Search Results (4,036)

Content caching and recommendation for content delivery over the Internet are two key techniques for improving the content delivery effectiveness determined by delivery efficiency and user satisfaction, which is increasingly important in the booming Internet of Things (IoT). While content caching seeks the “greatest common denominator” among users to reduce end-to-end delay in content delivery, personalized recommendation, on the contrary, emphasizes users’ differentiation to enhance user satisfaction. Existing studies typically address them separately rather than jointly due to their contradictory objectives. They focus mainly on heuristics and deep reinforcement learning methods without the provision of performance guarantees, which are required in many real-world applications. In this paper, we study the problem of joint optimization of caching and recommendation in which recommendation is performed in the cached contents instead of purely according to users’ preferences, as in the existing work. We show the NP-hardness of this problem and present a greedy solution with a performance guarantee by first performing content caching according to user request probability without considering recommendations to maximize the aggregated request probability on cached contents and then recommendations from cached contents to incorporate user preferences for cache hit rate maximization. We prove that this problem has a monotonically increasing and submodular objective function and develop an efficient algorithm that achieves a $1-{\displaystyle \frac{1}{e}}\approx 0.63$ approximation ratio to the optimal solution. Experimental results demonstrate that our algorithm dramatically improves the popular least-recently used (LRU) algorithm. We also show experimental evaluations of hit rate variations by Jensen–Shannon Divergence on different parameter settings of cache capacity and user preference distortion limit, which can be used as a reference for appropriate parameter settings to balance user preferences and cache hit rate for Internet content delivery. Full article

Background: Around one-quarter of all people in the developed world die of cancer, with primary care being the main setting in which the disease is first suspected because the majority of patients consult a general practitioner (GP) when they present with symptoms. Diagnostic delay may thus be attributable to the patient, the GP, or the healthcare system. While some findings suggest that as much as half of the total delay consists of patient delay, more research is nonetheless needed into how GPs can facilitate access to diagnostic evaluation when patients experience symptoms. Methods: A retrospective observational study will be conducted to evaluate a cohort of patients diagnosed with cancer, with data being obtained from both primary and specialised care settings. Different time intervals will be analysed, dating from onset of first symptoms to diagnosis or initiation of treatment, and will be classified as: patient interval; primary-care interval; healthcare-system interval; diagnostic interval; treatment interval; and total interval. Study variables will include patient characteristics (socio-demographic, risk factors, morbidity, etc.), tumour characteristics (tumour stage, symptom onset, alarm symptoms, etc.), and healthcare characteristics (place of initial consultation, referral to specialised care, etc.). Discussion: The study will describe diagnostic delays in patients with cancer in primary care, considering the time elapsed between symptom onset and initial consultation, request for tests and/or patient referral, first evaluation in the hospital setting, and date of diagnostic confirmation and treatment initiation. Additionally, the study will make it possible to identify the patient-, healthcare-, and disease-related variables that intervene in the duration of such delays. Full article

Background/Objectives: The aim of this study was to assess the structure of comorbidities, the reasons for seeking medical care, and the main causes of fatal outcomes in patients with atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD). Methods: A retrospective analysis of 40,772 electronic medical records in the database of the medical information system with the analysis of medical care requests and causes of fatal outcomes over a 4-year period (from 1 February 2021 to 1 February 2025) was performed. The study participants were divided into three groups. The first group included 1247 participants with AF and COPD (AF + COPD group). The second group included 25,474 patients with AF and without COPD (AF group), and the third group included 14051 patients with COPD and without AF (COPD group). Results: Patients with AF + COPD compared to patients with AF alone and COPD alone were more likely to have anemia (5.21% vs. 3.64% and 2.8%, respectively), pulmonary embolism (2.0% vs. 0.52% and 0.46% respectively), type 2 diabetes mellitus (28.2% vs. 22.7% and 14.32%), obesity (24.86% vs. 22.2% and 17.72%), chronic ischemic heart disease (89.25% vs. 78.69% and 49.31%), and chronic heart failure (16.76% vs. 9.47% and 3.22%). In addition, patients with AF + COPD demonstrated the highest mortality among all groups. Conclusions: Patients who have both AF and COPD have more comorbidities, seek medical care more frequently, and have worse survival compared with patients with only AF or only COPD. Full article

Background: Pneumococcal vaccination rates in the United States (US) remain suboptimal, especially for adults aged 19 to 64 with high-risk medical conditions. Community-pharmacy-based immunization services increase vaccine access, particularly in rural areas. This study describes the provision of pneumococcal immunization services, assesses the processes used to identify and confirm patient eligibility, and determines barriers to immunization services in rural community pharmacies. Methods: A cross-sectional survey was emailed to members of the Rural Research Alliance of Community Pharmacies, located in the southeastern US. The survey assessed which pneumococcal vaccines were offered, age groups, prescription requirements, and how patient eligibility was determined. In addition, participants were asked to rate a series of patient-related and organizational barriers to pneumococcal vaccination. Results: Ninety-four pharmacies completed the survey, with most (96.8%) offering pneumococcal vaccines, most commonly PCV20 (95.6%). Most pharmacies vaccinated patients upon request (98.9%) or when patients presented with a prescription (82.4%), but few proactively contacted patients to schedule the vaccination (17.6%). Pharmacists most often administered pneumococcal vaccines to patients aged 65 and older and used patient age and immunization information systems to identify eligible patients. The most common patient-related barrier was the patient’s belief that they do not need the vaccine. The most common organizational barriers were inadequate reimbursements for vaccine administration and vaccine products. Conclusions: Pneumococcal vaccinations are commonly offered in rural community pharmacies, which play an important role in immunization access. With recent guideline changes to the age-based recommendation, there is an opportunity to optimize strategies to increase vaccine uptake. Full article

This paper presents an online efficiency optimization strategy for a six-phase induction generator (6PIG) operating in both healthy and faulty modes for micro-hydropower applications. The proposed method is based on an extended Loss Model Control (LMC) approach, in which the direct axis stator current Id is dynamically optimized in real time to minimize the total electrical losses. Unlike conventional LMC strategies, this method explicitly incorporates switching losses into the loss model, along with stator and rotor copper losses and iron losses. The optimization problem is solved using a numerical minimization routine, allowing the control system to adapt continuously to variations in torque requests. The proposed approach is validated under both healthy and faulty configurations of the 6PIG. It is implemented and tested through simulation in MATLAB/Simulink^® and experimentally validated on a 24 kW squirrel cage six-phase induction generator (SC6PIG). The results are compared in terms of power losses, energy saving, and efficiency. Full article

In Next-Generation Low-Earth-Orbit (LEO) satellite networks, securing inter-satellite communication links (ISLs) through robust authentication is critical due to the dynamic and distributed nature of non-terrestrial environments. Traditional authentication frameworks often fall short under these conditions, prompting the adoption of Zero-Trust Security (ZTS) models. However, existing ZTS protocols incur significant computational overhead, especially as the number of satellite nodes increases, thereby affecting both communication network efficiency and security. To address this, a novel bio-inspired intelligent ZTS approach, i.e., Manta Ray Foraging Cost-Optimized Zero-Trust Security (MRFCO-ZTS), has been developed to leverage bio-inspired data-enabled learning principles to enhance secure satellite communication. The model ingests high-density satellite network data and continuously verifies access requests by formulating a cost function that balances the risk level, attack likelihood, and computational delay in an effective manner. The Manta Ray Foraging Optimization (MRFO) algorithm is applied to minimize this cost function and to enable efficient classification of nodes as detector or attacker based on historical authentication as well as nodes dynamic behaviors. MRFCO-ZTS enables precise identification of attacker behavior while ensuring secure data transmission among verified satellites. The developed MRFCO-ZTS framework is evaluated using a series of numerical simulations under varying satellite user loads, with performance assessed in terms of security accuracy, latency, and operational efficiency. Full article

In modern multi-core processors, memory request latency critically constrains overall performance. Prefetching, a promising technique, mitigates memory access latency by pre-loading data into faster cache structures. However, existing core-side prefetchers lack visibility to the DRAM state and may issue suboptimal requests, while conventional memory-side prefetchers often default to simple next-line policies that miss complex access patterns. We propose a comprehensive memory-side prefetch optimization scheme, which includes a prefetcher that utilizes advanced prefetching algorithms and an optimization module. Our prefetcher is capable of detecting more complex memory access patterns, thereby improving both prefetch accuracy and coverage. Additionally, considering the characteristics of DRAM memory access, the optimization module minimizes the negative impact of prefetch requests on DRAM by enhancing coordination with memory operations. Additionally, our prefetcher works in synergy with core-side prefetchers to deliver superior overall performance. Simulation results using Gem5 and SPEC CPU2017 workloads show that our approach delivers an average performance improvement of 10.5% and reduces memory access latency by 61%. Our prefetcher also operates in conjunction with core-side prefetchers to form a multi-level prefetching hierarchy, enabling further performance gains through coordinated and complementary prefetching strategies. Full article

Background/Objectives: Lung cancer screening with low-dose computed tomography (LDCT) has reduced lung cancer mortality in high-risk smokers. However, the evidence on LDCT screening in the elderly is limited, with there being few older participants in major trials and ongoing debate about the benefits, risks, and appropriate age limits of LDCT. This study aimed to investigate the prevalence of pulmonary nodules and lung cancer detection rates in men aged 70 and above who underwent a single round of LDCT screening. Methods: We retrospectively analyzed data from elderly male participants aged 70 years or older who underwent a single low-dose CT lung cancer screening at the Veterans Health Service Medical Center between 2010 and 2023. The participants included those who requested screening or were asymptomatic but recommended by physicians. Individuals with prior lung cancer, symptoms suggestive of lung cancer, or suspicious findings on previous imaging were excluded. The nodule prevalence, lung cancer diagnoses, pathological subtypes, and clinical stages were reviewed. Results: A total of 1409 individuals with a mean age of 74.2 years were included. The median follow-up duration was 3.6 years. Among the included individuals, 1304 (92.6%) had a history of smoking. Positive nodules were detected in 179 patients (12.7%, 95% CI: 11.0–14.5%), and lung cancer was diagnosed in 31 patients (2.2%, 95% CI: 1.5–3.1%). Of the diagnosed cases, 14 (45.2%) were adenocarcinomas and 12 (38.7%) were squamous cell carcinomas. Nineteen patients (51.3%) were diagnosed with stage I or II cancer, while seven (22.6%) were diagnosed at stage IV. Conclusions: A single round of LDCT screening in elderly men resulted in a relatively high lung cancer detection rate, with over half of the diagnosed cases being identified at an early stage. This highlights the potential clinical benefit of even one-time screening in enabling timely treatment, which may still be feasible in older adults. However, potential harms such as overdiagnosis should also be considered. Full article

Cone-beam computed tomography (CBCT) offers low radiation, cross-sectional imaging that is a suitable alternative to conventional fan-beam computed tomography (FBCT). The initial experience using in-clinic CBCT in the Rhinology outpatient clinic at Waikato Hospital, New Zealand, is described. The first 5 months of CBCT use for Rhinologic imaging was compared to FBCT use in the equivalent 5-month period one year prior. Data relating to 61 CBCTs and 115 FBCTs was analysed. We compared the time and number of hospital visits required for a confirmed treatment decision (CTD) to be made and the duration of the clinic appointment at which the scan was requested between the two groups. The CBCT group required significantly less time (171 vs. 316 days, p < 0.001) and fewer hospital visits (1.5 vs. 3.2 visits, p < 0.001) before a CTD was made, but a longer appointment duration (86 vs. 53 min, p < 0.001). The use of in-clinic CBCT in Rhinology was therefore associated with reduced time and fewer hospital visits before definitive management was decided, but longer clinic appointments were observed. Increased access to CT imaging may result in increased demand. Expertise is required to optimise the quality of imaging, and we recommend that a dedicated Radiographer be allocated. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. Full article

Background: Sustainability is a critical concern in transportation, notably in light of governmental initiatives such as cap-and-trade systems and eco-label regulations aimed at reducing emissions. In this context, collaborative approaches among carriers, which involve the exchange of shipment requests, are increasingly recognized as effective strategies to enhance efficiency and reduce environmental impact. Methods: This research proposes a novel collaborative planning model for multimodal transport designed to minimize the total costs associated with freight movements, including both transportation and $C{O}_2$ emissions costs. Transshipments of freight between vehicles are modeled in the proposed formulation, promoting carrier coalitions. This study incorporated eco-labels, representing different emission ranges, to capture shipper sustainability preferences and integrated authority-imposed low-emission zones as constraints. A bi-objective approach was adopted, combining transportation and emission costs through a weighted sum method. Results: A case study on the Naples Bypass network (Italy) is presented, highlighting the model’s applicability in a real-world setting and demonstrating the effectiveness of collaborative transport planning. In addition, the model quantified the benefits of collaboration under low-emission zone (LEZ) constraints, showing notable reductions in both total costs and emissions. Conclusions: Overall, the proposed approach offers a valuable decision support tool for both carriers and policymakers, enabling sustainable freight transportation planning. Full article

Objective: The present study is a qualitative analysis of participant experiences and perspectives from people who received cognitive remediation therapy for adult obesity (CRT-O). Method: Post-intervention data were generated from an open-ended question requesting the participants to write, in the form of a letter to their therapist, about their experiences and reflections upon taking part in cognitive remediation therapy for adult obesity. Participants’ letters were thematically analyzed. Results: Four themes and nested subthemes emerged from participant responses, including (1) motivation and initial response to CRT-O for the adult obesity study eligibility process with the nested subthemes of initial apprehension pre-intervention and awareness and acknowledgement of one’s problematic eating behaviors; (2) perceived benefits from cognitive remediation therapy for adult obesity with the nested subthemes of the strategies and techniques that were found beneficial and the role of the cognitive remediation therapy for adult obesity therapists in facilitating positive change; (3) perceived outcomes post-intervention with the nested subthemes of changed relationship with food, self-acceptance and gaining control to effect positive lifestyle change; and (4) expectations and beliefs about the longer-term impact of cognitive remediation therapy for adult obesity with the nested subthemes of using the cognitive remediation therapy for adult obesity strategies as a lifestyle routine, apprehension about not having follow-up therapist support, and concern about potential relapse. Conclusion: Our analysis found helpful insights into the consumer perception of this novel intervention and highlighted the clinical utility of implementing cognitive remediation therapy in those living with a higher body weight. Full article

Background/Objectives: The role of the primary health care nurse has evolved since the Spanish Ministry of Health officially established the professional profile of the nurse specialist in Primary Health Care in 2005. Despite the potential benefits of this new professional profile in the population’s health, their actual scope of practice is still unknown and still largely underutilized. This study aimed to explore demands and expectations of adult primary health care service users regarding the role of the nurse specialist in primary health care. Methods: A nationwide cross-sectional study consisted of a computer-assisted telephone survey to a random sample of 1200 adults living in Spain. A self-developed 19-item instrument assessed population’s perception of the role of the nurse specialist in primary health care. Descriptive, bivariate and logistic regression models explored associations between sociodemographic characteristics with perception of the nurse specialist role. Results: Most participants (82.3%) would choose a nurse specialist in primary health care and consider that the nurse specialist in primary health care should expand their scope of practice requesting diagnostic test (70%) and prescribing medications for chronic diseases (63.8%). Conclusions: Results show a population’s positive perception towards expanding the scope of practice of the nurse specialist in primary health care in the Spanish healthcare system. Primary health care models should acknowledge the potential of expanding the competencies of this professional profile. Full article

The rapid increase in the deployment of Internet of Things (IoT) sensor networks has led to an exponential growth in data generation and an unprecedented demand for efficient resource management infrastructure. Ensuring end-to-end communication across multiple heterogeneous network domains is crucial to maintaining Quality of Service (QoS) requirements, such as low latency and high computational capacity, for IoT applications. However, limited computing resources at multi-access edge computing (MEC), coupled with increasing IoT network requests during task offloading, often lead to network congestion, service latency, and inefficient resource utilization, degrading overall system performance. This paper proposes an intelligent task offloading and resource orchestration framework to address these challenges, thereby optimizing energy consumption, computational cost, network congestion, and service latency in dynamic IoT-MEC environments. The framework introduces task offloading and a dynamic resource orchestration strategy, where task offloading to the MEC server ensures an efficient distribution of computation workloads. The dynamic resource orchestration process, Service Function Chaining (SFC) for Virtual Network Functions (VNFs) placement, and routing path determination optimize service execution across the network. To achieve adaptive and intelligent decision-making, the proposed approach leverages Deep Reinforcement Learning (DRL) to dynamically allocate resources and offload task execution, thereby improving overall system efficiency and addressing the optimal policy in edge computing. Deep Q-network (DQN), which is leveraged to learn an optimal network resource adjustment policy and task offloading, ensures flexible adaptation in SFC deployment evaluations. The simulation result demonstrates that the DRL-based scheme significantly outperforms the reference scheme in terms of cumulative reward, reduced service latency, lowered energy consumption, and improved delivery and throughput. Full article