Search Results (1,445)

This paper presents a 3D vision-based autonomous navigation system for wheeled mobile robots equipped with an RGB-D camera. The system integrates SLAM (simultaneous localization and mapping), motion planning, and obstacle avoidance to operate in both static and dynamic environments. A real-time pipeline is developed to construct sparse and dense maps for precise localization and path planning. Navigation meshes (NavMeshes) derived from 3D reconstructions facilitate efficient A* path planning. Additionally, a dynamic “U-map” generated from depth data identifies obstacles, enabling rapid NavMesh updates for obstacle avoidance. The proposed system achieves real-time performance and robust navigation across diverse terrains, including uneven surfaces and ramps, offering a comprehensive solution for 3D vision-guided robotic navigation. Full article

Kazakhstan’s pasture, as a spatially extended agricultural resource for sustainable animal husbandry, requires effective monitoring with connected rational uses. Ranking number nine globally in terms of land size, Kazakhstan, with an area of about three million square km, requires proper assessment technologies for climate change and anthropogenic impact to track the pasture lands’ degradation. Remote sensing (RS)-based adaptive approaches for assessing pasture load, combined with field cross-checking of pastures, have been applied to evaluate the quality of vegetation cover, economic potential, service function, regenerative capacity, pasture productivity, and changes in plant species composition for five pilot regions in Kazakhstan. The current stages of these efforts are presented in this project report. The pasture lands in five regions, including Pavlodar (8,340,064 ha), North Kazakhstan (2,871,248 ha), Akmola (5,783,503 ha), Kostanay (11,762,318 ha), Karaganda (19,709,128 ha), and Ulytau (18,260,865 ha), were evaluated. Combined RS data were processed and the Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Fraction of Vegetation Cover (FCover), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Canopy Chlorophyll Content (CCC), and Canopy Water Content (CWC) indices were determined, in relation to the herbage of pastures and their growth and development, for field biophysical analysis. The highest values of LAI, FCOVER, and FARAR were recorded in the Akmola region, with index values of 18.5, 126.42, and 53.9, and the North Kazakhstan region, with index values of 17.89, 143.45, and 57.91, respectively. The massive 2024 spring floods, which occurred in the Akmola, North Kazakhstan, Kostanay, and Karaganda regions, caused many problems, particularly to civil constructions and buildings; however, these same floods had a very positive impact on pasture areas as they increased soil moisture. Further detailed investigations are ongoing to update the flood zones, wetlands, and swamp areas. The mapping of proper flood zones is required in Kazakhstan for pasture activities, rather than civil building construction. The related sustainable permissible grazing husbandry pasture loads are required to develop also. Recommendations for these preparation efforts are in the works. 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

As potato is an important crop, potato disease detection and classification are of key significance in guaranteeing food security and enhancing agricultural production efficiency. Aiming at the problems of tiny spots, blurred disease edges, and susceptibility to noise interference during image acquisition and transmission in potato leaf diseases, we propose a CBSNet-based potato disease recognition method. Firstly, a convolution module called Channel Reconstruction Multi-Scale Convolution (CRMC) is designed to extract the upper and lower features by separating the channel features and applying a more optimized convolution to the upper and lower features, followed by a multi-scale convolution operation to capture the key changes more effectively. Secondly, a new attention mechanism, Spatial Triple Attention (STA), is developed, which first reconstructs the spatial dimensions of the input feature maps, then inputs the reconstructed three types of features into each of the three branches and carries out targeted processing according to the importance of the features, thereby improving the model performance. In addition, the Bat–Lion Algorithm (BLA) is introduced, which combines the Lion algorithm and the bat optimization algorithm and makes the optimization process more adaptive by using the bat algorithm to adjust the gradient direction during the updating process of the Lion algorithm. The BLA not only boosts the model’s ability to recognize potato disease features but also ensures training stability and enhances the model’s robustness in handling noisy images. Experimental results showed that CBSNet achieved an average Accuracy of 92.04% and a Precision of 91.58% on the self-built dataset. It effectively extracts subtle spots and blurry edges of potato leaf diseases, providing strong technical support for disease prevention and control in large-scale potato farming. Full article

Systems may depend on parameters that can be controlled, serve to optimise the system, are imposed externally, or are uncertain. This last case is taken as the “Leitmotiv” for the following discussion.A reduced-order model is produced from the full-order model through some kind of projection onto a relatively low-dimensional manifold or subspace. The parameter-dependent reduction process produces a function mapping the parameters to the manifold.One now wants to examine the relation between the full and the reduced state for all possible parameter values of interest. Similarly, in the field of machine learning, a function mapping the parameter set to the image space of the machine learning model is learned from a training set of samples, typically minimising the mean square error. This set may be seen as a sample from some probability distribution, and thus the training is an approximate computation of the expectation, giving an approximation of the conditional expectation—a special case of Bayesian updating, where the Bayesian loss function is the mean square error. This offers the possibility of having a combined view of these methods and also of introducing more general loss functions. Full article

National Forest Inventories (NFIs) provide valuable land cover (LC) information but often lack spatial continuity and an adequate update frequency. Satellite-based remote sensing offers a viable alternative, employing machine learning to extract thematic data. State-of-the-art methods such as convolutional neural networks rely on fully pixel-level annotated images, which are difficult to obtain. Although reference LC datasets have been widely used to derive annotations, NFIs consist of point-based data, providing only sparse annotations. Weakly supervised and self-supervised learning approaches help address this issue by reducing dependence on fully annotated images and leveraging unlabeled data. However, their potential for large-scale LC mapping needs further investigation. This study explored the use of NFI data with deep learning and weakly supervised and self-supervised methods. Using Sentinel-2 images and the Portuguese NFI, which covers other LC types beyond forest, as sparse labels, we performed weakly supervised semantic segmentation with a convolutional neural network to create an updated and spatially continuous national LC map. Additionally, we investigated the potential of self-supervised learning by pretraining a masked autoencoder on 65,000 Sentinel-2 image chips and then fine-tuning the model with NFI-derived sparse labels. The weakly supervised baseline achieved a validation accuracy of 69.60%, surpassing Random Forest (67.90%). The self-supervised model achieved 71.29%, performing on par with the baseline using half the training data. The results demonstrated that integrating both learning approaches enabled successful countrywide LC mapping with limited training data. Full article

In the current operational landscape, organizations face a growing and diverse array of cybersecurity challenges, necessitating the development and implementation of innovative and effective security solutions. This paper presents a novel methodology for dynamic risk assessment and mitigation suggestions aimed at assessing and reducing cyber risks. The proposed approach gathers information from publicly available cybersecurity-related open sources and integrates it with environment-specific data to generate a comprehensive understanding of potential risks. It creates multiple distinct risk scenarios based on the identification of vulnerabilities, network topology, and the attacker’s perspective. The methodology employs Bayesian networks to proactively and dynamically estimate the probability of threats and Fuzzy Cognitive Maps to dynamically update vulnerability severity values for each risk scenario. These elements are combined with impact estimations to provide dynamic risk assessments. Furthermore, the methodology offers mitigation suggestions for each identified vulnerability across all risk scenarios, enabling organizations to effectively address the assessed cybersecurity risks. To validate the effectiveness of the proposed methodology, a case study is presented, demonstrating its practical application and efficacy. Full article

The ant colony algorithm is an approach for path planning that is used in multiple industries. This paper proposes an improved robot path planning method, referred to as Improved-ACO. First, the heuristic information calculation is optimized to increase algorithm efficiency and shorten convergence time. Secondly, an enhanced Tanh function is included into the heuristic information, allowing dynamic modifications during the search period and preventing the algorithm’s convergence to local optima. Then, a novel pheromone update strategy is employed to accelerate convergence. Next, a novel pheromone diffusion mechanism is proposed to strengthen the ants’ search capability. Additionally, a collision avoidance system and improved B-spline curves are included for path smoothing, guaranteeing that the optimized pathways conform to the robot’s kinematic limitations. Simulation results indicate that the improved ant colony algorithm decreases the average number of turns by $37.5\%$ and accelerates convergence time by $39.45\%$ relative to existing methods across diverse map dimensions. The experiments confirm that Improved-ACO achieves rapid convergence and constructs smooth curves that adhere to the robot’s kinematic constraints. Consequently, Improved-ACO is confirmed as an efficient and adaptable route planning method for robotic navigation under complicated situations. Full article

Erosion is a significant environmental challenge in Serbia, shaped by natural and human factors. Pronounced relief, fragile geological substrate, a developed hydrographic network, and a climate characterized by an uneven distribution of precipitation throughout the year make this area prone to activating erosion processes and flash floods whenever there is a significant disruption in ecological balance, whether due to the removal of vegetation cover or inadequate land use. Researchers have recorded approximately 11,500 torrents in Serbia, most of which were activated during the 19th century, a period of significant social and political change, as well as intensive deforestation and the irrational exploitation of natural resources. By the mid-19th century, the effects of land degradation were impossible to ignore. As the adequate assessment of soil erosion intensity is the initial step in developing a prevention and protection strategy and the type and scope of anti-erosion works and measures, this article presents the path that the anti-erosion field in Serbia has taken from the initial observations of erosion processes through the first attempts to create the Barren Land Cadastre and Torrent Cadastre to the creation of the Erosion Potential Method (EPM) and its modification by Dr. Lazarević that resulted in the creation of the first Erosion Map of SR Serbia in 1971 (published in 1983). In 2020, a new Erosion Map of Serbia was created with the application of Geographic Information System (GIS) technologies and based on the original method by Professor Slobodan Gavrilović—the EPM—without the modifications introduced by Lazarević. We compared the 1983 and 2020 erosion maps in a GIS environment, where the change in soil erosion categories was analyzed using a confusion matrix. The updated erosion maps mirror the shift in methodology from a traditional approach (Lazarević’s modification) to the modern GIS-based method (Gavrilović’s original EPM) and reflect technological improvements and changes in land use, conservation practices, and environmental awareness. Full article

Wheel loaders play a crucial role in daily production and transportation. With the rapid development of intelligence in passenger vehicles, freeing loader operators from high-risk and repetitive tasks has become a pressing issue. This paper presents a novel and efficient path planning and tracking framework tailored to the unique body structure and specific operating environment of loaders. We improve the Hybrid A* search algorithm based on the operational characteristics of loaders and integrate it with dynamically updated grid maps to enable the autonomous planning of loader operating paths in unstructured environments, meeting the efficiency requirements of production. Additionally, to address the challenge of poor trajectory tracking control accuracy caused by hydraulic articulated steering, we propose a new loader trajectory tracking controller based on the idea of hierarchical control. We use an extended state observer to compensate for unknown disturbances in the steering execution layer and employ fuzzy fractional-order PID to handle the nonlinearity of loaders. Field experiments using the proposed approach demonstrate that loaders can autonomously and in real-time complete tasks in dynamically changing operating scenarios. Full article

Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection strategy includes options for fast and slow charging types, as well as the choice of whether to comply with grid-controlled charging. Charging types are selected based on the ability to reach the desired state of charge (SOC), while compliance with grid-controlled charging is determined by comparing the unit charging cost (CC). An objective function is established to minimize the peak valley load difference (PVLD) rate of PGs and users’ CC. To achieve this, an improved non-dominated sorting whale optimization algorithm (INSWOA) is proposed which initializes the population through logistic mapping, introduces nonlinear convergence factors for position updates, and uses adaptive inertia weights to improve population diversity, enhance global optimization ability, reduce premature convergence, and improve solution accuracy. Finally, simulating distribution networks in a certain region, the results obtained from the INSWOA were compared with those from the non-dominated sorting whale optimization algorithm (NSWOA) and other algorithms. The comparisons demonstrated that the INSWOA significantly reduced the PVLD rate of the PG load and users’ CCs, highlighting its high practical value. Full article

This study presents relevant elements of seismic resilience strategy containing an innovative digital mapping tool tailored for Bucharest, one of Europe’s most seismically vulnerable areas. The framework integrates seismic resilience indicators and expert input with Bucharest’s seismic micro-zonation map to systematically identify critical relocation areas, including educational institutions, medical facilities, and open spaces for emergency use. A seven-step methodology underpins the strategy: identifying resilience indicators, gathering local data, conducting expert workshops, mapping vulnerable areas, designating emergency open spaces, incorporating educational institutions as shelters, and evaluating the framework through a SWOT (strengths, weaknesses, opportunities, and threats) analysis. The digital mapping tool developed using Google My Maps provides a practical and accessible platform for emergency management professionals and the public, enabling real-time response coordination and informed long-term planning. District 2 is identified as the most vulnerable area due to high population density and peak ground acceleration (PGA), while District 4 faces challenges stemming from limited medical and relocation resources, despite experiencing lower seismic activity. The SWOT analysis demonstrates the tool’s potential as a robust disaster management framework, while highlighting the need for continuous updates, enhanced collaboration, and integration of additional data. This study offers a scalable model for other urban contexts, bridging the gap between strategic planning and operational readiness for seismic risk reduction. Full article

This research expands and updates a previous analysis of social entrepreneurship, incorporating the academic literature published between 2017 and 2024. Using the Web of Science database and Latent Semantic Analysis (LSA) technique, 1262 articles were analyzed, organized into three time periods (2017–2018, 2019–2021, and 2022–2024). The results show a clear evolution of the field, where sustainability, digitization, and resilience emerge as fundamental axes. Compared to the original research, which identified the convergence between the third sector and corporate social responsibility (CSR), this research reveals a consolidation of hybrid models, aligned with the Sustainable Development Goals (SDGs). Sustainability is positioned as a transverse axis, integrating economic, social, and environmental objectives. Digital transformation, driven by the pandemic, has facilitated scalability, organizational efficiency, and social impact measurement, but also poses challenges in terms of technological equity. In addition, organizational and community resilience takes center stage as an adaptive response to global crises. Research provides a comprehensive and up-to-date view of social entrepreneurship, identifying key trends and emerging challenges, while mapping new lines of research needed to strengthen the field in an increasingly globalized and technological world. Full article

Among the land use–land cover products, tree cover maps are essential tools for assessing forest functionality and ecosystem services, and implementing sustainable forest management. By combining open-source and ancillary high-resolution cartographic datasets, this study aims to map trees and forests in the Latium region in central Italy, highlighting their spatial configuration, function, and forest typology. The main findings show that trees cover 44.2% of the regional land area. Forests cover 508,056 ha, forming the core matrix of the Latium mountain landscape, providing significant ecological and socio-economic value for forest management and the regional wood supply chain. Although trees outside the forest represent only 3.1% of regional tree cover, they play a crucial role in enhancing ecological connectivity and landscape resilience. Approximately 2% of the tree and forest cover occurs in urban areas, contributing significantly to climate regulation and air quality in densely populated environments. The dominant forest types in Lazio include Turkey oak, temperate broadleaf, beech, downy oak, and Holm oak, which together account for 58.6% of the total tree cover. The accuracy tests confirm the feasibility of using open-source data for reliable, cost-effective forest mapping. Regular updates of these maps can support continuous monitoring and promote sustainable forest management practices. Full article

Traditional Chinese villages and architectural cultural resources are abundant. Against the backdrop of rapid development in contemporary socioeconomic and urbanization processes, rural construction is facing multiple challenges such as imbalanced urban–rural development, gradually fading cultural traditions, and disharmonious living environments. The cultural elements of rural architecture urgently need more systematic and effective protection, integration, and reuse. Therefore, the precise extraction of traditional architectural features and their translation applications in modern contexts are gradually becoming key issues in current research and practice fields. This study takes traditional architecture of the Goulou Cluster of Yue Dialects in Guangxi, China, as an example. Through field investigations and mathematical and GIS spatial analysis, architectural samples were identified and extracted typologically, and a database of traditional architecture was constructed, delineating architectural cultural zones and summarizing type characteristics to create a genealogy map. Based on the results of the architectural genealogy study, modern translation pathways for traditional architecture were proposed through spatial modeling, technical analysis, and iterative optimization. Modern translation experiments were conducted on selected typical villages and their traditional buildings, exploring the application model system of traditional architecture in modern contexts. This study not only deepens the scientific understanding of the genealogy zoning characteristics of traditional architecture in the Goulou Cluster of Yue Dialects in Guangxi but also provides a reference for the modern translation and optimization path of traditional architecture, providing important theoretical basis and application guidance for promoting the inheritance and innovation of rural culture, and realizing the protection and updating of rural architectural style. Full article