Search Results (3,000)

Over the past few years, life expectancy has increased significantly. However, elderly individuals living independently often require assistance due to mobility issues, symptoms of dementia, or other health-related challenges. In these situations, high-quality elderly care systems for the aging population require innovative approaches to guarantee Quality of Service (QoS) and Quality of Experience (QoE). Traditional remote elderly care methods face several challenges, including high latency and poor service quality, which affect their transparency and stability. This paper proposes an Edge Computational Intelligence (ECI)-based haptic-driven ECI-TeleCaring system for the remote caring and monitoring of elderly people. It utilizes a Software-Defined Network (SDN) and Mobile Edge Computing (MEC) to reduce latency and enhance responsiveness. Dual Long Short-Term Memory (LSTM) models are deployed at the edge to enable real-time location-aware activity prediction to ensure QoS and QoE. The results from the simulation demonstrate that the proposed system is proficient in managing the transmission of data in real time without and with an activity recognition and location-aware model by communication latency under 2.5 ms (more than 60%) and from 11∼12 ms (60∼95%) for 10 to 1000 data packets, respectively. The results also show that the proposed system ensures a trade-off between the transparency and stability of the system from the QoS and QoE perspectives. Moreover, the proposed system serves as a testbed for implementing, investigating, and managing elder telecaring services for QoS/QoE provisioning. It facilitates real-time monitoring of the deployed technological parameters along with network delay and packet loss, and it oversees data exchange between the master domain (human operator) and slave domain (telerobot). Full article

Background: This study aimed to analyze the effect of cardiac rehabilitation programs on the health-related quality of life of patients after a coronary cardiac event using patient-reported outcome measures (PROMs) for up to 6 months of evaluation. Methods: A comprehensive search was carried out in the MEDLINE, CINAHL, CENTRAL, and Web of Science databases for randomized controlled trials comparing the cardiac rehabilitation program with usual care. Two independent reviewers assessed the studies for inclusion, risk of bias using the Cochrane tool, and quality of evidence through the GRADE system. A meta-analysis was performed on studies assessing health-related quality of life with the SF-12 (Physical Component Summary and Mental Component Summary) up to 6 months after the program. Results: Twelve studies encompassed 2260 patients who participated in a cardiac rehabilitation program after a coronary event, with a mean age of 60.06 years. The generic PROMs used to assess quality of life were the SF-12, SF-36, EQ-5D-3L, EQ-5D-5L, and GHQ, and the specific coronary heart disease PROMs were MacNew and HeartQoL. There was a positive effect of participation in cardiac rehabilitation on the physical component of health-related quality of life at 6 months (MD [7.02]; p = 0.04] and on the mental component (MD [1.06]; p = 0.82) after applying the SF-12. Conclusions: This study highlights the significant benefits of cardiac rehabilitation programs on health-related quality of life, particularly in the physical domain at 6 months. Assessing outcomes over time through PROMs after coronary heart events is essential, thus making it possible to personalize patients’ care and improve their health status. Full article

Background and Objectives: The aim of this study was to find the factors associated with the severe impairment of QoSL and the factors associated with a better score in QoSL, as well as the evaluation of pain symptoms and QoSL after the complete and incomplete excision of rectovaginal nodules. Materials and methods: The present prospective study was conducted in a single tertiary center for endometriosis where 116 patients underwent laparoscopic surgery for deep endometriosis during a 3-year period. The goal of the intervention was to excise all endometriotic implants while conserving the rectum. Intraoperative findings were recorded after the intervention, and the patients were classified according to the ENZIAN classification and rASRM scores. QoSL was assessed using the EHP-30 Module C (QoSL Score). Results: When comparing the mean scores before and 2 years after the surgery, a highly significant improvement was found for QoSL and dysmenorrhea (p < 0.0001). The complete excision of rectovaginal nodules led to a significantly better QoSL and lower dyspareunia (p < 0.0001) than incomplete resection (p < 0.02). Conclusions: This prospective study proves that the complete laparoscopic excision of all endometriotic implants improved the QoSL and decreased the pain score of dyspareunia. Incomplete rectovaginal nodule excision was correlated with a poorer QoSL and a lower improvement of dysmenorrhea, dyspareunia, and chronic pelvic pain scores than complete excision. Full article

Background: Considering the high prevalence of UI in the rural areas of Jordan and the limited clinical data on its occurrence in central Jordan, this study aims to investigate the prevalence, risk factors, and impact of urinary incontinence (UI) on the quality of life (QoL) of women in central Jordan. Method: This cross-sectional study was conducted from September to December 2022, using online the Incontinence Impact Questionnaire short form (IIQ-7) and Urogenital Distress Inventory short form (UDI-6). Participation was voluntary, and anonymous. Internal consistency was assessed using Cronbach’s α. Results: A total of 128 women participated in the study. More than half of the participants (54.33%, N = 69) experienced UI symptoms. Body mass index was the sole statistically significant factor linked to UI. Obese patients had the highest risk (OR 35, CI 95% 2.577–475.308, p < 0.05) compared to those with a healthy weight. Multivariate regression indicated significant associations of severe UI with smoking and vaginal births with a moderate impact of UI on QoL. Conclusions: The study’s findings emphasize the need for women’s health centers in the center of Jordan to develop comprehensive UI prevention and management programs to improve women’s health and well-being. Full article

The classification of Software-Defined Networking (SDN) traffic is an essential tool for network management, network monitoring, traffic engineering, dynamic resource allocation planning, and applying Quality of Service (QoS) policies. The programmability nature of SDN, the holistic view of the network through SDN controllers, and the capability for dynamic adjustable and reconfigurable controllersare fertile ground for the development of new techniques for traffic classification. Although there are enough research works that have studied traffic classification methods in SDN environments, they have several shortcomings and gaps that need to be further investigated. In this study, we investigated traffic classification methods in SDN using publicly available SDN traffic trace datasets. We apply a series of classifiers, such as MLP (BFGS), FC2 (RBF), FC2 (MLP), Decision Tree, SVM, and GENCLASS, and evaluate their performance in terms of accuracy, detection rate, and precision. Of the methods used, GenClass appears to be more accurate in separating the categories of the problem than the rest, and this is reflected in both precision and recall. The key element of the GenClass method is that it can generate classification rules programmatically and detect the hidden associations that exist between the problem features and the desired classes. However, Genetic Programming-based techniques require significantly higher execution time compared to other machine learning techniques. This is most evident in the feature construction method where at each generation of the genetic algorithm, a set of learning models is required to be trained to evaluate the generated artificial features. Full article

This study investigates the transformative impact of smart intelligence, leveraging the Internet of Things and edge-cloud platforms in smart urban development. Smart urban development, by integrating diverse digital technologies, generates substantial data crucial for informed decision-making in disaster management and effective urban well-being. The edge-cloud platform, with its dynamic resource allocation, plays a crucial role in prioritizing tasks, reducing service delivery latency, and ensuring critical operations receive timely computational power, thereby improving urban services. However, the current method has struggled to meet the strict quality of service (QoS) requirements of complex workflow applications. In this study, these shortcomings in edge-cloud are addressed by introducing a multi-objective resource optimization (MORO) scheduler for diverse urban setups. This scheduler, with its emphasis on granular task prioritization and consideration of diverse makespans, costs, and energy constraints, underscores the complexity of the task and the need for a sophisticated solution. The multi-objective makespan–energy optimization is achieved by employing a deep reinforcement learning (DRL) model. The simulation results indicate consistent improvements with average makespan enhancements of 31.6% and 70.09%, average cost reductions of 62.64% and 73.24%, and average energy consumption reductions of 25.02% and 17.77%, respectively, by MORO over-reliability enhancement strategies for workflow scheduling (RESWS) and multi-objective priority workflow scheduling (MOPWS) for SIPHT workflow. Similarly, consistent improvements with average makespan enhancements of 37.98% and 74.44%, average cost reductions of 65.53% and 74.89%, and average energy consumption reductions of 29.52% and 24.73%, respectively, by MORO over RESWS and MOPWS for CyberShake workflow, highlighting the proposed model’s efficiency gains. These findings substantiate the model’s potential to enhance computational efficiency, reduce costs, and improve energy conservation in real-world smart urban scenarios. Full article

Multiple sclerosis (MS) is a neurodegenerative disorder of the central nervous system characterized by a variety of symptoms such as fatigue, spasticity, tremors, and cognitive disorders. Individuals with MS may employ different coping strategies to manage these symptoms, which in turn can significantly impact their quality of life (QoL). This review aims to analyze these coping strategies and their impact on QoL. Furthermore, it seeks to identify the key factors that influence the choice and effectiveness of these coping strategies, providing insights into which strategies are most beneficial for enhancing QoL in people with MS. Methods: Systematic searches were performed in Scopus, PubMed, Web of Science, and Scopus databases. This systematic review has been registered in OSF with the number DOI 10.17605/OSF.IO/QY37X. Results: A total of 1192 studies were identified. After reading the full text of the selected studies and applying predefined inclusion criteria, 19 studies were included based on their pertinence and relevance to the topic. The results revealed that emotional variables, demographic factors, personality traits, and family support significantly influence the choice of coping strategies used to manage the symptoms of MS. Problem-solving and task-oriented coping were prevalent among MS patients and associated with better QoL outcomes. Emotional-focused and avoidance strategies were generally linked to poorer QoL, though avoidance provided temporary relief in certain contexts. Social support, emotional health, and cognitive reframing were crucial in enhancing QoL. Conclusions: The findings underscore the importance of tailored psychoeducational and therapeutic interventions focusing on emotional health, social support, and adaptive coping strategies. These interventions can significantly improve the long-term outcomes for individuals with MS. Future research should explore the dynamic interactions between coping strategies and QoL over time, providing a comprehensive understanding of how to best support MS patients in managing their disease. Full article

Managing modern, web-based, distributed applications effectively is a complex task that requires coordinating several aspects, including understanding the relationships among their components, the way they interact, the available hardware, the quality of network connections, and the providers hosting them. A distributed application consists of multiple independent and autonomous components. Managing the application involves overseeing each individual component with a focus on global optimization rather than local optimization. Furthermore, each component may be hosted by different resource providers, each offering its own monitoring and control interfaces. This diversity adds complexity to the management process. Lastly, the implementation, load profile, and internal status of an application or any of its components can evolve over time. This evolution makes it challenging for a Quality of Service (QoS) manager to adapt to the dynamics of the application’s performance. This aspect, in particular, can significantly affect the QoS manager’s ability to manage the application, as the controlling strategies often rely on the analysis of historical behavior. In this paper, the authors propose an extension to a previously introduced QoS manager through the addition of two new modules: (i) an associative memory module and (ii) an event forecast module. Specifically, the associative memory module, functioning as a cache, is designed to accelerate inference times. The event forecast module, which relies on a Weibull Time-to-Event Recurrent Neural Network (WTTE-RNN), aims to provide a more comprehensive view of the system’s current status and, more importantly, to mitigate the limitations posed by the finite number of decision classes in the classification algorithm. Full article

Alternaria brown spot (ABS), caused by Alternaria alternata, is one of the main citrus diseases that causes heavy production losses and reductions in fruit quality worldwide. The application of chemical fungicides has a key role in the management of ABS. In this study, 48 isolates of A. alternata collected from citrus orchards since 2014 were tested in vitro for their sensitivity to pyraclostrobin and fludioxonil, the latter being temporarily registered in Italy since 2020. Pyraclostrobin sensitivity was determined using spore germination and mycelial growth assays. The effective concentration inhibiting 50% of fungal growth (EC₅₀) was determined for each isolate. The sensitivity assays showed that the majority of A. alternata isolates tested were sensitive to pyraclostrobin. EC₅₀ values of fludioxonil in a mycelial growth assay indicated that 100% of isolates were sensitive to this fungicide. The analysis of the cytochrome b gene showed that none of the 40 isolates with a different sensitivity profile had the G143A mutation, and the subgroup of 8 isolates analyzed by real-time PCR did not carry the G137R and F129L mutations. A subset of four more sensitive and two reduced-sensitive isolates was chosen to assess sensitivity on detached citrus leaves treated with pyraclostrobin at the maximum recommended label rate. Disease incidence and symptom severity were significantly reduced, with a small reduction reported in leaves inoculated with the reduced-sensitive isolates. Furthermore, there was no correlation between sensitivity and fitness parameters evaluated in vitro (mycelium growth and sporulation rate). These findings help the development of monitoring resistance programs and, consequently, set up effective anti-resistance strategies for managing ABS on citrus orchards. Full article

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics. Full article

Background: The purpose of the study was to determine the effect of insulin pen training using the Teach-Back method in older patients with type 2 diabetes (T2D) on their self-management of insulin treatment, quality of life (QoL), and glycated hemoglobin (HbA1c) levels. Methods: Participants included 25 patients in the intervention group, with a mean age of 80.76 ± 6.132 years, and 24 patients in the control group, with a mean age of 81.29 ± 4.920 years. All participants were older people who had previously been diagnosed with T2D, had been using insulin for at least 6 months, and lived in rural areas. Teach-Back pen training was provided to the intervention group, while general diabetes education was provided to the control group. One-way variance analysis, paired-samples t-test and independent sample t-test were used. The self-management of insulin treatment, QoL and HbA1c levels were determined before training and after 3 months. The study was conducted between December 2022 and April 2023. Results: A significant difference was found in the mean scale scores between the intervention group and control group after training. The mean self-management of insulin treatment and QoL scale scores of the intervention group were significantly higher than those of the control group after training. The post-training HbA1c levels in the intervention group were lower than the pre-training levels. Conclusions: Teach-Back training improved diabetes self-management and QoL and decreased HbA1c levels in older patients with T2D living in a rural community. Full article

Aim: Among the severe organ complications occurring in patients with Turner syndrome (TS), ophthalmic dysmorphia and visual impairment are usually marginalized. There are only a few studies that take into account the prevalence of ophthalmic disorders in female patients with TS. Material and methods: Articles in PubMed, Scholar, and Website were reviewed, considering the prevalence of various ocular disorders in patients with X chromosome deficiency. Current standards for the management of patients with TS in the context of the prevalence of ophthalmic disorders were also analyzed. Results: Identification of visual impairment in people is important because it significantly impairs quality of life (QoL) along with other health problems. QoL affects cognitive and behavioral functioning and significantly increases self-esteem, acceptance of treatment, and, consequently, physical and mental health. Low self-esteem makes patients feel helpless and unable to plan their social development. Patients with TS are relatively more frequently diagnosed with various eye defects compared to the healthy population. Therefore, special attention should be paid to the early assessment of the visual system in people with TS to eliminate any factors that could potentially impair their QoL. Conclusions: Patients with TS should be referred to specialist ophthalmologists, pediatricians, or optometrists for preventive care or early treatment of visual impairment. The authors point out the need for comprehensive ophthalmological examinations as standard management in patients with TS. Full article

UAV-based air-ground integrated networks offer a significant benefit in terms of providing ubiquitous communications and computing services for Internet of Things (IoT) devices. With the empowerment of edge intelligence (EI) technology, they can efficiently deploy various intelligent IoT applications. However, the trajectory of UAVs can significantly affect the quality of service (QoS) and resource optimization decisions. Joint computation offloading and UAV trajectory optimization bring many challenges, including coupled decision variables, information uncertainty, and long-term queue delay constraints. Therefore, this paper introduces an air-ground integrated architecture with EI and proposes a TD3-based joint computation offloading and UAV trajectory optimization (TCOTO) algorithm. Specifically, we use the principle of the TD3 algorithm to transform the original problem into a cumulative reward maximization problem in deep reinforcement learning (DRL) to obtain the UAV trajectory and offloading strategy. Additionally, the Lyapunov framework is used to convert the original long-term optimization problem into a deterministic short-term time-slot problem to ensure the long-term stability of the UAV queue. Based on the simulation results, it can be concluded that our novel TD3-based algorithm effectively solves the joint computation offloading and UAV trajectory optimization problems. The proposed algorithm improves the performance of the system energy efficiency by 3.77%, 22.90%, and 67.62%, respectively, compared to the other three benchmark schemes. Full article

Background: Childhood hearing loss is a common chronic condition that may have a broad impact on children’s communication and motor and cognitive development, resulting in functional challenges and decreased quality of life (QoL). Objectives: This pilot study aimed to compare executive functions (EFs) as expressed in daily life and QoL between deaf and hard-of-hearing (D/HH) children and children with typical hearing. Furthermore, we examined the relationship between EFs and QoL in D/HH children. Methods: The participants were 76 children aged 7–11 yr: 38 D/HH and 38 with typical hearing. Parents completed the Behavior Rating Inventory of Executive Function (BRIEF) and Pediatric Quality of Life Inventory (PedsQL), while the child performed a shopping task in the virtual action planning supermarket (VAP-S) to reflect the use of EFs in daily activity. Results: D/HH children showed significantly poorer EFs (as measured by BRIEF and VAP-S) and reduced QoL. Difficulties in EFs were correlated with lower QoL. BRIEF scores were significant predictors of QoL domains. Conclusions: Difficulties in EFs may characterize children with D/HH and reduce their QoL. Therefore, EFs should be screened and treated. VAP-S and BRIEF are feasible tools for evaluating EFs that reflect children’s challenges due to EF difficulties in real-life contexts. Full article

To balance the tradeoff between quality of service (QoS) and operating expenditure (OPEX), the service provider should request the appropriate amount of resources to the cloud operator based on the estimated variation of service requests. This paper proposes a popularity-aware service provisioning framework (PASPF), which leverages the network data analytics function (NWDAF) to obtain analytics on service popularity variations. These analytics estimate the congestion level and list of top services contributing most of the traffic change. Based on the analytics, PASPF enables the service provider to request the appropriate amount of resources for each service for the next time period to the cloud operator. To minimize the OPEX of the service provider while keeping the average response time of the services below their requirements, we formulate a constrained Markov decision process (CMDP) problem. The optimal stochastic policy can be obtained by converting the CMDP model into a linear programming (LP) model. Evaluation results demonstrate that the PASPF can achieve less than $50\%$ OPEX of the service provider compared to a popularity-non-aware scheme while keeping the average response time of the services below the requirement. Full article