Search Results (785)

The chemical looping steam reforming of methane (CLSR) employing Fe-containing oxygen carriers can produce syngas and hydrogen simultaneously. However, Fe-based oxygen carriers exhibit low CH₄ activation ability and cyclic stability. In this work, oxygen carriers with fixed Fe content and different Fe/Ni ratios were synthesized by the sol–gel method to investigate the effects of Ni-Fe-Al interactions on CLSR performance. Ni-Fe-Al interactions promote the growth of the spinel structure and regulate both the catalytic sites and the available lattice oxygen, resulting in the CH₄ conversion and CO selectivity being maintained at 96–98% and above 98% for the most promising oxygen carrier, with an Fe₂O₃ content of 20 wt% and Fe/Ni molar ratio of 10. The surface, phase, and particle size were kept the same over 90 cycles, leading to high stability. During the CLSR cycles, conversion from Fe³⁺ to Fe²⁺/Fe⁰ occurs, along with transformation between Ni²⁺ in NiAl₂O₄ and Ni⁰. Overall, the results demonstrate the feasibility of using spinel containing earth-abundant elements in CLSR and the importance of cooperation between oxygen release and CH₄ activation. Full article

There is a strong coupling between two arms in cooperative operations of dual-arm robots. To enhance the coordination and cooperation ability of dual-arm robots, a force-closure-based weighted hybrid force/position fuzzy coordination control method is proposed. Firstly, to improve the grasping stability of dual-arm robots, the force-closure dynamic constraints are established by combining the friction cone constraints with the force and torque balance constraints. Then the optimal distribution of contact force is performed according to the minimum energy consumption principle. Secondly, to enhance the coordination of dual-arm robots, the weighted hybrid force/position control method is modified by adding the synchronization error between two arms. Then the Lyapunov method is adopted to prove the stability of the proposed coordination control method. Thirdly, the fuzzy self-tuning technique is adopted to adjust the control gains automatically. Lastly, a simulation and experiment are performed for collaborative transport. The results show that, compared with the position coordination control and the traditional hybrid force/position control, the weighted hybrid force/position fuzzy coordination control can improve control accuracy and has good cooperation ability and strong robustness. Therefore, the proposed method can effectively realize the coordination control of dual-arm robots. Full article

Mobile Cognitive Radio Networks (MCRNs) have emerged as a promising solution to address spectrum scarcity by enabling dynamic access to underutilized frequency bands assigned to Primary or Licensed Users (PUs). These networks rely on Cooperative Spectrum Sensing (CSS) to identify available spectrum, but this collaborative approach also introduces vulnerabilities to security threats—most notably, Spectrum Sensing Data Falsification (SSDF) attacks. In such attacks, malicious nodes deliberately report false sensing information, undermining the reliability and performance of the network. This paper investigates the application of machine learning techniques to detect and mitigate SSDF attacks in MCRNs, particularly considering the additional challenges introduced by node mobility. We propose a hybrid detection framework that integrates a reputation-based weighting mechanism with Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers to improve detection accuracy and reduce the influence of falsified data. Experimental results on software defined radio (SDR) demonstrate that the proposed method significantly enhances the system’s ability to identify malicious behavior, achieving high detection accuracy, reduces the rate of data falsification by approximately 5–20%, increases the probability of attack detection, and supports the dynamic creation of a blacklist to isolate malicious nodes. These results underscore the potential of combining machine learning with trust-based mechanisms to strengthen the security and reliability of mobile cognitive radio networks. Full article

At present, although there are a variety of assessment systems to rate the financing ability of enterprises, these systems suffer from the problems of outdated indicators and subjective weighting methods. In this paper, the impact of ESG performance on financing ability is taken as an evaluation index and combined with 13 other indexes to construct a new TOPSIS assessment system. Cooperative game theory in the form of the entropy weight method and a BP neural network is used to avoid the subjectivity of weighting. After establishing the evaluation model, we selected cross-sectional data from 4590 listed companies on the Shanghai and Shenzhen stock exchanges in 2023 to train the evaluation model and explore the impact of various indicators on financing capabilities. The results show the following: (1) Total revenue and total assets of main board companies are the main factors affecting financing ability. (2) Total revenue growth rate, total revenue, and R&D costs of Science and Technology Innovation Board Market (STAR Market) companies are the main factors affecting the financing ability. (3) Growth Enterprise Market (GEM) companies’ total revenue and R&D costs are the main factors affecting financing ability. This study uses data from 2023. In practical applications, it is recommended to use the latest data for evaluation and analysis, and to update the weights every six months. Full article

Industrial symbiosis network (ISN) is crucial to improving resource utilization efficiency and promoting sustainable development. In order to mitigate the damage caused to symbiotic systems by risk propagation, this paper constructs a directed weighted multilayer hypernetwork model that considers bounded rational decision and risk propagation coupling ($\mathrm{U}{\mathrm{A}}^{\mathrm{P}}{\mathrm{A}}^{\mathrm{H}}\mathrm{U}-\mathrm{S}\mathrm{I}\mathrm{S}$), providing a new method for risk management in industrial symbiosis networks. This paper constructs a weighted hypernetwork model to simulate the interaction of risk information in a symbiotic network and uses a time-varying adaptive propagation mechanism to describe the changes in bounded rational decisions made by enterprises during the risk information interaction process. A directed weighted network is developed to simulate the evolution process of an industrial symbiosis network, with the network topology representing the risk propagation path. The study also considers the roles of mass media and crowd effects and innovatively introduces the assumption of decision incubation periods. The proposed coupled dynamic model is theoretically analyzed and numerically simulated by using the Microscopic Markov Chain Approach (MMCA). The findings indicate that enhancing the enterprises’ risk response willingness and risk perception ability, improving the risk recovery ability, and cooperating with timely and accurate media reports can effectively inhibit the risk propagation on ISN. Full article

In this study, we investigated environmentally responsive photoluminescence color changes in water using an amphiphilic flavin derivative (1a) functionalized with an alkylsulfonate group. At low concentrations and room temperature, 1a exhibited a green emission. Upon increasing the concentration, thermodynamically stable micelle-like aggregates were formed, leading to a yellow emission. In contrast, under rapid freezing conditions, fibrous aggregates were formed under kinetic control, which also exhibited a yellow emission. These distinct aggregation modes are attributed to the cooperative effects of molecular design: the π-stacking ability of the tricyclic isoalloxazine core, flexible long alkyl chains, and the hydrophilic sulfonate moiety. This work demonstrates photoluminescent color switching based on aggregation-state control of a biogenic and potentially sustainable flavin luminophore, offering a new perspective for designing responsive and sustainable photofunctional materials. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

Collagen hydrogels serve as biomimetic scaffolds that closely resemble the natural extracellular matrix, thus providing an ideal 3D biocompatible environment for cells. However, based on our previous experience, not all collagen isolates are capable of gelling, which appears to depend on the type, origin, species, age and sex of the source animal and the collagen isolation method applied. We therefore decided to evaluate porcine collagen-rich materials isolated from two different porcine genotypes applying two different specific isolation methods, and to analyse other main components, i.e., lipids and glycosaminoglycans, as well as amino acid composition and structural and morphological properties. While all the collagen isolates obtained were subjected to the gelling process, only one of them successfully gelled. In addition, the gelling ability of this isolate was confirmed repeatedly on collagens that were isolated from other pigs of the same porcine genotype. The results revealed that the gelling process proceeds via cooperation between the composition and the structure of the collagen isolate. With respect to the composition, one of the most important factors in terms of the success of the gelation process of collagen isolates concerns elevated glycosaminoglycan contents. The structural factors that characterise collagen isolates, i.e., cross-links (immature and mature) and their mutual ratio, as well as the presence of telopeptides, strongly impact the progress of the gelling process and the resulting character of the hydrogel structure. All these factors are influenced by the isolation procedure. Full article

The final phase of the manufacturing process for any artefact involves their surface finishing operations. This phase entails the precise removal of small volumes of material to achieve a specific surface roughness, which is essential for ensuring the artefact’s post-production performance and endurance. For certain tooling, such as molds and dies, the finishing operation can be particularly significant, often equating to fifty percent of the total production time and a fifth of the overall manufacturing cost. In recent years, collaborative robotics has come to the fore. These advanced systems allow manufacturers to harness the positive attributes of robots, such as their repeatability, endurance, and strength, while simultaneously leveraging the unique benefits of human workers, including their process knowledge, problem-solving abilities, and adaptability. This co-operation between human and robotic capabilities has opened new avenues for efficiency and precision in the finishing process. This paper investigates the current advancements in collaborative robotic finishing, providing a comprehensive overview of the latest technologies and methodologies. It also highlights existing research gaps that need to be addressed to further enhance the effectiveness of these systems. Additionally, the paper suggests potential areas for future investigation, aiming to drive continued innovation and improvement in the field of collaborative robotic finishing operations. Full article

Currently, environmental issues negatively affect both firm performance and economic development, prompting society to expect enterprises to address these issues more effectively. In response, organizations, particularly manufacturing enterprises, have begun to adopt green innovation. This study examines how humble leadership in enterprise management affects organizational green innovation performance. Additionally, this study explores the mediating role of the organizational caring ethical climate and the moderating roles of the organizational structure and unabsorbed organizational resource slack. This study involved top managers from 357 manufacturing enterprises in Zhejiang Province, yielding 306 valid questionnaires. The Hierarchical regression technique is used to analyze the survey data. An analysis of the data shows that humble leadership positively affects organizational green innovation performance, with the organizational caring ethical climate serving as a mediator. Furthermore, the organizational structure and organizational resource slack positively moderate the effect of the organizational caring ethical climate on green innovation performance. This study validates and enriches social learning theory; social exchange theory; conservation of resource theory; and ability, motivation, and opportunity theory. It also provides new insights into the relationship between humble leadership and green innovation performance and expands research on the moderators of the relationship between the organizational caring ethical climate and green innovation performance. The findings suggest that managers of manufacturing enterprises should adopt humble leadership, promote a caring ethical climate, and enhance cooperation with stakeholders. 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

Background/Objectives: Pediatric acquired brain injury (ABI) often results in persistent challenges that extend beyond motor impairments, affecting quality of life (QoL), social participation, and engagement in physical activity. Given the complexity and chronicity of these outcomes, there is a pressing need for multidimensional interventions grounded in the International Classification of Functioning, Disability and Health (ICF). Sport-based exercise interventions, when developmentally adapted and tailored to individual interests, may promote intrinsic motivation, peer connection, and sustainable engagement—factors especially relevant in pediatric ABI populations, who often experience reduced physical activity and social isolation. However, standardized, replicable protocols specifically tailored to this population remain scarce. This study presents the protocol for a randomized controlled trial evaluating the effects of a 16-week sport-based intervention on QoL, social participation, physical activity engagement, and motor functioning tailored for adolescents with pediatric ABI. Methods: Participants will be randomly assigned to an intervention group or a control group receiving usual care. The intervention consists of one weekly 60-minute session, led by trained professionals in adapted physical activity and pediatric neurorehabilitation. It combines sport-based motor skill training, cooperative games, and group activities specifically tailored to each child’s developmental level, motor abilities, and preferences. Outcomes will be assessed at baseline and following the 16-week intervention period, focusing on QoL, participation, physical activity engagement, and motor functioning. Discussion: This study introduces a structured, child-centered model that bridges clinical rehabilitation and community-based sport. By integrating motor and psychosocial targets through a group sport-based intervention, it aims to enhance recovery across ICF domains. Findings may inform interdisciplinary practice and support the development of sustainable strategies to promote long-term engagement and well-being in adolescents with ABI. Full article

To address the challenge of developing the first squaramide-based ion pair receptor acting as an iniferter in the polymerization process, a well-known BDPA molecule with specific radical functions was incorporated into its structure. The developed ditopic receptor demonstrated the ability to cooperatively bind ion pairs. Moreover, it proved to be an effective iniferter in the polymerization reaction using methyl methacrylate. The polymerization process preserved the ion-binding properties of the receptor, enabling the formation of functional polymeric materials. It was shown that the resulting polymer with the embedded receptor can be used for salt extraction from both solid and liquid phases, whereas the reference receptor lacking the BDPA unit did not exhibit this capability. This opens new avenues for the design of intelligent and selective polymeric materials for applications in supramolecular chemistry and separation technologies. Full article

Core problems in agile software project scheduling, such as resource-constrained balancing and iteration cycle optimization, embody the pursuit of symmetry. Simultaneously, optimization algorithms find extensive applications in symmetry problems, for example, in graphs and pattern recognition. Considering the cooperation among multiple teams and environmental changes in complex agile software development, a dynamic periodic scheduling model for multi-team agile software project is constructed, which includes three tightly coupled sub-problems, namely user story selection, user story-development team allocation, and task-employee allocation. To solve the model, a group learning particle swarm optimization algorithm is proposed, which includes three novel strategies. First, the population is divided into four groups based on dual indicators of objective values and potential values. Second, different learning objects are selected according to the characteristic of each group so that the search diversity can be improved. Third, to react to the environmental changes and enhance the mining ability, heuristic population initialization and local search strategies are designed by utilizing the problem-specific information. Systematic experimental results on 13 instances indicate that compared with the state-of-the-art algorithms, the proposed algorithm is able to provide a schedule with better precision for the project manager in each sprint of the agile development. Full article

Power distribution systems are witnessing a growing deployment of distributed, inverter-based renewable resources such as solar generation. This poses certain challenges such as rapid voltage fluctuations due to the intermittent nature of renewables. Volt-Var control (VVC) methods have been proposed to utilize the ability of inverters to supply or consume reactive power to mitigate fast voltage fluctuations. These methods usually require a detailed power network model including topology and impedance data. However, network models may be difficult to obtain. Thus, it is desirable to develop a model-free method that obviates the need for the network model. This paper proposes a novel model-free cooperative control method to perform voltage regulation and reduce inverter aging in power distribution systems. This method assumes the existence of time-series voltage and load data, from which the relationship between voltage and nodal power injection is derived using a feedforward artificial neural network (ANN). The node voltage sensitivity versus reactive power injection can then be calculated, based on which a cooperative control approach is proposed for mitigating voltage fluctuation. The results obtained for a modified IEEE 13-bus system using the proposed method have shown its effectiveness in mitigating fast voltage variation due to PV intermittency. Moreover, a comparative analysis between model-free and model-based methods is provided to demonstrate the feasibility of the proposed method. Full article