Search Results (969)

The use of robots in various industries has increased significantly in recent years, with mobile robots playing a central role in automation. Their applications range from service robotics and automated material handling to bomb disposal and planetary exploration. A rapidly growing area of mobile robotics involves coordinated groups of autonomous robots, commonly referred to as swarms. However, only a limited number of studies have addressed systems in which ropes or wires physically connect robots. Connecting multiple autonomous robotic vehicles with a tensioned wire can form a movable fence, enabling coordinated motion as a single dynamic entity. This paper presents a real-time control approach for the off-road motion of physically connected skid-steering robotic vehicles. A numerical-simulation-driven artificial neural network is employed as a surrogate model to estimate wheel–ground load distribution online, enabling stable steering control and accurate trajectory tracking on rough terrain while accounting for wire-induced coupling effects. Full article

This report describes the development and deployment of the Public Health Quality Tool (PHQTool), an online resource designed to help mobile health clinics (MHCs) assess and improve the quality of their public health services. MHCs provide essential clinical and public health services to underserved populations but have historically lacked tools to assess and improve the quality of their work. To address this gap, the PHQTool was developed as an online, evidence-based, self-assessment resource for MHCs, hosted on the Mobile Health Map (MHMap) platform. This report documents the collaborative development process of the PHQTool and presents preliminary evaluation findings related to usability and relevance among mobile health clinics. Drawing from national public health frameworks and Honore et al.’s established public health quality aims, the PHQTool focuses on six aims most relevant to mobile care: Equitable, Health Promoting, Proactive, Transparent, Effective, and Efficient. Selection of the six quality aims was guided by explicit criteria developed through pilot testing and stakeholder feedback. The six aims were those that could be directly implemented through mobile clinic practices and were feasible to assess within diverse mobile clinic contexts. The remaining three aims (“population-centered,” “risk-reducing,” and “vigilant”) were determined to be less directly actionable at the program level or required system-wide or data infrastructure beyond the scope of individual mobile clinics. Development included expert consultation, pilot testing, and iterative refinement informed by user feedback. The tool allows clinics to evaluate practices, identify improvement goals, and track progress over time. Since implementation, 82 MHCs representing diverse organizational types have used the PHQTool, reporting high usability and identifying common improvement areas such as outreach, efficiency, and equity-driven service delivery. Across pilot and post-pilot implementation phases, a majority of respondents agreed or strongly agreed that the tool was user-friendly, relevant to their work, and appropriately scoped for mobile clinic practice. Usability and acceptance were assessed using descriptive statistics, including percentage agreement across Likert-scale items as well as qualitative feedback collected during structured debriefs. Reported findings reflect self-reported perceptions of feasibility, clarity, and relevance rather than inferential statistical comparisons. The PHQTool facilitates systematic quality assessment within the mobile clinic sector and supports consistent documentation of public health efforts. By providing a standardized, accessible framework for evaluation, it contributes to broader efforts to strengthen evidence-based quality improvement and promote accountability in MHCs. Full article

Map matching is a fundamental technique for aligning noisy GPS trajectory data with digital road networks and constitutes a key component of Intelligent Transportation Systems (ITS) and Location-Based Services (LBS). Nevertheless, existing approaches still suffer from notable limitations in complex environments, particularly urban and urban-like scenarios characterized by heterogeneous GPS noise and sparse observations, including inadequate adaptability to dynamically varying noise, unavoidable trade-offs between real-time efficiency and matching accuracy, and limited generalization capability across heterogeneous driving behaviors. To overcome these challenges, this paper presents a Meta-learning-driven Progressive map-Matching (MPM) method with a symmetry-aware design, which integrates a two-layer pattern-mining-based noise-robust meta-learning mechanism with a dynamic weight adjustment strategy. By explicitly modeling topological symmetry in road networks, symmetric trajectory patterns, and symmetric noise variation characteristics, the proposed method effectively enhances prior knowledge utilization, accelerates online adaptation, and achieves a more favorable balance between accuracy and computational efficiency. Extensive experiments on two real-world datasets demonstrate that MPM consistently outperforms state-of-the-art methods, achieving up to 10–15% improvement in matching accuracy while reducing online matching latency by over 30% in complex urban environments. Furthermore, the symmetry-aware design significantly improves robustness against asymmetric interference, thereby providing a reliable and scalable solution for high-precision map matching in complex and dynamic traffic environments. Full article

The transition from high school to university-level mathematics is often accompanied by significant challenges. During the COVID-19 pandemic, these difficulties were further exacerbated by the abrupt shift to online learning. In response, educators increasingly turned to gamification—“a process of enhancing a service with affordances for gameful experiences in order to support users’ overall value creation”—as a strategy to address the limitations of remote instruction. In this study, we designed a gamified environment for a first-year Number Theory course. The system was constructed using targeted game elements such as leaderboards, optional challenge exams, and recognition for elegant solutions. These features were then integrated into a comprehensive point-based assessment system, which accounted for weekly quizzes and active participation. Following a quasi-experimental design, this study compared two groups of pre-service mathematics teachers: the class of 2017 (N = 62), which received traditional in-person instruction (control group), and the class of 2020 (N = 61), which participated in an online, gamified version of the course (experimental group). Both groups were taught by the same lecturer, using identical content, concepts, and similar tasks throughout the course. Academic performance was measured using midterm exam results. While no significant difference emerged on the first midterm in week 6 (their average percentages were 50% and 51%), the experimental group significantly outperformed the control group on the second midterm at the end of the term (their average percentages were 65% and 49%). These results suggest that a thoughtfully designed, gamified approach can enhance learning outcomes in an online mathematics course. Full article

Although product returns present significant challenges for retailers, the service recovery paradox suggests they can also generate value. When return services are managed effectively, they can offset initial customer dissatisfaction and increase repurchase likelihood beyond what would occur without a return. However, prior research often treats returns as homogeneous, overlooking how different return types trigger distinct customer responses. Using transaction-level data from 27,178 orders at a major U.S. online apparel retailer between 2016 and 2019, this study investigates how customer-reported return reasons influence subsequent repurchase behavior. Return reasons are categorized by locus of responsibility—customer-, retailer-, or intermediary-attributed—and analyzed using logistic regression. The findings reveal substantial heterogeneity in post-return outcomes: customer-attributed returns are positively associated with repurchase, retailer-attributed returns are negatively associated, and intermediary-attributed returns show no significant effect. By demonstrating that return recovery effects depend on attribution, this study provides both theoretical insights and practical guidance for managing returns in a sustainable manner that enhances customer retention, improves operational efficiency, and strengthens the long-term sustainability of retail return management systems. Full article

In indoor positioning systems, it is common to use existing AP deployments within buildings to build a fingerprint database, providing positioning information during the online phase. However, AP layouts inside buildings often contain a large number of redundant APs, which leads to the improvement in positioning accuracy leveling off as the number of redundant APs increases, while also increasing the computational load of indoor positioning services. To address this problem, the thesis proposes a method for calculating the AP location discrimination capability and combines the location discrimination capability with coverage to eliminate redundant APs. Experiments conducted in real indoor scenarios, as well as on the Crowdsourced dataset and the SODIndoorLoc dataset, validate the results. The results show that the redundant AP removing strategy ensures that the average positioning accuracy fluctuates by no more than 5% compared to the unfiltered case, while significantly reducing the number of APs in the fingerprint database—by 64.43%, 72.78%, and 59.62%, respectively. In the position estimation phase, this paper uses affinity propagation clustering for coarse positioning and combines Bayesian methods for fine positioning. Compared with GMM, K-Means, and the pointwise algorithm, the average positioning error of the proposed method is reduced by 11% to 39%. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

Microservices as an emerging architectural approach have been widely applied in the development of online applications. However, in large-scale service systems, frequent data communications, complex invocation dependencies, and strict latency requirements pose significant challenges to efficient microservice orchestration. In addition, microservices need to frequently access the database to achieve data persistence, creating a mutual dependency between the two, and this symmetry further increases the complexity of service orchestration and coordinated deployment. In this context, the strong coupling of service deployment, database layout, and request routing makes effective local optimization difficult. However, existing research often overlooks the impact of databases, fails to achieve joint optimization among databases, microservice deployments, and routing, or lacks fine-grained orchestration strategies for multi-instance models. To address the above limitations, this paper proposes a joint optimization framework based on the Database-as-a-Service (DaaS) paradigm. It performs fine-grained multi-instance queue modeling based on queuing theory to account for delays in data interaction, request queuing, and processing. Furthermore this paper proposes a proximal policy optimization algorithm based on multi-stage joint decision-making to address the orchestration problem of microservices and database instances. In this algorithm, the action space is symmetrical between microservices and database deployment, enabling the agent to leverage this characteristic and improve representation learning efficiency through shared feature extraction layers. The algorithm incorporates a two-layer agent policy stability control to accelerate convergence and a three-level experience replay mechanism to achieve efficient training on high-dimensional decision spaces. Experimental results demonstrate that the proposed algorithm effectively reduces service request latency under diverse workloads and network conditions, while maintaining global resource load balancing. Full article

With the rapid evolution of computing technologies and the increased proliferation of online services, secure remote user authentication methods have become essential. Among these methods, password-based authentication remains dominant due to its straightforward implementation and ease of use. Nevertheless, password-based systems are particularly prone to credential theft from keylogging attacks, making user passwords easily compromised. To address these risks, image-based authentication methods were developed, allowing users to enter passwords through mouse clicks rather than keyboard input, thereby reducing vulnerabilities associated with conventional password entry. However, subsequent studies have shown that mouse movement and click information can still be obtained using APIs such as the GetCursorPos() function or WM_INPUT message, thus undermining the intended security benefits of image-based authentication. In response, various defense strategies have sought to inject artificial or random mouse data through functions such as SetCursorPos() or by utilizing the WM_INPUT message, in an effort to disguise authentic user input. Despite these defenses, recent machine learning-based attacks have demonstrated that such naïve bogus input can be distinguished from legitimate mouse data with up to 99% classification accuracy, resulting in substantial exposure of actual user actions. To address this, a technique leveraging Generative Adversarial Networks (GAN) was introduced to produce artificial mouse data closely mimicking genuine user input, which has been shown to reduce the attack success rate by roughly 37%, offering enhanced protection for mouse-driven authentication systems. This article seeks to advance GAN-based mouse data protection by integrating multiple adversarial generative models and conducting a comprehensive evaluation of their effectiveness with respect to data processing techniques, feature selection, generation intervals, and model-specific performance differences. Our experimental findings reveal that the enhanced approach reduces attack success rates by up to 48%, marking an 11% performance gain over previous mouse data protection approaches, and providing stronger empirical support that our method offers superior protection for user authentication data compared to prior techniques. Full article

Violence against women remains prevalent, yet many survivors do not engage with services even where health infrastructure exists. This study investigated the role of institution-facing resources, Institutional Trust (ITR) and Procedural Justice (PJ), and the role of interpersonal resources, Social Support Provided (SSP), in women’s formal care-seeking intentions, as mediated by Psychological Distress (PSS) and General Self-Efficacy (GSE). An online survey was administered to women in Greece (n = 392) and the United Kingdom (n = 328), yielding a sample of 718. To compare the structural paths in the model across the two countries, measurement invariance was first explored, while the model was estimated through multi-group structural equation modeling. Across the pooled sample, PJ and GSE predicted HSB firmly, while ITR had no direct link to the construct. SSP did not directly predict HSB, but was linked to GSE in all models. The results of the interaction and group-difference models showed PJ and SSP had a slight indirect effect through GSE, while distress-based pathways were weaker and context-dependent. Multi-group models revealed significant cross-national differences: the direct effect of ITR and PSS on GSE was stronger in the United Kingdom than in Greece. The direct effect of PJ/GSE and SSP/GSE also had a stronger impact in Greece than in the United Kingdom. Overall, the results indicate that the willingness of women to seek help is less driven by their trust in institutions and more driven by their expectations of fairness in provider interaction and their perceived personal capability, where social support plays a role as the antecedent increasing women’s Perceived Self-Efficacy. The implications include prioritizing procedurally just practices, designing interventions that enhance self-efficacy for system navigation, and mobilizing informal networks as partners in the help-seeking process. Full article

People living in rural communities are typically underserved by public transportation services and face challenges in accessing healthcare, jobs, stores, and other destinations. Understanding the lived experiences of people who use public transportation in rural communities can help to inform a more equitable transportation system. This qualitative study gathered the perspectives of community health center employees about the public bus system for Hawai‘i Island, a rural county in the United States. Using a community-engaged research approach, the evaluation team interviewed 10 employees through either in-person small group interviews or online individual interviews between April and July 2023. Transcripts were coded and analyzed using a thematic analysis approach. While all study participants were selected for their interest in commuting to work by bus, most believed the bus was not a reliable or convenient option. Participants shared their experiences about not being able to rely on the bus schedule, feeling unsafe while walking to bus stops or waiting for the bus, and other barriers to using the bus system. Participants also shared their insights about how a reliable bus system would benefit community health center patients who needed transportation to more than just their medical appointments, but also to places like pharmacies, laboratory services, and grocery stores. These findings can be used to initiate discussions around the ways that community health centers can further address transportation as a social determinant of health and inform transportation providers about how to best plan and invest in transportation infrastructure and services to meet the needs of rural populations. Full article

This study empirically examines how organizational learning influences problem-solving competency and organizational effectiveness in the context of online travel agencies (OTAs) and tests the moderating role of digital empowerment. Using agency lists registered under Korea’s Tourism Promotion Act, we employed stratified sampling by region and simple random sampling within strata. Data collection was commissioned by the Tourism/Leisure HRD Council. A survey was carried out from 2 to 19 June 2025; of the 210 questionnaires returned, 204 valid responses were analyzed. Measures were adapted from prior studies on a five-point Likert scale. Analyses conducted in SPSS 27.0 included descriptive statistics, exploratory factor analysis (EFA), reliability testing (Cronbach’s α), correlation analysis, and simple and hierarchical regressions. The results indicate that (1) organizational learning has a significant positive effect on problem-solving competency (β = 0.541, p < 0.001, R² = 0.293); (2) organizational learning positively affects organizational effectiveness (β = 0.436, p < 0.001, R² = 0.190); and (3) problem-solving competency positively influences organizational effectiveness (β = 0.624, p < 0.001, R² = 0.389). Regarding moderation, digital empowerment did not significantly moderate the organizational learning → problem-solving link but did significantly moderate the organizational learning → organizational effectiveness relationship (p < 0.05), suggesting that digital empowerment enhances the conversion efficiency of learning into performance. Theoretically, this study substantiates the learning–problem-solving–performance mechanism in a service/tourism setting and identifies digital empowerment as a catalytic moderator that strengthens the translation of learning into organizational outcomes. Practically, the findings imply that OTAs can amplify organizational effectiveness by building digital empowerment structures—data-driven decision systems, process automation, and real-time customer-response capabilities—which enable learned knowledge to materialize into performance. Future research should incorporate digital maturity, leadership, customer orientation, and related variables into extended models. Full article

The Agricultural Knowledge and Innovation System (AKIS) plays a pivotal role in fostering innovation and disseminating knowledge within the agricultural sector. This paper proposes a framework for strengthening AKIS in Greece. The study adopts a multi-actor approach, incorporating perspectives from farmers, researchers, policymakers, advisory services, and other relevant stakeholders. A structured questionnaire, administered online from December 2022 to March 2023, gathered responses from 61 senior managers across AKIS actors. By exploring their perceptions and opinions, this study aims to identify challenges and opportunities for enhancing the efficiency and effectiveness of AKIS in Greece. The findings contribute to policy dialogue and offer strategic directions for reinforcing collaborative innovation in the agricultural sector. Full article

Introduction: This study aimed (1) to describe services involved in healthcare provision for children with special healthcare needs (CSHCN) and explore changes in the frequency of service provision reported by parents during the first wave of the COVID-19 pandemic; (2) to analyze associations between healthcare service provision and disease complexity; (3) to explore potential associations of changes in frequency of service provision with disease complexity, socioeconomic status (SES), and psychosocial factors; and (4) to generate actionable insights for building crisis-resilient care systems. Methods: A sequential series of cross-sectional online surveys at three points in time was conducted among caregivers of children with and without special healthcare needs in Germany. We analyzed data from the first survey (08/2020–10/2020). Results: Among CSHCN, reductions in treatment reached up to 88.4%. Positive associations between the reduction in treatment during the pandemic and disease complexity could be shown. There was no evidence for associations between reductions in healthcare provision, SES, and/or mental health. Structural vulnerabilities within existing care pathways for children with and without special healthcare needs could be identified. Conclusions: The findings highlight major gaps in healthcare continuity and underscore the urgent need for crisis-resilient care structures. CSHCN with more complex needs require prioritized, consistent, and structurally protected access to multidisciplinary services. The study calls for long-term investment in integrated, cross-sectoral, and family-centered healthcare frameworks to safeguard CSHCN in future public health emergencies. Full article

Country parks are an important instrument for implementing China’s strategies on ecological civilization and integrated urban–rural development. This study conducted field surveys in seven country parks of Shanghai. Meanwhile, stakeholder seminars were organized with local residents and park authorities. To assess visitor satisfaction, a questionnaire survey was administered both on-site and online. Through case analysis and a policy review, this study systematically identifies key challenges in leveraging country parks for rural revitalization. The findings indicate that visitors highly value the ecological qualities of the parks, and basic infrastructure like roads and resting facilities generally meets expectations. However, shuttle services and smart guiding systems remain notable shortcomings that hinder the overall visitor experience. Moreover, gaps in service quality, local cultural representation, and the depth of nature education constitute the primary weaknesses affecting visitor satisfaction. Regarding rural revitalization, this study identifies four main limitations in the contribution of country parks: (1) Inadequate functional positioning and weak integration with surrounding resources; (2) Low land use efficiency and an unbalanced provision of supporting facilities; (3) Homogenized industrial formats with limited innovation and integration capacity; and (4) Restricted participation of local farmers and underdeveloped multi-stakeholder governance mechanisms. To address these issues, this study proposes four strategic recommendations: (1) Develop distinctive local brands and strengthen synergies with surrounding resources; (2) Promote mixed land use and enhance supporting service facilities; (3) Foster diversified business formats and facilitate the value realization of ecological products; and (4) Expand income-generation channels for farmers and improve multi-stakeholder governance frameworks. The research demonstrates that optimizing the functions of country parks can improve ecological and recreational services and help establish an integrated “ecology–industry–community” framework through industrial chain extension and community participation, thereby supporting rural revitalization. Full article