Search Results (1,506)

This study numerically investigates the thermo-energetic behaviour of partitioned cavity walls integrating hypothetical phase change material (PCM) arrangements with single and staggered transition temperatures under cyclic thermal excitation representative of building-envelope operating conditions. The investigated configurations included single-PCM cases with transition temperatures of $20{}^{\circ }\mathrm{C}$, $22{}^{\circ }\mathrm{C}$, and $24{}^{\circ }\mathrm{C}$, as well as two staggered multi-PCM arrangements, namely $(20,22,24{}^{\circ }\mathrm{C})$ and $(24,22,20{}^{\circ }\mathrm{C})$. A two-dimensional transient numerical model based on the enthalpy–porosity approach was developed and validated against previously published numerical and experimental studies available in the literature. Several thermo-energetic indicators were introduced, including temperature amplitude reduction, damping factor, heat-flux attenuation, thermal time lag, cumulative transmitted thermal energy, and liquid-fraction evolution. A normalized multi-objective thermo-energetic assessment was additionally performed to identify the most balanced PCM arrangement. The results demonstrated that the $20{}^{\circ }\mathrm{C}$ PCM provided the strongest indoor-side thermal attenuation, reducing the temperature amplitude and heat-flux amplitude at facet $x8$ by $66.34\%$ and $62.20\%$, respectively, while increasing the thermal time lag to approximately $7.41\mathrm{h}$. The liquid-fraction analysis further revealed that latent heat activation remained strongly localized and spatially selective within the partitioned cavity structure. The staggered multi-PCM arrangements generated broader and spatially redistributed latent heat activation patterns, promoting more progressive thermal regulation over time. In particular, the $(20,22,24{}^{\circ }\mathrm{C})$ arrangement produced the highest partial latent activation, with a maximum liquid fraction approaching $0.1596$, corresponding to the highest latent activation ratio observed in the present study (≈15.96%), whereas the reversed arrangement $(24,22,20{}^{\circ }\mathrm{C})$ provided enhanced indoor-side stabilization associated with delayed and spatially redistributed latent heat activation. The combined thermo-energetic assessment further revealed important trade-offs between peak thermal damping, delayed thermal response, and distributed latent heat activation. Overall, the obtained findings demonstrate that both PCM transition temperature and spatial ordering strongly influence the transient thermal behaviour of partitioned cavity walls and should therefore be carefully considered in the design of adaptive PCM-integrated building envelopes. Full article

Introduction: The Atlantic bluefin tuna Thunnus thynnus (ABFT) is a large pelagic apex predator with adaptations for a life of ceaseless swimming during long-distance oceanic migrations. The environmental physiology and energetics of tunas have interested researchers for many decades, but they are notoriously challenging to study because they are so difficult to keep in captivity. Adult ABFT are, however, now fattened in cages at various sites in the Mediterranean, while juveniles are reared from hatching every year at the Unique Scientific and Technological Infrastructure for ABFT aquaculture (ICAR-IEO), near Cartagena in Spain. These facilities provide access to animals, but the fish remain very problematic to study because of their highly active but physiologically delicate nature and, for adults, their very large sizes. Objective: To study the in vivo physiology and behaviour of ABFT. Methodology: We used heart rate biologging and high residency acoustic tracking to follow cardiac and swimming activity over a year in n = 24 adult ABFT (mass range 25 to 200 kg) held in a cage off the coast of Malta (Malta Fish Farming). We then performed swim tunnel respirometry on young of the year juveniles (500g) at ICRA-IEO, but subsequently took a ‘hands-off’ approach, using video analyses and group respirometry on free-swimming animals. Results: The descriptive approach on the caged adults provided understanding of how seasonal water temperatures (15 to 28 °C) affect tuna physiology and behaviour. The swimming respirometry on juveniles revealed that their performance was constrained by confinement in the tunnel, compared to when they were swimming at their spontaneous preferred speed in their rearing tank. Video analyses provided insights into the effects of size (25 to 200 cm bodylength) on spontaneous swimming speeds and coupled with tank respirometry, revealed how progressive hypoxia affects the metabolic rate and schooling behaviour of juveniles. Conclusions: These opportunistic and disparate pieces of information are nonetheless valuable for such a fascinating but data-deficient species, and can be useful in mechanistic models for management of an extremely valuable fishery in a context of global change. Full article

Background/Objectives: Antimicrobial resistance (AMR) is a major public health problem driven partly by inappropriate antibiotic use. Students of health studies represent future healthcare professionals with an important role in patient education, infection prevention, and antimicrobial stewardship. This study assessed knowledge, attitudes, and behaviours regarding antibiotic use and AMR among students of the Faculty of Health Studies, University of Mostar. Methods: An anonymous cross-sectional online survey was conducted in March 2025 using a self-selected convenience sample. The questionnaire was adapted from a previously published survey among Cypriot university students and distributed through student WhatsApp groups and by e-mail. Of 1113 invited students, 220 completed the survey, yielding a response rate of 19.8%. Results: During the previous 12 months, 39.5% of respondents reported antibiotic use. Most respondents reported adherence to medical instructions regarding dosage and duration of therapy, while 20.5% reported self-medication with antibiotics and 29.5% reported keeping unused antibiotics at home. Approximately 42% perceived antibiotics as easy or very easy to obtain without a prescription. Only 36.4% of respondents correctly distinguished antibiotics from other medications. Although most respondents recognised that bacteria can develop resistance, misconceptions persisted regarding humans and viruses. Differences between study programmes were observed for some attitudes and perceptions, whereas gender and year of study were not significantly associated with most responses. Conclusions: Health studies students demonstrated partial knowledge of antibiotics and AMR, together with behaviours that may contribute to inappropriate antibiotic use. Strengthened curricular content on rational antibiotic use, infection management, infection prevention, and antimicrobial stewardship appears justified. The findings are also consistent with the need to consider broader stewardship measures, including better enforcement of existing prescription-only dispensing requirements in Bosnia and Herzegovina. Full article

Farmer Producer Organizations (FPOs) have gained increasing attention as institutional mechanisms for improving the resilience of smallholder farming systems under changing climatic conditions. This study examines the role of FPOs in promoting the adoption of Climate-Smart Agriculture (CSA) practices, improved irrigation strategies, and sustainable land management in the arid region of Pali district, Rajasthan, India. A comparative assessment was conducted between FPO-associated member and non-member farmers to evaluate differences in climate change perception, adoption behaviour, and adaptive capacity. The study employed a mixed-methods research design using primary data collected from 408 farm households through structured interviews, focus group discussions, and key informant consultations. Descriptive statistics, mean comparison tests and regression analysis were used to examine adoption patterns and identify the major factors influencing farmers’ responses to climate risks. The findings indicate that delayed rainfall, rising temperatures, and increasing drought frequency are widely perceived by farmers as major threats to agricultural production. FPO membership was associated with higher levels of climate-risk awareness and greater reported adoption of CSA practices; however, these findings should be interpreted as associations rather than causal effects. Farmers linked with FPOs reported stronger uptake of improved and stress-tolerant crop varieties, crop diversification, mixed farming systems, agroforestry, soil moisture conservation, rainwater harvesting, improved irrigation methods, and integrated pest management practices. Education, farm size, access to extension services, market linkages, and climate information were also found to significantly influence adoption decisions. The study highlights the important contribution of FPOs in reducing transaction costs, improving access to inputs, technical knowledge, credit and markets, and encouraging collective responses to climate stress. Strengthening FPO governance, expanding extension support, and targeting vulnerable farmer groups can substantially enhance climate resilience and support sustainable agricultural transitions in arid regions. The findings demonstrate that farmer organizations can serve as effective intermediary institutions linking household-level adaptation strategies with broader goals of irrigation efficiency, land management, and rural sustainability. Full article

Information infrastructure is essential for digital transformation and AI-enabled services, but its operation also involves high electricity consumption and carbon emissions. This study develops a tripartite evolutionary game model involving the government, information-infrastructure operators and the public, and integrates it with system dynamics to examine how regulatory mechanisms influence operators’ net-zero behaviours. The model focuses on operational-stage information infrastructure. Initial parameters are calibrated using the 2023 China Statistical Yearbook on Resources and Environment and expert consultation, with key variables measured by operational revenue, net-zero costs, regulatory costs, incentives, penalties, public scrutiny costs and environmental losses. The results show that operators’ net-zero behaviours may fluctuate under weak or static regulation. Government incentives, penalties and public scrutiny can promote net-zero operations, while dynamic reward–penalty mechanisms are more effective in stabilising behavioural evolution. This study extends evolutionary game theory and system dynamics to the net-zero governance of information infrastructure and provides an adaptive regulatory framework for coordinating government regulation, operator behaviour and public participation. Full article

The transition to automated vehicles (AVs) introduces complex human factors and system-level challenges across Society of Automotive Engineers (SAE) Levels 0–5, with profound implications for the long-term viability of future transport infrastructure. Drawing on a synthesis of socio-technical, cognitive, and behavioural adaptation theories, this study develops an integrated framework to analyse the evolving relationships among driving automation, human behaviour, system risks, and urban sustainability. The findings demonstrate that human-factor risks are inherently nonlinear, meaning they do not decrease proportionally as technology advances; instead, risk profiles peak significantly at intermediate automation levels (SAE 2–3) due to supervisory fatigue and delayed takeovers, introducing severe traffic flow volatility and localised micro-congestion that directly compromise the environmental efficiency of sustainable transport systems. As these risks reconfigure into institutional and digital infrastructure dependencies at higher levels (SAE 4–5), the primary constraint shifts toward network readiness. Through an analysis of real-world AV deployment case studies and a structured narrative literature review, this paper identifies critical operational discontinuities and mixed-traffic complexities that threaten urban grid resilience. This study proposes a conceptual framework that translates these cross-level socio-technical insights into actionable deployment pathways, providing policymakers with adaptive governance models, transportation planners with mixed-traffic management strategies aimed at preserving network efficiency, infrastructure agencies with physical and digital readiness criteria for long-term asset sustainability, and AV developers with human–machine interface optimisation frameworks to secure human-centric safety within sustainable smart city networks. Full article

Decision-making is a fundamental component of autonomous driving, where complex urban scenarios require safe, robust, and adaptable behaviours. This work presents a curriculum learning approach to reduce the dynamics-related reality gap in autonomous driving decision-making through a hybrid architecture that combines learning-based tactical decisions with classical planning and control methods. The proposed methodology follows a staged sim-to-real process: first, the decision-making policies are trained in a lightweight simulator to learn basic kinematic behaviours; then, they are transferred and refined in CARLA to account for vehicle dynamics; subsequently, a digital twin of the real platform and test environment is used for scenario-specific fine-tuning; finally, the resulting architecture is validated through parallel execution with a real vehicle. The proposed approach focuses on vehicle dynamics, actuation response, and scenario geometry rather than on the complete sim-to-real problem for autonomous driving. The approach is evaluated across multiple urban driving scenarios in simulation, including lane changing, roundabouts, merging, and crossroads, while real-world validation is conducted in a controlled merge scenario. Experimental results show that the proposed curriculum improves training efficiency and final performance across the different stages, achieving success rates above 91% in SMARTS. In CARLA, the proposed architecture completes the evaluated scenarios up to 50% faster than the Autopilot baseline while improving comfort and safety-related metrics in terms of acceleration and jerk. The real-world parallel execution experiment further demonstrates the feasibility of transferring the decision-making architecture to a physical vehicle under controlled conditions. Finally, an ablation study quantifies the contribution of each curriculum stage to the overall system performance. Full article

The use of sophisticated software for greenhouse microclimate analysis often requires advanced modelling expertise and significant computational effort, which may not always be available to greenhouse designers. This study proposes an integrated and modular workflow aimed at supporting greenhouse design through coupled thermal and evapotranspiration simulations. The design methodology is based on three steps. In the initial phase, the greenhouse environmental conditions are evaluated through the implementation of a dynamic thermal analysis, which is conducted by the DesignBuilder software (version 4.2). Subsequently, a plant evapotranspiration model is employed in MATLAB/Simulink (version R2025b) to evaluate crop transpiration, moisture production, and irrigation water consumption. In the final phase, the simulated moisture production is used to estimate the required ventilation rates and to support the sizing of greenhouse systems, including irrigation and HVAC components. Plant moisture production is a crucial factor in determining the sizing of greenhouse subsystems, such as the irrigation system, the ventilation rate, and the HVAC system. Nonetheless, the implementation of the evapotranspiration model necessitates a bespoke calibration to a case study. Indeed, the proposed models are more generally applicable and must be adapted to real-world applications. The methodology was applied to a small greenhouse used for the cultivation of aeroponic lettuce (Lactuca sativa cv. Romana) in a Mediterranean environment. The aim of the study was to explore the potential of the proposed integrated modelling framework to estimate annual irrigation water demand and the minimum ventilation rate required to mitigate excess moisture production, using a coupled MATLAB/Simulink implementation. The proposed approach should be interpreted as an exploratory design-support methodology rather than a fully validated predictive model, intended to investigate system behaviour under the specific conditions of the case study. Full article

Intelligent autonomous driving systems must not only predict the appropriate driving manoeuvre but also provide human-interpretable evidence that justifies the decision. However, existing methods typically address these objectives separately, leading to three practical limitations: multi-stage perception-to-language pipelines can propagate upstream perception errors into downstream explanations; post hoc saliency methods often produce pixel-level highlights that are difficult to interpret semantically; and decoupled decision and explanation modules cannot guarantee that the explanation reflects the same scene evidence used for behaviour prediction. In this paper, we propose a unified framework that jointly performs vehicle behaviour prediction and human-centric interpretation from a shared visual backbone. Specifically, a hierarchical Swin Transformer encodes the driving scene into a sequence of spatial tokens, which are processed by two complementary branches. The first branch, termed the Object Selection Module (OSM), learns a compact scene-level semantic representation through query-guided cross-attention, while the second branch extracts a small set of class-agnostic object-centric tokens without requiring bounding-box or segmentation supervision. These two representations are subsequently integrated by a Semantic–Object Fusion (SOF) module based on scaled dot-product attention, residual connections, and a feed-forward network. The behaviour prediction head operates on the fused representation, whereas the interpretation head leverages the semantic representation through a skip connection to preserve decision-relevant context. For surround-view perception, learnable per-camera embeddings are introduced to maintain viewpoint identity with negligible additional parameter cost. Furthermore, a compact language model fine-tuned via Low-Rank Adaptation (LoRA) generates fluent, label-conditioned natural-language justifications. Extensive experiments on two public benchmarks, BDD-OIA and nu-AD, demonstrate that the proposed framework consistently delivers superior performance and provides effective, human-readable interpretations of driving decisions. Full article

Demand-side management is increasingly important for low-carbon transport governance. However, many studies assume relatively stable travel preferences and pay limited attention to behavioural changes under sudden external shocks. This study proposes an Event–Behaviour–Resilience framework and applies Natural Language Processing to Sina Weibo data to examine travel responses to extreme heat and refined oil price adjustments. The results show asymmetric response patterns. Oil price increases were associated with cost-based low-carbon substitution, with new-energy vehicle intentions accounting for 64.4% of the share. In contrast, extreme heat was associated with both trip reduction and motorised travel. Travel reduction reached 52.4%, while ride-hailing or taxi responses accounted for 24.6%. A quadratic fitting analysis identified 38.0–39.0 °C as an observed transition interval, within which high-carbon motorised willingness began to exceed low-carbon slow mobility willingness. Group-level analysis showed unequal behavioural flexibility. While 80.0% of the general population reduced travel under extreme heat, the forced mobility group showed limited travel reduction and maintained a high level of low-carbon willingness at 86.87%. XGBoost-SHAP results indicated that temperature, emotional valence, and behavioural constraints contributed to low-carbon mobility intention. These findings suggest that behavioural responses can help identify spatial interventions for low-carbon transport, especially in relation to heat exposure, mobility flexibility, and access to adaptive travel options. Full article

This paper explores how children perceive and use outdoor spaces in their everyday urban environment, and which spatial characteristics encourage engagement, autonomy, and diverse play. This study was conducted using child-led sensory walks—an exploratory qualitative method in which children acted as active research guides—with ten children aged 6 to 11 in residential areas of Zagreb. Verbal comments, movement patterns, and play behaviours were recorded and analysed through thematic analysis. Following the walks, eleven public playgrounds were assessed from a landscape architecture perspective, integrating children’s observations with an expert evaluation of spatial organisation, shade provision, connectivity with surrounding green spaces, and potential for unstructured play. The results reveal a pronounced preference for natural and semi-natural spaces, where children exhibited longer stays, more diverse physical and symbolic play, and a greater sense of autonomy. These findings affirm the relevance of affordance theory and multisensory experience in understanding children’s spatial behaviour and demonstrate the potential of the sensory walk as a transferable research and design tool in landscape architecture practice. At a broader scale, they point to the untapped role that playgrounds—redesigned as genuine green infrastructure nodes—could play in advancing urban climate adaptation goals at the neighbourhood scale. Full article

This study presents a replicable diagnostic framework for analysing latent safety vulnerability at complex junctions by integrating UAV-based observation, trajectory extraction, movement-level operational performance modelling, and regulatory benchmarking. Grounded in Vision Zero/Safe System principles, the approach is demonstrated at Junction 50 of the A-66 motorway in Mieres (Spain), a constrained urban interchange where motorway access/egress overlaps with local cross-town movements. Two one-hour UAV datasets were collected during peak periods and processed with GoodVision to extract trajectories, turning-movement counts, origin–destination patterns and recurrent irregular manoeuvres. These outputs were combined with SIDRA INTERSECTION indicators (e.g., LOS, delay and capacity utilisation) and assessed against the Spanish geometric design standard to identify design–operation misalignment, including a targeted 3D sight-distance check at the stop-controlled exit. The results show systematic behavioural adaptations at critical decision points, including informal side-by-side queuing at nominally single-lane exits and queue bypassing via adjacent auxiliary areas, co-occurring with capacity-strained movements (LOS E–F) and normative inconsistencies in lane type/length and channelisation. The framework translates high-resolution behavioural evidence into intervention-relevant outputs (critical movements, concentration zones and explicit design–behaviour mismatches) without relying on crash frequency as the primary signal, supporting proactive prioritisation in constrained legacy layouts. Full article

The water, food, and energy nexus has become central to debates on sustainability and climate adaptation, yet students’ capacity to understand these interdependencies remains unclear. This study examined knowledge and attitudes toward the nexus among 397 students enrolled in nine faculties at Bursa Uludağ University in Türkiye. Data were collected through a structured questionnaire covering demographic characteristics, conceptual knowledge, attitudes, behavioural tendencies and perceived barriers. Nexus knowledge was operationalised as a summative index of ten equally weighted items scored as correct (1) or incorrect/uncertain (0). Hierarchical regression was used to identify the determinants of nexus knowledge, and path analysis was used to test the indirect effects of knowledge on behavioural intention through professional confidence. Only 38.8% of respondents correctly identified the nexus as an interconnected resource framework and 60.7% reported no prior coursework on sustainability or climate change. Information exposure and course participation explained substantially more variance in nexus knowledge (ΔR² = 0.192) than demographic (ΔR² = 0.043) and socioeconomic variables (ΔR² = 0.074) combined. Knowledge had a significant indirect effect on behavioural intention through professional confidence (indirect effect = 0.16, 95% CI [0.09, 0.24]; final model R² = 0.398, Adjusted R² = 0.385). Attitudes were generally moderate rather than strongly environmental, and behavioural responses were highly polarised, pointing to a persistent gap between concern and action. Students identified knowledge deficits and technological limitations as the main barriers to nexus management and strongly preferred interdisciplinary coursework as the most effective educational intervention. The findings indicate that formal educational exposure plays a larger role than background characteristics in shaping nexus literacy and that embedding systems thinking into disciplinary curricula may strengthen sustainability competence among future professionals. Full article

Background: Dementia and Type 2 diabetes (T2D) represent two of the most pressing global public health challenges of our time, both exacerbated by physical inactivity. These conditions disproportionately affect Global Majority populations, who experience earlier onset, higher prevalence, and poorer access to culturally appropriate preventive care. However, conventional research and interventions often overlook the sociocultural and structural factors that underpin this disparity. This study synthesises current evidence to understand how these three conditions intersect and to identify equitable pathways for prevention and support. Methods: A meta-narrative review approach was employed to integrate evidence from diverse biomedical, public health, sociocultural and intervention science traditions. Searches were undertaken across MEDLINE/PubMed-adapted searches, CINAHL, PsycINFO, Web of Science Core Collection, AMED and ASSIA, supplemented by grey literature searching and citation chasing. Five meta-narratives were identified: biomedical and epidemiological, public health, health disparities, sociocultural and behavioural, and intervention science. Cross-narrative synthesis produced a conceptual framework linking upstream determinants, lifestyle factors, and disease outcomes. Results: The review revealed that structural inequities such as deprivation, environmental barriers and sociocultural factors including stigma, gendered norms, limited access to culturally appropriate facilities that restrict physical activity (PA) opportunities within Global Majority communities. These constraints elevate T2D and dementia risk through biological pathways involving insulin resistance, vascular injury, and neuroinflammation. Community-based participatory research (CBPR) interventions particularly those delivered in trusted cultural or faith settings emerged as effective strategies to improve PA, glycaemic control, and cognitive well-being. Conclusions: This synthesis reframes dementia and diabetes as interlinked within a wider syndemic driven by structural and sociocultural inequities. The proposed framework underscores the importance of culturally grounded, community-led approaches to promote brain health, reduce risk, and achieve equitable healthy ageing among Global Majority populations. Full article

The rapid evolution of online learning environments has generated diverse and complex data ecosystems. Recommender systems play a central role in leveraging such heterogeneous data to support personalised learning experiences. However, many deep learning-based recommender systems still rely on identifier-based representations that capture co-occurrence and collaborative patterns while overlooking the semantic information embedded in educational activities and the temporal dynamics of learner behaviour. To address these limitations, this study proposes a collaborative-enhanced semantic–sequential recommendation framework for educational platforms that combines structured semantic representation learning, sequential behavioural modelling, and collaborative preference modelling. The proposed architecture integrates a parameter-efficient MiniLM adaptation strategy to extract semantic representations from structured item-related educational metadata and a bidirectional recurrent encoder to model temporal learning patterns from behavioural logs. A gated fusion mechanism is then used to combine semantic and contextual information into learner representations, which are further integrated with collaborative user–item embeddings for top-K recommendation using a Bayesian personalised ranking objective. Experiments conducted on the EdNet-KT1 dataset under chronological splitting, full-corpus ranking, and fixed candidate-sampling protocols show that the collaborative-enhanced model achieves the highest-ranking performance among popularity-based, matrix factorisation, neural collaborative filtering, recurrent sequential, self-attention sequential, and ablation baselines. The model obtains an NDCG@10 of 0.1344 under full-corpus ranking and 0.5383 under candidate sampling, with statistically significant but practically modest improvements over the strongest baselines. Additional ablation, efficiency, and gate analyses indicate that semantic–contextual modelling is most effective when used as a residual enhancement to collaborative recommendation rather than as a standalone replacement. These results suggest that parameter-efficient semantic–sequential modelling, when combined with collaborative preference signals, offers a promising direction for scalable and evidence-based educational recommender systems. Full article