Cooperative Clusters Research Articles

The future landscape of immunology in COPD: A bibliometric analysis

BackgroundThis study was conducted to provide a comparative and comprehensive analysis of the last 50 years of research in the immunology of chronic obstructive pulmonary disease (COPD) and to identify future research priorities and directions. MethodsWe obtained publication information from the Web of Science Core Collection (WoSCC) database. Scimago Graphica, CiteSpace, VOSviewer, GraphPad Prism, Microsoft Excel 365, and the R package “bibliometrix” were used for bibliometric analysis. The methodologies used were burst citation, clustering and co-citation, keyword co-occurrence, and cooperative network graph clustering algorithms. ResultsA total of 2239 articles and reviews were included in the analysis. The number of annual publications showed an upward trend amidst fluctuations. The countries, institutions, journals, and authors with the highest number of publications were the USA, Imperial College London (ICL), Journal of Allergy and Clinical Immunology, and Murphy TF. There are 58 references and 13 keywords with a burst citation nature in the last 3 years, related to the study of inflammatory mechanisms. ConclusionWe used bibliometric analysis to understand the research progress regarding immune function in COPD. Meanwhile, we found that the hotspots of COPD immunology in basic research focus on the analysis of etiology and risk factors, and the exploration of the immune system; and the hotspots of COPD in clinical research focus on epidemiology, and the prevention and management of different subtypes of COPD.

A novel coalition game based distributed cooperative content caching in mobile edge networks

In mobile edge networks with mobile vehicles, road-side units (RSUs) can be used as edge nodes to cache some popular contents for moving vehicles in advance, and therefore improving the transmission efficiency and quality of user experience during vehicle-to-infrastructure (V2I) content communication. Transferring large files to a fast-moving car is a challenging task because of the limited communication time between the vehicle and the RSU. In this paper, we propose a cooperative content placement model for RSUs, in which multiple RSUs can jointly complete the cache and transmission of large files, so that the vehicle users can obtain the target content in the process of high-speed movement. To form cooperative coalitions between RSUs and place as much content as possible, a novel coalition game based distributed cooperative content caching scheme, namely CGD3C, is proposed, in which an RSU cooperative clustering algorithm and a resource balancing based coalition formation algorithm is proposed to merge and split the coalitions, so as to obtain a stable coalition structure. Finally, a cooperative content caching determination algorithm is proposed to determine the content placement with objective of maximizing the current interests. Our simulation results show that our proposed CGD3C can achieve stable and effective content delivery for high-speed moving vehicles, form an optimal RSU coalition structure independently, as well as improve the profit by 15%, the caching resource utilization by 24%, and the request hit rate by 12% on average in comparison with existing schemes.

A Bibliometric Study on Global Snakebite Research Indexed in Web of Science

Objective: To conduct a bibliometric analysis of the global snakebite literature to provide a reference for the future development of snakebite research.Methods: The Web of Science citation analysis tools, VOSviewer and CiteSpace V were used to carry out the bibliometric analysis of the literature and generate visualization maps.Results: The number of publications has increased at a considerably accelerated rate in the past 8 years. Nine distinct cooperation clusters were formed between institutions and countries. Keyword clustering yielded nine well-structured clusters covering two major topics, i.e., snakebite envenoming and antivenom. Burstiness detection revealed eight keywords with strong emergence, including neglected tropical diseases, Elapidae, Viperidae, and Russell’s viper, which have sustained popularity up to the present.Conclusion: Current research on snakebites has gradually garnered attention from the academic community. Cooperation papers between nations severely affected by snakebite and those with higher economic status received more attention. The continued exploration of therapeutic mechanisms, the development of antivenoms or alternative medicines, and primary prevention of snakebites to ensure the safety of populations in impoverished regions should be prioritized by international scholars. The epidemiological evidence and the timely translation of research findings should be valued by policymakers.

Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks.

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)'s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.

Self-organization in mobile populations promotes the evolution of altruistic punishment

Altruistic punishment has been shown to sustain cooperation in social dilemmas. However, the influence of individual migration has often been overlooked in previous studies investigating the emergence of altruistic punishment. In this study, we address this gap by exploring the evolution of altruistic punishment in a mobile population. To capture the complexities of individual migration decisions, we propose a weighted migration model that incorporates individuals’ assessments of potential gains and losses. Our findings reveal that migration prioritizing potential benefits contributes to population clustering, and such spatial self-organization patterns play a vital role in promoting the flourishing of altruistic punishment. Conversely, when individuals are more inclined to avoid losses, it intensifies the dissolution of cooperative clusters and inhibits the evolution of cooperation. Moreover, our results suggest that pro-social punishment can still be maintained during the migration process, even in scenarios with low punishment efficiency, as long as individuals are capable of imposing sufficiently large amounts of punishments. This study provides valuable insights into the emergence and maintenance of altruistic punishment.

Evolution of cooperation in spatial public goods game based on heterogenous social preferences and dynamic punishment

The mechanism of promoting cooperation in the public goods game has always been concerned by scholars. However, most of the existing studies are based on the premise that participants are self-interested. In order to explore why some sellers on e-commerce platforms voluntarily maintain the platform’s reputation, we incorporate heterogeneous social preferences of sellers into the spatial public goods game. We find that heterogeneous social preferences can enhance cooperation by improving collective rationality. Specifically, the altruistic preference of sellers can greatly reduce free-riding behavior, while the inequality aversion preference has a little inhibitory effect. Interestingly, when the benefit of maintaining the platform’s reputation is relatively high, the reciprocal preference can inhibit cooperation, but it can promote cooperation when the benefit is relatively small. This is due to the existence of some loosely connected but stable cooperative or defective clusters of sellers in e-commerce platforms. Furthermore, we propose a dynamic punishment mechanism to punish free riders. We observe that the dynamic punishment mechanism is more effective than the static punishment mechanism in solving the second-order free-riding problem faced by punishers. Increasing the enhancement factor of public goods is identified as a fundamental approach to mitigating this problem.

Industrial Clusters in Slovakia-Urban Development

For almost three decades, Slovakia has been undergoing reform processes related to restructuring industry and in expectation of the sustainability of the industrial structure with an important link to the landscape. The aim of this article is the presentation of the development models of the industrial clusters in synergy with the economic dimension, landscape, settlement structure, and sustainability. The background of the article is an evaluation of industry and its impact on the landscape between the 19th and 21st centuries. The development of industry influenced the territorial and spatial development of towns and landscapes, forcing a new identity on them. Development mirrored the rate of innovation in the domain of technologies and construction. The issues examined herein required the application of a combination of quantitative and qualitative research methods, including a creative method called SCAMPER. Our scheme and models of industrial parks and cooperation clusters are a result of theoretical and field research and reflect variable mobility and sustainability. The conclusions of the research indicate a turnover in the original order of importance in the industrial activity: environment–man–production. This is the contribution of evolution and the building of eco-friendly industrial structures.

Optimal Broadcast Scheduling Algorithm for a Multi-AUV Acoustic Communication Network

In systems of multiple autonomous underwater vehicles (AUVs), to achieve cooperative operation and cluster intelligence, information is often disseminated via broadcasting. However, due to the long propagation delay and slow transmission rate of underwater acoustic communication, traditional broadcast scheduling algorithms require a long broadcast period to avoid signal collision. To improve the channel utilization rate as much as possible and improve the update rate for broadcast information, we propose an optimal broadcast scheduling algorithm. This algorithm uses the location information of AUVs to adjust the broadcast sequence and broadcast schedule, to achieve the shortest possible collision-free broadcast period in the broadcast network for the current node distribution. Simulation experiments show that this algorithm can achieve a broadcast period much shorter than that of traditional TDMA and higher channel utilization without signal collision. In addition, the simulations prove the feasibility of applying the algorithm in an actual MAC protocol.

Energy and Distribution-Aware Cooperative Clustering Algorithm in Internet of Things (IoT)-Based Federated Learning

In Internet of Things (IoT)-based federated learning (FL), if IoT devices are located far from the base station (BS), they consume lots of energy to transmit the updated parameters to BS, which can cause energy depletion of IoT devices. To mitigate this problem, IoT devices can be clustered and the clustered IoT devices transmit the updated parameters to their cluster headers (CHs) instead of BS. However, if the aggregated data distribution in each cluster is non-independent and identically distributed (non-IID), the desired accuracy of the model cannot be achieved. In this paper, we propose an energy and distribution-aware cooperative clustering algorithm (EDA-CCA) where several IoT devices with a sufficient energy level are selected as CHs. By considering the distance to these CHs and BS and the data distribution of IoT devices, other IoT devices are clustered, and then a hierarchical parameter aggregation (i.e., sequential aggregation within each cluster and synchronous aggregation between CHs and FL server) is conducted. Evaluation results demonstrate that EDA-CCA can make the model having the desired accuracy with the lowest energy consumption among the comparison schemes.

Big Data Processing over Cloud Computing Environment

The topics of this special paper are mainly devoted to the most recent research, development and applications in the field of cloud computing, big data processing in the various fields, e.g. Web blog, search engine, image processing, and industry. Among all of the submitted manuscripts, 15 papers were selected and included in this special paper. Creative thoughts and interesting inspirations are presented, discussed, and disseminated in this paper. First, there are three papers on the cloud architecture and optimization for big data processing. J. Peng, et al. discussed the problems of load balance for massive data processing on cloud considering the data processing efficiency and nodes’ capability. A load balance approach for massive data, based on the consistent hashing method, was presented to improve the data processing equalization and high processing efficiency. Large-scale sensor networks and internet of things are one of the most important application fields in big data. Y. Sun, et al. proposed a cloud computing system to support PaaS in energy power applications—PROXZONE. PROXZONE provides the cloud services uniform monitoring and authorization, and cloud message service, which can support good performance when dealing with big data. Meanwhile, S. Xu, et al. discussed an improved cooperative dynamic cluster model in multitasks mobile network, and proposed a cooperative message forwarding mechanism to improve the network performance.

Harmful strong agents and asymmetric interaction can promote the frequency of cooperation in the snowdrift game

The emergence and mechanism of cooperation in social dilemmas have always been fundamental issues in evolutionary game theory. In this paper, we study the snowdrift game, in which individuals in a stronger position can gain additional benefits in cooperation with weaker individuals due to differences in status. Meanwhile, innocuous-type strong individuals will not harm their partners’ interests, while harmful-type ones will. In a mixed population, the strong individuals in these two asymmetric snowdrift games are more inclined to cooperate, but the weaker ones are more willing to choose defection. The average cooperation frequency is not lower than in symmetric populations. In structured populations, on the other hand, asymmetry promotes the formation of a configuration with a cooperator cluster with a core of strong cooperators, thereby promoting cooperation among individuals. However, only within a small range of parameters the frequency of cooperation will be slightly reduced. Harmful-type strong individuals can be more conducive to promoting cooperation than innocuous-type ones in some cases. The existence of harmful and strong individuals in the snowdrift game further helps to solve the social loafing effect.

Evolution of cooperation with tag-based expulsion in spatial public goods game

We introduce tag-based expulsion and explore its dynamic influence on cooperative behavior within a spatial public goods game. Our model employs a multi-agent system where agents, characterized by their strategies and tags, participate in public goods games and expel neighbors with dissimilar tags. The expulsion process effectively reshapes the interaction structure, providing a protective shield for cooperative clusters against defector infiltration. We demonstrate that expulsion, tag mutability, and tolerance are pivotal in driving cooperation levels and the optimal interaction structure. Even in less favorable conditions characterized by low enhancement factors, cooperation can emerge given appropriate tolerance levels. Our research underscores the importance of moderate population density, tag-based expulsion, and well-calibrated expulsion intensity in fostering cooperation, introducing fresh perspectives on the interplay between social dynamics and evolutionary processes. Additionally, we provide evidence of the dynamic adaptability of expulsion mechanisms, suggesting they may be integral to understanding cooperation in various societal contexts. These findings open up new avenues for further exploration of the complexity of cooperative behaviors and the roles of expulsion mechanisms in the evolution of cooperation.

Articles on hemorrhagic shock published between 2000 and 2021: A CiteSpace-Based bibliometric analysis

ObjectiveTo conduct a bibliometric analysis of literature on hemorrhagic shock published between 2000 and 2021 with the help of Citespace to explore the current status, hotspots and research trends in this regard, with the results presented in a visualized manner. MethodsThe data over the past 22 years were retrieved from the Web of Science (WOS) Core Collection database and downloaded as the “Full Record and Cited References”. Cooperative analysis, cluster analysis, co-citation analysis, and burst analysis were performed based on the data on countries/regions, institutions, journals, authors, and keywords through Citespace. ResultsA total of 2027 articles were retrieved. The number of annual publications fluctuated but was generally on an upward trend. The United States stands out as the most productive country (989 articles), the University of Pittsburgh the most productive publishing institution (109 articles), SHOCK the most cited journal (1486 articles), TAO LI the most productive author (40 articles), DEITCH EA the most cited author (261 times of citation), hemorrhagic shock the most frequent keyword (725 times of occurrence), and “traumatic brain injury” the most covered article in keyword clustering (29 articles). The burst analysis revealed Harvard University as the institution with the highest strength value and the Journal of Trauma and Acute Care Surgery the most important journal. It was also concluded that HASAN B ALAM, AARON M WILLIAMS, and LIMIN ZHANG may continue to publish high-quality articles in the future. In the meanwhile, both “protect” and “transfusion” were considered the hotspots and trends in current research. ConclusionsThe United States has been a major contributor to the publication of the articles over the past 22 years, with the most productive publishing institution, the most cited journal, and the most cited author all coming from the US. Hemorrhagic shock, injury, resuscitation, trauma, models, activation, expression, fluid resuscitation, rats, and nitric oxide are hot topics in relevant research. According to the keyword burst analysis, the areas related to “protect” and “transfusion” may rise as the research directions in the future. However, since the hotspots in the research of hemorrhagic shock are short-lived and fast-changing, the researchers should pay more attention to the development trend in this field.

Deep Reinforcement Learning for Energy Efficiency Maximization in Cache-Enabled Cell-Free Massive MIMO Networks: Single- and Multi-Agent Approaches

Cell-free massive multiple-input multiple-output (CF-mMIMO) is an emerging beyond fifth-generation (5 G) technology that improves energy efficiency (EE) and removes cell structure limitation by using multiple access points (APs). This study investigates the EE maximization problem. Forming proper cooperation clusters is crucial when optimizing EE, and it is often done by selecting AP–user pairs with good channel quality or aligning AP cache contents with user requests. However, the result can be suboptimal if we determine the clusters based solely on either aspect. This motivates our joint design of user association and content caching. Without knowing the user content preferences in advance, two deep reinforcement learning (DRL) approaches, i.e., single-agent reinforcement learning (SARL) and multi-agent reinforcement learning (MARL), are proposed for different scenarios. The SARL approach operates in a centralized manner which has lower computational requirements on edge devices. The MARL approach requires more computation resources at the edge devices but enables parallel computing to reduce the computation time and therefore scales better than the SARL approach. The numerical analysis shows that the proposed approaches outperformed benchmark algorithms in terms of network EE in a small network. In a large network, the MARL yielded the best EE performance and its computation time was reduced significantly by parallel computing.

Asymmetric effects of interaction and dispersal graphs on evolutionary dynamics

Population structure is a critical factor in shaping evolutionary dynamics. It constrains not only who interacts with whom, but also who learns from whom. In many real-world cases, such spatial constraints can lead to asymmetric interaction and strategy dispersal. For example, individuals who are geographically and socially too distant to interact physically may still learn from each other through social media. Despite the ubiquity of the asymmetry, their impact on evolutionary outcomes, particularly in the presence of structural heterogeneity, is not well understood. In this paper, we investigate their influence on evolutionary dynamics. Our analytical and simulation results demonstrate that although population structure can lead to the formation of clusters of cooperators, disrupting the balance between the interaction and dispersal graphs can severely impede cooperation. Moreover, we find that increased network heterogeneity exacerbates the cooperation-inhibiting effects. We also examine the roles of the interaction and dispersal networks separately. By modifying the connections in each network, we find that altering the dispersal network has a more pronounced effect on cooperation compared to the interaction network. Our work emphasizes the importance of the correlation between interaction and strategy dispersal and highlights that modifying the paths of strategy or information dissemination can be a more effective way of promoting social prosperity than modifying the interaction networks.

Research on task assignment algorithm of heterogeneous aircraft cooperative cluster in dynamic scene

With the aim of minimizing task completion time and reducing resource consumption, this paper investigates the dynamic task allocation problem of a heterogeneous aircraft cooperative cluster with multiple fire spots in the forest. Firstly, we establish the fire point propagation model and task assignment model to enhance task assignment accuracy. Based on this, we propose the Chaotic Cosine Fusing T-Distribution Variation and Quasi-Reflection Learning Grey Wolf Optimization algorithm (CTQGWO), which integrates distributed mutation and quasi-reflective learning. By incorporating chaos population initialization of the Tent map, adaptive control parameter adjustment strategy, distributed mutation, and quasi-reflective learning strategy, we can enhance the convergence speed and accuracy of the algorithm. Finally, taking into account the complexity of the forest and the instability of the aircraft, we develop a task reassignment algorithm that can quickly allocate and reduce the number of communications. Through experiments, our algorithm enables rapid decision-making for forest fire emergency scheduling, under the condition of limited forest fire resources, effectively reducing the total rescue time and minimizing the consumption of rescue resources.

Inhibition and activation of interactions in networked weak prisoner's dilemma.

In the framework of the coevolution dynamics of the weak prisoner's dilemma, inspired by prior empirical research, we present a coevolutionary model with local network dynamics in a static network framework. Viewing the edges of the network as social interactions between individuals, when individuals play the weak prisoner's dilemma game, they accumulate both payoffs and social interaction willingness based on a payoff matrix of the social interaction willingness we constructed. The edges are then inhibiting or activating based on the social interaction willingness of the two individuals, and individuals only interact with others through activated edges, resulting in local network dynamics in a static network framework. Individuals who receive more cooperation will be more likely to activate the edges around them, meaning they will participate in more social interactions. Conversely, individuals who receive more defects will do the opposite. Specifically, we investigate the evolutionary dynamics of cooperation under different levels of sensitivity to social interaction willingness and the temptation to defect. Through the simulation, we find that sparse cooperator clusters can expand greatly when social interaction sensitivity and temptation to defect are low. In contrast, dense cooperator clusters form rapidly in a high social interaction sensitivity, which protects the cooperation from high temptation.

COOPERATIVE LEARNING CLUSTER IN HISTORY CLASSES

The article mentions the problems of using a cooperative educational cluster that is compatible with the modernization processes of teaching history in general education schools. Collaborative learning cluster in history teaching is interpreted as an important tool for improving the level of students' cognitive activity, methodological recommendations are presented within the topic.

The Evolution of Cooperation in Multigames with Uniform Random Hypergraphs

How to explain the emergence of cooperative behavior remains a significant problem. As players may hold diverse perceptions on a particular dilemma, the concept of multigames has been introduced. Therefore, a multigame is studied within various binary networks. Since group structures are common in human society and a person can participate in multiple groups, this paper studies an evolutionary multigame with high-order interaction properties. For this purpose, a uniform random hypergraph is adopted as the network structure, allowing players to interact with all nodes in the same hyperedge. First, we investigate the effect of the multigame payoff matrix differences on the evolution of cooperation and find that increasing the differences in the payoff matrix promotes cooperation on the hypergraph network. Second, we discover that an increase in the average hyperdegree of the hypergraph network promotes network reciprocity, wherein high-hyperdegree nodes influence surrounding nodes to form a cooperator cluster. Conversely, groups with a low hyperdegree are more susceptible to betrayal, leading to a decline in cooperation.

Cyclops States in Repulsive Kuramoto Networks: The Role of Higher-Order Coupling.

Repulsive oscillator networks can exhibit multiple cooperative rhythms, including chimera and cluster splay states. Yet, understanding which rhythm prevails remains challenging. Here, we address this fundamental question in the context of Kuramoto-Sakaguchi networks of rotators with higher-order Fourier modes in the coupling. Through analysis and numerics, we show that three-cluster splay states with two distinct coherent clusters and a solitary oscillator are the prevalent rhythms in networks with an odd number of units. We denote such tripod patterns cyclops states with the solitary oscillator reminiscent of the Cyclops' eye. As their mythological counterparts, the cyclops states are giants that dominate the system's phase space in weakly repulsive networks with first-order coupling. Astonishingly, the addition of the second or third harmonics to the Kuramoto coupling function makes the cyclops states global attractors practically across the full range of coupling's repulsion. Beyond the Kuramoto oscillators, we show that this effect is robustly present in networks of canonical theta neurons with adaptive coupling. At a more general level, our results suggest clues for finding dominant rhythms in repulsive physical and biological networks.