Search Results (46,215)

In this study, a modified heat pump system has been designed for heating applications in cold climate regions, enabling the replacement of system components such as the evaporator and heat exchanger within the same system. In this modified heat pump system, which uses R407C and R417A refrigerants instead of the restricted R22 refrigerant, the optimal system conditions that provide the best performance were determined using the Taguchi experimental method, prepared using five different parameters. As a result of the experiments, the optimum operating conditions of the system were determined from the analysis of variance table obtained by considering the COP and exergetic efficiency values. As a result, it was determined that the COP and exergetic efficiency values were higher in the case of using the 8 mm lamella spacing evaporator type and horizontal heat exchanger type. Full article

As the development of the ocean extends to the deep and open seas, the application of multi-hull floating systems is becoming increasingly widespread, covering offshore oil and gas transfer and material replenishment operations. In multi-body floating systems, the hydrodynamic interactions between adjacent floating bodies significantly affect the overall motion response and load distribution. However, there is currently a lack of systematic experimental research on systems involving three or more units under the combined action of wind, waves, and currents. This study presents a 1:50 scale model experiment on a five-body offshore replenishment station, comprising a central transfer platform and four surrounding vessels. Absolute six-degree-of-freedom motions and relative displacements between the transfer platform and neighboring vessels were measured. The results indicate distinct differences among the units. The peripheral vessels have greater horizontal and yaw motions, while the central units are more restricted. The relative motions are substantially increased for beam and oblique wave conditions, implying increased interaction effects in the gaps between neighboring bodies. Moreover, the combined oblique environmental loading and asymmetric mooring stiffness result in increased global drift and yaw motions. These findings provide benchmark data for numerical validation and practical guidance for the design and operation of multi-body floating systems. Full article

This study investigates the optimization of fabrication and operating parameters for poly(ether sulfone) (PES) ultrafiltration membranes embedded with Bismuth tungstate and multi-walled carbon nanotubes (MWCNTs) Bi₂WO₆/MWCNTs for the removal of dye pollutants from wastewater. Response surface methodology (RSM) coupled with Analysis of Variance (ANOVA) was employed to develop regression models for evaluating membrane performance in terms of dye rejection and permeate flux. A central composite design (CCD) was used to conduct a systematic series of ultrafiltration experiments. The effects of key variables, including Bi₂WO₆/MWCNTs loading (0–0.1 wt.%), operating pressure (5–9) bar, and methyl red (MR) dye concentration (50–150 ppm), on membrane separation performance were comprehensively examined. The developed models demonstrated strong statistical significance and accurately described the experimental data. Optimization results revealed that the operating parameters exerted a more pronounced influence on membrane performance than fabrication variables. The maximum MR rejection of 96.8457% was achieved at an optimal Bi₂WO₆/MWCNTs loading of 0.08 wt.%, dye concentration of 112.6 ppm, and operating pressure of 9 bar. Experimental validation confirmed the reliability and predictive capability of the proposed models. In order to provide high-performance membranes with enhanced permeability, antifouling resistance, and dye removal efficiency for useful wastewater treatment applications, this study attempts to optimize the operating and preparation parameters for adding Bi₂WO₆/MWCNT nanocomposites into PES membranes. Full article

Against the global carbon neutrality target, predictive maintenance (PdM) of automotive engines represents a core technical strategy to advance the sustainable development of the automotive industry. Conventional single-modal diagnostic approaches for engine abnormal sound defects suffer from low accuracy and weak anti-interference capability. Existing multi-modal fusion methods fail to deeply mine the physical coupling between cross-modal features and often entail excessive model complexity, hindering deployment on resource-constrained on-board edge devices. To resolve these limitations, this study proposes a Physical Prior-Embedded Cross-Modal Attention (PPE-CMA) mechanism for lightweight multi-modal fusion diagnosis of engine abnormal sound defects. First, wavelet packet decomposition (WPD) and mel-frequency cepstral coefficients (MFCC) are integrated to extract time-frequency features from engine audio signals, while a channel-pruned ResNet18 is employed to extract spatial features from engine thermal imaging and vibration visualization images. Second, the PPE-CMA module is designed to adaptively assign attention weights to audio and image features by exploiting the physical coupling between engine fault acoustic and visual characteristics, enabling efficient cross-modal feature fusion with redundant information suppression. A rigorous theoretical derivation is provided to link cosine similarity with the physical correlation of engine fault acoustic-visual features, justifying the attention weight constraint (β = 1 − α) from the perspective of fault feature physical coupling. Third, an improved lightweight XGBoost classifier is constructed for fault classification, and a hybrid data augmentation strategy customized for engine multi-modal data is proposed to address the small-sample challenge in industrial applications. Ablation experiments on ResNet18 pruning ratios verify the optimal trade-off between diagnostic performance and computational efficiency, while feature distribution analysis validates the authenticity and effectiveness of the hybrid augmentation strategy. Experimental results on a self-constructed multi-modal dataset show that the proposed method achieves 98.7% diagnostic accuracy and a 98.2% F1-score, retaining 96.5% accuracy under 90 dB high-level environmental noise, with an end-to-end inference speed of 0.8 ms per sample (including preprocessing, feature extraction, and classification). Cross-engine and cross-domain validation on a 2.0T diesel engine small-sample dataset and the open-source SEMFault-2024 dataset yield average accuracies of 94.8% and 95.2%, respectively, demonstrating strong generalization. This method effectively enhances the accuracy and robustness of engine abnormal sound defect diagnosis, offering a lightweight technical solution for on-board real-time fault diagnosis and in-plant online quality inspection. By reducing engine fault-induced energy loss and spare parts waste, it further promotes energy conservation and emission reduction in the automotive industry. Quantified experimental data on fuel efficiency improvement and carbon emission reduction are provided to substantiate the ecological benefits of the proposed framework. Full article

Accurate fault diagnosis of rotating machinery in complex industrial environments demands an optimal trade-off between feature representation capability and computational efficiency. Existing single-modality models relying solely on 1D time-series signals or heavy 2D time-frequency images often fail to simultaneously capture high-frequency transient impacts and long-range degradation trends. CLiST (Complementary Lightweight Spatiotemporal Network), a novel lightweight multimodal framework driven by time-frequency fusion, was proposed to overcome this limitation. The architecture of CLiST employs a synergistic dual-stream design: a LightTS module efficiently extracts global operational trends from 1D vibration signals with linear complexity, while a structurally pruned LiteSwin integrated with Triplet Attention captures local high-frequency textures from 2D continuous wavelet transform (CWT) images. This mechanism establishes explicit cross-dimensional dependencies, effectively eliminating feature blind spots without excessive computational overhead. The experimental results show that CLiST not only achieves perfect accuracy on the fundamental CWRU benchmark but also exhibits exceptional spatial generalization when independently evaluated on non-dominant sensor axes of the XJTUGearbox dataset. Furthermore, validation on the real-world dataset (Guangzhou port) proves that the framework has excellent robustness to the attenuation of the signal transmission path and reduces the performance fluctuation between remote measurement points. Ultimately, CLiST delivers highly reliable AI-driven image and signal-processing solutions for vibration monitoring in industrial equipment. Full article

Finite element analysis is widely used to study spinal biomechanics and to compare surgical strategies under controlled loading conditions. By allowing variation in alignment, fixation, and implant design, these models provide insight into stress redistribution and motion changes that are difficult to isolate experimentally. This review examines spine surgery-focused finite element studies published between 2018 and 2024, with emphasis on interbody fusion techniques, adjacent segment mechanics, and implant-related stress behavior. Across lumbar fusion models, constructs incorporating anterior column support demonstrate lower posterior instrumentation stress than posterior-only approaches, with lateral lumbar interbody techniques showing reduced rod and screw stresses across multiple loading conditions compared with posterior lumbar interbody or posterolateral fusion constructs. In the cervical spine, comparisons of plated and zero-profile anterior cervical discectomy and fusion devices show smaller increases in adjacent-level motion and intradiscal pressure with zero-profile constructs, alongside higher localized stress at fixation interfaces. More recent studies apply finite element methods to implant optimization, alignment planning, and patient-specific modeling. Together, these findings suggest that finite element analysis is increasingly used to support surgical planning and implant design, with continued advances in validation and patient-specific simulation likely to strengthen its clinical relevance. Full article

This study explores an integrated multi-physics design approach for a high-speed Interior Permanent Magnet Synchronous Motor (IPMSM) optimized for marine diesel engine Exhaust Gas Recirculation (EGR) blower systems. To satisfy the rigorous operational demands of marine environments, an IPMSM with a rated output of 150 kW and a base speed of 9000 rpm was developed. The design validity was rigorously verified through a comprehensive multi-physics framework using the Finite Element Method (FEM), ensuring a balance between electromagnetic, thermal, and mechanical performance. The investigation established a mathematical model for the IPMSM driven by a Space Vector Pulse-Width Modulation (SVPWM) inverter, facilitating a detailed analysis of steady-state characteristics within the EGR system. To guarantee long-term reliability at high rotational speeds, the study performed an integrated thermal analysis based on precise electrical loss separation and a rotor-dynamic evaluation focusing on unbalanced vibration responses of the shaft. Finally, the proposed design was validated by integrating the IPMSM into a full-scale EGR blower system. Experimental evaluations across the entire operating range confirm that the integrated design successfully achieves the high power density and mechanical robustness required for marine diesel applications. Full article

This article presents the synthesis, real-time implementation, and experimental validation of an approximated adaptive dynamic programming (AADP) actor–critic controller for precise flow rate regulation of a variable-displacement axial-piston pump designed for open-circuit hydraulic systems. Replacing the conventional hydro-mechanical regulator with an electrohydraulic proportional spool valve, the model-free controller employs two compact two-layer neural networks: the actor generates valve PWM signals from the flow tracking error, its integral, and measured discharge pressure, while the critic approximates the infinite-horizon quadratic cost-to-go via the online solution of the Bellman equation through gradient descent on Bellman residuals. Lyapunov analysis establishes closed-loop stability under bounded learning rates, with initial weights tuned via nominal plant simulation to ensure convergence from feasible starting policies. After extensive laboratory testing across four fixed loading conditions and dynamic load variations, the adaptive controller demonstrated superior performance compared with a proportional-integral (PI) controller, a Lyapunov model-reference adaptive controller (LMRAC), and an H_∞ controller (Hinf). Real-time metrics confirm bounded critic signals and near-zero Bellman errors, validating optimal policy convergence amid unmodeled hydraulic nonlinearities. Full article

The NA64 experiment at the CERN H4 beamline recently started a high-energy positron-beam program to search for light dark matter particles through a thick-target, missing-energy measurement. To fulfill the energy resolution requirement of the physics measurement ${\sigma }_E/E\simeq 2.5\%/\sqrt{E\left[\mathrm{GeV}\right]}\oplus 0.5\%$ and cope with the constraints and performance requests of the NA64 setup, a new high-resolution homogeneous electromagnetic calorimeter PKR-CAL has been designed. The detector is based on PbWO₄ crystals, each read by multiple SiPM sensors to maximize the light collection. The PKR-CAL design has been optimized to mitigate and control unavoidable SiPM saturation effects at high light levels, as well as to minimize the gain fluctuations induced by instantaneous variations of the H4 beam intensity. The R&D program culminated in the construction of a small-scale prototype, POKERINO. In this work, we present the results from the experimental characterization campaign of the POKERINO, aiming at demonstrating that the obtained performances are compatible with the application requirements. Full article

Diamond antireflection techniques are of high interest for optical windows operating at extreme conditions. Herein, diamond antireflective microstructures in mid-infrared (MIR) spectral range were theoretically designed and experimentally fabricated. Finite difference time domain (FDTD) simulations were used to optimize the transmission performance of the diamond microstructures. Based on the simulation results, the optimized microstructures were fabricated by femtosecond (fs) laser direct writing (1030 nm, 300 fs, 25 kHz) followed by wet etching. After wet etching, the laser-modified zones and the accumulated graphitized clusters were effectively removed, thereby achieving the desired depth. The influences of laser power and scanning strategy on the morphology evolution of diamond microstructures were investigated. It was found that at the optimal conditions, the transmittance of the diamond increased from 70.9% to 81.4% (single-side) over a broad spectrum from 8 to 22 μm. This work demonstrates a promising hybrid fs laser/wet etching technique for diamond antireflective microstructures in MIR spectral range. Full article

In the automotive industry, the most critical factor affecting dimensional stability during the forming of Advanced High-Strength Steels (AHSSs) is the springback phenomenon. This study systematically investigates the springback behavior of four distinct dual-phase steel grades (DP450, DP600, DP800, and DP1000) in U-shaped body-in-white (BIW) structures across 180 distinct scenarios. The experimental design varied sheet thicknesses (1.2, 1.6, 2 mm), die clearance angles (5°, 10°, 15°), and bending radii (R6, R8, R10, R12, R14). Numerical simulations using Autoform R8 were validated against Atos 3D optical scanning data, achieving values exceeding 0.90 for all grades. Quantitative validation metrics showed exceptional fidelity for lower-strength grades with error margins below 1.1%, while the maximum deviation was limited to 3.1% for the ultra-high-strength DP1000 grade. The findings demonstrate that while increasing material strength substantially intensifies springback, the strategic augmentation of sheet thickness and optimization of die radius effectively mitigate these deviations, thereby enhancing process stability. Full article

Virtual plant models combined with ray-tracing simulations are an established tool for evaluating plant–light interactions. Current approaches often simplify leaf surface properties by assuming diffuse reflectance behavior, despite experimental evidence that leaf reflectance is direction-dependent across much of the visible spectrum. This study investigates whether incorporating measured, spectrally resolved and direction-dependent (BRDF) reflectance properties into these models affects simulation outcomes. Using virtual 3D cucumber (Cucumis sativus) plant models with PhongShader-based optical leaf characteristics for BRDF consideration, light absorption and local photon flux densities were simulated under a wide range of lighting conditions, including diffuse and directed sunlight scenarios. While total light absorption at the leaf level is only marginally affected (mean absolute percentage error, MAPE < 2%), spectral distortions in leaf surroundings, especially under direct light, exceeded 8% in the blue wavelength range. Beyond their relevance for estimating photosynthetic rates, such distortions directly affect the spectral composition within the canopy, which is particularly critical in greenhouse applications where optical sensors are used to monitor spectral ratios and, therefore, require the accurate prior simulation of canopy light conditions. This is particularly relevant for setups with directional artificial lighting. The findings suggest that BRDF modeling is not critical for calculating photosynthetic rates under most conditions, but is required in spectral analyses or for optimizing artificial lighting designs. Full article

In recent decades, bathroom design has undergone significant changes driven by technological advances, aesthetic trends, and social transformations. Despite their relevance to daily routines and quality of life, bathrooms remain underexplored with regard to how cladding materials influence users’ psychological experience. This study aims to analyze the effects of different bathroom cladding materials on perception, emotional response, and purchase intention. An online experiment was conducted using a single-factor experimental design with five types of cladding materials (ceramic, vinyl, wood, microcement, and natural stone). A total of 58 participants evaluated five virtual bathroom stimuli through self-report measures assessing perceived well-being, perceived stress, perceived functionality, perceived aesthetic preference, perceived cost, emotional valence, and purchase intention. Data were analyzed using nonparametric statistical tests. The results revealed significant differences across all perceptual dimensions depending on the cladding material. Wood and natural stone were associated with higher levels of perceived well-being and more pleasant emotional responses, whereas microcement was linked to higher perceived stress and lower aesthetic evaluations. In addition, affective variables—particularly aesthetic preference—emerged as the strongest predictors of purchase intention. These findings highlight the importance of bathroom materiality in shaping emotional experience and decision-making processes, and emphasize the role of cladding selection in promoting psychological well-being within domestic environments. Full article

4D millimeter-wave radar provides high-precision ranging capability and exhibits strong robustness under adverse weather and low-visibility conditions, but its point clouds are relatively sparse and suffer from severe elevation-angle measurement noise. Monocular cameras, by contrast, provide rich semantic information and high recall, yet are fundamentally limited by scale ambiguity. To exploit the complementary characteristics of these two sensors, this paper proposes a radar-camera fusion 3D multi-object tracking framework that does not rely on complex 3D annotated data. First, on the radar signal-processing side, a Gaussian distribution-based adaptive angle compression method and IMU-based velocity compensation are introduced to effectively suppress measurement noise, and an improved DBSCAN clustering scheme with recursive cluster splitting and historical static-box guidance is employed to generate high-quality radar detections. Second, a disparity-domain metric depth recovery method is proposed. This method uses filtered radar points as sparse metric anchors, performs robust fitting with RANSAC, and applies Kalman filtering for temporal smoothing, thereby converting the relative depth output of the visual foundation model Depth Anything V2 into metric depth. Finally, a hierarchical fusion strategy is designed at both the detection and tracking levels to achieve stable cross-modal state association. Experimental results on a self-collected dataset show that the proposed method achieves an overall MOTA of 77.93%, outperforming single-modality baselines and other comparison methods by 11 to 31 percentage points. This study provides an effective solution for low-cost and robust environment perception in complex dynamic scenarios. Full article

Background: The decoding of motor imagery electroencephalography (MI-EEG) is constrained by core issues including low signal-to-noise ratio (SNR) and cross-session as well as cross-subject domain shift, which seriously impedes the practical deployment of brain–computer interfaces (BCIs). Methods: To address these challenges, this paper proposes a novel end-to-end MI-EEG decoding method named BARN-DA. Two innovative modules, Band-Aware Channel Attention (BACA) and Multi-Scale Kernel Perception (MSKP), are designed: one enhances discriminative channel features by modeling channel information fused with frequency band feature representation, and the other captures complex data correlations via multi-scale parallel convolutions to improve the discriminability of the network’s feature extraction. Subsequently, the features are mapped onto the Riemannian manifold. For the source and target domain features residing on this manifold, a Riemannian Maximum Mean Discrepancy (R-MMD) loss is designed based on the log-Euclidean metric. This approach enables the effective embedding of Symmetric Positive Definite (SPD) matrices into the Reproducing Kernel Hilbert Space (RKHS), thereby reducing cross-domain discrepancies. Results: Experimental results on four public datasets demonstrate that the BARN-DA method achieves average cross-session classification accuracies of 84.65% ± 8.97% (BCIC IV 2a), 89.19% ± 7.69% (BCIC IV 2b), and 61.76% ± 12.68% (SHU), as well as average cross-subject classification accuracies of 65.49% ± 11.64% (BCIC IV 2a), 78.78% ± 8.44% (BCIC IV 2b), and 78.14% ± 14.41% (BCIC III 4a). Compared with state-of-the-art methods, BARN-DA obtains higher classification accuracy and stronger cross-session and cross-subject generalization ability. Conclusions: These results confirm that BARN-DA effectively alleviates low SNR and domain shift problems in MI-EEG decoding, providing an efficient technical solution for practical BCI systems. Full article