Search Results (215)

Background: Age-related declines in respiratory muscle strength and ventilatory efficiency can impair functional capacity and metabolic health in older adults. Inspiratory muscle training (IMT) has been proposed as a practical intervention to counteract these changes, yet its systemic effects remain unclear. This study aimed to examine the effects of short-term IMT on functional capacity, diaphragm thickness, and liver tissue characteristics in healthy elderly men. Methods: Thirty community-dwelling men aged 60–80 years were randomly assigned to an IMT or control group. The IMT group performed four weeks of breathing exercises using a POWERbreathe^® device at 40% of maximal inspiratory pressure, with a weekly 10% increase in pressure. Pre- and post-intervention assessments included the six-minute walk test (6MWT), diaphragm thickness and liver density via computed tomography, and quality of life (QoL; SF-12). Results: Four weeks of inspiratory muscle training significantly improved diaphragm thickness (11.7%), fatty liver density (FLD) (+16.7%), and six-minute walk performance (+5.3%), with large time × group effects favoring the IMT group. While the physical quality of life showed modest, comparable improvements, mental health outcomes demonstrated a moderate, time-dependent improvement without a significant group-by-time interaction. Conclusions: Short-term IMT improved diaphragmatic function and functional capacity in older men and was associated with favorable changes in a liver-related biomarker; however, given that only a single liver-related metric was assessed, these findings should not be interpreted as evidence of overall improvements in liver health. Full article

The increasing integration of Artificial Intelligence (AI) in education (AIEd) and its dependence on contemporary communication infrastructures (5G/6G, the Internet of Things (IoT), and Multi-Access Edge Computing (MEC)) has prompted a surge of research into applications, infrastructural dependencies, and deployment constraints. This is giving rise to a new paradigm termed AI-Enabled Telecommunication-Based Education (AITE). This review synthesises the recent literature (2022–2025) to examine how telecommunications and AI technologies converge to enhance educational ecosystems through adaptive learning systems, intelligent tutoring systems, AI-driven assessment, and administration. The findings reveal that low-latency, high-bandwidth connectivity, combined with edge-deployed analytics, enables real-time personalisation, continuous feedback, and scalable learning models that extend beyond traditional classrooms. In addition, persistent critical challenges are also reported, including issues with ethical governance, data privacy, algorithmic fairness, and uneven access to digital infrastructure, all affecting equitable adoption. By linking pedagogical transformation with telecom performance metrics—namely, latency, Quality of Service (QoS), and device interconnectivity—this work outlines a unified cross-layer framework for AITE. This review concludes by identifying future research avenues in ethical AI deployment, resilient architectures, and inclusive policy design to ensure transparent, secure, and human-centred educational transformation. Full article

With the development of service-based and cloud-based architectures, the number of web services is rapidly increasing, and when users use these services, they expect reliability, security, and performance. However, existing trust evaluation approaches are predominantly based on QoS characteristics (e.g., availability, throughput, and compliance) and do not fully reflect user satisfaction and expectations. This research addresses this gap by posing the following research question: How can a fuzzy-based multi-criteria decision-making (MCDM) approach incorporate user experience to enhance web service trust score evaluation under uncertainty? To address this, we propose a trust score–weighted model and an evaluation approach that extends traditional methods by integrating a fuzzy-based MCDM approach, such as fuzzy TOPSIS, fuzzy VIKOR, and fuzzy WASPAS, thereby allowing both objective service metrics and subjective user expectations to be jointly considered when evaluating service trust. The results of the proposed approach demonstrate the evaluation of trust scores through a fuzzy-based MCDM approach, allowing for the ranking of WSs. Our case study validates the model’s ability to incorporate predictive quality-of-service performance and its relevance to real-world, user-centric service selection scenarios. Full article

Background: Heart failure with reduced ejection fraction (HFrEF) markedly impairs quality of life (QoL) and life expectancy. The main therapeutic goals are to reduce mortality, improve functional capacity, and enhance QoL. Exercise training is an evidence-based, non-pharmacological component of standard care that improves functional capacity and clinical outcomes in HFrEF. This review examines the effects of endurance and resistance training on peak oxygen uptake (VO₂peak), ventilatory efficiency (VE/VCO₂ slope), health-related QoL, and cardiovascular outcomes in patients with HFrEF. Methods: A structured narrative review was conducted using comprehensive searches of PubMed, EMBASE, and the Cochrane Library for English-language studies published between January 2004 and October 2024. Eligible studies included adult HFrEF populations undergoing aerobic and/or resistance training with reported effects on VO₂peak, ventilatory efficiency, QoL, or clinical outcomes. Given the heterogeneity of interventions, comparators, and outcome metrics, data were synthesized descriptively. Results: Across 18 studies (plus one sub-analysis) including 3401 patients, 17 trials assessed VO₂peak and 16 reported significant improvements, with an average increase of approximately 2 mL·kg⁻¹·min⁻¹. Six studies assessed ventilatory efficiency, and five demonstrated reductions in VE/VCO₂ slope averaging 4.4 units. Eleven studies analyzed QoL, and nine reported significant improvements corresponding to an ≈5-point decrease in the Minnesota Living with Heart Failure Questionnaire (MLHFQ). In the largest trial, exercise training was associated with modest but statistically significant reductions in all-cause mortality or hospitalization (HR 0.89) and cardiovascular mortality or heart-failure hospitalization (HR 0.85) after adjustment for baseline prognostic factors. Conclusions: Structured exercise training improves aerobic capacity, ventilatory efficiency, and QoL in patients with HFrEF, with supportive evidence for reduced morbidity and mortality. These findings underscore the value of structured exercise as a core component of modern HFrEF management. Full article

Video-centric applications have seen significant growth in recent years with HTTP Adaptive Streaming (HAS) becoming a widely adopted method for video delivery. Recently, low-latency (LL) adaptive bitrate (ABR) algorithms have recently been proposed to reduce the end-to-end delay in HTTP adaptive streaming. This study investigates whether low-latency adaptive bitrate (LL-ABR) algorithms, in their effort to reduce delay, also compromise video quality. To this end, this study presents both objective and subjective evaluation of user experience with traditional DASH and low-latency ABR algorithms. The study employs crowdsourcing to evaluate user-perceived video quality in low-latency MPEG-DASH streaming, with a particular focus on the impact of short segment durations. We also investigate the extent to which quantitative QoE (Quality of Experience) metrics correspond to the subjective evaluation results. Results show that the Dynamic algorithm outperforms the low-latency algorithms, achieving higher stability and perceptual quality. Among low-latency methods, Low-on-Latency (LOL+) demonstrates superior QoE compared to Learn2Adapt-LowLatency (L2A-LL), which tends to sacrifice visual consistency for latency gains. The findings emphasize the importance of integrating subjective evaluation into the design of ABR algorithms and highlight the need for user-centric and perceptually aware optimization strategies in low-latency streaming systems. Our results show that the subjective scores do not always align with objective performance metrics. The viewers are found to be sensitive to complex or high-motion content, where maintaining a consistent user experience becomes challenging despite favorable objective performance metrics. Full article

Future programmable networks such as 5G/6G and large-scale IoT deployments demand dynamic and intelligent bandwidth control mechanisms to ensure stable Quality of Service (QoS) under highly variable traffic conditions. Conventional queue-based schedulers and emerging machine learning techniques still struggle with slow reaction to congestion, unstable fairness, and high computational costs. To address these challenges, this paper proposes a Dynamic Predictive Feedback (DPF) mechanism that integrates clustered-LSTM based short-term traffic prediction with meta-control driven adaptive bandwidth adjustment in a Software-Defined Networking (SDN) architecture. The prediction module proactively estimates future queue depth and arrival rates using in-band network telemetry (INT), while the feedback controller continuously adjusts scheduling weights based on congestion risk and fairness metrics. Extensive emulation experiments conducted under Static, Bursty IoT, Mixed, and Stress workloads show that DPF consistently outperforms state-of-the-art solutions, including A-WFQ and DRL-based schedulers, achieving up to 32% higher throughput, up to 40% lower latency, and 10–12% lower CPU and memory usage. Moreover, DPF demonstrates strong fairness (Jain’s Index ≥ 0.96), high adaptability, and minimal performance variance across scenarios. These results confirm that DPF is a scalable and resource-efficient solution capable of supporting the demands of future programmable, 5G/6G-ready network infrastructures. Full article

The air–ground integrated Internet of Vehicles (IoV), which incorporates unmanned aerial vehicles (UAVs), is a key component of a three-dimensional intelligent transportation system. Task offloading is crucial to improving the overall efficiency of the IoV. However, blackhole attacks and false-feedback attacks pose significant challenges to achieving secure and efficient offloading for heavily loaded roadside units (RSUs). To address this issue, this paper proposes a reputation-aware, multi-objective task offloading method. First, we define a set of multi-dimensional Quality of Service (QoS) metrics and combine K-means clustering with a lightweight Proximal Policy Optimization variant (Light-PPO) to realize fine-grained classification of offloading data packets. Second, we develop reputation assessment models for heterogeneous entities—RSUs, vehicles, and UAVs—to quantify node trustworthiness; at the same time, we formulate the RSU task offloading problem as a multi-objective optimization problem and employ the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to find optimal offloading strategies. Simulation results show that, under blackhole and false-feedback attack scenarios, the proposed method effectively improves task completion rate and substantially reduces task latency and energy consumption, achieving secure and efficient task offloading. Full article

Advancements in Non-Terrestrial Network (NTN) technology facilitate ubiquitous network access for users, whereas satellite-based Quantum Key Distribution (QKD) offers a viable solution for long-distance quantum key exchange in scenarios lacking terrestrial network infrastructure. This study explores the feasibility and practical utility of integrating NTN technology with satellite-based QKD and proposes a novel quantum-enhanced security framework for next-generation space–terrestrial networks. We have developed and deployed the first-of-its-kind 5G-enabled (fifth generation mobile communication) NTN prototype system leveraging satellite-based QKD key encryption. This system comprises a quantum satellite system, a communication satellite system, a 5G network infrastructure, and end-to-end encryption/decryption modules, aiming to validate the feasibility and usability of the proposed quantum-encrypted NTN security framework. Comprehensive tests and performance evaluations were carried out on the testbed constructed based on this prototype system, which collected critical Quality of Experience (QoE) metrics, including Round-Trip Time (RTT) and jitter, during user-plane ping measurements. Experimental results demonstrate that the integration of quantum encryption capabilities incurs an RTT overhead of 5 ms (0.75%), a necessary trade-off for systems incorporating supplementary quantum-encrypted transmission. Concurrently, the deployment of Virtual Private Network (VPN) infrastructure mitigates network jitter by 50%. These results hold critical theoretical and practical implications for the development of next-generation NTN security frameworks enabled by satellite-based QKD. Full article

Objective: May–Thurner syndrome typically refers to symptoms and signs arising from the compression of the left common iliac vein by the right common iliac artery. However, such clinical manifestations can occur in the setting of compression of the right common iliac vein and/or either external iliac vein. Given this scenario, the more appropriate term for the condition would be non-thrombotic iliac vein lesion(s) [NIVL]. The goal of this review of large sample size studies is to evaluate outcomes following stenting for chronic iliofemoral venous obstruction (CIVO) due to NIVL, including clinical, quality-of-life, and stent-related outcomes. Additionally, where evidence gaps or controversies exist, expert opinion has been offered for guidance. Methods: A review of the literature was undertaken to determine the role of stenting for NIVL. Appropriate search terms were used to search PubMed, Cochrane Central Register of Controlled Trials, and EMBASE. Studies were only included if they had a sample size of at least 100 limbs that underwent stenting for NIVL and had at least 12 months of follow-up. Additionally, every study needed to have at least one metric of objective clinical evaluation [Venous clinical severity score (VCSS)] and/or a quality-of-life (QoL) measure (generic or venous disease-specific). Results: A total of six studies met the eligibility criteria and included 1404 limbs that underwent stenting for NIVL. All except three studies had a combination of PTS and NIVL limbs, with all six studies having at least 100 limbs that underwent stenting for NIVL. Follow-up varied from 12 to 50 months post-stenting. Improvements in VCSS and quality-of-life measures were noted post-stenting. Additional outcome measures, like grade of swelling or visual analog scale pain score, when utilized, also demonstrated improvement. Recurrence-free ulcer healing rates of 63% to 82% were observed. Good long-term stent primary stent patencies (74–98%) were also reported, irrespective of stent type. Conclusions: This review notes that good outcomes can be expected following stenting for CIVO due to NIVL. Gaps, however, exist with regard to patient selection, peri/post-procedural antithrombotic strategies, and long-term follow-up in this context. A CEAP clinical class-based algorithm is provided to help with patient selection in addition to guidance on antithrombotic therapy and follow-up. Further study of these areas is merited. Full article

The rapid growth of Internet of Things (IoT) deployments has created an urgent need for energy-efficient communication strategies that can adapt to dynamic operational conditions. This study presents a novel adaptive protocol selection framework that dynamically optimizes IoT communication energy consumption through context-aware decision making, achieving up to 34% energy reduction compared to static protocol selection. The framework is grounded in a comprehensive empirical evaluation of three widely used IoT communication protocols—MQTT, CoAP, and HTTP—using Intel’s Running Average Power Limit (RAPL) for precise energy measurement across varied network conditions including packet loss (0–20%) and latency variations (1–200 ms). Our key contribution is the design and validation of an adaptive selection mechanism that employs multi-criteria decision making with hysteresis control to prevent oscillation, dynamically switching between protocols based on six runtime metrics: message frequency, payload size, network conditions, packet loss rate, available energy budget, and QoS requirements. Results show MQTT consumes only 40% of HTTP’s energy per byte at high volumes (>10,000 messages), while HTTP remains practical for low-volume traffic (<10 msg/min). A novel finding reveals receiver nodes consistently consume 15–20% more energy than senders, requiring new design considerations for IoT gateways. The framework demonstrates robust performance across simulated real-world conditions, maintaining 92% of optimal performance while requiring 85% less computation than machine learning approaches. These findings offer actionable guidance for IoT architects and developers, positioning this work as a practical solution for energy-aware IoT communication in production environments. Full article

Background: Abdominal aortic aneurysm (AAA) is a multifactorial vascular disease with limited therapeutic options, as no pharmacological treatments currently exist to mitigate the progression of small AAAs. Quality of life (QoL) has emerged as a valuable supplementary metric for assessing the efficacy of pharmacological interventions. This study evaluated QoL scores of MetAAA trial patients on metformin therapy compared to those with placebo intake. Methods: Overall, 54 patients with AAA were included in the MetAAA trial (ClinicalTrials.gov-Identifier:NCT03507413) and randomized to either metformin or placebo treatment. All participants were asked to complete three established and validated (in total 659 longitudinally collected) QoL questionnaires: (1) the 36-Item Short Form Health Survey (SF-36), (2) the Aneurysm Symptom Rating Questionnaire (ASRQ), and (3) the Aneurysm-Dependent Quality of Life questionnaire (ADQoL). Results: A superior health-related QoL was found in metformin-treated AAA patients compared to enrolled AAA patients receiving a placebo. In detail, AAA patients undergoing metformin treatment showed a superior overall current QoL score (p = 0.038), general health perception (p = 0.013), improved physical functioning (p = 0.004), and increased energy/lower fatigue scores (p = 0.008). Furthermore, fewer limitations due to cognitive distress (p = 0.001) and lower limb function (p = 0.021) were detected. Other QoL subscales did not show statistical significance. Inflammatory blood parameters suggest that while systemic inflammation may have some impact on perceived QoL, the relationship is largely limited. Conclusions: In patients with small AAA, metformin led to a limited improvement in health-related QoL compared to a placebo. Full article

Task scheduling in cloud computing becomes significantly more challenging under Distributed Denial-of-Service (DDoS) attacks, as malicious workload injection disrupts resource availability and degrades Quality of Service (QoS). To address this issue, this study proposes an improved Wild Horse Optimizer (A-WHO) that incorporates a stagnation detection mechanism and a stagnation-driven adaptive leader perturbation strategy. The proposed mechanism dynamically applies a noise-guided perturbation into the stallion position only when no improvement is observed over a predefined threshold, enabling A-WHO to escape local optima without modifying the standard behavior of WHO in normal iterations. In addition, a DDoS-aware CloudSim environment is developed by generating attacker virtual machines and high-MI malicious cloudlets to emulate realistic resource exhaustion scenarios. A-WHO’s performance is assessed using makespan, SLA violation rate, each of the QoS metrics, and energy consumption on normal and DDoS conditions. The experimental results indicate that A-WHO achieves the best absolute makespan and QoS metrics during an attack and competitive results under normal conditions. In comparison with the WHO, PSO, ABC, GA, SCA, and CSOA, the proposed approach demonstrates improved robustness and greater resilience to resource degradation attacks. These findings indicate that integrating stagnation-aware diversification into metaheuristic schedulers represents a promising direction for securing cloud task scheduling frameworks. Full article

The rapid proliferation of heterogeneous applications, from latency-critical video delivery to bandwidth-intensive file transfers, poses increasing challenges for modern communication networks. Traditional traffic engineering approaches often fall short in meeting diverse Quality of Experience (QoE) requirements under such conditions. To overcome these limitations, this study proposes a QoE-driven distribution framework for mixed traffic in Software-Defined Networking (SDN) environments. The framework integrates flow categorization, adaptive path selection, and feedback-based optimization to dynamically allocate resources in alignment with application-level QoE metrics. By prioritizing delay-sensitive flows while ensuring efficient handling of high-volume traffic, the approach achieves balanced performance across heterogeneous service demands. In our 15-RSU Mininet tests under service number = 1 and offered demand = 10 ms, JOGAF attains max end-to-end delays of 415.74 ms, close to the 399.64 ms achieved by DOGA, while reducing the number of active hosts from 5 to 3 compared with DOGA. By contrast, HNOGA exhibits delayed growth of up to 7716.16 ms with 2 working hosts, indicating poorer suitability for latency-sensitive flows. These results indicate that JOGAF achieves near-DOGA latency with substantially lower host activation, offering a practical energy-aware alternative for mixed traffic SDN deployments. Beyond generic communication scenarios, the framework also shows strong potential in Intelligent Transportation Systems (ITS), where SDN-enabled vehicular networks require adaptive, user-centric service quality management. This work highlights the necessity of coupling classical traffic engineering concepts with SDN programmability to address the multifaceted challenges of next-generation networking. Moreover, it establishes a foundation for scalable, adaptive data distribution strategies capable of enhancing user experience while maintaining robustness across dynamic traffic environments. Full article

Background/Objectives: Head and neck cancers significantly affect patients’ functional and psychosocial well-being. Multidisciplinary tumor boards have a central role in optimizing treatment strategies, but the relationship between tumor characteristics, comorbidities, and quality of life (QoL) remains insufficiently explored. Methods: We conducted a retrospective study of 94 patients with head and neck cancers evaluated by the oncology committee of Coltea Clinical Hospital in 2024. QoL was assessed post-surgery using the EORTC QLQ-C30 and H&N35 questionnaires. Descriptive statistics, non-parametric tests, correlations, and multivariate regression analyses were performed to examine associations between clinical variables and QoL outcomes. Results: The cohort comprised 82 men (87.2%) and 12 women (12.8%), with a mean age of 61.5 ± 9.8 years. The most common tumor site was the larynx (43.6%). Global QoL was low (mean = 42.3, SD = 11.7), and fatigue scores were high (mean = 61.5, SD = 13.5). All EORTC domains showed non-normal distributions (Shapiro–Wilk, p < 0.05). Kruskal–Wallis analysis revealed significantly lower QoL scores in patients with metastatic adenopathy with aunknown primary (p = 0.03). Spearman’s correlation indicated a moderate negative association between Charlson Comorbidity Index and QoL (r = −0.38, p = 0.01). Multivariate regression confirmed comorbidities (β = −2.5, p = 0.02) and tumor type (metastatic adenopathy, β = −8.0, p = 0.04) as independent predictors of reduced QoL. Conclusions: Patients with advanced disease and higher comorbidity burden experience significantly poorer QoL after head and neck cancer surgery. Tumor board decisions facilitate individualized treatment planning; however, systematic integration of QoL metrics is essential to optimize both oncological and functional outcomes. Full article

6G low-power and lossy network (6G LLN) is a kind of distributed network designed for IoT and edge computing scenarios of the sixth-generation mobile communication technology. Its routing technologies should fully consider characteristics of green and low carbon, constrained nodes, lossy links, etc. This paper proposes an improved routing protocol for low-power and lossy networks (I-RPL) to better suit the characteristics of 6G LLN and meet its application requirements. I-RPL has designed new context-aware routing metrics, which include the residual energy indicator, buffer utilization ratio, ETX, delay, and hop count to meet multi-dimensional network QoS requirements. The candidate parent and its preferred parent’s residual energy indicator and buffer utilization ratio are calculated recursively to reduce the effect of upstream parents. ETX and delay calculating methods are improved to ensure a better performance. Moreover, I-RPL has optimized the network construction process to improve energy and protocol efficiency. I-RPL has designed scientific multiple routing metrics evaluation theories (Lagrangian multiplier theories), proposed new rank computing and optimal route selecting mechanisms to simplify protocol, and optimized broadcast suppression and network reliability. Finally, theoretical analysis and experiment results show that the average end-to-end delay of I-RPL is 13% lower than that of RPL; the average alive node number increased 11% and so on. So, I-RPL can be applied well to the 6G LLN and is superior to RPL and its improvements. Full article