Search Results (3,908)

Background/Objectives: This exploratory, cross-sectional study examined the relationships between personal factors and the nutrition intentions of participants in Healthy Choices for Your Recovering Body (HCYRB), a nutrition education program for limited-resource adults in substance use recovery (SUR). Methods: The study used a single-sample survey design where HCYRB participants (n = 2163) completed a post-participation survey. Linear regression models were tested to assess the effects of personal factors such as nutrition knowledge, cooking skills, self-efficacy beliefs and current nutrition and physical activity practices on participants’ nutrition intentions. Variables were measured with a self-reported survey that participants completed after participation in HCRYB. Results: The final model (R² = 0.39) showed statistically significant effects of self-efficacy beliefs, specifically, food resource management confidence and confidence to choose nutritious foods; current levels of water, soda, and energy drink consumption; physical activity level; and gender on nutrition intentions. Conclusions: Overall, the findings suggest that nutrition-related self-efficacy and current practices influence nutrition intentions of HCYRB participants. Future programs may focus on building participants’ nutrition-related confidence during SUR to enhance intentions and eventual behavior change. Such strategies may include programming activities that promote and affirm participants’ current positive nutrition-related behaviors (e.g., adequate consumption of water and involvement in physical activity). As participants master these healthy practices throughout the nutrition education experience, they will be more likely to gain confidence and motivation toward continuing the behavior throughout their recovery journey. Full article

Under growing urbanization and environmental challenges, sustainable urban mobility has become a critical priority for cities worldwide. Public Transport (PT) systems play a central role in reducing car dependency, lowering emissions, increasing network capacity, and promoting more equitable and efficient access to urban spaces for all users. Hence, the present paper aims to investigate PT preferences in the city of Larissa, Greece. Larissa is a medium-sized city currently serviced only by buses, and is currently focusing on the potential introduction of a new tram system to operate in parallel with existing bus services. To this end, a SP survey was designed and implemented, resulting in 972 observations that were collected for further statistical analysis. Survey results show a slight preference for trams over buses, with 54.63% selecting the tram and 45.37% favoring the buses. Moreover, a context-based segmentation pipeline was established using PCA, DBSCAN and t-SNE algorithms, aiding the visualization of existing clusters for transport choice approaches. Afterwards, a series of mixed logit models was applied, and statistically significant variables influencing mode choice were determined. The study also examines Value of Time (VoT) metrics and finds that respondents assign lower VoTs to trams than to buses, especially in out-of-vehicle segments of the journey, such as waiting and walking, and therefore consider trams as more pleasant and less burdensome. The findings also indicate that passengers place a high value on the quality of infrastructure related to access and waiting times, underlining the need to improve the overall user experience beyond the vehicle itself. In summary, the present research offers valuable insights into how the introduction of a tram system could possibly reshape PT usage patterns when compared with the legacy existing bus services. Full article

Parental involvement is widely recognized as a key component of effective schooling, particularly in educational environments that emphasize community, developmental continuity, and holistic pedagogy. Alternative education models such as Waldorf schools have expanded internationally, yet empirical evidence on how parents perceive and structure their experiences within these institutions remains limited. This study investigates parental perceptions of Waldorf education in Hungary through a nationwide questionnaire survey of 585 parents whose children attend Waldorf schools. To explore the latent structure of parental evaluations, Exploratory Factor Analysis was conducted, followed by Confirmatory Factor Analysis to test the stability of the resulting model. The analyses identified four coherent dimensions of parental experience: Trust and Pedagogy, Community and Engagement, Perceived Long-Term Educational Prosperity, and Information and Transparency. Additional analyses examined how these dimensions vary according to institutional characteristics, parental participation in school community activities, and intentions regarding long-term enrollment. The results indicate that pedagogical trust constitutes a relatively stable evaluative dimension across institutions, while perceptions related to community engagement, long-term educational prospects, and transparency are more strongly associated with institutional maturity. Parents who intend to remain in Waldorf education until the completion of upper secondary schooling report consistently higher evaluations across all dimensions. By empirically identifying the structure of parental experiences in a European alternative education context, the study contributes to research on parental engagement, school choice, and the institutional cultures of alternative schooling. Full article

Managing blood glucose in type 1 diabetes (T1D) remains a daily clinical challenge, and accurate short-term prediction of glucose levels can meaningfully improve insulin dosing decisions while reducing the risk of dangerous hypoglycaemic episodes. Although numerous machine learning approaches have been proposed for this task, comparing their relative merits is difficult because published studies differ widely in datasets, preprocessing choices, and evaluation criteria. In this work, we address this research gap by benchmarking ten machine learning methods—from a naïve persistence baseline through classical linear regressors, gradient-boosted ensembles, and recurrent neural networks to a novel hybrid that couples LightGBM with stochastic differential equation (SDE)-based glucose–insulin simulation—on two multi-patient datasets comprising 34 T1D subjects, across prediction horizons of 15, 30, 60, and 120 min. Every method is trained and tested under identical preprocessing and temporal splitting conditions to ensure a fair comparison. The proposed Hybrid LightGBM-SDE model consistently outperforms all alternatives, recording RMSE values of 22.42 mg/dL at 15 min, 28.74 mg/dL at 30 min, 33.89 mg/dL at 60 min, and 37.22 mg/dL at 120 min—an improvement of between 13.6% and 27.0% relative to standalone LightGBM. At the clinically important 30 min horizon, 99.7% of predictions lie within the acceptable A and B zones of the Clarke Error Grid. Wilcoxon signed-rank tests confirm that performance differences are statistically significant (p < 10⁻¹⁰), and SHAP-based analysis shows that the SDE-derived simulation features are among the most influential predictors, especially at longer horizons. All source code and evaluation scripts are publicly released to support reproducibility. Due to temporary data access constraints, all experiments reported here use physics-based synthetic datasets generated from the Bergman minimal model, replicating the structural properties of the D1NAMO and HUPA-UCM collections; validation on the original clinical recordings is planned. Among the two synthetic datasets, the D1NAMO-equivalent cohort (nine patients) proves more challenging, with systematically higher per-patient RMSE variance. The clinically acceptable prediction accuracy at the 30 min horizon (99.7% in Clarke zones A + B) suggests potential for integration into insulin dosing decision-support systems. Full article

Background and Objectives: Venous thromboembolic disease (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a major cause of morbidity and mortality worldwide and imposes a substantial financial burden on health systems due to both the direct and indirect costs of acute management and long-term complications. This systematic review aimed to assess patient satisfaction with anticoagulation therapy for VTE and to highlight potential differences according to the type of anticoagulant. The review focused on factors influencing the patient experience, such as perceived efficacy, ease of use, adverse effects, and health-related quality of life. Materials and Methods: A systematic review, without quantitative meta-analysis, was conducted in accordance with PRISMA 2020 guidelines. Articles were identified through searches in major databases (PubMed, Scopus, Cochrane Library and others) using keywords including “patient satisfaction”, “anticoagulation”, “venous thromboembolic disease”, and “quality of life”. In total, 21 studies published between 2009 and 2025 met the inclusion criteria. The studies assessed patient satisfaction with different types of anticoagulation, including vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs), and low-molecular-weight heparin (LMWH) injections. Results: Across the included studies, patients generally reported higher levels of treatment satisfaction with DOACs compared with VKAs, mainly due to the absence of routine laboratory monitoring and fewer dietary restrictions. However, satisfaction varied according to age, sex, and clinical status. In specific patient populations, such as those with cancer-associated thrombosis, factors including fewer drug–drug interactions and perceptions of safety with LMWH appeared to influence treatment choice and satisfaction. Adverse effects, particularly bleeding, were identified as major drivers of dissatisfaction. Several studies suggested that higher treatment satisfaction was associated with better adherence, while quality of life appeared to improve in patients treated with DOACs in comparison with VKAs. Conclusions: Patient satisfaction is a critical component of successful VTE management. Overall, DOACs appear to be associated with higher treatment satisfaction than traditional therapies such as VKAs, although further high-quality research is needed to individualise anticoagulation strategies. Systematic incorporation of patient-reported satisfaction into clinical decision-making and into international guidelines may improve adherence, enhance quality of life, and ultimately increase the effectiveness of anticoagulation therapy. Full article

Parallel, data-intensive applications are now commonly executed on infrastructures that combine Cloud, Fog, and Edge resources. In these environments, execution takes place on devices with markedly different computational power and over networks whose latency and bandwidth can fluctuate over time. Under these conditions, overall performance is influenced not only by processing speed but also by communication delays arising from data dependencies between tasks. This leads to a basic issue: whether scheduling strategies developed under computation-focused assumptions continue to perform well once communication costs are made explicit. This work examines the behavior of simple and widely adopted scheduling heuristics when network effects are modeled directly within the system. No new scheduling algorithms are introduced. Instead, the analysis focuses on how execution time and monetary cost change for deterministic parallel workloads deployed on hierarchical Cloud–Edge infrastructures exposed to stochastic latency and bandwidth variations. For this purpose, we introduce CLOWNSim, a lightweight discrete-event simulation framework that supports large-scale Monte Carlo experiments on fixed task graphs, allowing infrastructural and scheduling effects to be examined independently of workload variability. The experimental analysis covers fully centralized Cloud deployments, intermediate Fog configurations, and resource-constrained IoT scenarios. Scheduling policies based on computational speed, execution cost, or random device selection are evaluated across these settings. In Cloud and Fog environments, communication latency and data transfers represent a substantial portion of the overall makespan, weakening the impact of scheduling decisions driven primarily by computation. In IoT scenarios, limited processing capacity becomes the main limiting factor, while communication overhead remains present but less influential in comparison. The results indicate that performance trends across the Cloud–Edge continuum cannot be attributed to scheduler choice alone. Execution behavior arises from the combined effects of workload structure, placement decisions, and network properties, with different elements becoming dominant depending on the deployment context. The proposed simulation framework offers a practical way to study these interactions and to assess cost–performance trade-offs under communication conditions that reflect realistic operating environments. Full article

Background: Adults with disabilities living in low-resource communities experience persistent inequities in access to healthcare, mental health services, and community participation. However, qualitative data capturing lived experiences in the Deep South remain limited. This study aimed to identify priority needs among adults with mobility disabilities residing in economically distressed communities near Birmingham, Alabama, to inform future telehealth programming. Methods: Fifteen adults (mean age = 60 ± 10 years), predominantly African American and female, completed semi-structured phone interviews exploring basic needs, neighborhood accessibility, health priorities, and perceived supports. Interviews were audio-recorded, transcribed verbatim, and analyzed using Braun and Clarke’s six-phase thematic analysis. Results: Five themes emerged: (1) seeking stability amid severe mental health strain and inadequate supports; (2) constrained food environments shaped by cost, location, and safety; (3) feeling forgotten: systemic neglect and restricted participation in community life; (4) physical health deprioritized by competing needs and structural barriers; and (5) remote support as a viable but unrealized option. Participants described how safety concerns, transportation barriers, and rising food costs constrained daily functioning, while unmet mental health needs compounded isolation. Despite widespread cardiometabolic disease, immediate needs related to mental health, food, and housing consistently superseded physical health. Mental health support was identified as the most feasible area for remote delivery, though poor awareness of available resources limited engagement with any service model. Conclusions: Findings demonstrate that disability-related disparities in low-resource communities are driven largely by structural and environmental factors rather than individual choice. Telehealth and mobile-based services may provide a feasible access strategy for mental health and supportive care in under-resourced settings, particularly when integrated with broader community supports. Addressing foundational needs is essential for advancing health equity among people with disabilities in the Southeast. Full article

Artificial intelligence (AI) has gradually been applied to food production. Many companies now face a choice between adopting AI technology and adhering to the traditional methods of food production. Existing studies have reported inconsistent findings regarding consumer perceptions of AI-produced food, yet little research has examined how consumers form motive inferences on businesses that transition from traditional practices to adopting AI in new food development. Based on motivation inference theory, this paper investigates the impact of food production methods on consumer inferences and purchase intention. Through three experiments, we find that AI-produced food evokes more negative motive inference in trust building and lowers purchase intention than traditionally produced food. Furthermore, such effect is driven by a serial mediating effect through cost-cutting attribution and perceived ulterior motive. Additionally, it is attenuated when the food company has a high corporate reputation. This research advances research on AI application in food from a consumer motive inference perspective, providing suggestions on firms’ adoption of AI-based practices in food production. Full article

While existing research has established that air pollution-induced “avoidance behavior” significantly drives the growth of online food delivery volumes, the Average Order Value (AOV) remains unexplored. This study utilizes micro-transactional data provided by the store owner and employs machine learning algorithms to detect the impact of air quality (measured by the AQI) on online food delivery AOV and analyze the underlying consumer behavior. The findings indicate that: (1) Air quality deterioration significantly drives up the AOV. The global average response coefficient is 0.0053, showing a 2.4-fold acceleration effect once the AQI crosses the median (66). (2) Crucially, this growth stems from a directional divergence in consumer decision-making. Air pollution leads to the simultaneous occurrence of a reduction in average item quantity (impact coefficient: −0.0014) and a surge in Average Item Price (AIP) (impact coefficient: 0.0066). (3) Causal analysis further identifies a “substitution mechanism.” Specifically, every one-unit decrease in average item quantity induces a CNY 1.098 jump in average item price. These findings suggest a plausible behavioral logic where environmental stress may induce psychological fatigue but does not necessarily trigger “defensive frugality.” Instead, the observed pattern is consistent with a “decision avoidance” mode where consumers streamline item quantities; simultaneously, to hedge against potential experience risks resulting from simplified choices, they appear to utilize saved cognitive resources to target high-value “signature” items. Theoretically, this study fills the gap in environmental stress research regarding the price dimension of online consumption and reveals a behavioral evolution from “pure avoidance” to “value-oriented selection.” Practically, it provides empirical support for online food delivery merchants to optimize product selection, differentiate pricing, and implement precision marketing in dynamic environments. Full article

Background/Objectives: Urology residency training widely varies across countries, and evidence comparing residents’ experiences at an international level is limited. This study reports the results of an international survey of urology residents from different countries worldwide, aiming to characterize training environments, educational exposure, and trainee expectations across diverse healthcare systems. Methods: A 39-item online survey was administered to urology residents during the Société Internationale d’Urologie (SIU) Regional Meeting (Florence, November 2024), assessing demographics, training exposure, educational resources, workload, satisfaction, and career perspectives. The results were compared between trainees at different postgraduate years (PGYs) to explore associations for key outcomes. Results: Overall, 208 urology residents from 21 countries completed the survey. Most residents were actively involved in research (76.4%), although confidence in independent scientific production was moderate (significantly lower among junior trainees). Surgical exposure increased with PGY, with good experience in endoscopy but limited hands-on exposure and expected autonomy in laparoscopic, robotic, and major open surgery. Despite high overall satisfaction with urology, residents described heavy workloads, inconsistent access to structured teaching and international fellowships, and a long-term shift in career expectations toward private practice. Conclusions: Urology residents worldwide report high engagement in research, strong satisfaction with their specialty choice, and interest in international mobility. Nonetheless, persistent disparities in surgical exposure, research confidence, workload, and gender representation highlight the need for competency-based curricula, structured mentorship, and improved training organization to promote equitable and high-quality urology education globally. Full article

Parallel discrete-event simulation (PDES) of register-transfer-level (RTL) designs on multi-socket NUMA platforms demand dynamic load balancing to mitigate barrier-induced tail latency. However, the ultra-fine event granularity of RTL simulation makes migration cost non-negligible, and the non-uniform memory hierarchy of NUMA turns migration cost into a topology-dependent variable rather than a constant. Existing approaches either ignore this topology dependence or rely on heuristic thresholds that lack theoretical justification. This paper formulates NUMA-aware dynamic load balancing as a constrained optimization problem in which the migration cost is an explicit function of the socket locality between the source and destination cores. We introduce a unified net benefit function $G(m,i\to j,f)$ that jointly captures the tail-latency reduction, migration overhead, and cache warm-up penalty for migrating module m from core i to core j at frequency f. We prove that G is jointly concave in migration scale and frequency, yielding two analytical results: (i) a closed-form admission inequality that prescribes when migration is strictly beneficial, and (ii) a conservative fixed-frequency design rule that guides the choice of a global epoch length for the proposed epoch-based controller. We further show that when the initial static partition satisfies a bounded-quality condition, the total migration volume is provably bounded, formalizing the intuition that restraint is optimal, not merely conservative. We implement the proposed topology-aware admission control (TAC) framework in TACVS (Topology-Aware Admission Control Verilog Simulator), our event-driven parallel RTL simulation prototype. Experiments on four open-source RTL designs running on a 2-socket NUMA platform show that TAC reduces the tail-latency ratio by 18.0% on average (up to 28.5%) and improves normalized throughput by 27.1% on average (up to 34.1%) relative to topology-oblivious baselines. An ablation study further shows that admission control and cooldown are critical for performance, with throughput dropping by 15.9% and 22.8% on average (up to 22.4% and 32.5%) when each is removed, respectively. Full article

Background: Variability in procedural exposure among internal medicine junior doctors may result in inconsistent preparedness for essential bedside procedures. Simulation-based education grounded in mastery learning has been proposed to standardise skill acquisition, but less is known about how such training is integrated into clinical practice. Methods: We conducted a theory-informed mixed-methods evaluation of a mastery-oriented procedural simulation workshop. The quantitative component used pre–post assessments, including a multiple-choice knowledge test and self-reported measures of perceived knowledge, skill, and confidence using a 5-point Likert scale. Participants were required to achieve predefined competency benchmarks based on ACGME-aligned procedural checklists. The qualitative component, conducted two years later, involved semi-structured interviews exploring how participants integrated workshop learning into clinical practice. Results: Thirty-eight participants completed paired pre–post assessments. Knowledge scores and self-reported perceived knowledge, skill, and confidence improved significantly across all procedures (p < 0.01), with large effect sizes. Ten participants participated in follow-up interviews. Thematic analysis identified four themes: (1) standardization of procedural practice, (2) activation of prior knowledge, (3) hands-on experience and skill development, and (4) relevance to clinical practice. Conclusions: A mastery-oriented simulation workshop was associated with improvements in knowledge and perceived procedural readiness. Structured simulation may influence how junior doctors approach procedural learning in clinical practice. Full article

Energy efficiency in computing has emerged as a critical concern due to escalating environmental and financial costs, particularly in the context of cluster computing, where there is an ever-increasing software workload. Achieving meaningful improvements in energy efficiency requires a comprehensive understanding of the interplay between hardware and software. This research investigates how algorithmic optimisations, language choice, and parallelisation strategies influence energy efficiency and how hardware-level strategies such as underclocking, overclocking, cooling, and on-demand computing can further impact energy usage. A set of measures that can be used generally to show the impact trade-off of power and performance are defined, including the Energy Factor (EF) and a new Efficiency–Performance Score (EPS). Validation experiments on a custom-built Raspberry Pi Bramble cluster used workloads like Monte Carlo Pi simulations in Python and C. Energy and performance trade-offs were evaluated using the Energy Factor and Efficiency–Performance Score on a small example cluster to validate the approach. Results show parallelisation greatly improves energy efficiency over serial execution. Cooling slightly boosts speed under heavy loads but increases total energy use. Perhaps counter-intuitively, underclocking actually raises total energy consumption, while overclocking reduces it. Language choice also impacts efficiency, with C offering notable energy savings over Python. The findings support the hypothesis that software optimisation alone can improve energy efficiency, but the most impactful results are achieved when both software and hardware strategies are jointly considered. These insights contribute to the design of future energy-aware computing systems and provide a foundation for sustainable, high-performance computing architectures. Full article

Cow’s milk allergy (CMA) remains one of the most common food allergies in infancy, requiring the avoidance of cow’s milk and its derivatives. Breast milk is the best source of nutrition for infants. For those infants with CMA whose mothers are unable to breastfeed or choose not to, extensively hydrolysed formulas (eHFs) are widely recommended as first-line milk substitutes, whereas hydrolysed rice formulas (HRFs) are increasingly recognised as a viable alternative. This concept paper provides a healthcare professional (HCP) perspective on HRF, drawing on expert consensus from two meetings convened in 2025. Discussions noted the long history of safe and effective HRF use in Europe, its nutritional adequacy, and the evolving international guidelines supporting HRF as an alternative first-line option. A key meeting outcome was the development of a practical decision tree to help UK clinicians decide when HRF should be the preferred choice. Key considerations for its use in non-breastfed infants include the following: parental/caregiver stress related to persistent symptoms; ongoing symptoms despite multiple interventions; cultural and lifestyle choices; religious dietary requirements; and specialists’ recommendations. Secondary considerations highlighted by HCPs include the following: proven reactions whilst infants are breast-milk-fed together with parental request for formula; faltering growth; multiple symptoms; taste acceptance (older infants); and parental preference based on experience. The role of functional components, such as prebiotics and human milk oligosaccharides (HMOs), was noted in regard to the emerging evidence of benefits to the microbiome and immune development. The experts emphasised the importance of engaging HCPs across all levels of CMA care and addressing challenges in translating current guidance into treatment practice. It was concluded that, overall, HRF represents a nutritionally complete, plant-based alternative that has been shown to be well tolerated (taste, symptoms) in clinical studies. It can be used to broaden therapeutic options for infants with CMA in the UK who are not exclusively fed breast milk. Full article

The low participation of men in primary education is a persistent and structural phenomenon that cannot be adequately understood through homogeneous views of teachers’ motivations and experiences. This study is conducted in the Chilean context, which is characterized by a highly feminized teaching workforce and persistent challenges related to working conditions, social valuation of teaching, and teacher retention. It aims to analyze profiles of male primary school teachers, considering their motivations, perceptions, and the meanings they attribute to the teaching profession. A sequential explanatory mixed-methods design (QUAN → qual) was employed. First, 144 male in-service primary teachers completed the FIT-Choice scale and a latent class analysis was conducted. Subsequently, in-depth interviews were carried out with an intentionally selected subsample of 20 teachers, which were analyzed using qualitative content analysis. Three distinct motivational profiles were identified: altruistic, pragmatic, and critical. The qualitative findings complemented these profiles, highlighting the influence of personal trajectories and working conditions on teachers’ career choice and retention in the profession. Overall, the findings suggest that policies for training, support, and professional induction must recognize teacher heterogeneity and promote inclusive working environments, moving beyond approaches that focus exclusively on increasing the number of men in primary education. Implications for the design of policies aimed at attracting and retaining male primary school teachers are discussed. Full article