Search Results (1,788)

With the increase in the global aging population, the demand for elderly-friendly game products is growing rapidly. To address existing limitations, particularly in user demand extraction and design parameter setting, this study proposed a design framework integrating the BTM–AHP–AD–TOPSIS methods. The goal was to accurately identify the core needs of elderly users and translate them into effective design solutions. User reviews of elderly-friendly game products were collected from e-commerce platforms using Python 3.8-based web scraping. The Biterm Topic Model (BTM) was employed to extract user needs from review texts. These needs were prioritized using the Analytic Hierarchy Process (AHP) and translated into specific design parameters through Axiomatic Design (AD). Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to comprehensively evaluate multiple design schemes and select the optimal solution. The results demonstrate that the proposed design path offers a holistic method for progressing from need extraction to design evaluation. It effectively overcomes previous limitations, including inefficient need extraction, limited scope, unclear need weighting, and unreasonable design parameters. This method enhances user acceptance and satisfaction while establishing rigorous design processes and scientific evaluation standards, making it well suited for developing elderly-friendly products. Full article

(1) Background: The Peruvian healthcare system is widely regarded as deficient, with ongoing improvements identified as a key area of need. This study sought to assess user satisfaction and the quality of care in two public hospitals in Trujillo. (2) Methods: A non-experimental, cross-sectional, and correlational study was carried out. A group of 384 people who used two public hospitals in the city of Trujillo was studied. The people in the study were chosen based on the researchers’ convenience sampling. Information was collected using a survey based on the SERVQUAL model. This survey was used to evaluate the quality of service. Descriptive and inferential analyses were performed, including Spearman’s correlation and multinomial logistic regression to assess associations and identify key predictors of perceived service quality. (3) Results: The results indicated that 97.66% of the users perceived a low quality of care and 100% expressed dissatisfaction with the services. The most critical dimensions were reliability and responsiveness, while tangible items obtained better results. A positive correlation (rho = 0.723) was identified between quality of care and user satisfaction, with empathy (rho = 0.559) and safety (rho = 0.543) emerging as the most influential dimensions. (4) Conclusions: Responsiveness and Security were identified as key predictors of the perceived service quality in two public hospitals in Trujillo, Peru. Despite high empathy correlations, only timely care and safety significantly influenced satisfaction. The findings align with SDG 3, calling for improved efficiency and humanized care in public health services. Full article

This study introduces an educational framework that merges 3D modeling with AI-assisted narrative interaction to apply digital technology in cultural heritage education, exemplified by an ancient carriage culture. Through immersive tasks and contextual narratives, the course notably improved learners’ professional skills and cultural awareness. Experimental results revealed significant knowledge acquisition among participants post-engagement. Additionally, the user experience improved, with increased satisfaction in the narrative interaction design course. These enhancements led to heightened interest in cultural heritage and deeper knowledge acquisition. Utilizing Norman’s three-layer interaction model, Ryan’s contextual narrative theory, and Falk and Dierking’s museum learning experience model, the study developed a systematic course for multi-sensory design and contextual interaction, confirming the positive impact of multimodal interaction on learning outcomes. This research provides theoretical support for the digital transformation of cultural education and practical examples for educational practitioners and cultural institutions to implement in virtual presentations and online learning. Full article

The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t-tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, $p<0.001$) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ($k=4$, silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ($R^2=0.68$, $p<0.001$) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use. Full article

Teaching of embedded systems, including communication protocols such as SMBus, is commonly faced with difficulties providing the students with interactive and personalized, practical learning experiences. To overcome these shortcomings, this report presents a new conceptual framework that exploits generative artificial intelligence (GenAI) via customized DV-GPT. Coupled with prepromises techniques, DV-GPT offers timely targeted support to students and engineers who are studying SMBus protocol design and verification. In contrast to traditional learning, this AI-based tool dynamically adjusts feedback based on the users’ activities, providing greater insight into challenging concepts, including timing synchronization, multi-master arbitration, and error handling. The framework also incorporates the industry de facto standard UVM practices, which helps narrow the gap between education and the professional world. We quantitatively compare with a baseline GPT-4 and show significant improvement in accuracy, specificity, and user satisfaction. The effectiveness and feasibility of the proposed GenAI-enhanced educational approach have been empirically validated through the use of structured student feedback, expert judgment, and statistical analysis. The contribution of this research is a scalable, flexible, interactive model for enhancing embedded systems education that also illustrates how GenAI technologies could find applicability within specialized educational environments. Full article

This paper introduces Planogen, a modular procedural generation plug-in for the Unity game engine, which is composed of two primary components: a character generation module (CharGen) and an airplane generation module (PlaneGen). Planogen facilitates the rapid generation of varied and interactive aircraft cabin environments populated with diverse virtual passengers. The presented system is intended for use in research experiment scenarios, particularly those targeting the fear of flying (FoF), where environmental variety and realism are essential for user immersion. Leveraging Unity’s extensibility and procedural content generation techniques, Planogen allows for flexible scene customization, randomization, and scalability in real time. We further validate the realism and user appeal of Planogen-generated cabins in a user study with 33 participants, who rate their immersion and satisfaction, demonstrating that Planogen produces believable and engaging virtual environments. The modular architecture supports asynchronous updates and future extensions to other VR domains. By enabling on-demand, repeatable, and customizable VR content, Planogen offers a practical tool for developers and researchers aiming to construct responsive, scenario-specific virtual environments that can be adapted to any research domain. Full article

The efficient management of urban parking systems has emerged as a pivotal issue in today’s smart cities, where increasing vehicle populations strain limited parking infrastructure and challenge sustainable urban mobility. Aligned with the United Nations 2030 Agenda for Sustainable Development and the strategic goals of smart city planning, this study presents a sustainability-driven, multiagent simulation-based framework to model, analyze, and optimize smart parking dynamics in congested urban settings. The system architecture integrates ground-level IoT sensors installed in parking spaces, enabling real-time occupancy detection and communication with a centralized system using low-power wide-area communication protocols (LPWAN). This study introduces an intelligent parking guidance mechanism that dynamically directs drivers to the nearest available slots based on location, historical traffic flow, and predicted availability. To manage real-time data flow, the framework incorporates message queuing telemetry transport (MQTT) protocols and edge processing units for low-latency updates. A predictive algorithm, combining spatial data, usage patterns, and time-series forecasting, supports decision-making for future slot allocation and dynamic pricing policies. Field simulations, calibrated with sensor data in a representative high-density urban district, assess system performance under peak and off-peak conditions. A comparative evaluation against traditional first-come-first-served and static parking systems highlights significant gains: average parking search time is reduced by 42%, vehicular congestion near parking zones declines by 35%, and emissions from circling vehicles drop by 27%. The system also improves user satisfaction by enabling mobile app-based reservation and payment options. These findings contribute to broader sustainability goals by supporting efficient land use, reducing environmental impacts, and enhancing urban livability—key dimensions emphasized in sustainable smart city strategies. The proposed framework offers a scalable, interdisciplinary solution for urban planners and policymakers striving to design inclusive, resilient, and environmentally responsible urban mobility systems. Full article

Navigational aids play a vital role in enhancing the mobility and independence of blind and visually impaired (VI) individuals. However, existing solutions often present challenges related to discomfort, complexity, and limited ability to provide detailed environmental awareness. To address these limitations, we introduce SnapStick, an innovative assistive technology designed to improve spatial perception and navigation. SnapStick integrates a Bluetooth-enabled smart cane, bone-conduction headphones, and a smartphone application powered by the Florence-2 Vision Language Model (VLM) to deliver real-time object recognition, text reading, bus route detection, and detailed scene descriptions. To assess the system’s effectiveness and user experience, eleven blind participants evaluated SnapStick, and usability was measured using the System Usability Scale (SUS). In addition to the 94% accuracy, the device received an SUS score of 84.7%, indicating high user satisfaction, ease of use, and comfort. Participants reported that SnapStick significantly improved their ability to navigate, recognize objects, identify text, and detect landmarks with greater confidence. The system’s ability to provide accurate and accessible auditory feedback proved essential for real-world applications, making it a practical and user-friendly solution. These findings highlight SnapStick’s potential to serve as an effective assistive device for blind individuals, enhancing autonomy, safety, and navigation capabilities in daily life. Future work will explore further refinements to optimize user experience and adaptability across different environments. Full article

Uncoordinated electric vehicle (EV) charging can significantly complicate power system operations. In this paper, we develop a hybrid EV charging method that seamlessly integrates centralized EV charging and distributed control schemes to address EV energy demand challenges. The proposed method includes (1) a centralized day-ahead optimal scheduling mechanism and EV shifting process based on mixed-integer linear programming (MILP) and (2) a distributed control strategy based on a genetic algorithm (GA) that dynamically adjusts the charging rate in real-time grid scenarios. The MILP minimizes energy imbalance at overloaded slots by reallocating EVs based on supply–demand mismatch. By combining full and minimum charging strategies with MILP-based shifting, the method significantly reduces network stress due to EV charging. The centralized model schedules time slots using valley-filling and EV-specific constraints, and the local GA-based distributed control adjusts charging currents based on minimum energy, system availability, waiting time, and a priority index (PI). This PI enables user prioritization in both the EV shifting process and power allocation decisions. The method is validated using demand data on a radial feeder with residential and commercial load profiles. Simulation results demonstrate that the proposed hybrid EV charging framework significantly improves grid-level efficiency and user satisfaction. Compared to the baseline without EV integration, the average-to-peak demand ratio is improved from 61% to 74% at Station-A, from 64% to 80% at Station-B, and from 51% to 63% at Station-C, highlighting enhanced load balancing. The framework also ensures that all EVs receive energy above their minimum needs, achieving user satisfaction scores of 88.0% at Stations A and B and 81.6% at Station C. This study underscores the potential of hybrid charging schemes in optimizing energy utilization while maintaining system reliability and user convenience. Full article

Objectives: This study evaluates the economic impact of digital health interventions (DHIs) developed under the HosmartAI EU-funded program, focusing on obstetrics, cardiology, and gastroenterology. Methods: A Cost Consequence Analysis (CCA) was chosen in order to be able to examine the costs and consequences of AI technologies in early diagnosis of preterm births, echocardiography, coronary computed tomography angiography (CCTA), and capsule endoscopy (CE). Results: The results show that in obstetrics and CCTA, the AI technologies are cost-saving, with the AI-based preterm birth detection leading to savings of 99,840 EUR due to reduced severity of prematurity. In the echocardiography scenario, the new AI technology slightly increased costs (9409 vs. 2116 EUR), but offered benefits in diagnostic accuracy and shorter interpretation duration, particularly for less experienced physicians. Similarly, the capsule endoscopy AI technology raised annual costs by 6626 EUR but improved productivity, accuracy, and user satisfaction. Conclusions: The findings emphasize the need for standardized frameworks to guide economic evaluations of DHIs, ensuring informed healthcare investment and reimbursement decisions in the future. Full article

This study presents an analysis of England’s 2023 national assessment of assistive technology (AT) access and use, with a particular focus on the qualitative impact of AT as described by users. It aims to address limitations in conventional AT impact assessments, which often prioritize clinical outcomes or user satisfaction, by offering a deeper account of how impact is experienced in everyday life. Drawing on data from a nationally representative survey of 7000 disabled adults and children, as well as six focus group discussions and 28 semi-structured interviews with stakeholders across the WHO 5Ps framework (People, Providers, Personnel, Policy, and Products), the study applies Amartya Sen and Martha Nussbaum’s Capability Approach to explore these experiences. Using inductive thematic analysis, we identify three main domains of user-reported impact: Functions and Activities (e.g., mobility, communication, vision, leisure, daily routines, and cognitive support), Outcomes (e.g., autonomy, quality of life, safety, social participation, wellbeing, and work and learning), and Lived Experience (e.g., access barriers, essentiality, identity and emotional connection, peace of mind, and sense of control and confidence). These findings offer a more user-centered understanding of AT impact and can inform the development of future measurement tools, research design, and government-led interventions to improve AT provision. Full article

The rapid growth of electric vehicle (EV) adoption necessitates advanced energy management strategies to ensure sustainable, reliable, and efficient operation of charging infrastructure. This study proposes a hybrid AI-based framework for optimizing residential EV charging systems through the integration of Reinforcement Learning (RL), Linear Programming (LP), and real-time grid-aware scheduling. The system architecture includes smart wall-mounted chargers, a 120 kWp rooftop solar photovoltaic (PV) array, and a 60 kWh lithium-ion battery energy storage system (BESS), simulated under realistic load conditions for 800 residential units and 50 charging points rated at 7.4 kW each. Simulation results, validated through SCADA-based performance monitoring using MATLAB/Simulink and OpenDSS, reveal substantial technical improvements: a 31.5% reduction in peak transformer load, voltage deviation minimized from ±5.8% to ±2.3%, and solar utilization increased from 48% to 66%. The AI framework dynamically predicts user demand using a non-homogeneous Poisson process and optimizes charging schedules based on a cost-voltage-user satisfaction reward function. The study underscores the critical role of intelligent optimization in improving grid reliability, minimizing operational costs, and enhancing renewable energy self-consumption. The proposed system demonstrates scalability, resilience, and cost-effectiveness, offering a practical solution for next-generation urban EV charging networks. Full article

Effective management of urban development requires robust decision-support tools, including land suitability analysis and its visual communication. This study introduces and evaluates seven 3D geovisualization methods—Horizontal Planes, Point Cloud, 3D Surface, Vertical Planes, 3D Graduated Symbols, Prism Map, and Voxels—for visualizing land suitability for residential development in Jihlava, Czechia. Using five raster-based data layers derived from a multi-criteria evaluation (Urban Planner methodology) across three time horizons (2023, 2028, 2033), the visualizations were implemented in ArcGIS Online and assessed by 19 domain experts via a structured questionnaire. The evaluation focused on clarity, usability, and accuracy in interpreting land suitability values, with the methods being rated on a five-point scale. Results show that the Horizontal Planes method was rated highest in terms of interpretability and user satisfaction, while 3D Surface and Vertical Planes were considered the least effective. The study demonstrates that visualization methods employing visual variables (e.g., color and transparency) are better suited for land suitability communication. The methodological contribution lies in systematically comparing 3D visualization techniques for thematic spatial data, providing guidance for their application in planning practice. The results are primarily intended for urban planners, designers, and local government representatives as supportive tools for efficient planning of future built-up area development. Full article

Background/Objectives: Adherence to healthy behaviors after open-heart surgery is crucial for recovery and long-term health. Traditional patient education methods can be enhanced by using technology to improve engagement and self-care. This study aimed to develop and assess the feasibility of the “Term-Jai” smartphone application for promoting healthy heart behaviors in open-heart surgery patients. Methods: The “Term-Jai” psychological theory-based application was tested quantitatively and qualitatively over a 30-day period with 13 patients (age 44–78 years) following open-heart surgery between November 2023 and March 2024. Participant engagement, healthy behaviors, user experience, and usability were assessed using the System Usability Scale (SUS), satisfaction ratings, healthy behavior questionnaires, and semi-structured interviews. Results: The application was feasible, with 70% of participants remaining engaged during the intervention. The average SUS score was 80.2 ± 10.3, indicating good usability. Participants found the application’s information useful, clear, and easy to understand, showing improvements in health behaviors following application usage. The qualitative analysis highlighted the application’s intuitive design and potential for supporting cardiac rehabilitation. High satisfaction scores suggested its effectiveness despite some barriers to application usage around technical support and personalized exercise progression. Conclusions: The “Term-Jai” application is a promising tool for promoting healthy behaviors in patients following open-heart surgery. The application shows good usability and participant satisfaction, indicating its potential for broader implementation after further refinements. Full article

The aim of this study is to investigate how consumers’ cognitive and affective responses to web-based augmented reality affect their intention to continue to use augmented reality. The novelty of this study is the integration of the Stimulus–Organism–Response model with Technology Continuance Theory, allowing for an investigation of the relationships among the following critical variables: augmented reality (AR), utilitarian value, perceived risk, user satisfaction, attitude toward AR, and continuance intention. The study sample consisted of 452 participants. Data were analyzed using the Partial Least Squares–Structural Equation Modeling (PLS-SEM) approach. The results indicate significant direct relationships between all variables. Furthermore, this study demonstrated an indirect relationship between AR and continuance intention, mediated sequentially by cognitive responses, namely, utilitarian value and perceived risk, and affective responses, including user satisfaction and attitude toward AR. Consequently, it was revealed that all indirect relationships were significant, except for the pathways from AR to continuance intention involving perceived risk. This study presents key insights for online retailers, demonstrating how the integration of AR technology into conventional online shopping platforms can optimize user experiences by enhancing the cognitive and affective responses of customers. This, in turn, strengthens their intention to continue using AR technology, fostering sustained engagement and the long-term adoption of AR technology. Full article