Search Results (246)

The objective of this study is to verify the usability of gesture interactions such as tilting or shaking, rather than conventional touch gestures, on mobile devices. To this end, a prototype was developed that manipulates the text size in a mobile text messaging application through tilt gestures. In the text input interface, three types of tilt gesture interaction methods (‘Shaking’, ‘Leaning’, and ‘Acceleration’) were implemented to select the text size level among five levels (extra-small, small, normal, large, and extra-large). Along with the gesture-based interaction methods, the conventional button method was also evaluated. A total of 24 participants were asked to prepare text messages of specified font sizes using randomly assigned interaction methods to select the font size. Task completion time, accuracy (setting errors and input errors), workload, and subjective preferences were collected and analyzed. As a result, the ‘Shaking’ method was generally similar to the conventional button method and superior to the other two ‘Leaning’ and ‘Acceleration’ methods. This may be because ‘Leaning’ and ‘Acceleration’ are continuous operations, while ‘Shaking’ is an individual operation for each menu (font size level). According to subjective comments, tilting gestures on mobile devices can not only be useful if users take the time to learn them, but also provide ways to convey intentions with simple text. Although tilting gestures were not found to significantly improve text editing performance compared to conventional screen touch methods, the use of motion gestures beyond touch on mobile devices can be considered for interface manipulations such as app navigation, gaming, or multimedia controls across diverse applications. Full article

This study considers a simple automotive supply chain that includes an automobile manufacturer with demand information and financial advantages and a financially constrained automobile lessor. The manufacturer can decide whether to provide financing support to the lessor, as follows: when the manufacturer offers trade credit contracts, this is seller financing, and the lessor does not need to borrow from banks; if the manufacturer only provides wholesale price contracts, then the lessor must rely on bank financing. By constructing a signaling game, we delve into the interactive relationship between the manufacturer’s contract decisions and the lessor’s optimal financing strategies under both symmetric and asymmetric demand information scenarios. The findings show that, under symmetric information, the decisions of the manufacturer and the lessor are primarily driven by demand price sensitivity, with no significant financing conflicts between the two parties. However, under asymmetric information, their decisions are also closely related to the degree of demand fluctuation, leading to the emergence of financing conflicts. The innovation of this study lies in its incorporation of demand information asymmetry into the analytical framework governing manufacturers’ contract decisions and lessors’ financing strategies. This provides valuable theoretical insights and practical guidance for automotive supply chains operating under financial constraints. Full article

Robustness is an important issue in deep learning, and Bayesian neural networks (BNNs) provide means of robustness analysis, while the minimax method is a conservative choice in the classical Bayesian field. Recently, researchers have applied the closed-loop idea to neural networks via the minimax method and proposed the closed-loop neural networks. In this paper, we study more conservative BNNs with the minimax method, which formulates a two-player game between a deterministic neural network and a sampling stochastic neural network. From this perspective, we reveal the connection between the closed-loop neural and the BNNs. We test the models on some simple data sets and study their robustness under noise perturbation, etc. Full article

Hand impairment affects millions of people. There are multiple factors that cause deficits, varying from physical injuries to neurological disorders. Upper-limb patients face significant difficulties in daily life. Rehabilitation aims at supporting them to regain functionality and increasing their independence and quality of life. Assessment is key to therapy, as it offers an evaluation of the condition of patients, leading to suitable treatments. Unfortunately, rehabilitation relies on clinical resources, making it expensive and time-consuming. Digital technology can provide solutions that make treatments more flexible and affordable. With the use of computer vision, we created an online platform that includes several exercises and serious games, based on movements and gestures performed in real-world treatments. Difficulty levels vary, and therapists can monitor these procedures remotely, while performance can be stored and tracked over time, identifying improvement. There is no need for any special equipment, as the platform can be accessed like a common website and all its applications require only a simple computer camera and stable Internet connection. In this article, we present our research approach, we analyze the development of the platform, and we provide a brief demonstration of its use in practice. Furthermore, we address some technical challenges and we share the results derived from preliminary test phases, concluding by outlining future plans. Full article

Megaprojects are complex systems comprising interdependent subsystems and diverse stakeholders, each contributing to the project’s resilience and long-term outcomes. Traditional methods for assigning stakeholder influence often assume that stakeholders operate independently when evaluating subsystem resilience. However, these approaches overlook the intricate dynamics—such as competition and collaboration—that frequently characterize stakeholder interactions in megaprojects. This study addresses this gap by introducing a novel framework based on game theory and network analysis to assess megaproject resilience. The model incorporates both stakeholder interactions and subsystem interdependencies, using a networked game approach to dynamically allocate stakeholder weights. These weights reflect cooperative and conflicting relationships among stakeholders. The framework optimizes a stakeholder’s utility function by balancing marginal benefits, costs, and interaction effects, ensuring rational and adaptive weight distribution. The resulting solution represents a unique Nash equilibrium, identified as the optimal configuration for stakeholder influence. To validate the framework, the study applies it to the Jakarta–Bandung High-Speed Railway (JBHSR) megaproject, demonstrating its capacity to integrate theoretical rigor with practical application. Through mathematical proofs and simulations, the research explores how model parameters influence two critical solution properties: order consistency and stability. Comparative analysis with established methods, such as the Analytic Hierarchy Process (AHP) and simple averaging, highlights the proposed model’s superior ability to capture stakeholder dynamics and adapt to the evolving nature of megaprojects throughout their lifecycle. The findings emphasize the model’s utility in delivering more nuanced resilience evaluations by accounting for stakeholder roles, relationships, and contributions. Specifically, this framework advances theory by merging network analysis with game theory to capture dynamic stakeholder influences, while offering practitioners a real-time mechanism to manage and optimize stakeholder interactions for improved resilience across the entire megaproject lifecycle. Its adaptability to full lifecycle assessments makes it a robust and scalable tool for managing resilience in large-scale infrastructure projects, offering valuable insights for both practitioners and researchers. Full article

We study a duel game in which each player has incomplete knowledge of the game parameters. We present a simple, heuristically motivated and easily implemented algorithm by which, in the course of repeated plays, each player estimates the missing parameters and consequently learns his optimal strategy. Full article

Background/Objectives: Recent research suggests that video games may serve as cognitive training tools to enhance visual working memory (VWM) capacity. However, the effectiveness of game-based cognitive training remains debated, and the underlying neural mechanisms, as well as the relationship between training efficacy and game design factors, are unclear. This study aimed to evaluate the impact of video game training on VWM capacity and explore its neural correlates. Methods: Two groups underwent 56 daily 20 min training sessions with two distinct video games targeting different cognitive skills: a reaction-time training game and a VWM-specific training game. Behavioral assessments included accuracy, hit response times, correct rejection response times, and Cowan’s K values. Neural correlates were measured through Negative Slow Wave (NSW) activity using EEG. Decision tree classification analyses were applied to NSW data across sessions and set sizes to identify patterns linked to VWM capacity. Results: Preliminary results are that both groups showed improvements in behavioral measures (accuracy, response times, and Cowan’s K values). NSW analyses revealed a main effect of set size in both groups, and classification results indicated that NSW patterns differed between groups, across sessions, and set sizes, supporting the relationship between NSW and VWM capacity. Conclusions: These findings contribute to understanding NSW as a neurophysiological correlate of VWM capacity, demonstrating its plasticity through video game training. Simple video games could effectively enhance behavioral and neural aspects of VWM, encouraging their potential as accessible cognitive training tools. Full article

Developing AI algorithms for the game of Go has long been a challenging task. While tools such as AlphaGo have revolutionized gameplay, their focus on maximizing win rates often leads to moves that are incomprehensible to human players, limiting their utility as training aids. This work introduces a novel approach to bridge this gap by leveraging a Vision Transformer (ViT) to develop an AI model that achieves professional-level play while mimicking human decision-making. Using a dataset from the KGS Go server, our ViT-based model achieves 51.49% accuracy in predicting expert moves with a simple feature set. Comparative analysis against CNN-based models highlights the ViT’s superior performance in capturing patterns and replicating expert strategies. These findings establish ViTs as promising tools for enhancing Go training by aligning AI strategies with human intuition. Full article

The design of bridges must balance sustainability and construction simplicity. A game-theory-based optimization method was applied in this research to find a sustainable steel–concrete composite bridge design. The sustainability was evaluated through cost and environmental and social impact using the Life Cycle Assessment method. The optimization process considered four criteria simultaneously, using a discrete version of the SCA algorithm and a transfer function for discretization. The preferred solutions were selected using the Minkowski distances approach. Results showed a decrease in slab reinforcement and an increase in the amount of steel in the cross-section, leading to only an 8.2‰ increase in cost compared to similar studies. Regarding the cross-section, the geometry obtained considers cells in the upper and lower parts of the webs to improve the bending resistance. The proposed method allows for the simultaneous optimization of multiple criteria and provides a sustainable yet simple bridge design solution. Full article

Musculoskeletal disorders (MSDs) can significantly impact individuals’ quality of life (QoL), often requiring effective rehabilitation strategies to promote recovery. However, traditional rehabilitation methods can be expensive and may lack engagement, leading to poor adherence to therapy exercise routines. An exergame system can be a solution to this problem. In this paper, we investigate appropriate hand gestures for controlling video games in a rehabilitation exergame system. The Mediapipe Python library is adopted for the real-time recognition of gestures. We choose 10 easy gestures among 32 possible simple gestures. Then, we specify and compare the best and the second-best groups used to control the game. Comprehensive experiments are conducted with 16 students at Andalas University, Indonesia, to find appropriate gestures and evaluate user experiences of the system using the System Usability Scale (SUS) and User Experience Questionnaire (UEQ). The results show that the hand gestures in the best group are more accessible than in the second-best group. The results suggest appropriate hand gestures for game controls and confirm the proposal’s validity. In future work, we plan to enhance the exergame system by integrating a diverse set of video games, while expanding its application to a broader and more diverse sample. We will also study other practical applications of the hand gesture control function. Full article

Public touchscreens, such as those used in automated teller machines or ticket payment systems, which are accessed by different people in a short period of time, could transmit pathogens and thus spread infections. Therefore, the aim of this study was to develop and test a prototype of a touchscreen system for the public sector that disinfects itself quickly and automatically between two users without harming any humans. A quartz pane was installed in front of a commercial 19” monitor, into which 120 UVC LEDs emitted laterally. The quartz plate acted as a light guide and irradiated microorganisms on its surface, but—due to total reflection—not the user in front of the screen. A near-infrared commercial touch frame was installed to recognize touch. The antibacterial effect was tested through intentional staphylococcus contamination. The prototype, composed of a Raspberry Pi microcomputer with a display, a touchscreen, and a touch frame, was developed, and a simple game was programmed that briefly switched on the UVC LEDs between two users. The antimicrobial effect was so strong that 1% of the maximum UVC LED current was sufficient for a 99.9% staphylococcus reduction within 25 s. At 17.5% of the maximum current, no bacteria were observed after 5 s. The residual UVC irradiance at a distance of 100 mm in front of the screen was only 0.18 and 2.8 µW/cm² for the two currents, respectively. This would allow users to stay in front of the system for 287 or 18 min, even if the LEDs were to emit UVC continuously and not be turned off after a few seconds as in the presented device. Therefore, fast, automatic touchscreen disinfection with UVC LEDs is already possible today, and with higher currents, disinfection durations below 1 s seems to be feasible, while the light guide approach virtually prevents the direct irradiation of the human user. Full article

In spatial public goods games, groups consisting solely of defectors do not benefit. Consequently, intelligent defectors are inclined to incur the cost of punishing other defectors to enable cooperators to flourish within the group, thereby safeguarding their own advantages. Drawing from real-world observations where early preparation often dictates future success, we integrated probabilistic punishment into the public goods game and analyzed two scenarios. In the first scenario, a probabilistic punishment mechanism was established, wherein the higher the cost was of monitoring and enforcement, the greater was the probability of punishment. In the second scenario, a compensation and fine distribution mechanism was introduced alongside probabilistic punishment, where the outcome of the punishment determined whether the smart defector recovered part of the fine or rewarded cooperators with additional benefits. This incentivized smart defectors to judiciously assess the punishment cost required to effectively protect their interests. The study demonstrated that both mechanisms significantly enhanced cooperation, with the probabilistic punishment model involving fine distribution and compensation proving more effective than simple probabilistic punishment alone. These results offer novel insights into the dynamics of probabilistic punishment and the role of fine distribution in fostering cooperation. Full article

The use of serious games on virtual learning platforms as a learning support resource is increasingly common. They are especially effective in helping students acquire mainly applied curricular content. However, a process is required to monitor the effectiveness and students’ perceived satisfaction. The objectives of this study were to (1) identify the most significant characteristics; (2) determine the most relevant predictors of learning outcomes; (3) identify groupings with respect to the different serious game activities; and (4) to determine students’ perceptions of the usefulness of the simple and complex serious game activities. We worked with a sample of 130 university students studying health sciences and biomedical engineering. The serious game activities were applied in a Moodle environment, UBUVirtual, and monitored using the UBUMonitor tool. The degree type and the type of serious game explained differing percentages of the variance in the learning results in the assessment tests (34.4%—multiple choice tests [individual assessment]; 11.2%—project performance [group assessment]; 25.6%—project presentation [group assessment]). Different clusters were found depending on the group of students and the algorithm applied. The Adjusted Rang Index was applied to determine the most appropriate algorithm in each case. The student satisfaction was high in all the cases. However, they indicated complex serious games as being more useful than simple serious games as learning resources for the practical content in both health sciences and biomedical engineering degrees. Full article

Graphical games describe strategic interactions among a specified network of players. The threshold protocol game is a graphical game that models the adoption of a lesser-used product in a population when individuals benefit by using the same product. The threshold protocol game has historically been considered using infinite, simple graphs. In general, however, players might value some relationships more than others or may have different levels of influence in the graph. These traits are described by weights on graph edges or vertices, respectively. Relative comparisons on arbitrarily weighted graphs have been studied for a variety of graphical games. Alternatively, graph labelings are functions that assign values to the edges and vertices of graphs based on a particular set of rules. This work demonstrates that the outcome of the threshold protocol game can be characterized on a magic square-generalization labeled graph. There are a variety of graph labelings that generalize the concept of magic squares. In each, the labels on similar sets of graph elements sum to a constant. The constant sums of magic square-generalization labelings mean that each player experiences a constant level of influence without needing to specify the value of players relative to one another. The game outcome is compared across different types and features of labelings. Full article