Drift-aware, Explainable and Adaptive Intrusion Detection System), a unified framework integrating SHAP-based explainability, statistical drift analysis via the Kolmogorov–Smirnov statistic and Wasserstein distance, drift-aware stable feature selection, and few-shot adaptation, evaluated on a CICIoT2023-to-CICIoMT2024 cross-domain transfer scenario. Under a leakage-free protocol in which drift statistics and few-shot samples are drawn exclusively from the target training split, DEA-IDS reduces FPR from 0.5468 to 0.0004 while maintaining an F1-score of 0.9944; threshold-, sample-size-, and feature-selection-control sensitivity analyses confirm this reduction reflects drift-aware stable feature selection rather than test-set leakage or dimensionality reduction alone. A per-attack-family analysis shows this improvement is concentrated in high-volume flood-style attacks and is accompanied by reduced detection of ARP spoofing, malformed-MQTT, and reconnaissance traffic, reported here as an explicit limitation. These results demonstrate that explicitly modeling feature stability before adaptation improves operational robustness for cross-domain intrusion detection in heterogeneous IoT–IoMT environments. Full article

Reliable national-scale, multi-depth soil property maps remain scarce across much of Africa, particularly in arid regions. Here, we present the first national-scale digital soil mapping (DSM) for Namibia: sixteen physical and chemical soil properties predicted on a 90 m prediction grid for three depth intervals (0–30, 30–60 and 60–100 cm). The framework draws on 4958 legacy profiles and augerings and 65 covariates, including locally produced datasets that capture soil–environment relationships not fully represented by global covariates. A semi-automated workflow combined Random Forest modelling in Google Earth Engine with R-based depth harmonisation, Boruta-feature selection and hyperparameter tuning. Performance and bootstrap variability were quantified over 20 bootstrap iterations per property–depth combination, with pedological evaluation and independent validation. pH was the most consistently predicted property, and base saturation, sand and silt captured useful broad spatial patterns, whereas bulk density, organic carbon, phosphorus, magnesium and clay were less predictable, and electrical conductivity and sodium showed low predictive skill. Performance generally declined with depth, and bootstrap variability was highest in sparsely sampled regions and deeper layers. Against independent samples from arable land, root mean square error was lower than SoilGrids in all 14 property–depth combinations evaluated and lower than iSDA in 8 of 12. Predicted patterns were consistent with known Namibian soil–landscape relationships and pedogenic processes, except for sodium. National-scale DSM is therefore feasible in data-sparse arid environments when harmonised legacy data are combined with global and locally relevant covariates, using simple but operationally robust methods. The products are intended for national and regional assessment, rather than site-specific decisions, and the modular workflow supports future updates and is potentially adaptable to other data-limited regions. Full article

Urban Nature-based Solutions (NbSs) provide ecological, hydrological, cultural and wellbeing benefits; however, conventional economic appraisal often privileges monetised, short-term outcomes and overlooks a broader range of benefits. This study demonstrates the application of an economic valuation Decision Support Framework (DSF) using four rain garden typologies identified in Auckland, New Zealand. The DSF’s complexity-aware decision pathway, adapted from Cynefin, guided the selection of context-appropriate valuation methods. Benefits were assessed qualitatively and semi-quantitatively using the More Than Water (MTW) Tool, alongside a quantitative life cycle costing (LCC) evaluation. MTW results showed that rain garden typologies exhibited clear differences in assessed benefit potential. Although hydraulic and water-quality outcomes were broadly comparable, greater surface area, soil and vegetation volume, vegetation complexity and landscape integration were associated with higher assessed levels of ecological, resilience and wellbeing benefits. LCC results indicated that larger typologies achieved lower costs per unit area through economies of scale. The case study demonstrates that the DSF provides a practical method for combining monetised and non-monetised values within stormwater appraisal. Its application reveals differences in potential benefit realisation that conventional cost-efficiency analysis may overlook, supporting more comprehensive NbS investment decisions in New Zealand, and offering a transferable model for other jurisdictions, subject to local calibration of costs and benefit data. Full article

Machine learning (ML) models for hypertension risk prediction have predominantly relied on traditional demographic and clinical risk factors, often overlooking the temporal heterogeneity of environmental exposures. This study developed and validated an interpretable ML framework that systematically integrates multi-window cumulative PM_2.5 exposure features for hypertension risk assessment. We designed a modular analytical pipeline comprising five ML algorithms—Generalized Linear Model (GLM), Lasso regression, Decision Tree, Random Forest (RF), and XGBoost—coupled with a three-layer interpretability module (variable importance, SHAP values, and partial dependence plots). The framework ingests traditional risk factors alongside cumulative PM_2.5 exposure across five temporal windows (0-day, 7-day, 15-day, 30-day, and 60-day). As a validation case, the framework was applied to 2523 participant-visits from the Beijing subsample of the China Health and Nutrition Survey. Analyses were performed at the participant level, systolic and diastolic blood pressure were excluded from the predictors of the hypertension classifiers, and models were validated with person-level and year-based splits. After these corrections the five models showed realistic discrimination, with test AUCs of 0.69–0.77 and Brier scores of 0.17–0.23. Age, body mass index (BMI) and waist circumference were consistently among the most important predictors, and the 60-day PM_2.5 window was the most important exposure feature. Adding the five PM_2.5 window features improved test AUC by ≈0.02–0.03 in the ensemble models (p = 0.03–0.05). Window-specific adjusted analyses showed inverse associations of PM_2.5 with hypertension that were stronger for longer windows. The proposed framework provides a reusable interpretable approach for incorporating multi-window environmental exposure data into cardiovascular risk prediction. Its modular design enables adaptation to other environmental exposures, health outcomes, and population cohorts, supporting both risk screening and personalized intervention strategies. Full article

Vertical axis wind turbines (VAWTs) have become an attractive option for use in cities and other built environments because they can capture wind from different directions. This gives them an advantage over horizontal axis wind turbines in locations where wind direction changes frequently. However, VAWTs still face several challenges, including unstable airflow, dynamic stall, flow separation, and negative torque during certain parts of their rotation cycle. These issues can reduce overall performance and efficiency. This systematic literature review focuses on three closely related areas: VAWT aerodynamic performance; the use of flow deflectors to improve airflow; and the application of artificial intelligence for prediction, optimization, and control. The review followed a Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA)-based method and collected studies published between 2008 and 2026 from Scopus, Web of Science, ScienceDirect, and Google Scholar. Following PRISMA guidelines, 1127 records were identified, 792 remained after duplicate removal, 136 full texts were assessed, and 21 studies were included in the final review. The selected studies were organized according to turbine type, deflector design, operating conditions, research methods, and the role of artificial intelligence. An assessment was also carried out to compare evidence from experiments, validated simulations, optimization studies, and emerging AI applications. The findings show that well designed deflectors can improve the aerodynamic performance of VAWTs when compared with their original configurations. However, the level of improvement depends on factors such as turbine design, wind speed, Reynolds number, tip speed ratio, and deflector shape. Because of these differences, reported performance gains should be considered specific to each study rather than a general result. Artificial intelligence has mainly been used for performance prediction, optimization, surrogate modelling, and turbine control. Reinforcement learning appears promising for adaptive deflector control. However, very few studies have tested a complete system that combines sensors, an adjustable deflector, artificial intelligence, and real-time closed-loop control. Based on the reviewed studies, a five-layer research framework is proposed. The framework includes aerodynamic and mechanical design, data collection and processing, AI model development, real-time control and actuation, and experimental validation. This framework is presented as a research direction for future investigation. Future studies should compare reinforcement learning with traditional control methods, evaluate energy consumption, address the gap between simulation and real-world operation, and conduct more experimental testing. Full article

Long-term space missions traveling beyond the Earth’s magnetosphere will expose astronauts to prolonged periods of social isolation (SI) and exposure to space radiation (SR). Throughout these missions, astronauts will have to cope with these stressors and adapt to their environment to carry out mission objectives. It is known that SI and SR can impact mission performance and overall health. Additionally, behavioral stress responses can differ between sexes. However, the independent and interactive effects of SI and SR on fear behavior and extinction learning between sexes is unknown. This study compared freezing behavior and stress-induced hyperthermia (SIH) in female and male Wistar rats using a conditioned fear (CF) paradigm. Comparisons were made in SHAM-treated rats and in ground-based models of SI and SR alone and in combination (dual flight stressors (DFSs)). SHAM and SI females froze significantly more during shock training (ST), context re-exposure (CTX) and extinction learning (EXT) compared to their male counterparts, suggesting sex differences in fear learning and memory. There were minimal changes in freezing behavior between sexes in SR and DFS animals; however, SR and DFS females had significantly lower temperatures than their male counterparts. These findings suggest the presence of sex differences in stress responses, and that stress-based learning and memory can be differentially impacted by spaceflight hazards. Baseline differences suggest that males and female rats may have sex-specific characteristics attuned to their behavioral capacities or needs, whereas differences after exposure to spaceflight hazards suggest differences in stress vulnerability. In general, these data suggest that understanding within-sex alterations will be important for assessing the effects of spaceflight hazards on performance and developing mitigation strategies that may need to be tailored for males and females. Full article

Cross-well lithology recognition is important for reservoir characterization, but its application to newly drilled wells is constrained by inter-well variations in logging responses and limited lithological labels. This study proposes a geoscience knowledge-guided machine-learning method for lithology recognition under limited target-well labels. Geochemical indicators and conventional logging data are combined to construct geologically interpretable features, while few-shot transfer learning is used to reduce class imbalance and inter-well distribution differences. Geological knowledge is introduced as probabilistic constraints and integrated with model predictions through uncertainty-aware fusion. The method is evaluated using volcanic reservoir well-logging data from the Pearl River Mouth Basin. Three representative wells are used as the source domain and an independent newly drilled well as the target domain, with three labeled samples per lithology class used for adaptation and the remaining samples for independent testing. Repeated experiments with 10 random seeds yield an accuracy of 88.03% ± 6.09%, with improved classification performance and lower variability than the compared baseline methods. The results show that combining geological knowledge with few-shot learning can improve the robustness of cross-well lithology recognition and provide a practical approach for lithology prediction in heterogeneous volcanic reservoirs. Full article

The widespread adoption of remote and hybrid work has profoundly reshaped organizational processes, offering greater flexibility and autonomy while simultaneously introducing new psychosocial challenges. Drawing on the Job Demands–Resources (JD-R) model, the present study examined the relationship between employees’ perceived disadvantages of remote work and work engagement, considering the parallel indirect associations involving technology-related demands and work–family conflict. Data were collected through a cross-sectional survey involving 3762 hybrid workers employed by an Italian IT company. Primary analyses were conducted on 3028 participants with complete observed data on the four focal constructs. Participants completed an adapted measure of he Remote Working Benefits and Disadvantages Scale, together with the Utrecht Work Engagement Scale (UWES-3), the Emerging Technology-related Stressors Scale, and the Work–Family Conflict Scale. Greater perceived disadvantages were associated with higher technology-related demands and work–family conflict, both of which were in turn associated with lower work engagement. The standardized indirect associations were β = −0.086 through technology-related demands and β = −0.044 through work–family conflict, while the remaining direct association was small (β = −0.058). The overall model explained 8.3% of the variance in work engagement, indicating modest explanatory power. These findings support an appraisal-based interpretation of remote and hybrid work, suggesting that employees’ subjective evaluations of their work arrangements may represent one relevant correlate of engagement. The study underscores the importance of implementing sustainable remote work practices that minimize perceived disadvantages, support effective boundary management, and reduce technology-related demands to foster employee engagement and well-being. Full article

This study presents a context-grounded conceptual design for an environmentally sustainable hotel in Shiraz, Iran, integrating three complementary strategies: a biomimicry-inspired responsive façade, a Voronoi-based façade and landscape design, and solar energy generation. Inspired by the adaptive behavior of morning glory flowers, the responsive façade is designed to adjust dynamically to environmental conditions with the aim of improving daylight utilization and thermal performance. Voronoi patterns are incorporated into the façade and landscape with the design intent of enhancing natural light distribution, airflow, structural efficiency, and aesthetic quality. In addition, photovoltaic panels installed on the hotel and parking roofs provide renewable energy to improve building sustainability. The façade and landscape geometry are developed parametrically in Rhinoceros 3D with Grasshopper, and the rooftop photovoltaic system is simulated using site-specific climate data for the project location. The System Advisor Model (SAM) simulations indicate that the proposed solar system can generate approximately 4,281,172 kWh of electricity annually, corresponding to approximately 9,889,507 lb (4486 t) of avoided CO₂ relative to the cited U.S. coal generation benchmark. The integration of biomimicry, computational design, and renewable energy demonstrates an integrated conceptual approach to environmentally sustainable hotel architecture, with quantified solar energy generation and emissions reduction potential, and design-intent strategies for daylighting, thermal comfort, and occupant well-being that warrant further validation. This work presents an integrated design framework, not a validated or constructed building. Consistent with this scope, the term sustainability is used here in its environmental sense; the economic and social pillars are not assessed, and the study does not claim that Voronoi geometry is necessary for, or superior to, more regular and repetitive façade alternatives, a question that would require a controlled comparative study. The proposed design illustrates how nature-inspired strategies and advanced digital design methods can be combined to create innovative, resilient, and environmentally responsible buildings, providing a context-specific design proposition for future sustainable hospitality research. Full article