Search Results (1,112)

Mobile robots are widely used, with research focusing on autonomy and functionality. However, long-term deployment requires their health and safety to be ensured. Terrain-induced vibrations accelerate wear. Hence, self-awareness and optimal path selection, avoiding such terrain anomalies, is essential. This study proposes an RL-based vibration-aware path planning framework, incorporating terrain roughness level classification, a vibration cost map, and an optimized vibration-aware path planning strategy. Terrain roughness is classified into four levels using IMU sensor data, achieving average prediction accuracy of 97% with a 1D CNN model. A vibration cost map is created by assigning vibration costs to each predicted class on a 2D occupancy grid, incorporating obstacles, vibration-prone areas, and the robot’s pose for navigation. An RL model is applied that adapts to changing terrain for path planning. The RL agent uses an MDP-based policy and a deep RL training model with PPO, taking the vibration cost map as input. Finally, the RL-based vibration-aware path planning framework is validated through virtual and real-world experiments using an in-house mobile robot. The proposed approach is compared with the A* path planning algorithm using a performance index that assesses movement and the terrain roughness level. The results show that it effectively avoids rough areas while maintaining the shortest distance. Full article

This study investigates vertical soil movement, a subsidence phenomenon affecting infrastructure and communities in the Emilia-Romagna region (Italy). Building upon previous research—initially based on leveling and GNSS observations and later expanded with interferometric synthetic aperture radar (InSAR)—this study focuses on recent data from 2016 to 2021. A key innovation is the use of dual-geometry ascending and descending acquisitions to derive the vertical and the east–west movement components, a technique not previously applied at a regional scale in this area. The integration of advanced geodetic techniques involved processing 1208 Sentinel-1 satellite images with the SqueeSAR^® algorithm and analyzing data from 28 GNSS permanent stations using the precise point positioning (PPP) methodology. By calibrating the InSAR data with GNSS measurements, we generated a comprehensive subsidence map for the study period, identifying trends and anomalies. The analysis produced 13.5 million measurement points, calibrated and validated using multiple GNSS stations. The final dataset, processed through geostatistical methods, provided a high-resolution (100-m) regional subsidence map covering nearly 11,000 square kilometers. Finally, the vertical soil movement map for 2016–2021 was developed, featuring isokinetic curves with an interval of 2.5 mm/year. The results underscore the value of integrating these geodetic techniques for effective environmental monitoring in subsidence-prone areas. Furthermore, comparisons with previous subsidence maps reveal the evolution of soil movement in Emilia-Romagna, reinforcing the importance of these maps as essential tools for precise subsidence monitoring. Full article

The skeletal muscle of cattle is the main component of their muscular system, responsible for supporting and movement functions. However, there are still many unknown areas regarding the ranking of the importance of different types of cell populations within it. This study conducted in-depth research and made a series of significant findings. First, we trained 15 bovine skeletal muscle models and selected the best-performing model as the initial model. Based on the SHAP (Shapley Additive exPlanations) analysis of this initial model, we obtained the SHAP values of 476 important genes. Using the contributions of these 476 genes, we reconstructed a 476-gene SHAP value matrix, and relying solely on the interactions among these 476 genes, successfully mapped the single-cell atlas of bovine skeletal muscle. After retraining the model and further interpretation, we found that Myofiber cells are the most representative cell type in bovine skeletal muscle, followed by neutrophils. By determining the key genes of each cell type through SHAP values, we conducted analyses on the correlations among key genes and between cells for Myofiber cells, revealing the critical role these genes play in muscle growth and development. Further, by using protein language models, we performed cross-species comparisons between cattle and pigs, deepening our understanding of Myofiber cells as key cells in skeletal muscle, and exploring the common regulatory mechanisms of muscle development across species. Full article

In recent years, due to the expansion of cities, the transformation of agricultural areas, the forestry activities carried out solely for wood production, and the spread of transportation networks, natural habitats have become increasingly fragmented, and suitable habitats for wildlife are rapidly decreasing. As a result, natural areas are being divided, connections are being cut off, species’ living spaces are shrinking, and species and habitats are being isolated. This fragmentation significantly hinders the natural movements of large mammals (Ursus arctos, Sus scrofa, and Canis lupus), leading to reduced genetic diversity and threatening the long-term viability of their populations. This research, conducted in the Ballıdağ and Kurtgirmez regions of the Western Black Sea Region of Türkiye, aimed to determine the movement corridors of the brown bear (Ursus arctos), wild boar (Sus scrofa), and wolf (Canis lupus) in the area and to evaluate the habitat connections for these species. This study relied on data obtained through field studies and the previous literature. Ecological modeling was used with the Maximum Entropy Method (MaxEnt) to understand the relationships between these species and environmental variables, and the barriers posed by the latter. Ecological corridor maps were created to evaluate the effects of habitat fragmentation in the region and species’ potential to maintain critical connection points despite this fragmentation using Circuitscape software based on the Circuit Theory approach. As a result of the analysis, it was determined that the AUC values were between 0.75 and 0.8; the most important variables in the models were road density, vegetation, and elevation; focal points and resistance surface areas were determined for three large mammal species; and important ecological corridors were defined between the Ballıdağ and Kurtgirmez regions. This study revealed that preserving habitat connections and reducing fragmentation is critical for the long-term existence of predator species and ecosystem health. Full article

Path planning is a key technology in the automation of construction robots, and its implementation mainly relies on navigation algorithms and the generation of environment maps. However, the original A* algorithm has issues such as not considering dimensional constraints, a high number of turning nodes, and unsmooth paths. Moreover, traditional environment map generation is time-consuming and energy-consuming. Therefore, this paper proposes a path-planning method for construction robots based on an improved A* algorithm and BIM. First, the A* algorithm is improved by incorporating dimensional constraints for both the building and the robot and by reasonably setting virtual obstacles to enhance the feasibility of robot movement. Additionally, B-spline curves are used to optimize the path, reduce turning points, and achieve a smoother trajectory. Second, Dynamo visual programming is used to process the building’s BIM model, enabling efficient generation of the environment map while preserving the rich semantic and geometric information of the building. Finally, the improved A* algorithm is employed to perform path planning within the environment map generated from the BIM model, resulting in the generation of the optimal path. Through experimental verification on the civil engineering building of a university, the results show that the improved A* algorithm can successfully navigate within the BIM-generated map. It not only ensures the safety and smoothness of path planning but also enables the fast and efficient generation of environment maps. This effectively advances the research and application of BIM technology in the field of robot navigation. Full article

Background: There has been a substantial increase in research on the new 24-hour movement paradigm, emphasizing the importance of considering the “whole day” and investigating integrated movement behaviors (physical activity, sedentary behavior, and sleep). This scoping review aims to map the evidence from reviews that have summarized information on integrated 24-hour movement behaviors in children and adolescents. Methods: Eight databases were searched in May 2023, with an update in October 2024. The review followed the PRISMA-ScR framework and the guidelines of the Joanna Briggs Institute Reviewer’s Manual. Results: National 24-hour movement guidelines for children and adolescents exist in only a few countries (Australia, Canada, New Zealand, and South Africa). There is a lack of valid and reliable measurement tools for assessing 24-hour movement. Globally, children and adolescents, with and without disabilities, show low adherence to these guidelines. Reallocating time to moderate-to-vigorous physical activity was beneficial, while other reallocations had mixed results to health. COVID-19 reduced physical activity and increased screen time and sleep. Healthy movement behaviors are positively associated with better health outcomes in children and adolescents. There is a possible relationship between adherence to 24-hour movement behaviors and cognitive function, pollution measures, and eHealth interventions. Inconsistencies were identified in the terms used. Conclusions: High-quality research is needed to develop measurement tools that assess the long-term health impact of 24-hour movement and to create solutions for improving adherence, mainly in countries lacking specific guidelines. Full article

The generation of 3D digital humans has traditionally relied on multi-view imaging systems and large-scale datasets, posing challenges in cost, accessibility, and real-time applicability. To overcome these limitations, this study presents an efficient pipeline that constructs high-fidelity 3D digital humans from a single frontal image. By leveraging generative AI, the system synthesizes additional views and generates UV maps compatible with the SMPL-X model, ensuring anatomically accurate and photorealistic reconstructions. The generated 3D models are imported into Unity 3D, where they are rigged for real-time motion synchronization using BlazePose-based lightweight pose estimation. To further enhance motion realism, custom algorithms—including ground detection and rotation smoothing—are applied, improving movement stability and fluidity. The system was rigorously evaluated through both quantitative and qualitative analyses. Results show an average generation time of 211.1 s, segmentation accuracy of 92.1%, and real-time rendering at 64.4 FPS. In qualitative assessments, expert reviewers rated the system using the SUS usability framework and heuristic evaluation, confirming its usability and effectiveness. This method eliminates the need for multi-view cameras or depth sensors, significantly reducing the barrier to entry for real-time 3D avatar creation and interactive AI-driven applications. It has broad applications in virtual reality (VR), gaming, digital content creation, AI-driven simulation, digital twins, and telepresence systems. By introducing a scalable and accessible 3D modeling pipeline, this research lays the groundwork for future advancements in immersive and interactive environments. Full article

The Jiroft Dam, situated in Kerman province, Iran, serves as a crucial infrastructure for water management, flood control, and agricultural development in the region. However, the surrounding mountainous terrain presents considerable geotechnical challenges that threaten the stability of access roads and other essential infrastructure. This study is based on comprehensive field surveys and mapping, which have revealed significant ground displacements and evidence of slope instabilities in the area. The investigation identifies key factors, including soil composition, rock formations, groundwater flow, and seismic activity, that contribute to these shifts in the terrain. To ensure the accuracy of the elevation data, the study employed Monte Carlo simulation techniques to analyze the statistical distribution of the collected survey data. By simulating various possible outcomes, this study enhanced the precision of the elevation models, allowing for better identification of critical instability zones. Additionally, the Overall Equipment Effectiveness (OEE) was utilized to evaluate the effectiveness of the current monitoring equipment and infrastructure, providing a clearer understanding of operational efficiency and areas for improvement. The findings of this study highlight the immediate need for effective risk management strategies to mitigate the potential hazards of landslides and infrastructure failure. Addressing these challenges is essential to ensure the long-term sustainability of the region’s infrastructure. In response to these observations, this research proposes practical engineering solutions such as slope stabilization techniques and improved drainage systems to address the identified instabilities. Furthermore, this study underscores the necessity of the continuous monitoring and the implementation of early warning systems to detect further ground movements and mitigate associated risks.In addition to technical interventions, this research emphasizes the importance of integrating local knowledge and expertise into the risk management process. Full article

The advent of geomatic techniques and novel sensors has opened the road to new approaches in mapping, including morphological ones. The evolution of a land portion and its graphical representation constitutes a fundamental aspect for scientific and land planning purposes. In this context, new paradigms for geomorphological mapping, which are useful for modernizing traditional, geomorphological mapping, become necessary for the creation of scalable digital representation of processes and landforms. A fully remote mapping approach, based on multi-source and multi-sensor applications, was implemented for the recognition of landforms and processes. This methodology was applied to a study site located in central Italy, characterized by the presence of ‘calanchi’ (i.e., badlands). Considering primarily the increasing availability of regional LiDAR products, an automated landform classification, i.e., Geomorphons, was adopted to map landforms at the slope scale. Simultaneously, by collecting and digitizing a time-series of historical orthoimages, a multi-temporal analysis was performed. Finally, surveying the area with an unmanned aerial vehicle, exploiting the high-resolution digital terrain model and orthoimage, a local-scale geomorphological map was produced. The proposed approach has proven to be well capable of identifying the variety of processes acting on the pilot area, identifying various genetic types of geomorphic processes with a nested hierarchy, where runoff-associated landforms coexist with gravitational ones. Large ancient mass movement characterizes the upper part of the basin, forming deep-seated gravity deformation, highly remodeled by a set of widespread runoff features forming rills, gullies, and secondary shallow landslides. The extended badlands areas imposed on Plio-Pleistocene clays are typically affected by sheet wash and rill and gully erosion causing high potential of sediment loss and the occurrence of earth- and mudflows, often interfering and affecting agricultural areas and anthropic elements. This approach guarantees a multi-scale and multi-temporal cartographic model for a full-coverage representation of landforms, representing a useful tool for land planning purposes. Full article

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The use of IME in a human–machine interface (HMI) provides a favorable experience to the users and helps reduce production costs due to a smaller list of parts and lower material costs. A few functional components that are compatible with one another are crucial to the final product’s properties in the IME structure. Of these components, the dielectric layers are an important component in the smart surface industry, providing insulation for the prevention of leakage currents in multilayered printed structures and capacitance sensing on the surface of specially designed shapes in IME. Advanced dielectric materials are non-conductive materials that impend and polarize electron movements within the material, store electrical energy, and reduce the flow of electric current with exceptional thermal stability. The selection of a suitable dielectric ink is an integral stage in the planning of the IME smart touch surface. The ink medium, solvent, and surface tension determine the printability, adhesion, print quality, and the respective reaction with the bottom and top conductive traces. The sequence in which the components are deposited and the heating processes in subsequent thermoforming and injection molding are other critical factors. In this study, various commercially available dielectric layers were each printed in two to four consecutive layers with a mesh thickness of 50–60 µm or 110–120 µm, acting as an insulator between conductive silver traces overlaid onto a polycarbonate substrate. Elemental mapping and optical analysis on the cross-section were conducted to determine the compatibility and the adhesion of the dielectric layers on the conductive traces and polycarbonate substrate. The final selection was based on the functionality, reliability, repeatability, time-stability, thickness, total processing time, appearance, and cross-sectional analysis results. The chosen candidate was then placed through the final product design, circuitry design, and plastic thermoforming process. In summary, this study will provide a general guideline to optimize the selection of dielectric inks for in-mold electronics applications. Full article

Biomechanical asymmetries between children’s left and right feet can affect stability and coordination, especially during dynamic movements. This study aimed to examine plantar pressure distribution, foot balance, and center of pressure (COP) trajectories in children during walking, running, and turning activities to understand how different movements influence these asymmetries. Fifteen children participated in the study, using a FootScan plantar pressure plate to capture detailed pressure and balance data. The parameters, including time-varying forces, COP, and Foot Balance Index (FBI), were analyzed through a one-dimensional Statistical Parametric Mapping (SPM1d) package. Results showed that asymmetries in COP and FBI became more pronounced, particularly during the tasks of running and directional turns. Regional plantar pressure analysis also revealed a more significant load on specific foot areas during these dynamic movements, indicating an increased reliance on one foot for stability and control. These findings suggest that early identification of asymmetrical loading patterns may be vital in promoting a balanced gait and preventing potential foot health issues in children. This study contributes to understanding pediatric foot biomechanics and provides insights for developing targeted interventions to support healthy physical development in children. Full article

The paper introduces a novel method for creating a photographic map of the seabed using images captured by the on-board photo and video systems of autonomous underwater vehicles (AUVs) during various missions, while incorporating navigation parameters. Additionally, it presents a new approach for storing this photo map on the on-board device in a mosaic format (tiles), which significantly accelerates operational visual inspection by enabling the automatic search and recognition of underwater objects that may exceed the coverage area of a single photograph. This capability is achieved by organizing the photo map into layers with varying zoom levels. Semi-natural experiments were conducted with data from actual missions using the real underwater vehicle demonstrate the high efficiency of the proposed method and algorithm. Unlike existing methods that form photo maps after the underwater vehicle has taken pictures of the bottom using special high-performance computers, the developed method forms a photo map directly during the movement of the vehicle, using only the computing power of the on-board computer. In addition, in the event of accidents, when it is necessary to detect objects of interest on the seabed as quickly as possible, it is necessary to provide a quick visual inspection of the generated photo map. For this purpose, we have developed an algorithm for saving a photo map in the form of a mosaic, which is widely used in interactive geographic maps, such as Google Maps. This algorithm differs from existing methods in that it selectively saves data to the on-board storage device to reduce the number of read and write operations, thus ensuring the timely operation of the entire process of creating a photo map at a given frequency of photography. After the generated map has been stored as a mosaic and a high-speed connection with the vehicle has appeared, the operator can immediately view the entire generated map using a regular web browser. Full article

Multiple sclerosis (MS) is an autoimmune disease classified as neurodegenerative because it can be associated with the more or less progressive development of neurological symptoms and cognitive deficits. In recent years, various studies have started to investigate eye movements in relation to cognitive impairment in persons with MS by means of eye-tracking equipment. However, the high heterogeneity of the paradigms used in different studies, as well as the different methodologies included, makes it difficult to provide a complete and precise picture of this important research and clinical issue. The purpose of the present in-depth scoping review was to map the existing literature in this field to determine which metrics may be relevant when dealing with the neurocognitive profile of people with MS. From the analyses of the included studies, the anti-saccade latency and errors were the most frequently proposed metrics. Correlation analyses between these metrics and cognitive measures showed significant associations between them, calling for a deeper investigation of this promising research and clinical field. The results of the present scoping review strongly suggest that eye tracking may play a crucial role in clinical practice during the early detection of neurocognitive disorders. There is a great need for primary research that addresses the full complexity of MS in its different phenotypes and the disease-related variables from a multidisciplinary perspective. Future research should clarify whether oculomotor dysfunction in MS follows or precedes cognitive deficits. Full article

The application of robotic arms in the industrial field is continuously becoming greater and greater. The impact force generated by a robotic arm in a gripping operation leads to vibration and wear. To address this problem, this paper proposes a trajectory planning method based on the improved Slime Mould Algorithm. An interpolation curve under the joint coordinate system is constructed by using seven non-uniform B-spline functions, with time and impact force as the optimization objectives and angular velocity, angular acceleration, and angular acceleration as the constraints. The original algorithm introduces Bernoulli chaotic mapping to increase the diversity of the population, adaptively adjusts the feedback factor, improves the crossover operator to accelerate the global convergence, and combines the original algorithm with an improved artificial bee colony search strategy guided by the global optimal solution, adding a quadratic interpolation method to increase the diversity of the population and to accelerate the global convergence speed. Combined with the improved artificial swarm search strategy guided by the global optimal solution, the quadratic interpolation method is added to enhance the local utilization ability. The simulation and real-machine experimental results show that the improved algorithm shortens the movement time of the robotic arm, reduces the joint impacts, minimizes the vibration and wear, and prolongs the service life of the robotic arm. Full article

Customer context analysis (CCA) in brick-and-mortar shopping malls can support decision makers’ marketing decisions by providing them with information about customer interest and purchases from merchants. It makes offline CCA an important topic in marketing. In order to analyze customer context, it is necessary to analyze customer behavior, as well as to obtain the customer’s location, and we propose an analysis system for customer context based on these two aspects. For customer behavior, we use a modeling approach based on the time-frequency domain, while separately identifying movement-related behaviors (MB) and semantic-related behaviors (SB), where MB are used to assist in localization and the positioning result are used to assist semantic-related behavior recognition, further realizing CCA generation. For customer locations, we use a deep-learning-based pedestrian dead reckoning (DPDR) method combined with a node map to achieve store-level pedestrian autonomous positioning, where the DPDR is assisted by simple behaviors. Full article