Search Results (3,863)

Autonomous driving (AD) has emerged as a transformative technology that holds the potential to free humans from the need for manual driving and provide a safer, more comfortable and efficient driving experience. However, most AD systems make decisions solely based on vehicle dynamics and environmental factors such as road conditions and surrounding vehicles, while the occupant’s mental states, such as subjective feelings and experience, are neglected. As a result, autonomous vehicles (AVs) often fail to meet the occupant’s physical and mental demands, ultimately leading to a compromised driving experience. In this study, we propose an occupant-aware decision-making paradigm (ODP) for AD systems. ODP first perceives the occupant’s physical and physiological states that are closely related to mental states, such as facial expressions and physiological signals, through the occupant monitoring system (OMS). Then, a large vision-language model (VLM) processes the occupant’s physical and physiological states via the chain of thought (CoT) technique to analyze the occupant’s mental states and infer the occupant’s needs. Finally, the VLM makes driving decisions that match the occupant’s demands and preferences. Experimental results show that ODP can make decisions that are significantly better aligned with the occupant’s actual needs than existing methods. Full article

The combined effects of plateau pikas and yak grazing on the distribution or occupancy of endemic passeridae birds on the Qinghai-Tibetan plateau, China remain largely unknown. To assess habitat selection patterns within the frameworks of niche construction theory and the rivet hypothesis, we measured the occupancy rates of passeridae species along five sample strips of transects established in a treeless ecosystem. Each transect was surveyed three times within each seasonal sampling window (spring, summer, and autumn 2024), and repeated visits were treated as detection occasions for occupancy modeling. We used plateau pika density and yak grazing patterns as key variables to investigate their influence on the occupancy of alpine passeridae birds. We found that the occupancy of both the White-rumped and Rufous-necked Snowfinch was positively associated with proximity to yak bedding sites and high densities of plateau pika burrows. However, the occupancy of both species declined with increasing distance from yak bedding areas. In contrast, the Ground Tit showed no detectable association with these variables. This strong interspecific variation underscores the importance of disentangling mechanistic linkages among large herbivores, ecosystem engineers, and avian niche specialization in this fragile biome. Further research should explore how cross-taxa interactions mediate habitat availability and species resilience under ongoing environmental change. Full article

This study benchmarks three ROS 2 Navigation2 local controllers—Dynamic Window Approach Based (DWB), Regulated Pure Pursuit (RPP), and Model Predictive Path Integral (MPPI)—under three complementary operational stressors in simulation: (i) a structured corridor with a transient dynamic obstacle, (ii) a sloped environment where terrain inclination biases a planar 2D LiDAR costmap through spurious occupancy projections, and (iii) a narrow corridor that amplifies inflation effects. A reproducible rosbag2-based protocol records five key performance indicators per trial: time-to-goal, lateral tracking RMSE, stopped time, heading oscillations, and control effort. With 15 independent repetitions per cell (scene × controller × direction), the design yields 270 trials. The results expose complementary value profiles: RPP minimizes mission time, DWB produces the fewest heading oscillations through critic-based shaping, and MPPI achieves the lowest control effort via smooth trajectory generation. In the sloped scene, the tracking RMSE differences compress across all controllers—a signature of a perception-limited regime in which costmap bias overshadows controller logic. These findings translate into an actionable controller-selection guide and a reproducible baseline for quantifying gains from upstream perception and cost-representation improvements. In concrete terms, we contribute (i) a controlled benchmark with fixed planning, localization, and costmaps, (ii) full configuration disclosure (controller parameters, costmap settings, and software versions with package pinning), and (iii) a scene-specific costmap distortion index that links slope-induced local cost bias to measurable performance shifts, underpinning a decision matrix for controller selection in semi-structured environments. Full article

Urban curbside loading and unloading zones are increasingly affected by competing non-logistics uses, such as outdoor terraces or resident parking, leading to reductions in effective curbside length. These design decisions can significantly alter service capacity and generate environmental externalities in urban freight operations that are rarely quantified. This study introduces the Factor of Occupancy (F_o) as a space–time design indicator for curbside unloading zones, defined as the product of effective curbside length and the maximum authorised dwell time. Using direct observational data from an urban block in Zaragoza (Spain), the analysis focuses on a loading and unloading zone whose effective length was reduced by approximately 6 m due to the installation of a restaurant terrace. Two curbside configurations are compared: a reduced configuration (8 m) and a restored configuration (14 m), keeping demand and temporal constraints constant. F_o is integrated into a loss-based queueing model (M/M/1/1) to estimate blocking probabilities and the number of served and rejected freight operations. To capture the environmental implications of curbside capacity loss, the paper proposes the Hidden Carbon Emissions (HCE) indicator, which quantifies the additional CO₂ emissions generated by rejected vehicles through block recirculation and idling during illegal occupancy, based on observed behaviour and publicly available emission factors. The results show that restoring curbside length substantially increases effective service capacity and reduces rejected vehicles, leading to a marked decrease in hidden CO₂ emissions per operation. The findings highlight that minor curbside design decisions can produce measurable impacts on both urban freight efficiency and environmental performance. Full article

Natural and human-made hazards are increasing due to global warming and human activities. Occupant evacuation in complex buildings remains challenging due to unfamiliar building layouts, communication failures, and unpredictable occupant behavior. Therefore, this study aims to explore how integrating digital technologies enhances emergency preparedness, supports occupant decision-making during evacuation, and improves occupants’ situational awareness. We conducted a PRISMA-guided systematic literature review across Scopus, IEEE Xplore, and ProQuest Discover, analyzing 31 high-quality journal articles relevant to the research. The focus was on integrating digital technologies to support occupant situational awareness and evacuation outcomes. This review explores the integration of Internet of Things (IoT), Building Information Modeling (BIM), Virtual Reality (VR) /Augmented Reality (AR), Artificial Intelligence (AI), and Digital Twins (DTs) for emergency preparedness, supporting real-world applications. This review highlights three research questions: (1) Evaluate how current digital technologies affect occupant emergency preparedness in buildings; (2) Identify the challenges that limit the effectiveness of digital technologies across key emergency preparedness stages; (3) Understand how digital technologies can support occupant emergency preparedness. The review compiles evidence and presents a conceptual framework to support the integration of digital technologies into occupant-focused emergency preparedness, providing practical guidance for the future direction of risk management research. Full article

Understanding how seat-induced whole-body vibration (WBV) is transmitted to and actively compensated by the human body is essential for accurately assessing discomfort, fatigue, and postural control in vehicle occupants. This study proposes a visual–inertial fusion framework utilizing IMU-RGB-D data to isolate seated human body vibration in dynamic vehicular environments. In real-cabin monitoring systems, measured motion is a superposition of platform vibration, passive transmission through the body, active postural compensation, and camera jitter. Existing WBV and driver monitoring studies typically rely on single modality sensing, such as inertial or visual approaches, without decomposing these components or modelling camera vibration. The framework synchronized three IMUs with RGB-D landmarks. Seat, human body, and camera accelerations are separated, and body vibration velocity is derived from body–seat differential acceleration via band-pass filtering and spectral integration. The 3D landmarks enable rotational-translational Postural Compensation Index metrics, axis-wise energy distributions, and anthropometric consistency checks. The study is held in an in-service urban tram case. Torso vibration is dominated by 40% anteroposterior components, while head postural is predominantly >50% lateral sway. Near static anthropometric evaluation was also studied, resulting in shoulder width errors that remain within 10–20 mm. The results show that the framework can distinguish passive ride phases from strongly compensated phases, separate camera jitter from true body motion, and reveal anisotropic postural strategies, providing a structured basis for vibration and posture analysis in in-vehicle monitoring. Full article

Rural shrinkage is increasingly expressed through changing residential mobility, housing under occupancy, and intermittent dwelling use, rather than a simple linear process of permanent outmigration and abandonment. Yet empirical measurement of occupancy dynamics and the service-mediated mechanisms shaping residence stability remains limited. This study proposes an evidence-driven and explainable assessment framework that links energy-informed occupancy dynamics with settlement building area and mechanism identification, using Fuyuan City as a case study. Daily electricity consumption time series from 2021 to 2024 are used to infer occupancy dynamics and detect behavioral signatures of long term residence, seasonal residence, return visits, and vacancy. Shape-based temporal clustering identifies six occupancy trajectories, revealing pronounced heterogeneity in mobility rhythms within the rural settlement system. Settlement vacancy-related built-environment changes are characterized from 2 m remote sensing imagery, using a trained YOLO-based building detection workflow, producing settlement-level total building area as a physical indicator of the development intensity. Integrating these behavioral measures with multi-source spatial factors, the mechanism model shows that development, governance, and environmental conditions influence residence stability primarily through service provision. Among service domains, education services exhibit the strongest direct association with long-term residence stability, while transport and daily life services show modest positive effects and healthcare presents a smaller positive effect. Development conditions positively promote all service types, whereas governance and environmental context display differentiated and, in some pathways, opposing effects across services. Overall, the framework enables interpretable monitoring of rural shrinkage dynamics by jointly quantifying occupancy trajectories, settlement morphology, and service-mediated pathways shaping residential outcomes. Full article

Background/Objectives: Adolescence is a critical developmental period in which individuals are required to orient themselves toward the future and construct a coherent life plan, including educational and career aspirations. Future orientation is closely linked to identity development and self-understanding, which allow adolescents to integrate past, present, and anticipated future selves. Among the personal resources supporting this process, problem-solving skills play a key role by enabling effective coping with challenges and informed, goal-directed decision-making. This study examined the association between problem-solving skills and adolescents’ aspirations for an ideal occupation, and tested whether this relationship was mediated by identity acquisition and self-understanding, with attention to gender differences. Methods: A quantitative study design was adopted. Participants were 2443 Italian adolescents (aged 15–19 years) attending upper secondary schools. They completed self-report measures assessing perceived problem-solving skills, identity acquisition, self-understanding, and aspiration for an ideal occupation. Two multigroup mediation models were tested using structural equation modeling, examining identity acquisition and self-understanding as mediators and comparing pathways across genders. Results: Problem-solving skills were indirectly associated with stronger aspirations toward an ideal occupation through identity-related processes. Identity acquisition mediated this association only among females, whereas self-understanding emerged as a significant mediator for both females and males, with partial mediation among females and full mediation among males. Conclusions: Overall, although constrained by the cross-sectional design, the findings are consistent with the notion that problem-solving skills contribute to future-oriented career aspirations chiefly by promoting identity coherence and self-clarity. These findings highlight the importance of integrating problem-solving training with identity-focused interventions in educational and career guidance programs, while considering gender-specific developmental pathways. Full article

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is becoming the most common liver disease, affecting between 30 and 40% of the global population. MASLD is a multifaceted disease spectrum that is closely associated with obesity, insulin resistance, type 2 diabetes mellitus and, more broadly, metabolic syndrome. All these conditions increase the risk of liver-related mortality, which explains the intense research efforts in recent years to better elucidate its pathogenesis. The crucial impact of environmental pollutants on the development of MASLD is now well recognized. Polychlorinated biphenyls (PCBs) are environmental contaminants that act as endocrine disruptors. Recently, they have been associated with the development of diabetes, obesity, MASLD, and cancer. The association between liver diseases, namely toxicant-associated steatotic liver disease and steatohepatitis (TASLD and TASH, respectively), and occupational exposure to PCBs and other industrial chemicals has been documented by several lines of evidence, whereas the potential role of low-level environmental pollution in liver disease and in MASLD remains incompletely understood. Previous studies on animal models have shown that PCB exposure is associated with steatosis/steatohepatitis, fibrosis, cirrhosis, hepatocellular carcinoma (HCC), altered liver enzymes, and mortality in exposed populations. This review investigates the mechanisms underlying hepatic steatogenesis in preclinical and animal models and analyzes the existing literature on the possible role of PCBs, together with the other conventional risk factors, in the development of MASLD in humans. Full article

City-scale forecasting is essential for both electric-vehicle (EV) charging operations (e.g., peak management and resource allocation) and urban mobility management (e.g., road-network flow monitoring and incident response). Spatio-temporal graph neural networks (STGNNs) are a natural candidate for these problems, yet their performance often critically depends on the choice of a predefined graph prior and the availability/quality of exogenous signals. Importantly, we do not intentionally construct a poor graph; rather, we treat any predefined adjacency as a testable hypothesis and verify its alignment with the forecasting target via no-graph ablations and lightweight diagnostics ($\Delta corr$, $E_D$). In this work, we present a unified experimental pipeline based on a spatio-temporal graph convolutional network (STGCN) backbone and conduct systematic ablations on (i) whether and how a predefined static graph is used and (ii) how feature sets influence multi-step forecasting accuracy. We evaluate on two city-scale hourly datasets with heterogeneous node counts (UrbanEV: 275 nodes; CHARGED-AMS_remove_zero: 1388 nodes) and a 24 h input/6 h output setting. Across datasets, we find that a static graph can be beneficial only when it matches the true dependency structure; otherwise, it may degrade accuracy substantially. On UrbanEV, removing the graph component improves overall MAE from 116.21 ± 5.43 to 66.53 ± 1.71 (S = 5 seeds, 0–4), outperforming a persistence baseline (MAE 94.16). Feature ablations further analyze how occupancy and price signals affect UrbanEV accuracy (e.g., MAE 87.32 with all features under the evaluated feature setting). On CHARGED, the volume-only setting performs best among tested feature combinations (MAE 0.127), closely tracking a persistence baseline (MAE 0.139), while additional covariates may introduce noise under static modeling. We provide detailed multi-horizon results and discuss practical implications for when graph priors help or hurt in real deployments. Full article

Increasing energy demand, decarbonization commitments, and growing expectations for thermal comfort are driving the need for more adaptive and efficient climate control in residential buildings. This review synthesizes contemporary intelligent HVAC control strategies, including model-predictive control (MPC), deep reinforcement learning (DRL), data-driven forecasting, and hybrid approaches. Following PRISMA guidelines, a set of studies published between 2010 and 2025 was systematically screened and analyzed to identify the dominant methodological trends, data requirements, implementation architectures, and evaluation practices reported in the literature. This review highlights how these methods differ in modeling assumptions, computational complexity, robustness to uncertainty, and suitability for residential environments characterized by stochastic occupancy and heterogeneous building stock. In addition, we examine enabling technologies such as sensing infrastructures, pricing signals, and embedded computation, as well as barriers to real-world deployment, including data availability, interpretability, and integration with existing building systems. The findings provide a consolidated framework for understanding the capabilities and limitations of intelligent HVAC control and outline research gaps that remain for achieving scalable, user-centered, and energy-efficient operation in residential buildings. Full article

This study investigates the spatial dimensions of labour market polarization in Hungary by examining the widening gap between developed agglomerations and lagging peripheral regions. It explores how educational inequality, technology-driven risks, and constrained mobility affect the spatial aspects of labour market polarization. It covers all 197 districts of Hungary on the LAU-1 level. Using cluster analysis and OLS regression models, we shall explore relationships between employment rates, educational attainment, automation exposure—as based on occupation-level data—and a composite mobility index. From the data, we detected distinct labour market zones, which are dynamic agglomerations, industrial transition zones, and peripheral lagging. The data confirms that the “triple trap” is clearly experienced by the peripheral regions, with lower educational attainment, high exposure to automation impacting nearly 50%, and mobility constraints keeping the workforce bound to local public works employment. These results provide evidence that labor market polarization is a self-reinforcing spatial process. It implies that successful policy interventions should be comprehensive, addressing the interrelated elements of transport infrastructure, skill development, and regional economic diversification in one stroke to break the vicious circle of immobility. Full article

This study evaluates the feasibility of continuous indoor pollutant monitoring as an indirect method for assessing extended ventilation performance in residential buildings. This research addresses key limitations of conventional short-term tracer-gas methods, which cannot account for occupant lifestyle, environmental fluctuations, and extended ventilation variability. The study employs a diffusion-based framework to interpret pollutant-concentration equalization across the residential space over extended monitoring periods. We conducted field experiments in an apartment unit equipped with both ducted and non-ducted ventilation systems. Pollutants (PM_2.5, CO₂, HCHO, and aromatic VOCs (BTEX + styrene)) were uniformly emitted. PM_2.5 and CO₂ were continuously monitored at six spatially distributed points using calibrated sensors, while HCHO and aromatic VOCs were quantified by repeated active sampling and laboratory analysis. Under ducted ventilation, average pollutant reduction rates reached 86.8% for PM_2.5, 58.3% for CO₂, and 53.6% for HCHO. Simultaneously, spatial concentration variance decreased by up to 71% within 120 min, indicating strong diffusion-driven equalizations. These results support the feasibility of extended ventilation performance monitoring using continuous pollutant sensing, with implications for IAQ management, energy optimization, and future integration with data-driven predictive models. Full article

The design of thermal strategies applied in buildings based on the use of renewable energies can play an important role in the development of a built environment that is better adapted to the climate. This paper is focused on the application of a renewable solar energy system coupled with a Heating, Ventilation and Air-Conditioned (HVAC) system to promote occupants’ thermal comfort (TC) and indoor air quality (IAQ) in buildings during heating season. In the building thermal design, a building thermal dynamic model is used to calculate the temperatures of the opaque and transparent building surfaces, the temperature of the water supply ducts, the TC level and the IAQ level, among other variables. The TC conditions of the occupants were evaluated using the Predicted Mean Vote index, commonly used in the literature in similar studies. IAQ was assessed by the usual carbon dioxide concentration in environments where most of the pollution is of human origin. The numerical study was carried out in a virtual residential building consisting of two floors and seven compartments. The building is occupied at night and at midday. Two cases were studied, considering, respectively, the non-use and use of the solar HVAC system. The solar HVAC system consists of solar water collectors, installed above the roof area, and thermo-convector heat exchangers, installed inside each occupied space. The results show that the application of this solar HVAC system in a Mediterranean-type climate is able to guarantee, during occupancy, acceptable TC levels in three compartments and near acceptable TC levels in one compartment. Regarding IAQ, acceptable level can be achieved throughout the day. Full article

People counting and occupancy monitoring play a vital role in applications such as intelligent building management, safety control, and resource optimization in future smart cities. Conventional camera and infrared-based methods often suffer from privacy risks, lighting dependency, and limited robustness in complex indoor environments. In this paper, we present a 60 GHz millimeter-wave (mmWave) radar-based occupancy monitoring system that enables accurate and privacy-preserving people counting. The proposed system leverages echo signals processed through Doppler and range spectrogram and analyzed by an enhanced ResNet-50 deep learning model to classify motion states and count individuals. Experimental results collected in a typical indoor environment demonstrate that the system achieves 95.45% accuracy across 6 classes of movements and 98.86% accuracy for people counting (0–3 persons). The method also shows strong adaptability under limited data and robustness to Gaussian blur interference, providing an efficient and reliable solution for intelligent indoor occupancy monitoring. Full article