Search Results (1,612)

Autism Spectrum Disorder (ASD) necessitates comprehensive management, addressing complex challenges in social communication, behavioral regulation, and sensory processing, for which wearable technologies offer valuable tools to monitor and support interventions. Therefore, this review explores recent advancements in wearable technology, categorizing devices based on executive function, psychomotor skills, and the behavioral/emotional/sensory domain, highlighting their potential to improve ongoing management and intervention. To ensure rigor and comprehensiveness, the review employs a PRISMA-based methodology. Specifically, literature searches were conducted across diverse databases, focusing on studies published between 2014 and 2024, to identify the most commonly used wearables in ASD research. Notably, 55.45% of the 110 devices analyzed had an undefined FDA status, 23.6% received 510(k) clearance, and only a small percentage were classified as FDA Breakthrough Devices or in the submission process. Additionally, approximately 50% of the devices utilized sensors like ECG, EEG, PPG, and EMG, highlighting their widespread use in real-time physiological monitoring. Our work comprehensively analyzes a wide array of wearable technologies, including emerging and advanced. While these technologies have the potential to transform ASD management through real-time data collection and personalized interventions, improved clinical validation and user-centered design are essential for maximizing their effectiveness and user acceptance. Full article

The widespread adoption of the Industry 5.0 paradigm puts people and their applications at the center of attention and, with the increasing automation and robotization of work, the need for workers to acquire new, more advanced skills increases. The development of artificial intelligence (AI) means that expectations for workers are further raised. This leads to the need for multiple career changes from life and throughout life. Belonging to a previous generation of workers makes this retraining even more difficult. The authors propose the use of machine learning (ML), virtual reality (VR) and brain–computer interface (BCI) to assess the conditions of work–life balance for employees. They use machine learning for prediction, identifying users based on their subjective experience of work–life balance. This tool supports intelligent systems in optimizing comfort and quality of work. The potential effects could lead to the development of commercial industrial systems that could prevent work–life imbalance in smart factories for Industry 5.0, bringing direct economic benefits and, as a preventive medicine system, indirectly improving access to healthcare for those most in need, while improving quality of life. The novelty is the use of a hybrid solution combining traditional tests with automated tests using VR and BCI. This is a significant contribution to the health-promoting technologies of Industry 5.0. Full article

Urban memory involves the re-creation of a city’s physical, historical, social, and cultural elements in the memories of its inhabitants. However, urban transformation and commercial tourism-oriented projects may threaten the continuity of this memory. This study aims to provide an understanding of the relationship between urban memory and spatial experience while exploring how urban memory elements convey meanings to daily users and local inhabitants of a touristic settlement. The research focuses on Misi Village in Bursa, Turkey, a settlement with a 2000-year history known for its traditional architecture and natural beauty. Over the past two decades, local authorities have pursued extensive restoration projects to rebrand Misi Village as an Art and Tourism Village. The research employs the oral history method, focusing on two user groups: tourists and locals. The findings reveal that while tourists appreciate Misi Village for its natural beauty and recreational activities, they lack a deeper understanding of its history and the transformation of its identity. Instead, they mostly focus on commerce-oriented spatial experiences. In contrast, local residents emphasize daily life and traditional practices as they strive to sustain their livelihoods. By highlighting this difference, strategic planning is proposed to preserve Misi Village’s unique urban memory and promote sustainable, culturally centered tourism. Full article

This paper aims to help data center administrators choose thermal simulation tools, which manage thermal conduction from power for energy savings. When evaluating and suggesting data center thermal simulators for users, questions such as “What are the simulator’s differences? Are they easy to use? Which is the best choice?” are frequently asked. To answer these questions, this paper reviews the thermal simulation works for data centers in the last ten years. After that, it proposes the versatility and dexterity metrics for these simulators and discovers that it is difficult to choose them despite their similar design purpose and functions. Empowered by the survey, we claim that the widespread practice simulators still need more enhancement in data center scenarios. We back up our claim by comparing typical simulators and propose improvements to thermal simulators for future studies. Full article

People with disabilities (PWD), in their routine commutes, confront hindrances associated with road infrastructure in busy urban centers. The present study developed a user perception-based methodology to evaluate optimal routes for PWD in urban settlements in the Kingdom of Saudi Arabia (KSA). A survey captured the preferences for 105 PWD, consisting of 37 powered wheelchair users, 62 manual wheelchair users, and 6 artificial limb users. The multi-criteria decision analysis evaluated the accessibility index for PWD based on four criteria: length, number of junctions, absence of footpath, and slope. This study revealed that manual wheelchair users prefer the length criterion, powered wheelchair users emphasized the absence of footpaths, and artificial limb users were concerned about slope. The result showed that only two routes out of ten showed medium, while those remaining exhibited low accessibility. Most routes were relatively long for people with disabilities, focusing on the need for public transportation with special arrangements in most small and medium-sized cities, like the study area of Hail and Qassim province of the KSA, to reduce the distance and travel time. The proposed framework provides valuable insights to route evaluation for persons with special needs in the KSA and elsewhere. Full article

The current research methods employed in the study of gardens are largely concentrated in the fields of landscape architecture, architecture, and ecology. However, there is a paucity of analytical methods dedicated to the domain of garden design. However, the design content of gardens is within the scope of design studies. It is therefore imperative to develop or refine research methods for the analysis of gardens that are firmly grounded in design principles. The primary contribution of this study is the development of a design analysis framework, centered on the interrelationships between ‘user–garden–environment’, which can be applied to the analysis and investigation of gardens from a ‘people, objects, and environment’ perspective, in alignment with design studies. Influenced by similar geography, environment, culture, customs, etc., the garden design of China, Japan, and South Korea presents a very East Asian design style of forms, elements, features, etc., but also formed the differences of each characteristic. This paper takes China’s Chengde Mountain Resort, Japan’s Shugakuin Imperial Villa, and South Korea’s Changdeokgung, which are listed on the World Heritage List, as examples Based on the design analysis method proposed in this paper, a relevant design comparative study is conducted in three aspects: users of East Asia’s royal gardens, the design object—the gardens—and the external environment, and the similarities and differences between the Chinese, Japanese, and Korean royal gardens are analyzed. This study aims to verify the feasibility of the analytical framework of ‘user–garden–environment’ design by analyzing typical cases of royal gardens in China, Japan, and South Korea. This will enable the creation of multiple values conducive to the development of gardens. Furthermore, the analytical framework of ‘user–garden–environment’ is subjected to a process of deconstruction through the case studies of typical royal gardens in the three East Asian countries. This is done to overcome the limitations of existing research methodologies, provide a novel systematic research methodology for garden research, and facilitate the protection and inheritance of the historical and cultural heritage of gardens in East Asia. Additionally, this approach offers a reference point for related garden research in the context of modern lifestyles. Full article

We present KnowVID-19, a knowledge-based system that assists medical researchers and scientists in extracting targeted information quickly and efficiently from online medical literature repositories, such as PubMed, PubMed Central, and other biomedical sources. The system utilizes various open-source machine learning tools, such as GROBID, S2ORC, and BioC to streamline the processes of data extraction and data mining. Central to the functionality of KnowVID-19 is its keyword-based text classification process, which plays a pivotal role in organizing and categorizing the extracted information. By employing machine learning techniques for keyword extraction—specifically RAKE, YAKE, and KeyBERT—KnowVID-19 systematically categorizes publication data into distinct topics and subtopics. This topic structuring enhances the system’s ability to match user queries with relevant research, improving both the accuracy and efficiency of the search results. In addition, KnowVID-19 leverages the NetworkX Python library to construct networks of the most relevant terms within publications. These networks are then visualized using Cytoscape software, providing a graphical representation of the relationships between key terms. This network visualization allows researchers to easily track emerging trends and developments related to COVID-19, long COVID, and associated topics, facilitating more informed and user-centered exploration of the scientific literature. KnowVID-19 also provides an interactive web application with an intuitive, user-centered interface. This platform supports seamless keyword searching and filtering, as well as a visual network of term associations to help users quickly identify emerging research trends. The responsive design and network visualization enables efficient navigation and access to targeted COVID-19 literature, enhancing both the user experience and the accuracy of data-driven insights. Full article

Edge computing has emerged as a critical technology for meeting the needs of latency-sensitive applications and reducing network congestion. This goal is achieved mainly by distributing computational resources closer to end users and away from traditional data centers. Optimizing the utilization of limited edge cloud resources and improving the performance of edge computing systems requires efficient resource-management techniques. In this paper, we primarily discuss the use of simulation tools—EdgeSimPy in particular—to assess edge cloud resource management methods. We give a summary of the main difficulties in managing a limited pool of resources in edge cloud computing, and we go over how simulation programs like EdgeSimPy work and evaluate resource management algorithms. The scenarios we consider for this evaluation involve edge computing while taking into account variables like user location, resource availability, and network structure. We evaluate four resource management algorithms in a fixed, simulated edge computing environment to determine their performance regarding their CPU usage, memory usage, disk usage, power consumption, and latency performance metrics to determine which method performs better in a fixed scenario. This allows us to determine the optimal algorithm for tasks that prioritize minimal resource use, low latency, or a combination of the two. Furthermore, we outline areas of unfilled research needs and potential paths forward for improving the reliability and realism of edge cloud simulation tools. Full article

Presidential elections are an important political event that often trigger intense debate. With more than 139 million users, YouTube serves as a significant platform for understanding public opinion through sentiment analysis. This study aimed to implement deep learning techniques for a multi-label sentiment analysis of comments on YouTube videos related to the 2024 Indonesian presidential election. Offering a fresh perspective compared to previous research that primarily employed traditional classification methods, this study classifies comments into eight emotional labels: anger, anticipation, disgust, joy, fear, sadness, surprise, and trust. By focusing on the emotional spectrum, this study provides a more nuanced understanding of public sentiment towards presidential candidates. The CRISP-DM method is applied, encompassing stages of business understanding, data understanding, data preparation, modeling, evaluation, and deployment, ensuring a systematic and comprehensive approach. This study employs a dataset comprising 32,000 comments, obtained via YouTube Data API, from the KPU and Najwa Shihab channels. The analysis is specifically centered on comments related to presidential candidate debates. Three deep learning models—Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (Bi-LSTM), and a hybrid model combining CNN and Bi-LSTM—are assessed using confusion matrix, Area Under the Curve (AUC), and Hamming loss metrics. The evaluation results demonstrate that the Bi-LSTM model achieved the highest accuracy with an AUC value of 0.91 and a Hamming loss of 0.08, indicating an excellent ability to classify sentiment with high precision and a low error rate. This innovative approach to multi-label sentiment analysis in the context of the 2024 Indonesian presidential election expands the insights into public sentiment towards candidates, offering valuable implications for political campaign strategies. Additionally, this research contributes to the fields of natural language processing and data mining by addressing the challenges associated with multi-label sentiment analysis. Full article

In the context of low-carbon energy transformation, fully utilizing the integrated demand response (IDR) resources on the load side can improve the operational flexibility and economy of the integrated energy system (IES). However, establishing a reasonable trading mechanism to enhance users’ participation in IDR has become a key issue that IES urgently needs to solve. To this end, this paper first establishes an IES model that includes electricity, heat, and gas. To reduce carbon emissions, a ladder-type carbon trading mechanism is introduced while adding low-carbon technologies such as carbon capture devices and power-to-gas conversion. Secondly, a bilateral IDR mechanism centered on the load aggregator (LA) is proposed, and a multi-agent operation model including IES, LA, and users is established. The IDR subsidy price is dynamically determined through a two-level Stackelberg game model involving IES, LA, and users. Then, through KKT conditions and the Big M method, the two-level game model is turned into an IES-LA game model, which is solved using a combination of the White Shark Optimization method and the Gurobi solver. The final simulation results show that the scheduling model can fully reflect the time value of IDR resources, and the IES cost is decreased by USD 152.22, while LA and user benefits are increased by USD 54.61 and USD 31.85. Meanwhile, the ladder-type carbon trading mechanism and low-carbon technology have effectively achieved low-carbon operation of the system. Full article

Energy storage (ES) units are vital for the reliable and economical operation of the power system with a high penetration of renewable distributed generators (DGs). Due to ES’s high investment costs and long payback period, energy management with shared ESs becomes a suitable choice for the demand side. This work investigates the sharing mechanism of ES units for low-voltage (LV) energy prosumer (EP) communities, in which energy interactions of multiple styles among the EPs are enabled, and the aggregated ES dispatch center (AESDC) is established as a special energy service provider to facilitate the scheduling and marketing mechanism. A shared ES operation framework considering multiple EP communities is established, in which both the energy scheduling and cost allocation methods are studied. Then a shared ES model and energy marketing scheme for multiple communities based on the leader–follower game is proposed. The Karush–Kuhn–Tucker (KKT) condition is used to transform the double-layer model into a single-layer model, and then the large M method and PSO-HS algorithm are used to solve it, which improves convergence features in both speed and performance. On this basis, a cost allocation strategy based on the Owen value method is proposed to resolve the issues of benefit distribution fairness and user privacy under current situations. A case study simulation is carried out, and the results show that, with the ES scheduling strategy shared by multiple renewable communities in the leader–follower game, the energy cost is reduced significantly, and all communities acquire benefits from shared ES operators and aggregated ES dispatch centers, which verifies the advantageous and economical features of the proposed framework and strategy. With the cost allocation strategy based on the Owen value method, the distribution results are rational and equitable both for the groups and individuals among the multiple EP communities. Comparing it with other algorithms, the presented PSO-HS algorithm demonstrates better features in computing speed and convergence. Therefore, the proposed mechanism can be implemented in multiple scenarios on the demand side. Full article

In the development of complex embedded interactive systems, a tension arises between, on the one hand, ever shorter and highly iterative design processes, and, on the other hand, the need for user testing with early prototypes to validate systems from a user-centred design perspective. This study focuses on the integration of Virtual Reality (VR) into prototyping embedded interactive systems, examining its potential to bridge the gap between rapid prototyping and user-centered design validation. Adopting a comparative research approach, we analyze a case study: the development of a cultural smart city experience. It juxtaposes in situ, low-fidelity prototype testing with VR-based testing, evaluating their realism, interactivity, functionality, presence and task difficulty. This mixed-method research design incorporates both qualitative and quantitative methodologies, engaging 27 design students in a comparative study, conducting participatory research and 8 expert interviews. These findings reveal divergent roles in field testing and VR in the new product development process, highlighting VR’s strengths in visualizing procedures and facilitating discussion. This study identifies the limitations of VR in mimicking realistic interactions and incorporating social context yet underscores its superiority over paper prototypes in its realism and interactivity. Where field testing can hold broader contextual insights, the VR prototype gives more concrete and applied insights. The main advantage of VR testing is its visualisation of procedures and its final materialisation according to the participants interviewed. According to the experts interviewed, VR can be used as a useful tool within the development process especially for visualisation and testing user flows of complex interfaces. Full article

Existing buildings in the European Union account for 40% of its energy consumption. To significantly reduce this impact, annual deep energy renovation rates should triple by the end of the 2020s. However, the lack of automation in the construction industry has hindered energy renovation efforts. Horizon Europe’s INPERSO project (Industrialised and Personalised Renovation for Sustainable Societies) aims to create a user-centered energy rehabilitation method based on industrialized technologies and systems, enhancing efficiency and building performance. To bridge the gap between predictions and real-world outcomes, the 22 project partners—using a multi-criteria decision analysis (MCDA) process—devised a list of key performance indicators (KPIs) for evaluating rehabilitation based on economic, energy, environmental, social, and technological factors. Adopting a human-centric approach, these project partners aim to minimize the technologies’ environmental impact while optimizing users’ comfort and experience. The indicators are designed to evaluate performance at every stage of the renovation process, enabling continuous feedback and user engagement and ultimately ensuring that projected energy savings are met throughout the building’s lifespan. The KPIs selected for INPERSO provide a solid framework for evaluating and monitoring sustainable renovation. However, challenges such as administrative reluctance and user disruption must be addressed to further boost the adoption of deep energy renovations. Full article

Background/Objectives: This study aimed to explore the effects of Instagram use on body satisfaction and self-esteem in young adults 20 to 40 years (N = 95). Given the widespread use of social media and its potential influence on body image, we sought to understand how Instagram use may contribute to body dissatisfaction and self-esteem, particularly through quantitative analysis of self-report measures. Methods: A cross-sectional survey design in which the Rosenberg Self-Esteem Scale (RSES), the Body Shape Questionnaire (BSQ), and additional ad hoc questions designed to assess Instagram usage patterns were employed. Results: The results indicated that greater Instagram use is associated with increased body dissatisfaction (p = 0.005), although it did not significantly affect self-esteem (p = 0.211). Gender did not play a significant role in these relationships (p = 0.173). Notably, a significant positive correlation was found between body satisfaction and self-esteem, showing that individuals with higher body satisfaction also reported higher self-esteem (p < 0.001). Further analyses indicated that users exposed to appearance-centered content were more likely to report body dissatisfaction. Conclusions: These findings suggest that Instagram usage, particularly in the context of appearance-focused content, has a considerable impact on body dissatisfaction among young adults but does not appear to influence self-esteem. This highlights the importance of developing interventions focused on promoting healthy social media habits and critical content engagement to mitigate negative impacts on body image. Social media exposure should be a key component in future interventions designed to improve body image and overall psychological well-being. Full article

This study takes a unique approach by investigating the integration of Brain–Computer Interfaces (BCIs) and Building Information Modeling (BIM) within residential architecture. It explores their combined potential to foster neuro-responsive, sustainable environments within the framework of Construction 5.0. The methodological approach involves real-time BCI data and subjective evaluations of occupants’ experiences to elucidate cognitive and emotional states. These data inform BIM-driven alterations that facilitate adaptable, customized, and sustainability-oriented architectural solutions. The results highlight the ability of BCI–BIM integration to create dynamic, occupant-responsive environments that enhance well-being, promote energy efficiency, and minimize environmental impact. The primary contribution of this work is the demonstration of the viability of neuro-responsive architecture, wherein cognitive input from Brain–Computer Interfaces enables real-time modifications to architectural designs. This technique enhances built environments’ flexibility and user-centered quality by integrating occupant preferences and mental states into the design process. Furthermore, integrating BCI and BIM technologies has significant implications for advancing sustainability and facilitating the design of energy-efficient and ecologically responsible residential areas. The study offers practical insights for architects, engineers, and construction professionals, providing a method for implementing BCI–BIM systems to enhance user experience and promote sustainable design practices. The research examines ethical issues concerning privacy, data security, and informed permission, ensuring these technologies adhere to moral and legal requirements. The study underscores the transformational potential of BCI–BIM integration while acknowledging challenges related to data interoperability, integrity, and scalability. As a result, ongoing innovation and rigorous ethical supervision are crucial for effectively implementing these technologies. The findings provide practical insights for architects, engineers, and industry professionals, offering a roadmap for developing intelligent and ethically sound design practices. Full article