Search Results (7,918)

Microrobot navigation in constrained environments requires path planning methods that ensure both efficiency and collision avoidance. Conventional approaches, which typically combine graph-based path finding with geometric path simplification, effectively reduce path complexity but often generate collision-prone paths because wall boundaries are not considered during simplification. Therefore, to overcome this limitation, we present Secure Angle-based Geometric Elimination (SAGE), a single-pass path-simplification algorithm that converts pixel-level shortest paths into low-complexity trajectories suitable for real-time collision-free navigation of microrobots. SAGE inspects consecutive triplets (p_i, p_i+1, p_i+2) and removes the middle point when the turning angle is smaller than threshold (∠p_ip_i+1p_i+2 ≤ θ_th) or the direct segment (p_i → p_i+2) is collision-free. Quantitative analysis shows that SAGE achieves approximately 5% shorter path length, 20% lower turning cost and 0% collision rate, while maintaining computation comparable to the Ramer–Douglas–Peucker algorithm. Integration with Dijkstra and RRT planners confirms scalability across complex maze and vascular environments. Experimental microrobot demonstrations show navigation with complete collision avoidance, establishing SAGE as an efficient and reliable framework for high-speed microrobot navigation and automation in lab-on-a-chip, chemical-reaction and molecular-diagnostic systems. Full article

The Arctic Puffin Optimization (APO) algorithm, proposed in 2024, is a swarm intelligence optimization. Similar to other swarm intelligence optimization algorithms, it suffers from issues such as slow convergence in the early stage, being easy to fall into local optima, and insufficient balance between exploration and exploitation. To address these limitations, an improved APO (IAPO) algorithm incorporating multiple strategies is proposed. Firstly, a mirror opposition-based learning mechanism is introduced to expand the search scope, improving the efficiency of searching for the optimal solution, which enhances the algorithm’s convergence accuracy and optimization speed. Secondly, a dynamic differential evolution strategy with adaptive parameters is integrated to improve the algorithm’s ability to escape local optima and achieve precise optimization. Comparative experimental results between IAPO and eight other optimization algorithms on 20 benchmark functions, as well as CEC2019 and CEC2022 test functions, show that IAPO achieves higher accuracy, faster convergence, and superior robustness, securing first-place average rankings of 1.35, 1.30, 1.25, and 1.08 on the 20 benchmark functions, CEC 2019, 10- and 20-dimensional CEC 2022 test sets, respectively. Finally, simulation experiments were conducted on three engineering optimization design problems. IAPO achieved optimal values of 5.2559 × 10⁻¹, 1.09 × 10³, and 1.49 × 10⁴ for these engineering problems, ranking first in all cases. This further validates the effectiveness and practicality of the IAPO algorithm. Full article

The Crested Porcupine Optimizer (CPO), as a newly emerging swarm intelligence algorithm, demonstrates advantages in balancing global exploration and local exploitation but still suffers from limitations in convergence speed and local exploitation precision. To address these issues, this paper proposes an enhanced variant, the Butterfly Search and Triangular Walk Crested Porcupine Optimizer (BTCPO). The method achieves a dynamic balance between exploration and exploitation by combining triangular walk to boost local exploitation and butterfly search to increase global variety. Experimental results on 23 classical benchmark functions and the CEC2021 test suite show that BTCPO outperforms CPO as well as seven state-of-the-art algorithms (DBO, HBA, BKA, HHO, GWO, GOOSE, and SSA). Specifically, BTCPO achieves the best performance on more than 80% of CEC2021 functions, with convergence speed improved by approximately 25% compared to CPO. Furthermore, BTCPO exhibits higher efficiency and usefulness in engineering design problems such as trusses, welded beams, and cantilever beams. These findings demonstrate the theoretical and practical advantages of BTCPO, making it a workable approach to solving difficult optimization problems. Full article

Postharvest operations are cost intensive in microalgae production, and when electrocoagulation–electroflotation (EC/EF) with aluminum anodes is used, aluminum can remain associated with biomass and wash streams; hence, a selective postwash process is needed. Accordingly, this study defined an operational window for aluminum desorption that preserves the energetic advantage of EC/EF. A response-surface design (I-optimal/CCD) was used to evaluate the effects of the EDTA concentration (1–100 mM), contact time (5–20 min), mixing speed (100–300 rpm), and pH (6–10) on EC/EF-harvested Chlorella sp. biomass, with ANOVA and model diagnostics supporting adequacy. EDTA concentration and mixing emerged as significant factors, whereas time and pH acted mainly through interactions; moreover, quadratic terms for EDTA and mixing indicated diminishing returns at high levels. Consequently, the surface predicted an optimum near EDTA ≈ 65 mM, time ≈ 20 min, pH 10, and 100 rpm, corresponding to ~97% aluminum removal. Importantly, a confirmation run under these conditions across eight chlorophyte strains consistently achieved >95% removal, revealing narrow dispersion yet statistically distinguishable means. Taken together, coupling EC/EF with an EDTA postwash operation in the identified window effectively limits aluminum carry-over in microalgal biomass and, therefore, provides a reproducible basis for downstream conditioning and potential recirculation within biorefinery schemes. Full article

Packet delivery in vehicular ad hoc networks degrades under realistic road dynamics, where mobility and local density vary over time and across road layouts. This study revisits route lifetime control in AODV and introduces Vehicular Traffic Condition-Based AODV, which adjusts the Active Route Timeout and the Delete Period Constant online at each HELLO reception using locally observable cues on neighbor density and short-term speed variation. The design is empirically informed by OpenStreetMap and SUMO mobility with OMNeT++/Veins/INET co-simulation. The analysis highlights two recurrent patterns that guide the method: (i) an intermediate neighbor-density range—roughly from the mid-teens to about twenty neighbors—where average speed tends to vary more rapidly; and (ii) a distribution of short-term speed-change magnitudes, sampled at the instants of HELLO reception, that is concentrated within a narrow interval with a light upper tail. Accordingly, the proposed method increases or decreases route-entry lifetimes with heightened responsiveness inside this density range, while applying conservative updates elsewhere to mitigate oscillations. Evaluation across multiple vehicular-traffic conditions spanning three fleet sizes (200, 300, 400 vehicles) and three speed-limit settings (10, 20, 30 km/h) demonstrates consistent packet delivery ratio gains over conventional AODV and close tracking of the best static lifetime configurations identified per condition. The gains are attributable to timely pruning of unstable paths and sustained retention of stable paths, which increases valid forwarding opportunities without centralized coordination. Full article

Current research and development in understanding road users’ driving behaviors play a key role in improving traffic safety. Recently, several driving simulators have been employed as a suitable approach to investigate several drivers’ responses in challenging traffic scenarios. Although professional drivers represent a particular category among driving populations, the body of literature about their comparative behavioral and psychological characteristics remains limited. This study examined the differences in driving performance and visual and physiological responses between civilian and professional drivers in a simulated environment. A total of 30 drivers, with an equal split between professional and civilian categories, took part in a series of driving simulations. The simulations incorporated various infrastructure types, including four cone avoidance tasks and a high-speed motorway task. This study collected comprehensive data on performance metrics, hand usage, heart rate, and eye movements. Eye-tracking technology was used to measure visual attention. The findings revealed that during cone avoidance scenarios, civilian drivers exhibited a similar performance, visual behavior, and physiological response, except for the speed, experiment duration, and throttle, to professional drivers. In the motorway scenario, all metrics showed no significant difference between the two driver groups. These results highlight the need for cautious interpretation, particularly given the limitations of the sample. Revalidation is needed in larger studies, especially for understanding the differences between drivers’ metrics, which is crucial to elevate drivers’ safety, and assessing training programs in Hungary. Full article

This study presents a streamlined and accurate approach for modeling the performance of hermetic reciprocating compressors under variable-speed conditions. Traditional compressor models often neglect the influence of motor frequency, leading to considerable deviations at low-speed operation. To address these limitations, a frequency-dependent numerical framework was developed using one-dimensional (1-D) and two-dimensional (2-D) polynomial regressions to represent volumetric efficiency (${\eta }_v$) and isentropic efficiency (${\eta }_{isentr}$) as functions of compression ratio (r) and motor speed frequency (f). The proposed model integrates manufacturer data and thermodynamic property databases to predict compressor behavior across a wide range of operating conditions. Validation using the Bitzer 4HTE-20K CO₂ compressor demonstrated strong agreement with experimental data, maintaining prediction errors within ±10% for both power input and discharge temperature. Moreover, the model enhanced accuracy by up to 19.4% in the low-frequency range below 40 Hz, where conventional models typically fail. The proposed method provides a practical and computationally efficient tool for accurately simulating the performance of hermetic reciprocating compressors that support improved design, optimization, and control of refrigeration and heat pump systems. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article

As climate change intensifies, enhancing numerical weather prediction (NWP) accuracy has been increasingly critical. While data assimilation optimizes NWP initial conditions, its effectiveness over complex terrain requires further systematic evaluation. This study implemented a high-resolution WRF/4D-Var data assimilation framework, overcoming its inherent limitation of not supporting two-layer nested assimilation across domains by designing a two-layer nested “assimilation-forecast” workflow. Representative winter and summer cases from February and June 2019 were selected to evaluate improvements in near-surface and upper-air meteorological parameters. The results indicated that the 4D-Var data assimilation significantly improved the correlation coefficients of near-surface variables during winter by 2.9% (temperature), 14.5% (relative humidity), 6.6% (wind speed), and 10.4% (wind direction), with even greater improvements observed in summer reaching 13.3%, 5.8%, 35.3%, and 42.3%, respectively. Meanwhile, 4D-Var considerably enhanced the atmospheric vertical profiling, with the middle troposphere (300–700 hPa) exhibiting the most pronounced improvement. Among different surface types, water bodies exhibited the strongest assimilation response. Results also revealed systematic corrections to the background fields, with February exhibiting more uniform adjustments in contrast to June’s complex spatiotemporal patterns. Positive effects persisted throughout the 24-h forecasts, with the maximum benefit occurring within the first 12 h. These results demonstrate the effectiveness of 4D-Var in regional meteorological forecasting, highlighting its value for constructing high-precision multidimensional meteorological fields to support both weather and air quality simulations. Full article

The Zernike polynomial method is extensively used for atmospheric phase screen generation but is limited by insufficient high-frequency components. Calculating higher-order terms introduces challenges in computational efficiency and numerical instability when using the direct method. This paper analyzes these issues and proposes replacing the direct method with recursive radial formulas. We evaluate four recursive algorithms (Barmak’s, q-recursive, Prata’s and Kintner’s) for their performance in phase screen generation, focusing on computational speed and numerical stability. Our results demonstrate that recursive methods achieve a 10–20-times improvement in computational efficiency and maintain numerical stability even for high-order expansions. The main novelty of this study lies in the comprehensive comparison and validation of these recursive strategies for high-accuracy atmospheric phase screen simulation. Full article

With the advancement of industrial technology toward high speed, heavy load, precision, and automation, traditional sliding bearing materials have been unable to meet modern industrial demands. Surface coating technology, as an efficient surface modification method, has become a key means to enhance the tribological properties, wear resistance, corrosion resistance, and fatigue resistance of sliding bearings, thus extending their service life. This paper systematically reviews the research progress of coating technology for sliding bearings in the past, aiming to fill the gap in comprehensive summaries of multi-material systems and multi-process technologies in existing reviews. In terms of materials, it focuses on the performance characteristics and application scenarios of three major coating types—metal-based, ceramic-based, and polymer-based—clarifying their advantages and limitations. In terms of processes, it analyzes the technical characteristics of mainstream methods including electroplating, magnetron sputtering, and laser cladding, as well as their innovative applications in replacing traditional processes. Furthermore, this review summarizes the latest research results in coating performance evaluation, such as tribological testing via pin-on-disk testers and corrosion resistance analysis via salt spray tests. Finally, it discusses future development trends in new materials, new process applications, and environmental sustainability. This work is expected to provide a valuable reference for related research and engineering applications in the field of sliding bearing coatings. Full article

Object detection of small and occluded targets in complex scenarios is a vital yet challenging task in computer vision, with applications in intelligent systems (e.g., kitchen safety supervision). To address limitations of existing models, this study proposes an improved YOLOv10 algorithm with three key innovations. We first introduce a Mosaic-9 data augmentation strategy to enhance small target density in training samples. The traditional PANet in YOLOv10 is replaced by the Bidirectional Feature Pyramid Network (BiFPN), which uses cross-scale bidirectional connections and learnable weights to optimize multi-scale feature fusion. A Squeeze-and-Excitation (SE) channel attention module is integrated into the CSPDarknet backbone to emphasize key feature channels and mitigate background interference for occluded objects. Experiments on a self-constructed dataset (6508 images with multi-scale and occluded targets) show the improved YOLOv10 achieves 69.5% mAP@0.5, a 7.7 percentage point increase over YOLOv10n, while maintaining 12.1 ms inference speed. Ablation studies verify Mosaic-9 enhances small target perception, BiFPN boosts mAP@0.5 by 5.7%, and SE improves occlusion robustness by 4.8%. This work offers a generalizable multi-module optimization framework for YOLO-series models, applicable to various small and occluded target detection tasks, advancing lightweight object detection algorithms and intelligent vision systems. Full article

Accurate and efficient detection of small and complex surface defects on hot-rolled steel plates remains a significant challenge in industrial quality assurance. Current deep learning detectors often exhibit limitations in detection accuracy and training convergence speed, particularly for small objects, which limits their practical deployment in real-time industrial inspection systems. To overcome these deficiencies, this paper proposes a multi-scale deformable transformer iterative query refinement network (MDT-Net). MDT integrates three key innovations: a Swin Transformer backbone for robust multi-scale feature representation, a deformable attention mechanism to significantly reduce computational complexity and accelerate convergence, and an iterative bounding box refinement strategy for precise localization. Extensive experiments on the NEU-DET dataset demonstrate MDT’s superior performance, achieving 82.7% mAP₅₀. Crucially, MDT significantly outperforms other mainstream detectors in small object detection, reaching an mAP_50:95 of 0.55, and exhibits remarkably faster training convergence. These findings confirm MDT’s effectiveness and robustness for accurate and efficient steel surface defect detection, thereby providing a crucial tool for enhancing sensor-based quality control and offering a promising solution for industrial quality management. Full article

Titanium alloy Ti-6Al-4V possesses excellent mechanical and corrosion-resistant properties; therefore, it is widely employed in aerospace, automotive, and biomedical fields. However, its poor machinability restricts traditional processing methods. To overcome this limitation, the current work presents a symmetry analysis approach to evaluate the effects of tool coating on the micro-electric discharge machining (micro-EDM) characteristics of Ti-6Al-4V. Tungsten carbide (WC) microelectrodes were fabricated in three forms: uncoated, copper-coated, and carbon-coated. The chemical vapor deposition (CVD) method was used to coat the carbon layer, and the integrity of the coating was confirmed by Energy-Dispersive X-ray Spectroscopy/Analysis (EDS/EDX). The effect of input variables—namely, voltage, capacitance, and spindle rotational speed—on two responses was studied—the machining depth (Z-axis displacement) and tool wear rate (TWR)—using a Taguchi L9 orthogonal array. Analysis conducted using Minitab statistical software 17 revealed that both voltage and capacitance contributed to the response parameters as optimized variables. The comparative study showed that the copper- and carbon-coated WC microtool could obtain a better Z coordinate and lower tool wear ratio compared with those of the uncoated tool. The findings confirm that applying thin conductive coatings to WC tools can significantly improve the stability, precision, and overall symmetry of the micro-EDM process when machining difficult-to-cut titanium alloys. Full article

Background: The chronic manifestations of stroke are commonly multisystemic, affecting motor function, perception, cognition, and more. Conventional interventions have limitations when it comes to cost and their mundane nature, which are often perceived as boring. A high prevalence of risk factors has resulted in the adult population experiencing a stroke, many of whom require medical intervention, whose limitations strain both the patient and the healthcare system. Recently, extended reality (XR) has demonstrated promise as a rehabilitative aid for cognition, proprioception, and motor function following stroke without conventional therapy constraints. Methods: This case series explores the relationship between mixed reality (MR; one modality of XR) and cognitive performance in three post-stroke patients. Three post-stroke participants completed 12, one-hour MR training sessions over 4 weeks. Cognitive performance was assessed and changes were compared across three timepoints: baseline, immediately following the intervention, and following a 90-day washout period. Results: Participants demonstrated improvement in memory, executive function, and processing speed. Additionally, two out of the three participants demonstrated trends for improvement in attention and working memory. Conclusions: While these promising results tentatively suggest that 12 h of mixed reality training may yield cognitive improvement in post-stroke patients, a larger sample size is needed before drawing definitive conclusions. Full article