Search Results (169)

To address the challenge of coordinating economic cost control and low-carbon objectives in microgrid scheduling, while overcoming the performance limitations of the traditional Zebra Optimization Algorithm (ZOA) in complex problems, this paper proposes a Synergistic Zebra Optimization Algorithm (SZOA) and integrates it with innovative management concepts to enhance the microgrid scheduling process. The SZOA incorporates three core strategies: a multi-population cooperative search mechanism to strengthen global exploration, a vertical crossover–mutation strategy to meet high-dimensional scheduling requirements, and a leader-guided boundary control strategy to ensure variable feasibility. These strategies not only improve algorithmic performance but also provide technical support for innovative management in microgrid scheduling. Extensive experiments on the CEC2017 (d = 30) and CEC2022 (d = 10, 20) benchmark sets demonstrate that the SZOA achieves higher optimization accuracy and stability compared with those of nine state-of-the-art algorithms, including IAGWO and EWOA. Friedman tests further confirm its superiority, with the best average rankings of 1.20 for CEC2017 and 1.08/1.25 for CEC2022 (d = 10, 20). To validate practical applicability, the SZOA is applied to grid-connected microgrid scheduling, where the system model integrates renewable energy sources such as photovoltaic (PV) generation and wind turbines (WT); controllable sources including fuel cells (FC), microturbines (MT), and gas engines (GS); a battery (BT) storage unit; and the main grid. The optimization problem is formulated as a bi-objective model minimizing both economic costs—including fuel, operation, pollutant treatment, main-grid interactions, and imbalance penalties—and carbon emissions, subject to constraints on generation limits and storage state-of-charge safety ranges. Simulation results based on typical daily data from Guangdong, China, show that the optimized microgrid achieves a minimum operating cost of USD 5165.96, an average cost of USD 6853.07, and a standard deviation of only USD 448.53, consistently outperforming all comparison algorithms across economic indicators. Meanwhile, the SZOA dynamically coordinates power outputs: during the daytime, it maximizes PV utilization (with peak output near 35 kW) and WT contribution (30–40 kW), while reducing reliance on fossil-based units such as FC and MT; at night, BT discharges (−20 to −30 kW) to cover load deficits, thereby lowering fossil fuel consumption and pollutant emissions. Overall, the SZOA effectively realizes the synergy of “economic efficiency and low-carbon operation”, offering a reliable and practical technical solution for innovative management and sustainable operation of microgrid scheduling. Full article

This article deals with the analysis and exploration of the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The authors point out that the implementation of advanced AI technologies into already functioning and often complex systems, such as enterprise resource planning (ERP), presents significant technical challenges and requires a well-thought-out integration strategy. The complexity arises from the need to align new solutions with existing processes, resources, and data. Using the example of a fuel distribution system, the authors present the concept of integrating human knowledge (HK) and artificial intelligence (AI) in the management process. The article presents a comprehensive analysis of the smart upgrade of fuel delivery management (FDM) architecture by incorporating an AI app to solve complex problems, such as predicting demand or traffic flows, as well as correctly detecting near-miss events. Technological convergence enables the mutual pursuit of improving the management process by developing soft skills and expanding knowledge managers. The authors’ findings show that an important factor for successful convergence is horizontal and vertical matching of the human knowledge and artificial intelligence cooperation for archive max positive synergy. Some recommendations could be useful for tank truck operators as a starting point to predict demand patterns, smart route planning, etc., where an AI app could be very successful. Full article

Background: Class II is considered one of the most common malocclusions, influencing 37% of schoolchildren in Europe and 33% of orthodontic patients in the United States. When this type of malocclusion is combined with increased overjet with proclined teeth and maxillary excess, then moving maxillary molars distally is suggested. According to the recent literature, modern appliances that lack patient cooperation can be combined with temporary anchorage devices to provide absolute and skeletal anchorage while supporting the non-compliance appliances to eliminate their side effects, such as anterior and posterior anchorage loss along with maxillary molar inclination and rotation. To counteract these limitations, the Advanced Molar Distalization Appliance (amda^®), a non-compliance appliance for maxillary molar distalization supported by two mini-implants (MIs) with anterior abutments, was recently developed. Methods: In this preliminary prospective clinical trial, eight consecutive patients treated with the amda^® are evaluated through lateral cephalometric radiographs, while its application, construction, and anchorage is presented and discussed. The evaluation of dentoalveolar and skeletal changes has been made with 14 variables measured on the pre- and post-cephalometric radiographs before and immediately after maxillary molar distalization (T₀ and T₁, respectively), along with cephalometric superimpositions by the structural method. Results: In total, the mean distal molar movement was 4.2 ± 1.37 mm, the mean distal tipping was 1.7 ± 1.9 degrees, and the vertical movement was 1.6 ± 2.6 mm. Conclusions: The amda^® seems to provide an ideal option for treating patients with Class II malocclusion, achieving bodily movement of the maxillary molars with only minimal distal tipping and no anchorage loss. Full article

Vertical alliances within agricultural supply chains serve as critical institutional vehicles for deepening triple-sector integration (primary–secondary–tertiary) in rural economies, driving agricultural modernization, and advancing rural revitalization. However, sustaining alliance stability constitutes a complex dynamic process wherein inadequate stakeholder engagement and collaborative failures frequently precipitate alliance instability or even dissolution. Existing scholarship exhibits limited systematic examination of the micro-mechanisms and regulatory pathways through which multi-agent strategic interactions affect alliance stability from a dynamic evolutionary perspective. To address this gap, this research focuses on China’s core agricultural innovation vehicle—the Agricultural Industrialization Consortium—and examines the tripartite structure of “Leading Enterprise–Family Farm–Integrated Agricultural Service Providers.” We construct a tripartite evolutionary game model to systematically analyze (1) the influence mechanisms governing cooperative strategy selection, and (2) the regulatory effects of key parameters on consortium stability through strategic stability analysis and multi-scenario simulations. Our key findings are as follows: Four strategic equilibrium scenarios emerge under specific conditions, with synergistic parameter optimization constituting the fundamental driver of alliance stability. Specific mechanisms are as follows: (i) compensation mechanisms effectively mobilize leading enterprises under widespread defection, though excessive penalties erode reciprocity principles; (ii) strategic reductions in benefit sharing ratios coupled with moderate factor value-added coefficients are critical for reversing leading enterprises’ defection; (iii) dual adjustment of cost sharing and benefit sharing coefficients is necessary to resolve bilateral defection dilemmas; and (iv) synchronized optimization of compensation, cost sharing, benefit sharing, and value-added parameters represents the sole pathway to achieving stable (1,1,1) full-cooperation equilibrium. Critical barriers include threshold effects in benefit sharing ratios (defection triggers when shared benefits > cooperative benefits) and the inherent trade-off between penalty intensity and alliance resilience. Consequently, policy interventions must balance immediate constraints with long-term cooperative sustainability. This study extends the application of evolutionary game theory in agricultural organization research by revealing the micro-level mechanisms underlying alliance stability and providing a novel analytical framework for addressing the ‘strategy–equilibrium’ paradox in multi-agent cooperation. Our work not only offers new theoretical perspectives and methodological support for understanding the dynamic stability mechanisms of agricultural vertical alliances but also establishes a substantive theoretical foundation for optimizing consortium governance and promoting long-term alliance stability. Full article

To address the challenges of dispersing aerial targets such as bird flocks at civilian airports and drones conducting low-altitude surveillance in critical areas, including ports and convention centers, this paper proposes a hybrid Artificial Potential Field-Particle Swarm Optimization (APF–PSO) algorithm. The proposed solution integrates the real-time collision-avoidance capability of the artificial potential field method with the global network-optimization characteristics of the particle swarm algorithm to maximize protective coverage. Simulation results demonstrate that the hybrid algorithm achieves optimal performance in dispersion of aerial targets based on protective coverage under safety constraints, confirming its superior performance. The key innovations lie in implementing a dynamic repulsion field with exponential gain for emergency maneuvers, introducing a vertical avoidance module to resolve deadlock issues, and establishing a novel decoupled cooperative paradigm for scalable aerial protection networks. Full article

In off-road scenes, segmentation targets exhibit significant scale progression due to perspective depth effects from oblique viewing angles, meaning that the size of the same target undergoes continuous, boundary-less progressive changes along a specific direction. This asymmetric variation disrupts the geometric symmetry of targets, causing traditional segmentation networks to face three key challenges: (1) inefficientcapture of continuous-scale features, where pyramid structures and multi-scale kernels struggle to balance computational efficiency with sufficient coverage of progressive scales; (2) degraded intra-class feature consistency, where local scale differences within targets induce semantic ambiguity; and (3) loss of high-frequency boundary information, where feature sampling operations exacerbate the blurring of progressive boundaries. To address these issues, this paper proposes the ProCo-NET framework for systematic optimization. Firstly, a Progressive Strip Convolution Group (PSCG) is designed to construct multi-level receptive field expansion through orthogonally oriented strip convolution cascading (employing symmetric processing in horizontal/vertical directions) integrated with self-attention mechanisms, enhancing perception capability for asymmetric continuous-scale variations. Secondly, an Offset-Frequency Cooperative Module (OFCM) is developed wherein a learnable offset generator dynamically adjusts sampling point distributions to enhance intra-class consistency, while a dual-channel frequency domain filter performs adaptive high-pass filtering to sharpen target boundaries. These components synergistically solve feature consistency degradation and boundary ambiguity under asymmetric changes. Experiments show that this framework significantly improves the segmentation accuracy and boundary clarity of multi-scale targets in off-road scene segmentation tasks: it achieves 71.22% MIoU on the standard RUGD dataset (0.84% higher than the existing optimal method) and 83.05% MIoU on the Freiburg_Forest dataset. Among them, the segmentation accuracy of key obstacle categories is significantly improved to 52.04% (2.7% higher than the sub-optimal model). This framework effectively compensates for the impact of asymmetric deformation through a symmetric computing mechanism. Full article

As global temperatures continue to rise despite international mitigation efforts, geoengineering has emerged as a potential avenue for climate intervention. One of the most promising and ambitious concepts is the Planetary sunshade—a large-scale structure located at Lagrange Point L1, designed to reduce solar irradiance by physically blocking or redirecting incoming photons. This paper presents a structural design solution for this ambitious system, focusing on deployable mechanisms, frame architecture, and sail configurations that enable rapid mass production and deployment of solar sails components. The design process follows the European Cooperation for Space Standardization (ECSS) methodology through its early-phase stages, utilizing weighted decision matrices for concept selection and material evaluation. Finite element analysis (FEA) was used to validate structural integrity under Atlas V launch and operational conditions. The final design features a 1297 m² sail composed of four triangular segments, deployed via booms and stowed using a vertical folding pattern around a central spool. The booms incorporate arch-shaped cross-sections to enhance stiffness. This configuration achieves a radius expansion ratio of 25 and a sail efficiency factor of 0.5, ensuring survivability under Atlas V launch loads. Full article

While escalating environment and food safety challenges underscore the need for sustainable agri-food systems, promoting green agri-food production provides a promising pathway. The green agri-food value chain integrates green agri-food production with coordinated value-adding activities across the value chain. In developing such value chains, agricultural cooperatives emerge as a key player. This research integrates sustainability and value chain theories, aiming to study the role of China’s cooperatives in enabling green production and green value chains. It used qualitative methodology and interviews with management and members of three green vegetable cooperatives in Shandong Province, China, to offer an initial examination into this research area. The findings reveal that cooperatives play an important role in the green vegetable value chain and have a different level of vertical integration, with some having control over the whole value chain from input supply to retail. They also provide essential input, technical, and market support to enable green vegetable production and facilitate various value-adding activities. The study offers valuable insights into recommendations for enhancing value addition and facilitating green value chains. It also holds practical implications for practitioners and policymakers to strengthen cooperative development in China as an important intermediary for advancing agriculture sustainability. Full article

In the field of high-altitude operations, the frequent occurrence of fall accidents is usually closely related to safety measures such as the incorrect use of safety locks and the wrong installation of safety belts. At present, the manual inspection method cannot achieve real-time monitoring of the safety status of the operators and is prone to serious consequences due to human negligence. This paper designs a new type of high-altitude operation safety device based on the STM32F103 microcontroller. This device integrates ultra-wideband (UWB) ranging technology, thin-film piezoresistive stress sensors, Beidou positioning, intelligent voice alarm, and intelligent safety lock. By fusing five modes, it realizes the functions of safety status detection and precise positioning. It can provide precise geographical coordinate positioning and vertical ground distance for the workers, ensuring the safety and standardization of the operation process. This safety device adopts multi-modal fusion high-altitude operation safety monitoring technology. The UWB module adopts a bidirectional ranging algorithm to achieve centimeter-level ranging accuracy. It can accurately determine dangerous heights of 2 m or more even in non-line-of-sight environments. The vertical ranging upper limit can reach 50 m, which can meet the maintenance height requirements of most transmission and distribution line towers. It uses a silicon carbide MEMS piezoresistive sensor innovatively, which is sensitive to stress detection and resistant to high temperatures and radiation. It builds a Beidou and Bluetooth cooperative positioning system, which can achieve centimeter-level positioning accuracy and an identification accuracy rate of over 99%. It can maintain meter-level positioning accuracy of geographical coordinates in complex environments. The development of this safety device can build a comprehensive and intelligent safety protection barrier for workers engaged in high-altitude operations. Full article

This paper presents a bionic dual-fin underwater robot, inspired by the ocean sunfish, that achieves multiple swimming motions using only two vertically arranged fins. This work demonstrates that a mechanically simple platform can execute complex 2-D and 3-D motions through advanced control strategies, eliminating the need for auxiliary actuators. We control the two fins independently so that they can perform cooperative actions in the water, enabling the robot to perform various motions, including high-speed cruising, agile turning, controlled descents, proactive ascents, and continuous spiraling. The swimming performance of the dual-fin robot in executing multi-modal locomotion is experimentally analyzed through visual measurement methods and onboard sensors. Experimental results demonstrate that a minimalist dual-fin propulsion system of the designed ocean sunfish robot can provide speed (maximum cruising speed of 1.16 BL/s), stability (yaw amplitude less than 4.2°), and full three-dimensional maneuverability (minimum turning radius of 0.89 BL). This design, characterized by its simple structure, multiple motion capabilities, and excellent motion performance, offers a promising pathway for developing robust and versatile robots for diverse underwater applications. Full article

With the current acceleration of climate change, there is a global demand for sustainable development and carbon emission reduction. As a major link in the global supply chain, the logistics industry’s green and low-carbon transformation has become a critical breakthrough in achieving the objective of reducing carbon emissions. This study develops a multidimensional assessment index method for the green logistics level. The study selects 51 major economies worldwide from 2000 to 2022 as research subjects. The cloud model–entropy value–TOPSIS method is applied to measure the green logistics level. The results of the green logistics level are analyzed from the perspectives of developed and developing countries, and their spatiotemporal evolution characteristics are explored. The study shows that (1) the green logistics level in developed countries is relatively high, mainly due to policy-driven, core technology advantages. However, they continue to encounter issues, such as regional imbalance and excessive green costs. (2) The green logistics level in developing countries is in the middle to lower level, limited by technological dependence, outdated infrastructure, and so on. They are generally caught in a “high-carbon lock-in” situation. (3) From the perspective of time, the global level of green logistics shows a rising trend year by year. The peak of the kernel density curve of the green logistics level is characterized by an “I” shape. There is a significant disparity in each country’s green logistics level, although it is narrowing every year. (4) From the spatial perspective, the green logistics level in each country shows a rising trend year by year vertically, while the horizontal disparity between countries is enormous. The development of the green logistics level between continents is unbalanced. The study presents several recommendations, including boosting technology transfer, giving financial support, strengthening international cooperation, and developing green infrastructure, to promote the global logistics industry’s green and low-carbon transformation to accomplish sustainable development goals. Full article

This study presents a cooperative unmanned aerial system (UAS) designed to enable precise autonomous landings in unstructured environments using low-cost onboard vision technology. This approach involves a carrier UAV with a stabilized RGB camera and a neural inference system, as well as a lightweight tailsitter payload UAV with an embedded grayscale vision module. The system relies on visually recognizable landing markers and does not require additional sensors. Field trials comprising full deployments achieved an 80% success rate in autonomous landings, with vertical touchdown occurring within a 1.5 m radius of the target. These results confirm that vision-based marker detection using compact neural models can effectively support autonomous UAV operations in constrained conditions. This architecture offers a scalable alternative to the high complexity of SLAM or terrain-mapping systems. Full article

To address the challenge of evaluating a radar cross-section (RCS) for a non-cooperative aircraft with limited aerodynamic shape information, this paper presents a multi-source, data-driven inverse reconstruction method. This approach integrates data fusion techniques to facilitate an initial shape reconstruction, followed by an iterative optimization process that utilizes computational fluid dynamics (CFD) to enhance the shape, accounting for the aerodynamic performance. Additionally, an inverse deduction analysis is effectively employed to ascertain the characteristics of the power system, leading to the design of a double S-curved tail nozzle layout with stealth capabilities. An aerodynamic analysis demonstrates that at Mach 0.6, the lift-to-drag ratio peaks at 27.3 for the attack angle of 4°, after which it declines as the angle increases. At higher angles of attack, complex flow separation occurs and expands with the increasing angle. The electromagnetic simulation results indicate that under vertical polarization, the omnidirectional RCS reaches its peak as the incident angle is deflected downward by 10° and reduces with the growth of the angle, demonstrating angular robustness. Conversely, under horizontal polarization, the RCS is more sensitive to edge-induced rounding. The findings illustrate that this methodology enables accurate shape modeling for non-cooperative targets, thereby providing a fairly solid basis for stealth performance evaluation and the assessment of surprise effectiveness. Full article

This study addresses the heterogeneous formation control problem for cooperative unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) operating under input quantization constraints. A unified mathematical framework is developed to harmonize the distinct dynamic models of UAVs and USVs in the horizontal plane. The proposed control architecture adopts a hierarchical design, decomposing the system into kinematic and dynamic subsystems. At the kinematic level, an artificial potential field method is implemented to ensure collision avoidance between vehicles and obstacles. The dynamic subsystem incorporates neural network-based estimation to compensate for system uncertainties and unknown parameters. To address communication constraints, a linear quantization model is introduced for control input processing. Additionally, adaptive control laws are formulated in the vertical plane to achieve precise altitude tracking. The overall system stability is rigorously analyzed using input-to-state stability theory. Finally, numerical simulations demonstrate the effectiveness of the proposed control strategy in achieving coordinated formation control. Full article

As urban populations expand and congestion intensifies, traditional ground transportation struggles to satisfy escalating mobility demands. Unmanned Electric Vertical Take-Off and Landing (eVTOL) aircraft, as a key enabler of Urban Air Mobility (UAM), leverage low-altitude airspace to alleviate ground traffic while offering environmentally sustainable solutions. However, supporting high bandwidth, real-time video applications, such as Virtual Reality (VR), Augmented Reality (AR), and 360° streaming, remains a major challenge, particularly within bandwidth-constrained metropolitan regions. This study proposes a novel Unmanned Aerial Vehicle (UAV)-enabled video streaming architecture that integrates 6G wireless technologies with intelligent routing strategies across cooperative airborne nodes, including unmanned eVTOLs and High-Altitude Platform Systems (HAPS). By relaying video data from low-congestion ground base stations to high-demand urban zones via autonomous aerial relays, the proposed system enhances spectrum utilization and improves streaming stability. Simulation results validate the framework’s capability to support immersive media applications in next-generation autonomous air mobility systems, aligning with the vision of scalable, resilient 3D transportation infrastructure. Full article