Game-based Model Research Articles

Effects of Situated Game Teaching Through Set Plays on Badminton Tactical Knowledge, Technical Ability, and Game Performance Among Turkish Secondary School Students

Background: The Situated Game Teaching Through Set Plays (SGTSP) model, a newly proposed curricular model, extends the previous game-based approaches by adopting the Theory of Situated Learning as a framework to focus on the relational and situational nature of the changing relations of all game parameters in a specific momentary game scenario. However, as a new game-based model, SGTSP warrants further empirical research investigations to establish its effectiveness at advancing student learning such as skill acquisition and game competence. Purpose: To examine the effects of the SGTSP curricular model on secondary school students’ tactical knowledge, technical ability, and game performance in badminton when compared with a technique-focused approach during a 10-lesson unit. Methods: A completely randomized block design with a repeated measure was used to address the research purpose. A sample of 158 sixth graders from eight classes at two Turkish middle schools (67 girls and 89 boys; Mage = 12.62 ± 0.47) were assigned to either the SGTSP (n = 79 from four classes) or the technique-focused approach conditions (n = 79 from four classes) for the experiment. Assessments were conducted before and after the badminton instructional unit. The statistical models consisted of one of the four technical ability performances, components of game performance, or tactical knowledge as a dependent variable and eight independent variables: teacher, treatment, class nested within teacher and treatment, gender, skill levels, and three 2-way interactions between treatment, gender, and skill levels. A series of repeated-measure multivariate analyses of variance were conducted to analyze the data. Results: Participants in both groups improved tactical knowledge, forehand long serve, forehand clear, smash, and drop skills, and game performances from pre to post. Participants in the SGTSP condition showed significantly greater improvements (). Discussion and Conclusion: The findings of this study substantiated the positive effects of the SGTSP model on developing secondary school students’ badminton tactical knowledge, technical ability, and game performances in badminton compared with the traditional technique-focused approach.

Multi-Objective optimization for stable and efficient cargo transportation of partial space elevator

This paper proposed a new libration decoupling analytical speed function (LD-ASF) in lieu of the classic analytical speed function to control the climber’s speed along a partial space elevator to improve libration stability in cargo transportation. The LD-ASF is further optimized for payload transportation efficiency by a novel coordinate game theory to balance competing control objectives among payload transport speed, stable end body’s libration, and overall control input via model predictive control. The transfer period is divided into several sections to reduce computational burden. The validity and efficacy of the proposed LD-ASF and coordinate game-based model predictive control are demonstrated by computer simulation. Numerical results reveal that the optimized LD-ASF results in higher transportation speed, stable end body’s libration, lower thrust fuel consumption, and more flexible optimization space than the classic analytical speed function.

Strengthening Clinician-Researchers' Communication and Knowledge Translation Skills: An Innovative Game Model From the Breathe Well Group.

Communication is a core component of a clinician's role; however, when clinicians conduct research, communicating the emerging findings and recommendations to different types of stakeholders can be unfamiliar territory. Communicating research to advocate for change can be even more challenging. Clinician researchers seeking to be agents for change need to conceive and craft specific, evidence-based messages and communicate these effectively to different stakeholders to negotiate action. As part of a global health research program, we developed and tested a novel game-based model to strengthen the communication skills of clinician researchers, from 4 countries, for improving services for chronic obstructive pulmonary disease. This model focused on communication with 3 key stakeholder groups for knowledge translation: Patients/carers, healthcare providers and policy makers/healthcare managers. Delivered through a series of facilitated, online meetings, this model consisted of 2 parts: developing and rehearsing advocacy messages with coaching support, and then testing them with a panel of 3 representative stakeholders, and an audience of fellow researchers. All the country teams reported increased confidence in crafting advocacy messages for specific stakeholders and have applied lessons learned from the model. Delivering this model within a global health research program requires mentoring, time, commitment, resources and translation support to address language barriers. It offers an exemplar to build the communication skills of clinician and non-clinician researchers so that they can go beyond dissemination toward translation of evidence into policy and practice.

A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response

In the context of national efforts to promote country-wide distributed photovoltaics (DPVs), the installation of distributed energy storage systems (DESSs) can solve the current problems of DPV consumption, peak shaving, and valley filling, as well as operation optimization faced by medium-voltage distribution networks (DN). In this paper, firstly, a price elasticity matrix based on the peak and valley tariff mechanism is introduced to establish a master–slave game framework for DN-DESSs under the DPV multi-point access environment. Secondly, the main model optimizes the pricing strategy of peak and valley tariffs with the objective of the lowest annual operating cost of the DN, and the slave model establishes a two-layer optimization model of DESSs with the objective of the maximum investment return of the DESSs and the lowest daily operating costs and call the CPLEX solver and particle swarm optimization algorithm for solving. Finally, the IEEE33 node system is used as a prototype for simulation verification. The results show that the proposed model can not only effectively reduce the operating cost of the distribution network but also play a role in improving the energy storage revenue and DPV consumption capacity, which has a certain degree of rationality and practicality.

Practical Certificate-Less Infrastructure with Application in TLS

We propose highly efficient certificate-less (CL) protocols for the infrastructure used by authenticated key exchange (AKE). The construction is based on elliptic curves (EC) without pairing, which means it can be easily supported by most industrial cryptography libraries on constrained devices. Compared with other pairing-free CL solutions, the new CL-AKE protocol enjoys the least number of scalar multiplications over EC groups. We use a unified game-based model to formalize the security of each protocol, while most previous works only assess the security against a list of attacks, provide informal theorems without proper modeling, or use separate models for protocols in different stages. We also present an efficient integration of the core protocols into the TLS cipher suites and a stand-alone implementation for constrained devices. The performance is evaluated on constrained devices in real-world settings, which further confirms the efficiency of our proposal.

Cooperative decision-making of multiple autonomous vehicles in a connected mixed traffic environment: A coalition game-based model

Advances in vehicle-networking technologies have enabled vehicles to cooperate in mixed traffic. However, realizing the cooperative decision-making of multiple connected autonomous vehicles (CAVs) when influenced by the presence of connected manual vehicles (CMVs) is a challenging area in current research. In this study, we propose a coalition game-based (CG-based) model for multi-CAV cooperative decision-making in a connected mixed traffic environment. First, the model integrates the perceived risk field theory, quantifying the driving risk from the perspective of different CMVs; this risk is used to determine the uncertainty of the motion state of CMVs. Second, the model can identify the conflicts caused by multiple lane-changing vehicles and decouple the conflict problem into multiple two-vehicle lane-changing games, including a cooperative game between two CAVs and a non-cooperative game between a CAV and a CMV. To test the proposed model, four scenarios that blocked the passage of multiple CAVs were set up; in these scenarios, the average speed of the CG-based model was 21.05, 16.76, 23.17, and 12.55% higher than that of the LC2013 model. The simulation results showed that the CG-based model could improve the efficiency of multiple CAVs while ensuring safety in a mixed traffic flow.

Pricing for Reconfigurable Intelligent Surface Aided Wireless Networks: Models and Principles

Owing to the recent advancements of meta-materials and meta-surfaces, the concept of reconfigurable intelligent surface (RIS) has been embraced to meet the spectral- and energyefficient, and yet cost-effective solutions for the sixth-generation (6G) wireless networks. From an operational standpoint, RISs can be easily deployed on the facades of buildings and indoor walls. Albeit promising, in the actual network operation, the deployment of RISs may face challenges because of the willingness and benefits of RIS holders from the aspect of installing RISs on their properties. Accordingly, RIS-aided wireless networks are faced with a formidable mission: how to balance the wireless service providers (WSPs) and RIS holders in terms of their respective interests. To alleviate this deadlock, we focus on the application of pricing models in RIS-aided wireless networks in pursuit of a win-win solution for both sides. Specifically, we commence with a comprehensive introduction of RIS pricing with its potential applications in RIS networks, meanwhile the fundamentals of pricing models are summarized in order to benefit both RIS holders and WSPs. In addition, a Stackelberg game-based model is exemplified to illustrate the operation of utility-maximization pricing. Finally, we highlight open issues and future research directions of applying pricing models to the RIS-aided wireless networks.

Effects of Competition and Reputation on Auditing Cloud Service Provider's Security Commitment

Auditing a cloud service provider's (CSP's) compliance with data backup requirements is of great value to data availability and redundancy. As the checking incurs extra costs, a lot of research has been done to find the optimal checking strategy. However, there remain two problems: 1) What kind of influence does the competition between CSPs for clients have on the checking strategies? 2) How to incorporate the influence of CSP's reputation in a reasonable and practicable way? To solve these problems, we propose a one-population evolutionary game model. The competition for clients is first captured via the game between CSPs. Then, we introduce a reputation term in the game model, which is defined as the function of checking probability. We analyze the model, and then, derive the thresholds of checking probability. A verifier can optimize the use of checking resources whatever checking methods are using according to the thresholds. Finally, we conduct various comparison experiments between our model and another game-based model. The comparison results show that the long-term effects of competition and reputation can help a verifier save checking resources or promote the checking effect.

STSIR: An individual-group game-based model for disclosing virus spread in Social Internet of Things

Social Internet of Things (SIoT) with deep integration of Internet of Things and social networks has become a target of a large number of hackers who attempt to spread viruses for breaching data confidentiality and service reliability. Therefore, exposing the law of virus spread with social characteristics and addressing historical dependence of infection and recovery rates in an SIoT are urgent problems to be solved at present. To this end, we propose a novel virus spread model (STSIR) based on an epidemic theory's analysis framework and individual-group game theory, which more reasonably describes viruses spread among devices considering people behavior. Aiming at the characteristics of SIoTs including limited social distance and dynamic number variation of people and devices, we adopt and improve the traditional epidemic model SIR to reveal the form of viruses continuously spreading to neighbor nodes. We then introduce an individual-group game to establish the attack and defense model between infected SIoT nodes and susceptible SIoT nodes, in order to not only obtain the mixed Nash equilibrium solution by using a payoff matrix but also solve the dependence of the infection and recovery rates on historical experience. Further, we establish differential equations to represent the model STSIR, which are the basis of proving the existence of model equilibrium points and analyzing stability mathematically. Finally, the effectiveness of the model STSIR in curbing virus spread is verified by simulating two equilibrium points. Under the same conditions in an SIoT, the model STSIR reduce viruses by ∼45% more than the model SIS, and saves stabilization time cost by ∼66.7% compared with the model SIR, which proves that the model STSIR is obviously more effective.

Efficacy of the 4F Feedback Model: A Game-Based Assessment in University Education

Feedback is a critical aspect of optimised learning design, but there are few, if any, feedback models that map different types of feedback and how they may assist students to increase performance and enhance their learning experience. This research paper outlines a feedback model as an extension of the four-dimensional framework which includes a consideration of the type, the content, the format, and the frequency of feedback, as well as the agent which delivers it. This model is based upon an understanding of learning in the context of designing learning experiences and utilises a game-based model of learning to understand the importance of motivation and autonomy in learners to enhance and accelerate learning. The framework is developed and reflected upon by analysing two cases: a medical triage case in which the timing and frequency of feedback proved critical, and a business simulation which illuminated the need for a range of types of feedback and to be aware of the possibility of different agents (instructor peer and game) that can deliver feedback. The extended model may help game and learning designers alike to discern different types of feedback, both in games and more generally, in more explicit and nuanced ways.

Formal Synthesis of Adaptive Droplet Routing for MEDA Biochips

A digital microfluidic biochip (DMFB) enables the miniaturization of immunoassays, point-of-care clinical diagnostics, and DNA sequencing. A recent generation of DMFBs uses a microelectrode-dot-array (MEDA) architecture, which provides fine-grained control of droplets and real-time droplet sensing using CMOS technology. However, microelectrodes in a MEDA biochip can degrade due to charge trapping when they are repeatedly charged and discharged during bioassay execution; such degradation leads to the failure of microelectrodes and erroneous bioassay outcomes. To address this problem, we first introduce a new microelectrode-cell design such that we can obtain the health status of all the microelectrodes in a MEDA biochip by employing the inherent sensing mechanism. Next, we present a stochastic game-based model for droplet manipulation, and a formal synthesis method for droplet routing that can dynamically change droplet transportation routes. This adaptation is based on the real-time health information obtained from microelectrodes. Comprehensive simulation results for four real-life bioassays show that our method increases the likelihood of successful bioassay completion with negligible impact on time-to-results.

Optimal Transactive Energy Trading of Electric Vehicle Charging Stations With On-Site PV Generation in Constrained Power Distribution Networks

This paper presents a two-level transactive energy market framework, that enables energy trading among electric vehicle charging stations (EVCSs). At the lower level, the discharging capability of EVs and on-site PV generation are leveraged by individual EVCS for participating in the transactive trading with their peers. Once the lower-level trading is completed, EVCSs trade energy at the upper level through the power grid network managed by the distribution system operator (DSO). The upper-level market is cleared while satisfying the power distribution network constraints. A cooperative game-based model is proposed to model the energy trading among EVCSs. To this end, the asymmetric Nash bargaining method is applied to allocate the grand coalition’s payoff to each EVCS at the upper-level market, while a weighted proportional allocation method is used to allocate individual EVCS’s payoff to its respective EVs at the lower-level market. The upper-level market formulation is further decomposed into two subproblems representing an energy scheduling and trading subproblem which maximizes EVCS payoffs, and a bargaining subproblem which allocates EVCS payoffs. The effectiveness of the proposed framework for incentivizing transactive trades among EVs and EVCSs is validated in case studies.

Investigating the Online Game-based and Group-Based Flipped Online Learning of Drug Abuse Prevention on Learning Achievement Among Grade 10 Students in Bangkok, Thailand

This study was examined the effect of the online Game-based learning versus the Group-Based Flipped Classroom online learning experiences about drug abuse prevention on the learning achievement among grade 10 students in Bangkok, Thailand. Classroom action research was implemented among grade 10 students at high school in Bangkok selected through purposive. Two comparable classrooms were assigned to either the online game-based learning or group-based flipped online learning for 2 weeks. The instruments employed in the study consisted of: (1) a flipped classroom model with online group investigation, (2) an online game-based model as individual learning, (3) the learning achievement—drug abuse prevention cognitive test. Independent t-test was analyzed to test the effectiveness of the two interventions. It revealed there was a significant difference in the learning achievement of drug abuse prevention given to those who were exposed to online game-based learning and those who were exposed to group-based flipped classroom online learning (t= 3.945, n1= 43, n2= 39, p= 0.000). From the study, high school students who were exposed to the online game-based learning performed better to those exposed to group-based flipped classroom online learning. Teachers are to encourage students to use individual learning to improve performance.

Advancing the Use of Games as a Model for Scientific Practice

Abstract Science is similar to a game, as both involve rules-based participation in search of optimal outcomes. Supported by key texts in history and philosophy of science, the authors propose a game-based model for understanding scientific inquiry and practice, particularly through computational resources. They conclude that this model creates space for more speculative and reflective approaches to scientific practice and can contribute to the design and development of better scientific software, simulation and visualization.

Bottom-up capping (BUC) policy under bargaining techniques for inter-sectoral groundwater trading: a case study from Iran

Abstract Cap-and-trade (C&T) policy has led to environmental benefits in some groundwater markets by restricting and economically reallocating water permits. However, top-down approaches for capping permits may face resistance from every affected stakeholder. This paper presents an efficient policy framework to improve the implementation of C&T policies in a real shared aquifer in Iran. To this end, groundwater permits for water-selling farms are capped through a bottom-up capping (BUC) policy. A policy analysis that employs static and dynamic bargaining techniques incorporates farms' utilities. Results reveal that the bargaining techniques propose more acceptable capping strategies than the top-down approach. The BUC policy analysis introduces the proposed strategy by dynamic bargaining as the tradable groundwater permits. The effects of irrigation water sales to the industry sector, evaluated using a cooperative game-based optimization model, show that with the fair reallocation of water trading benefits, the current net benefits of agriculture and industry sectors increase by 55 and 27%, respectively. Furthermore, farms reduce their groundwater withdrawals by 35% compared with the current mode. Therefore, the BUC policy for inter-sectoral groundwater trading under dynamic bargaining can lead to the sustainable use of limited groundwater resources by facilitating the capping strategies and improving the water permits productivity.

Composable security for multipartite entanglement verification

We present a composably secure protocol allowing $n$ parties to test an entanglement generation resource controlled by a possibly dishonest party. The test consists only in local quantum operations and authenticated classical communication once a state is shared among them and provides composable security, namely it can be used as a secure subroutine by $n$ honest parties within larger communication protocols to test if a source is sharing quantum states that are at least $\epsilon$-close to the GHZ state. This claim comes on top of previous results on multipartite entanglement verification where the security was studied in the usual game-based model. Here, we improve the protocol to make it more suitable for practical use in a quantum network and we study its security in the Abstract Cryptography framework to highlight composability issues and avoid hidden assumptions. This framework is a top-to-bottom theory that makes explicit any piece of information that each component (party or resource) gets at every time-step of the protocol. Moreover any security proof, which amounts to showing indistinguishability between an ideal resource having the desired security properties (up to local simulation) and the concrete resource representing the protocol, is composable for free in this setting. This allows us to readily compose our basic protocol in order to create a composably secure multi-round protocol enabling honest parties to obtain a state close to a GHZ state or an abort signal, even in the presence of a noisy or malicious source. Our protocol can typically be used as a subroutine in a Quantum Internet, to securely share a GHZ state among the network before performing a communication or computation protocol.

Cyber Insurance Design for Validator Rotation in Sharded Blockchain Networks: A Hierarchical Game-Based Approach

Sharding is a promising solution to achieving scalability within the blockchain network. A sharded blockchain network consists of a beacon chain and several committees powered by the participants (i.e., validators) through the Proof-of-Stake (PoS) consensus protocol. Efficient and scalable as it can be, the sharded blockchain based on PoS is vulnerable to discouragement attack. A discouragement attack occurs when malicious validators censor messages to discourage validators from participating in the network. Furthermore, no rate-limiting validator rotation (enter/exit quickly) makes it more challenging to detect such an attack. In this paper, considering the undetermined rotation and the discouragement attack, we render the beacon chain an intermediary, allowing the beacon chain to interact with validators and the cyber-insurer, aiming to encourage the validators' stable rotation through insurance compensation. Specifically, we utilize a two-stage hierarchical game-based model to formulate the complicated interactions under the cyber insurance framework. In the first stage, the beacon chain develops compensatory strategies according to the insurer's profile. In the second stage, the beacon chain designs a series of contracts for validators, including insurance items, compensatory strategies, and rotation requirements. Consequently, the proposed scheme incentivizes validators to remain online by transferring risk to the cyber insurer and enables the sharded blockchain network to weaken the attack's impact through validators' stable rotation. This paper presents closed-form solutions for the proposed model, in which the beacon chain and the cyber insurer can gain maximized profits. The simulations demonstrate the feasibility and superiority of the proposed model.

The Basic Motion Learning Model of Small Game Based Throwing in Elementary School

This study aims to develop a small game-based basic motion learning model at Elementary School Number 65 Rejang Lebong. The research method used is research and development (R&D) using ten stages from Borg and Gall. The research instruments used were locomotor, non-locomotor tests, and manipulative basic movement skills tests. The treatment in this study was in the form of a basic motion learning model based on small games given for 4 weeks. The subjects in this study were students in grades 4 and 5 of Elementary School Number 65 Rejang Lebong. Subjects in this study were 30 people. The validation results obtained in small-scale trials regarding the quality of the learning model have entered into the validation criteria filled by the validator with a percentage of 63% and the results of large group trials are declared valid with a percentage of 81.25% which means very feasible. Furthermore, the effectiveness test was carried out using the t-test. In the significance test of the difference using the SPSS tool, the mean value = 0.8666 which shows the difference between the pre-test and post-test results, the results of T-count = 13.730, df = 29, and P-Value = 0.000 <0.05, which means that there are There is a significant difference between before and after being treated with the small game-based learning model of throwing basic motion learning. Where the post-test results have positive results with a significant difference compared to the pre-test results. So that the learning model of the basic motion of throwing based on small games can be used in learning physical education and health in elementary schools. The implication of this research is that the basic motion learning model of throwing based on small games can be used as a form of learning the basic motion of throwing in elementary school children.

The Influencing Factors and Promotion Countermeasures of Industrial Generic Technology Diffusion Based on Differential Game

Many factors affect the diffusion of generic technology due to the variety of generic technology and the multiplicity of diffusion carriers. Therefore, it is very urgent to determine the main factors that influence generic technology diffusion. Based on theories of industrial generic technology diffusion, this paper constructs a differential game-based model between government and enterprises. Then, this study explores the key factors that affect the diffusion strategy of industrial generic technology innovation and analyzes the possible method for improving industrial generic technology innovation diffusion. The following conclusions are obtained. Incentive measures, rewards, and penalties play a key role in the diffusion of generic technology, and the costs and benefits incurred by the diffusion are critical to the diffusion of generic technology. Also, the net income of enterprises has a positive impact on the adoption of generic technology diffusion. After that, this paper carries out data simulation for result verification and provides some policy implications.

A New Deep Q-Network Design for QoS Multicast Routing in Cognitive Radio MANETs

In this paper, we propose a new deep Q-network (DQN) design for quality-of-service (QoS) multicast routing (DQMR) protocol to establish efficient QoS multicast (EQM) trees in cognitive radio mobile ad hoc networks (CR-MANETs). An EQM tree is a shortest-path multicast tree with minimum end-to-end (E2E) cost (a combination of queuing size ratio and link stability) subject to QoS constraints such as queuing size ratio, link stability, number of hops, number of time slots and avoiding the licensed channel of primary users. Particularly, we propose an NP-complete optimization problem such that its feasible solution is an EQM tree. To address this problem, we design a new DQN model and a new game-based model to form EQM trees in real-time by offline training instead of online training as done in previous papers. Moreover, the DQMR protocol is also guaranteed to have high stability, low routing delay, low control overhead, and high packet delivery ratio (PDR). Furthermore, one more new contribution of the paper is that exact closed-form expressions for the E2E queuing delay of a multicast routing tree are also derived assuming random waypoint mobility and the reference point group mobility models to compare with simulation results of routing delay. Simulation results show that the DQMR protocol outperforms multicast ad hoc on-demand distance vector routing protocol in terms of routing delay, control overhead, and PDR.