Search Results (227)

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

The increasing complexity of airway management, particularly in high-stakes or emergency settings, demands a holistic approach that accounts not only for technical skill but also for the systems in which clinicians operate. Advances in airway devices such as videolaryngoscopes, videolaryngeal mask airways, flexible intubation scopes, combined techniques, and single-use technologies offer new opportunities for improving outcomes—but also introduce new challenges. This article explores the intersection of human factors and the implementation of new airway devices, using a systems-based lens informed by the SEIPS 3.0 framework. Drawing on recent guidelines, real-world case studies, and design principles, we examine how technological changes affect team dynamics, decision-making, equipment layout, and cognitive load. We also highlight the importance of standardized processes, training, and environmental design in mitigating risk and enhancing performance. Ultimately, we propose actionable strategies to integrate human factors into airway device adoption to improve both patient safety and clinician well-being. This review underscores the fact that embedding human factor principles into the adoption and use of airway technologies is essential to build safer, more resilient, and team-centered airway management systems. Full article

In the context of intelligent manufacturing, the integrated scheduling problem of dual rail-guided vehicles (RGVs) and multiple parallel processing equipment in flexible manufacturing systems has gained increasing importance. This problem exhibits spatiotemporal coupling and dynamic constraint characteristics, making traditional optimization methods ineffective at finding optimal solutions. At the problem formulation level, the dual resource scheduling task is modeled as a mixed-integer optimization problem. An intelligent scheduling framework based on action mask-constrained Proximal Policy Optimization (PPO) deep reinforcement learning is proposed to achieve integrated decision-making for production equipment allocation and RGV path planning. The approach models the scheduling problem as a Markov Decision Process, designing a high-dimensional state space, along with a multi-discrete action space that integrates machine selection and RGV motion control. The framework employs a shared feature extraction layer and dual-head Actor-Critic network architecture, combined with parallel experience collection and synchronous parameter update mechanisms. In computational experiments across different scales, the proposed method achieves an average makespan reduction of 15–20% compared with numerical methods, while exhibiting excellent robustness under uncertain conditions including processing time fluctuations. Full article

Background and Objectives: The interplay between motor tasks and cognition in Alzheimer’s dementia (AD) remains insufficiently characterised. We hypothesised that prefrontal-mediated cognitive functions could contribute to motor impairments in older adults with AD. Materials and Methods: Cross-sectional data from the National Alzheimer’s Coordinating Centre (NACC) were analysed. Our sample included older adults (≥60 years) with a baseline diagnosis of AD. The Unified Parkinson’s Disease Rating Scale Part-III was used to assess the presence or absence of motor signs. Episodic memory, language, confrontation naming, attention, processing speed, and executive function were assessed using a neuropsychological battery. Binary logistic models examined the relationship between cognitive performance and motor manifestations. Results: Of 44,713 NACC participants, 5124 individuals with complete covariate data were included in the analysis, 1339 with and 3785 without motor signs. Participants were predominantly female (~55%), with an average age of 76.5 ± 7.9 years and mean education of 14.2 ± 3.7 years. The presence of motor manifestations was related to slower processing speed (Trail Making Test—Part A) and impaired executive function (Trail Making Test—Part B). No covariate modified these associations. Among specific motor domains, impaired chair rise was related to executive dysfunction, whereas postural instability, impaired posture–gait, and bradykinesia were related to slower mental processing. Hypophonia, masked facies, resting tremor, action–postural tremor and rigidity were not associated with any cognitive measure. Conclusions: Processing speed and, to a lesser extent, executive function emerged as the main cognitive functions associated with motor manifestations in older adults with AD. Further research is needed to clarify the nature of this association, including potential causal pathways. Full article

Unsafe behavior of workers is a leading cause of construction accidents. However, existing monitoring systems remain limited by low efficiency and poor adaptability to dynamic on-site environments. This study proposes an adaptive dual-stream vision framework that integrates Dynamic Adaptive Image Enhancement (DAIE) and a Lightweight Real-Time Behavior Network (LR-BehaviorNet) to improve the accuracy and responsiveness of unsafe behavior detection. The DAIE module dynamically adjusts brightness, contrast, and sharpness according to scene conditions, ensuring visual clarity under varying lighting and weather. LR-BehaviorNet combines efficient convolutional blocks with Transformer-based temporal modeling to identify critical actions from both enhanced and raw image streams. Additionally, an adaptive thresholding mechanism fine-tunes detection sensitivity under complex visual interference. Experiments using open-source construction datasets demonstrate that the proposed framework outperforms conventional models—including Faster R-CNN, YOLO, and Mask R-CNN—in precision, recall, and F1-score, achieving 93.2%, 91.4%, and 92.3%, respectively. These results validate the robustness of the proposed method for real-time safety supervision and its potential integration with intelligent construction management platforms. Overall, the framework offers a scalable and efficient solution for automated safety monitoring, advancing the digital transformation of construction safety management. Full article

Accurate documentation is critical in ophthalmology, yet clinical notes often contain subtle errors that can affect decision-making. This study prospectively compared contemporary large language models (LLMs) for detecting clinically salient errors in emergency ophthalmology encounter notes and generating actionable corrections. 129 de-identified notes, each seeded with a predefined target error, were independently audited by four LLMs (o3 (OpenAI, closed-source), DeepSeek-v3-r1 (Deepseek, open-source), MedGemma-27B (Google, open-source), and GPT-4o (OpenAI, closed-source)) using a standardized prompt. Two masked ophthalmologists graded error localization, relevance of additional issues, and overall recommendation quality, with within-case analyses applying appropriate nonparametric tests. Performance varied significantly across models (Cochran’s Q = 71.13, p = 2.44 × 10⁻¹⁵). o3 achieved the highest error localization accuracy at 95.7% (95% CI, 89.5–98.8), followed by DeepSeek-v3-r1 (90.3%), MedGemma-27b (80.9%), and GPT-4o (53.2%). Ordinal outcomes similarly favored o3 and DeepSeek-v3-r1 (both p < 10⁻⁹ vs. GPT-4o), with mean recommendation quality scores of 3.35, 3.05, 2.54, and 2.11, respectively. These findings demonstrate that LLMs can serve as accurate “second-eyes” for ophthalmology documentation. A proprietary model led on all metrics, while a strong open-source alternative approached its performance, offering potential for privacy-preserving on-premise deployment. Clinical translation will require oversight, workflow integration, and careful attention to ethical considerations. Full article

The rapid proliferation of photovoltaic (PV) generation has transformed conventional distribution systems, resulting in frequent reverse power flow (RPF) and associated overvoltage issues. This paper presents a deep reinforcement learning (DRL)-based topology control method to autonomously mitigate RPF and voltage violations. A novel multi-discrete Maskable Proximal Policy Optimization (MPPO) algorithm is proposed, combining topology-aware action masking with a multi-discrete action representation to ensure constraint satisfaction and enhance training stability. The approach efficiently explores the feasible switching space while maintaining network radiality, load connectivity, and power flow solvability. Extensive case studies based on one year of operational data from a practical distribution system show that the proposed agent achieves an average RPF reduction of 24.3% across the test cases and restores normal voltage conditions in about 65% of scenarios, while satisfying other operational constraints. The results confirm that the proposed method provides a scalable, data-driven solution for topology reconfiguration in PV-rich distribution networks. Full article

Conserving near-shore island endemics requires workflows that are robust to small, spatially clustered samples and that translate Species Distribution Model (SDM) into regulation-ready actions. We formalize a transferable SDM-to-action blueprint—(i) cluster-aware spatial holdout (leave-one-cluster-out, LOCO), (ii) conservative, high-specificity binarization paired with simple ecological filters, and (iii) explicit area-band uncertainty—and apply it to the Ulleungdo (Republic of Korea) endemic Scrophularia takesimensis. We combined 2008–2024 field records with a 5 m resolution MaxEnt model (linear–quadratic features; regularization RM = 1.40) using 28 unique presences versus 744 background points sampled within an accessible coastal belt (300 m from shore). Under LOCO, the model generalized well (AUC = 0.984 ± 0.014; partial AUC at specificity of at least 0.90 = 0.935; RelRMSE = 0.107) and mapped a narrow near-shore suitability belt with a continuous northern–northeastern core and fragmented southern–eastern satellites. To obtain a regulation-ready map, we converted continuous suitability to binary using a cutoff that achieved specificity of at least 0.98 under spatial holdout (threshold: 0.472; baseline: 300 m) and applied two ecological filters (retain areas within 90 m of shoreline; remove patches < 75 m²), yielding a CORE of 1.148 km² that captured 71.4% of recent records with zero leakage beyond the belt after post-processing. Accessible-mask sensitivity (masks of 300, 450, and 600 m) bounded the post-processed CORE to 0.930–1.593 km² (coverage: 0.607–0.789), which we carry forward as a planning area band. We translate these results into a tiered plan: protect the near-shore core, reconnect the fragmented southern and eastern stretches, and survey the highest-ranked coastal segments. Beyond this case, the blueprint generalizes to other small-n near-shore endemics, offering a transparent path from the SDM to policy while clarifying that, given static predictors, inferences concern present-day suitability rather than climate change forecasting. Full article

Modern electrical vehicles (EVs) are equipped with sizable batteries that possess significant potential as energy prosumers. EVs are poised to be transformative assets and pivotal contributors to the virtual power plant (VPP), enhancing the performance and profitability of VPPs. The number of household EVs is increasing yearly, and this poses new challenges to the optimization of VPP operations. The computational cost increases exponentially as the number of decision variables rises with the increasing participation of EVs. This paper explores the role of a large number of EVs as prosumers, interacting with a VPP consisting of a photovoltaic system and battery energy storage system. To accommodate the large quantity of EVs in the modeling, this research adopts the decentralized control structure. It optimizes EV operations by regulating their charging and discharging behavior in response to pricing signals from the VPP. A two-stage optimization framework is proposed for VPP-EV operation using a reinforcement algorithm and gradient-based programming. Action masking for reinforcement learning is explored to eliminate invalid actions, reducing ineffective exploration, thereby accelerating the convergence of the algorithm. The proposed approach is capable of handling a substantial number of EVs and addressing the stochastic characteristics of EV charging and discharging behaviors. Simulation results demonstrate that the VPP-EV operation optimization increases the revenue of the VPP and significantly reduces the electricity costs for EV owners. Through the optimization of EV operations, the charging cost of 1000 EVs participating in the V2G services is reduced by 26.38% compared to those that opt out of the scheme, and VPP revenue increases by 27.83% accordingly. Full article

Single Event Upsets (SEUs) in Commercial Off-The-Shelf (COTS) Software-Defined Radios (SDRs) are frequent in a erospace applications, especially in GEO (Geostationary Orbit) orbit during severe solar activity, and can lead to unexpected register corruption and communication failures. This work presents a purely software-based Fault Detection, Isolation, and Recovery (FDIR) framework tailored for the AD936x RF agile transceiver, requiring no hardware modifications. The proposed method classifies all device registers into four impact categories and applies dedicated scrubbing strategies—standard refresh, masked refresh, procedural refresh, and forced refresh—combined with real-time register health monitoring and adaptive recovery actions. Fault injection experiments comprising 10,000 diverse test cases achieved 100% fault coverage for the tested scenarios, with an average recovery time of 0.75 s for typical SEUs and a guaranteed worst-case recovery under 4.4 s for critical failures, while maintaining a CPU load below 1.3%. The approach ensures continuous SDR operation under SEU events and offers a scalable, lightweight, and cost-effective reliability enhancement for CubeSats and other resource-constrained aerospace platforms. Full article

Traditional mechanical processing techniques are confronted with significant challenges when machining advanced materials possessing excellent mechanical properties. Electrochemical machining (ECM), as a material removal technology based on the principle of anodic dissolution, demonstrates distinctive advantages including the absence of contact stress, independence from material hardness, and elimination of mechanical residual stress and recast layers. These characteristics render ECM particularly suitable for high-precision applications requiring superior surface quality. This review systematically summarizes the applications, recent progress, and current challenges of ECM in surface processing. According to diverse surface requirements, ECM technology is classified into two core directions based on primary objectives. The first direction focuses on surface quality enhancement, where nanoscale planarization, residual stress reduction, and uniform surface performance are achieved through precise regulation of anodic dissolution. The second direction concerns material shaping, which is subdivided into macro-scale and micro-scale processing. Macro-scale forming combines electrochemical dissolution with mechanical action to maintain high material removal rate (MRR) while achieving micron-level precision. Micro-scale forming employs nanosecond pulse power supplies and precision electrode/mask designs to overcome manufacturing limitations of micro-nano features on hard-brittle materials. Despite progress achieved, key technical bottlenecks persist, including unstable dynamic control of the inter-electrode gap, environmental concerns regarding electrolytes, and tooling degradation. Future research should prioritize the development of green processing technologies, intelligent control systems, multi-scale manufacturing strategies, and multi-energy field hybrid technologies to enhance the capability of ECM in meeting increasingly stringent surface requirements for advanced materials. Full article

Extreme ultraviolet (EUV) lithography faces critical challenges in aberration control and patterning fidelity as technology nodes shrink below 3 nm. This work demonstrates how Source–Mask Optimization (SMO) simultaneously addresses both illumination and mask design to enhance pattern transfer accuracy and mitigate aberrations. Through a comprehensive optimization framework incorporating key process metrics, including critical dimension (CD), exposure latitude (EL), and mask error factor (MEF), we achieve significant improvements in imaging quality and process window for 40 nm minimum pitch patterns, representative of 2 nm node back-end-of-line (BEOL) requirements. Our analysis reveals that intelligent SMO implementation not only enables robust patterning solutions but also compensates for inherent EUV aberrations by balancing source characteristics with mask modifications. On average, our results show a 4.02% reduction in CD uniformity variation, concurrent with a 1.48% improvement in exposure latitude and a 5.45% reduction in MEF. The proposed methodology provides actionable insights for aberration-aware SMO strategies, offering a pathway to maintain lithographic performance as feature sizes continue to scale. These results underscore SMO’s indispensable role in advancing EUV lithography capabilities for next-generation semiconductor manufacturing. Full article

The Nile, Indus, and Yellow River deltas are historically significant and have experienced extensive shoreline changes over the past 50 years, yet the roles of human interventions and natural events remain unclear. In this study, the Net Shoreline Movement and End Point Rate (EPR) were calculated to quantify the erosion and accretion of the shoreline, respectively. Subsequently, linear trend analysis was employed to identify potential directional shifts in shoreline behavior. These measures are combined with segment-scale cumulative area and the EPR trend to reveal where erosion or accretion intensifies, weakens, or reverses through time. Results show distinct, system-specific trajectories, the Nile lost ~27 km² from 1972 to1997 as a result of the dam construction and sediment reduction, and lost only ~3 km² more from 1997 to 2022, with local stabilization. The Indus switched from intermittent gains before 1990s to sustained loss after that, totaling ~300 km² of cumulative land loss mainly due to upstream dam constructions and storm events. The Yellow River gained ~500 km² from 1973 to 1996 then lost ~200 km² after main-channel relocation and reduced sediment supply despite active-mouth management. These outcomes indicate that deltas are very vulnerable to system wide human activities and natural events. Combined, satellite-derived metrics can help prioritize locations, guide feasible interventions, establish annual monitoring and trigger action. A major caveat of this study is that yearly shoreline rates and 5–10-yearaverages can mask short-lived or very local shifts. Targeted field surveys and finer-scale modeling (hydrodynamics, subsidence monitoring, bathymetry) are therefore needed to refine the design and inform better policy choices. Full article

Hybrid rule-based and reinforcement-learning (RL) signal control is gaining traction for urban coordination by pairing interpretable cycles, splits, and offsets with adaptive, data-driven updates. However, systematic evidence on their architectures, safeguards, and deployment prerequisites remains scarce, motivating this review that maps current hybrid controller designs under corridor coordination. Searches across major databases and arXiv (2000–2025) followed PRISMA guidance; screening is reported in the flow diagram. Eighteen studies were included, nine with quantitative comparisons, spanning simulation and early field pilots. Designs commonly use rule shields, action masking, and bounded adjustments of offsets or splits; effectiveness is assessed via arrivals on green, Purdue Coordination diagrams, delay, and travel time. Across the 18 studies, the majority reported improvements in arrivals on green, delay, travel time, or related coordination metrics compared to fixed-time or actuated baselines, while only a few showed neutral or mixed effects and very few indicated deterioration. These results indicate that hybrid safeguards are generally associated with positive operational gains, especially under heterogeneous traffic conditions. Evidence specific to Indonesia remains limited; this review addresses that gap and offers guidance transferable to other developing-country contexts with similar sensing, connectivity, and institutional constraints. Practical guidance synthesizes sensing choices and fallbacks, controller interfaces, audit trails, and safety interlocks into a deployment checklist, with a staged roadmap for corridor roll-outs. This paper is not only a systematic review but also develops a practice-oriented framework tailored to Indonesian corridors, ensuring that evidence synthesis and practical recommendations are clearly distinguished. Full article

Autonomous beach-cleaning robots require reliable, low-cost navigation on sand. We study Sim-to-Real transfer of deep reinforcement learning (DRL) policies using a minimal sensor suite—wheel-encoder odometry and a single 2-D LiDAR—on a 30 kg differential-drive platform (Raspberry Pi 4). Two policies, Proximal Policy Optimization (PPO) and a masked-action variant (PPO-Mask), were trained in Gazebo + Gymnasium and deployed on the physical robot without hyperparameter retuning. Field trials on firm sand and on a natural loose-sand beach show that PPO-Mask reduces tracking error versus PPO on firm ground (16.6% ISE reduction; 5.2% IAE reduction) and executes multi-waypoint paths faster (square path: 112.48 s vs. 103.46 s). On beach sand, all waypoints were reached within a 1 m tolerance, with mission times of 115.72 s (square) and 81.77 s (triangle). These results indicate that DRL-based navigation with minimal sensing and low-cost compute is feasible in beach settings. Full article