Search Results (378)

This article explores the structural paradox of the byeong-gut (Korean shamanic healing ritual): why it adheres to the rigid and canonical format of the jaesu-gut (shamanic blessing ritual) instead of adopting a specialized clinical procedure. Critiquing the instrumental trap of previous scholarship that reduces shamanic healing to psychological comfort or social liberation, this study proposes a relational displacement model by integrating Roy Rappaport’s theory of ritual invariance with the relational ontologies of Bruno Latour and Tim Ingold. The article demonstrates that shamanic healing operates through a dual mechanism. First, at the non-discursive (material) level, the ritual functions as an ontological technology that objectifies and displaces individual suffering onto external surrogates. Second, at the discursive (linguistic) level, a meticulous analysis of the manse-baji (invocation chant) illustrates how the patient’s fragmented life is re-assembled into a meshwork of human and non-human agencies. Ultimately, this article argues that the byeong-gut transcends mere functional curing; it serves as a sophisticated knowledge system that re-maps the isolated ego onto a relational cosmology, transforming the Geertzian bafflement of suffering into an intelligible event within a shared and sacred cosmic order. Full article

Urban planners need scalable ways to monitor pedestrian conditions across heterogeneous cities, but conventional Level-of-Service (LOS) methods are often locally calibrated and difficult to transfer. This study proposes a city-adaptive framework for pedestrian LOS mapping using spatio-temporal graph models and few-shot transfer learning. Pedestrian count data from Melbourne, Dublin, and Zurich were converted into six ordinal LOS classes using city-specific percentile thresholds computed from the training data, yielding a relative congestion measure rather than an absolute cross-city standard. We developed a spatio-temporal graph transformer with an ordinal prediction head and evaluated it under in-domain, zero-shot, few-shot, and domain-adaptive settings. The results show strong in-domain performance in Melbourne (accuracy 79.7%; Acc ± 1 99.1%) and effective adaptation to the city-adaptive ordinal classification task. Few-shot fine-tuning with only 5% labeled target city data recovered 95–99% of in-domain performance, suggesting that small amounts of local supervision can substantially reduce calibration requirements in data-scarce environments. KernelSHAP analysis indicates that short-term temporal lag features dominate predictions across cities, whereas spatial and contextual features vary more strongly with local urban structure. The findings suggest that few-shot transfer learning can support pedestrian LOS estimation in cities with limited labeled data; however, the proposed LOS formulation should be interpreted as a city-specific relative indicator rather than an absolute measure of pedestrian comfort, crowding, or service quality. While the framework was evaluated across three cities, additional validation in diverse urban contexts and against perceptual measures of pedestrian experience remains necessary. Overall, the study contributes a city-adaptive framework for transferable relative LOS prediction rather than a universal cross-city LOS standard. Full article

Climate change adaptation in buildings is an expanding field, yet methodological approaches remain diverse and fragmented, with no widely adopted unified assessment framework. This study aims to identify and synthesize existing methods and frameworks used to assess climate change adaptation in buildings. A scoping review was conducted to map key concepts, methodological trends, and knowledge gaps in the literature. Following database screening and study selection, 50 articles published between 2010 and 2026 (including one online-first paper with a nominal publication year 2026) were analysed according to assessment methods, frameworks, hazards, building typologies, life-cycle stages, and climate zones. The findings indicate a strong reliance on simulation-based and indicator-based approaches. Heatwaves are the most frequently examined hazard, while thermal comfort is the most commonly assessed adaptation-related outcome; comprehensive multi-hazard assessments remain relatively scarce. Established sustainability certification schemes and technical standards are often adapted for this purpose, whereas dedicated climate adaptation assessment frameworks remain limited. Overall, the field is characterized by considerable methodological fragmentation, highlighting the need for integrated multi-criteria evaluation frameworks that better connect building-scale technical assessment with broader climate policy objectives. Full article

Research on rural thermal environments has grown rapidly in recent years. However, it still relies on urban-based paradigms and lacks a framework for rural contexts. This study uses a systematic mapping and critical analysis approach to examine key assumptions in existing research. It identifies three common biases: the direct use of urban thermal comfort models, the underestimation of passive strategies, and the limited focus on behavioral and psychological adaptation. The results show a clear high-constraint–high-adaptation feature in rural areas. Traditional spatial forms and low-tech materials can improve thermal conditions at low cost. At the same time, behavioral adjustments and psychological adaptation can widen the acceptable thermal range. Based on these findings, this study proposes a low-cost–high-adaptation framework and develops three testable hypotheses. The study reduces urban bias in current research and provides a clear direction for low-cost climate adaptation design in rural areas. Full article

This paper proposes a calibration-oriented framework for cooperative adaptive cruise control based on a linear quadratic regulator formulation. A simulation-based architecture is developed by integrating the controller with a nonlinear longitudinal platoon model that explicitly accounts for actuator saturation and tyre–road friction limits, enabling the analysis of platoon behaviour under realistic operating conditions. A systematic offline calibration methodology is introduced based on multidimensional performance maps, relating key performance indicators associated with collision avoidance, comfort, and energy efficiency to controller and spacing-policy tuning parameters. The map-based approach enables a structured exploration of competing objectives and provides a quantitative assessment of controller sensitivity. The results show that the proposed framework can identify calibration regions that preserve collision-free operation in safety-critical manoeuvres while maintaining satisfactory tracking and comfort-related performance. In addition, the off-nominal model parameters analysis confirms that the proposed calibration approach remains effective under heterogeneous operating conditions, including vehicle parametric variation of mass, rolling resistance coefficient and drag. Overall, the results support the use of the proposed methodology as a practical tool for robust and performance-oriented controller calibration. Full article

Hospital gardens are increasingly recognized as therapeutic environments; however, many Post-Occupancy Evaluation (POE) studies remain limited to perception-based assessments without explicitly linking spatial characteristics to user experience. This study develops an evidence-based evaluation framework integrating spatial analysis with user-centered data. A mixed-methods approach was employed, combining systematic observations, spatial mapping, and a structured short-form questionnaire administered to 350 users. The study area was classified into three micro-climatic zones—hot, moderate, and cool/shaded—based on solar exposure, vegetation density, and surface characteristics. User experience was evaluated through composite indices of comfort, satisfaction, and perceived restorativeness (Cronbach’s α = 0.83). The results indicate a clear spatial gradient: mean comfort scores increased from 2.8 in hot zones to 3.5 in moderate zones and 4.2 in cool/shaded areas. These differences were statistically significant (p < 0.05) and supported by meaningful effect sizes (Cohen’s d). Furthermore, multiple linear regression analysis demonstrated that natural components—specifically mature vegetation and clean air—are strongly associated with psychological recovery (β = 0.54, p < 0.001). Spatial analysis also revealed a mismatch between design configuration and environmental performance. The findings provide a transferable, spatially grounded framework for optimizing therapeutic landscapes in healthcare settings. Full article

Background/Objectives: Chronic Kidney Disease (CKD) is a prevalent condition requiring ongoing patient counseling and engagement, yet little is known about how physicians communicate with patients about CKD in routine clinical practice. We conducted a qualitative study to examine physician communication approaches related to CKD and to assess how these approaches align with Picker’s principles of patient-centered care framework. Methods: Semi-structured interviews were conducted with primary care physicians and nephrologists practicing in community and safety-net settings. Using directed content analysis, we identified patterns in how clinicians describe educating patients, contextualizing clinical information, and deferring aspects of counseling to other providers. Results: Physicians predominantly emphasized information-giving and the use of laboratory data to explain disease status. In contrast, practices such as explicit patient preference elicitation, addressing fear, anxiety, or physical comfort, and involving family or support persons were infrequently described. Mapping these communication behaviors to patient-centered care principles highlighted specific elements that are routinely enacted and others that remain underutilized in everyday CKD counseling. Conclusions: These findings identify concrete, feasible opportunities to strengthen patient-centered communication through brief, practice-ready strategies such as plain-language explanations, teach-back, values checks, and shared decision-making prompts. Enhancing these communication practices represents a pragmatic opportunity to improve the quality and patient-centeredness of CKD care. Full article

With the development of autonomous driving and intelligent connected vehicle technologies, the vehicle cabin is shifting from a simple transportation space to an intelligent mobile space integrating infotainment, interaction, and rest, and passenger comfort has gradually become an important factor affecting user experience, system trust, and perceived safety. Focusing on three categories of cabin environmental factors, namely the acoustic, optical, and thermal environments, this study develops an experimental design and comprehensive modeling method for passenger comfort evaluation. First, controlled single-factor experiments were conducted to establish quantitative mapping relationships between physical environmental parameters and subjective comfort ratings. The analytic hierarchy process (AHP) was then used to determine the weights of each indicator, and a penalty-based aggregation mechanism was introduced to construct a comprehensive comfort evaluation model. Finally, external validation was performed on an independent vehicle platform to examine the model’s applicability and consistency. The results show that acoustic comfort decreases as the sound pressure level increases, whereas optical and thermal comfort exhibit nonlinear behavior with optimal intervals. AHP weight results show that the thermal environment has the highest weight (0.4280), followed by the acoustic environment (0.3305) and the optical environment (0.2415). The external validation results indicate that the proposed model exhibits good predictive consistency across three steady-state operating conditions, with a mean absolute error of 0.122, a root-mean-square error of 0.150, and a Pearson correlation coefficient of 0.960. The findings show that the penalty-based aggregation model can effectively characterize the limiting-factor effect under the joint action of multiple environmental factors, providing a computable and interpretable evaluation framework for intelligent cockpit environmental control and automotive engineering experimental teaching. The conclusions of this study are mainly applicable to the current experimental platform and steady-state operating conditions, and further validation is still required with more vehicle models, dynamic road scenarios, and complex multi-environment factor disturbances. Full article

Autonomous ground vehicle navigation in dynamic real-world environments demands path planning systems that simultaneously accommodate real-time environmental hazards and diverse user-defined objectives requirements that classical algorithms, with their static, single-objective cost functions, cannot fulfil. This paper presents the Semantic Personalised Path Planning (SPPP) system, centred on a novel Semantic Personalised Cost (SPC) algorithm that augments the A* search framework with a dynamically computed personalised cost term. The SPC function integrates eight real-time semantic obstacle categories including traffic congestion, weather severity, road surface conditions, and construction activity with eight user-defined preference dimensions spanning safety, travel time, emergency response, comfort, and battery efficiency. An adaptive scaling mechanism amplifies obstacle penalties near the goal, and a gradient-based weight evolution rule refines preference weights iteratively over successive route segments. The user-defined preference activation directly personalises the routing objective to individual operational needs, with the gradient-based evolution further refining preference alignment over successive route segments. Experiments were conducted in two phases: 500 randomised obstacle configurations on a controlled $8\times 8$ grid, and a real 847-node road graph extracted from OpenStreetMap around SRM Institute of Science and Technology, Kattankulathur, representing a single 1.4 km urban corridor, with obstacle scores derived from live Mapbox Traffic and OpenWeatherMap application programming interface data. Under the full emergency preference scenario, SPPP achieves 94.3% obstacle avoidance versus 31.7% for the Euclidean distance threshold A* baseline, a difference statistically significant at $p < 0.001$ under the Wilcoxon signed-rank test with Cohen’s d ≈ 18.9. Real-world computation time of 1.91 ms on a standard laptop and 3.76 ms on a Raspberry Pi 4 confirms deployability on embedded autonomous vehicle hardware. Full article

Forests in Poland play a key recreational role, and the growing interest in sylvaturism requires optimized management. Despite the growing body of research on forest recreation, existing studies rarely address the role of small-scale infrastructure in shaping user preferences and its integration into spatial planning frameworks, which constitutes a research gap in this study. This study aimed to identify user preferences for small infrastructure and to develop an application-oriented, socially sensitive model for forest trail design that supports sustainable management. The research was conducted in 2021–2024 using the CAWI method on a group of 402 adult Poles. Data analysis included descriptive statistics, Pearson’s chi-square tests to assess demographic differences, and correspondence analysis to identify user preference profiles. The results not only confirmed a clear hierarchy of needs but also demonstrated that differences between user groups relate primarily to the intensity rather than the structure of preferences. A clear hierarchy of needs was confirmed, with route map boards (86.32%), educational boards (72.64%), and benches (71.14%) dominating. Based on the results, a modular design model was developed (modules: basic, comfort, accessibility, and activity), which constitutes a conceptual advancement over existing planning approaches by introducing a flexible, user-oriented framework that links social preferences with spatial decision-making. By integrating empirical social data into the planning process, the proposed framework extends current knowledge on recreation planning and provides a structured basis for adaptive forest trail design. This tool could help managers efficiently channel tourist traffic, protect ecosystems, and promote public health. Full article

The housing crisis in rapidly transforming earthquake zones represents the exhaustion of conventional construction paradigms. Unlike single-focused analyses, this study compares conventional reinforced concrete and modular steel systems from a holistic lifecycle perspective, using Turkey as a strategic laboratory for urban transformation. Employing qualitative content analysis, it maps in-depth interviews with 14 sector experts onto a ‘Dialectical Life Cycle Matrix’ via frequency-based consensus indicators. Expert assessments indicate that conventional methods face a structural bottleneck driven by architectural uniformity, labour-related weaknesses, rising costs, and prolonged durations, triggering seismic vulnerability, compromised living quality, and non-circular end-of-life outcomes. Modular systems counter this through factory-controlled rapid production, QA/QC mechanisms, and economies of scale, integrating guaranteed safety and the robust option of steel with R&D-driven human comfort. However, transitioning requires relinquishing deep-rooted advantages—financial flexibility, established order, regulatory comfort, cultural perception, and morphological harmony—introducing local trade-offs: high initial investment, geometric plot and logistical constraints, cultural barriers, and design concerns. Consequently, universal technologies cannot be directly transferred. To overcome Turkey’s local barriers, this study proposes a three-stage transition model: (I) civil and public-led legislative and workforce reforms; (II) financial innovation and gradual hybrid adaptation; and (III) industrial maturation transforming housing into a continuously updated living product. Full article

Urban destinations increasingly incorporate green–blue infrastructure, sensory-balanced public spaces, and microclimate-responsive design to mitigate visitor fatigue and support sustainable tourism experiences. To understand how insights from broader tourism environments, particularly nature-based contexts, can inform emerging urban well-being strategies, this study conducts a global bibliometric review (2005–2025) on psychological restoration, stress relief, and visitor well-being. Using Scopus and a Boolean search combining mental health constructs, tourism setting, and analytical approaches, 825 records were identified, and 149 articles were retained after applying eligibility criteria. Science mapping and performance analyses reveal accelerated post-2018 growth and three dominant knowledge clusters centered on restoration pathways, environmental determinants, and behavioral/hospitality components. Based on these patterns, this study introduces the RESTOR-URBAN model, integrating environmental moderators, psychological mechanisms, and behavioral interactions that jointly shape stress reduction and emotional well-being across urban tourism systems. The results show increasing relevance of micro-restorative experiences, thermal comfort management, and stress-aware service design, while highlighting persistent methodological heterogeneity and limited integration of environmental co-data (Universal Thermal Climate Index (UTCI), Physiological Equivalent Temperature (PET), and Discomfort Index (DI)). The findings suggest that restoration-based evidence from nature-based tourism can inform sustainable urban tourism planning, hospitality practice, and visitor experience design, and propose a research agenda emphasizing standardized well-being indicators, longitudinal and structural equation modeling (SEM)-based approaches, and environmental quality variables for resilient, health-oriented urban destinations. Full article

Recent advancements in autonomous driving technology are transforming the automotive industry, with advanced driver assistance systems (ADAS) recognized as a crucial transitional technology toward fully autonomous driving. ADAS enhances driver safety and comfort through features such as emergency braking, lane-keeping, and adaptive cruise control, ultimately aiding in traffic accident prevention and reduction in driver fatigue. However, commercial ADAS implementations show substantial variability due to differences in sensor configurations, operational design domain (ODD) definitions, and operational criteria across automakers. To address this gap, this study provides a structured comparative review of commercialized ADAS technologies across 11 major Western and Asian automakers. By encompassing both Western and Asian OEMs, this study compares manufacturer-declared sensor configurations, ODD settings, activation conditions, driver-monitoring requirements, takeover and fallback logic, and update-related characteristics. The review identifies implementation-level differences that affect comparability, user understanding, validation requirements, and standardization needs. Rather than ranking OEM systems by safety performance, this study clarifies the trade-offs among redundancy-oriented, camera-centric, HD-map-dependent, geofenced, and OTA-driven ADAS strategies. The findings support future work on standardized ODD communication, user-centered HMI design, independent validation, and update-aware review frameworks for commercial ADAS. Full article

Mobile robot navigation remains challenging when fast convergence, collision avoidance and deployability must be satisfied simultaneously. The original Q-learning with Artificial Potential Field (QAPF) paradigm is extended in this paper with three coordinated mechanisms that together yield a reported-horizon convergence reduction of approximately four orders of magnitude (from $\sim 3\times {10}^6$ episodes to $\sim 200$ to 230 episodes under the present protocol) and an internal-ablation collision-rate reduction of approximately one order of magnitude ($6.2\%$ to $0.3\%$), and that open a new capability frontier covering dynamic obstacles, multi-robot coordination, energy-aware velocity modulation and embedded-deployable inference timing. The first mechanism is a potential-based reward-shaping schedule whose unclipped fixed-weight form follows the policy-invariant shaping theorem, while the implemented clipped and time-varying form is used as an empirically stable approximation. Under the present experimental protocol, the reported convergence horizon is reduced from the $\sim 3\times {10}^6$ episodes reported for the original QAPF formulation to approximately 200 to 230 episodes; this comparison is protocol-dependent and is not claimed as a controlled one-to-one runtime speedup. The second mechanism is a discrete Control Barrier Function (CBF)-inspired action filter (thediscrete filter described in this paper is inspired by the continuous-time CBF literature, but does not carry a forward-invariance proof; it is used as an empirical safety mechanism rather than as a formal Control Barrier Function in the formal continuous-time sense) with per episode visit memory by which the held-out collision rate is reduced from $6.2\%$ for QAPF alone to $0.3\%$ while $93.8\%$ task completion is maintained, where this collision-rate comparison is internal to the QAPF ablation because the prior QAPF reference does not report a comparable held-out collision metric. The third mechanism is a set of extensions to dynamic obstacles, two-robot cooperative navigation under a centralized scheme (with an explicit $\mathcal{O}(N^2)$ scaling-cost analysis and three decentralization strategies for fleets beyond the small-N regime), curriculum learning and energy-aware velocity modulation. Disturbance robustness tests, empirical timeout/stagnation detection for unreachable-goal cases, i7 reference inference timing with projected embedded-device latencies, multi-axis generalization over obstacle density and grid size, scalability analysis for centralized multi-robot coordination and a scope comparison against A* and RRT* are added by the revised evaluation. Across 30 independent seeds on held-out static maps, $94.5\pm 2.1\%$ success is achieved by adaptive QAPF while $93.8\pm 2.3\%$ success with $0.3\pm 0.4\%$ collisions is achieved by QAPF+CBF. Under a separate finite robustness suite, $85.0\pm 4.1\%$ success is retained by QAPF+CBF in the combined disturbance regime. The timing study indicates that the 20 Hz real-time threshold is comfortably exceeded by all methods on the measured i7 reference platform and by all projected embedded-device equivalents. The results show that a lightweight and safety-oriented navigation policy for grid-based mobile-robot settings can be provided by APF-guided tabular reinforcement learning when it is paired with a discrete safety filter and a clarified energy and robustness analysis. Full article

Glamping tourism has expanded rapidly as travelers increasingly seek nature-based experiences combined with comfort and privacy, particularly in the post-COVID-19 period. Online reviews provide a valuable source of insight into how guests perceive such experiential accommodation, yet large-scale, data-driven analyses of glamping sentiment remain limited. This study applies machine-learning techniques to classify customer sentiment expressed in online reviews of glamping sites in South Korea. A total of 3233 reviews were collected from ten leading glamping locations on Naver Map, cleaned, and translated from Korean to English. Sentiment labels (negative, neutral, and positive) were generated using VADER (Valence Aware Dictionary and sEntiment Reasoner), a lexicon-based sentiment scoring tool validated for short informal texts and the labeled corpus was subsequently used to train and evaluate six supervised classifiers. Six supervised classifiers—Naïve Bayes, k-Nearest Neighbors, Random Forest, Logistic Regression, Gradient Boosting, and Support Vector Machine (SVM)—were trained and evaluated through stratified ten-fold cross-validation using accuracy, AUC, F1-score, and Matthews Correlation Coefficient (MCC). Results indicate that SVM achieved the strongest overall discriminatory performance, particularly in identifying minority sentiment classes under substantial class imbalance. These findings suggest that automated sentiment classification holds practical potential for supporting evidence-based service monitoring and reputation management in glamping tourism, although further validation in operational settings is needed before deployment can be recommended. Full article