Search Results (167)

Large-reclined seating has emerged as a favored configuration in the luxury transport sector. While the static advantages are evident, the effect of this posture on dynamic comfort is not clear. This study investigated the objective biodynamics and subjective discomfort of the human body sitting in a large-reclined posture (58° from the vertical) under single axis vertical and lateral vibration excitations. The transmissibility of the human–seat system and apparent mass of the human body were measured respectively. The results revealed a critical transition between static and dynamic comfort: while the 58° posture offers superior static relaxation, dynamic discomfort dominates the overall perception when the excitation intensity exceeds a threshold of 0.249 m/s² r.m.s. Objective measurements indicated that dynamic comfort degradation in large-reclined postures is primarily driven by altered inherent biodynamic characteristics. These findings highlight that future luxury vehicle seating must incorporate targeted dynamic isolation to compensate for posture-induced comfort degradation and ensure premium ride quality. Full article

Retrofitting existing buildings has become a critical strategy for reducing energy consumption, improving thermal comfort, and achieving carbon reduction targets in the built environment. Among retrofit measures, wall insulation plays a pivotal role in minimizing heat loss and enhancing building energy efficiency. Conventional insulation materials, although effective, are often associated with high embodied energy, limited recyclability, and environmental concerns. Consequently, bio-based composite materials derived from natural fibers, agricultural residues, and renewable binders have emerged as promising sustainable alternatives. However, the moisture sensitivity of lignocellulosic materials remains a major challenge that can compromise thermal performance, durability, and long-term service life. This review provides a comprehensive and critical assessment of bio-based hydrophobic composite panels for wall insulation in retrofit applications. Unlike previous reviews that have primarily examined bio-based insulation materials, natural-fiber composites, or hydrophobic modifications separately, this study integrates these interconnected research domains within a unified framework. The review systematically examines raw material selection, composite panel manufacturing processes, hydrophobic surface-engineering strategies, thermal and moisture-related performance, durability characteristics, retrofit implementation approaches, and sustainability considerations. The analysis demonstrates that hydrophobic modification significantly reduces moisture uptake, enhances dimensional stability, and preserves thermal-insulation performance under varying environmental conditions. Natural-fiber-based composites, including hemp, flax, jute, bamboo, coconut fiber, and agricultural residues, exhibit competitive thermal conductivity (λ) values while offering reduced environmental impacts compared with conventional insulation materials. Furthermore, the integration of advanced hydrophobic treatments improves resistance to water penetration, biological degradation, and freeze–thaw damage, thereby increasing the long-term reliability of retrofit insulation systems. Full article

This paper proposes a finite-time adaptive backstepping active suspension control strategy, integrating command filtering and composite learning, to address the degradation of ride comfort and attitude stability in heavy-duty vehicles caused by shifting loads and harsh roads. First, a nonlinear dynamic vehicle model is established, treating multi-source complex disturbances as a single lumped disturbance and accounting for suspension stiffness and damping nonlinearities. To stabilize the body attitude, a tri-axis controller governing the vertical, pitch, and roll motions is developed, incorporating the practical physical constraints of actuators. By employing a composite learning Radial Basis Function neural network, the controller achieves smooth approximation and precise compensation of lumped disturbances, significantly enhancing the system’s active disturbance rejection performance under complex excitations. Furthermore, the finite-time stability of the closed-loop system is rigorously proven using Lyapunov stability theory. Finally, the strategy is evaluated under a 40% load mass mismatch and continuous random road excitations. Results indicate that the proposed strategy effectively curbs the deterioration of suspension nonlinearities during overloads, ensuring smoother dynamic transitions across all three axes. Compared to conventional backstepping control, the proposed approach reduces the root mean square values of vertical, pitch, and roll accelerations by 19%, 13%, and 35%, respectively. Ultimately, this framework effectively improves vehicle stability and disturbance rejection, providing a robust reference for heavy-duty vehicle chassis control. Full article

Flexible pressure sensors with a porous architecture are highly desirable for wearable health monitoring and intelligent human–machine interaction, owing to their excellent comfort and conformability to human motion. However, conventional porous sensors often suffer from poor signal accuracy and unstable output, which limit their capability for precision sensing. To address these challenges, we designed and fabricated a flexible pressure sensor with exceptional linearity by mimicking the unique surface structure of Iron Cross Begonia (Begonia masoniana) leaves. The sensor is constructed using a readily available melamine foam as the backbone: a porous sensing scaffold is first obtained via a simple dip-coating process, and a film featuring bioinspired protrusions is fabricated by repeated replica molding. Lamination of these two components yields a stacked sensor device. Characterization demonstrates that the sensor achieves a broad pressure detection range of up to 350 kPa, with a minimum resolvable pressure of 250 Pa, and exhibits an excellent linearity of 0.999 over its entire working range (0–350 kPa). Moreover, the sensor shows stable responses under varying loading frequencies, is capable of detecting low-frequency signals, and retains its performance without notable degradation even after 5000 repeated loading-unloading cycles. In practical applications, the sensor accurately monitors flexion and extension movements of the wrist, finger, neck, and knee, capturing human motion signals with high fidelity. Furthermore, it enables information encoding and transmission through finger gestures. The proposed bioinspired structural design strategy effectively enhances the overall performance of porous pressure sensors, offering a new paradigm for the development of flexible sensing devices with promising applications in wearable health monitoring, human motion detection, and human–machine interaction. Full article

Indoor temperature time series in district-heating buildings are often contaminated by anomalies embedded in nonstationary, multiscale thermal dynamics. This study proposes a hybrid Empirical Wavelet Transform and Long Short-Term Memory (EWT-LSTM) framework for adaptive anomaly detection and localized reconstruction. Evaluated on 15 min interval data from 45 residential units over a 112-day heating season, the framework operates via a highest-frequency branch for anomaly detection and a full-modal branch for signal repair. Quantitative results show that the EWT Highest-Frequency LSTM (EWT(HF)-LSTM) achieved the best anomaly discrimination among decomposition variants with an average F1-score of 0.531. For anomaly repair, the full EWT-LSTM produced the highest fidelity with a localized Root Mean Square Error ($RMS{E}_a$) of 0.818 °C. Furthermore, thermal comfort validation demonstrated that EWT-LSTM successfully prevented the severe comfort degradation of up to −82% in Exceeded Degree-Hours caused by unstable Empirical Mode Decomposition (EMD)-based reconstructions. These concrete results confirm that the proposed framework effectively provides clean, physically coherent temperature data for downstream district heating operations. Full article

This paper presents a comprehensive investigation of the impact of I/Q (In-phase/Quadrature) imbalance on the performance of a six-port receiver operating in the millimeter-wave band, specifically in the 60–65 GHz frequency range. Unlike traditional heterodyne architectures, the six-port junction offers a low-cost and low-power alternative for direct conversion; however, it is highly sensitive to hardware imperfections. This study demonstrates that manufacturing tolerances in passive components, such as 90° hybrid couplers and power dividers, introduce significant amplitude and phase disparities. These imbalances geometrically distort the ideal QPSK constellation, transforming the circular decision boundaries into an elliptical profile. The research methodology employs a robust co-simulation approach in Advanced Design System (ADS), integrating measured S-parameters with mathematical analysis to quantify signal degradation. Performance is evaluated using the Error Vector Magnitude (EVM) metric. The experimental findings reveal that even at the higher end of the spectrum (65 GHz), where the amplitude imbalance reaches 0.7 dB and the phase error is approximately 5°, the six-port QPSK receiver maintains an EVM of 8.7%. This result is comfortably below the 17.5% limit mandated by modern wireless communication standards, such as LTE and 5G. These results confirm the architectural resilience of the six-port receiver, validating its effectiveness as a reliable solution for high-speed, short-range data transmission in future ultra-wideband telecommunication infrastructures. Full article

Railway track assets may suffer from different types of degradation due to aging, traffic conditions, environmental conditions, and natural and man-made hazards, which affect their performance in terms of reliability and availability, as well as passenger safety and comfort. By knowing which variables influence the degradation and performance of railway tracks, and the most appropriate maintenance and renewal actions, it is possible to define the most appropriate Performance Indicators. The use of predictive models to forecast these indicators can support the decision-making process during the maintenance management over time. In this work, a proposal including the selection of the most appropriate Performance Indicators is presented, together with a brief overview of predictive models used for railway systems. Based on that, a holistic framework to forecast the railway track performance aiming to support the decision-making process is given and its applicability is discussed. The proposed framework integrates the selection, processing, and aggregation of different types of Performance Indicators within a predictive modelling framework, enabling the analysis even when data availability is limited. The applicability of the framework is demonstrated through an illustrative example based on inspection data. The results illustrate the evolution of track condition states over time within a probabilistic framework. Full article

To address the complexities inherent in evaluating electric motorcycle ride comfort across diverse road profiles and operating speeds, this study establishes a systematic evaluation framework utilizing a multi-level fuzzy comprehensive assessment approach. Empirical investigations were conducted on asphalt, Belgian block, and speed-bump terrains at varying velocities. Triaxial acceleration data were acquired from the seat, footrest, and handlebar interfaces to compute weighted Root Mean Square (RMS) acceleration, Vibration Dose Value (VDV), and Power Spectral Density (PSD). By synthesizing subjective ratings, a correlation between tactile perception and objective metrics was derived to calibrate the two-level fuzzy model. Analysis reveals that vibration energy is predominantly concentrated in the vertical low-frequency domain (0–20 Hz) independent of test conditions. Notably, a 50% increase in velocity precipitated a 22.4% decrement in the comprehensive ride comfort index, degrading the classification from “Moderate” to “Fair.” The proposed framework provides a rigorous quantitative paradigm for vibration mitigation strategies and informed speed management in electric vehicle engineering. Full article

To improve the indoor thermal comfort of embedded atriums and corridors in office buildings during summer, this study aims to optimize air conditioning airflow organization in atriums using computational fluid dynamics (CFD) simulations. Field measurements were carried out to collect air parameters, which were subsequently used to validate the established CFD model. Taking a six-story office building in Xi’an as the research subject and stratified air conditioning as the baseline case, this study investigated the effects of air inlet layout, air inlet type, and air volume distribution on the indoor thermal environment. The results revealed significant vertical temperature stratification within the atrium, with average temperatures ranging from 23.5 °C to 46.1 °C. Based on comparative analysis of multiple optimization scenarios, the following conclusions are drawn: adopting swirl diffusers in the corridors with an air inlet quantity ratio of 1:1:1:1:2 from the first to fifth floors, combined with uniform air supply volume across the first to fourth floors, can maintain the average Predicted Mean Vote (PMV) of each floor within the range of −0.1 to 0.3. Conversely, excessive air supply volume on upper floors and insufficient air supply volume on lower floors significantly degrade the corridor thermal comfort. Full article

This study investigates the safety limitations of autonomous vehicles operating under dense fog conditions, where sensor performance is severely degraded, and explores the potential of cooperative control for collision avoidance. A comparative framework is developed using the CARLA simulator to analyze four driving configurations: no perception and no communication, degraded LiDAR–radar sensing, V2V-assisted Model Predictive Control (MPC), and V2V-assisted MPC enhanced with predictive buffering. The methodology integrates fog-dependent perception modeling, cooperative hazard messaging, and real-time MPC-based longitudinal control, and evaluates system performance through multiple simulation trials under urban and highway conditions. Key performance indicators include time-to-collision, reaction time, maximum deceleration, jerk, and collision occurrence. The results demonstrate that perception-only strategies lead to late reactions and unsafe emergency braking, with minimum TTC values as low as 0.29 s and frequent collision events. In contrast, V2V-assisted MPC significantly improves anticipation and driving comfort, while the proposed predictive buffering approach achieves a 0% collision rate and increases the minimum TTC to approximately 1.93 s. The inclusion of predictive buffering further enhances robustness against communication losses, enabling smoother deceleration and consistently safe inter-vehicle spacing. Overall, the findings confirm that cooperative V2V communication combined with predictive control effectively compensates for fog-induced perception degradation and represents a viable solution for improving safety and reliability in low-visibility autonomous driving scenarios. Full article

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

This study aimed to design a bioabsorbable, biocompatible poly(lactic-co-glycolic acid) (PLGA)-based tissue stabilizer for a new tympanoplasty method and to evaluate its feasibility. A PLGA copolymer with a lactic acid: glycolic acid ratio of 50:50 was used to fabricate the stabilizers via melt molding using custom-designed molds. The surface morphology of the fabricated stabilizers was analyzed by scanning electron microscopy (SEM). In vitro degradation profiles were evaluated over a 60-day period in phosphate buffered saline (PBS), simulated body fluid (SBF), and trypsin environments, and biocompatibility was assessed using direct and indirect proliferation assays with human fibroblasts. SEM analyses revealed a smooth and homogeneous surface morphology. Degradation studies demonstrated a controlled and progressive decrease in residual mass over time. Cell proliferation assays indicated that the PLGA stabilizer exhibited no cytotoxic effects. In rabbit models, the tissue stabilizer improved fascia graft stabilization, resulting in more regular epithelialization and higher tympanic membrane closure rates compared with the control and fat myringoplasty groups. This approach may improve surgical efficiency and patient comfort by enabling shorter operative times, reduced surgical invasiveness, and the potential use of local anesthesia. Full article

The fuel consumption of power-split hybrid electric vehicles (PSHEVs) can be effectively reduced via mode transition that includes the engine process. However, factors such as engine torque ripple, system parameter uncertainties, and variations in torsional vibration characteristics can easily induce drivetrain vibration. These factors not only degrade ride comfort but also lead to a fundamental control challenge. The inherent trade-off between rapid response and stability is difficult to reconcile. In addition, the lack of adaptive mechanisms further limits consistent performance under varying conditions. To tackle these problems, a scenario-adaptive composite robust control (SACRC) strategy is proposed. The strategy consists of a UIO (unknown input observer)-based torque observation module, an adaptive VSS-LMS approach, and an H∞ controller with self-tuning parameters. Firstly, a six-degree-of-freedom dynamic model of the PSHEV transmission system is established with excitation sources, considering the characteristics of dual elastic elements. Secondly, a UIO-based torque observer is designed using a simplified dual-elastic-element model. By using engine speed and output shaft speed, the observer can accurately identify the torque transmitted by the torsional damper and drive shaft. Then, an adaptive VSS-LMS and H∞ controller with self-tuning parameters is constructed to ensure a balanced performance between fast torsional vibration suppression and control stability. Finally, simulation and experimental results demonstrate that the proposed strategy provides favorable adaptability to complex scenarios, and unifies the performance goals of rapidity, stability, and robustness. Full article

Fungal growth and insufficient thermal comfort degrade building durability and indoor quality, especially in humid and high-radiation regions. Zinc oxide (ZnO) stands out for its strong antifungal activity and radiative cooling potential. In this study, a commercial coating was modified with ZnO nanoparticles synthesized via a green chemistry route using Cymbopogon citratus (lemongrass) leaf extract as a reducing agent. Structural and morphological characterization by XRD, SEM, and EDS confirmed the formation of hexagonal wurtzite-phase nanoparticles with hemispherical and ellipsoidal morphologies, presenting average sizes of 50.27 ± 19.84 nm and 128.25 ± 33.43 nm, respectively, and an average crystallite size of 20.32 nm. Antifungal activity, evaluated using the poisoned food technique against Aspergillus niger and Penicillium spp., showed significant growth inhibition, reaching up to 94.63% for A. niger and 72.64% for Penicillium at a concentration of 3 mg/mL after 120 h of incubation. Thermal comfort performance was assessed to direct sunlight, in which coatings modified with 5% w/w ZnO nanoparticles achieved an average internal temperature reduction of 0.6 °C and a maximum reduction of 2.4 °C compared to uncoated surfaces. These results demonstrate that ZnO nanoparticles synthesized through environmentally friendly methods can effectively enhance both antifungal resistance and passive cooling performance. Full article

Background and Objectives: Soft denture lining materials improve stress distribution and patient comfort but can lose mechanical stability under routine chemical cleansing. This study aimed to evaluate the time-dependent effects of two alkaline peroxide-based denture cleansing tablets (i.e., Efferdent and Protefix) on Shore A hardness and weight change of three soft lining materials (i.e., Ufi Gel P, Ufi Gel SC, and Visco-gel) at days 1, 7, and 30. Materials and Methods: Ninety specimens (n = 10/group) were assigned to a 3 × 3 factorial design. Specimens were immersed in cleansing solutions for 8 h daily and stored in artificial saliva for 16 h; controls remained solely in artificial saliva. Shore A hardness was measured using a durometer, and weight was recorded with a precision scale. Data were analyzed by mixed-design ANOVA and linear regression (α = 0.05). Results: Material type significantly affected hardness and weight change (p < 0.001). Visco-gel showed a marked increase in Shore A hardness (from about 15–16 to 26–27 HA) and greater weight loss (approximately 0.04–0.06 g), whereas silicone-based materials (Ufi Gel P and Ufi Gel SC) demonstrated more stable hardness values (from about 24–25 to 31–32 HA) with minimal weight variation (generally below about 0.02 g). The type of cleansing tablet had a smaller but significant effect (p = 0.004), with Protefix causing greater alterations. Weight change was negatively correlated with hardness increase (R² = 0.33, p < 0.001). Conclusions: Within the limitations of this in vitro study, material composition was identified as the main determinant of degradation resistance, with silicone-based liners demonstrating greater durability under the tested conditions, while Efferdent may be considered a milder option for long-term cleansing. Full article