Search Results (3,458)

While the influence of the urban built environment on public emotions has garnered extensive attention, existing studies predominantly focus on temperate climates or warmer seasons. As a result, they rarely extend their scope to winter-specific emotions in cold-region cities, thereby overlooking the complex human–environment emotional interactions under extreme climates. To bridge this seasonal research gap, this study develops an innovative analytical framework integrating Large Language Models (LLMs) with Multiscale Geographically Weighted Regression (MGWR). Drawing on social media data, this framework leverages the powerful zero-shot reasoning capabilities of LLMs to precisely quantify the two-dimensional emotional characteristics of Valence and Arousal. Concurrently, by incorporating the multi-scale spatial modeling strengths of MGWR, it thoroughly investigates the spatial patterns and driving mechanisms of public emotions within the winter context of typical cold-region cities. The results indicate that, first, extreme climates do not lead to urban emotional suppression; instead, frozen rivers transform into vibrant emotional corridors, with the public demonstrating a high degree of thermal-psychological adaptability. Second, by incorporating winter-specific environmental variables, the research reveals a cold-region paradox of emotional valence. Specifically, under snow cover, lower winter Land Surface Temperature (LST) and winter Normalized Difference Vegetation Index (NDVI) paradoxically evoke positive emotions by reconstructing the aesthetic experience of ice-snow landscapes. Furthermore, the impact of urban service facilities on emotional arousal exhibits a significant pattern of diminishing marginal utility. Overall, the LLMs-MGWR framework achieves a closed loop of high-throughput, multi-dimensional semantic decoding and multi-scale spatial interpretation, demonstrating exceptional cross-regional generalizability. Ultimately, this study not only provides a novel paradigm for understanding human–environment interactions in complex environments but also offers transferable planning guidelines for microclimate design, facility decentralization, and the reshaping of winter blue-green infrastructure in global cold-region cities. Full article

The primary objective of this article is to examine the organizational, economic, and sustainability-related implications of implementing artificial intelligence (AI) systems in small and medium-sized enterprises (SMEs) in Poland. The study combines a survey of 112 SMEs in the Kuyavian–Pomeranian region, including 70 AI-using firms, with 13 in-depth interviews with managers. The quantitative analysis applies logit models to identify determinants of perceived AI effects on internal processes: working time and workload reduction, automation, cost effects, and creativity. The qualitative component explains how AI is adopted and embedded in business practice. The results show that AI adoption in SMEs is increasingly common but remains uneven and mostly operational. The strongest effects concern workload reduction and time efficiency, particularly in service firms and where AI is used intensively. Advanced AI adoption increases the probability of perceiving workload and cost-related effects. However, these effects should not be interpreted simply as direct cost reduction. Rather, AI improves productivity and work capacity while creating new costs related to paid tools, data preparation, integration, output verification, and governance. The interviews show that AI implementation follows a staged path: from curiosity-driven experimentation, through cognitive work augmentation, to workflow integration and, in selected cases, AI-enabled business model innovation. The transition from ad hoc use to strategic implementation depends less on firm size alone and more on process maturity, capabilities, and data readiness. Barriers also change with maturity: early-stage firms face a lack of knowledge, time, and clear use cases, whereas advanced users encounter data quality, hallucinations, security, integration, and governance problems. The study finds that sustainability considerations, particularly environmental impacts and ESG-related implications of AI, remain largely unperceived in SME decision-making. Entrepreneurs primarily interpret sustainability through the lenses of organizational resilience, long-term competitiveness, adaptability, and responsible digital transformation rather than through formal environmental metrics. The findings suggest that SME managers should implement AI gradually, link adoption to measurable process-level outcomes, and invest in AI literacy and governance. They should also integrate responsible AI principles into organizational strategy to support sustainable digital transformation. The study contributes to the literature by showing that AI adoption in SMEs should be understood not only as a productivity-enhancing process but also as a broader organizational transition shaping long-term sustainability and resilience. Full article

This paper proposes an integrated formation control framework with a finite-time nonlinear disturbance observer (FT-NDO) for automated guided vehicles (AGVs) operating in port environments, where constrained workspace, narrow formation spacing, and complex external disturbances pose significant challenges. An adaptive leader–follower formation strategy with dynamic inter-vehicle spacing is developed to enhance maneuverability during turning. Within a hierarchical control structure that decouples lateral and longitudinal dynamics, two sliding mode controllers (SMCs) are designed: (a) a lateral SMC that prioritizes heading accuracy, limiting yaw angle error to within ±2°; and (b) a nonsingular terminal SMC (NTSMC) for longitudinal control, improving error convergence speed compared to conventional SMC. An FT-NDO is further incorporated into both control loops to estimate and compensate for external disturbances in real time, achieving a disturbance estimation accuracy of over 95% and significantly attenuating the impact of environmental disturbances. Validation through simulation and physical experiment of a dual-AGV formation in a realistic port scenario demonstrates that the proposed approach restricts formation deviation to 0.015 m and maintains stable operation under various disturbance conditions. This study provides a practical solution for dual-AGV collaborative transportation in spatially constrained and dynamically disturbed environments, with direct implications for improving operational efficiency and safety in port logistics. Full article

Global agriculture faces a dual imperative: increase food production to meet rising demand while simultaneously reducing environmental impacts and resource inefficiencies. Addressing this challenge requires repositioning ruminant nutrition as the intelligent nexus linking crop and livestock production within Integrated Crop–Livestock Systems (ICLS). In this role, nutrition becomes central to restoring ecological, nutritional, and economic synergies that have been fragmented by decades of agricultural specialization. While ICLS provides the ecological foundation, Precision Livestock Farming delivers the technological and analytical infrastructure necessary to operationalize integration at the individual-animal level. Real-time sensing, Internet of Things platforms, and Artificial Intelligence (AI) enable dynamic monitoring of animal physiology, behavior, and environmental interactions across scales. A key advancement in this evolution is the development of Hybrid Intelligent Mechanistic Models (HIMM), which integrate biologically grounded mechanistic models with data-driven AI approaches. By combining interpretability with adaptive learning, HIMM enhances predictive accuracy, extrapolative capacity, and decision transparency, enabling the creation of digital twins that simulate biological responses before management interventions are implemented. Such architectures extend precision nutrition beyond feed efficiency and methane mitigation to include nutrient density and product quality, thereby linking different ecosystem processes directly to human dietary needs. Integrating nutrition with advanced modeling and monitoring tools can help livestock systems move beyond static “net-zero” benchmarks toward sustainable strategies that are responsive to local production contexts. In this reframed paradigm, nutrition is not merely a production input but the central analytical framework that computationally links biological mechanisms, environmental stewardship, technological innovation, and human health within sustainable ruminant systems. Full article

Cape Coast is a prominent tourism destination in Ghana, distinguished by its historical landmarks, coastal ecosystems, and cultural heritage. Yet the city faces mounting threats from environmental hazards such as coastal erosion, flooding, extreme heat, and lagoon degradation, which directly compromise the sustainability of its tourism sector. Guided by the Sustainable Tourism Development Theory (STDT) and the Tourism Resilience and Adaptation Theory (TRAT), this study investigates the impacts of these hazards on tourism development, the effectiveness of current disaster risk reduction (DRR) strategies, and the roles of key stakeholders in building sectoral resilience. Using a qualitative research design, data were collected through in-depth interviews with eighteen stakeholders comprising four policymakers, six community leaders, five tourism business operators, and three representatives from non-governmental organisations, alongside documentary analysis of four institutional reports. The study contributes to the literature by demonstrating that fragmented, reactive DRR strategies and weak stakeholder coordination undermine Cape Coast’s tourism resilience, and by showing how urban natural assets, a dimension largely neglected in existing tourism–DRR scholarship, are central to both hazard exposure and adaptive capacity. The findings call for integrated, ecosystem-based DRR frameworks that align governance mechanisms with sustainable tourism imperatives. Full article

Hybrid RF-FSO-THz communication systems are promising candidates for future Internet of Things (IoT) and 6G networks because they combine the robustness of radio frequency links, the high-capacity potential of Free-Space Optical communications, and the ultra-wideband capabilities of terahertz transmission. Adaptive channel selection in such systems depends on multiple correlated environmental and physical-layer variables, including distance, rain intensity, humidity, visibility, turbulence strength, signal-to-noise ratio, channel capacity, and energy-efficiency metrics. This paper presents a physics-supported benchmark framework for supervised adaptive channel selection in hybrid RF-FSO-THz systems and systematically investigates the impact of linear and nonlinear dimensionality-reduction techniques on predictive performance, statistical robustness, computational complexity, and physical interpretability. A multi-scenario dataset comprising 5000 samples was generated using calibrated RF, FSO, and THz propagation models under clear, rain, fog, and worst-case environmental conditions. Principal Component Analysis (PCA) and Kernel PCA were evaluated together with Random Forest, Support Vector Machines (SVMs), XGBoost, Gradient Boosting (GB), Multi-Layer Perceptron (MLP), Logistic Regression, and Decision Trees. The results demonstrate that PCA preserves nearly all predictive capabilities while reducing the original 33-dimensional feature space by approximately 81.8%, maintaining accuracies close to 97–98% with the best-performing classifiers. Statistical significance analysis confirms that PCA introduces only modest degradations, whereas Kernel PCA consistently reduces the predictive performance while increasing memory requirements and inference latency. Additional environmental-only validation experiments indicate that adaptive channel selection remains highly learnable even when only pre-selection environmental descriptors are available, partially mitigating concerns regarding self-consistency bias. Overall, the results suggest that PCA provides an advantageous compromise among predictive accuracy, computational efficiency, statistical robustness, and physical interpretability for supervised adaptive channel selection in physics-supported hybrid wireless communication systems. Full article

Abiotic stresses, such as drought, salinity, and aluminum toxicity (Al³⁺), affect the growth and initial establishment of wheat plants, limiting crop yield in restrictive growing environments. Therefore, the early selection of tolerant genotypes adapted to multiple production environments is essential to optimize wheat production. A laboratory experiment was conducted to identify and recommend wheat cultivars that simultaneously combine adaptability and stability for initial morphological responses when subjected to stressful environmental conditions. Plants from 12 wheat cultivars were grown under non-stressful (control) and stressful conditions (drought, salinity and Al³⁺ stress), using a 4 × 12 factorial arrangement with four replicates. On the 28th day, the emergence rate, length, dry matter and vigor of the plants were measured. Abiotic stresses limit the initial growth and vigor of wheat plants, with drought causing the greatest limitation for plant growth and biomass accumulation, while salinity had the greatest impact on plant vigor indices. Aluminum toxicity limits root development and biomass allocation. Principal component analysis explained 67.76% of the total variability and distinguished the plant growing environments. The multi-trait index proved effective in cultivar selection, highlighting the cv. ORS Feroz due to its proximity to the ideotype and adaptability to multiple abiotic stresses. Full article

Building energy consumption accounts for 30% of global energy use, making building management pivotal to achieving global sustainability. Occupants have profound impacts on the building environment. Incorporating occupant-related factors into the environmental control process is essential for optimizing the efficiency of building management systems (BMSs), which thus gives rise to the concept of occupant-centric control (OCC). Conventional methods rely on simplified models and fixed schedules that fail to satisfy environmental control and occupant requirements, while constructing credible models places strict requirements on the dataset. In this paper, we propose a Model-Aware Predictive Control (MAPC) framework that can construct credible models with limited data and provide room-level control strategies to optimize the trade-off between occupant comfort and energy consumption. The technological innovations of this research are twofold. On the one hand, we design a model construction and fine-tuning method that combines data-driven subspace projection approach with physical priors that can construct credible thermal dynamic models with limited data. On the other hand, to balance the potential conflicts between enhancing occupant comfort and saving energy, we present a hierarchical decision-making mechanism that enables adaptive multi-objective room-level control considering dynamic occupant comfort requirements and energy usage. The experimental results obtained on an EnergyPlus-based simulation dataset and a publicly available dataset demonstrate that MAPC can provide room-level control strategies based on dynamic occupant requirements and user preferences and achieve superior trade-offs between occupant comfort and energy consumption. The ablation experiments also demonstrated the superiority of MAPC in constructing reliable models on limited datasets. MAPC provides pivotal support for the advancement of the intelligent buildings and sustainable indoor environment. Full article

Indoor air quality has a significant impact on occupant health, comfort, and productivity in residential and commercial indoor environments. This paper proposes an IoT-edge enabled deep–fuzzy hybrid framework for real-time IAQ prediction and adaptive control. The proposed system integrates IoT-based environmental sensing, Temporal Fusion Transformer-based multivariate forecasting, knowledge distillation, edge-deployed Bi-LSTM inference, and Mamdani fuzzy logic control within a unified IAQ management architecture. A composite Comfort Risk Index is introduced to combine environmental parameters and occupant discomfort feedback into a single adaptive control indicator. Experimental evaluation under varying indoor conditions demonstrated strong forecasting performance, with prediction accuracies reaching 96.3% for CO₂ and 95.7% for PM_2.5 prediction, while reducing inference latency from 575 ms to 295 ms. Comparative analysis against baseline threshold-based control strategies further indicated improved comfort stability, smoother actuator behavior, and reduced estimated actuator operating intensity during deployment. The proposed framework also demonstrated resilient operation under simulated sensor-failure conditions while maintaining low computational overhead suitable for resource-constrained IoT-edge environments. Overall, the results indicate that combining lightweight deep learning models with interpretable fuzzy control can provide an effective, scalable, and energy-aware solution for intelligent real-time IAQ optimization in smart indoor environments. Full article

Landfill tire fires are complex environmental disasters generating toxic pollutants with severe health risks. This study quantified emission dynamics and toxicological consequences of a large-scale tire fire in an urban ecosystem. A comprehensive source-to-receptor approach was applied, integrating Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) atmospheric dispersion modeling with comparison against air quality monitoring data. Soil samples collected from the fireground and surrounding urban allotment gardens were analyzed for tire-specific tracers (Zn) and 16 priority polycyclic aromatic hydrocarbons (PAHs). Human health risks were assessed using Incremental Lifetime Cancer Risk (ILCR), Toxic Equivalency Quotient (TEQ), and Mutagenic Equivalency Quotient (MEQ) metrics. Fire emissions were dominated by particulate matter (PM₁₀: 1.34 t) and PAHs (17.7 kg). Soil at the fire site showed severe contamination (Σ PAHs: 148.9 mg/kg), with benzo[a]pyrene as the primary carcinogen. The cumulative ILCR for children reached 9.7 × 10⁻⁴, exceeding the commonly used upper regulatory benchmark of 10⁻⁴. Dermal contact was identified as the dominant exposure pathway for pyrogenic PAHs. Elevated risk levels persisted at distal residential sites (ILCR: 10⁻⁵–10⁻⁴), indicating long-term environmental contamination Ecological risk quotients (RQ) exceeded unity for PAHs across all fire-impacted locations and for Zn and Cu in the immediate vicinity of the fire scene. These findings demonstrate that acute tire fire events can evolve into persistent terrestrial health hazards, highlighting the critical role of dermal exposure in PAH uptake and the need for long-term environmental monitoring and adaptive land-use management strategies to mitigate chronic health risks in urban populations. Full article

Emerging sustainability demands and calls for lowering materials’ environmental impact have directed authors to examine a class of polymers characterised as covalent adaptable networks and referred to as vitrimers. In this study, composite plates were made using vitrimer resin as the matrix material and continuous unidirectional flax fibre fabrics as the reinforcement. A specific early-stage composite part production method is proposed to make the multi-ply flax/vitrimer composite plate. The development of natural fibre–reinforced vitrimer composites is of clear research interest as a promising approach towards sustainable and recyclable novel material systems. Specimens prepared with all the plies oriented 0° exhibited a 129.4 MPa tensile strength and a 12.4 GPa tensile modulus, indicating a 334% increase in tensile strength when compared to the average value of 29.8 MPa obtained for neat vitrimer specimens and a 1140% improvement in the tensile modulus compared to the 1.0 GPa reached for neat vitrimer. The specimens whose plies were oriented 90° are found to deliver a tensile strength of 12.2 MPa and a 1.3 GPa tensile modulus. Applying the classical composite material micromechanics equation to calculate the 0°-direction tensile modulus demonstrated a good agreement with the experimentally obtained value—a 9.6% difference was discovered. Proper fibre/matrix interfacial adhesion was detected when the flax/vitrimer specimens’ surfaces after fracture were examined under scanning electron microscope. The research findings on tensile mechanical properties reveal that the observed flax/vitrimer composites may be potential candidates for replacing typical synthetic fibre–reinforced materials rated for automotive applications and intended for in-plane loaded parts, particularly some inner-body vehicle elements. Full article

Environmental Impact Assessment (EIA) is a key instrument for integrating environmental considerations into development planning; however, its effectiveness remains a subject of ongoing debate. This study evaluated the quality of ecological information across all stages of the EIA process, including baseline studies, impact assessment, mitigation measures, and monitoring programs. A total of 121 Environmental Impact Statements (EISs) from land development projects in central Thailand (2019–2024) were analyzed using structured content analysis. The results indicated that baseline ecological studies were generally comprehensive, particularly in species identification and habitat characterization. However, impact assessments remained largely descriptive, with limited use of quantitative and spatial analytical methods. Ecological mitigation measures were often generic, weakly linked to identified impacts, and particularly unclear in land development projects, indicating limited alignment with the mitigation hierarchy. Monitoring programs were even less frequently included and rarely functioned as a mechanism for evaluating mitigation effectiveness or supporting adaptive management. To address these gaps, this study proposes an integrated DPSIR–EIA–Ecosystem Services framework that strengthens linkages across EIA stages and enhances the analytical and decision-support capacity of ecological assessment for sustainable environmental governance. Full article

Sensory impairment affects over 1.3 billion people worldwide, yet the field still lacks a mechanistically specified theoretical framework explaining how communication inaccessibility contributes to stress-related health disparities in these populations. Existing models remain fragmented, addressing biological, psychological, and social factors in isolation rather than as interconnected systems. This concept paper presents the Communication-Endocrine-Stress Adaptive Regulation (CESAR) Model, an integrative biopsychosocial framework that integrates communication access, social support, stress regulation, and neuroendocrine function into a unified causal pathway. The CESAR model proposes that sensory impairment creates communication barriers may reduce social support, increase perceived stress, dysregulate the hypothalamic–pituitary–adrenal (HPA) axis, and ultimately impact reproductive health and psychological well-being. This integrative framework synthesizes evidence from disability studies, stress physiology, and communication sciences to provide a comprehensive theoretical foundation for understanding adaptation in sensory-impaired populations. The model incorporates feedback loops, moderating factors (sex, age, impairment type, duration), and environmental contexts (accessibility policies, healthcare access) that influence adaptive outcomes. By proposing specific causal pathways and testable hypotheses, the CESAR model provides a roadmap for future empirical research and targeted interventions that address the root causes of health disparities in sensory-impaired populations rather than merely treating symptoms. Full article

This study evaluates the ecological impacts of seawater desalination discharge on coastal marine ecosystems through a sequential analytical framework linking systematic literature synthesis, field-monitoring evidence, spatial analysis, and predictive ecological modeling. The novelty of the study lies in combining multi-regional evidence from Mediterranean coastal zones, Persian Gulf waters, and Pacific coastal environments with threshold-based ecological risk assessment, thereby linking discharge-related environmental stressors with biological responses and ecosystem-function alterations. The systematic review first retained 750 studies published between 2004 and 2024 for qualitative synthesis. On this basis, 59 high-quality references with sufficient numerical information were selected for the main quantitative meta-analysis, while field-monitoring data were used to support the interpretation of distance-based discharge gradients. Spatial interpolation and hierarchical modeling were then applied to evaluate exposure–response patterns and ecological threshold behavior. The results showed that desalination facilities generated measurable ecological impacts mainly within 50–200 m of discharge points, with a critical transition distance of approximately 127 m where hypersaline conditions, typically 1.5–2.0 times ambient seawater levels, were associated with marked changes in marine community structure. Benthic assemblages showed taxon-specific responses, with mollusks and echinoderms exhibiting greater sensitivity than polychaetes and small crustaceans. Marine vegetation declined strongly under combined salinity, thermal, and chemical stress, while phosphonate-based antiscalants accumulated in filter-feeding organisms and produced bioaccumulation factors up to 42.1 times ambient levels. Ecosystem-function indicators, including microbial community composition and sediment organic matter processing, remained altered up to 300 m from discharge points, indicating that functional impacts may extend beyond the primary hypersaline plume. The predictive modeling framework further demonstrated that ecological risk decreased nonlinearly with distance and varied according to discharge intensity, local hydrodynamics, and biological sensitivity. These findings indicate that conventional uniform buffer-based assessment may underestimate the ecological footprint of desalination discharge. Sustainable desalination management should therefore adopt site-specific monitoring, species-sensitive protection thresholds, improved brine-management technologies, and adaptive mitigation strategies based on real-time environmental feedback. Full article

Disentangling the coupled, nonlinear impacts of climate change and anthropogenic activities on vegetation dynamics is critical yet challenging for global change research. The Qinghai–Tibetan Plateau (QTP), a highly climate-sensitive and ecologically strategic region, serves as a vital arena for examining such complex socio-ecological attributions. Based on multi-source environmental datasets from 2000 to 2020, this study developed an integrated, spatially explicit framework coupling residual trend analysis (RESTREND) and GeoDetector to quantify individual drivers and nonlinear climate–human interactions. The QTP exhibited a significant, widespread greening trend during 2000–2020, featuring prominent spatial clustering with “High–High” clusters in the southeast and “Low–Low” clusters in the northwest. Attribution modeling revealed that vegetation dynamics were governed not by isolated variables, but by multifaceted, nonlinear synergies among precipitation, temperature, topography, vegetation type, and land-use change. Key interactive pairs, particularly elevation–temperature and slope–precipitation, dramatically increased explanatory power over single-factor models. Crucially, climate–human synergies explained substantially more variance than climate variables alone, bounded by a distinct elevational threshold: human activities dominated vegetation dynamics at mid-elevations (2500–3500 m), while climate factors took over as the primary controller at high altitudes (above 3500 m). Quantitatively, human activities induced vegetation improvement across 38.6% of the plateau, maintained stability in 35.8%, and caused degradation in 25.6%. By successfully merging trend decomposition with spatial stratified heterogeneity analysis, this study provides a transferable approach to uncoupling complex environmental interactions. These insights highlight the intensifying human footprint on alpine ecosystems and advocate for zone-specific adaptive management: mitigating human disturbances at mid-elevations and fostering climate adaptation in higher zones to preserve plateau resilience. Full article