Cooperative Framework Research Articles

A decentralized framework for economic‐resilient operation of networked microgrids, considering demand response program

AbstractThe occurrence of natural disasters can disrupt the normal functioning of distribution networks. In this situation, the activeness of distribution networks in the sense of benefiting from energy sources and related structures such as microgrids (MGs) makes them benefit from the capability of structures such as MGs, in case of High‐Impact Low‐Probability (HILP) events, so that consumers are able to disconnect from main grid and trade with other MGs. Thus, the energy management of networked MGs needs a new operation structure to encourage them providing each other with the required energy by creating the necessary incentives. This paper presents a novel cooperative framework aimed at enhancing the resilience of MGs through the establishment of peer‐to‐peer (P2P) energy trading among their constituents. In the present model, MGs engage in energy transactions with one another, aiming to minimize costs through the cooperative operation of networked MGs. The utilization of the suggested model enables MGs to enhance network resilience within the framework of peer‐to‐peer energy transactions, while also achieving cost reductions in comparison to islanding and non‐cooperative operation. The numerical results obtained demonstrate the efficacy of the proposed methodology in enhancing the resilience of MGs and mitigating their operational expenses.

Efektivitas Program Gotong Royong dan Perwiritan dalam Meningkatkan Pemahaman Konsep Metodologi Islam

This study investigates the effectiveness of mutual cooperation and consultation programs to improve understanding of Islamic methodological concepts. This study uses descriptive qualitative research. The results of this study show that people who understand that their life cannot be separated from the help and support of others understand Islamic teachings within the framework of mutual cooperation. A strong sense of collectivity in society can be interpreted as a result of Islamic values ​​that promote unity and mutual aid. The strong social integrity with which society participates in the enforcement of punishment also reflects the influence of Islamic values ​​of justice and collective responsibility. Therefore, the practice of mutual cooperation in a society influenced by Islamic teachings not only creates unity and harmony, but also the practice of Islamic methodology, which emphasizes the importance of unity, mutual aid and social responsibility in everyday life. It also becomes mutual cooperation and consultation programs to improve the understanding of Islamic methodological concepts will be carried out with careful planning. First, a specific goal was set to improve the understanding of Islamic methodological concepts in the village Muslim community. The program begins with a counseling session led by a clergyman to discuss Islamic methodological concepts such as scientific approaches and practices in daily life.mAlthough mutual cooperation and consultation programs have great potential to improve the understanding of Islamic methodological concepts, there are several inhibiting factors that may limit the effectiveness of their implementation

A novel dual-arm adaptive cooperative control framework for carrying variable loads and active anti-overturning

This paper aims to tackle the issue of carrying variable loads and disturbances in an impedance-based dual-arm robot. When robots are engaged in transportation tasks, deviations in trajectory due to changing loads and the risk of objects falling under external disturbances or unstable gripping can lead to mission failure. To address these issues, a novel Dual-Arm Adaptive Cooperative Control Framework (ACCF) is proposed. The ACCF is designed to ensure adherence to trajectory constraints in the presence of load variations and to actively respond to sliding or overturning caused by disturbances. The framework is user-friendly and robust, featuring a two-loop arrangement. The inner-loop incorporates an adaptive force control law to robustly control internal forces for dual-arm gripping. The outer-loop utilizes centralized impedance control, incorporating a fast gravity estimation scheme to compensate for trajectory deviations and an active anti-overturning scheme to resist sliding or overturning of objects during disturbances. Experimental evaluations and comparisons are conducted. The results successfully demonstrate the ACCF's adaptability under variable loads and external disturbances, providing a solution for practical dual-arm applications, such as transportation, in future scenarios.

U.S. Cooperation with Central Asian Countries within the C5+1 Initiative: Trends and Prospects

The article examines the United States’ engagement with Central Asian countries through the C5+1 initiative, highlighting its evolution, challenges, and strategic implications. Central Asia's significant geostrategic position and history have made it a focal point for global powers. The C5+1 initiative, encompassing the U.S. and five Central Asian nations, represents a strategic realignment of U.S. policies in the region post-9/11. This initiative aims to balance the dominant Russian influence and China’s growing economic power. The article explores how the C5+1 initiative has established foundational frameworks for cooperation, addressing a range of issues from economic development to security concerns. The analysis underscores the initiative's role in shaping U.S. foreign policy towards Central Asia, considering regional dynamics and international relations.

Overcoming Distance Education Challenges in Nigeria: A Call for Collaborative ICT Integration

This article looks at the benefits of Distance Education (DE) or Open Learning Systems as the means of providing education that meets the growing demand for education on the account of the financial concerns and changing priorities. The historical development of communication methods is one of the factors that shaped the education sector considerably and now we have the open learning systems as a popular way of. The aim of the establishment of DE as highlighted by scholars is to create another parallel system which can be integrated with formal education. Other objectives include promoting flexibility, constructing an egalitarian society, facilitating in-service and lifelong education and utilizing communication technology for wider outreach. ICTs hold the key to global educational initiatives, providing an environment of collaboration and disseminating ideas in the most effective manner. Although ICTs belong to one of the most transformative technologies, Nigeria is faced with challenges of integration of ICTs, among them deficiencies in ICT infrastructure. Distance education has proved to be successful for the purpose of widening educational chances but Nigeria faces problems like low ICT penetration, erratic power supply, and meagre economy. It is noted that ICTs play a critical role in the development of distance education in Nigeria and that this research stresses the need for collaboration among all relevant stakeholders— government, private companies, and international organizations—to enhance ICT infrastructure, implement international cooperation frameworks, and integrate ICT literacy and use into academic courses. Keywords: Distance Education (DE), Information and Communication Technologies (ICTs), OLS, Education Issues, Flexibility in Education, ICT Infrastructure, and Inclusive Learning.

A hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures, but current approaches take a long time to cover the entire area. Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments. This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time. The proposed framework consists of two stages, the exploration stage and the exploitation stage, resolving the task in a sequential manner. At the exploration stage, the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs, and the Voronoi-based planner is used to enable the UAVs to reach their best region. After obtaining the global map, viewpoints are generated and divided while taking into account the battery capacity of each UAV. Finally, a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction. Several experiments are conducted in both a simulated and real environment to show the time efficiency, robustness, and effectiveness of the proposed method. Furthermore, the whole system is implemented in real applications.

Legal aspects of international cooperation in combating organised crime

In the context of intensification of globalisation processes, researchers’ attention is drawn to organised forms of crime, which are substantially more difficult to counteract if criminal offences have transnational features. Considering this, it is vital to investigate the content of international treaties aimed at combating transnational organised crime. Thus, the purpose of this study was to identify certain legal features of international cooperation in combating organised crime for further implementation of promising provisions in the work of law enforcement agencies in this area. The methodological framework of the study was formed by both general scientific and special methods of scientific cognition. The study also employed systemic, informational and functional approaches, as well as terminological, systemic-structural, formal-logical, and comparative legal methods of scientific cognition. The study confirmed that organised crime does not recognise the existing borders of states and constantly crosses them. At the same time, law enforcement agencies are quite limited in their actions by these borders, which substantially affects their ability to combat crime, especially organised crime. It was found that the legal framework for international cooperation in combating crime, including organised crime, is gradually being formed, but this process is influenced by the concept of primacy of national law over international law. The study proved that the current terminology of international treaties may not correspond to the terminology used in the national legislation of modern countries, but unification of legislation is reasonably necessary for effective crime prevention. The practical value of the findings obtained is that they can be used to further improve the legal framework for international cooperation

Adaptive overlay selection at the SD-WAN edges: A reinforcement learning approach with networked agents

Software-Defined Wide Area Networks (SD-WANs) have emerged as a promising solution to address the connectivity demands of modern distributed enterprises. However, the effective application of the Software Defined Networking (SDN) paradigm in such broad and dynamic environments remains a significant challenge. In this paper, we present two novel contributions. First, we design a decentralized control plane for SD-WANs that leverages edge-based network monitoring and overlays’ configuration. Then we present a Reinforcement Learning-based orchestration plane that leverages local information for the enforcement of SD-WAN policies. Since traditional approaches suffer either a lack of scalability due to the problem’s complexity or suboptimal performance due to isolated decision-making, the proposed approach leverages a cooperative Multi-Agent Reinforcement Learning framework. Our novel cooperative approach is based on per-site agents that exchange a small amount of information to enhance performance while preserving scalability. To validate the efficacy of our proposed approach, we conducted an extensive experimental evaluation considering diverse SD-WAN scenarios. Results show that our framework is able to satisfy global network policies for a multi-site SD-WAN with different QoS requirements and cost constraints.

Transforming Justice: Implications of Artificial Intelligence in Legal Systems

The present literature review explores the growing impact of artificial intelligence (AI) on the justice system. It sheds light on the prospects, obstacles, and probable consequences of its assimilation. Utilizing a broad array of scholarly resources, we examine the implementation of AI in various domains, including but not limited to predictive law enforcement, risk evaluation, evidentiary analysis, and judicial decision-making. The review recognizes the advantages of artificial intelligence, such as enhanced efficacy, precision, and impartiality in legal proceedings, while also expressing apprehensions regarding potential partialities, ethical predicaments, and risks to confidentiality and human liberties. Furthermore, it is crucial to underscore the significance of interdisciplinary cooperation and comprehensive regulatory frameworks in guaranteeing the judicious and impartial integration of AI technologies in the justice system. The present study endeavors to make a significant scholarly contribution to the ongoing discourse surrounding artificial intelligence and its intersection with the legal field. By examining the opportunities and challenges of integrating AI in legal systems, this review provides specific insights into formulating policies around algorithmic accountability, transparency, and ethical safeguards to ensure responsible AI adoption. 
 
 Received: 27 October 2023 / Accepted: 29 February 2024 / Published: 5 March 2024

A novel implicit decision variable classification approach for high-dimensional robust multi-objective optimization in order scheduling

This paper efficiently addresses the high-dimensional robust order scheduling problem. A novel algorithm named dynamic cooperative coevolution based on an implicit decision variable classification approach (DCC/IDVCA) is developed to search for robust order schedules. To significantly reduce the computational resources required for solving the high-dimensional robust order scheduling problem, we propose decomposing the original decision variables through implicit classification methods. First, a novel estimation method is introduced to evaluate the weighted contribution of variables to robustness. This method utilizes historical information, including the variation of the overall mean effective fitness and the frequency of variables being classified into highly robustness-related subcomponents in previous cycles, for evaluating their weighted contribution to robustness. Then, based on the corresponding weighted robustness contributions, the original variables are classified into highly and weakly robustness-related variables. Finally, these two types of variables are decomposed into highly and weakly robustness-related subgroups within a dynamic cooperative coevolution framework and optimized separately. In the experimental section, the proposed algorithm is applied to two practical order scheduling problems in discrete manufacturing industry. The experimental results demonstrate that the proposed algorithm achieves competitive outcomes compared to state-of-the-art high-dimensional robust multi-objective optimization algorithms.

Distributed Cooperative Control Strategy for Stable Voltage Restoration and Optimal Power Sharing in Islanded DC Microgrids

Conventionally, voltage regulation and economic dispatch in DC microgrid are performed at different time scales, which cannot keep the distributed generators (DGs) at the optimal operating point in real time. In this paper, a novel distributed cooperative control strategy is proposed to synchronously achieve stable bus voltage restoration and optimal power sharing in islanded DC microgrids. The proposed strategy breaks the constraints of hierarchical control strategies, and incorporates the objectives of voltage regulation and economic dispatch into a distributed cooperative control framework to eliminate average bus voltage deviation and minimize total power generation costs in real time. The stability of the proposed strategy is verified by eigenvalue analysis and steady-state analysis. Furthermore, the concept of “incremental cost (IC) margin” is introduced in cooperative control considering capacity constraints to avoid the converter output power violation and performance degradation. Finally, the MATLAB/Simulink simulation and StarSim HIL experimental results verify the effectiveness and robustness of the proposed strategy in an islanded DC microgrid under various testing scenarios.

Volt–Var curve determination method of smart inverters by multi-agent deep reinforcement learning

Reactive power control of PV inverters can be applied to mitigate the voltage increase caused by reverse power flow and voltage fluctuations caused by PV output fluctuations in the distribution system. This paper focuses on the Volt–Var control of PV smart inverters to minimize power losses. It proposes a multi-agent type cooperative voltage control framework to optimize the blind band and slope of the VVC. The proposed method utilizes the optimized VVC to eliminate voltage deviations using only local information. The optimization problem is formed as a Markov decision process and solved using a deep reinforcement learning algorithm called multi-agent soft-actor critic, which achieves fast control that fully accounts for uncertainty while achieving global optimization. Therefore, the proposed method can contribute to reducing power loss, which is a system-wide problem, with only local information. Case studies were conducted using a small and medium-sized distribution network model with IEEE 33 buses and 12 demand cases. The comparison results demonstrate that the proposed method is superior to other benchmark methods in terms of minimizing power losses and mitigating voltage variations, as the proposed method can reduce power losses by about 9% while achieving avoidance of voltage deviations.

Eğitim Yönetiminde Program Geliştirme Süreçleri ile İlgili Alanyazında Yapılan Araştırma Makalelerinin İncelenmesi

As stated under the Tenth Development Plan, basic and professional skills development program, Dec. 2014-2018, published in line with the goals of 2023 in our country, critical thinking is among the basic skills that individuals should have in terms of ensuring rapid adaptation of individuals to work and living conditions and developing human resources. the teacher strategy document, which draws the roadmap for teacher training, development and employment periods in our country between 2017-2023, lists raising free individuals with program development skills among the final goals of education. Dec.Dec. However, when Deciphering the framework reports defining the human qualities of the twenty-first century that guide the national education policies of countries, program development is emphasized among the important and priority skills. 21, which is recognized worldwide for its work on twenty-first century skills. 21, prepared by the century skills partnership. cooperation framework for century skills and 21. the project on the evaluation and teaching of Decennial skills highlights program development among the twenty-first century competencies. In addition to its role in the changing educational world, program development also has an important role in shaping individuals and societies. Nowadays, the ability to develop a program to find the most accurate and reliable one that will work for you from the increasing amount of data in each profession field is the most important personality trait that can be relied on by acting as a mental filter. An individual armed with educational processes does not accept information in a stationary way and questions the reliability of the information he encounters by applying his own reasoning methods. He may convert the information he questions when necessary, or he may not accept it. From the point of view of individuation, the ability to develop a program is important for a person to be independent and original-minded individuals, to create the right solutions for his life and to make the right decisions. Key Words: Educational Management, Program Development, Research and Review Articles

CrossPrune: Cooperative pruning for camera–LiDAR fused perception models of autonomous driving

Deep neural network pruning is effective in enabling high-performance perception models to be deployed on autonomous driving platforms with limited computation and memory resources. With their rapid development, state-of-the-art autonomous driving perception (ADP) models have advocated the use of multimodal sensors to extract diverse feature categories. However, existing pruning studies in the ADP area focus on single-modal models and neglect multimodal models. Compared with conventional pruning, multimodal pruning presents a new type of redundancy, namely modal-wise redundancy that is caused by multimodal branches extracting similar perception information. When a specific type of information is extracted by more than one modal branch, although this extraction is deemed essential from an individual single-modal standpoint, it becomes redundant when viewed from a modal-wise perspective. Therefore, modal branches must be handled cooperatively to eliminate modal-wise redundancy while concurrently preserving the original perception accuracy. Building on this, we propose CrossPrune, a modal cooperative pruning framework designed for camera–LiDAR fused perception in autonomous driving. The primary objective of CrossPrune is to effectively eliminate redundancy and achieve nondestructive pruning for multimodal ADP models. This was accomplished by approaching the problem as a multi-objective optimization task, encompassing both weight pruning and the restriction of feature distortions caused by pruning. Experiments conducted on the nuScenes and KITTI datasets demonstrated that CrossPrune attained superior pruning ratios while minimizing accuracy loss, surpassing the performance of the baselines. The key results indicated that the proposed CrossPrune achieved relative improvements of 9.6% in mAP and 11.5% in NDS under 89.8% pruning sparsity.

Metal-bonded perovskite lead hydride with phonon-mediated superconductivity exceeding 46 K under ambient pressure

In the search for high-temperature superconductivity in hydrides, a plethora of multi-hydrogen superconductors have been theoretically predicted, and some have been synthesized experimentally under ultrahigh pressures of several hundred GPa. However, the impracticality of these high-pressure methods has been a persistent issue. In response, we propose a new approach to achieve high-temperature superconductivity under ambient pressure by implanting hydrogen into lead to create a stable few-hydrogen binary perovskite, Pb4H. This approach diverges from the popular design methodology of multi-hydrogen covalent high critical temperature (Tc ) superconductors under ultrahigh pressure. By solving the anisotropic Migdal–Eliashberg equations, we demonstrate that perovskite Pb4H presents a phonon-mediated superconductivity exceeding 46 K with inclusion of spin–orbit coupling, which is six times higher than that of bulk Pb (7.22 K) and comparable to that of MgB2, the highest Tc achieved experimentally at ambient pressure under the Bardeen, Cooper, and Schrieffer framework. The high Tc can be attributed to the strong electron–phonon coupling strength of 2.45, which arises from hydrogen implantation in lead that induces several high-frequency optical phonon modes with a relatively large phonon linewidth resulting from H atom vibration. The metallic-bonding in perovskite Pb4H not only improves the structural stability but also guarantees better ductility than the widely investigated multi-hydrogen, iron-based and cuprate superconductors. These results suggest that there is potential for the exploration of new high-temperature superconductors under ambient pressure and may reignite interest in their experimental synthesis in the near future.

Digital empowerment and win-win co-operation for green and low-carbon industrial development: Analysis of regional differences based on GMM-ANN intelligence models

The study employs two sophisticated computational model validation mechanisms, Artificial Neural Network (ANN) and System Generalized Method of Moments (SYS-GMM), as the theoretical foundation for sustainable development theory. Within this research framework, ANN serves as an intelligent model for autonomously learning input data features by capturing the nonlinear and dynamic relationships within the data. This study thoroughly simulates and analyzes the intricate relationship between digital empowerment and win-win cooperation in green and low-carbon industrial development. The findings indicate a regional decreasing trend in digital empowerment in China, with percentages of 87.39%, 79.05%, and 65.85%, respectively. These regional variations may result from policy, economic structure, and social factors. Notably, a significant trend of win-win cooperation exists between digital empowerment and green low-carbon initiatives. This discovery implies that promoting digital empowerment contributes to reducing green carbon emissions, offering empirical evidence for the positive impact of digital technology on promoting environmental sustainability. This study unveils the empirical trend of digitally-enabled green, low-carbon industry development within the win-win cooperation framework and presents specific recommendations for future sustainability strategies. In addition to offering a scientific foundation for policymakers, this study provides a forward-looking perspective for academics engaged in the study of digital transformation and environmental sustainability. Through a comprehensive analysis of the intricate relationship between digital empowerment and win-win cooperation in the context of green and low-carbon industrial development, this study offers a profound understanding of the application of digital technology in sustainable development across diverse regions, contributing empirical research findings to advance sustainable development in China.

ASEAN cooperation to combat transnational crime: progress, perils, and prospects

The Association of Southeast Asian Nations (ASEAN) has grappled with the persistent threat of transnational crime (TC) in Southeast Asia since its establishment in 1967. The rising tide of globalization and technological advancement has only exacerbated the problem, leading to the evolution and organization of various criminal activities. As ASEAN faces a multitude of non-traditional security challenges that transcend national borders, including climate change, infectious diseases, and resource scarcity, TC remains a critical concern that could jeopardize regional stability and security. This article undertakes an extensive analysis of ASEAN's endeavors to combat TC. It delves into key documents outlining ASEAN's commitment, highlights the significant achievements, and identifies the challenges facing the region. The qualitative study evaluates the effectiveness of ASEAN's response to TC within its cooperative framework and core principles. Through content analysis, it draws insights from ASEAN's official documents, as well as reports from Interpol and the United Nations Office on Drugs and Crime, to understand the regional efforts in countering TC. The research reveals a range of collaborative efforts initiated by ASEAN to tackle TC. By focusing on examples of successful operations, such as Operation Maharlika III against human trafficking and Operation HAECHI-I targeting cybercrime, the study underscores the vital role of cross-border cooperation. However, the analysis highlights the challenges posed by the complex nature of TC and the varying capabilities of ASEAN member states. It underscores the importance of regional cooperation and external partnerships to address the evolving threats of TC effectively. In essence, ASEAN's commitment to combating TC is evident through the adoption of strategic documents, joint operations, and capacity-building initiatives. Nevertheless, the evolving nature of TC and its diverse manifestations pose ongoing challenges. A balanced approach in addressing TC is needed that encompasses not only securitization but also a criminal justice response.

Sky-Worker: a heterogeneous dual-arm robot with dynamic authority assignment for live-line working

Purpose The current difficulties of distribution network working robots are mainly in the performance and operation mode. On the one hand, high-altitude power operation tasks require high load-carrying capacity and dexterity of the robot; on the other hand, the fully autonomous mode is uncontrollable and the teleoperation mode has a high failure rate. Therefore, this study aims to design a distribution network operation robot named Sky-Worker to solve the above two problems. Design/methodology/approach The heterogeneous arms of Sky-Worker are driven by hydraulics and electric motors to solve the contradiction between high load-carrying capacity and high flexibility. A human–robot collaborative shared control architecture is built to realize real-time human intervention during autonomous operation, and control weights are dynamically assigned based on energy optimization. Findings Simulations and tests show that Sky-Worker has good dexterity while having a high load capacity. Based on Sky-Worker, multiuser tests and practical application experiments show that the designed shared-control mode effectively improves the success rate and efficiency of operations compared with other current operation modes. Practical implications The designed heterogeneous dual-arm distribution robot aims to better serve distribution line operation tasks. Originality/value For the first time, the integration of hydraulic and motor drives into a distribution network operation robot has achieved better overall performance. A human–robot cooperative shared control framework is proposed for remote live-line working robots, which provides better operation results than other current operation modes.

Research on Dynamic Target Search for Multi-UAV Based on Cooperative Coevolution Motion-Encoded Particle Swarm Optimization

Effectively strategizing the trajectories of multiple Unmanned Aerial Vehicles (UAVs) within a dynamic environment to optimize the search for and tracking of mobile targets presents a formidable challenge. In this study, a cooperative coevolution motion-encoded particle swarm optimization algorithm called the CC-MPSO search algorithm is designed to tackle the moving target search issue effectively. Firstly, a Markov process-based target motion model considering the uncertainty of target motion is investigated. Secondly, Bayesian theory is used to formulate the moving target search as an optimization problem where the objective function is defined as maximizing the cumulative probability of detection of the target in finite time. Finally, the problem is solved based on the CC-MPSO algorithm to obtain the optimal search path nodes. The motion encoding mechanism converts the search path nodes into a set of motion paths, which enables more flexible handling of UAV trajectories and improves the efficiency of dynamic path planning. Meanwhile, the cooperative coevolution optimization framework enables collaboration between different UAVs to improve global search performance through multiple swarm information sharing, which helps avoid falling into local optimal solutions. The simulation results show that the CC-MPSO algorithm demonstrates efficacy, reliability, and superior overall performance when compared to the five commonly used swarm intelligence algorithms.

Lightweight Imitation Learning for Real-Time Cooperative Service Migration

Due to the revolution of communication technology, the rapidly increasing number of mobile devices in edge networks generates various real-time service requests, requiring a considerable volume of heterogeneous resources all the time. However, edge devices with limited resources cannot afford substantial learning cost, while migrating services requires heterogeneous resources, especially for dynamic networks. To address these issues, we first establish a cooperative service migration framework and formulate a bi-objective optimization problem to optimize service performance and cost. By analyzing the optimal migration ratio of service cooperative migration, we propose an offline expert policy based on global states to provide optimal expert demonstrations. To realize real-time service migration based on observable states, we design a lightweight online agent policy to imitate expert demonstrations and leverage meta update to accelerate the model transfer. Experimental results show that our algorithm is exceptional in training cost and accuracy, and has significant superiors in multiple metrics such as the service latency and payment under different workloads, compared to other representative algorithms.