Search Results (1,493)

This paper addresses the trajectory tracking control problem of underactuated ships in ocean engineering, which faces the dual challenges of tracking error time–performance regulation and robustness design due to the system’s underactuated characteristics, model uncertainties, and external disturbances. Aiming to address the issues of traditional finite-time control (convergence time dependent on initial states) and fixed-time control (control chattering and parameter conservativeness), this paper proposes a predefined-time adaptive control framework that integrates an event-triggered mechanism and neural networks. By constructing a Lyapunov function with time-varying weights and designing non-periodic dynamically updated dual triggering conditions, the convergence process of tracking errors is strictly constrained within a user-prespecified time window without relying on initial states or introducing non-smooth terms. An adaptive approximator based on radial basis function neural networks (RBF-NNs) is employed to compensate for unknown nonlinear dynamics and external disturbances in real-time. Combined with the event-triggered mechanism, it dynamically adjusts the update instances of control inputs, ensuring prespecified tracking accuracy while significantly reducing computational resource consumption. Theoretical analysis shows that all signals in the closed-loop system are uniformly ultimately bounded, tracking errors converge to a neighborhood of the origin within the predefined-time, and the update frequency of control inputs exhibits a linear relationship with the predefined-time, avoiding Zeno behavior. Simulation results verify the effectiveness of the proposed method in complex marine environments. Compared with traditional control strategies, it achieves more accurate trajectory tracking, faster response, and a substantial reduction in control input update frequency, providing an efficient solution for the engineering implementation of embedded control systems in unmanned ships. Full article

Given the severe global traffic safety challenges—including threats to human lives and socioeconomic impacts—this study analyzes visual behavior to promote sustainable transportation, improve road safety, and reduce resource waste and pollution caused by accidents. Four typical road sections, namely, turning, straight ahead, uphill, and downhill, were selected, and the eye movement data of 23 drivers in different driving stages were collected by aSee Glasses eye-tracking device to analyze the visual gaze characteristics of the drivers and their transfer patterns in each road section. Using Markov chain theory, the probability of staying at each gaze point and the transfer probability distribution between gaze points were investigated. The results of the study showed that drivers’ visual behaviors in different road sections showed significant differences: drivers in the turning section had the largest percentage of fixation on the near front, with a fixation duration and frequency of 29.99% and 28.80%, respectively; the straight ahead section, on the other hand, mainly focused on the right side of the road, with 31.57% of fixation duration and 19.45% of frequency of fixation; on the uphill section, drivers’ fixation duration on the left and right roads was more balanced, with 24.36% of fixation duration on the left side of the road and 25.51% on the right side of the road; drivers on the downhill section looked more frequently at the distance ahead, with a total fixation frequency of 23.20%, while paying higher attention to the right side of the road environment, with a fixation duration of 27.09%. In terms of visual fixation, the fixation shift in the turning road section was mainly concentrated between the near and distant parts of the road ahead and frequently turned to the left and right sides; the straight road section mainly showed a shift between the distant parts of the road ahead and the dashboard; the uphill road section was concentrated on the shift between the near parts of the road ahead and the two sides of the road, while the downhill road section mainly occurred between the distant parts of the road ahead and the rearview mirror. Although drivers’ fixations on the front of the road were most concentrated under the four road sections, with an overall fixation stability probability exceeding 67%, there were significant differences in fixation smoothness between different road sections. Through this study, this paper not only reveals the laws of drivers’ visual behavior under different driving environments but also provides theoretical support for behavior-based traffic safety improvement strategies. Full article

Continuous monitoring of environmental and physiological parameters is essential for early diagnostics, real-time decision making, and intelligent system adaptation. Recent advancements in bio-microelectromechanical systems (BioMEMS) sensors have significantly enhanced our ability to track key metrics in real time. However, continuous monitoring demands sustainable energy supply solutions, especially for on-site energy replenishment in areas with limited resources. Artificial intelligence (AI), particularly large language models, offers new avenues for interpreting the vast amounts of data generated by these sensors. Despite this potential, fully integrated systems that combine self-powered BioMEMS sensing with AI-based analytics remain in the early stages of development. This review first examines the evolution of BioMEMS sensors, focusing on advances in sensing materials, micro/nano-scale architectures, and fabrication techniques that enable high sensitivity, flexibility, and biocompatibility for continuous monitoring applications. We then examine recent advances in energy harvesting technologies, such as piezoelectric nanogenerators, triboelectric nanogenerators and moisture electricity generators, which enable self-powered BioMEMS sensors to operate continuously and reducereliance on traditional batteries. Finally, we discuss the role of AI in BioMEMS sensing, particularly in predictive analytics, to analyze continuous monitoring data, identify patterns, trends, and anomalies, and transform this data into actionable insights. This comprehensive analysis aims to provide a roadmap for future continuous BioMEMS sensing, revealing the potential unlocked by combining materials science, energy harvesting, and artificial intelligence. Full article

This research addresses the challenges of proactive scheduling and routing in manufacturing systems governed by the Material Requirement Planning (MRP) method. Such systems often face capacity constraints, difficulties in resource balancing, and limited traceability of component requirements. The lack of seamless integration between customer orders and production tasks, combined with the manual and time-consuming nature of schedule adjustments, highlights the need for an automated and optimized scheduling method. We propose a novel optimization-based approach that leverages mixed-integer linear programming (MILP) combined with a proprietary procedure for reducing the size of the modeled problem to generate feasible and/or optimal production schedules. The model incorporates dynamic routing, partial resource utilization, limited additional resources (e.g., tools, workers), technological breaks, and time quantization. Key results include determining order feasibility, identifying unfulfilled order components, minimizing costs, shortening deadlines, and assessing feasibility in the absence of available resources. By automating the generation of data from MRP/ERP systems, constructing an optimization model, and exporting the results back to the MRP/ERP structure, this method improves decision-making and competes with expensive Advanced Planning and Scheduling (APS) systems. The proposed innovation solution—the integration of MILP-based optimization with the proprietary PT (data transformation) and PR (model-size reduction) procedures—not only increases operational efficiency but also enables demand source tracking and offers a scalable and economical alternative for modern production environments. Experimental results demonstrate significant reductions in production costs (up to 25%) and lead times (more than 50%). Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

Real-time wildlife monitoring on edge devices poses significant challenges due to limited power, constrained bandwidth, and unreliable connectivity, especially in remote natural habitats. Conventional object detection systems often transmit redundant data of the same animals detected across multiple consecutive frames as a part of a single event, resulting in increased power consumption and inefficient bandwidth usage. Furthermore, maintaining consistent animal identities in the wild is difficult due to occlusions, variable lighting, and complex environments. In this study, we propose a lightweight hybrid tracking framework built on the YOLOv8m deep neural network, combining motion-based Kalman filtering with Local Binary Pattern (LBP) similarity for appearance-based re-identification using texture and color features. To handle ambiguous cases, we further incorporate Hue-Saturation-Value (HSV) color space similarity. This approach enhances identity consistency across frames while reducing redundant transmissions. The framework is optimized for real-time deployment on edge platforms such as NVIDIA Jetson Orin Nano and Raspberry Pi 5. We evaluate our method against state-of-the-art trackers using event-based metrics such as MOTA, HOTA, and IDF1, with a focus on detected animals occlusion handling, trajectory analysis, and counting during both day and night. Our approach significantly enhances tracking robustness, reduces ID switches, and provides more accurate detection and counting compared to existing methods. When transmitting time-series data and detected frames, it achieves up to 99.87% bandwidth savings and 99.67% power reduction, making it highly suitable for edge-based wildlife monitoring in resource-constrained environments. Full article

This paper proposes a lightweight gaze-tracking architecture named GhostBlock-Augmented Look to Coordinate Space (L2CS), which integrates GhostNet-based modules and depthwise separable convolution to achieve a better trade-off between model accuracy and computational efficiency. Conventional lightweight gaze-tracking models often suffer from degraded accuracy due to aggressive parameter reduction. To address this issue, we introduce GhostBlocks, a custom-designed convolutional unit that combines intrinsic feature generation with ghost feature recomposition through depthwise operations. Our method enhances the original L2CS architecture by replacing each ResNet block with GhostBlocks, thereby significantly reducing the number of parameters and floating-point operations. The experimental results on the Gaze360 dataset demonstrate that the proposed model reduces FLOPs from 16.527 × 10⁸ to 8.610 × 10⁸ and parameter count from 2.387 × 10⁵ to 1.224 × 10⁵ while maintaining comparable gaze estimation accuracy, with MAE increasing only slightly from 10.70° to 10.87°. This work highlights the potential of GhostNet-augmented designs for real-time gaze tracking on edge devices, providing a practical solution for deployment in resource-constrained environments. Full article

This study extends our prior blockchain-based traceability framework, WEave, for application to a furniture supply chain scenario, while using the original multi-tier apparel supply chain as an anchoring use case. We integrate circular economy principles such as product reuse, recycling traceability, and full lifecycle transparency to bolster sustainability and resilience in supply chains by enabling data-driven accountability and tracking for closed-loop resource flows. The enhanced approach can track post-consumer returns, use of recycled materials, and second-life goods, all represented using a closed-loop supply chain topology. We describe the extended network architecture and smart contract logic needed to capture circular lifecycle events, while proposing new metrics for evaluating lifecycle traceability and reuse auditability. To validate the extended framework, we outline simulation experiments that incorporate circular flows and cross-industry scenarios. Results from these simulations indicate improved transparency on recycled content, audit trails for returned products, and acceptable performance overhead when scaling to different product domains. Finally, we offer conclusions and recommendations for implementing WEave functionality into real-world settings consistent with the goals of digital, resilient, and sustainable supply chains. Full article

In the landslide chain from pre-disaster conditions to landslide mitigation and recovery, time is an important factor in understanding the geological hazards process and managing landsides. Static knowledge graphs are unable to capture the temporal dynamics of landslide events. To address this limitation, we propose a systematic framework for constructing a multi-temporal knowledge graph of landslides that integrates multi-source temporal data, enabling the dynamic tracking of landslide processes. Our approach comprises three key steps. First, we summarize domain knowledge and develop a temporal ontology model based on the disaster chain management system. Second, we map heterogeneous datasets (both tabular and textual data) into triples/quadruples and represent them based on the RDF (Resource Description Framework) and quadruple approaches. Finally, we validate the utility of multi-temporal knowledge graphs through multidimensional queries and develop a web interface that allows users to input landslide names to retrieve location and time-axis information. A case study of the Zhangjiawan landslide in the Three Gorges Reservoir Area demonstrates the multi-temporal knowledge graph’s capability to track temporal updates effectively. The query results show that multi-temporal knowledge graphs effectively support multi-temporal queries. This study advances landslide research by combining static knowledge representation with the dynamic evolution of landslides, laying the foundation for hazard forecasting and intelligent early-warning systems. Full article

Currently, exploring digital immersive experiences is a new trend in the innovation and development of cultural tourism. This study addresses the growing demand for digital immersion in cultural tourism by examining the integration of spatial narrative and digitally gamified architectural scenarios. This study synthesizes an optimized framework for narrative design in digitally gamified architectural scenarios, integrating spatial narrative theory and feedback-informed design. The proposed model comprises four key components: (1) developing spatial narrative design methods for such scenarios; (2) constructing a spatial language system for spatial narratives using linguistic principles to organize narrative expression; (3) building a preliminary digitally gamified scenario based on the “Wuhu Jiaoji Temple Renovation Project” after architectural and environmental enhancements; and (4) optimization through thermal feedback experiments—collecting visitor trajectory heatmaps, eye-tracking heatmaps, and oculometric data. The results show that the optimized design, validated in the original game Dreams of Jiaoji, effectively enhanced spatial narrative execution by refining both on-site and in-game architectural scenarios. Post-optimization visitor feedback confirmed the validity of the proposed optimization strategies and principles, providing theoretical and practical references for innovative digital cultural tourism models and architectural design advancements. In the context of site-specific architectural conservation, this approach achieves two key objectives: the generalized interpretation of architectural cultural resources and their visual representation through gamified interactions. This paradigm not only enhances public engagement through enabling a multidimensional understanding of historical building cultures but also accelerates the protective reuse of heritage sites, allowing heritage value to be maximized through contemporary reinterpretation. The interdisciplinary methodology promotes sustainable development in the digital transformation of cultural tourism, fostering user-centered experiences and contributing to rural revitalization. Ultimately, this study highlights the potential use of digitally gamified architectural scenarios as transformative tools for heritage preservation, cultural dissemination, and rural community revitalization. Full article

This paper proposes a speed-adaptive autonomous driving path-tracking framework based on the soft actor–critic (SAC) and pure pursuit (PP) methods, named the SACPP controller. The framework first analyzes the obstacles around the vehicle and plans an obstacle-free reference path with the minimum curvature using the hybrid A* algorithm. Next, based on the generated reference path, the current state of the vehicle, and the vehicle motor energy efficiency diagram, the optimal speed is calculated in real time, and the vehicle dynamics preview point at the future moment—specifically, the look-ahead distance—is predicted. This process relies on the learning of the SAC network structure. Finally, PP is used to generate the front wheel angle control value by combining the current speed and the predicted preview point. In the second layer, we carefully designed the evaluation function in the tracking process based on the uncertainties and performance requirements that may occur during vehicle driving. This design ensures that the autonomous vehicle can not only quickly and accurately track the path, but also effectively avoid surrounding obstacles, while keeping the motor running in the high-efficiency range, thereby reducing energy loss. In addition, since the entire framework uses a lightweight network structure and a geometry-based method to generate the front wheel angle, the computational load is significantly reduced, and computing resources are saved. The actual running results on the i7 CPU show that the control cycle of the control framework exceeds 100 Hz. Full article

Autism Spectrum Disorder (ASD) encompasses various neurological disorders with symptoms varying by age, development, genetics, and other factors. Core symptoms include decreased pain sensitivity, difficulty sustaining eye contact, incorrect auditory responses, and social engagement issues. Diagnosing ASD poses challenges as signs can appear at early stages of life, leading to delayed diagnoses. Traditional diagnosis relies mainly on clinical observation, which is a subjective and time-consuming approach. However, AI-driven techniques, primarily those within machine learning and deep learning, are becoming increasingly prevalent for the efficient and objective detection and classification of ASD. In this work, we review and discuss the most relevant related literature between January 2016 and May 2024 by focusing on ASD detection or classification using diverse technologies, including magnetic resonance imaging, facial images, questionnaires, electroencephalogram, and eye tracking data. Our analysis encompasses works from major research repositories, including WoS, PubMed, Scopus, and IEEE. We discuss rehabilitation techniques, the structure of public and private datasets, and the challenges of automated ASD detection, classification, and therapy by highlighting emerging trends, gaps, and future research directions. Among the most interesting findings of this review are the relevance of questionnaires and genetics in the early detection of ASD, as well as the prevalence of datasets that are biased toward specific genders, ethnicities, or geographic locations, restricting their applicability. This document serves as a comprehensive resource for researchers, clinicians, and stakeholders, promoting a deeper understanding and advancement of AI applications in the evaluation and management of ASD. Full article

Background: Cervical cancer is a major global health concern, causing approximately 350,000 deaths annually. It is also preventable through effective prevention and early detection. To facilitate elimination, the World Health Organization (WHO) set targets for HPV vaccination, screening, and treatment. Achieving these goals requires frameworks to monitor screening program performance. As many regions transition to HPV primary screening, a standardized Cervical Cancer Screening Cascade can track performance, identify gaps in follow-up, and optimize resource allocation. Methods: This paper introduces a structured cascade developed to monitor uptake, retention, and outcomes in HPV-based screening programs. The Cascade was created through collaboration between public health experts, clinicians, and researchers at the University of British Columbia (UBC), the Women’s Health Research Institute, and BC Cancer. Results: The Cascade outlines four phases: screening, triage, detection, and treatment. Each phase includes two substages: “uptake” and “results,” with an additional substage in screening (“invitation”). “Screening” assesses invitation effectiveness and participation. “Triage” tracks follow-up after a positive screen. “Detection” evaluates attendance at diagnostic appointments, and “Treatment” measures the treatment rate for those with precancerous lesions. Conclusions: The Cascade can guide emerging and existing HPV screening programs within Canada and other similarly resourced settings and serve as a benchmark tool for programs to assess their progress towards cervical cancer elimination. Full article

Introduction: Scattering of kernels due to angular dehiscence is a key bottleneck in mechanized harvesting of oilseed rape. Materials and Methods: In this study, a dual-track “genotype–phenotype” screening strategy was established by innovatively integrating high-throughput KASP molecular marker technology and a standardized random collision phenotyping system for the complex quantitative trait of angular resistance. Results: Through the systematic evaluation of 634 oilseed rape hybrid progenies, it was found that the KASP marker S12.68, targeting the cleavage resistance locus (BnSHP1) on chromosome C9, achieved a 73.34% introgression rate (465/634), which was significantly higher than the traditional breeding efficiency (<40%). Phenotypic characterization screened seven excellent resources with cracking resistance index (SRI) > 0.6, of which four reached the high resistance standard (SRI > 0.8), including the core materials NR21/KL01 (SRI = 1.0) and YuYou342/KL01 (SRI = 0.97). Six breeding intermediate materials (44.7–48.7% oil content, mycosphaerella resistance MR grade or above) were created, combining high resistance to chipping and excellent agronomic traits. For the first time, it was found that local germplasm YuYou342 (non-KL01-derived line) was purely susceptible at the S12.68 locus (SRI = 0.86), but its angiosperm vascular bundles density was significantly increased by 37% compared with that of the susceptible material 0911 (p < 0.01); and the material 187308 (SRI = 0.78), although purely susceptible at S12.68, had a 2.8-fold downregulation in expression of the angiosperm-related gene, BnIND1, and a 2.8-fold downregulation of expression of the angiosperm-related gene, BnIND1. expression was significantly downregulated 2.8-fold (q < 0.05), indicating the existence of a novel resistance mechanism independent of the primary effector locus. Conclusions: The results of this research provide an efficient technical platform and breakthrough germplasm resources for oilseed rape crack angle resistance breeding, which is of great practical significance for promoting the whole mechanized production. Full article

In water-scarce regions such as Jordan, accurate tracking of water flows is critical for informed water management. This study applied the Water Accounting Plus (WA+) framework using open-source remote sensing data from the FAO WaPOR portal to develop agricultural water accounting (AWA) for the Amman–Zarqa Basin (AZB) during 2014–2022. Inflows, outflows, and water consumption were quantified using WaPOR and other open datasets. The results showed a strong correlation between WaPOR precipitation (P) and rainfall station data, while comparisons with other remote sensing sources were weaker. WaPOR evapotranspiration (ET) values were generally lower than those from alternative datasets. To improve classification accuracy, a correction of the WaPOR-derived land cover map was performed. The revised map achieved a producer’s accuracy of 15.9% and a user’s accuracy of 86.6% for irrigated areas. Additionally, ET values over irrigated zones were adjusted, resulting in a fivefold improvement in estimates. These corrections significantly enhanced the reliability of key AWA indicators such as basin closure, ET fraction, and managed fraction. The findings demonstrate that the accuracy of P and ET data strongly affects AWA outputs, particularly the estimation of percolation and beneficial water use. Therefore, calibrating remote sensing data is essential to ensure reliable water accounting, especially in agricultural settings where data uncertainty can lead to misleading conclusions. This study recommends the use of open-source datasets such as WaPOR—combined with field validation and calibration—to improve agricultural water resource assessments and support decision making at basin and national levels. Full article