Cooperative Scheme Research Articles

Pricing Power Conflict and Cooperation Strategies of Competing Two‐Sided Platforms: Impact of the Differentiated Value‐Added Services and Cross‐Network Effects

ABSTRACTSince pricing power plays a pivot role in platforms' pricing strategies and services investment, thereby influencing the competitive landscape between the two platforms. This study investigates the pricing power preferences within a two‐sided manufacturing platform ecosystem, where a large‐scale platform (platform L) competes with a small‐specialized platform (platform R) for bilateral users through differentiated value‐added services (VAS) level. Both platforms exhibit cross‐network externality and engage in pricing power competition. Three endogenous pricing power modes are available, including horizontal Nash (mode B), platform L‐price‐leader Stackelberg (mode L), and platform R‐price‐leader Stackelberg (mode R). The optimal solutions under each mode and characterize the equilibrium evolution of pricing power preferences are derived. Findings reveal that at the low VAS level stage for platform L, mode R realizes equilibrium when the hassle cost is low; mode L realizes equilibrium when the hassle cost is high. At the high VAS level stage for platform L, mode L realizes equilibrium when the hassle cost is high; mode R realizes equilibrium only when the hassle cost is low and cross‐network externality is in a high range. Notably, competing platforms will trap into the pricing power conflict when both platforms strategically second moves in announcing prices. Therefore, this study proposes two feasible strategies for platform cooperation: joint revenue and VAS cost‐sharing and transfer payment. These strategies can reach a “win–win” outcome when sharing ratios are moderate or payment meets suitable thresholds. This study not only enhances the theory of two‐sided markets but also provides strategic guidance for platform competition and cooperation.

Cooperative, Collaborative Learning Enhances Students’ Ability to Improve Their Oral Communication in an English Classroom

This study examines the effectiveness of cooperative and collaborative learning methodologies in enhancing oral English communication skills, particularly in real-life situations within the classroom. It focuses on the practical benefits of these approaches in developing L2 learners' oral proficiency through mutual support and interaction. Conducted with 49 11th-grade students from a public high school, the research involved an educational intervention that applied cooperative and collaborative strategies, such as role-plays, discussions, debates, and group projects. These activities provided students with diverse language inputs and communicative strategies suited to their English level. Besides, data collection included an observation checklist before and after the intervention, as well as a survey that analyzed students’ collaborative experiences, the challenges they faced, and strategies to overcome language-speaking barriers. The study found that both cooperative and collaborative learning fostered active participation, peer interaction, and authentic communication, which are vital for improving L2 oral proficiency. In conclusion, the research highlights the value of these methodologies in creating opportunities for students to engage in real-life communication, strengthening their oral skills, and empowering them to communicate more effectively in English.

Cooperation Dynamics in Multiagent Systems: Modeling Vehicular Cooperation through Game Theory

<div>Cooperation lies at the core of multiagent systems (MAS) and multiagent reinforcement learning (MARL), where agents must navigate between individual interests and collective benefits. Advanced driver assistance systems (ADAS), like collision avoidance systems and adaptive cruise control, exemplify agents striving to optimize personal and collective outcomes in multiagent environments. The study focuses on strategies aimed at fostering cooperation with the aid of game-theoretic scenarios, particularly the iterated prisoner’s dilemma, where agents aim to optimize personal and group outcomes. Existing cooperative strategies, such as tit-for-tat and win-stay lose-shift, while effective in certain contexts, often struggle with scalability and adaptability in dynamic, large-scale environments. The research investigates these limitations and proposes modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. By analyzing existing cooperative strategies, the research investigates their effectiveness in encouraging group-oriented behavior in repeated games. It suggests modifications to align individual gains with collective rewards, addressing real-world dilemmas in distributed systems. Furthermore, it extends to scenarios with exponentially growing agent populations (<i>N</i> → +∞), addressing computational challenges using mean-field game theory to establish equilibrium solutions and reward structures tailored for infinitely large agent sets. Practical insights are provided by adapting simulation algorithms to create scenarios conducive to cooperation for group rewards. Additionally, the research advocates for incorporating vehicular behavior as a metric to assess the induction of cooperation, bridging theoretical constructs with real-world applications.</div>

Cooperative Control Strategy for Power Quality Based on Heterogeneous Inverter Parallel System

Multi-functional grid-connected inverters (MFGCIs) have attracted much attention for their auxiliary services to improve power quality in microgrids and new energy generation systems. However, power quality enhancement based on MFGCIs often only considers a single control method. The regulation potential of multiple control-type inverters is not fully utilized. To suppress harmonic and support voltage, a cooperative control strategy based on the parallel system of a grid-following (GFL) inverter and a grid-forming (GFM) inverter is proposed. The topology of the MFGCIs is introduced and a mathematics model is deduced. Secondly, a specific-order harmonic compensation method is proposed for the harmonic suppression at the point of common coupling (PCC). The harmonic current is realized to be shared according to the inverter capacity. The reactive power output of the heterogeneous inverters is coordinated by calculating the reactive power compensation and reactive power allocation so that the PCC voltage always stays near the rated voltage. Finally, the effectiveness of the proposed control strategy is verified by simulation and experiment.

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.

Chimeric aggregative multicellularity in absence of kin discrimination.

Aggregative multicellularity is a cooperative strategy employed by some microorganisms. Unlike clonal expansion within protected environments during multicellular eukaryotic development, an aggregation strategy introduces the potential for genetic conflicts and exploitation by cheaters, threatening the stability of the social system. Myxococcus xanthus, a soil-dwelling bacterium, employs aggregative multicellularity to form multicellular fruiting bodies that produce spores in response to starvation. Studies of natural fruiting bodies show that this process is restricted to close kin or clonemates. Here, we investigate the mechanisms underlying kin recognition during development in M. xanthus. By co-culturing two distantly related M. xanthus strains under vegetative and starvation conditions, we observed that the strains segregate in both contexts. During vegetative growth, one strain antagonized the other using the type VI secretion system (T6SS). T6SS-mediated antagonism was also observed during development, resulting in monoclonal fruiting bodies when WT strains were mixed. In contrast, mixtures of T6SS knockout strains formed chimeric fruiting bodies, that produced viable spores from both strains. These findings suggest that T6SS is the primary mechanism of kin discrimination in distantly related M. xanthus strains, and its use ensures the development of monoclonal fruiting bodies and social integrity.

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.

Suboptimal distributed cooperative control for linear multi-agent system via Riccati design.

In this paper, we propose a suboptimal distributed cooperative control scheme for the continuous-time linear multi-agent system (MAS) with a specified global quadratic cost functional over both undirected and directed graph scenarios. For undirected graphs, we first derive the cost functional for a given strictly linear feedback distributed protocol. It is shown that the cost functional is upper bounded by a quadratic form of the MAS's initial state, and the minimum upper bound can be derived by solving a parametric algebraic Riccati equation (PARE) depends solely on the algebraic connectivity of the graph and is independent of the largest eigenvalue compared with the existing work. Based on this, a suboptimal distributed design method is proposed, where the resulting cost functional is less than a specified positive scalar. Then, we extend the theoretical results and design method to the directed graph scenario by introducing the row and column Laplacian matrices associated with the directed graph. Finally, numerical examples are provided to verify the effectiveness of the obtained results.

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”.