Search Results (14,931)

Vehicular communication networks demand highly efficient and accurate channel estimation to ensure reliable data exchange in high mobility scenarios. The IEEE 802.11p standard is widely regarded as the foundation of the Vehicle-to-Vehicle (V2V) communication channel; however, it is constrained by limited pilot resources and a fixed pilot structure, which degrade the performance and effectiveness of traditional estimation techniques, particularly in dynamic environments. Recent advances in deep learning offer significant potential for addressing these issues by improving estimation accuracy and modelling complex channel dynamics. Though deep learning-based methods introduce trade-offs in computational complexity and accuracy, these are crucial constraints in latency-sensitive V2V scenarios. This article presents a comprehensive review of deep learning-based channel estimation techniques, analysing methods for the IEEE 802.11p standard and critically examining their limitations in both classical and deep learning-based approaches. Additionally, the article highlights improvements introduced by IEEE 802.11bd, which features an enhanced pilot structure and advanced modulation schemes, providing a more robust framework for adaptive, efficient channel estimation. By identifying future research pathways that balance delay, complexity, and accuracy, an intelligent and effective transportation system can be established. Full article

Autonomous mobile robots require robust localization in complex and dynamic environments, where single-sensor solutions often fail due to accumulated drift or signal degradation. LiDAR–inertial odometry provides accurate short-term motion estimation, but suffers from long-term error accumulation, whereas RTK-GNSS offers absolute positioning that becomes unreliable under occlusion or multipath effects. To solve the above problems, this paper proposes an adaptive multi-sensor fusion positioning framework that dynamically fuses LiDAR, IMU, and RTK-GNSS data based on the real-time quality evaluation of sensors. The system uses the front-end tightly coupled LiDAR–IMU iterative extension Kalman filter (IEKF) as the core estimator and combines loop detection with incremental factor graph optimization to suppress long-term drift. In addition, a degradation detection method based on the minimum eigenvalue of the Jacobian matrix is proposed to identify unreliable matching constraints in real time. In order to avoid abrupt changes in positioning results caused by fluctuations in sensor data quality, the system adopts a smooth fusion strategy based on covariance weighting. Experiments on the KITTI benchmark and self-collected datasets demonstrate that the proposed method significantly improves localization accuracy and robustness compared with pure LiDAR-based approaches, achieving stable centimeter-level performance while maintaining real-time capability on embedded platforms. Full article

Antimicrobial resistance (AMR) poses a severe threat to global health by limiting treatment options and increasing clinical and economic burdens. This review synthesizes evidence showing that resistance evolution is strongly shaped by antibiotic pressure, leading to the accumulation of adaptive mutations, activation of efflux systems, and widespread dissemination of resistance determinants across clinical, animal, and environmental settings. We highlight recent genomic, metagenomic, and structural findings that elucidate the molecular basis of AMR, with particular emphasis on horizontal gene transfer mediated by mobile genetic elements such as plasmids, integrons, and transposons. Analyses across One Health interfaces reveal extensive sharing of antimicrobial resistance genes among humans, livestock, and environmental reservoirs, identifying Enterobacteriaceae and ESKAPE pathogens as key hubs of resistance dissemination. Special focus is placed on Acinetobacter baumannii, where phylogenetic and three-dimensional structural analyses of class D β-lactamases OXA-23 and OXA-24/40 demonstrate a conserved catalytic framework coupled with substantial sequence and conformational variability. These structural differences likely influence carbapenem specificity and resistance levels. Collectively, the findings underscore how genetic diversity, mobile elements, and structural adaptation converge to drive AMR, reinforcing the need for integrated genomic and structural approaches to guide surveillance and antimicrobial development. Full article

Manual wheelchair users with spinal cord injury (SCI) rely heavily on upper-limb function for independent mobility, which often leads to cumulative musculoskeletal loading due to repetitive propulsion. To address limitations associated with conventional unidirectional pushrim propulsion, this study presents the design and development of a detachable bidirectional wheelchair propulsion system that enables mode-dependent push and pull inputs through a mechanically reconfigurable lever mechanism. The proposed system allows conventional forward propulsion through forward pushing, while enabling alternative propulsion patterns through lever mode switching. Depending on the selected mode, either pushing or pulling inputs can be mechanically coupled to forward or backward wheel rotation, without requiring powered actuation or permanent modification of the wheelchair structure. This design expands the range of feasible propulsion strategies by allowing a selectable relationship between propulsion input direction and wheelchair movement direction through mechanical mode switching via a purely mechanical transmission architecture. The system is designed as a modular add-on compatible with standard manual wheelchairs, incorporating a clamp-based detachable interface and a gear-driven bidirectional transmission mechanism. Design considerations emphasize mechanical simplicity, controllability, and compatibility with existing wheelchair configurations, while preserving baseline pushrim functionality. This design-focused study reports the engineering rationale, mechanical architecture, and feasibility of a detachable bidirectional propulsion concept for manual wheelchairs. By explicitly documenting the system configuration and mode-switching logic, this work aims to provide a transparent design framework that can support future experimental validation and user-centered evaluation of bidirectional propulsion strategies for manual wheelchair users with SCI. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

Dynamic wireless power transfer (DWPT) has attracted significant interest due to its ability to transfer power to moving electric vehicles. Most existing DWPT research focuses on vehicles traveling along fixed paths. However, modern warehouses increasingly employ indoor electric vehicles (IEVs), such as autonomous mobile robots, that move along non-fixed paths. Although several solutions have been proposed for large-area DWPT systems applicable to IEVs with non-fixed trajectories, these approaches are predominantly based on inductive DWPT. Such systems require a large number of densely arranged transmitting coils and expensive ferrite pads, resulting in high system cost. To the authors’ best knowledge, no published work has addressed large-area capacitive DWPT systems for IEVs moving along non-fixed paths. This paper aims to fill this research gap. The main novelty of this work is the first proposal of a capacitive DWPT system for lightweight IEVs operating along non-fixed paths. The feasibility of the proposed solution is validated through simulation studies conducted in PSIM. The simulation results demonstrate that the proposed DWPT system, employing an advanced transmitting-metal-plate activation strategy, can maintain an almost constant mutual capacitance, thereby ensuring a smooth output voltage at the receiving side for a moving IEV. Full article

With global urbanisation anticipated to reach 68% by 2050, there is a significant risk of exacerbating urban transport emissions. Urban transport decarbonisation is a complex adaptive system challenge, the understanding and optimisation of which could be supported by digital twins (DTs). Although prior research has explored digital and big data technology applications, creating actionable insights requires human-centred designs. We conducted a structured workshop to gather practitioner views on how urban-scale DTs can support transport decarbonisation. Specifically, we explored the outcomes they aim to achieve, the interventions they are interested in, and the value digital twinning offers compared to current methods. The data was synthesised and analysed to identify (1) impacts, (2) interventions, (3) location types, (4) data sources and (5) feedback mechanisms of importance to participants. These five aspects are proposed as a framework to support the definition of digital twinning use cases targeting urban transport decarbonisation. Application of the framework encourages creators to explicitly consider the services to be provided to users, how the derived insights influence the real world and the data connections between the physical and digital, noting that these are often overlooked in reported research. A framework application is illustrated through an example use case described for the West Midlands, UK. Full article

The enormously growing demand for seafood has resulted in the over-exploitation of marine resources, pushing certain species to the brink of extinction. Overfishing is one of the main issues in sustainable marine development. To support marine resource protection and sustainable fishing, this study proposes advanced fish classification techniques using state-of-the-art machine learning (ML). Specifically, the proposed method enables the precise identification of protected fish species, among other features. In this paper, we present a system-level optimization of the MobileNet architecture, termed M-MobileNet, designed to operate efficiently on resource-limited hardware environments. Our classifier is constructed by a refined modification of the well-known MobileNet neural network, resulting in a reduction of parameters. Furthermore, we have collected, organized, and compiled an original and comprehensive labeled dataset of 37,462 images of fish native to the Indonesian archipelago. The proposed model is trained on this dataset to classify images of captured fish and accurately identify their respective species. Furthermore, the system provides recommendations regarding the consumability of the catch. Compared to the MobileNet deep neural network structure, our model utilizes only 50% of the top-layer parameters, with approximately 42% GTX 860M utility. This configuration results in achieving up to 97% accuracy of classification. Considering the constrained computing capacity prevalent on many fishing vessels, our proposed model offers a practical solution for on-site fish classification. Moreover, synchronized implementation of the proposed model across multiple vessels can provide valuable insights into the movement and location of various fish species. Full article

In the San Diego–Tijuana border region, characterized by human mobility dynamics, institutional vulnerability, and inequalities, experiences of financial exclusion among Latin American migrants are deeply intertwined. This study draws on in-depth interviews with 14 migrants from Central America, South America, and the Caribbean, including both men and women. It analyzes factors such as undocumented status, institutional mistrust, cultural barriers, and regulatory requirements that shape access to financial services. In contexts of exclusion, migrants resort to social support networks, informal strategies, and emerging digital options such as fintech. The study adopts a qualitative design, using an analysis based on emerging categories to construct analytical dimensions grounded in participants’ trajectories and voices. The findings show that financial inclusion does not depend solely on access to services, but also on recognizing the lived experiences, emotions, and everyday obstacles faced by migrants. Far from being passive recipients, migrants play an active role in building responses to exclusion. The study concludes that making these practices visible and designing policies grounded in migrants’ realities are essential steps toward a more just and accessible financial system. Full article

As a critical step in industrial quality control, surface defect detection in aluminum materials remains challenging for minor defects despite advances in deep learning. To address this, this paper proposes an enhanced YOLOv8-based model, BFI-YOLO, that incorporates a Bidirectional Multi-scale Residual Network. Specifically, we design a Bidirectional Multi-scale Feature Pyramid Network (BM-FPN) based on BiFPN to strengthen cross-scale feature fusion. The parameter-free SimAM attention module is embedded to enhance subtle defect responses while suppressing background texture interference, without introducing additional computational overhead.Furthermore, we develop a Multi-scale Residual Convolution (MSRConv) module to capture defects of varying sizes on aluminum surfaces comprehensively. MSRConv utilizes multi-scale convolutional kernels to adapt to cross-scale defect features and retains shallow details via residual connections, thereby strengthening the model’s representation of fine defects. Extensive experiments on the public TAPSDD dataset show that BFI-YOLO achieves a precision of 91.3%, a recall of 89.8%, and mAP@0.5 of 92.1%, with only 1.8 M parameters. Compared to the baseline, BFI-YOLO reduces parameters by 40% while increasing mAP@0.5 by 4.2%, effectively balancing detection accuracy and lightweight performance. Optimized for resource-constrained industrial platforms such as embedded systems and mobile robots, BFI-YOLO meets real-time monitoring requirements while achieving competitive detection accuracy, providing an efficient and practical solution for metal surface defect detection. Full article

With the rapid development of intelligent manufacturing and industrial automation, defect recognition and detection of hot-rolled strip steel have become crucial to ensuring both production efficiency and product quality. However, existing hot-rolled strip steel detection systems often rely on expensive, energy-intensive, stationary equipment, making them unsuitable for mobile applications, such as outdoor use. To address this challenge, this paper proposes and designs a lightweight dual-surface defect recognition model for hot-rolled steel strips that can be implemented on mobile low-power devices (e.g., Raspberry Pi). First, to train the lightweight model, the NEU-CLS dataset is augmented through image generation via StyleGAN3, denoising with a water-wave-like noise removal algorithm, and super-resolution with Real-ESRGAN. Then, MMAM-EfficientNet-B0 is pruned during training, and the Network Slimming algorithm is applied to optimize it on the expanded NEU-CLS dataset, removing 70% of the network structure. Finally, the pruned recognition model is deployed on a Raspberry Pi, achieving an accuracy of 96.333%, with a classification time of 1.527 s per image, a reduction of 155.010% compared to the original model. Our experiments confirm the real-time effectiveness and practical application value of the model. Full article

In this paper, a trajectory planning method based on an improved beluga whale optimization algorithm is proposed for the trajectory planning of plasma-spraying robot with complex surfaces. Firstly, the system architecture, kinematics model and trajectory planning constraints of the 6-DOF mobile plasma robot are analyzed, including kinematics, dynamics and environmental constraints, and a constrained-objective optimization function with time optimization, energy consumption and smoothness as objectives is established. Secondly, aiming at the shortage of the balance between global search and local development of the original beluga optimization algorithm, the tent chaotic mapping strategy is introduced to enhance the population diversity, and the sine and cosine algorithm is integrated to optimize the search process, so as to improve the convergence accuracy and stability. The experimental part is verified by the standard test function and the special index of trajectory planning. The results show that the IBWO algorithm is significantly better than the original beluga optimization, particle swarm optimization and other comparative algorithms in convergence accuracy, stability and comprehensive performance. In addition, the trajectory planning example shows that the joint trajectory generated by improved beluga whale optimization is smooth and has high constraint satisfaction, which is suitable for complex surface spraying tasks. Full article

Public transport is a critical instrument for mitigating traffic congestion, reducing environmental pollution, and promoting social inclusion in urban areas. This study presents the results of a quantitative survey conducted among 406 residents of Łódź, Poland, aimed at identifying the determinants of public transport use and the factors influencing modal choices. The findings indicate that 89% of respondents had used public transport within the past three years, with over half reporting the use of both buses and trams. However, public transport is predominantly chosen out of necessity rather than preference, driven by limited access to private vehicles, absence of a driver’s license, or the high costs of car ownership. Environmental considerations and service quality factors play a comparatively minor role. User satisfaction with public transport services in Łódź is moderate, and current users express limited intention to increase their usage or actively recommend the system, suggesting constrained potential for demand growth. In contrast, non-users declare a willingness to shift to public transport if travel costs are reduced and service quality is improved. Measures aimed at restricting private car use demonstrate limited motivational impact, whereas enhancing the reliability, accessibility, and affordability of public transport emerges as the most effective strategy. Methodologically, the study contributes by combining bibliometric mapping with quantitative survey analysis, providing a replicable framework for assessing urban mobility determinants in other cities with similar socio-economic and transport contexts. Full article

Rapid advancements in foundation models, including Large Language Models, Vision-Language Models, Multimodal Large Language Models, and Vision-Language-Action models, have opened new avenues for embodied AI in mobile service robotics. By combining foundation models with the principles of embodied AI, where intelligent systems perceive, reason, and act through physical interaction, mobile service robots can achieve more flexible understanding, adaptive behavior, and robust task execution in dynamic real-world environments. Despite this progress, embodied AI for mobile service robots continues to face fundamental challenges related to the translation of natural language instructions into executable robot actions, multimodal perception in human-centered environments, uncertainty estimation for safe decision-making, and computational constraints for real-time onboard deployment. In this paper, we present the first systematic review of foundation models in mobile service robotics, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA) guidelines. Using an OpenAlex literature search, we considered 7506 papers for the years spanning 1968–2025. Our detailed analysis identified four main challenges and how recent advances in foundation models, related to the translation of natural language instructions into executable robot actions, multimodal perception in human-centered environments, uncertainty estimation for safe decision-making, and computational constraints for real-time onboard deployment, have addressed these challenges. We further examine real-world applications in domestic assistance, healthcare, and service automation, highlighting how foundation models enable context-aware, socially responsive, and generalizable robot behaviors. Beyond technical considerations, we discuss ethical, societal, human-interaction, and physical design and ergonomic implications associated with deploying foundation-model-enabled service robots in human environments. Finally, we outline future research directions emphasizing reliability and lifelong adaptation, privacy-aware and resource-constrained deployment, as well as the governance and human-in-the-loop frameworks required for safe, scalable, and trustworthy mobile service robotics. Full article

The most recent National Research Council Nutrient Requirements for Sheep and Goats was published in 2007, one of the most consequential nutrient requirement recommendations for sheep and goats in the world. The enhancement of production efficiency, minimization of carbon footprint, and maximization of resource economy, among others, motivate the continuing discussion of nutrient utilization and refinement of nutrient requirements in sheep and goats that are increasingly important in various parts of the world. Progress has been made in the estimation of energy and protein requirements in sheep and goats, mainly utilizing empirical feeding experimentation, comparative slaughter techniques and minimum endogenous loss methods. In sheep, newer estimates of energy and protein requirements for maintenance and growth and partial efficiencies has been reported since 2007. There were suggestions that energy and protein requirements could have been affected by breed, wool growth, gender and size, with these reported values being similar or lower than the recommended values in international feeding systems such as NRC, ARC, INRA and AFRC. In goats, energy and protein requirements for growing goats were reported to be either higher or lower than the established recommendations, depending upon meat or dairy breeds. Effect of gender on energy requirement appeared to be related to the stage of growth or degree of maturity. Newer data also suggested that existing recommendations on nutrient requirements may not be adequate for non-pregnant and non-lactating pubertal females. In multiparous pregnant goats, energy and protein requirements for maintenance did not appear to be affected by days of pregnancy, but efficiencies of metabolizable energy and metabolizable protein utilization for pregnancy were. There were suggestions that metabolizable protein can be predicted from energy intake using equations that encompass both sheep and goats, but more data on goats were called for to account for specific differences in nutrition. In addition to sulfur, there has been progress made on the estimation of maintenance and growth requirements of calcium, phosphorus, potassium and magnesium in goats, with suggestions on the consideration of gender and breed differences. While conventional factors such as breeds and species, genotype, stage of maturity, gender, body composition, mobilization of tissue energy for production, and additional activity energy required due to resource limitation and acclimatization remain as important considerations for the estimation of nutrient requirements in sheep and goats, emerging factors such as climate change, heat stress, parasitism and secondary plant compounds that can affect nutrient utilization should also be considered in the estimation of nutrient requirements. Model equations and partial efficiencies used by NRC to predict energy and protein requirements for maintenance, growth, lactation, and fiber have been highlighted and discussed for the purpose of a more focus discussion and refinement for the future. Potential limitations of both traditional and emerging methodologies in determining the nutrient requirements in sheep and goats were discussed. The advancement in nutrigenomics can potentially move nutrient requirements beyond its population-based guidelines. To justify the research investment, emerging methodologies such as nutrigenomics will have to be linked more directly to the improvement of production efficiency via more precise prediction of nutrient requirement. With the assistance of artificial intelligence and more data obtained from sensor technology, precision nutrition has the potential to deliver nutrients precisely to individual animals and meet nutrient requirements in sheep and goats. Full article