Cooperative Scheme Research Articles

Gamification in Flipped Classrooms for Sustainable Digital Education: The Influence of Competitive and Cooperative Gamification on Learning Outcomes

This study examines the effects of competitive versus cooperative gamification mechanics on the learning achievement of female first-year middle school students in flipped classrooms, highlighting its implications for designing sustainable learning environments. Employing a quasi-experimental design, 60 students were randomly assigned to two experimental groups, each engaged in a flipped classroom environment with either competitive or cooperative gamified elements. While both groups showed significant improvement in post-test scores, no significant differences were observed in cognitive achievement or skills acquisition between the groups. These findings emphasize that competitive and cooperative gamification mechanics can be equally effective in enhancing learning, suggesting that the choice of mechanic does not critically impact learning outcomes. The study provides practical guidance for educators and instructional designers in developing balanced gamified learning environments that optimize competitive and cooperative strategies, thus fostering a more versatile and adaptable approach to student motivation and engagement in sustainable technology-enhanced education.

Overview of NGO Strategies and Practices in Addressing Global Challenges

Against the backdrop of growing global challenges, non-governmental organisations (NGOs) are emerging and playing an important role. This paper examines in depth the strategies and practices of NGOs in addressing global challenges. Firstly, the background of the rise of NGOs and the purpose of the study are explained, followed by an analysis of the concept and characteristics of NGOs, including definition, main features and types. It then discusses the strategies of NGOs in addressing global challenges, such as the use of information tools and the implementation of cooperation strategies. The practice of NGOs in global ocean governance and global environmental governance is then described. The roles and challenges of NGOs are also analysed, including their involvement in public crises and their position in global crisis governance, while internal and external challenges faced by NGOs are identified. Finally, the conclusions of the study are summarised and an outlook is given on future research directions, including strengthening the capacity building of NGOs, expanding areas of cooperation, deepening policy influence and exploring innovative development.

The Impact of the Pacific Alliance on Trade Creation and Trade Diversion in the COVID-19 Period: A Robust Econometric Analysis

(1) Background: The COVID-19 pandemic severely disrupted global trade, posing challenges for regional trade agreements such as the Pacific Alliance, comprising Chile, Colombia, Mexico, and Peru. This study investigates the Pacific Alliance’s impact on trade creation and diversion during the pandemic, exploring how regional cooperation mitigated trade disruptions. (2) Methods: A quantitative approach utilizing econometric models, including ordinary least squares, Poisson pseudo-maximum likelihood, and the Heckman selection model, was employed. The gravity model of trade was applied to bilateral trade data from 1948 to 2021, incorporating COVID-19-specific variables to assess trade dynamics. (3) Results: The analysis revealed significant trade creation within the Pacific Alliance during the pandemic, with intra-bloc trade strengthening despite global disruptions. However, evidence of trade diversion from non-member countries was mixed and inconclusive. (4) Conclusions: The Pacific Alliance demonstrated resilience during the COVID-19 period, fostering regional trade creation and cushioning the impact of global trade shocks. While trade diversion remains uncertain, the study highlights the critical role of regional trade agreements in stabilizing trade flows during global crises, offering valuable insights for future policy and regional cooperation strategies.

Contingent convertible Ijara pricing model

Purpose This study aims to conceive and develop a pricing model for the Ijara contingent convertible contract (ICCC, hereafter), considering the possibility that the lessee may default. The ICCC model grants the lessor the option of converting the unpaid amount into equity or recovering the leased equipment and selling it at market price in case of financial distress. Design/methodology/approach The ICCC is consistent with the profit-sharing approach and the new risk management techniques, which are compatible with Islamic philosophy. Relying on real options theory and the contingent claim approach, a closed-form solution of the firm’s assets is developed in a dynamic environment, where the rate of return is generated by a Cox-Ingersoll-Ross stochastic process. Findings Examining the numerical analysis reveals the impact of the firm value, the conversion or sell decision and the conversion ratio and volatility on the ICCC value. The value of the ICCC can increase substantially as the value of the firm approaches the conversion threshold. The conversion ratio as well as the asset market price play equally an important role in the decision to convert or sell. Originality/value This paper develops a pricing model for a contingent Ijara contract, which incorporates a conversion option to mitigate the lessee’s credit risk during periods of economic instability. The ICCC is a cooperative strategy that would be advantageous to all parties, including the lessor and lessee. In the event of a conversion, businesses may be able to continue operating thanks to this financial innovation, and the lessor may profit from the company’s recovery by freeing up more resources for the use of more profitable ventures.

Building Bridges to European Integration: Albania and Kosovo's Joint Efforts in the Balkans

This study aims to look at the joint efforts of Albania and Kosovo in enhancing European integration within the Balkans. It tries to figure out, through an analysis of the joint programs and strategic partnerships, the extent to which these initiatives help them realize their shared goal of EU membership and regional stability. Prior Work: Previous scholarship has looked mostly into individual tracks on how to integrate into Europe by analyzing the political, economic, and social reforms undertaken by Albania and Kosovo separately. Other studies have tried to identify challenges and opportunities for the Western Balkans in this regard of EU integration. Yet little is known about cooperative work between Albania and Kosovo, and what it means for the wider regional context represents a gap in this literature. Key Findings: The two countries have managed, as shown by these findings, to build such a framework of cooperation together with dimensions being diplomatic, economic, and even cultural. Cross-border infrastructure projects, policy reforms, and joint diplomacy as well as collaboration in regional and international initiatives have played a bigger part in lifting up their prospects for integration. The paper shows how important it is for these two countries to ally strategically in tackling common challenges, like issues of political instability within their territories, fostering economic development programs, and social cohesion that make up the key tenets upon which EU membership is anchored.Value: This paper is therefore important in providing an insightful analysis into how synergistic cooperation between Albania and Kosovo can be used to enhance European integration prospects. It underscores the role bilateral cooperation can play in ensuring regional stability on the path to EU membership. The paper provides insight into how other aspiring member states from the Balkans can use cooperative strategies to inform the broader European integration agenda from a disadvantaged region. Future Longitudinal studies should be done to assess the impact that such joint initiatives by Albania and Kosovo could have on their EU integration trajectories over time. Comparative analyses with other regional Balkan partnerships could give a more nuanced knowledge of best practices and lessons learned. Moreover, investigating how international organizations and external stakeholders support these bilateral agreements will further make a lot of sense in the academic discussion on European integration in the region. Received: 2 August 2024 / Accepted: 12 November 2024 / Published: 3 December 2024

Coordination strategy and contract design of platform supply chain for large-scale sports events with low carbon preference.

The organization of sports events, while generating economic benefits, inevitably imposes significant environmental pressures. Conducting green and low-carbon sports events have become a global consensus. In addressing the carbon emissions and benefit coordination issues on the production end of infrastructure construction for large-scale sports events, we considers the significant role of digital platforms in the industry's low-carbon transformation and upgrade, and innovatively incorporates platforms as decision-making entities and investigates the equilibrium strategies for low-carbon cooperation under three different power structures: one led by the sports events materials supplier, one by the materials distributor, and one by the integrated service platform. Additionally, it designs related cost-sharing contracts. The findings suggest that: centralized decision-making is more conducive to aligning the interests of various entities within the sports events platform supply chain. Different power structures have distinct impacts on overall supply chain profits and carbon emissions. Specifically, the supply chain achieves the highest overall profit under the domination of the integrated service platform, while the lowest level of carbon reduction occurs under the domination of the materials distributor. These results provide strategic insights for the low-carbon development of sports events and the coordinated cooperation within platform supply chain.

A cooperative control strategy for balancing SoC and power sharing in multiple energy storage unit within DC microgrids

AbstractThis paper proposes a distributed cooperative control scheme for multiple energy storage unit (ESU) in DC microgrids to achieve the control objectives of SoC balancing, power sharing, and bus voltage recovery. In the primary control part, the proposed scheme constructs a control function between the SoC values of each ESU and the droop coefficients to dynamically adjust the droop coefficients. Through a communication network, information is exchanged with neighbouring ESUs to achieve SoC convergence. In the secondary control part, by exchanging power information with neighbouring ESUs, precise power distribution is achieved. Additionally, the proposed scheme maintains bus voltage stability. Finally, a DC microgrid simulation model and experimental platform were developed, demonstrating the feasibility and plug‐and‐play capability of the proposed control strategy through both simulation and experimental case tests.

PKDGAN: Private Knowledge Distillation With Generative Adversarial Networks

The deployment of deep learning applications has to address the increasing privacy concerns when using private and sensitive data for training. A conventional deep learning model is prone to privacy attacks that can recover the sensitive information from either model parameters or accesses to the inference model. Recently, differential privacy (DP) has been proposed to offer provable privacy guarantees by randomizing the training process of neural networks. However, many approaches tend to provide the worst case privacy protection for model publishing, inevitably impairing the accuracy of the trained models. Thus, we present a novel private knowledge transfer strategy, where the private teacher trained on sensitive data is not publicly accessible but the student models can be released with privacy guarantees. In this paper, a three-player (teacher-student-discriminator) learning framework, Private Knowledge Distillation with Generative Adversarial Networks (PKDGAN), is proposed, where the student acquires the distilled knowledge from the teacher and is trained with the discriminator to generate similar outputs as the teacher. Moreover, a cooperative learning strategy is also suggested to support the collective training of multiple students against the discriminator when each student is with insufficient unlabelled training data. To enforce rigorous privacy guarantees, PKDGAN applies a Rényi differential privacy mechanism throughout the training process, and use it with a moment accountant technique to track the privacy cost. PKDGAN allows students to be trained with unlabelled public data and very few epochs, which avoids the exposure of training data while ensuring model performance. In the experiments, PKDGAN is found to have consistently good performance on various datasets (MNIST, SVHN, CIFAR-10, and Market-1501). When compared to prior works [1], [2], PKDGAN exhibits 5-82% accuracy loss improvement without compromising any privacy guarantee.

Variable speed limit control strategy considering traffic flow lane assignment in mixed-vehicle driving environment

To accurately predict traffic flow and optimize the operations of freeway bottleneck areas in a mixed-vehicle driving environment, this paper proposes a traffic prediction model and a variable speed limit (VSL) cooperative control strategy. Firstly, a lane-level short-term traffic prediction model, physics informed Transformer and cell transmission model (PIT-CTM), is constructed by combining the Transformer neural network and lane-level cell transmission model (CTM) based on the physics-informed deep learning framework. On this basis, the accuracy and transferability of PIT-CTM are analysed. Secondly, a lane assignment decision model is presented, which enables the dynamic planning of the optimal traffic distribution across lanes. Furthermore, a lane-level VSL control model is constructed based on the model predictive control (MPC) framework. The model induces vehicles to change lanes earlier by setting the speed limit difference between lanes. By regulating the input flow in the bottleneck area of the freeway, it reduces conflicts between mainline vehicles and ramp vehicles. Finally, the feedback regulation between the lane assignment decision model and the lane-level VSL control model promotes the cooperative optimisation of the lateral and longitudinal flows and adapts the control strategy to the dynamic traffic characteristics. A three-lane freeway merging zone is selected, the numerical experiment is conducted and compared with differential lane-level VSL. The results show that the strategy can effectively optimise the mixed-vehicle traffic state and maintain better control performance under any connected and autonomous vehicle (CAV) penetration rates.

Multi-agent modeling for energy storage charging station scheduling strategies in the electricity market: A cooperative learning approach

With integration of an energy storage system (ESS), an energy storage charging station serves as pivotal intermediaries between the smart grid and electric vehicles (EVs). This station utilizes the ESS to enhance grid stability and facilitate energy management. Participation in electricity market transactions offers revenue opportunities for charging stations, but it also introduces operational challenges, due to fluctuating electricity market prices and diverse energy demands and supplies. In this paper, we study the operation strategy optimization problem for the charging station, addressing economic and service challenges influenced by market volatility and energy diversity. The optimization objective considers not only maximizing economic benefits from the electricity market and EV services but also minimizing penalties associated with EV service quality. We propose a model that accounts for the dynamics of the electricity market, uncertainties from EV demands, and disturbances from green power generation, optimizing the power scheduling of the ESS and multiple charging piles (CPs) to determine transaction power in the market. The cooperative scheduling strategies for the ESS and CPs are learned using the proposed heterogeneous Multi-agent Deep Deterministic Policy Gradient method. This approach features distributed agents learning to determine decision variables for both the ESS and CPs, while a joint critic network assesses the station’s overall objectives to guide their cooperative learning. The proposed method was tested against three state-of-the-art benchmark methods, which showed our method achieves better results.

Electric Vehicle Battery Supply Strategy Considering Brand Spillover Effect

Electric vehicle manufacturers ensure power battery supply through either direct sourcing or technology cooperation. This paper examines the battery supply strategy of an entrant manufacturer(m2), in a two-tier supply chain that includes a battery supplier (s), an incumbent manufacturer (m1), and the aforementioned entrant. It investigates these strategies under the influence of the brand spillover effect. It reveals that when m1 opts for direct battery procurement, if m2 boasts higher after-sales service standards, adopting a technology cooperation strategy becomes more advantageous. A sweet spot exists wherein, given m2's brand power and after-sales service level fall within a certain range, all supply chain participants can achieve mutual benefits. In scenarios where m1 depends on technology cooperation for its battery supply, it is advisable for m2 to mirror this approach by also engaging in technology cooperation.

Teachers as First Responders: Navigating Classroom Experiences and Mental Health Challenges

Educators have been responding to student mental health emergencies on the front lines more frequently in recent years, frequently taking on tasks that have historically been done by mental health professionals. This change emphasizes how important it is for educators to possess both the pedagogical know-how and the practical approaches needed to address mental health issues in the classroom. This abstract examines how teachers are increasingly acting as first responders when it comes to helping students with mental health concerns. The study shows that a variety of mental health issues, such as anxiety, depression, and symptoms associated with trauma, are commonly encountered by educators. These problems might show up as emotional anguish, behavioral challenges, or academic difficulties, and they all call for a sensitive and understanding approach. Important findings indicate that although a large number of educators are dedicated to promoting the mental health of their pupils, they frequently lack thorough training in crisis intervention and mental health first aid. The study comes to the conclusion that establishing supportive school settings requires a cooperative strategy combining educators, mental health specialists, and legislators. Schools can better assist students' learning by increasing teachers' preparation and providing the tools they need.

Coordinated control strategy for improving frequency security of LCC[sbnd]HVDC sending-end system with large-scale wind power

To improve the frequency security of the LCCHVDC sending-end system with high penetration of wind power system and achieve optimal utilization of frequency regulation resources, a cooperative control strategy involving wind power and LCCHVDC in frequency regulation is proposed. Firstly, under a variety of scenarios, the power support requirements of LCCHVDC sending-end system are analyzed according to the power regulation margin of various frequency regulation resources and the disturbance power, and a multi-time scale frequency regulation control strategy for DFIG based wind farms is proposed, which combines the advantages of variable speed control and variable pitch angel control. And a cooperative control strategy of DFIG and LCCHVDC considering the frequency regulation deadband is proposed. Then, based on the maximum power regulation margin of synchronous generators and wind farms, the key control parameters of relative control links are designed to enable them autonomously adjust the active power according to various scenarios to involve in inertia response and primary frequency regulation task. Finally, the effectiveness of the control strategy is verified by the simulation model of LCCHVDC sending-end system with large-scale wind power, which significantly improves the frequency security and stability.

Teaching Module for Creating Songs with STAD Cooperative Learning Strategy in Music Art Learning at High School in Yogyakarta

This research aims to: (1) produce a teaching module for creating songs with a cooperative learning strategy of the STAD type as a teacher's guide in learning music at high schools in Yogyakarta and (2) determine the feasibility of teaching modules for creating simple songs with a cooperative learning strategy of the STAD type in learning music at high schools in Yogyakarta. This research is a type of research and development with the ADDIE development model developed by Dick and Carry. The subjects in this study consisted of expert validators, teachers and students. The trial design in this study consisted of two stages, namely: (1) the preliminary test stage addressed to expert validators and (2) the trial stage addressed to teachers and students. Data collection techniques used validity instruments, interviews and questionnaires. The data analysis technique consists of analyzing product feasibility data, analyzing teacher and learner response data. The results of this study are: (1) a simple songwriting teaching module product has been developed, this module is equipped with a Cooperative Learning learning strategy and a guide to the use of Adobe Audition video tutorials, (2) this module reviewed by two experts, learning experts with a final score of 78% with the category “Feasible” and material experts with a final score of 83% with the category “Very Feasible”.

Low-carbon and economic operation of energy hub considering cooperative operation strategy with wind farm and photovoltaic station

Low-carbon and economic operation of energy hub considering cooperative operation strategy with wind farm and photovoltaic station

Optimizing Microgrid Performance: Integrating Unscented Transformation and Enhanced Cheetah Optimization for Renewable Energy Management

The increased integration of renewable energy sources (RESs), such as photovoltaic and wind turbine systems, in microgrids poses significant challenges due to fluctuating weather conditions and load demands. To address these challenges, this study introduces an innovative approach that combines Unscented Transformation (UT) with the Enhanced Cheetah Optimization Algorithm (ECOA) for optimal microgrid management. UT, a robust statistical technique, models nonlinear uncertainties effectively by leveraging sigma points, facilitating accurate decision-making despite variable renewable generation and load conditions. The ECOA, inspired by the adaptive hunting behaviors of cheetahs, is enhanced with stochastic leaps, adaptive chase mechanisms, and cooperative strategies to prevent premature convergence, enabling improved exploration and optimization for unbalanced three-phase distribution networks. This integrated UT-ECOA approach enables simultaneous optimization of continuous and discrete decision variables in the microgrid, efficiently handling uncertainty within RESs and load demands. Results demonstrate that the proposed model significantly improves microgrid performance, achieving a 10% reduction in voltage deviation, a 10.63% decrease in power losses, and an 83.32% reduction in operational costs, especially when demand response (DR) is implemented. These findings validate the model’s efficacy in enhancing microgrid reliability and efficiency, positioning it as a viable solution for optimized performance under uncertain renewable inputs.

IPDToolkit: An R package for simulation and Bayesian analysis of iterated prisoner’s dilemma game-play under third-party arbitration

Recently, researchers have begun studying the role that third-party arbitration may play in the evolution of cooperation. Using the iterated prisoner’s dilemma (IPD), they show that arbitration can mitigate the negative effects of perception errors on the stability of cooperative strategies. Open questions, both theoretical and empirical, however, remain. To promote research on the role of third-party arbitration, we introduce an R package, IPDToolkit, which facilitates both simulation of synthetic data and Bayesian analysis of empirical data. To address theoretical questions, IPDToolkit provides a Monte Carlo simulation engine that can be used to generate play between arbitrary strategies in the IPD with arbitration and assess expected pay-offs. To address empirical questions, IPDToolkit provides customizable, Bayesian finite-mixture models that can be used to identify the strategies responsible for generating empirical game-play data. We present a complete workflow using IPDToolkit to teach end-users its functionality.

Research on Control Strategy of Multi-energy Complementary Microgrid for Wind-solar Hydrogen Production

The multi-energy complementary microgrid system is an effective supplement to the areas not covered by the large power grid, and can effectively solve the problem of electricity consumption in remote areas. Based on the research of wind power, photovoltaic, energy storage, hydrogen production and fuel cell systems, this paper builds a wind-solar hydrogen storage multi-energy complementary micro-grid DC network system, and puts forward cooperative control strategies of the system under different working conditions, so as to achieve stable operation of the system and maximize energy utilization. Finally, PSCAD simulation software is used to establish a simulation model for each distributed unit to verify the stability and rationality of the control strategy. The simulation results show that the control strategy can achieve efficient, cooperative and stable operation between different energy sources on the premise of ensuring the power quality of the microgrid.

A Coordinated Bidding Strategy of Wind Power Producers and DR Aggregators Using a Cooperative Game Approach

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker’s attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies.

Multi-population coevolutionary algorithm for a green multi-objective flexible job shop scheduling problem with automated guided vehicles and variable processing speed constraints

This study focuses on addressing a multi-objective Flexible Job Shop Scheduling Problem with Automated Guided Vehicles (FJSP-AGVs) and variable processing speed constraints. First, a position-based mixed integer linear programming model (MILP) is proposed to optimize simultaneously the maximum completion time and the total energy consumption. Then, we decompose FJSP-AGVs into four interrelated subproblems and design a Multi-Population Coevolutionary Algorithm (MCEA) to solve them. In MCEA, (1) The effective encoding and decoding methods are used to accurately reflect the characteristics of the problem, and generate feasible scheduling solutions. (2) A multi-rule-based heuristic is proposed to enrich the diversity of four populations. (3) A disjunctive graph is constructed to depict and obtain the critical path(s). On this basis, (4) two cooperative evolution strategies based on critical paths are proposed to facilitate collaborative evolution between different populations and improve the global search capability of the algorithm. Furthermore, (5) a consumption reduction strategy is proposed by reducing the processing speed of operations on non-critical paths while ensuring that it does not affect the makespan. Finally, we validate the effectiveness of MCEA by GD, and IGD, and set coverage metrics on the four typical benchmark datasets. Based on the average GD (IGD) metric across 65 instances, MCEA shows reductions of 77.63% (93.60%), 95.30% (97.27%), and 96.17%(97.89%) relative to EHA, EMOEA, and mop-BRKGA, respectively. The set coverage metric, MCEA outperforms EHA, EMOEA, and mop-BRKGA in 59, 64, and 64 instances, respectively. These results clearly indicate that MCEA can solve the FJSP-AGVs with variable processing speed constraints.