Search Results (669)

Work-related musculoskeletal disorders (WMSDs) are a leading concern in industrial ergonomics, often stemming from sustained non-neutral postures and repetitive tasks. This paper presents a vision-based framework for real-time, frame-level ergonomic risk classification using a lightweight Vision Transformer (ViT). The proposed system operates directly on raw RGB images without requiring skeleton reconstruction, joint angle estimation, or image segmentation. A single ViT model simultaneously classifies eight anatomical regions, enabling efficient multi-label posture assessment. Training is supervised using a multimodal dataset acquired from synchronized RGB video and full-body inertial motion capture, with ergonomic risk labels derived from RULA scores computed on joint kinematics. The system is validated on realistic, simulated industrial tasks that include common challenges such as occlusion and posture variability. Experimental results show that the ViT model achieves state-of-the-art performance, with F1-scores exceeding 0.99 and AUC values above 0.996 across all regions. Compared to previous CNN-based system, the proposed model improves classification accuracy and generalizability while reducing complexity and enabling real-time inference on edge devices. These findings demonstrate the model’s potential for unobtrusive, scalable ergonomic risk monitoring in real-world manufacturing environments. Full article

The paper presents an experimentally validated regression model for dry clutch friction lining wear, accounting for the influence of clutch temperature, initial slip speed, torque, and closing time. The experimental data have been collected by using a custom-designed disk-on-disk computer-controlled tribometer and conducting repetitive real operation-like clutch closing cycles for different levels of the above operating parameters. The model is designed to be cycle-wise, predicting cumulative worn volume expectation and standard deviation after each closing cycle. It is organized around three distinctive submodels, which provide predictions of: (i) wear rate expectation, (ii) wear rate variance, and (iii) elevated wear rate during run-in operation. Finally, the wear rate expectation and variance submodels and the overall, cumulative worn volume model are validated on independent experimental datasets. The main novelty of the presented research lies in the development of stochastic multi-input cycle-wise dry cutch wear model for clutch design and monitoring applications. Full article

Alzheimer’s disease (AD) is a leading cause of dementia, characterized by progressive cognitive and language impairments that significantly impact communication and quality of life. Neuromodulation techniques, including repetitive transcranial magnetic stimulation (rTMS), transcranial direct current stimulation (tDCS), and deep brain stimulation (DBS), have emerged as promising interventions. This study employs bibliometric analysis to evaluate global research trends in neuromodulation treatments for AD-related language impairments. A total of 88 publications from the Web of Science Core Collection (2006–2024) were analyzed using bibliometric methods. Key indicators such as publication trends, citation patterns, collaboration networks, and research themes were examined to map the intellectual landscape of this field. The analysis identified 580 authors across 65 journals, with an average of 34.82 citations per article. Nearly half of the publications were produced after 2021, indicating rapid recent growth. The findings highlight a predominant focus on non-invasive neuromodulation methods, particularly rTMS and tDCS, within neurosciences and neurology. While research activity is increasing, significant challenges persist, including ethical concerns, operational constraints, and the translational gap between research and clinical applications. This study provides insights into the current research landscape and future directions for neuromodulation in AD-related language impairments. The results emphasize the need for novel neuromodulation techniques and interdisciplinary collaboration to enhance therapeutic efficacy and clinical integration. Full article

This study investigates the theological function of the contemporary worship song “The Blessing” by addressing the following guiding research question: in what ways does “The Blessing” function as a form of prophetic declaration and communal prayer in contemporary congregational worship? Drawing on frameworks from Pentecostal theology, lyrical theology, and performative speech-act theory, this study analyzes how the song’s language, structure, and performance embody Spirit-enabled proclamation and intercession. Engaging Rice’s Evagrian–LAPT grammar, Glenn Packiam’s theology of worship as encounter, and Steven Félix-Jäger’s model of New Testament prophecy, the textual analysis focuses on the song’s present-tense verbs of divine action and its lyrical constructions. Scripturally grounded in Numbers 6:24–26, “The Blessing” operates as a sung benediction that invokes God’s blessing, sanctification, divine favor and protection, covenantal presence, and peace. The repetitive use of “Amen” functions as a communal seal of affirmation, turning passive reception into active, prophetic participation when sung. This study contends that the song exemplifies how contemporary congregational song serves as primary theology—Spirit-inspired, embodied, and sounded—where proclamation and prayer are nurtured in lived worship. Ultimately, “The Blessing” functions as a pneumatological and ecclesial act of sung prophecy and intercession—an instance of primary theologizing that nurtures the worshiping community and mediates a Spirit-empowered encounter with divine hope. Full article

In recent years, global energy transition has pushed distributed generation (DG) to the forefront in relation to new energy development. Most existing studies focus on DG or energy storage planning but lack co-optimization and power tracking analysis. To address this problem, a multi-objective genetic algorithm-based collaborative planning method for photovoltaic (PV) and energy storage is proposed. On this basis, power flow tracking technology is further introduced to conduct a detailed analysis of distributed energy power allocation, providing support for system operation optimization and responsibility sharing. To verify the validity of the model, a 14-node distribution network is used as an example. Voltage stability, PV consumption rate, and economy are taken as objective functions. By solving the three scenarios, it is determined that the introduction of energy storage increases the PV consumption rate from 85.6% to 96.3%; the average network loss for the whole day increases from 1.81 MW to 2.40 MW. Utilizing power tracking techniques, various causes were analyzed; it was found that the placement of energy storage leads to a multidirectional and repetitive flow of power. Full article

Aiming at the unstable filling effect under high-speed operating conditions of the maize mechanical precision metering device, which easily causes the problem of leakage and multiple filling, a novel filling method was proposed to limit the number of seeds accumulation in front of the filling port by a composite seeding tray and improve the filling effect for single-seed. Meanwhile, a quantitative precision filling seed-metering device for maize was presented. The structural parameter design of the key components was completed, and the principle of improving the seed-filling effect was analyzed and elucidated. The optimal type of grooved teeth for the composite seeding tray was selected, and a Box–Behnken orthogonal optimization experiment was conducted using EDEM simulation. The high-speed seed-metering performance optimization results were validated through a platformed performance experiment. The results indicated that the seed-metering device had higher seed supply capacity, better seed-filling effect, and superior seed-metering performance under the type A grooved teeth condition. When the opening height of the seed barrier was 19.4 mm, the depth of the grooved teeth was 1.2 mm, and the operating speed was 10.7 km·h⁻¹, the seed-metering performance was optimal. The passing, repetitive, and miss rates were 95.1%, 1.6%, and 3.3%, respectively. When the operating speed was 8–14 km·h⁻¹, the passing rate of the seed-metering device was higher than 94.1%, the repetitive rate was lower than 2.3%, and the miss rate was lower than 3.7%. This work provides a reference for enhancing the seed-filling effect of mechanical precision metering devices under high-speed operating conditions. Full article

In this paper, an iterative learning control without resetting conditions based on a finite-time zeroing neural network (NRCILC-FTZNN) is designed for trajectory tracking of a robotic manipulator operating under external disturbances and executing repetitive tasks. A finite-time zeroing neural network (FTZNN) is developed to eliminate external disturbances and enhance convergence. Furthermore, an iterative learning control without resetting conditions based on the FTZNN is proposed to automatically provide the initial state value in each iteration, thereby eliminating the need for reset conditions. The trajectory-tracking errors, measured by the mean absolute error (MAE), are reduced by 46.89% and 63.29% compared to other schemes. Furthermore, the tracking errors of the proposed NRCILC-FTZNN method converge to zero in fewer iterations than those of the other methods. Simulation results demonstrate the convergence of the robotic manipulator system under disturbances to confirm the effectiveness of NRCILC-FTZNN scheme. Full article

High-current electron beams can significantly enhance the productivity of variety of applications including medical radioisotope (RI) production and wastewater purification. High-power superconducting radio frequency (SRF) linacs are capable of producing such high-current electron beams due to the key advantage to operate in continuous wave (CW) mode. However, this requires an injector capable of generating electron bunches with high repetition rate and in CW mode, while minimizing beam losses to avoid damage to SRF cavities due to quenching. RF gating to the grid of a thermionic electron gun is a promising solution, as it ensures CW bunch generation at the repetition rate same as the fundamental or sub-harmonics of the accelerating RF frequency, with minimal beam loss. This paper presents detailed beam dynamics simulations demonstrating that an RF-gated gun operating at 1.3 GHz can generate bunches with 148 ps full width with 8.96 pC charge. Full article

The electrical grid is undergoing increasing integration of decentralized power sources connected to the low-voltage network. In this context, the concept of a microgrid has emerged as a system comprising small-scale energy sources, loads, and storage devices, coordinated to operate as a single controllable entity capable of functioning in either grid-connected or islanded mode. The microgrid may be organized in a centralized configuration, such as a master-slave scheme, wherein the centralized converter, i.e., the grid-forming converter (GFC), plays a pivotal role in ensuring system stability and control. This paper introduces a plug-in repetitive controller (RC) strategy tuned to even harmonic orders for application in a three-phase GFC, diverging from the conventional approach that focuses on odd harmonics. The proposed control is designed within a synchronous reference frame and is targeted at centralized AC microgrids, particularly during islanded operation. Simulation results are presented to assess the microgrid’s power flow and power quality, thereby evaluating the performance of the GFC. Additionally, the proposed control was implemented on a Texas Instruments TMS320F28335 digital signal processor and validated through hardware-in-the-loop (HIL) simulation using the Typhoon HIL 600 platform, considering multiple scenarios with both linear and nonlinear loads. The main results highlight that the RC improves voltage regulation, mitigates harmonic distortion, and increases power delivery capability, thus validating its effectiveness for GFC operation. Full article

Trajectory planning for autonomous vehicles is essential for ensuring driving safety, passenger comfort, and operational efficiency. Collision avoidance constraints introduce significant computational complexity due to their inherent non-convex and nonlinear characteristics. Previous research has proposed the drivable corridor (DC) method, which transforms complex collision avoidance constraints into linear inequalities by constructing time-varying rectangular corridors within the spatiotemporal domains, thereby enhancing optimization efficiency. However, the DC construction process involves repetitive collision detection, leading to an increased computational burden. To address this limitation, this study proposes a novel approach that integrates grid-based obstacle representation with dynamic grid merging to accelerate collision detection and dynamically constructs the DC by adaptively adjusting the expansion strategies according to available spatial dimensions. The feasibility and effectiveness of the proposed method are validated through simulation-based evaluations conducted over 100 representative scenarios characterized by diverse and unstructured environmental configurations. The simulation results indicate that, with appropriately selected grid resolutions, the proposed approach achieves up to a 60% reduction in trajectory planning time compared to conventional DC-based planners while maintaining robust performance in complex environments. Full article

We present a model of a Cr²⁺-doped saturable absorber (SA), which is employed in passively Q-switched (PQS) Tm:CaF₂ lasers. The overall round-trip loss, the time evolution of the intracavity photon density, and the effective population inversion density can all be obtained through numerical solutions. Under the mode-matching condition, this model can be used to easily determine the PQS laser’s main output parameters, including the average output power, repetition frequency, peak power, pulse energy, and pulse width. This concept is also applicable to a range of thulium-doped solid-state lasers (SSLs) operating on the transition from the ³F₄ level to the ³H₆ level, which are Q-switched by a Cr²⁺-doped SA. This model is helpful for the design and optimization of this kind of laser. Full article

In the context of sustainable manufacturing practices, minimum quantity lubrication (MQL) has been extensively employed in machining operations involving hard-to-cut materials. While substantial experimental and numerical investigations on MQL-assisted machining have been conducted, existing simulation approaches remain inadequate for modeling the dynamic flow field variations inherent to MQL processes, significantly compromising the predictive reliability of current models. This study introduced an innovative bidirectional iterative coupling framework integrating finite element (FE) analysis and computational fluid dynamics (CFD) to enhance simulation accuracy. Since fluid flow characteristics critically influence tribological and thermal management at the tool–workpiece interface during machining, CFD simulations were initially performed to evaluate how MQL parameters govern fluid flow behavior. Subsequently, an integrated FE-CFD modeling approach was developed to simulate Ti-6Al-4V alloy turning under MQL conditions with varying feed rates. The novel methodology involved transferring thermal flux data from FE simulations to CFD’s heat source domain, followed by incorporating CFD-derived convective heat transfer coefficients back into FE computations. This repetitive feedback process continued until the thermal exchange parameters reached convergence. Validation experiments demonstrated that the proposed method achieved improved alignment between the simulated and experimental results for both cutting temperature profiles and principal force components across different feed conditions, confirming the enhanced predictive capability of this coupled simulation strategy. Full article

Defect repair on custom-curved glulam beams is still performed manually because knots are irregular, numerous, and located on elements that cannot pass through linear production lines, limiting the scalability of timber-based architecture. This study presents Woodot, an autonomous mobile robotic platform that combines an omnidirectional rover, a six-dof collaborative arm, and a fine-tuned Segment Anything computer vision pipeline to identify, mill, and plug surface knots on geometrically variable beams. The perception model was trained on a purpose-built micro-dataset and reached an F1 score of 0.69 on independent test images, while the integrated system located defects with a 4.3 mm mean positional error. Full repair cycles averaged 74 s per knot, reducing processing time by more than 60% compared with skilled manual operations, and achieved flush plug placement in 87% of trials. These outcomes demonstrate that a lightweight AI model coupled with mobile manipulation can deliver reliable, shop-floor automation for low-volume, high-variation timber production. By shortening cycle times and lowering worker exposure to repetitive tasks, Woodot offers a viable pathway to enhance the environmental, economic, and social sustainability of digital timber construction. Nevertheless, some limitations remain, such as dependency on stable lighting conditions for optimal vision performance and the need for tool calibration checks. Full article

Current research on dosimeters based on radiation-induced attenuation (RIA) primarily focused on enhancing radiation sensitivity or reducing dependencies from interference factors. However, their intrinsic sensing performance has received limited attention. This work proposed application and analysis methods for RIA-based dosimeters, validated by a low-cost apparatus using commercial fibers. Initially, a generic protocol of high-dose detection after low-dose calibration was suggested to overcome the various dependencies of RIA, enabling repetitive monitoring of near-stable radiation by simple replacement of commercial fibers. Experiments comparing three dose-loss models demonstrated that the saturation-exponential model exhibited superior accuracy, achieving absolute errors below 4 Gy within a measurable range of up to ~300 Gy. Subsequently, the system’s RIA-based sensitivity was ~125.6 dB·Gy⁻¹·km⁻¹. The resolution and sensitivity expressed by optical power were newly defined, effectively quantifying the decline in precision and response ratio during detection. Moreover, an additional structure was introduced to extend the measurable range. Simulations and experiments under 1-MeV electron irradiation verified that adjustable ranges could be achieved through configuration of attenuation layers. In summary, these advancements provided critical guidance for component selection and operational evaluation, facilitating the commercialization and practical deployment of RIA-based dosimeters. Full article

This study reports a novel MAX phase material, Mo₂TiAlC₂, as a passively mode-locking (PML) saturable absorber (SA) for a Tm:YAP laser operating in the 2 μm wavelength range. The systematic characterization of its nonlinear optical properties was quantitatively analyzed using I-scan methodology, demonstrating a significant modulation depth of 3.5%, which indicated strong nonlinear optical activity. Within the realm of optimal cavity conditions, a remarkable performance by the PML configuration can be discerned. A stable pulsed emission was manifested at 1937 nm, wherein an average output power reaching 620 mW was achieved. A pulse temporal span of 989.5 ps was acquired with a corresponding repetition frequency of 103.1 MHz, indicating robust mode-locked synchronization. Notably, the beam quality factors (M²) along the orthogonal spatial axes were observed with values measuring 1.12 and 1.18, respectively, indicating propagation characteristics close to those of diffraction-limited beams. Full article