An efficient method was proposed to fabricate the mold cavity for a helical cylindrical pinion based on a plastic torsion forming concept. The structure of the spur gear cavity with the same profile as the end face of the target helical gear cavity was first fabricated by low-speed wire electrical discharge machining (LS-WEDM). Then, the structure of the helical gear cavity could be obtained by twisting the spur gear cavity plastically around the central axis. In this way, the fabrication process of a helical cylindrical gear cavity could be greatly simplified, compared to the fabrication of a multi-stage helical gear core electrode and the highly difficult and complex spiral EDM process in the current gear manufacturing method. Moreover, several experiments were conducted to verify this novel processing concept, and a theoretical model was established to show the relationship between the machine torsion angle and the helical angle of a helical gear. Based on this theoretical model, the experimental results showed that it is feasible to precisely control the shape accuracy of a helical cylindrical pinion mold cavity by adjusting the machine torsion angle.

In the context of rapid advancements in the automotive industry and intelligent transportation systems, assessing cognitive load during driving has become a key factor for driving safety and user experience. This paper presents a comprehensive narrative review of theories, methods, and technological advancements in assessing cognitive load during driving. Rather than following a systematic review protocol,the structure of the review is organized around key research questions and critical gaps identified in the current literature. We assess the applicability and performance of major evaluation methods, including physiological indicators, behavioral measures, subjective self-report scales, and data-driven approaches, across various driving scenarios. The review also discusses the integration of multi-source information and propose a conceptual framework for holistic and adaptive cognitive load assessment. Furthermore, it highlights challenges in current practices, such as technical constraints, environmental variability, and individual differences. Special emphasis is placed on the relevance of cognitive load assessment for industrial informatics, particularly in the context of advanced driver assistance systems (ADAS), autonomous driving technologies, and driver training programs. This review aims to provide a structured synthesis of current approaches, offer practical insights for application and system design, and guide future research toward developing more robust, generalizable, and context-aware assessment tools. By analyzing the state of the art, we contribute a timely reference to support both academic development and industrial implementation for next-generation intelligent vehicle systems.

Parallel sensing can shorten multi-UAV mapping missions, yet late-stage exploration remains costly when low-yield frontiers continue to trigger flight, reassignment, and communication. We formulate this problem as pre-allocation candidate regulation under sparse summary sharing and propose \method. Each frontier is represented by an inspectable record containing gain, viewpoint quality, travel cost, and peer-state information. Normalized gain evidence lowers the assignment cost within a fixed cap, and an auxiliary peer-connectivity term penalizes moves that increase the peer-distance burden. The allocator selects the eligible candidate with minimum adjusted cost; if none is available, the UAV retains its current target or receives no new assignment. We evaluate the method in three forest-like 3D simulation scenes using five matched runs per scene. Compared with the dense-communication reference (\dcr), final volume was $0.02\%$ lower, while completion time, flight distance, communication volume, and message count decreased by $12.72\%$, $12.01\%$, $37.54\%$, and $29.61\%$, respectively. Compared with the sparse-sharing baseline (\ssb), final volume was $0.08\%$ lower; completion time, flight distance, and message count decreased by $13.26\%$, $12.85\%$, and $7.32\%$, respectively. Communication volume decreased by $2.53\%$, but its confidence interval crossed zero. Module ablations indicate that gain evidence and reward capping both contribute to the observed regulation behavior. Removing the peer-connectivity term produced no significant paired change, so we treat it as an auxiliary regularizer.

Detecting unmanned aerial vehicles (UAVs) in complex low-altitude scenes remains challenging due to limited target details and background interference. To address these challenges, this paper proposes DARA-Net, a lightweight UAV detection framework that coordinates detail preservation, efficient feature extraction, and multiscale aggregation. Built upon YOLOv11, DARA-Net integrates adaptive window reduction (AWR) to retain informative local features during downsampling, a separable aggregation block (SAB) to improve feature representation with fewer parameters, and a directional weighted pyramid (DWP) to enhance cross-scale feature fusion. Experiments on a visible-light UAV dataset containing 25,941 images demonstrate that DARA-Net achieves 97.0% precision, 91.1% recall, 95.1% mAP@0.5, and 62.0% mAP@0.5:0.95. Compared with the YOLOv11 baseline, recall improves by 2.6 percentage points, while the parameter count decreases by approximately 30.6% and model size decreases by 29.1%, reaching 1.79 M parameters and 3.9 MB, respectively. Compared with YOLOv26n, DARA-Net achieves a higher mAP@0.5:0.95 (62.0% versus 60.77%) with approximately 24.6% fewer parameters and a 28.2% smaller model file, while maintaining comparable mAP@0.5. Additional validation on an external UAV dataset further supports its competitive detection performance. These results highlight the value of DARA-Net as a compact UAV detector that prioritizes localization performance and reduced parameter and storage requirements.

The outbreak of many energy and environmental problems is greatly related to the increasing consumption of resources. In recent years, there has been obvious advancement in the fields of new energy and energy recovery. For example, laying a piezoelectric energy-collector on the road is capable of collecting the mechanical energies generated by passing vehicles. It is known that the