Search Results (164)

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

Objective: A self-management telehealth program to improve hand strength and function in systemic sclerosis (SSc) patients may improve their quality of life, so we investigated its efficacy. Methods: A 6-week prospective randomized controlled study was conducted in adults with SSc with a hand mobility in scleroderma (HAMIS) score > 1 or a limited range of motion in at least one hand joint. Participants were randomly allocated into three groups for six weeks of health education: (a) typical education, (b) watching video guides as needed, in addition to typical education, and (c) watching video guides and receiving weekly telephone notifications, in addition to typical education. The endpoints were the differences in self-management behavior, HAMIS scores, hand grip strength (HGS), and quality of life (QoL) using the European Quality of Life-5 Dimensions between groups, as well as the changes in these parameters compared to baseline. Results: A total of 24 patients per group were enrolled, with the majority diagnosed with diffuse cutaneous SSc (79.2%). Six weeks post-intervention, HGS improved significantly in both the video guide and telephone notification groups compared to typical education in both hands (p = 0.028, p = 0.044). Pincer grip differed between the groups in the non-dominant hand. Household modifications were more frequent in the video guide and telephone notification groups than in the typical education group (p = 0.023). All groups showed significant improvements in HGS and HAMIS scores in both hands, as well as in self-management behaviors, compared to baseline. QoL, as measured using a visual analog scale, improved significantly after the intervention in both the video guide and telephone notification groups, but not in the typical education group. Conclusions: Self-management telehealth programs effectively enhance hand strength, function, and self-management behaviors in patients with SSc with limited hand function. Weekly telephone notifications further reinforced continuity and engagement in these patients. Full article

In recent years, with the rapid development of the Internet and mobile devices, the high-resolution video industry has ushered in a booming golden era, making video content the primary driver of Internet traffic. This trend has spurred continuous innovation in efficient video coding technologies, such as Advanced Video Coding/H.264 (AVC), High Efficiency Video Coding/H.265 (HEVC), and Versatile Video Coding/H.266 (VVC), which significantly improves compression efficiency while maintaining high video quality. However, during the encoding process, compression artifacts and the loss of visual details remain unavoidable challenges, particularly in high-resolution video processing, where the massive amount of image data tends to introduce more artifacts and noise, ultimately affecting the user’s viewing experience. Therefore, effectively reducing artifacts, removing noise, and minimizing detail loss have become critical issues in enhancing video quality. To address these challenges, this paper proposes a post-processing method based on Convolutional Neural Network (CNN) that improves the quality of VVC-reconstructed frames through deep feature extraction and fusion. The proposed method is built upon a high-resolution dual-path residual gating system, which integrates deep features from different convolutional layers and introduces convolutional blocks equipped with gating mechanisms. By ingeniously combining gating operations with residual connections, the proposed approach ensures smooth gradient flow while enhancing feature selection capabilities. It selectively preserves critical information while effectively removing artifacts. Furthermore, the introduction of residual connections reinforces the retention of original details, achieving high-quality image restoration. Under the same bitrate conditions, the proposed method significantly improves the Peak Signal-to-Noise Ratio (PSNR) value, thereby optimizing video coding quality and providing users with a clearer and more detailed visual experience. Extensive experimental results demonstrate that the proposed method achieves outstanding performance across Random Access (RA), Low Delay B-frame (LDB), and All Intra (AI) configurations, achieving BD-Rate improvements of 6.1%, 7.36%, and 7.1% for the luma component, respectively, due to the remarkable PSNR enhancement. Full article

This work proposes a power-efficient framework for adaptive video streaming in UAV-assisted wireless networks specially designed for disaster-hit areas where existing base stations are nonfunctional. Delivering high-quality videos requires higher video rates and more resources, which leads to increased power consumption. With the increasing demand of mobile video, efficient bandwidth allocation becomes essential. In shared networks, users with lower bitrates experience poor video quality when high-bitrate users occupy most of the bandwidth, leading to a degraded and unfair user experience. Additionally, frequent video rate switching can significantly impact user experience, making the video rates’ smooth transition essential. The aim of this research is to maximize the overall users’ quality of experience in terms of power-efficient adaptive video streaming by fair distribution and smooth transition of video rates. The joint optimization includes power minimization, efficient resource allocation, i.e., transmit power and bandwidth, and efficient two-dimensional positioning of the UAV while meeting system constraints. The formulated problem is non-convex and difficult to solve with conventional methods. Therefore, to avoid the curse of complexity, the block coordinate descent method, successive convex approximation technique, and efficient iterative algorithm are applied. Extensive simulations are performed to verify the effectiveness of the proposed solution method. The simulation results reveal that the proposed method outperforms 95–97% over equal allocation, 77–89% over random allocation, and 17–40% over joint allocation schemes. Full article

Mobile virtual reality (VR) is considered a killer application for future mobile broadband networks. However, for cloud VR, the long content delivery path and time-varying transmission rate from the content provider’s cloud VR server to the users make the quality-of-service (QoS) provisioning for VR users very challenging. To this end, in this paper, we design a 360° VR video service system that leverages proactive caching and mobile edge computing (MEC) technologies. Furthermore, we propose a multiuser access control algorithm tailored to the system, based on analytical results of the delay violation probability, which is derived considering the impact of both the multi-hop wired network from the cloud VR server to the MEC server and the wireless network from the MEC server-connected base station (BS) to the users. The proposed access control algorithm aims to maximize the number of served users by exploiting real-time and dynamic network resources, while ensuring that the end-to-end delay violation probability for each accessed user remains within an acceptable limit. Simulation results are presented to analyze the impact of diverse system parameters on both the user access probability and the delay violation probability of the accessed users, demonstrating the effectiveness of the proposed multiuser access control algorithm. It is observed in the simulation that increasing the computing capacity of the MEC server or the communication bandwidth of the BS is one of the most effective methods to accommodate more users for the system. In the tested scenarios, when the MEC server’s computing capacity (the BS’s bandwidth) increases from 0.8 Tbps (50 MHz) to 3.2 Tbps (150 MHz), the user access probability improves on average by 92.53% (85.49%). Full article

The deployment of Augmented Reality (AR) is a necessity as an enabling technology for intelligent transportation systems (ITSs), with the potential to boost the implementation of Vehicle-to-Everything (V2X) networks while improving driver experience and increasing driving safety to fulfill AR functionality requirements. In this regard, V2X networks must maintain a high quality of service AR functionality, which is more challenging because of the nature of 5G V2X networks. Moreover, the execution of diverse traffic requirements with varying degrees of service quality is essential for seamless connectivity, which is accomplished by introducing efficient handover (HO) techniques. However, existing methods are still limited to basic services, including conversional, video streaming, and general traffic services. In this study, a Multiple Criteria Decision-Making (MCDM) technique is envisioned to address the handover issues posed by high-speed vehicles connected to ultra-high-density (UDN) heterogeneous networks. Compared with existing methods, the proposed HO mechanism handles high mobility in dense 5G V2X environments by performing a holistic evaluation of network conditions and addressing connection context requirements while using cutting-edge applications such as AR. The simulation results show a reduction in handover delays, failures, and ping-pong, with 84% prevention of unnecessary handovers. Full article

In Japan, local governments implore residents to remove the batteries from small-sized electronics before recycling them, but some products still contain lithium-ion batteries. These residual batteries may cause fires, resulting in serious injuries or property damage. Explosive materials such as mobile batteries (such as power banks) have been identified in fire investigations. Therefore, these fire-causing items should be detected and separated regardless of whether small-sized electronics recycling or other recycling processes are in use. This study focuses on the automatic detection of fire-causing items using deep learning in recycling small-sized electronic products. Mobile batteries were chosen as the first target of this approach. In this study, MATLAB R2024b was applied to construct the You Only Look Once version 4 deep learning algorithm. The model was trained to enable the detection of mobile batteries. The results show that the model’s average precision value reached 0.996. Then, the target was expanded to three categories of fire-causing items, including mobile batteries, heated tobacco (electronic cigarettes), and smartphones. Furthermore, real-time object detection on videos using the trained detector was carried out. The trained detector was able to detect all the target products accurately. In conclusion, deep learning technologies show significant promise as a method for safe and high-quality recycling. Full article

This systematic review analyzed the role of microlearning in basic education, identifying the most widely used Digital Information and Communication Technologies, relevant learning theories, and the role of social technologies from a Science, Technology, Society, and Environment (STSE) perspective. Following PRISMA 2020, searches were conducted in Web of Science, Scopus, ERIC, and IEEE Xplore databases. Studies on microlearning were selected based on previously defined eligibility criteria. The review process in Rayyan involved deduplication, screening, and full-text analysis. Data were qualitatively analyzed using content analysis, and methodological quality was assessed with CASP and the Downs and Black. The findings highlight that microlearning, integrated with digital tools such as online platforms, mobile apps, and short videos, significantly enhances student motivation, performance, and interaction; content in short modules facilitates knowledge retention and connects concepts to real-life situations. Promising trends include mobile technologies and gamification, which foster active, meaningful learning. Grounded in theories like Self-Determination, Constructionism, and Constructivism, microlearning personalizes teaching and promotes engagement, critical thinking, and accessibility, contributing to inclusive and sustainable education. From a STSE perspective, social technologies enhance autonomy, social interaction, and ethical–environmental awareness. In Brazil, further research on digital platforms and gamified strategies is needed to drive innovative educational practices. Full article

Background/Objectives: Pediatric cerebral palsy (CP) is a neurological disorder that affects motor skills, posture, and muscle coordination, impacting children’s daily functioning and quality of life. Treatment approaches in occupational therapy aim to enhance motor function and functional independence through a variety of rehabilitative techniques. Recently, new technologies—such as virtual reality, robotics, and assistive devices—have emerged as promising tools in occupational therapy to complement traditional interventions and potentially enhance motor and sensory outcomes in children with CP. This systematic review aims to analyze the effectiveness of these innovative technological interventions in the rehabilitation of children with CP. Methods: A systematic review was conducted and different scales were used to assess the risk of bias and methodological quality. The degree of evidence and level of recommendation were established according to the Grading of Recommendations Assessment, Development, and Evaluation (GRADE). The review adhered to PRISMA guidelines, with a comprehensive literature search performed across multiple databases, including Cochrane, Web of Science, and PubMed. This systematic review has been registered in the PROSPERO database with the registration number CRD42025630162. Results: The reviewed studies indicate that technologies such as functional electrical stimulation, robotic assistance, and adaptive devices have shown improvements in mobility, motor control, posture, strength, and autonomy in children with CP. Similarly, virtual environments support the correction of facial dysfunctions and enhance social interaction through video games and social networks. While these tools hold significant potential for rehabilitation, further adjustments are needed to achieve more consistent results. Conclusions: Emerging technologies appear to be effective tools in occupational therapy for pediatric CP, with positive impacts on motor skills and functional capabilities. Nevertheless, further studies with larger sample sizes and rigorous methodological designs are necessary to confirm these findings and establish more robust evidence on their long-term efficacy. Full article

With the rapid development of mobile networks and devices, real-time video transmission has become increasingly important worldwide. Constrained by the bandwidth limitations of single networks, extensive research has shifted towards video transmission in multi-network environments. However, differences in bandwidth and latency in heterogeneous networks (such as LTE and Wi-Fi) lead to high latency and packet loss issues, severely affecting video quality and user experience. This paper proposes a Forward Error Correction (FEC)-based Low-Delay Multipath Scheduling algorithm (LDMP-FEC). This algorithm combines the Gilbert model with a continuous Markov chain to adaptively adjust FEC redundancy, thereby enhancing data integrity. Through the FEC Recovery Priority Scheduling (FEC-RPS) algorithm, it dynamically optimizes the transmission order of data packets, reducing the number of out-of-order packets (OFO-packets) and end-to-end latency. Experimental results show that LDMP-FEC significantly reduces the number of out-of-order packets in heterogeneous network environments, improving performance by 50% compared to the round-robin and MinRtt algorithms, while maintaining end-to-end latency within 150 ms. Under various packet loss conditions, LDMP-FEC sustains a playable frame rate (PFR) above 90% and a Peak Signal-to-Noise Ratio (PSNR) exceeding 35 dB, providing an efficient and reliable solution for real-time video and other low-latency applications. Full article

Objectives: To summarize the impact of various telerehabilitation interventions on motor function, balance, gait, activities of daily living (ADLs), and quality of life (QoL) among patients with stroke and to determine the existing telerehabilitation interventions for delivering physiotherapy sessions in clinical practice. Methods: Six electronic databases were searched to identify relevant quantitative systematic reviews (SRs). Due to substantial heterogeneity, the data were analysed narratively. Results: A total of 28 systematic reviews (n = 245 primary studies) were included that examined various telerehabilitation interventions after stroke. Motor function was the most studied outcome domain across the reviews (20 SRs), followed by ADL (18 SRs), and balance (14 SRs) domains. For primary outcomes, our findings highlight moderate- to high-quality evidence showing either a significant effect or no significant difference between telerehabilitation and other interventions. There was insufficient evidence to draw a conclusion regarding feasibility outcomes, including participant satisfaction, adherence to treatment, and cost. Most reviews under this umbrella included patients with stroke in the subacute or chronic phase (12 SRs). Simple and complex telerehabilitation interventions such as telephone calls, videoconferencing, smartphone- or tablet-based mobile health applications, messaging, virtual reality, robot-assisted devices, and 3D animation videos, either alone or in combination with other interventions, were included across reviews. Conclusions: Various telerehabilitation interventions have shown either a significant effect or no significant difference compared to other interventions in improving upper and lower limb motor function, balance, gait, ADLs, and QoL, regardless of whether simple or complex approaches were used. Further research is needed to support the delivery of rehabilitation services through telerehabilitation intervention following a stroke. Full article

With the evolution of cellular networks and wireless-local-area-network-based communication technologies, services for smart device users have appeared. With the popularity of 4G and 5G, smart device users can now consume larger bandwidths than before. Consequently, the demand for various services, such as streaming, online games, and video conferences, has increased. For improved quality of experience (QoE), streaming services utilize adaptive bitrate (ABR) algorithms to handle network bandwidth variations. ABR algorithms use network bandwidth history for future network bandwidth prediction, allowing them to perform efficiently when network bandwidth fluctuations are minor. However, in environments with frequent network bandwidth changes, such as wireless networks, the QoE of video streaming often degrades because of inaccurate predictions of future network bandwidth. To address this issue, we utilize the gated recurrent unit, a time series prediction model, to predict the network bandwidth accurately. We then propose a buffer-based ABR streaming technique that selects optimized video-quality settings on the basis of the predicted bandwidth. The proposed algorithm was evaluated on a dataset provided by Zeondo by categorizing instances of user mobility into walking, bus, and train scenarios. The proposed algorithm improved the QoE by approximately 11% compared with the existing buffer-based ABR algorithm in various environments. Full article

Mobile gaming accounts for more than 50% of global online gaming revenue, surpassing console and browser-based gaming. The success of mobile gaming titles depends on optimizing applications for the specific hardware constraints of mobile devices, such as smaller displays and lower computational power, to maximize battery life. Additionally, these applications must dynamically adapt to the variations in network speed inherent in mobile environments. Ultimately, user engagement and satisfaction are critical, necessitating a favorable comparison to browser and console-based gaming experiences. While Quality of Experience (QoE) subjective evaluations through user surveys are the most reliable method for assessing user perception, various factors, termed influence factors (IFs), can affect user ratings of stimulus quality. This study examines human influence factors in mobile gaming, specifically analyzing the impact of user delight towards displayed content and the effect of gaze tracking. Using Pupil Core eye-tracking hardware, we captured user interactions with mobile devices and measured visual attention. Video stimuli from eight popular games were selected, with resolutions of 720p and 1080p and frame rates of 30 and 60 fps. Our results indicate a statistically significant impact of user delight on the MOS for most video stimuli across all games. Additionally, a trend favoring higher frame rates over screen resolution emerged in user ratings. These findings underscore the significance of optimizing mobile gaming experiences by incorporating models that estimate human influence factors to enhance user satisfaction and engagement. Full article

One of the continuous challenges related to the growing popularity of mobile devices and embedded systems with limited memory and computational power is the development of relatively fast methods for real-time image and video analysis. One such example is Optical Character Recognition (OCR), which is usually too complex for such devices. Considering that images captured by cameras integrated into mobile devices may be acquired in uncontrolled lighting conditions, some quality issues related to non-uniform illumination may affect the image binarization results and further text recognition results. The solution proposed in this paper is related to a significant reduction in the computational burden, preventing the necessity of full text recognition. Conducting only the initial image binarization using various thresholding methods, the computation of the mutual similarities of binarization results is proposed, making it possible to build a simple model of binary image quality for a fast prediction of the OCR results’ quality. The experimental results provided in the paper obtained for the dataset of 1760 images, as well as the additional verification for a larger dataset, confirm the high correlation of the proposed quality model with text recognition results. Full article

User-perceived throughput is a novel performance metric attracting a considerable amount of recent attention because it characterizes the quality of the experience in mobile multimedia services. For instance, it gives a data rate of video streaming with which a user will not experience any lag or outage in watching video clips. However, its performance limit remains open. In this paper, we are interested in the achievable upper bound of user-perceived throughput, also referred to as the user-perceived capacity, and how to achieve it in typical wireless channels. We find that the user-perceived capacity is quite limited or even zero with channel state information at the receiver (CSIR) only. When both CSIR and channel state information at the transmitter (CSIT) are available, the user-perceived throughput can be substantially improved by power or even rate adaptation. A constrained Markov decision process (CMDP)-based approach is conceived to compute the user-perceived capacity with joint power–rate adaptation. It is rigorously shown that the optimal policy obeys a threshold-based rule with time, backlog, and channel gain thresholds. With power adaptation only, the user-perceived capacity is equal to the hard-delay-constrained capacity in our previous work and achieved by joint diversity and channel inversion. Full article