Search Results (305)

Amid intensified global technological competition and increasing restrictions on cross-border knowledge transfer, enhancing the ability to identify, integrate, and recombine diverse technological knowledge has become a critical strategy for strengthening the innovation capabilities of multinational enterprises (MNEs). Based on multidimensional proximity theory and dynamic capability theory, this study takes R&D units within Huawei’s global R&D network as the research object. It constructs a cross-border collaboration framework under the dual boundaries of organization-geography to explore the differences in the role of technological distance on the innovation performance of R&D units in different cooperation scenarios. This study also introduces technological diversification as a mediating variable to reveal the conversion path from heterogeneous knowledge input to innovation output. The findings indicate: (1) A nonlinear relationship exists between technological distance and innovation performance. In local-internal and international-internal collaborations, this relationship follows an inverted U-shaped pattern, whereas in local-external collaborations, it shows a significant positive effect. (2) In international-external collaboration, due to the dual absence of geographical and organizational proximity, the positive effect of technological distance on innovation performance is not significant. (3) The technological diversification capability of R&D units is a crucial mediating factor in the process by which technological distance affects innovation performance, thereby fostering the efficiency of heterogeneous knowledge absorption and recombination. The study examines the micro-mechanisms underlying cross-border collaborations and capability building in MNEs’ R&D units from dual perspectives of contextual fit and capability development, providing theoretical support and practical guidance for MNEs to optimize international technological collaboration mechanisms and improve innovation performance. Full article

In this paper, we propose a cooperative security method for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to address the scenario of unauthorized rogue drones (RDs) intruding into an airport’s restricted airspace. The proposed method integrates artificial intelligence techniques with engineering solutions to enhance the autonomy and effectiveness of air–ground cooperation in airport security. Specifically, the MADDPG algorithm enables the Security Interception UAVs (SI-UAVs) to autonomously detect and counteract RDs by optimizing their decision-making processes in a multi-agent environment. Additionally, Particle Swarm Optimization (PSO) is employed for distance-based target assignment, allowing each SI-UAV to autonomously select intruder targets based on proximity. To address the challenge of limited SI-UAV flight range, a power replenishment mechanism is introduced, where each SI-UAV automatically returns to the nearest UGV for recharging after reaching a predetermined distance. Meanwhile, UGVs perform ground patrols across different airport critical zones (e.g., runways and terminal perimeters) according to pre-designed patrol paths. The simulation results demonstrate the feasibility and effectiveness of the proposed security strategy, showing improvements in the reward function and the number of successful interceptions. This approach effectively solves the problems of target allocation and limited SI-UAV range in multi-SI-UAV-to-multi-RD scenarios, further enhancing the autonomy and efficiency of air–ground cooperation in ensuring airport security. Full article

The scalability of coordination policies is a critical challenge in swarm robotics, where agent numbers may vary substantially between deployment scenarios. Reinforcement learning (RL) offers a promising avenue for learning decentralized policies from local interactions, yet a fundamental question remains: can policies trained on one swarm size transfer to different population scales without retraining? This zero-shot transfer problem is particularly challenging because the traditional RL approaches learn fixed-dimensional representations tied to specific agent counts, making them brittle to population changes at deployment time. While existing work addresses scalability through population-aware training (e.g., mean-field methods) or multi-size curricula (e.g., population transfer learning), these approaches either impose restrictive assumptions or require explicit exposure to varied team sizes during training. Graph Neural Networks (GNNs) offer a fundamentally different path. Their permutation invariance and ability to process variable-sized graphs suggest potential for zero-shot generalization across swarm sizes, where policies trained on a single population scale could deploy directly to larger or smaller teams. However, this capability remains largely unexplored in the context of swarm coordination. For this reason, we empirically investigate this question by combining GNNs with deep Q-learning in cooperative swarms. We focused on well-established 2D navigation tasks that are commonly used in the swarm robotics literature to study coordination and scalability, providing a controlled yet meaningful setting for our analysis. To address this, we introduce Deep Graph Q-Learning (DGQL), which embeds agent-neighbor graphs into Q-learning and trains on fixed-size swarms. Across two benchmarks (goal reaching and obstacle avoidance), we deploy up to three times larger teams. The DGQL preserves a functional coordination without retraining, but efficiency degrades with size. The ultimate goal distance grows monotonically (15–29 agents) and worsens beyond roughly twice the training size ($\approx 20$ agents), with task-dependent trade-offs. Our results quantify scalability limits of GNN-enhanced DQL and suggest architectural and training strategies to better sustain performance across scales. Full article

With the rapid growth of demands in marine resource exploitation, environmental monitoring, and maritime safety, cooperative operations based on Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) have emerged as a promising paradigm for intelligent ocean missions. UAVs offer flexibility and high coverage efficiency but suffer from limited endurance due to restricted battery capacity, making them unsuitable for large-scale tasks alone. In contrast, USVs provide long endurance and can serve as mobile motherships and energy-supply platforms, enabling UAVs to take off, land, recharge, or replace batteries. Therefore, how to achieve cooperative path planning and energy replenishment scheduling for USV–UAV systems in complex marine environments remains a crucial challenge. This study proposes a USV–UAV cooperative path planning and energy replenishment optimization method based on BeiDou high-precision positioning. First, a unified system model is established, incorporating task coverage, energy constraints, and replenishment scheduling, and formulating the problem as a multi-objective optimization model with the goals of minimizing total mission time, energy consumption, and waiting time, while maximizing task completion rate. Second, a bi-level optimization framework is designed: the upper layer optimizes the USV’s dynamic trajectory and docking positions, while the lower layer optimizes UAV path planning and battery replacement scheduling. A closed-loop interaction mechanism is introduced, enabling the system to adaptively adjust according to task execution status and UAV energy consumption, thus preventing task failures caused by battery depletion. Furthermore, an improved hybrid algorithm combining genetic optimization and multi-agent reinforcement learning is proposed, featuring adaptive task allocation and dynamic priority-based replenishment scheduling. A comprehensive reward function integrating task coverage, energy consumption, waiting time, and collision penalties is designed to enhance global optimization and intelligent coordination. Extensive simulations in representative marine scenarios demonstrate that the proposed method significantly outperforms baseline strategies. Specifically, it achieves around higher task completion rate, shorter mission time, lower total energy consumption, and shorter waiting time. Moreover, the variance of energy consumption across UAVs is notably reduced, indicating a more balanced workload distribution. These results confirm the effectiveness and robustness of the proposed framework in large-scale, long-duration maritime missions, providing valuable insights for future intelligent ocean operations and cooperative unmanned systems. Full article

In recent years, the number of satellites in space has experienced explosive growth, and the number of non-cooperative satellites requiring close attention and precise tracking has also increased rapidly. Despite this, the world’s satellite precision tracking equipment is constrained by factors such as a slower growth in numbers and a scarcity of available deployment sites. To rapidly and efficiently identify satellites with potential new anomalies among the large number of cataloged non-cooperative satellites currently transiting, we have constructed a Bi-Directional Minimal GRU deep learning network model incorporating an attention mechanism based on Minimal GRU. This model is termed the Attention-based Bi-Directional Minimal GRU model (ABMGRU). This model utilizes tracking data from relatively inexpensive satellite observation equipment such as phased array radars, along with catalog information for non-cooperative satellites. It rapidly detects anomalies in target satellites during the initial phase of their passes, providing decision support for the subsequent deployment, scheduling, and allocation of precision satellite tracking equipment. The satellite tracking observation data used to support model training is predicted through Satellite Tool Kit simulation based on existing catalog information of non-cooperative satellites, encompassing both anomaly free data and various types of data containing anomalies. Due to limitations imposed by relatively inexpensive observation equipment, satellite tracking data is restricted to the following categories: time, azimuth, elevation, distance, and Doppler shift, while incorporating realistic noise levels. Since subsequent precision tracking requires utilizing more satellite pass time, the duration of tracking data collected during this phase should not be excessively long. The tracking observation time in this study is limited to 1000 s. To enhance the efficiency and effectiveness of satellite anomaly detection, we have developed an Attention-based Bi-Directional Minimal GRU deep learning network model. Experimental results demonstrate that the proposed method can detect non-cooperative anomalous satellites more effectively and efficiently than existing lightweight intelligent algorithms, outperforming them in both completion efficiency and detection performance. It exhibits superiority across various non-cooperative satellite anomaly detection scenarios. Full article

On 18 September 2023, the Spanish State recognised the Notorio Arraigo (firmly established, or literally, notorious deep rootedness) of the Bahá’í Community of Spain through a Ministerial Order, integrating it into the group of recognised religious denominations, albeit without any signed cooperation agreements. This milestone reflects the evolving legal–political frameworks of religious freedom since the arrival of the Bahá’í Faith in Spain in the mid-20th century, as well as the strategies employed to consolidate and institutionalise itself as a religious denomination, influenced by Bahá’í principles. This paper examines how these principles have inspired the Community’s secular strategies throughout different historical phases: from a restrictive Catholic confessional framework to the current non-confessional state, which guarantees religious freedom. It also analyses the actions undertaken to obtain recognition of Notorio Arraigo and considers the challenges and needs faced by the Bahá’í Community at this new level of recognition. Full article

This paper investigates actuator fault-tolerant cooperative control of multiple quadrotor unmanned aerial vehicles (multi-QUAVs) under restricted communication conditions, where only relative output measurements are available. By appropriately transforming and scaling the control inputs and outputs of the multi-QUAVs, an observable subsystem is constructed. A decoupled fault estimation observer is then designed for this subsystem to estimate actuator faults and the leader’s input signal. Based on the fault estimation information and relative measurement information among QUAVs, a node-based active fault-tolerant cooperative control law is developed. This approach enables multi-QUAVs to achieve consensus-based formation solely relying on relative output information, even in the presence of actuator faults. Finally, the effectiveness of the proposed active fault-tolerant cooperative control method is verified by simulation. Full article

Objective: This longitudinal study examined how preceptors’ perceived difficulty and role performance in instructing newly graduated nurses impacted by restricted clinical practicum opportunities because of COVID-19 impact their mental health outcomes, including stressors, burnout, and turnover intention. Methods: The study surveyed 426 preceptors responsible for newly graduated nurses across 39 hospitals during fiscal year 2022. Data were collected at three time points: June, September, and December 2022. The questionnaire assessed personal attributes, perceived instructional difficulty (PID) due to limited clinical practice, self-rated preceptor role performance, nursing job stressors, burnout, and intention to resign. Two-way ANOVA was conducted to analyze the effects of perceived difficulty (high/low) and role performance (high/low) on mental health indicators. Results: Seventy-six preceptors (6 males, 70 females) completed all three surveys. In June 2022, preceptors reporting high perceived difficulty demonstrated significantly higher scores in role performance subscales, including “goal achievement and accident prevention” and “continuation of instruction with cooperation.” Significant main effects of perceived difficulty and role performance were observed on stressors such as role conflict, physician conflict, and death-related stress, as well as on burnout dimensions such as emotional exhaustion and personal accomplishment. By December, significant interaction effects emerged for outcomes related to “intention to quit nursing” and “desire to change departments.” Conclusions: Preceptors’ PID and role performance significantly influence their stress, burnout, and turnover intentions. Those experiencing both high difficulty and high role performance experience increased psychological burdens. This underscores the importance of targeted mental health support for preceptors. Full article

Metal additive manufacturing (AM) has become a crucial technology for rapid prototyping and enhancing the efficiency of producing lighter components. Despite these advantages, many challenges remain. We examine how mineral-rich developing countries can upgrade in the metal AM global value chain (GVC). We do so by applying the theory of GVCs and the multi-level perspective (MLP) framework to the metal powder segment. We investigate how Kazakhstan can link itself to the metal AM GVC by cooperating with China. Our case studies are based on 20 interviews with metal AM industry experts and scholars from Kazakhstan, China, and Europe. Using the MLP framework, we identify eight drivers that have enabled China to become prominent in the global metal AM industry. In addition, we identify eight barriers restricting Kazakhstan’s upgrading. For Kazakhstan to begin producing metal powders for AM, we suggest that its government start by implementing three policies, based on China’s experience: improve education and training systems, with a focus on advanced metallurgy; target AM industry segments in which cost, not quality, is a primary focus; and adopt international standards for metal AM-related activities. Our findings offer important lessons for other mineral-rich developing countries that may be more relevant than experiences from developed nations. Full article

Agricultural cooperatives are essential in mitigating climate change and food insecurity through the promotion of sustainable agricultural practices and the conservation of biodiversity. However, weaknesses in governance, economic restrictions, market pressures, and regulatory obstacles frequently hinder their efficacy. This study investigates the main factors leading to cooperative failures through qualitative analysis of twenty-three (23) expert interviews. Research demonstrates that strong governance, efficient communication, financial stability, and supportive policies are crucial for the viability of cooperatives. Leadership issues, bureaucratic inefficiencies, and market competition were seen as significant roadblocks. It is essential to tackle these difficulties via governance adjustments, economic resilience approaches, and policy advocacy to strengthen the role of cooperatives in climate change mitigation and food security. Full article

This article discusses the possibilities and methods for rescuing people from abandoned mine workings and the cooperation of the components of the Integrated Rescue System of the Slovak Republic when carrying out rescue work in underground spaces, specifically the Bankov mine. Additionally, the basic legislative restrictions on the level of rescue work that can be performed in underground spaces in Slovakia and abroad are characterized. In the study itself, exercises in a mining environment were designed and tested by rescuers from the fire and rescue corps of the Slovak Republic, while several methods for rescuing people from underground spaces using climbing equipment were tested. Since the research setting was an abandoned mine, the rescue methods were carried out with regard to the maximum achievable safety of the firefighters. With the demise of the Mine Rescue Service in the Slovak Republic in 2025, rescue activities passed into the hands of the fire and rescue corps, and it is therefore necessary to determine the best method for rescue from mining spaces that can be performed by firefighters when the priority is the rescue time: the most important factor for saving human life. Using the analysis of the data obtained in this study, the most effective method specifically for rescuing people from underground spaces was determined. Based on the information obtained, proposals and measures were established to make rescue work in underground spaces more efficient. The research met all standards set for firefighters, and all rescuers agreed to publish this research. Full article

In the Eastern Cape Province of South Africa, rural women agripreneurs encounter ongoing structural challenges in accessing formal finance, securing land rights, and gaining leadership roles, despite their vital contribution to agriculture and food security. This research combines a thematic review of secondary sources from 2018 to 2024 with an embedded case study based on primary qualitative data with women involved in the Citrus Growers Association–Grower Development Company (CGA–GDC) public–private partnership. This dual approach connects local, real-world entrepreneurial experiences with global financial inclusion initiatives, especially the G20 Women’s Empowerment Principles and the G20 Development Agenda. The findings highlight a consistent gap between policy and practice: while frameworks at both national and international levels advocate for women’s financial inclusion, actual implementation in rural agribusiness often neglects gender differences. Women’s engagement is limited by insecure land rights, restricted access to formal credit, male-controlled cooperative management, and insufficient gender-specific data monitoring. Drawing comparative insights from Kenya, India, and West Africa, the study proposes seven interconnected policy suggestions, such as establishing gender-disaggregated data systems, expanding women-led cooperatives, reforming land tenure laws, including entrepreneurial financial literacy in capacity-building programmes, and utilising gender-sensitive digital finance solutions. By connecting grassroots empirical evidence with global policy discussions, this study aims to contribute to academic debates and practical efforts to develop gender-responsive financial ecosystems, thereby boosting women’s economic independence, entrepreneurial activity, and rural progress in South Africa and similar contexts in the Global South. Full article

Land resources are the basis of human production and life, and they face many challenges in the process of urbanization, such as the prominent contradiction between land supply and demand and the inefficient use of land, which in turn restricts the socio-economic development and the promotion of urbanization. This paper takes China’s ten largest urban agglomerations as its research object and constructs a land resource drag effect model based on the C-D production function. The geographical weighted regression method is used to calculate the coefficient of the land drag effect. Combining kernel density analysis and spatial autocorrelation analysis, the paper reveals the temporal and spatial evolution patterns of the drag effect and discusses the impact of land resources on new urbanization and its temporal and spatial differentiation characteristics. The study shows that during the period of 2006–2022, China’s new-type urbanization as a whole rises, but the development of each urban agglomeration has significant differences, showing a spatial pattern of “east high, west low”; the drag effect of land resources shows a decreasing trend, but regional differences are obvious, showing a distribution of “east strong, west weak”; the kernel density curve of drag effect of land shows a “right-skewed-left-skewed” change, with the overall level weakening and the degree of concentration increasing; the drag effect of land resources shows significant positive global autocorrelation, and there are spatial proximity effect and spillover effect in space. The findings provide a theoretical basis for land resource utilization and spatial development in China’s new-type urbanization process. Therefore, it is necessary to implement differentiated land resource allocation and urban planning policies according to different types of urban spatial agglomeration and to give full play to the cooperative linkage effect of urban agglomerations in reducing the drag effect of land resources. Full article

Underwater fish image segmentation is the key technology to realizing intelligent fisheries and ecological monitoring. However, the problems of light attenuation, blurred boundaries, and low contrast caused by complex underwater environments seriously restrict the segmentation accuracy. In this paper, RetinalConet, an underwater fish segmentation network based on bionic retina dual-channel and multi-module cooperation, is proposed. Firstly, the bionic retina dual-channel module is embedded in the encoder to simulate the separation and processing mechanism of light and dark signals by biological vision systems and enhance the feature extraction ability of fuzzy target contours and translucent tissues. Secondly, the dynamic prompt module is introduced, and the response of key features is enhanced by inputting adaptive prompt templates to suppress the noise interference of water bodies. Finally, the edge prior guidance mechanism is integrated into the decoder, and low-contrast boundary features are dynamically enhanced by conditional normalization. The experimental results show that RetinalCoNet is superior to other mainstream segmentation models in the key indicators of mDice, reaching 82.3%, and mIou, reaching 89.2%, and it is outstanding in boundary segmentation in many different scenes. This study achieves accurate fish segmentation in complex underwater environments and contributes to underwater ecological monitoring. Full article

This article presents the results of a participatory urban diagnosis conducted in District 2 of La Paz, Bolivia, as part of an educational cooperation project aimed at exploring the spatial and symbolic dimensions of urban insecurity. Drawing on feminist and intersectional frameworks, this research combined participatory action methods, digital surveys, and collective mapping to identify patterns of fear and exclusion in public space. The analysis revealed significant disparities in how insecurity is perceived and experienced by different social groups—especially women, Indigenous peoples, and LGTBQ+ individuals—highlighting the importance of spatial configuration, social presence, and care infrastructure in shaping everyday urban life. The project also served as a pedagogical innovation, integrating architecture students into a process of civic engagement and co-production of knowledge. The resulting ‘Map of Gender Insecurity’ contributed to local planning efforts through the “Seguras, No Valientes” initiative. While the limited representation of some groups restricts statistical generalization, the approach offers a replicable model for linking research, education, and public action in pursuit of more inclusive and safer cities. Full article