Search Results (4,299)

Energy-aware Adaptive Cruise Control (Eco-ACC) has become an essential approach for enhancing the energy efficiency of electric vehicles while ensuring safe and comfortable driving. This paper presents a systematic review, following the PRISMA methodology, of 60 recent studies published between 2021 and 2025. The review provides a structured analysis of control strategies, validation approaches, computational demands, and battery-related considerations in Eco-ACC systems. The results indicate that Model Predictive Control (MPC) remains the most widely adopted technique (41.7%), primarily due to its ability to handle system constraints and address multi-objective optimization problems. Reinforcement Learning (RL) approaches (33.3%) are increasingly explored for their capability to adapt to uncertain and dynamic driving conditions. In addition, hybrid MPC–AI methods (16.7%) show strong potential for balancing optimal control performance with real-time implementation requirements. A key observation is the clear imbalance in validation practices: more than 73% of the studies rely on simulation-based evaluation, whereas only 10% include real-world experiments, revealing a pronounced simulation-to-reality (sim2real) gap. Furthermore, two critical research gaps are identified. First, the computational energy paradox highlights the trade-off between improved control performance and increased computational cost. Second, battery-aware control remains insufficiently addressed, as most existing methods overlook long-term battery degradation effects. Based on these findings, this review proposes a deployment-oriented research framework that prioritizes hybrid control architectures, real-time feasibility, and robust validation strategies, including Hardware-in-the-Loop and field testing. The presented insights aim to support the development of practical and energy-efficient Eco-ACC systems suitable for real-world deployment in next-generation electric vehicles. Full article

Fully leveraging the four-wheel independent drive characteristics of distributed-drive electric vehicles has become essential for enhancing their driving range. However, conventional regenerative braking strategies applied to such vehicles often fail to consider individual wheel slip ratios, which can easily lead to wheel lock and low energy recovery efficiency. To address these issues, this paper proposes a novel energy management method that integrates hybrid braking control with intelligent connected speed planning. A hierarchical control strategy for the hybrid braking system is first developed, explicitly accounting for the slip ratio of each wheel. The upper-level controller calculates the slip ratio for each wheel based on vehicle speed and wheel speed information and subsequently determines the braking torque distribution between the front and rear axles. The lower-level controller then allocates the motor braking torque and hydraulic braking torque to each wheel, subject to system constraints such as battery status and motor torque limits. Building on this framework, vehicle state and road information are incorporated as inputs to formulate a Markov decision process, which optimizes traffic efficiency, energy economy, and ride comfort as multiple objectives. The deep deterministic policy gradient (DDPG) algorithm is employed to achieve collaborative optimization of speed planning and energy management. Simulation results demonstrate that the proposed DDPG-based control strategy outperforms both rule-based control methods and classical dynamic programming algorithms in terms of comprehensive performance across traffic efficiency, energy consumption, and ride comfort. These findings validate its superiority in complex traffic conditions. Full article

Advanced Driver Assistance Systems (ADAS) are increasingly shaping urban mobility and road safety, yet their benefits depend not only on technical performance, but also on driver acceptance. This study examines how Hungarian drivers perceive and evaluate key ADAS functions, Adaptive Cruise Control (ACC), Lane Keeping/Centering Assist (LKA/LCA), and Forward Cross Traffic Alert (FCTA), in urban driving contexts. The research is based on qualitative focus group discussions conducted in Győr, Hungary, involving drivers aged 20–50 from different age cohorts. Data were analyzed using thematic analysis. The findings show that the acceptance of ADAS is strongly context-dependent and function specific. ACC was perceived primarily as a comfort-enhancing tool, especially on longer or more monotonous routes, while LCA was often regarded intrusive and less reliable in urban conditions due to poor road markings, potholes, and frequent stop-and-go situations. On the contrary, blind spot and cross-traffic-related functions were evaluated more positively due to their direct safety benefits. Trust, perceived risk, and control emerged as key dimensions of acceptance, with many participants emphasising the importance of warning-based support rather than a strong autonomous intervention. In general, the study concludes that urban acceptance of ADAS is shaped by the interaction of infrastructure conditions, perceived usefulness, and driver trust, highlighting the need for more transparent, context sensitive, and user-centered system design in support of safer urban mobility. Full article

Background: Recovery following total joint arthroplasty varies substantially among patients, and psychological factors may partly account for this variability. Although anxiety and depression have been widely investigated, the specific contribution of preoperative surgical fear to postoperative pain and quality of recovery remains unclear. This study aimed to examine the association between preoperative surgical fear and postoperative pain intensity and quality of recovery. Methods: This prospective, hospital-based observational study enrolled 89 patients undergoing primary total knee or hip arthroplasty. Preoperative surgical fear was measured using the Surgical Fear Questionnaire (SFQ). Pain intensity was assessed with the Numeric Rating Scale (NRS) preoperatively and at three postoperative time points. Recovery quality at 24 h was evaluated using the Quality of Recovery-40 (QoR-40). Pearson correlation and multiple linear regression analyses were performed to evaluate associations and identify variables independently associated with recovery outcomes, controlling for potential confounders, including age, sex, ASA physical status, and type of surgery. Results: The mean SFQ score was 26.62 ± 15.19, and the mean QoR-40 score was 157.63 ± 16.66. Surgical fear was moderately and negatively correlated with overall recovery quality (r = −0.546, p < 0.001). In multiple linear regression analysis, surgical fear was most strongly associated with poorer overall recovery quality (β = −0.563, p < 0.001), within a model explaining 30.3% of the variance (adjusted R² = 0.303). At the subscale level, surgical fear was significantly associated with emotional state, pain, physical comfort, and perceived support. Pain intensity at 12 h postoperatively was significantly associated with reduced physical independence (β = −0.218, p = 0.038). Pain intensity peaked at 12 h postoperatively (p < 0.001). Conclusions: Higher levels of preoperative surgical fear are associated with poorer quality of recovery following total joint arthroplasty. These findings highlight surgical fear as a potentially relevant perioperative factor and support the integration of routine psychological assessment into perioperative care pathways in relation to early postoperative recovery outcomes. From a clinical perspective, early identification of patients with high surgical fear may facilitate targeted perioperative counseling and supportive interventions by healthcare professionals, potentially improving recovery outcomes. 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

Rapid urbanization combined with global climate change is intensifying the Surface Urban Heat Island (SUHI) effect worldwide, posing significant risks to human health, thermal comfort, and quality of life in cities. Characterized by notably higher temperatures in urban areas compared to their rural surroundings, the SUHI phenomenon is driven by factors such as increased built-up density and reduced vegetation cover. In this context, open-source remote sensing data, particularly from the Landsat satellite series, play a crucial role in studying surface urban heat islands. Available freely, Landsat’s multispectral and thermal imagery provides extensive spatial coverage and consistent temporal frequency, enabling long-term diachronic analyses. This study leverages a 40-year time series (1984–2024) of Landsat thermal data to map surface temperature variations in urban environments between Kenitra and Rabat cities, facilitating the identification of heat-excess zones linked to anthropogenic factors. Based on the results obtained, the LU/LC maps show that the study area is characterized by the notable growth of urbanization over the period 1984–2024, particularly in the dynamic poles of the region such as the city centers of Kénitra, Rabat, and Sale. This dynamic is highlighted by an increase from 1.8% to 3% in the total area of the region, accompanied by a remarkable decrease in agricultural land and bare soils. The evaluation of the Random Forest (RF) model’s performance also indicates that it successfully classified the data and predicted the LU/LC classes effectively, as confirmed by metric indices such as the Receiver Operating Characteristic curve and the Kappa index, which present very high average values exceeding 90%. Furthermore, the exploitation of the thermal bands of Landsat images provided relevant information on surface temperature variation. The SUHI maps show that the Rabat-Sale-Kenitra (RSK) region experienced a progressive increase in temperature over the study period, rising from 27 °C in 1984 to 44 °C in 2024. This value could increase further due to the continuous dynamics of urbanization. Together, these tools provide a robust framework for understanding the spatiotemporal dynamics of surface urban heat islands and support sustainable urban planning. Full article

Demand response is shifting towards continuous coordination of flexible demand, storage, and distributed generation across buildings and prosumer communities. Multi-agent reinforcement learning has gained attention because it can support decentralized execution under partial observability while still learning coordinated behavior through centralized training. This systematic review follows PRISMA 2020 guidance and synthesizes $n=70$ peer-reviewed studies published in the 2021 to 2025 window, covering building clusters, grid-aware district coordination, program-level aggregation, industrial demand response, and transactive energy mechanisms. The results show that the dominant evaluation context is grid-responsive building clusters, with growing reliance on benchmark environments that standardize interfaces and encourage reproducible multi-KPI reporting. Across the methods, centralized training with decentralized execution is the prevailing pattern, often combined with attention-based critics or value factorization to handle heterogeneity and global rewards. Reward design and constraint handling emerge as primary determinants of stability, since objectives mix cost, peak, ramp, comfort, and emissions, while rebound and synchronized behavior are recurring risks. A descriptive and cross-variable quantitative synthesis is also provided, showing that publication activity increased from three studies (4.3%) in 2021 to 28 studies (40.0%) in 2025, with the strongest concentration in 2024–2025. Quantitatively, grid-responsive building clusters accounted for 26 of 70 studies (37.1%), actor–critic methods for 24 studies (34.3%), CityLearn for 16 studies (22.9%), and cost-based evaluation was reported in 64 studies (91.4%), whereas robustness testing appeared in only 16 studies (22.9%). Across the reviewed studies, peak reduction was reported in 55 (78.6%) studies, whereas robustness testing appeared in only 16 studies (22.9%) and transferability or deployment realism in 11 (15.7%), indicating that evaluation remains much stronger for operational performance than for real-world generalization. Full article

The growing integration of immersive technologies into education is opening new possibilities for teaching and learning, while also raising concerns about the reliability and potential distortion of knowledge in artificial intelligence-mediated environments. Understanding how users perceive and accept artificial intelligence-generated content in immersive learning systems is therefore essential. This study explores the factors that influence user acceptance of artificial intelligence-driven virtual reality educational applications and explains it through a multidimensional framework that extends the Technology Acceptance Model, the Theory of Reasoned Action, and the Theory of Planned Behavior—a new ED-SCALE model. We innovated the previous models by adding an ergonomic dimension, often overlooked in virtual reality-based education. To test the model, we developed an artificial intelligence-driven virtual reality educational escape room designed to simulate adaptive and interactive learning experiences. Data were collected from 213 participants using a questionnaire measuring subjective norms, perceived behavioral control, attitudes toward artificial intelligence-mediated instruction, perceived informational efficacy, and ergonomic quality. The findings show that ergonomic quality, intuitive interfaces, physical comfort, and social influence play an important role in shaping user trust and long-term adoption intentions. The results suggest that the success of artificial intelligence-driven immersive learning systems depends not only on technological performance but also on user experience and social context, confirming our first hypothesis regarding new variables that are conditional for virtual technology acceptance. Full article

With growing health awareness and an increasing preference for indoor exercise among the elderly, the demand for community indoor activity spaces is rising in the northern regions of China with cold winters and hot summers. While previous community studies have primarily focused on residential buildings, limited attention has been given to indoor activity spaces for the elderly. Moreover, field measurements expose critical thermal deficiencies in these spaces, where indoor temperatures remain substandard in both winter and summer, particularly falling substantially below the WHO health-based threshold (≥18 °C) in winter. Recognizing that transitional spaces are effective for improving indoor thermal conditions, this study explored their potential to enhance the indoor thermal environment, leading to targeted retrofitting schemes. The results showed that although additional transitional spaces effectively enhance the thermal performance, the strategies for winter and summer often conflict. Specifically, enclosed transitional spaces are effective for winter insulation but are prone to overheating in summer, whereas semi-outdoor configurations on the south and west facades are beneficial for summer heat prevention. Based on these findings, optimal retrofitting schemes were identified: for Site A, the existing interior corridor is transformed into a semi-outdoor transitional space; for Site B, an Adaptive Façade system is proposed for the south façade. Furthermore, despite the passive benefits, auxiliary HVAC systems remain necessary to maintain temperatures strictly within the comfort range during extreme weather. This study provides a scientific basis for research on transition spaces and offers a reference for retrofitting buildings in similar climatic regions. Full article

Outdoor thermal comfort in university courtyards is a key factor influencing students’ environmental experience and the usability of outdoor spaces in hot-arid climates. Courtyard design may also affect the environmental conditions of adjacent classrooms by modifying solar exposure, shading, air movement, and surface heat gain. Accordingly, this study aims to develop optimized design scenarios for improving outdoor thermal comfort in university courtyards through hybrid passive strategies, including vegetation, shading systems, and cool pavements. To achieve this goal, the research adopted a combined field-based and simulation-based methodology. Field measurements and student questionnaires for 292 students were conducted in courtyards and classrooms of three university buildings in Luxor, Egypt. These buildings represent different urban morphologies, courtyard aspect ratios, geometric configurations, and student densities. In parallel, simulation models were developed using ENVI-met V5.6.1 and Rhinoceros V8 with Grasshopper, to test and compare various design scenarios. Field monitoring revealed that wider courtyards with low aspect ratios (0.28–0.38), lacking trees and finished with concrete paving, recorded lower CO₂ concentrations (around 800 ppm), but experienced higher surface and air temperatures. These elevated temperatures negatively affected outdoor thermal comfort and increased heat gain in classrooms overlooking the courtyards. In contrast, courtyards with higher aspect ratios (0.63–0.82) demonstrated better microclimatic moderation and improved comfort conditions. Simulation results indicate that integrating a belt vegetation pattern of Cassia leptophylla, combined with textile shading and cool pavements with an albedo of 0.5, can reduce the Universal Thermal Climate Index (UTCI) by up to 14.7 °C, shifting conditions toward moderate heat stress. The findings provide practical design guidance for upgrading existing university courtyards and designing future educational buildings in hot-arid climates to enhance student comfort and environmental performance. Full article

Background/Objective: Playing-related musculoskeletal disorders are highly prevalent among bowed string musicians and may impair performance and career longevity. This study aimed to evaluate the effects of motor control-based interventions on pain, functional outcomes, range of motion, and neuromuscular parameters in musicians playing bowed string instruments. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Scopus, Web of Science Core Collection, and Cochrane CENTRAL were searched from inception to October 2025, and the search was updated before resubmission to identify any newly published eligible studies. Eligibility screening, full-text assessment, data extraction, and risk-of-bias assessment were independently verified by a second reviewer. Risk of bias was assessed according to study design using RoB 2 for the randomized controlled trial and ROBINS-I for non-randomized interventional studies. Results: Four interventional studies met the inclusion criteria. Three studies reported improvements in pain-related outcomes or PRMD severity, and three reported improvements in functional outcomes. One study demonstrated improved cervical range of motion, whereas one study reported altered shoulder girdle muscle activation patterns with reduced playing comfort. Overall, the certainty of the available evidence was limited by small sample sizes, non-randomized designs, and risk of bias. Conclusions: Limited evidence suggests that motor control-based interventions may be associated with improvements in pain and playing-related function in bowed string musicians; however, the evidence base remains small and methodologically heterogeneous, and conclusions should be interpreted with caution. Full article

Driven by the growing energy demand and severe challenges posed by climate change, reducing the high energy consumption of district heating systems while enhancing their flexibility and operational reliability has become an urgent priority. This study focuses on the heating system of a residential community in Zhengzhou, China, by developing a joint source-network-load simulation model and proposing a model predictive control (MPC) strategy tailored to the dynamic characteristics of the system. A white-box model of the building complex and heating system was established by coupling EnergyPlus and Modelica. Subsequently, the model was automatically calibrated using actual operational data and the GenOpt optimization tool, which further improved the simulation accuracy and optimal control performance of the model. The results show that the root mean square errors (RMSEs) of the calibrated secondary network supply water temperature, return water temperature, and indoor temperature decreased by 34.6% and 15.7%, respectively, verifying the effectiveness of the proposed calibration method. Furthermore, the proposed MPC strategy demonstrates significant advantages over conventional control baselines, greatly improving the temperature regulation accuracy and system stability. Compared to the baseline operation without MPC, the proposed strategy increases the user-side thermal comfort index from 56% to 100%, thereby significantly enhancing overall heating quality. Full article

The WELL Building Standard (WELL) is a certification system designed to enhance occupant health and well-being in indoor environments. Conventional building energy-saving strategies typically rely on fixed temperature setpoint adjustments, which may conflict with WELL thermal comfort requirements. However, achieving high energy efficiency remains essential. This study uses a quantitative evaluation framework with TRNSYSs to examine the effectiveness of integrating load reset control (LRC) into simultaneous heating and cooling (SHC) systems. It compares LRC with conventional fixed setpoint (SP) and predicted mean vote (PMV) control strategies, based on WELL’s thermal comfort criteria (maintaining the PMV between −0.5 and +0.5). Six simulation cases were analyzed, considering radiant (RAD) and convection (CONV) terminals. The results indicate that radiant terminals provide more stable PMV performance while consuming less energy than convection terminals, demonstrating better compliance with WELL objectives. Although PMV control achieves the highest thermal comfort, it substantially increases energy consumption. In contrast, LRC emerges as an optimal strategy, effectively balancing the energy efficiency of SP control with the comfort of PMV control. The RAD-LRC configuration delivers the best overall performance. It achieves higher thermal comfort than SP, with comparable energy consumption, making it a highly practical approach for modern building energy management. Full article

Prolonged standing and repetitive lifting are routine occupational stressors that elevate plantar pressures across workers. In those with diabetes, these demands represent additional risk factors for diabetic foot pathology, highlighting the need for ergonomic interventions beyond standard safety footwear. This study evaluated the perceived ergonomic performance of the MoonWalking^® insole, a novel adaptive pneumatic system designed for real-time pressure stabilization and offloading when integrated into safety footwear. A comparative experimental protocol tested two conditions: safety footwear with the manufacturer’s original insole and the same footwear with the MoonWalking prototype. Twenty participants assessed perceived comfort using a VAS and binary ergonomic questionnaires. The results showed statistically significant improvements in perceived cushioning, foot fit, and overall comfort when using the MoonWalking insole. Participants consistently identified pressure-stabilizing and offloading functions across all plantar regions, indicating that adaptive pressure control was clearly perceptible. No pain or movement restrictions were reported. Although perceived fatigue did not reach statistical significance, a decreasing trend was observed. A slight reduction in intention to reuse the footwear occurred with the prototype, possibly due to its increased weight. These findings provide evidence that integrating an adaptive pneumatic insole into safety footwear may improve plantar pressure redistribution and user comfort. Full article

This research presents an artificial intelligence (AI)-driven 3D scanning and printing system for the fabrication of personalized dental prosthetics, implants, and orthodontic appliances. The proposed system integrates high-resolution intraoral scanning, AI-based data analysis, and additive manufacturing to enhance precision, customization, and treatment efficiency. Patient-specific anatomical data and medical history are incorporated to optimize implant design, material selection, and functional performance. Nano-enhanced biocompatible materials are utilized to improve mechanical strength, durability, and antibacterial properties. Specifically, these materials demonstrate a 30% increase in overall precision and a 50% improvement in durability compared to traditional dental materials. In addition, the system adopts a zero-waste manufacturing strategy by recycling excess materials, supporting sustainable dental practices. The results demonstrate significant improvements in accuracy, patient comfort, and environmental responsibility in modern digital dentistry. Full article