Search Results (752)

In recent years, online streaming has encountered the challenge of retaining its user base. This study considers the role of transaction cost economics theory in consumer choices to continue subscribing. Participants respond to their top three streaming services, resulting in 797 responses, accounting for multiple selections by each respondent. Respondents could choose their top three services from a list of Netflix, Disney, Hulu, Amazon Prime Video, HBO Max, and Apple TV+. The study’s conclusions highlight the impact of uncertainty, a negative measure of streaming quality, on online subscription-based video streaming. Additionally, asset specificity, reflecting uniqueness and exclusive content, is found to be positively related to continuing a subscription. This research distinguishes itself by examining individuals who are already subscribers to provide insights and guidance through the lens of Transaction Cost Economics, to help marketing professionals seeking a deeper understanding of consumer behavior in the online streaming landscape. Full article

With the rapid development of video streaming services, adaptive bitrate (ABR) algorithms have become a core technology for ensuring optimal viewing experiences. Traditional ABR strategies, predominantly rule-based or reinforcement learning-driven, typically employ uniform quality assessment metrics that overlook users’ subjective preference differences regarding factors such as video quality and stalling. To address this limitation, this paper proposes an adaptive video bitrate selection system that integrates preference modeling with reinforcement learning. By incorporating a preference learning module, the system models and scores user viewing trajectories, using these scores to replace conventional rewards and guide the training of the Proximal Policy Optimization (PPO) algorithm, thereby achieving policy optimization that better aligns with users’ perceived experiences. Simulation results on DASH network bandwidth traces demonstrate that the proposed optimization method improves overall Quality of Experience (QoE) by over 9% compared to other mainstream algorithms. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Accurate and real-time classification of low-latency Internet traffic is critical for applications such as video conferencing, online gaming, financial trading, and autonomous systems, where millisecond-level delays can degrade user experience. Existing methods for low-latency traffic classification, reliant on raw temporal features or static statistical analyses, fail to capture dynamic frequency patterns inherent to real-time applications. These limitations hinder accurate resource allocation in heterogeneous networks. This paper proposes a novel framework integrating wavelet transform (WT) and artificial neural networks (ANNs) to address this gap. Unlike prior works, we systematically apply WT to commonly used temporal features—such as throughput, slope, ratio, and moving averages—transforming them into frequency-domain representations. This approach reveals hidden multi-scale patterns in low-latency traffic, akin to structured noise in signal processing, which traditional time-domain analyses often overlook. These wavelet-enhanced features train a multilayer perceptron (MLP) ANN, enabling dual-domain (time–frequency) analysis. We evaluate our approach on a dataset comprising FTP, video streaming, and low-latency traffic, including mixed scenarios with up to four concurrent traffic types. Experiments demonstrate 99.56% accuracy in distinguishing low-latency traffic (e.g., video conferencing) from FTP and streaming, outperforming k-NN, CNNs, and LSTMs. Notably, our method eliminates reliance on deep packet inspection (DPI), offering ISPs a privacy-preserving and scalable solution for prioritizing time-sensitive traffic. In mixed-traffic scenarios, the model achieves 74.2–92.8% accuracy, offering ISPs a scalable solution for prioritizing time-sensitive traffic without deep packet inspection. By bridging signal processing and deep learning, this work advances efficient bandwidth allocation and enables Internet Service Providers to prioritize time-sensitive flows without deep packet inspection, improving quality of service in heterogeneous network environments. Full article

Face-to-face interaction is one of the most natural forms of human communication. Unsurprisingly, Video Conferencing (VC) Applications have experienced a significant rise in demand over the past decade. With the widespread availability of cellular devices equipped with high-resolution cameras, Instant Messaging Video Call Applications (IMVCAs) now constitute a substantial portion of VC communications. Given the multitude of IMVCA options, maintaining a high Quality of Experience (QoE) is critical. While content providers can measure QoE directly through end-to-end connections, Internet Service Providers (ISPs) must infer QoE indirectly from network traffic—a non-trivial task, especially when most traffic is encrypted. In this paper, we analyze a large dataset collected from WhatsApp IMVCA, comprising over 25,000 s of VC sessions. We apply four Machine Learning (ML) algorithms and a Large Multimodal Model (LMM)-based agent, achieving mean errors of 4.61%, 5.36%, and 13.24% for three popular QoE metrics: BRISQUE, PIQE, and FPS, respectively. Full article

Personality is a fundamental psychological trait that exerts a long-term influence on human behavior patterns and social interactions. Automatic personality recognition (APR) has exhibited increasing importance across various domains, including Human–Robot Interaction (HRI), personalized services, and psychological assessments. In this study, we propose a multimodal personality recognition model that classifies the Big Five personality traits by extracting features from three heterogeneous sources: audio processed using Wav2Vec2, video represented as Skeleton Landmark time series, and text encoded through Bidirectional Encoder Representations from Transformers (BERT) and Doc2Vec embeddings. Each modality is handled through an independent Self-Attention block that highlights salient temporal information, and these representations are then summarized and integrated using a late fusion approach to effectively reflect both the inter-modal complementarity and cross-modal interactions. Compared to traditional recurrent neural network (RNN)-based multimodal models and unimodal classifiers, the proposed model achieves an improvement of up to 12 percent in the F1-score. It also maintains a high prediction accuracy and robustness under limited input conditions. Furthermore, a visualization based on t-distributed Stochastic Neighbor Embedding (t-SNE) demonstrates clear distributional separation across the personality classes, enhancing the interpretability of the model and providing insights into the structural characteristics of its latent representations. To support real-time deployment, a lightweight thread-based processing architecture is implemented, ensuring computational efficiency. By leveraging deep learning-based feature extraction and the Self-Attention mechanism, we present a novel personality recognition framework that balances performance with interpretability. The proposed approach establishes a strong foundation for practical applications in HRI, counseling, education, and other interactive systems that require personalized adaptation. Full article

This study explores the integration of digital media tools—specifically TikTok, online press news analysis, and digital portfolios—into pre-service chemistry teacher education to enhance student engagement, foster conceptual understanding, and highlight the relevance of chemistry in society. The educational intervention involved 138 pre-service teachers who analysed digital news articles to reflect on the societal and environmental implications of chemistry, promoting media literacy and awareness of socioscientific issues. Additionally, they created short-form TikTok videos, using social media to communicate scientific concepts creatively and interactively. All participants compiled their work into digital portfolios, which served as both a reflective and integrative tool. A post-course Likert-scale questionnaire (N = 77) revealed high overall satisfaction with the methodology, with 94.8% valuing the news analysis activity and 59.7% finding TikTok particularly engaging. Despite some limitations regarding access to technical infrastructure, the findings indicate that incorporating Information and Communication Technology (ICT) in this manner supports motivation, meaningful learning, and the development of key teaching competencies. This case study contributes practical insights into ICT use in science education. Full article

Police officers work in high-stress environments that demand emotional resilience, interpersonal skills, and effective communication. Occupational stress can negatively impact their motivation, conflict resolution abilities, and professional effectiveness. Loving-Kindness Meditation (LKM), a mindfulness-based intervention focused on cultivating compassion and empathy, has shown promise in enhancing prosocial attitudes and emotional regulation. With the rise of short video platforms, digital interventions like video-guided LKM may offer accessible mental health support for law enforcement. This study examines the effects of short video app-guided LKM on police officers’ mindfulness, public service motivation (PSM), conflict resolution skills (CRSs), and communication skills (CSSs). It aims to determine whether LKM can enhance these psychological and professional competencies. A randomized controlled trial (RCT) was conducted with 110 active-duty police officers from a metropolitan police department in China, with 92 completing the study. Participants were randomly assigned to either the LKM group (n = 46) or the waitlist control group (n = 46). The intervention consisted of a 6-week short video app-guided LKM program with daily 10 min meditation sessions. Pre- and post-intervention assessments were conducted using several validated scales: the Mindfulness Attention Awareness Scale (MAAS), the Public Service Motivation Scale (PSM), the Conflict Resolution Styles Inventory (CRSI), and the Communication Competence Scale (CCS). A 2 (Group: LKM vs. Control) × 2 (Time: Pre vs. Post) mixed-design MANOVA was conducted to analyze the effects. Statistical analyses revealed significant group-by-time interaction effects for PSM (F(4,177) = 21.793, p < 0.001, η² = 0.108), CRS (F(4,177) = 20.920, p < 0.001, η² = 0.104), and CSS (F(4,177) = 49.095, p < 0.001, η² = 0.214), indicating improvements in these areas for LKM participants. However, no significant improvement was observed for mindfulness (F(4,177) = 2.850, p = 0.930, η² = 0.016). Short video app-guided LKM improves public service motivation, conflict resolution skills, and communication skills among police officers but does not significantly enhance mindfulness. These findings suggest that brief, digitally delivered compassion-focused programs can be seamlessly incorporated into routine in-service training to strengthen officers’ prosocial motivation, de-escalation competence, and public-facing communication, thereby fostering more constructive police–community interactions. Full article

Augmented Reality Smart Glasses (ARSGs) allow users to engage in picture-taking and video recording, as well as real-time storage and sharing of pictures and videos through cloud services. Unlike smartphones, newer ARSGs resemble ordinary sunglasses, allowing for unobtrusive recording. As these devices become available on an international market, it is important to understand how different cultural attitudes towards privacy and the recording and sharing of images of bystanders could impact the acceptance and adoption of ARSGs. South Korea and the United States have vastly different culturally based perceptions of photography and recording in public. S. Korea has cultural and legal restrictions in place, while the U.S.’s values of freedom of expression and individual rights are reflected in limited restrictions. Accordingly, drawing upon the Technology Acceptance Model (TAM), this paper explored the impact of privacy concerns on key constructs of the TAM for U.S. and S. Korean participants. This paper examined how Americans’ (U.S. = 402) and S. Koreans’ (S. Korea = 898) perceived usefulness, perceived ease of use, attitude toward using, and behavioral intention to use ARSGs were impacted by privacy concerns. The results of this study found that S. Korean respondents had significantly greater privacy concerns about using ARSGs than U.S. respondents. However, they also had significantly more positive attitudes and greater behavioral intentions to use ARSGs. Path analyses examining ARSGs’ acceptance revealed that privacy concerns impacted attitudes towards ARSGs, but that these had a greater impact on U.S. participants than on Koreans. The results highlight the importance of considering nuanced cultural perspectives, specifically privacy concerns, in examining the development and adoption of new technologies. Raw data and scripts for this study are available to ensure reproducibility. Full article

Background/Objectives: The urine sodium/potassium (Na/K) ratio can potentially be used to detect dietary habits that contribute to hypertension. In this prospective cohort interventional trial, we aimed to verify whether private insurance sales staff can help clients change their lifestyle habits based on their urinalysis results. Methods: Clients of the life insurance company (20–65 years old) who were considered to have “high risk” lifestyle factors, which was defined as having high values for two or more of the following indicators: body mass index, blood pressure, triglycerides, liver enzymes, and glucose metabolism, were included. The clients were randomly assigned to three groups: a face-to-face (FF) intervention by sales staff (n = 83), non-FF (Non-FF) intervention via a social networking service (n = 87), and no intervention (Control) (n = 58). Urinalysis and surveys about diet and exercise habits were conducted before and after a 3-month interventional period in all groups. Three interventions were performed for the FF and Non-FF groups, including dietary advice based on urinalysis results, education encouraging reduced salt intake and increased locomotor activity, and viewing an educational video. The Control group only received their urinalysis results by mail. Results: The participants’ mean age was 44.0 years old. Significant improvements in estimated potassium intake were observed in the Non-FF group, and significant reductions in urine Na/K ratios were noted in both the FF and Non-FF groups. Multiple logistic regression analysis indicated that watching the video was the most effective factor for decreasing the urine Na/K ratio (odds ratio = 1.869). The total points for dietary behavior, based on the questionnaire, significantly improved among the individuals who watched the video. Conclusions: This study demonstrates the potential for private health insurance companies to contribute to health promotion and introduces a novel strategy for improving lifestyle habits among individuals at high risk of lifestyle-related diseases. Full article

This study presents an exhaustive analysis of LGBTQIA+ audiovisual production available on the main streaming platforms in Spain, covering both Spanish and international content. Using a sample of 1490 works from ten video-on-demand services (Apple TV+, Disney+, Filmin, FlixOlé, Max, Movistar Plus+, Netflix, Prime Video, Rakuten, and SkyShowtime), this study examines how the offered catalogues are configured and structured in response to the commercial dynamics of the LGBTQIA+ production market. Using quantitative methodology, the research addresses the industrial production models, the agents involved and the characteristics of the most widely offered narrative genres and formats, highlighting distribution patterns and visibility in the catalogues. The findings include a marked international abundance, a reflection of the global market guidelines and the hegemony of narratives aimed at transnational audiences. National productions, although less numerous, are a significant contribution to the audiovisual landscape, incorporating cultural identities with an LGBTQIA+ representation that is more aligned with local realities. The central role of independent producers is observed in production models where international agreements are outlined as a key strategy. In addition, it highlights the prevalence of genres such as drama and comedy, together with that of the film format. The visibility and representation of sexual and gender diversity indicates a positive commercial response, although with considerable challenges. Full article

Introduction: Few applications worldwide focus on psychosocial rehabilitation, and none specifically address psychosocial rehabilitation projects. This justifies the need for an application to assist mental health professionals in constructing and managing such projects in the Brazilian mental health scenario. Objective: This study aimed to present a web application, the “Psychosocial Rehabilitation Application” (Psychosocial Rehab App), and describe its development in detail through a technological survey conducted between May 2024 and February 2025. Method: The development process of the web app was carried out in the following four stages, adapted from the Novak method: theoretical basis, requirements survey, prototyping, and development with alpha testing. The active and collaborative participation of the main researcher (a psychiatric nurse) and two undergraduate software engineers, supervised by a software engineer and a professor of nursing and psychology, was essential for producing a suitable operational product available to mental health professionals. Interactions were conducted via video calls, WhatsApp, and email. These interactions were transcribed using the Transkriptor software and inserted into the ATLAS.ti software for thematic analysis. Results: The web app “Psychosocial Rehabilitation Application” displays a home screen for registration and other screens structured into the stages of the psychosocial rehabilitation project (assessment, diagnosis, goals, intervention, agreements, and re-assessment). It also has a home screen, a resource screen, and a function screen with options to add a new project, search for a project, or search for mental health support services. These features facilitate the operation and streamline psychosocial rehabilitation projects by mental health professionals. Thematic analysis revealed three themes and seven codes describing the entire development process and interactions among participants in collaborative, interrelational work. A collaborative approach between researchers and developers was essential for translating the complexity of the psychosocial rehabilitation project into practical and usable functionalities for future users, who will be mental health professionals. Discussion: The Psychosocial Rehab App was developed collaboratively by mental health professionals and developers. It supports the creation of structured rehabilitation projects, improving decision-making and documentation. Designed for clinical use, the app promotes autonomy and recovery by aligning technology with psychosocial rehabilitation theory and the actual needs of mental health services. Conclusions: The Psychosocial Rehab App was developed through collaborative work between mental health and technology professionals. The lead researcher mediated this process to ensure that the app’s functionalities reflected both technical feasibility and therapeutic goals. Empathy and dialog were key to translating complex clinical needs into usable and context-appropriate technological solutions. Full article

The growing demand for bandwidth-intensive vehicular applications—particularly ultra-high-definition streaming and immersive panoramic video—is pushing current network infrastructures beyond their limits, especially in urban areas with severe congestion and degraded user experience. To address these challenges, we propose an aerial-assisted vehicular network architecture that integrates 6G base stations, distributed massive MIMO networks, visible light communication (VLC), and a heterogeneous aerial network of high-altitude platforms (HAPs) and drones. At its core is a context-aware dynamic bandwidth allocation algorithm that intelligently routes infotainment data through optimal aerial relays, bridging connectivity gaps in coverage-challenged areas. Simulation results show a 47% increase in average available bandwidth over conventional first-come-first-served schemes. Our system also satisfies the stringent latency and reliability requirements of emergency and live infotainment services, creating a sustainable ecosystem that enhances user experience, service delivery, and network efficiency. This work marks a key step toward enabling high-bandwidth, low-latency smart mobility in next-generation urban networks. Full article

Background: Colonoscopy is an important and essential diagnostic and screening tool for colorectal cancer and other pathologies in the colon. High-quality bowel preparation (BP) is a key quality measure of colonoscopy and is critical for maximizing its effectiveness, including enhancing adenoma detection rates. However, inadequate bowel preparation (IBP) remains a frequent challenge and is influenced by multiple factors. This review aims to summarize and evaluate educational and technological interventions implemented before colonoscopy to improve BP quality. Methods: The methodology comprised a structured narrative review of studies published in English, including randomized controlled trials, prospective studies, observational cohorts, and meta-analyses. Interventions were categorized by their delivery mode and impact on BP adequacy. Interventions included written materials, internet-based education modules, short message service (SMS) reminders, visual aids, instructional videos, verbal communication, telephone support, smartphone applications, and virtual reality (VR) platforms. Results: Most studies reported significant improvements in BP quality with enhanced patient education, particularly with the use of instructional videos and smartphone applications. Verbal communication and telephone support also demonstrated positive outcomes but were limited by resource availability. VR represents a promising emerging technology, though its implementation remains costly and complex. Conclusions: Enhanced educational interventions are proven methods to optimize BP quality. The selection of an appropriate modality should consider patient characteristics, technological accessibility, and institutional resources. Personalized strategies targeting high-risk populations can further reduce IBP rates and improve overall colonoscopy outcomes. Full article

Urban traffic management faces challenges, including inadequate sensing capabilities and insufficient operational status evaluation. The rapid expansion of electronic toll collection (ETC) systems from highways to urban roads provides new opportunities to address these issues. The vast amount of “dormant” ETC data contains rich traffic information that urgently needs to be deeply mined and effectively utilized. This paper reviews the research status, key technologies, and development trends of urban traffic state sensing and analysis technologies based on ETC data. In terms of technological development, ETC systems have evolved from simple toll collection tools to comprehensive traffic management platforms, featuring unique advantages such as accurate vehicle identification, extensive spatiotemporal coverage, and stable data quality. ETC data-based traffic sensing technologies encompass traffic state representation at microscopic, mesoscopic, and macroscopic levels, enabling comprehensive sensing from individual vehicle behavior to overall network operations. The construction of multi-source data fusion frameworks enables effective complementarity between ETC data, floating car data, and video detection data, significantly improving traffic state estimation accuracy. In practical applications, ETC data has demonstrated enormous potential in real-time monitoring and signal control optimization, traffic prediction and artificial intelligence technologies, environmental impact assessment, and other fields. Meanwhile, ETC data-based urban traffic management is transitioning from passive responses to proactive prediction, from single functions to comprehensive services, and from isolated systems to integrated platforms. Looking toward the future, the deep integration of emerging technologies, such as vehicle–road networking, edge computing, and artificial intelligence, with ETC systems will further promote the intelligent, refined, and precise development of urban traffic management. Full article