Search Results (239)

Autonomous earthwork machinery is gaining traction as a means to boost productivity and safety on space-constrained urban sites, yet the fast-growing literature has not been fully integrated. To clarify current knowledge, we systematically searched Scopus and screened 597 records, retaining 157 peer-reviewed papers (2015–March 2025) that address autonomy, integrated control, or risk mitigation for excavators, bulldozers, and loaders. Descriptive statistics, VOSviewer mapping, and qualitative synthesis show the output rising rapidly and peaking at 30 papers in 2024, led by China, Korea, and the USA. Four tightly linked themes dominate: perception-driven machine autonomy, IoT-enabled integrated control systems, multi-sensor safety strategies, and the first demonstrations of fleet-level collaboration (e.g., coordinated excavator clusters and unmanned aerial vehicle and unmanned ground vehicle (UAV–UGV) site preparation). Advances include centimeter-scale path tracking, real-time vision-light detection and ranging (LiDAR) fusion and geofenced safety envelopes, but formal validation protocols and robust inter-machine communication remain open challenges. The review distils five research priorities, including adaptive perception and artificial intelligence (AI), digital-twin integration with building information modeling (BIM), cooperative multi-robot planning, rigorous safety assurance, and human–automation partnership that must be addressed to transform isolated prototypes into connected, self-optimizing fleets capable of delivering safer, faster, and more sustainable urban construction. Full article

Gait recognition is a reliable biometric approach that uniquely identifies individuals based on their natural walking patterns. It is widely used to recognize individuals who are challenging to camouflage and do not require a person’s cooperation. The general face-based person recognition system often fails to determine the offender’s identity when they conceal their face by wearing helmets and masks to evade identification. In such cases, gait-based recognition is ideal for identifying offenders, and most existing work leverages a deep learning (DL) model. However, a single model often fails to capture a comprehensive selection of refined patterns in input data when external factors are present, such as variation in viewing angle, clothing, and carrying conditions. In response to this, this paper introduces a fusion-based multi-model gait recognition framework that leverages the potential of convolutional neural networks (CNNs) and a vision transformer (ViT) in an ensemble manner to enhance gait recognition performance. Here, CNNs capture spatiotemporal features, and ViT features multiple attention layers that focus on a particular region of the gait image. The first step in this framework is to obtain the Gait Energy Image (GEI) by averaging a height-normalized gait silhouette sequence over a gait cycle, which can handle the left–right gait symmetry of the gait. After that, the GEI image is fed through multiple pre-trained models and fine-tuned precisely to extract the depth spatiotemporal feature. Later, three separate fusion strategies are conducted, and the first one is decision-level fusion (DLF), which takes each model’s decision and employs majority voting for the final decision. The second is feature-level fusion (FLF), which combines the features from individual models through pointwise addition before performing gait recognition. Finally, a hybrid fusion combines DLF and FLF for gait recognition. The performance of the multi-model fusion-based framework was evaluated on three publicly available gait databases: CASIA-B, OU-ISIR D, and the OU-ISIR Large Population dataset. The experimental results demonstrate that the fusion-enhanced framework achieves superior performance. Full article

This paper addresses the issues of Beidou navigation signal interference and blockage in complex substation environments by proposing an intelligent collaborative navigation model based on Beidou high-precision navigation and binocular vision recognition. The model is designed with Beidou navigation providing global positioning references and binocular vision enabling local environmental perception through a collaborative fusion strategy. The Unscented Kalman Filter (UKF) is used to integrate data from multiple sensors to ensure high-precision positioning and dynamic obstacle avoidance capabilities for robots in complex environments. Simulation results show that the Beidou–Binocular Cooperative Navigation (BBCN) model achieves a global positioning error of less than 5 cm in non-interference scenarios, and an error of only 6.2 cm under high-intensity electromagnetic interference, significantly outperforming the single Beidou model’s error of 40.2 cm. The path planning efficiency is close to optimal (with an efficiency factor within 1.05), and the obstacle avoidance success rate reaches 95%, while the system delay remains within 80 ms, meeting the real-time requirements of industrial scenarios. The innovative fusion approach enables unprecedented reliability for autonomous robot inspection in high-voltage environments, offering significant practical value in reducing human risk exposure, lowering maintenance costs, and improving inspection efficiency in power industry applications. This technology enables continuous monitoring of critical power infrastructure that was previously difficult to automate due to navigation challenges in electromagnetically complex environments. Full article

This review provides a comprehensive analysis of recent advancements in lower limb exoskeleton systems, focusing on applications, control strategies, hardware architecture, sensing modalities, human-robot interaction, evaluation methods, and technical innovations. The study spans systems developed for gait rehabilitation, mobility assistance, terrain adaptation, pediatric use, and industrial support. Applications range from sit-to-stand transitions and post-stroke therapy to balance support and real-world navigation. Control approaches vary from traditional impedance and fuzzy logic models to advanced data-driven frameworks, including reinforcement learning, recurrent neural networks, and digital twin-based optimization. These controllers support personalized and adaptive interaction, enabling real-time intent recognition, torque modulation, and gait phase synchronization across different users and tasks. Hardware platforms include powered multi-degree-of-freedom exoskeletons, passive assistive devices, compliant joint systems, and pediatric-specific configurations. Innovations in actuator design, modular architecture, and lightweight materials support increased usability and energy efficiency. Sensor systems integrate EMG, EEG, IMU, vision, and force feedback, supporting multimodal perception for motion prediction, terrain classification, and user monitoring. Human–robot interaction strategies emphasize safe, intuitive, and cooperative engagement. Controllers are increasingly user-specific, leveraging biosignals and gait metrics to tailor assistance. Evaluation methodologies include simulation, phantom testing, and human–subject trials across clinical and real-world environments, with performance measured through joint tracking accuracy, stability indices, and functional mobility scores. Overall, the review highlights the field’s evolution toward intelligent, adaptable, and user-centered systems, offering promising solutions for rehabilitation, mobility enhancement, and assistive autonomy in diverse populations. Following a detailed review of current developments, strategic recommendations are made to enhance and evolve existing exoskeleton technologies. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article

The European Charter for Wine Tourism (2005) promotes the sustainable development of tourism activities associated with viticulture. The document identifies the active role that wine-growing territories must play in the conservation, management, and valorization of their resources. This study aims to understand the degree of linkage that this Charter establishes with initiatives for the heritage of wine culture, specifically focusing on wine museums in Spain. It examines how these museums contribute to defining a tourism development program, constructing a common strategic vision, and encouraging cooperation between the social and economic agents involved in the territory. As case studies, the Vivanco Museum of Wine Culture (La Rioja), considered by World Tourism Organization (UNWTO) as the best in the world, and the Valdepeñas Wine Museum (Castilla-La Mancha), an example of rehabilitation and musealization in the region with the highest concentration of vineyards in the world, have been chosen. The results show that both museums exemplify management, development, and cooperation in their respective territories, aligning with the theoretical assumptions established in the Charter. Full article

Maritime vision sensing can be applied to maritime unmanned systems to perform search and rescue (SAR) missions under complex marine environments, as multiple unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) are able to conduct vision sensing through the air, the water-surface, and underwater. However, in these vision-based maritime SAR systems, collaboration between UAVs and USVs is a critical issue for successful SAR operations. To address this challenge, in this paper, we propose a long-endurance collaborative SAR scheme which exploits the complementary strengths of the maritime unmanned systems. In this scheme, a swarm of UAVs leverages a multi-agent reinforcement-learning (MARL) method and probability maps to perform cooperative first-phase search exploiting UAV’s high altitude and wide field of view of vision sensing. Then, multiple USVs conduct precise real-time second-phase operations by refining the probabilistic map. To deal with the energy constraints of UAVs and perform long-endurance collaborative SAR missions, a multi-USV charging scheduling method is proposed based on MARL to prolong the UAVs’ flight time. Through extensive simulations, the experimental results verified the effectiveness of the proposed scheme and long-endurance search capabilities. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article

Collaborative Problem Solving (CPS) is a pedagogy seldom used in math’s classrooms despite its relevance and ability to develop students’ 21st century learning skills. International reports from PISA have highlighted the necessity of both collaboration and problem solving as crucial 21st century skills and this particular study focusses on teachers learning about using CPS in maths. In this small case study, five Australian primary school teachers explore the introduction of this pedagogy into their maths classrooms whilst supporting each other in a professional learning network (PLN). The findings highlight the importance of the support and collaboration in a teacher team needed to assist with the development of this new pedagogy. They learned about teacher collaboration as their students too learned about student collaboration. Being a part of PLN helped them develop leadership as they were involved in common structured activities together, as they implemented CPS in their classrooms, and in the process, built trust. Other outcomes resulted, including development of collaborative leadership; a common vision; a collective vision developed alongside students, all leading to a deeper understanding of cooperative learning and collaborative problem solving. Full article

Multimodal amed entity recognition (MNER) is a natural language-processing technique that integrates text and visual modalities to detect and segment entity boundaries and their types from unstructured multimodal data. Although existing methods alleviate semantic deficiencies by optimizing image and text feature extraction and fusion, a fundamental challenge remains due to the lack of fine-grained alignment caused by cross-modal semantic deviations and image noise interference. To address these issues, this paper proposes a dual-enhanced hierarchical alignment (DEHA) framework that achieves dual semantic and spatial enhancement via global–local cooperative alignment optimization. The proposed framework incorporates a dual enhancement strategy comprising Semantic-Augmented Global Contrast (SAGC) and Multi-scale Spatial Local Contrast (MS-SLC), which reinforce the alignment of image and text modalities at the global sample level and local feature level, respectively, thereby reducing image noise. Additionally, a cross-modal feature fusion and vision-constrained CRF prediction layer is designed to achieve adaptive aggregation of global and local features. Experimental results on the Twitter-2015 and Twitter-2017 datasets yield F1 scores of 77.42% and 88.79%, outperforming baseline models. These results demonstrate that the global–local complementary mechanism effectively balances alignment precision and noise robustness, thereby enhancing entity recognition accuracy in social media and advancing multimodal semantic understanding. Full article

This study focuses on the issue of visual sustainability of colors in commercial historical districts, taking the historical area of Xueshi Street–Wuzoufang Street in Suzhou, China as a case study. It explores how to balance modern commercial development with the protection of historical culture. Due to the impact of commercialization and the introduction of various immature protection policies, historical districts often face the dilemma of coexisting “color conflict” and “color poverty”. Traditional color protection methods are either overly subjective or excessively quantitative, making it difficult to balance scientific rigor and adaptability. Therefore, this study provides a detailed literature review, compares and selects current quantitative color research methods, and proposes a comprehensive color analysis framework based on ViT (Vision Transformer), the CIEDE2000 color difference model, and K-means clustering (V-C-K framework). Using this framework, we conducted an in-depth analysis of the color-harmony situation in the studied area, aiming to accurately identify color issues in the district and provide optimization strategies. The experimental results show that the commercial colors of the Xueshi Street–Wuzoufang Street historical district exhibit a clear phenomenon of polarization: some areas have colors that are overly bright, leading to visual conflict, while others have colors that are too dull, lacking vitality and energy; furthermore, some areas display a mix of both conditions. Based on this situation, we then compared the extracted negative colors to the prohibited colors in the mainstream Munsell color system’s urban-color management guidelines. We found that colors with “high lightness and high saturation”, which are strictly limited by traditional color criteria, are not necessarily disharmonious, while “low lightness and low saturation” colors that are not restricted may not guarantee harmony either and could exacerbate the area’s “dilapidated feeling”. In other words, traditional color-protection standards often emphasize the safety of “low saturation and low lightness” colors unilaterally, ignoring that they can also cause dullness and discordance in certain environments. Under the $\Delta E$ (color difference value) threshold framework, color recognition is relatively more sensitive, balancing the inclusivity of “vibrant” colors and the caution against “dull” colors. Based on the above experimental results, this study proposes the following recommendations: (1) use the $\Delta E00$ threshold to control the commercial colors in the district, ensuring that the colors align with the historical atmosphere while possessing commercial vitality; (2) in protection practices, comprehensively utilize the ViT, CIEDE2000, and K-means quantitative methods (i.e., the V-C-K framework) to reduce subjective errors; (3) based on the above quantitative framework, while referencing the reasonable parts of existing protection guidelines, combine cooperative collaboration, cultural group color preference surveys, policy incentives, and continuous monitoring and feedback to construct an operable plan for the entire “recognition–analysis–control” process. Full article

This study investigates the factors that affect the adoption of mobile payment systems in Oman, focusing specifically on small and medium-sized enterprises (SMEs) within the expanding FinTech landscape. By utilizing secondary sources of data from the Central Bank of Oman and global FinTech reports, this research identifies essential enablers, such as security features and ease of use, which are propelled by developments in FinTech solutions. It also addresses the obstacles, such as high transaction fees and issues with authentication, that impede SMEs from embracing these technologies. Through an examination of worldwide FinTech adoption patterns, this research offers perspectives on Oman’s progress toward becoming a cashless society. This study employs sophisticated statistical techniques, including histograms and correlation analysis, to reveal significant trends in the rates of mobile payment adoption. The results emphasize the necessity for cooperative efforts among regulators, financial entities, and FinTech developers to minimize costs, strengthen digital infrastructure, and enhance user experiences. These findings are consistent with Oman’s Vision 2040, which aims to foster financial inclusion and propel the country’s shift toward a robust, digitally oriented economy powered by FinTech innovation. Full article

This study proposes a D-optimality-based viewpoint selection method to improve visual assistance for a manipulator by optimizing camera placement. The approach maximizes the information gained from visual observations, reducing uncertainty in object recognition and localization. A mathematical framework utilizing D-optimality criteria is developed to determine the most informative camera viewpoint in real time. The proposed method is integrated into a robotic system where a mobile robot adjusts its viewpoint to support the manipulator in grasping and placing tasks. Experimental evaluations demonstrate that D-optimality-based viewpoint selection improves recognition accuracy and task efficiency. The results suggest that optimal viewpoint planning can enhance perception robustness, leading to better manipulation performance. Although tested in structured environments, the approach has the potential to be extended to dynamic or unstructured settings. This research contributes to the integration of viewpoint optimization in vision-based robotic cooperation, with promising applications in industrial automation, service robotics, and human–robot collaboration. Full article

The circular economy has emerged as a pivotal strategy for cities to reconcile economic growth with environmental sustainability. However, its implementation in resource-dependent Gulf Cooperation Council contexts remains underexplored. This study is among the first to empirically assess circular economy readiness in a Gulf Cooperation Council industrial hub through a mixed-method approach, bridging the gap between expert perceptions and localized policy implementation. Focusing on the Dammam Metropolitan Area, Saudi Arabia, a critical industrial anchor for Saudi Vision 2030, this study combines a cross-sectional survey of 230 policymakers, industry leaders, and academics with descriptive/inferential statistics (SPSS) and qualitative thematic coding (NVivo). The findings identify renewable energy (mean = 4.10) and municipal waste management (mean = 3.78) as top sectoral priorities, aligning with national sustainability goals. Yet systemic challenges, including fragmented governance, limited public awareness (mean = 3.65), and funding gaps (mean = 3.52), underscore disparities between Vision 2030’s ambitions and localized capacities. Statistical analyses reveal strong associations between institutional fragmentation and financial inefficiencies (χ² = 23.45, * p = 0.010), while mid-career workforce dominance (54.8%) and underrepresentation of policymakers (6.5%) highlight governance gaps. The current study advocates hybrid strategies: stricter waste regulations (40.0% stakeholder priority), circular economy training programs, and public–private partnerships to scale waste-to-energy infrastructure and industrial symbiosis. Despite pragmatic optimism (48.7% foresee 21–40% recycling by 2030), limitations such as reliance on expert perspectives and exclusion of citizen voices necessitate future interdisciplinary and longitudinal research. By aligning regulatory rigor with inclusive governance, the Dammam Metropolitan Area can model a Gulf-centric circular economy transition, advancing regional sustainability while contributing actionable insights for resource-dependent economies globally. Full article

Background: Ocular disorders are not frequently addressed in primary care, which is more visible in remote rural settings. The aim of the study was to assess the prevalence and pattern of eye diseases in a remote rural population of Crete and to explore whether they represent a hidden morbidity. Materials and Methods: A community-based, cross-sectional study based on data collected through a comprehensive clinical investigation conducted by a mobile ophthalmological unit. Permanent inhabitants, aged over 40 years, living in one remote rural community located on the highest mountain of Crete, were invited to participate. The prevalence of eye diseases was measured during the comprehensive ophthalmological examination. Patients’ medical records were used to assess hidden morbidity. The National Eye Institute Visual Function Questionnaire-25 (NEI VFQ-25) was applied to measure self-reported vision-targeted health status. Results: A total of 239 individuals agreed to participate; 54.9% were females (n = 151), with a mean ageof 66.13 years (±14.56). The most common diagnoses were refractory errors (59%), cataract (21.7%), glaucoma (11.7%), maculopathy (8.8%), and dry eyes (8.8%). A previously undiagnosed eye disorder was detected in 34.3% (n = 82). Total scores of NEI VFQ-25 measured quality of life were highand significantly lower in Known Cases of eye diseases compared to patients with New or Without diagnosis (76.6 vs. 84.1 and 84.6, respectively, p = 0.009). Conclusions: Our study highlighted the need for increased awareness of primary care in rural areas concerning eye disorders. Local policies should focus on implementing public health interventions and encouraging close cooperation with specialists to overcome accessibility issues. Full article