Search Results (2,305)

In an era of information overload, artificial intelligence plays a pivotal role in supporting everyday decision-making. This paper introduces EverydAI, a virtual AI-powered assistant designed to help users make informed decisions across various daily domains such as cooking, fashion, and fitness. By integrating advanced natural language processing, object detection, augmented reality, contextual understanding, digital 3D avatar models, web scraping, and image generation, EverydAI delivers personalized recommendations and insights tailored to individual needs. The proposed framework addresses challenges related to decision fatigue and information overload by combining real-time object detection and web scraping to enhance the relevance and reliability of its suggestions. EverydAI is evaluated through a two-phase survey, each one involving 30 participants with diverse demographic backgrounds. Results indicate that on average, 92.7% of users agreed or strongly agreed with statements reflecting the system’s usefulness, ease of use, and overall performance, indicating a high level of acceptance and perceived effectiveness. Additionally, EverydAI received an average user satisfaction score of 4.53 out of 5, underscoring its effectiveness in supporting users’ daily routines. Full article

Modern scientific knowledge management systems increasingly demand methods capable of flexible modeling and dynamic analytics. This paper presents an approach to modeling for the needs of research knowledge base systems. The presented method integrates extensible XML-based data modeling with advanced analytical tools. The approach enables the structured definition of domain models characterized by hierarchical nesting, historical tracking, and semantic versioning. The presented analytical engine, based on this approach, uses XPath navigation to support dynamic pivot-table aggregations over complex, nested data. The underlying modeling is particularly useful for handling temporal data. It is suitable for broader contexts, resulting in flexible data structures, multi-version record management, interoperability with Linked Open Data (LOD) standards, and FAIR-compliant workflows. The results demonstrate that combining model-centric extensibility, semantic interoperability, and user-driven analytics provides a scalable and adaptable foundation for building information management systems across diverse institutional and organizational settings. A use case for a university knowledge database is presented, and acceptance of the system by users is discussed. Full article

The computational cost of the Transformer architecture is highly dependent on the length of the input sequence, with a computational complexity of $O\left(n^2\right)$ due to the self-attention mechanism. As a result, Transformer-based models, such as Large Language Models, incur significant computational and storage overhead when processing tasks involving long input sequences. To mitigate these challenges, we propose a compression method that allows users to manually adjust the trade-off between compression efficiency and model performance. The method employs a trainable model to minimize information loss, ensuring that the impact on accuracy remains minimal. The method demonstrated an accuracy degradation within acceptable limits on LongBench v2. Full article

To promote the sustainable development of urban smart transportation systems, this study constructs a structural equation model (SEM) based on the Technology Acceptance Model (TAM), incorporating extended variables including social influence, environmental awareness, privacy concerns, and service similarity to investigate users’ behavioral intentions toward Mobility as a Service (MaaS). The research systematically examines key factors influencing user adoption behavior and their underlying mechanisms, providing theoretical foundations and practical guidance for optimizing MaaS system design and policy making. Using SEM as the core analytical framework, this study employs mediation analysis, moderation analysis, and multigroup comparison to empirically examine the direct and indirect effects among variables, as well as group heterogeneity. Data were collected through an online questionnaire survey, with Analysis of Variance (ANOVA) applied to identify the differential impacts of demographic and travel behavior characteristics on users’ intentions and related psychological constructs, thereby supporting precise user segmentation and evidence-based policy interventions. Key findings include the following: (1) Social influence, ease of use, and environmental awareness boost MaaS adoption, while privacy concerns hinder it. (2) Freelancers/self-employed weaken the positive effects of usefulness, ease of use, and social influence on adoption. (3) Service similarity and ease of use effects vary significantly between single-mode and multimodal commuters. The findings extend the theoretical boundaries of TAM and provide both theoretical and practical support for the development of sustainable urban transportation systems. Full article

Background/Objectives: Advanced lung cancer is a highly distressing disease that negatively impacts older adults. Supportive care interventions designed for this population are scarce and often inaccessible due to competing demands and transportation access. We adapted and refined an evidence-based treatment, Self-System Therapy (SST), to address the unmet needs of older adults with advanced cancer. Methods: Guided by principles of implementation science, we conducted patient interviews, focus groups, and user testing to refine our new SST for the lung cancer (SST-LC) protocol. We then conducted a single-arm pilot trial (clinicaltrials.gov NCT04057196) for patients aged 65+ and above with Stage III or IV lung cancer (N = 30). Benchmarks for acceptability, feasibility, and preliminary changes in outcome measures were assessed. Results: Our study met the desired recruitment goals and demonstrated high treatment adherence rates (89%) and satisfaction rates (85%), indicating that SST-LC was feasible and well-received. Participants also showed reductions in distress and depression, and improvements in emotional and functional well-being from baseline to post-intervention, with effects mostly maintained at follow-up. Physical well-being, social well-being, and quality of life showed smaller, non-significant changes. Feedback from participants also suggested that SST enhanced their resilience and ability to cope with cancer-related challenges, but also indicated a preference for fewer sessions. Conclusions: SST for older adults living with advanced lung cancer is feasible and acceptable. Moreover, this supportive care intervention shows promise in addressing psychological distress, emotional well-being, and functional well-being in older adults. Future research will include testing the efficacy of SST in a larger randomized controlled trial. Full article

Growing environmental challenges and the need to practice sustainable human actions and attitudes make it necessary to search for new solutions that can minimize the negative impact of human activity on the environment. This also refers to the tourism industry. In this article, the authors propose the use of virtual reality (VR) to visit any destination worldwide as a potential green alternative to traditional forms of travel. The aim of this study presented in this article was to analyze public acceptance and user perceptions of VR as a tool supporting or replacing real-world tourism. This study involved 215 people who used the Google Earth VR application to visit their chosen destinations around the world. Respondents also completed a two-part survey (before and after the VR experience), in which they assessed, among other things, the realism of the VR visiting and the overall VR experience and expressed their opinions on the role of VR in tourism. The results indicate that while VR is not yet widely recognized as a full-fledged substitute for travel (46% of respondents stated that VR would not replace real travel but could be a good complement), it was rated as useful for people who cannot travel (79.1% of respondents) and before trips (79.1% of respondents). The realism of the Google Earth VR application received an average rating of 3.679 on a scale of 1 to 5, with a median of 4. This study addresses the yet unexplored area of VR in tourism, which has great potential given that the results show a quite positive attitude of participants towards using the application enabling virtual travel—nearly three-quarters of the sample (73.9%) expressed a willingness to return to VR-based sightseeing experiences. Full article

Background: Pain detection and management in older people with impaired cognition is an unmet need, leading to healthcare inequalities. This study aimed to enhance pain detection by translating and culturally adapting the PAIC-15 questionnaire into Brazilian Portuguese, establishing its content validity and comparing findings with a similar Dutch study. Methods: Following the same international standards used in the English-to-Dutch validation, we translated and culturally adapted PAIC-15 for Brazil. Content validity was assessed using two questions: (A) whether the item indicated pain and (B) whether it was specific to pain. An online form was distributed to ‘target users’ (nurses and geriatricians in LTCFs or other settings) and other healthcare professionals (HCPs) experienced in pain assessment in people with cognitive decline. Results were compared with the English-to-Dutch validation. Results: A total of 103 HCPs responded (mean age 45.2 ± 11.4 years)—76 (74%) were female and 54 (52%) had previously used pain assessment tools. Among the 63 target users, 13 items (87%) were validated as indicative and/or specific to pain. Comparing Brazilian and Dutch users, 10 items (67%) showed agreement on pain specificity. Conclusions: The Brazilian Portuguese PAIC-15 is now available, showing acceptable content validity. Linguistic challenges highlight the need for training and further validation in larger samples. Full article

The new era of FinTech services enabled the financial sector to benefit from innovative and cost-effective products via process automation, fostering a foundation for more sustainable business growth. Despite considerable research, the determinants of FinTech services adoption by businesses remain mostly unknown. For the first time, a mixed-method study is realized combining the perspectives of FinTech services providers (experts) and FinTech service users (businesses that use FinTech). To elicit the providers’ views, interviews have been conducted with experts from FinTech service providers. From the user side, data generated via online surveys was evaluated in an adjusted Unified Theory of Acceptance and Use of Technology (UTAUT2) model tailored to FinTech specifics using the R implementation of PLS-SEM. The results of this analysis enabled comparisons between the perspectives of providers and users to identify similarities and differences in adoption factors. Correspondingly, conclusions on FinTech adoption encourage FinTech service providers to adjust their solutions to better fit the business requirements. For business owners, they provide valuable insights on how to streamline their financials and foster sustainable growth through efficiency gains. Full article

Against the backdrop of the low-altitude economy, with the widespread application of drones in sports scenarios, the driving mechanism of users’ long-term usage intention has become a key issue in technology adoption research. To investigate the critical factors influencing the continuous use of drone products by sports-involved populations, this study builds a factor model for users’ continuous use of drones. It is based on the Unified Theory of Acceptance and Use of Technology (UTAUT) model, integrating the UTAUT2 model and specific user needs in sports scenarios. Both traditional structural equation modeling (SEM) and Bayesian structural equation modeling (BSEM) are employed for empirical testing. Through the analysis of 297 valid questionnaire responses, it is found that the Bayesian approach yields a better fit. Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, Hedonic Motivation, Safety and Environmental Compatibility all exert significant positive impacts on users’ continuous usage intention, with Effort Expectancy having the most prominent influence. On this basis, service strategies for drone brands are proposed to support product design and service provision. This study preliminarily indicates that Bayesian analysis possesses advantages and potential in this field. Meanwhile, the factor model for users’ long-term drone usage can meet the development needs in sports scenarios, and it has strong feasibility as a design model for users’ long-term drone usage. Full article

This study investigates the colloquial expression ʔinno that serves as a complementizer (C) and a discourse marker (DM) in Jordanian Arabic (JA). The data includes (422) instances of ʔinno collected from social media conversations of (60) JA speakers. The analysis shows that for ʔinno as a (C), there are (259) instances that are used to introduce subordinate clauses, and it is inflected with pronoun suffixes that specify person, gender, and number. It also serves various functions in verbal and nominal sentences. As a DM, Ɂinno are (163) instances that appear in the middle of sentences between two propositions. A list of contexts is developed featuring Ɂinno in JA. The pragmatic functions of Ɂinno are determined in each situation and validated by an Acceptability Judgment Task which is completed by 20 native speakers of JA. The pragmatic functions of ʔinno fall into six primary categories with sub-functions, such as explanatory functions (like giving reasons or expressing results), elaborative functions (including elaboration and giving examples and clarification), emotional and assessment functions (such as expressing surprise or criticism), emphatic and assertive functions (for emphasizing or warning), epistemic and uncertainty functions (covering hesitation and hedging), and a turn-taking function (specifically urging for continuity). This study concludes that Ɂinno is well established among social media users in the Jordanian context and the varied contexts play a vital role in exploring its pragmatic and syntactic functions. Full article

Heated tobacco products (HTPs) frequently induce user discomfort due to high mainstream smoke temperatures. To address this challenge and improve the inhalation experience, this experiment designed and prepared a cage-shaped braided tube as the cooling section of the filter for HTPs. The thermal, cooling, suction resistance, and smoke composition properties of the filter were tested and analyzed. Thermal analysis (DSC/TG) revealed a 116.53 J/g increase in endothermic enthalpy for PEG-impregnated samples, accompanied by maintained thermal stability (decomposition temperature ≈ approximately 350 °C). The 0.8 wt% Carbon Nanotube (CNT) composite achieved exceptional thermal conductivity (0.597 W/m·K), representing a 521% improvement over untreated controls. The braided tube optimal performance (3 mm inner diameter, 30% PEG/0.8% CNT) reduced the highest smoke temperature to 47.8 °C while maintaining acceptable suction resistance (68.5 Pa, 56.4% reduction vs. commercial IQOS filters). GC-MS analysis confirmed negligible alterations in smoke composition (p > 0.05). This innovation offers an effective thermal management solution that does not compromise sensory experiences. Full article

This paper presents the SMALLDERS project, a European initiative aimed at transforming smallholders’ business models through an innovative technological platform. The platform functions as an e-marketplace that connects small farmers directly with consumers while simultaneously promoting environmental sustainability and collaboration across the agri-food value chain. The study evaluates the platform’s commercial viability and acceptance through a mixed-methods approach, incorporating qualitative and quantitative data. Research methods include focus group sessions, interviews with key stakeholders—such as transport companies, large distributors, and public administrations—and a consumer survey assessing intentions and attitudes toward the e-marketplace. Results indicate limited overall consumer readiness to adopt the platform; however, 48.6% of respondents expressed willingness to use it provided competitive prices and personal benefits are assured. Smallholders regard e-commerce as a promising opportunity, yet they face significant barriers, including limited resources, low digital literacy, and logistical constraints. Stakeholders generally view the platform positively, emphasizing that its success depends on achieving a critical mass of business volume. To foster adoption, SMALLDERS proposes three business models for smallholders: sustainable, cooperative, and technological. The platform includes a user-friendly feature to assist smallholders in transitioning among these models, complemented by training and support services designed to encourage more resilient and innovative agricultural practices. Full article

This study aimed to validate the Robot Acceptance Questionnaire (RAQ), a self-report instrument designed to assess user acceptance toward social robots. Originally structured around four theoretical domains—pragmatic, hedonic (identity and feelings), and attractiveness—the RAQ was empirically found to converge into two robust and inversely related dimensions: Positive Attitude (PA) and Negative Attitude (NA). A total of 208 participants (mean = 43.1; S.D. = 21.4) viewed a short video of a humanoid robot (Pepper) and completed the RAQ. Factorial structure (Principal Component Analysis), internal reliability (Cronbach’s alpha), and construct validity were assessed. Results showed excellent internal consistency for both PA and NA (α = 0.93), and intuitive associations with independent measures of ease of use, mastery, and willingness to interact. The RAQ thus offers a concise and reliable tool for assessing general robot acceptance, especially suitable for remote and large-scale studies. Full article

This paper proposes a structured design framework and system architecture for a non-intrusive spatial system aimed at supporting child behavior in everyday domestic environments. Rooted in ethical considerations, our approach defines four core behavior-guided design strategies: routine recovery, emotion-responsive adjustment, behavioral transition induction, and external linkage. Each strategy is meticulously translated into a detailed system logic that outlines input conditions, trigger thresholds, and feedback outputs, designed for implementability with ambient sensing technologies. Through a comparative conceptual analysis of three sensing configurations—low-resolution LiDARs, mmWave radars, and environmental sensors—we evaluate their suitability based on technical feasibility, spatial integration, operationalized privacy metrics, and ethical alignment. Supported by preliminary technical observations from lab-based sensor tests, low-resolution LiDAR emerges as the most balanced option for its ability to offer sufficient behavioral insight while enabling edge-based local processing, robustly protecting privacy, and maintaining compatibility with compact residential settings. Based on this, we present a working three-layered system architecture emphasizing edge processing and minimal-intrusion feedback mechanisms. While this paper primarily focuses on the framework and design aspects, we also outline a concrete pilot implementation plan tailored for small-scale home environments, detailing future empirical validation steps for system effectiveness and user acceptance. This structured design logic and pilot framework lays a crucial foundation for future applications in diverse residential and care contexts, facilitating longitudinal observation of behavioral patterns and iterative refinement through lived feedback. Ultimately, this work contributes to the broader discourse on how technology can ethically and developmentally support children’s autonomy and well-being, moving beyond surveillance to enable subtle, ambient, and socially responsible spatial interactions attuned to children’s everyday lives. Full article

Technological advances have enabled the development of innovative educational tools, particularly those aimed at supporting English as a Second Language (ESL) learning, with a specific focus on oral skills. However, pronunciation remains a significant challenge due to the limited availability of personalized learning opportunities that offer immediate feedback and contextualized practice. In this context, the present research proposes the design, implementation, and validation of an immersive application that leverages virtual reality (VR) and artificial intelligence (AI) to enhance English pronunciation. The proposed system integrates a 3D interactive environment developed in Unity, voice classification models trained using Teachable Machine, and real-time communication with Firebase, allowing users to practice and assess their pronunciation in a simulated library-like virtual setting. Through its integrated AI module, the application can analyze the pronunciation of each word in real time, detecting correct and incorrect utterances, and then providing immediate feedback to help users identify and correct their mistakes. The virtual environment was designed to be a welcoming and user-friendly, promoting active engagement with the learning process. The application’s distributed architecture enables automated feedback generation via data flow between the cloud-based AI, the database, and the visualization interface. Results demonstrate that using 400 samples per class and a confidence threshold of 99.99% for training the AI model effectively eliminated false positives, significantly increasing system accuracy and providing users with more reliable feedback. This directly contributes to enhanced learner autonomy and improved ESL acquisition outcomes. Furthermore, user surveys conducted to understand their perceptions of the application’s usefulness as a support tool for English learning yielded an average acceptance rate of 93%. This reflects the acceptance of these immersive technologies in educational contexts, as the combination of these technologies offers a realistic and user-friendly simulation environment, in addition to detailed word analysis, facilitating self-assessment and independent learning among students. Full article