Search Results (459)

The growing complexity of distribution-level virtual power plants (VPPs) demands a rethinking of how flexible demand is modeled, aggregated, and dispatched under uncertainty. Traditional optimization frameworks often rely on deterministic or homogeneous assumptions about end-user behavior, thereby overestimating controllability and underestimating risk. In this paper, we propose a behavior-aware, two-stage stochastic dispatch framework for VPPs that explicitly models heterogeneous user participation via integrated behavioral economics and social interaction structures. At the behavioral layer, user responses to demand response (DR) incentives are captured using a Prospect Theory-based utility function, parameterized by loss aversion, nonlinear gain perception, and subjective probability weighting. In parallel, social influence dynamics are modeled using a peer interaction network that modulates individual participation probabilities through local contagion effects. These two mechanisms are combined to produce a high-dimensional, time-varying participation map across user classes, including residential, commercial, and industrial actors. This probabilistic behavioral landscape is embedded within a scenario-based two-stage stochastic optimization model. The first stage determines pre-committed dispatch quantities across flexible loads, electric vehicles, and distributed storage systems, while the second stage executes real-time recourse based on realized participation trajectories. The dispatch model includes physical constraints (e.g., energy balance, network limits), behavioral fatigue, and the intertemporal coupling of flexible resources. A scenario reduction technique and the Conditional Value-at-Risk (CVaR) metric are used to ensure computational tractability and robustness against extreme behavior deviations. Full article

The growing adoption of modular robotic systems presents new challenges in ensuring ease of assembly, deployment, and reconfiguration, especially for end-users with varying technical expertise. This study proposes and validates an Assembly Complexity Index (ACI) framework, combining subjective workload (NASA Task Load Index) and task complexity (Task Complexity Index) into a unified metric to quantify assembly difficulty. Twelve participants performed modular manipulator assembly tasks under supervised and unsupervised conditions, enabling evaluation of learning effects and assembly complexity dynamics. Statistical analyses, including Cronbach’s alpha, correlation studies, and paired t-tests, demonstrated the framework’s internal consistency, sensitivity to user learning, and ability to capture workload-performance trade-offs. Additionally, we propose an augmented reality (AR) and virtual reality (VR) integration workflow to further mitigate assembly complexity, offering real-time guidance and adaptive assistance. The proposed framework not only supports design iteration and operator training but also provides a human-centered evaluation methodology applicable to modular robotics deployment in Industry 4.0 environments. The AR/VR-assisted workflow presented here is proposed as a conceptual extension and will be validated in future work. Full article

The successful integration of Terrestrial and Non-Terrestrial Networks (T/NTNs) in 6G is poised to revolutionize demanding domains like Earth Observation (EO) and Intelligent Transportation Systems (ITSs). Still, it requires Distributed Machine Learning (DML) frameworks that are scalable, private, and efficient. Existing methods, such as Federated Learning (FL) and Split Learning (SL), face critical limitations in terms of client computation burden and latency. To address these challenges, this paper proposes a novel hierarchical DML paradigm. We first introduce Federated Split Transfer Learning (FSTL), a foundational framework that synergizes FL, SL, and Transfer Learning (TL) to enable efficient, privacy-preserving learning within a single client group. We then extend this concept to the Generalized FSTL (GFSTL) framework, a scalable, multi-group architecture designed for complex and large-scale networks. GFSTL orchestrates parallel training across multiple client groups managed by intermediate servers (RSUs/HAPs) and aggregates them at a higher-level central server, significantly enhancing performance. We apply this framework to a unified T/NTN architecture that seamlessly integrates vehicular, aerial, and satellite assets, enabling advanced applications in 6G ITS and EO. Comprehensive simulations using the YOLOv5 model on the Cityscapes dataset validate our approach. The results show that GFSTL not only achieves faster convergence and higher detection accuracy but also substantially reduces communication overhead compared to baseline FL, and critically, both detection accuracy and end-to-end latency remain essentially invariant as the number of participating users grows, making GFSTL especially well suited for large-scale heterogeneous 6G ITS deployments. We also provide a formal latency decomposition and analysis that explains this scaling behavior. This work establishes GFSTL as a robust and practical solution for enabling the intelligent, connected, and resilient ecosystems required for next-generation transportation and environmental monitoring. Full article

Gamified virtual reality (VR) environments are increasingly used to enhance engagement and learning, yet most designs still adopt a one-size-fits-all approach that overlooks motivational diversity. The HEXAD framework, which classifies users into six player types, provides a promising lens for addressing this gap, but its predictive validity in immersive VR remains contested. This study investigates how HEXAD profiles shape navigation, time allocation, and engagement dynamics in an open-ended gamified VR environment. Thirty-two undergraduate participants, all regular gamers, completed the HEXAD scale before exploring a VR setting with five thematic islands without predefined tasks. System logs and screen recordings captured first island choices, sequential visit patterns, and time spent, and data were analyzed using qualitative pattern analysis alongside nonparametric statistics. The results showed significant associations between player type and initial choices, with Players favoring Game Island, Socialisers choosing Social Island, Philanthropists engaging most with Library, and Achievers and Free Spirits drawn to Experience. Kruskal–Wallis tests of exploration breadth revealed moderate effect sizes across types, though significance was limited by sample size. Three emergent strategies, Focused Explorers, Wanderers, and Strategic Switchers, captured motivational orientations beyond single metrics, while heat map visualizations highlighted clustering around Game and Experience Islands. By situating these findings within flow theory and inclusive–adaptive design principles, this study demonstrates how behavioral traces can link motivational typologies with embodied interaction. Overall, the results advance debates on HEXAD’s robustness and contribute practical pathways for developing adaptive, motivation-sensitive VR environments that support sustained engagement and inclusivity. Full article

It is well established that effective management and restoration of freshwater ecosystems is often limited by the availability of reusable data. Although numerous public, private, and nonprofit organizations collect data from freshwater ecosystems, much of what is collected remains inaccessible or unusable by Rights holders and end users (including researchers, practitioners, community members, and decision-makers). In Canada, the federal government plans to improve freshwater data sharing practices through the newly formed Canada Water Agency, which is currently drafting a National Freshwater Data Strategy. Our study aimed to support these efforts by synthesizing insights from the technology sector, where data management and sharing practices are more mature. We interviewed 12 experts from the technology sector, asking them for advice on how to improve data sharing practices in the freshwater science sector. Using a Reflexive Thematic Analysis of participants’ responses to semi-structured interview questions, we identified nine broad recommendations. Recommendations centred on motivating open data sharing, promoting data reuse through data licences, training and skill building, and developing standards and digital solutions that enable data discovery, accessibility, interoperability, and reuse. These recommendations can support the numerous initiatives that are working to improve access to high-quality freshwater data and help address the pressing crisis of global freshwater ecosystem degradation. Full article

Spatial memory is a critical component of spatial cognition, particularly in unfamiliar environments. As navigation systems become integral to daily life, understanding how individuals with varying spatial abilities respond to different navigation modes is increasingly important. This study employed a virtual driving environment to examine how participants with varying spatial abilities (good or poor) performed under three navigation modes, namely visual, audio, and combined audio–visual navigation modes. A total of 78 participants were divided into two groups, good sense of direction (G-SOD) and poor sense of direction (P-SOD), according to their Santa Barbara Sense of Direction (SBSOD) scores. They were randomly assigned to one of the three navigation modes (visual, audio, audio–visual). Participants followed navigation cues and simulated driving behavior to the end point twice during the learning phase, then completed the route retracing task, recognizing scenes task and recognizing the order task. Significant main effects were found for both SOD group and navigation mode, with no interaction. G-SOD participants outperformed P-SOD participants in route retracing task. Audio navigation mode led to better performance in tasks involving complex spatial decisions, such as turn intersections and recognizing the order. The accuracy of recognizing scenes did not significantly differ across SOD groups or navigation modes. These findings suggest that audio navigation mode may reduce visual distraction and support more effective spatial encoding and that individual spatial abilities influence navigation performance independently of guidance type. These findings highlight the importance of aligning navigation modalities with users’ cognitive profiles and support the development of adaptive navigation systems that accommodate individual differences in spatial ability. Full article

The growing integration of renewable energy sources (RESs) into modern power systems calls for enhanced flexibility and control mechanisms. Conventional demand response (DR) strategies, such as price-based and incentive-driven methods, often encounter challenges that limit their effectiveness. This paper proposes a novel DR approach grounded in Customer Directrix Load (CDL) and formulated through Stackelberg game theory. A bilevel optimization framework is established, with air conditioning (AC) systems and electric vehicles (EVs) serving as the main DR participants. The problem is addressed using a genetic algorithm. Simulation studies on a modified IEEE 33-bus distribution system reveal that the proposed strategy significantly improves RES accommodation, reduces power curtailment, and yields mutual benefits for both system operators and end users. The findings highlight the potential of the CDL-based DR mechanism in enhancing operational efficiency and encouraging proactive consumer involvement. Full article

Background/Objectives: Low-density lipoprotein cholesterol (LDL-C) is a causal factor in the development of atherosclerosis and a predictor of cardiovascular disease. However, the association between LDL-C levels and cardiovascular outcomes in patients undergoing dialysis remains controversial, with current guidelines advising against initiating statin therapy in this population. This study investigated the relationship between LDL-C levels and cardiovascular outcomes in Korean adults undergoing dialysis, using nationwide data. Methods: A total of 21,692 patients with end-stage kidney disease undergoing dialysis between 2009 and 2017 were identified from the Korean National Health Insurance Service database. Statin non-users (primary cohort) and users (secondary cohort) comprised 15,414 and 6278 patients, respectively. LDL-C levels were categorized, and cardiovascular outcomes including composites of cardiovascular death, myocardial infarction, and ischemic stroke were analyzed. Results: Among statin non-users, LDL-C levels > 100 mg/dL were significantly associated with an increased risk of the composite outcome, in a dose-dependent manner, compared with LDL-C levels < 70 mg/dL. Specifically, participants with LDL-C levels ≥ 160 mg/dL demonstrated a 43% increased risk of the composite outcome and a 2.25-fold higher risk of myocardial infarction compared to those with LDL-C levels < 70 mg/dL. Among statin users, LDL-C levels > 130 mg/dL were associated with an increased risk of the composite outcome. Conclusions: This study highlights the significant association between elevated LDL-C levels and adverse cardiovascular outcomes in patients undergoing dialysis. These findings underscore the importance of close monitoring and proactive management of LDL-C levels in this high-risk population. Future research should focus on developing tailored lipid-lowering strategies to improve cardiovascular outcomes in these patients. Full article

Indoor positioning remains a significant challenge, particularly in environments such as museums, where the installation of specialized positioning infrastructure may be impractical. Recent advances in image processing offer effective and precise methods for object recognition, presenting a viable alternative. This study explores the feasibility of employing real-time image processing techniques for identifying points of interest (POIs) within museum settings. It outlines the ideation, design, development, and evaluation of an image-based POI identification system implemented in a real-world environment. To evaluate the system’s effectiveness, a user study was conducted with regular visitors at the Hecht Museum. The results showed that the algorithm successfully and quickly identified POIs in 97.6% of cases. Additionally, participants completed the System Usability Scale (SUS) and provided open-ended feedback, indicating high satisfaction with the system’s accuracy and speed while also offering suggestions for future improvements. Full article

This article presents an innovative model for managing cash logistics, grounded in the 4P concept of supply chain management. The 4P framework encompasses four interconnected elements: Product, Players, Processes and Policies. Developed with a focus on sustainability the 4P Cash Logistics Model is based on empirical research conducted in Poland, involving key participants in the cash supply chain—the central bank, commercial banks and cash handling companies. It also incorporates, albeit less explicitly, the perspectives of merchants and consumers as end-users of cash, offering a comprehensive view of the cash cycle management. The 4P Cash Logistics Model has been designed in a country-agnostic manner, employing the concept of a control tower, with the central bank positioned as the integrator of the cash supply chain. This paper proposes several improvements to cash logistics, including the introduction of a standardised electronic bank deposit slip and a multilateral platform for exchanging information on cash stocks and flows and for trading monetary value between banks and cash handling companies. Full article

Background/Objectives: DEXCOM™ continuous glucose monitoring devices (DCGMs) have been shown to improve glycaemic control and complication rates in people with Type 1 diabetes (T1DM). However, little qualitative data exists regarding user satisfaction, useful features and the overall lived experience of using a DCGM which will strongly impact one’s quality of life (QOL), compliance and the self-management of diabetes. This study aimed to assess DCGM users’ satisfaction rates and experiences with device features in patients with T1DM in Ireland. Methods: A questionnaire consisting of open- and closed-ended questions together with a glucose monitoring satisfaction survey (GMSS) was offered to all patients attending Sligo University Hospital (SUH) diabetes clinic who used a DCGM for at least six months. Results: Data was analysed for 73 participants. Self-reported QOL improved in 88% of participants and 52% of participants reported fewer hypoglycaemic events. The features most liked by participants were alerts given when the glycaemic target was not in range, improved quality of life, improved hypoglycaemia awareness and the need for reduced finger pricking. However, concerns were also identified about redundant alarms and sensor failures, phone incompatibility and skin reactions. DCGM was associated with good levels of glucose monitoring satisfaction with an overall satisfaction score of 3.67 ± 1.24 out of 5. Participants reported high openness (4.01 ± 0.91), increased trust (3.77 ± 1.16) and low emotional (1.70 ± 0.97) and behavioural burden (2.38 ± 1.10) with DCGM usage. Male participants who had diabetes for a mean duration of 20.06 ± 0.89 years and used DEXCOM^TM for approximately 2 years demonstrated significantly higher levels of satisfaction (p < 0.05). Conclusions: The findings of this study provide a first exploration of patients’ perspectives on DCGM devices in an Irish setting. Results suggest that DCGM users are highly satisfied with the device with an increase in self-reported QOL. Adaptations to features based on patient feedback should be considered to further enhance user satisfaction and maximise QOL benefits. Full article

The ability to effectively engineer robots with appropriate social behaviors that conform to acceptable social norms and with the potential to influence human behavior remains a challenging area in robotics. Given this, we sought to provide insights into “what can be considered a socially appropriate and effective behavior for robots charged with enforcing social compliance of various magnitudes”. To this end, we investigate how social robots can be equipped with context-inspired persuasive behaviors for human–robot interaction. For this, we conducted three separate studies. In the first, we explored how the android robot “ERICA” can be furnished with negative persuasive behaviors using a video-based within-subjects design with N = 50 participants. Through a video-based experiment employing a mixed-subjects design with N = 98 participants, we investigated how the context of norm violation and individual user traits affected perceptions of the robot’s persuasive behaviors in the second study. Lastly, we investigated the effect of the robot’s appearance on the perception of its persuasive behaviors, considering two humanoids (ERICA and CommU) through a within-subjects design with N = 100 participants. Findings from these studies generally revealed that the robot could be equipped with appropriate and effective context-sensitive persuasive behaviors for human–robot interaction. Specifically, the more assertive behaviors (displeasure and anger) of the agent were found to be effective (p < 0.01) as a response to a situation of repeated violation after an initial positive persuasion. Additionally, the appropriateness of these behaviors was found to be influenced by the severity of the violation. Specifically, negative behaviors were preferred for persuasion in situations where the violation affects other people (p < 0.01), as in the COVID-19 adherence and smoking prohibition scenarios. Our results also revealed that the preference for the negative behaviors of the robots varied with users’ traits, specifically compliance awareness (CA), agreeableness (AG), and the robot’s embodiment. The current findings provide insights into how social agents can be equipped with appropriate and effective context-aware persuasive behaviors. It also suggests the relevance of a cognitive-based approach in designing social agents, particularly those deployed in sensitive social contexts. Full article

Background: Internationally, digital health interventions have increasingly been adopted within hospital settings. Optimising their clinical implementation requires user involvement, but there is a lack of evidence regarding how this should be done. Objectives: This study was carried out to understand the acceptability and usability of a prototype Digital Antibiotic Review Tracking Toolkit and identify modifications required to optimise it ahead of a trial. Methods: The optimisation process involved online semi-structured interviews with a purposive sample of fifteen healthcare professionals recruited from Scotland and England, along with three service users, to gather feedback on the prototype’s design, content and delivery. Participants’ negative views were specifically sought to identify adaptations needed to ensure that the intervention’s components aligned optimally with end-user needs. Data were analysed using Framework Analysis guided by the RE-AIM implementation science framework (Reach, Effectiveness, Adoption, Implementation, and Maintenance) to identify key themes. Results: Participants mostly voiced positive views regarding the prototype, finding it acceptable, feasible and engaging. They also identified concerns relating to its adoption, system functionality, accessibility and maintenance that needed to be addressed. Anticipated low adoption rates were linked to issues surrounding computer literacy. This detailed user feedback informed rapid adjustments to the intervention to enhance its acceptability, perceived future credibility and usability in hospitals. Conclusions: This novel study illustrates how to identify, modify and adapt a digital intervention quickly and efficiently using qualitative iterative methods. Findings highlight the critical importance of contextualising end-user experience with health interventions to facilitate future engagement, uptake, and long-term use. This study also demonstrates how core elements of the MRC framework can be operationalised to help refine prototype digital interventions pre-trial. Full article

Background/Objectives: Sudden unexpected death in epilepsy (SUDEP) is a major cause of mortality in pediatric epilepsy. Cardiac arrhythmias, possibly reflected by electrocardiographic (ECG) abnormalities, are thought to contribute significantly to SUDEP risk. This study aimed to evaluate ECG indices associated with an increased risk of both atrial and ventricular arrhythmias and sudden cardiac death in pediatric patients with generalized and focal seizures, excluding those with underlying channelopathies. Materials and Methods: Pediatric patients aged 0–18 years with generalized or focal epilepsy followed at our center between October 2024 and April 2025 were enrolled. Comprehensive cardiac evaluations, including echocardiography and 12-lead ECG, were conducted. Patients with channelopathies, structural heart defects, or significant congenital heart disease were excluded. ECG parameters—QT dispersion (QT Disp), corrected QT interval (QTc), QTc dispersion (QTc Disp), P-wave dispersion (P Disp), and T peak-T end interval (Tp-e)—were analyzed across epilepsy subgroups and compared to healthy controls. Effects of antiepileptic drug (AED) use and gender were also assessed. Results: A total of 151 participants were included (generalized: n = 51; focal: n = 50; controls: n = 50). QTc and Tp-e intervals were prolonged in both epilepsy groups compared to controls (p = 0.001 and p = 0.036, respectively), however, they fell within the conventional parameters. AED use was associated with further prolongation of QTc (p = 0.035) and Tp-e (p = 0.037), these metrics were similarly found to be within the established normative boundaries. Phenobarbital and lamotrigine users showed the longest QTc, albeit not statistically significant. Males with generalized seizures had longer maximum P-wave duration (P Max) than females (p = 0.009). A moderate correlation was found between Tp-e and QTc (r = 0.557, p = 0.001). Conclusions: Although there are findings in our study that may suggest a relationship between SUDEP and arrhythmia according to electrocardiographic markers associated with arrhythmia risk, larger and prospective studies with long-term follow-up are needed in the future. Full article

Knowledge extraction and sharing is one of the biggest challenges organizations face to ensure successful and long-lasting knowledge repositories. The North Carolina Department of Transportation (NCDOT) commissioned a web-based knowledge management program called Communicate Lessons, Exchange Advice, Record (CLEAR) for end-users to promote employee-generated innovation and to institutionalize organizational knowledge. Reusing knowledge from an improperly managed database is problematic and potentially causes substantial financial loss and reduced productivity for an organization. Poorly managed databases can hinder effective knowledge dissemination across the organization. Data-driven dashboards offer a promising solution by facilitating evidence-driven decision-making through increased information access to disseminate, understand and interpret datasets. This paper describes an effort to create data visualizations in Tableau for CLEAR’s gatekeeper to monitor content within the knowledge repository. Through the three web-based strategic dashboards relating to lessons learned and best practices, innovation culture index, and website analytics, the information displays will aid in disseminating useful information to facilitate decision-making and execute appropriate time-critical interventions. Particular emphasis is placed on utility-related issues, as data from the NCDOT indicate that approximately 90% of projects involving utility claims experienced one or two such incidents. These claims contributed to an average increase in project costs of approximately 2.4% and schedule delays averaging 70 days. The data dashboards provide key insights into all 14 NCDOT divisions, supporting the gatekeeper in effectively managing the CLEAR program, especially relating to project performance, cost savings, and schedule improvements. The chronological analysis of the CLEAR program trends demonstrates sustained progress, validating the effectiveness of the dashboard framework. Ultimately, these data dashboards will promote organizational innovation in the long run by encouraging end-user participation in the CLEAR program. Full article