Search Results (256)

Localization of mobile robots is crucial for deploying robots in real-world applications such as search and rescue missions. This work aims to develop an accurate localization system applicable to swarm robots equipped only with low-cost monocular vision sensors and visual markers. The system is designed to operate in fully open spaces, without landmarks or support from positioning infrastructures. To achieve this, we propose a localization method based on equilateral triangular formations. By leveraging the geometric properties of equilateral triangles, the accurate two-dimensional position of each participating robot is estimated using one-dimensional lateral distance information between robots, which can be reliably and accurately obtained with a low-cost monocular vision sensor. Experimental and simulation results demonstrate that, as travel time increases, the positioning error of the proposed method becomes significantly smaller than that of a conventional dead-reckoning system, another low-cost localization approach applicable to open environments. Full article

With the global increase in maritime activities, the frequency of maritime accidents has risen, underscoring the urgent need for faster and more efficient search and rescue (SAR) solutions. This study presents an intelligent unmanned aerial vehicle (UAV)-based maritime rescue system that combines GPS-driven dynamic path planning with vision-based dual-target detection and tracking. Developed within the Gazebo simulation environment and based on modular ROS architecture, the system supports stable takeoff and smooth transitions between multi-rotor and fixed-wing flight modes. An external command module enables real-time waypoint updates. This study proposes three path-planning schemes based on the characteristics of drones. Comparative experiments have demonstrated that the triangular path is the optimal route. Compared with the other schemes, this path reduces the flight distance by 30–40%. Robust target recognition is achieved using a darknet-ROS implementation of the YOLOv4 model, enhanced with data augmentation to improve performance in complex maritime conditions. A monocular vision-based ranging algorithm ensures accurate distance estimation and continuous tracking of rescue vessels. Furthermore, a dual-target-tracking algorithm—integrating motion prediction with color-based landing zone recognition—achieves a 96% success rate in precision landings under dynamic conditions. Experimental results show a 4% increase in the overall mission success rate compared to traditional SAR methods, along with significant gains in responsiveness and reliability. This research delivers a technically innovative and cost-effective UAV solution, offering strong potential for real-world maritime emergency response applications. 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

The application of drones has contributed to automated herbicide spraying in the context of precision agriculture. Although drone technology is mature, the widespread application of agricultural drones and their numerous advantages still demand improvements in battery endurance during flight missions in agricultural operations. This issue has been addressed by optimizing the path planning to minimize the time of the route and, therefore, the energy consumption. In this direction, a novel framework for autonomous drone-based herbicide applications that integrates deep learning-based semantic segmentation and coverage path optimization is proposed. The methodology involves computer vision for path planning optimization. First, semantic segmentation is performed using a DeepLab v3+ convolutional neural network to identify and classify regions containing weeds based on aerial imagery. Then, a coverage path planning strategy is applied to generate efficient spray routes over each weed-infested area, represented as convex polygons, while accounting for the drone’s refueling constraints. The results demonstrate the effectiveness of the proposed approach for optimizing coverage paths in weed-infested sugarcane fields. By integrating semantic segmentation with clustering and path optimization techniques, it was possible to accurately localize weed patches and compute an efficient trajectory for UAV navigation. The GA-based solution to the Traveling Salesman Problem With Refueling (TSPWR) yielded a near-optimal visitation sequence that minimizes the energy demand. The total coverage path ensured complete inspection of the weed-infected areas, thereby enhancing operational efficiency. For the sugar crop considered in this contribution, the time to cover the area was reduced by 66.3% using the proposed approach because only the weed-infested area was considered for herbicide spraying. Validation of the proposed methodology using real-world agricultural datasets shows promising results in the context of precision agriculture to improve the efficiency of herbicide or fertilizer application in terms of herbicide waste reduction, lower operational costs, better crop health, and sustainability. Full article

The article compares the young generation’s perspectives on social and environmental problems in Greece and Poland. We examine the visions of young generation representatives for solving these problems and their role in developing sustainable enterprises. A quantitative analysis was conducted using a k-means clustering algorithm in Python to analyze youth perspectives on sustainable enterprises in both countries. The method offers several benefits, including uncovering unexpected patterns, simplifying the data, and highlighting shared views across both countries. The results show that the youth in Greece and Poland widely support the inclusion of social and environmental considerations into business strategies, and despite some differences in the strengths of opinions, they agree that businesses should integrate sustainability into their mission. However, different clusters reveal varying levels of youth knowledge and awareness. Young people believe that businesses should prioritize sustainability, but acknowledge the difficulty of balancing profits with social and environmental goals. Financial support, specific skills, and experience are necessary for establishing sustainable enterprises. An enabling environment and youth empowerment are critical for promoting youth’s sustainable entrepreneurship. Future research could focus on the enabling factors that impact the willingness of youth to engage in sustainable entrepreneurship and on the ways and tools to provide necessary support. Full article

There is an ongoing debate about whether Christian theology has had positive or negative effects on the natural environment. Included in this debate is the role of Christian missions acting in colonial environments. This article investigates the relationship between Protestant agricultural missions and their environments, using the documents of the first World Missionary Conference (Edinburgh 1910) and the third World Missionary Conference (Tambaram 1938), as well as several related documents. Although the history of agricultural missions can be backtracked into the 19th century, they were not regarded as an independent branch of missions until the early twentieth century. In 1910, neither the home boards of Protestant missions nor the older generation of missionaries had any vision for agricultural missions, and traditional culture—including agriculture—was seen as superstitious and full of heathen beliefs. However, agricultural missions developed rapidly in the decades between Edinburgh and Tambaram and broadened into rural missions due to a change in vision. The deplorable rural areas of the younger Christian churches called for ‘rural reconstruction’, and rural missions were welcomed as the most important agents to undertake this challenge. The environment of the church and countryside was enlarged and, by 1938, included economic and social environments, known as the fourth dimension of the church and missions after preaching, education, and medical care. Full article

Education, understood as the process of forming the human person in their integral dimensions—spiritual, intellectual, moral, and social—occupies a key place in the reflection on the evangelizing mission of the Church. This article presents education as a form of integral evangelization based on the thought and activity of the Polish religious sister and mystic Blessed Marcelina Darowska (1827–1911). The initial part outlines the development of the concept of “integral evangelization” in the teachings of the Catholic Church and its relation to personalist pedagogy. The following section presents the figure of Blessed Marcelina Darowska and her educational initiative, situated in the 19th-century socio-historical context of partitioned Poland. The main section of the analysis focuses on Darowska’s concept of education as the formation of the whole person, encompassing the discernment of talents, intellectual, religious, and social development, and preparation for responsible family and civic roles. It also considers to what extent this vision of education can be identified as an expression of integral evangelization in accordance with the criteria of contemporary Church teaching. This article further presents the development of Blessed Marcelina’s educational work and its impact on forming generations of women conscious of their identity. The conclusion offers a reflection on the relevance and potential of this educational method in the context of contemporary social and religious challenges, particularly in the work of new evangelization. Full article

Accurately estimating 3D human pose and body shape from a single monocular image remains challenging, especially under poor lighting or occlusions. Traditional RGB-based methods struggle in such conditions, whereas single-pixel imaging (SPI) in the Near-Infrared (NIR) spectrum offers a robust alternative. NIR penetrates clothing and adapts to illumination changes, enhancing body shape and pose estimation. This work explores an SPI camera (850–1550 nm) with Time-of-Flight (TOF) technology for human detection in low-light conditions. SPI-derived point clouds are processed using a Vision Transformer (ViT) to align poses with a predefined SMPL-X model. A self-supervised PointNet++ network estimates global rotation, translation, body shape, and pose, enabling precise 3D human mesh reconstruction. Laboratory experiments simulating night-time conditions validate NIR-SPI’s potential for real-world applications, including human detection in rescue missions. Full article

The coordination of school- and community-based mental health services is a promising strategy to respond to the rising mental health needs of youth. Out-of-school time (OST) programs are an under-utilized community-based resource that may be leveraged to help meet this need. We conducted a scoping review to (a) identify practices that facilitate the coordination of care between schools and OST programs and (b) examine whether such practices align with those used in existing models of expanded school mental health (ESMH). EBSCOhost, PsycINFO, PubMed, Google Scholar, ERIC Scopus, MedLine, ScienceDirect, and national organization websites were used to identify articles, including peer-reviewed (n = 2) and grey literature (n = 7) published from 2004 to 2024 that were written in English and described U.S.-based schools and programs. Coordination practices were coded inductively and categorized as a school/program, district/community, or state-level practice. Practices within the vision/mission alignment, coordinating daily operations, training, and resource/information sharing domains were identified at the highest frequency. Many of the strategies identified are included in existing models of ESMH. Thus, expanding coordination models to include OST may be a feasible direction for increasing the capacity of communities to meet the mental health needs of youth. Given the paucity of research in this area, further empirical research is needed. Full article

The Fourth Industrial Revolution (4IR), characterized by the integration of advanced digital technologies, is transforming industries globally and significantly impacting leadership practices, particularly in the healthcare sector. As organizations adapt to this digital evolution, the demand for effective leadership becomes increasingly critical. This paper explores Leadership 4.0, a framework that identifies the skills and competencies required for successful leadership in the digital age. The study employs a mixed-methods approach, combining a Systematic Literature Review (SLR) with qualitative insights gathered through case studies and the Delphi method, involving healthcare leaders, to investigate leadership practices in organizations undergoing digital transformation. Through case studies and literature reviews, the research seeks to provide a comprehensive understanding of the changing leadership landscape, addressing the unique challenges and opportunities presented by digital transformation in healthcare. The findings indicate that effective Leadership 4.0 requires a combination of digital literacy, strategic foresight, and emotional intelligence. Leaders must exhibit adaptability, drive innovation, and foster a culture of continuous learning to navigate the complexities of digital transformation successfully. The study also emphasizes the importance of internal branding as a critical strategy for aligning employees with the organization’s mission and digital vision, thereby fostering collective commitment to transformation objectives. Key challenges include resistance to change and the ongoing need for upskilling, while opportunities lie in leveraging digital innovations to enhance organizational performance. Full article

Societies need to develop more sustainable ways of living, working, and doing business. Education for sustainable development (ESD) plays a pivotal role in this endeavor. The aim is to provide people with the necessary knowledge, values, and skills for the needed transformation. Therefore, organizations of adult and continuing education must address issues of climate protection and sustainability in their programs and act accordingly in the way the organization is managed. This article reports on a qualitative study that involves leaders of adult education institutions in China, Germany, and the USA. This study seeks to investigate what these leaders do and how they act to implement ESD in their organizations. It specifically looks at current challenges, management strategies, beliefs, and values of the leaders. The results of this study show that leaders of adult and continuing education organizations recognize the need to address sustainability in adult education. However, they face many challenges in implementing ESD, including a lack of resources (e.g., finances and staff), structural barriers, and difficulties in reaching new target groups. Despite these challenges, many participants strive to develop their organizations in a more sustainable way, such as through curriculum design and campus management. Some leaders take a comprehensive approach, guided by the Whole Institution Approach, by integrating ESD into their institution’s vision and mission, establishing dedicated working groups, and forming new partnerships with external stakeholders. Overall, this study highlights the crucial role of leadership in advancing sustainability efforts. Full article

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in a 2U CubeSat. This research aims to address the onboard computing constraints in nanosatellite missions to boost space agricultural practices. Through the Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data were securely streamed through Amazon Web Service Internet of Things (AWS IoT) to an ESP-NOW receiver and Roboflow. Real-time plant growth predictor monitoring was implemented through the web application provisioned at the receiver end. On the other hand, sprouts on germination bed were determined through the custom-trained Roboflow computer vision model. The feasibility of remote data computational analysis and monitoring for a 2U CubeSat, given its minute form factor, was successfully demonstrated through the proposed cloud framework. The germination detection model resulted in a mean average precision (mAP), precision, and recall of 99.5%, 99.9%, and 100.0%, respectively. The temperature, humidity, heat index, LED and Fogger states, and bed sprouts data were shown in real time through a web dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. The scalability nature of the framework allows adaptation to various crops to support sustainable agricultural activities in extreme environments such as space farming. Full article

This review examines the meaning and scope of tarbiyah and its relationship to other key Islamic educational concepts, such as taʿlīm, tadrīs, and taʾdīb, in the English- and Arabic-language literature. The central question guiding this review is as follows: what does tarbiyah signify in primary and secondary sources, and how is it conceptually linked to taʿlīm, tadrīs, and taʾdīb? Employing a narrative review, this study critically examines relevant texts to clarify the distinctions and connections among these foundational concepts, offering insight into their roles within Islamic educational discourse. The English-language literature on the subject reveals divergent views on the meaning and the interplay between these concepts, their relationships, and their hierarchical order. This is less prominent in the Arabic-language literature. A deeper understanding of tarbiyah can help shape the vision and mission of Islamic schools and teacher education programmes, while also guiding the development of educational policies and praxes that are both academically rigorous and grounded in Islamic educational principles. Such an approach supports the holistic intellectual, moral, and spiritual development of learners. Full article

Pose measurement of non-cooperative targets in space is one of the key technologies in space missions. However, most existing methods simulate well-lit environments and do not consider the degradation of algorithms in low-light conditions. Additionally, due to the limited computing capabilities of space platforms, there is a higher demand for real-time processing of algorithms. This paper proposes a real-time pose measurement method based on binocular vision that is suitable for low-light environments. Firstly, the traditional point feature extraction algorithm is adaptively improved based on lighting conditions, greatly reducing the impact of lighting on the effectiveness of feature point extraction. By combining point feature matching with epipolar constraints, the matching range of feature points is narrowed down to the epipolar line, significantly improving the matching speed and accuracy. Secondly, utilizing the structural information of the spacecraft, line features are introduced and processed in parallel with point features, greatly enhancing the accuracy of pose measurement results. Finally, an adaptive weighted multi-feature pose fusion method based on lighting conditions is introduced to obtain the optimal pose estimation results. Simulation and physical experiment results demonstrate that this method can obtain high-precision target pose information in a real-time and stable manner, both in well-lit and low-light environments. Full article

With the rapid development of deep learning, weather recognition has become a research hotspot in the field of computer vision, and the research on field programmable gate array (FPGA) acceleration based on deep learning algorithms has received more and more attention, based on which, we propose a method to implement deep neural networks for weather recognition in a small-scale FPGA. First, we train a deep separable convolutional neural network model for weather recognition to reduce the parameters and speed up the performance of hardware implementation. However, large-scale computation also brings the problem of excessive power consumption, which greatly limits the deployment of high-performance network models on mobile platforms. Therefore, we use a lightweight convolutional neural network approach to reduce the scale of computation, and the main idea of lightweight is to use fewer bits to store the weights. In addition, a hardware implementation of this model is proposed to speed up the operation and save on-chip resource consumption. Finally, the network model is deployed on a Xilinx ZYNQ xc7z020 FPGA to verify the accuracy of the recognition results, and the accelerated solution succeeds in achieving excellent performance with a speed of 108 FPS and 3.256 W of power consumption. The purpose of this design is to be able to accurately recognize the weather and deliver current environmental weather information to UAV (unmanned aerial vehicle) pilots and other staff who need to consider the weather, so that they can accurately grasp the current environmental weather conditions at any time. When the weather conditions change, the information can be obtained in a timely and effective manner to make the correct judgment, to ensure the flight of the UAV, and to avoid the equipment being affected by the weather leading to equipment damage and failure of the flight mission. With the help of this design, the UAV flight mission can be better completed. Full article