Search Results (4,985)

This paper considers the pricing of a subscription service in a heterogeneous market with consumers having different discount rates. We show that in the case of a non-zero enrollment/cancellation cost, solutions of the Hamilton–Jacobi–Bellman equation naturally contain an equivalent of the well-known Preisach operator, a fundamental model of hysteresis in engineering applications. Singular perturbation expansions are used to approximate the optimal solution, assuming that enrollment/cancellation costs are small, relative to the total subscription cost. As a case study, we consider and compare markets with one and two consumers. Full article

Randomized clinical trials are considered the gold standard for the evaluation of new interventions and therapies. The results from randomized clinical trials are highly influential in treatment decision-making and decisions about the implementation of new therapeutic options within the field of oncology. This article describes a narrative review of the literature to further explore the strengths and limitations of real-world evidence in comparison to randomized clinical trials and provides a commentary on opportunities for real-world evidence in thoracic malignancies. However, randomized trials often exclude oncology patients with poorer functional status or comorbidities which are routinely considered for treatment in real-world practice. Real-world data may complement existing data from randomized clinical trials and play an important role in evaluating patterns and outcomes of care, informing everyday oncology practice. While real-world data is increasingly reported in the medical literature, strengths and limitations exist which can also limit their applicability. More work is needed to standardize methodologies for real-world studies. Full article

The potential for structural health monitoring (SHM) in fibre-reinforced polymers (FRPs) using electrical resistance measurements (ERMs) has gained increasing attention, particularly in carbon fibre-reinforced polymers (CFRPs). Most existing studies are limited to single-axis measurements on coupon-scale specimens, whereas industrial applications demand scalable solutions capable of monitoring large areas, with more complex sensing configurations. Structural health monitoring (SHM) of carbon fibre-reinforced polymers (CFRPs) using electrical resistance measurements offers a low-cost, scalable sensing approach. However, predicting surface resistance between arbitrarily placed electrodes on unidirectional (UD) CFRP laminates remains challenging due to anisotropic conductivity and geometric variability. This study introduces a practical analytical model based on two geometry-dependent parameters, effective width and effective distance, to estimate resistance between any two electrodes arbitrarily placed on UD CFRP laminates with 0° or 90° fibre orientations. Validation through finite element (FE) simulations and experimental testing demonstrates good matching, confirming the model’s accuracy across various configurations. Results show that the dominant electrical current path aligns with the fibre direction due to the material’s anisotropic conductivity, allowing simplification to a single-axis resistance model. The proposed model offers a reliable estimation of surface resistance and provides a valuable tool for electrode array configuration design in CFRP-based SHM. This work contributes to enabling low-cost and scalable electrical sensing solutions for the real-time monitoring of composite structures in aerospace, automotive, and other high-performance applications. Full article

Non-communicable diseases (NCDs) affect over 2.1 billion children globally, accounting for 15.9% of deaths in children under 20 and contributing 174 million years lived with disability. Integrating nutrition care into NCD management within health systems can save lives, reduce costs, and improve quality of life. Nutrition interventions have been found to improve survival rates in children with cancer by 30%. Incorporating early nutrition interventions in hospitals is associated with a 36% reduction in per-patient costs. Despite these clear benefits, nutrition care is often not readily accessible as part of NCD management in children. Access to trained nutrition professionals is limited, and nutrition training for healthcare workers is often inadequate. There are cost-effective and scalable models for delivering high-quality nutrition care, but scaling these models will require commitment to capacity building, training, technological innovation, and monitoring frameworks. Coordinated, multisectoral responses are needed urgently to incorporate nutrition sustainably into healthcare systems to confront the growing burden of childhood NCDs. Full article

Among bioactive peptides, those with antimicrobial activity have attracted increasing attention due to their potential as alternatives to traditional antibiotics. Antimicrobial peptides (AMPs) are small molecules, typically composed of 6 to 60 amino acid residues, and some with low cytotoxicity and minimal side effects. They exhibit broad-spectrum activity against bacteria, viruses, fungi, and parasites through diverse mechanisms of action and interactions with the immune system. This review presents the main aspects of AMPs, including their biochemical characteristics, sources, mechanisms of action, and computational tools used for their identification and analysis. It also examines recent progress in clinical trials and the current limitations that restrict the development and commercialization of AMPs. The review discusses the application of AMPs beyond human medicine, including their use in food preservation to prevent microbial contamination and in veterinary medicine to control infections in livestock, thereby reducing dependence on conventional antibiotics. Overall, AMPs represent a versatile class of antimicrobial agents whose effective implementation across health, food, and veterinary sectors will require integrated multidisciplinary approaches. Full article

Background and Objectives: The dopamine D2 receptor (DRD2) plays a central role in fronto-striatal circuits regulating cognitive control and reward processing. The rs1076560 polymorphism alters receptor isoform expression, potentially modifying impulsivity and vulnerability to stimulant use disorders. We examined gene–environment interactions between rs1076560 and stimulant dependence in relation to impulsivity, ADHD traits, and hedonic capacity. Methods: A total of 517 men (235 stimulant-dependent, 282 controls) completed the Barratt Impulsiveness Scale (BIS-11), Adult ADHD Self-Report Scale (ASRS v1.1), and Snaith–Hamilton Pleasure Scale (SHAPS). Genotyping for rs1076560 was performed using real-time PCR, and two-way ANOVAs tested genotype-by-group effects. Results: Significant genotype-by-group interactions were observed across all BIS-11 subscales and ASRS scores. In the stimulant-dependent group, C/C homozygotes showed the highest levels of attentional impulsivity and attentional dysregulation compared to both A/C and C/C controls. In contrast, within the control group, A/A homozygotes demonstrated higher motor impulsivity, non-planning impulsivity, and BIS-11 total scores than C/C controls. No significant main effects or interactions were found for SHAPS scores. Conclusions: DRD2 rs1076560 moderates impulsivity-related traits through dopaminergic pathways relevant to executive dysfunction in stimulant use disorders. These findings highlight a neurobiological mechanism of addiction vulnerability and may inform precision approaches in neurology and psychiatry. Full article

Background: Sleep disturbances in the multiple sclerosis (MS) population are increasingly recognized, but the factors driving this association remain understudied. This study aimed to determine the prevalence and associations of poor sleep quality in the relapsing–remitting MS (RRMS) population receiving disease-modifying therapy (DMT). Methods: We conducted a cross-sectional study that enrolled 399 individuals diagnosed with RRMS on DMT. Data on patient demographics, clinical presentation, and treatment were systematically evaluated. Sleep-related outcomes were assessed using validated questionnaires—the Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Fatigue Severity Scale (FSS), STOP-Bang questionnaire, and Hamilton Anxiety Rating Scale (HAM-A). Independent associations of poor sleep were examined using log-binomial regression to estimate risk ratios (RR). Results: Poor sleep was reported in 42% of the participants in our cohort. In multivariable analysis, only insomnia severity (RR = 1.07; 95% CI 1.05–1.09, p < 0.001) and anxiety (RR = 1.02; 95% CI 1.01–1.04, p = 0.001) remained independently associated with poor sleep. Conclusions: Sleep disturbances are common among patients with RRMS. Insomnia severity and anxiety, rather than demographic or disease-related characteristics, showed independent associations with impaired sleep. Routine screening and targeted interventions addressing insomnia and anxiety may improve sleep quality and, consequently, overall quality of life in this population. Full article

There has been an increasing demand for high-resolution image sensing technologies in recent years due to their diverse and advanced optical applications. With recent advances in nanofabrication technologies, this can be achieved through the realization of high-density pixels. However, the development of high-density and miniaturized pixels introduces challenges to the conventional color filters, which generally transmit and absorb different spectral components of light. A significant portion of the incident light is inherently lost using conventional color filters. Moreover, as the pixel size is shrunk, optical losses appear to be substantial. To address these fundamental limitations, a novel nanophotonic optical router is proposed in this work. Our router utilizes a single-layer, all-dielectric metasurface as a spectral router. The metasurface is designed through an inverse design approach that exploits adjoint sensitivity analysis. A novel figure of merit is developed and incorporated in the inverse design process, enabling the metasurface design to effectively sort and route the incoming light into four targeted channels, each corresponding to a distinct spectral component—red, green, blue, and near-infrared. We demonstrate that the proposed quad-spectral metasurface router, having a compact footprint of $2 \mathsf{\mu }\mathrm{m}\times 2 \mathsf{\mu }\mathrm{m}$, achieves an average optical efficiency of approximately 39% across the broad spectral range, i.e., 400–850 nm, with each spectral channel exceeding an efficiency of 25%. This surpasses the maximum efficiency attainable by the conventional four-channel color filters. Our proposed quad-spectral metasurface router offers a wide range of applications in low-light imaging, image fusion, computational photography, and computer vision. In addition, this work highlights the applicability of an adjoint-based inverse design approach to accelerate the development of compact, efficient, and high-performance nanophotonic devices for the next generation of imaging and sensing systems. Full article

South Asian (SA) communities in Ontario, Canada experienced disproportionately higher rates of COVID-19 infection. Moreover, these communities also faced racism fueled by COVID-19-related misinformation and xenophobic sentiments that placed blame on them for virus transmission. The aim of this research was to understand, from the perspective of local SA communities, the causes behind higher incidences of COVID-19. SA adults (N = 25) participated in a focus group (N = 3) investigating experiences during the early stages of the pandemic. Data, interpreted through the lens of the Public Health Critical Race Praxis, suggest that the structural determinants of health, alongside racism and xenophobia, negatively impacted health outcomes for these communities. By not taking an active anti-racist stance, media, health and government authorities were viewed as perpetuating discriminatory narratives and practices, fueling blame and stigma towards these South Asian communities for COVID-19 transmission. Local public health policies, practices and communications were perceived to be informed by, and best serve, white Anglo-European settlers. This research provides insight into the role that health officials can play in addressing local and regional discrimination and stigma to promote equity-centered disease prevention efforts. Our findings should be integral to current and ongoing research and action related to pandemic preparedness. Full article

Travel-time delays due to recurring congestion cause productivity loss, increase the likelihood of accidents, and lead to environmental pollution due to greenhouse gas emissions. The National Highway Traffic Safety Administration in the United States has listed several driver assistance technologies that are now common in most newer vehicles. While these technologies can help reduce the likelihood of traffic-related accidents, they do little to reduce recurring congestion in urban areas. Recurring congestion during rush hours is prevalent, for example, along Interstate 95 and Capital Beltway 495 in the Baltimore-Washington area. Such congestion also enhances the likelihood of crashes. Previous approaches to hotspot identification are primarily theoretical, which limits their practical applicability. In this paper, we develop a Machine Learning (ML) approach that integrates geospatial data with artificial neural networks to predict traffic congestion hotspots during rush hour. The approach uses live traffic sensor data. A case study from Maryland is presented. The result shows top hotspot segments across Maryland. Using a snapshot of hotspots at eight different time periods, the likelihood of hotspot locations is predicted using an artificial neural network. The framework is validated using live loop detector data (speed and volume) from Maryland freeways, particularly I-495 and I-95. The research can serve as a valuable tool for traffic congestion hotspot identification and travel-time prediction. Full article

This paper investigates a robust optimal reinsurance and investment problem for an insurance company operating in a Markov-modulated financial market. The insurer’s surplus process is modeled by a diffusion process with jumps, which is correlated with financial risky assets through a common shock structure. The economic regime switches according to a continuous-time Markov chain. To address model uncertainty concerning both diffusion and jump components, we formulate the problem within a robust optimal control framework. By applying the Girsanov theorem for semimartingales, we derive the dynamics of the wealth process under an equivalent martingale measure. We then establish the associated Hamilton–Jacobi–Bellman (HJB) equation, which constitutes a coupled system of nonlinear second-order integro-differential equations. An explicit form of the relative entropy penalty function is provided to quantify the cost of deviating from the reference model. The theoretical results furnish a foundation for numerical solutions using actor–critic reinforcement learning algorithms. Full article

The detection of high-consequence viral pathogens is essential for spillover prevention and reduction in transmission but is limited by the low sensitivity of next-generation sequencing technology. Low-titer field samples from a variety of hosts are primarily composed of non-viral genomic material, reducing the probability of obtaining usable sequence data. Targeted enrichment, such as VirCapSeq-VERT, removes background genomic material to improve virus detection but is mainly used for sequencing clinical samples. We customized the VirCapSeq-VERT probe system to aid in the detection of zoonotic viruses of interest and adapted it for use on the Oxford Nanopore sequencing platform. We validated the method on a variety of samples, including a mock virome consisting of seven RNA viruses, samples from an animal laboratory study, and a set of animal field samples. We also developed Nanite, a lightweight bioinformatics pipeline, to perform bioinformatic analyses. Results indicated that the optimized enrichment protocol improved sequencing by enhancing the detection of viruses, increasing read lengths, and, in some cases, improving genomic coverage. Most importantly, the sequencing of zoonotic viruses was improved in field samples with low titers, suggesting that this protocol is a useful tool for increasing the efficacy of Oxford Nanopore sequencing for field-oriented applications. Full article

Background: Older adults are a vulnerable population wherein their advancing age leads to limitations in physical and mental functionality that can compromise quality of life. Objective: The objective of this study was to analyze the relationship between mental health factors and quality of life in older adult users of long-term care services in Mexico. Methods: The present cross-sectional study was conducted with a convenience sample of 131 older adult users of long-term care services (three residential care homes and a day center) in Morelia, Michoacán, Mexico. A questionnaire including the World Health Organization Quality of Life Older Adults Scale (WHO-QoL-Old), Geriatric Depression Scale (GDS), Hamilton Anxiety Rating Scale (HARS), Cognitive Reserve Questionnaire (CRQ), and sociodemographic variables was administered. The analysis of the relationship between variables was performed using bivariate analysis (comparisons between groups and Pearson correlations). Due to the type of sampling, the representativeness of the sample obtained was not evaluated. Results: Depression and anxiety were found to inversely influence overall quality of life and its dimensions, while cognitive reserve is a factor that favors quality of life. Also, as related to cognitive reserve, level of education was found to be a factor that favors quality of life. Conclusions: Older adult users of long-term care services are a vulnerable group, given the negative impact on their quality of life that some mental health conditions could have, such as depression, anxiety, and low cognitive reserve. Full article

Objective: To determine whether a third-trimester drop in insulin requirements in pregnant people with pre-existing diabetes is associated with a subsequent occurrence of adverse pregnancy outcomes. Research Design and Methods: We conducted a retrospective cohort study of patients with type 1 and 2 diabetes who were followed at a tertiary referral center in Toronto, Canada. We collected data on insulin dosing in the third trimester (after 28 weeks of pregnancy) and compared outcomes in those with and without a third-trimester drop of 15% or more in their total insulin requirements. Our primary outcome was a composite of stillbirth, spontaneous preterm birth or preterm premature rupture of membranes, and iatrogenic preterm birth or cesarean birth for fetal wellbeing concerns, occurring following the drop in insulin requirements. We conducted regression analyses controlling for early pregnancy glycosylated hemoglobin, body mass index, and diabetes-related microvascular disease, and presented results as odds ratios (OR) with 95% confidence intervals (95%CI). Results: We included 350 pregnant people—146 with type 1 and 204 with type 2 diabetes. Of these, 54 (15.4%) had a third-trimester drop of 15% or more in their total insulin requirements. There was no difference in the primary outcome between groups (OR 0.97; 95% CI 0.41–2.10). Conclusions: Based on this single-center study, limited by sample size and analytic constraints, in people with pre-existing diabetes, a third-trimester drop of ≥15% in total insulin requirements was not associated with subsequent occurrence of adverse pregnancy outcomes. Larger prospective studies looking at associations between a drop in insulin requirements and subsequent occurrence of adverse pregnancy outcomes are necessary to inform meta-analyses and clinical decision making. Full article

Objectives: This in silico study sought to identify specific biomarkers for mild traumatic brain injury (mTBI) through the analysis of publicly available gene and miRNA databases, hypothesizing their influence on neuronal structure, axonal integrity, and regeneration. Methods: This study implemented a three-step process: (1) data searching for mTBI-related genes in Gene and MalaCard databases and literature review, (2) data analysis involved performing functional annotation through GO and KEGG, identifying hub genes using Cytoscape, mapping protein–protein interactions via DAVID and STRING, and predicting miRNA targets using miRSystem, miRWalk2.0, and mirDIP, and (3) RNA-sequencing analysis applied to the mTBI dataset GSE123336. Results: Eleven candidate hub genes associated with mTBI outcome were identified: APOE, S100B, GFAP, BDNF, AQP4, COMT, MBP, UCHL1, DRD2, ASIC1, and CACNA1A. Enrichment analysis linked these genes to neuron projection regeneration and synaptic plasticity. miRNAs linked to the mTBI candidate genes were hsa-miR-9-5p, hsa-miR-204-5p, hsa-miR-1908-5p, hsa-miR-16-5p, hsa-miR-10a-5p, has-miR-218-5p, has-miR-34a-5p, and has-miR-199b-5p. The RNA sequencing revealed 2664 differentially expressed miRNAs post-mTBI, with 17 showing significant changes at the time of injury and 48 h post-injury. Two miRNAs were positively correlated with direct head hits. Conclusions: Our bioinformatic analysis suggests that specific genes and miRNAs, particularly hsa-miR-10a-5p, may be involved in molecular pathways influencing mTBI outcomes. Our research may guide future mTBI diagnostics, emphasizing the need to measure and track these specific genes and miRNAs in diverse cohorts. Full article