Search Results (3,002)

While drone delivery has gained significant scholarly and industrial interest, rural residents’ acceptance of these systems remains underexplored, despite the region’s acute logistics challenges and service inequities. Using survey data from rural U.S. residents, this study first estimates an ordered logistic regression (OLR) model to identify factors associated with five-level drone delivery acceptance. The study then compares OLR, multinomial logistic regression (MNL), Random Forest (RF), XGBoost, and LightGBM under matched feature sets to evaluate whether nonlinear machine-learning models improve prediction beyond the interpretable statistical baseline. Open-ended responses are coded into LLM-derived sentiment labels and added as supplementary predictors to test whether unstructured feedback improves acceptance prediction. Results show that willingness to pay is the strongest predictor of acceptance, while equitable same-day delivery demand and post-pandemic attitude adjustment are also positively associated with higher acceptance. Household disability status and urban accessibility are not significant after adjustment. In the five-level analysis, OLR provides a strong ordinal baseline, while XGBoost and other tree-based models improve selected class-level prediction metrics. In the binary high-acceptance analysis, machine-learning models show stronger predictive performance, especially when structured predictors are combined with sentiment features. This study contributes to rural drone-delivery literature by linking service equity, perceived value, and LLM-derived sentiment within a comparable statistical and machine-learning framework for rural service design. Full article

Background/Objectives: Migraine is a complex neurovascular disorder with a multifactorial genetic basis and remains a leading cause of neurological disability worldwide. The angiotensin-converting enzyme (ACE) insertion/deletion (I/D) polymorphism has been implicated in vascular regulation and neurovascular reactivity; however, its role in migraine susceptibility and clinical expression remains unclear. This study aimed to evaluate the association between ACE I/D polymorphism and migraine susceptibility and clinical phenotypes in a diverse clinical cohort. Methods: A case–control study was conducted including 183 participants, comprising clinically diagnosed migraine patients (n = 82) and age- and sex-matched controls (n = 101). Genomic DNA was extracted from peripheral blood samples, and ACE I/D genotyping was performed using polymerase chain reaction. Genotype distribution was compared using chi-square tests, and odds ratios (ORs) with 95% confidence intervals (CIs) were calculated. Results: Overall genotype frequencies were Deletion/Deletion (DD) 43.7%, ID 38.8%, and Insertion/Insertion (II) 17.5%. Genotype distributions were comparable between migraine patients and controls (χ² ≈ 0.92, p = 0.632). No statistically significant associations were identified between ACE I/D polymorphism and migraine susceptibility, genotype models, or migraine subtypes. However, the II genotype was associated with higher Migraine Disability Assessment (MIDAS) scores among migraine patients. Conclusions: The ACE I/D polymorphism was not associated with migraine susceptibility or clinical phenotypes in this cohort. These findings suggest that ACE I/D polymorphism does not appear to be a major determinant of migraine susceptibility in this cohort. The results highlight the need to investigate broader genetic and molecular mechanisms and support the application of integrative genomic approaches to better understand migraine heterogeneity and inform precision medicine strategies. Full article

This prospective study investigated the association of dog ownership with the onset of disabling dementia and mortality using propensity score weighting based on the physical, social, and psychological characteristics of dog owners, and then examined the potential causality of this relationship using an instrumental variable model. Additionally, we examined the costs and effects of dog ownership using cost-effectiveness analysis. Overall, 11,224 older adults selected using stratified and random sampling strategies in 2016 were analyzed. Dog ownership was defined as current, past, and never. Disabling dementia was defined according to a physician rating under the long-term care insurance system in Japan and mortality was ascertained by the Japanese national vital statistics during the approximately 7.5-year follow-up period. Current and past dog owners had an odds ratio (OR) of 0.53 (95% CI: 0.43–0.66) and 0.97 (0.86–1.09) of the onset of disabling dementia compared to never owners. ORs for mortality were 0.63 (0.51–0.79) in current dog owners and 0.88 (0.78–0.99) in past dog owners. Causal analysis showed that current dog owners (OR = 0.52, 95% CI: 0.27–0.98) and past dog owners (OR = 0.75, 95% CI: 0.55–1.03) had low ORs for incident dementia compared to never dog ownership. Corresponding values for mortality showed low ORs, but no significant effect on current dog owners (OR = 0.59, 95% CI: 0.35–1.01) and past dog owners (OR = 0.77, 95% CI: 0.60–1.00) compared to never owners. Results of cost-effectiveness analysis showed that dog ownership produced small but positive gains in quality-adjusted life years (QALYs) and was dominant (cost-saving and QALY-increasing) from the public payer perspective, reducing publicly financed medical and long-term care costs while increasing QALYs; when private dog-ownership costs were included in a scenario analysis, the incremental cost-effectiveness ratio (ICER) was ¥6,744,174 per QALY compared with no-dog status. This prospective study shows that dog ownership has a potential protective effect against the onset of disabling dementia in older adults. The study also revealed the potential of dog ownership to reduce public spending with regard to long-term care and medical expenditures. Full article

Background: With the worsening of global population aging, individuals aged 45 and above face numerous challenges, including disability, pain, cognitive impairment, hearing, and depression. This study aims to utilize machine learning and deep learning methods to develop predictive models for forecasting the health outcomes and disease risk among middle-aged and elderly individuals, while identifying the key factors influencing disease prevalence and quality of life in this demographic. Methods: This research is grounded in the China Health and Retirement Longitudinal Study (CHARLS) database (2015–2018), encompassing 20,967 participants. Machine learning and deep learning techniques were employed to create predictive models for disability, pain, cognitive impairment, hearing, and depression, utilizing 69 variables. A minimum redundancy maximum relevance (MRMR) feature selection strategy and incremental feature selection (IFS) were applied to identify key variables. The optimal predictive models were evaluated for accuracy using the area under the receiver operating characteristic curve (ROC-AUC). Results: The analysis resulted in 15 predictive models, with TabPFN showing the highest performance in predicting disability, pain, cognitive impairment, hearing, and depression, achieving AUCs of 0.802, 0.860, 0.814, 0.769, and 0.856, respectively. Shapley interpretability analysis identified the top five critical features affecting these health outcomes. Conclusions: The proposed models can assess the disease risk of various conditions in middle-aged and elderly individuals, suggesting that early prevention and effective intervention may reduce incidence rates. Additionally, the study provides insights into key factors influencing health, offering a scientific foundation for tailored health management and precision intervention strategies. Full article

Background: Multiple sclerosis (MS) is a neurodegenerative and chronic inflammatory disease, often associated with psychiatric comorbidities, particularly depression and anxiety, which play a major role in disability, cognitive dysfunction, and quality of life. Growing evidence suggests that affective symptoms in MS are not only related to psychosocial burden but also to structural and functional abnormalities in the brain that can be identified using magnetic resonance imaging (MRI). Methods: A systematic database search was performed to identify studies for a structured narrative review. Original articles that examined MRI correlates of depression and anxiety in adult MS patients were included. Structural MRI, diffusion imaging, and functional MRI studies evaluating lesion distribution, cortical and subcortical atrophy, white matter microstructure, and network connectivity were analyzed alongside psychometric instruments used to assess affective symptoms. Results: Depression in MS was consistently associated with fronto-limbic and subcortical abnormalities including cortical thinning, hippocampal and thalamic atrophy, white matter disconnection and altered connectivity within the default mode, salience and executive control networks. Diffusion MRI studies have shown microstructural damage in associative white matter tracts, and functional MRI studies have supported a model of network-level dysfunction. In contrast, anxiety had less robust and reproducible associations with conventional MRI findings, indicating a more complex interaction between inflammatory, neurobiological and psychosocial mechanisms. Conclusions: Multimodal MRI approaches may increase our knowledge of the neurobiological substrates of depression and anxiety in MS and may contribute to more personalized diagnostic and therapeutic strategies. Full article

Background: Disease-modifying therapies have profoundly changed the natural history of spinal muscular atrophy (SMA) type I, shifting rehabilitation priorities beyond survival and motor function toward participation, autonomy, and quality of life. However, rehabilitation models have not evolved at the same pace, and the role of early powered mobility in this new clinical scenario remains insufficiently conceptualized. Methods: This narrative review integrates current evidence on early powered mobility in children with severe motor disabilities with contemporary rehabilitation frameworks, including the International Classification of Functioning, Disability and Health (ICF), participation-based therapy, family-centered care, and the concept of on-time mobility. Evidence from the AMEsobreRuedas research program is incorporated to develop a conceptual framework for early powered mobility in children with SMA type I receiving disease-modifying therapies. Results: Current evidence suggests that early powered mobility should be understood as a developmental rehabilitation intervention rather than solely as an assistive technology for transportation. Independent mobility facilitates exploration, play, social interaction, autonomy, and participation, while positively influencing family experiences and expectations. Findings from the AMEsobreRuedas program further indicate that the benefits of powered mobility extend beyond driving skill acquisition, supporting participation, quality of life, and family well-being when implemented within meaningful daily contexts. Based on this evidence, a conceptual framework is proposed in which independent mobility acts as an early facilitator of developmental opportunities, with participation emerging through the interaction between the child, family, and environment. Conclusions: In the era of disease-modifying therapies, rehabilitation in SMA should move from a motor-centered approach toward a participation-oriented model. Early powered mobility represents a key intervention for promoting developmental opportunities and meaningful participation rather than simply compensating for motor impairment. The proposed conceptual framework may support clinical decision making and provide a foundation for future rehabilitation research in pediatric neuromuscular disorders. Full article

Against the backdrop of population aging, the well-being of older adults with disabilities in daily life and its underlying mechanisms has increasingly become an important research topic. Using data from the 2020 wave of the China Health and Retirement Longitudinal Study (CHARLS), this study focuses on 509 individuals aged 60 and above with disabilities. It incorporates informal social support, depression risk, and life satisfaction into a unified analytical framework, and employs ordered Logit models, binary Logit models, and the KHB decomposition method to examine their interrelationships and underlying mechanisms. The results show that: (1) informal social support is positively associated with the life satisfaction of older adults with disabilities; (2) informal social support is negatively associated with depression risk; and (3) depression risk is negatively associated with life satisfaction and plays a significant mediating role in the relationship between informal social support and life satisfaction, accounting for 23.25% of the total effect. From a daily life perspective, this study reveals that the well-being of older adults with disabilities is not solely derived from institutional resource provision, but is generated and sustained through ongoing social interactions and relational networks. The study therefore deepens our understanding of the everyday and relational dimensions of subjective well-being in later life. Full article

Background/Objectives: Despite the increasing rates of disability associated with aging, obesity, and osteoarthritis requiring surgery, optimizing rehabilitation after hospital discharge remains a major challenge and a key determinant of care safety, quality, and sustainability. The aim of this exploratory study is to evaluate the predictive performance of machine learning (ML) models for predicting Inpatient Rehabilitation Length Of Stay (IRLOS), Function in the Activities of Daily Living (FADL) and discharge destination (DD) in patients who underwent total joint replacement for hip and knee osteoarthritis, using Real-World Data routinely collected in a tertiary orthopedic hospital. Methods: 2103 patients were included and temporally split into a development cohort (2019; n = 1711) and a temporal validation cohort (2018; n = 392). A total of 73 routinely collected perioperative variables were used to train multiple ML models, including both black-box and transparent methods. IRLOS and FADL were modeled as regression tasks, while DD was treated as a binary classification task. Model development followed a rigorous pipeline with feature selection, cross-validation, and hyperparameter tuning. Performance was assessed using appropriate metrics and evaluated across joint type (hip/knee) and via temporal validation. Model interpretability was examined using SHAP and model-specific analyses, further supported by clinical analysis. Results: In temporal validation, models achieved modest performance for IRLOS (R² = 0.17; MAE = 2.52 days) and FADL (R² = 0.25; MAE = 2.25 days), with no significant performance degradation over time. DD prediction showed good discrimination (AUC = 0.85; balanced accuracy = 0.80) despite outcome imbalance, with high sensitivity (0.92) and negative predictive value (≈1.00), but low positive predictive value (0.09). Performance was stable across hip and knee subgroups. The interpretability analysis further highlighted several key predictors related to perioperative complexity (e.g., surgical duration and transfusion), baseline functional status, and social factors (e.g., living situation and employment), confirming that rehabilitation outcomes are multidimensional and influenced by both medical and non-medical determinants. Conclusions: The analysis identified several relevant predictors related to perioperative complexity, baseline functional status, and social context. The models showed stable behavior across intervention type and across time through temporal validation, suggesting that they capture relevant patterns in rehabilitation pathways, although predictive performance was moderate. Full article

This study aimed to compare the burden of malaria and dengue worldwide and to project future incidence trends. Incidence and disability-adjusted life-years (DALYs) were obtained from the Global Burden of Disease Study 2021 (GBD 2021). Temporal trends were quantified using estimated annual percentage change (EAPC), age–period–cohort analysis was used to assess age, period, and cohort effects, and Bayesian age–period–cohort models were applied to project incidence to 2036. From 1992 to 2021, the global age-standardized incidence rate (ASIR) of malaria decreased (EAPC= −0.55%), whereas that of dengue increased (EAPC = 1.83%); the corresponding age-standardized DALY rates showed EAPCs of −1.64% and 1.29%, respectively. Across 21 GBD regions, SDI was inversely correlated with ASIR for malaria (r = −0.848) and dengue (r = −0.521), and with age-standardized DALY rates for malaria (r = −0.849) and dengue (r = −0.497). Malaria burden remained concentrated in low-SDI regions, particularly sub-Saharan Africa and Oceania, whereas dengue burden was highest in middle-SDI regions, especially tropical Latin America. From 2021 to 2036, the global ASIR of malaria was projected to decrease slightly by 1.36% (3485.27 to 3437.72). Whereas that of dengue was projected to increase by 10.58% (752.04 to 831.63). In males, the projected ASIR of malaria and dengue increased by 4.18% (3193.24 to 3326.58) and 9.96% (704.83 to 775.06), respectively. In females, the corresponding increases were 4.87% (3379.82 to 3544.27) and 8.83% (819.74 to 892.13). These findings suggest divergent epidemiological trajectories for malaria and dengue. The projections further suggest a consistent upward trajectory for dengue, with sex-specific relative changes differing descriptively by disease: the projected relative increase was numerically greater in females for malaria and in males for dengue. Full article

(1) Background: Motor imagery (MI) analysis via electroencephalography (EEG) is crucial for Brain–Machine Interfaces (BMIs) and neurorehabilitation, though inherent EEG noise challenges robust decoding algorithm development for assistive devices aiding patient recuperation. (2) Methods: This study introduces a deep learning pipeline utilizing the IFNet convolutional neural network with frequency filtering and two scaling methods to decode pedaling MI. To mitigate catastrophic forgetting during transfer learning, a rotative Elastic Weight Consolidation (rEWC)-based weight adjustment procedure was incorporated. The pipeline was validated with 23 non-disabled participants across four sessions each, evaluating four approaches via pseudo-online (open-loop) and real-time online (closed-loop) analyses. (3) Results: The decoding accuracy for MI exceeded 80% (up to 95% individually) in selected pseudo-online approaches, while real-time online classification averaged 70% accuracy with a slight bias towards the relax task, confirming statistical significance for both scenarios. (4) Conclusions: These findings demonstrate that mitigating catastrophic forgetting in transfer learning within deep learning models and applying proper scaling are crucial for optimal performance. Consequently, the proposed deep learning pipeline holds significant promise for effective, adaptive EEG-based neurorehabilitation systems. Full article

Background: Multiple sclerosis (MS) lacks a single invariant phenotypic core. Patients accumulate heterogeneous combinations of sensory, motor, cognitive, and autonomic impairments over time, reflecting lesions that are disseminated in time and space. Standard scales such as the Expanded Disability Status Scale (EDSS) distribute disability across functional systems, but do not explicitly represent MS phenotype as a mixture of latent symptom modules. Methods: We analyzed 4617 de-identified neurology progress notes from 577 patients with MS at a single academic medical center. A large language model (GPT-5.2) categorized each note with respect to 17 non-mutually-exclusive neurological phenotype features, and note-level features were aggregated to patient-level binary vectors. Non-negative matrix factorization (NMF) was applied to generate three-, four-, and five-module solutions. For each rank, we computed approximate variance captured, relative reconstruction error, and module-level feature loadings. In the preferred four-module solution, we derived patient-level module percentages, identified highly dominant (≥55%) and archetypal (≥70%) module profiles, and quantified admixture using Shannon entropy and the effective number of modules. Results: Three-, four-, and five-module NMF solutions showed similar approximate variance captured (52.7–54.3%) and reconstruction error (0.47–0.53), but the four-module solution provided the clearest clinical interpretation. The four latent modules were sensory-visual-pain, ataxic-spastic-falls, cognitive-psychologic-fatigue, and autonomic-bladder-bowel, aligning closely with established functional systems in MS. Most patients exhibited admixed phenotypes, with module entropies ranging from 0 (single-module dominance) to 1.386 (equal mixture) and effective modules spanning approximately 1 to 4. Using pre-specified thresholds, 154 patients (26.6%) were highly dominant in a single module and 72 (12.5%) were archetypal; these purer phenotypes were most often in the sensory-visual-pain module. Conclusions: MS phenotypic diversity in routine clinical practice can be parsimoniously represented as mixtures of four latent symptom modules rather than as positions along a single severity axis. Most patients show substantial admixture of sensory, motor, cognitive, and autonomic involvement, but a minority exhibit relatively pure or strongly dominant module patterns. This modular representation provides an interpretable framework for quantifying MS phenotype and for generating testable hypotheses about MS subtypes whose biological relevance remains to be established. Full article

The rapid expansion of artificial intelligence (AI) is accelerating data center construction and creating downstream implications for households. This study examines how AI-era data center costs (comprising construction, energy, water, grid upgrades, cooling, and lifecycle management) move through service supply chains and affect household prices in healthcare, transportation, education, banking, and commerce. It also considers the productivity and welfare benefits that AI may transmit. This study identifies four pass-through channels: utility-rate socialization of energy costs, cloud-platform pricing, sectoral pass-through from AI-adopting industries, and indirect effects through supply chains and labor markets. It introduces the AI-inflated net good basket, defined as transmitted cost minus transmitted benefit, to show how AI reshapes the overall net cost of household consumption rather than simply inflating individual prices. The study develops the AI Infrastructure Net Cost Pass-Through Model (AI-NCPM), a four-layer conceptual framework tracing net cost flows from data center investment to sectoral allocation and household outcomes. The model’s parameters are analytically specified but not empirically calibrated; numerical examples are illustrative rather than representing estimated effects. Its main contribution is an integrative framework linking cost pass-through, infrastructure cost socialization, two-sided platform allocation, environmental externalities, and household expenditure incidence within a single net-cost account. Because these effects originate in the design, construction, energy and cooling systems, and lifecycle operation of data centers, the analysis connects AI infrastructure economics to the built environment. The framework suggests that low-income, minority, rural, older adult, and disability-affected households may face disproportionate net burdens, as costs fall heavily on essential services while benefits accrue more readily to affluent and digitally connected households. Full article

As RISC-V processors are increasingly considered for embedded real-time and control-oriented systems, evaluating how timing behavior changes under increasing task concurrency becomes essential. Adding runnable tasks can amplify preemptions, context-switch activity, response-time variability, execution jitter, and deadline pressure. Existing RISC-V simulation and virtual-platform environments mainly target architectural exploration, functional validation, or full-system execution, and do not directly provide a controlled workflow for isolating scheduler-induced timing degradation across large configuration spaces. This paper presents nSim-RV, a configurable and reproducible RISC-V simulation and orchestration framework for scheduler-aware timing scalability evaluation. The framework combines automated campaign generation, deterministic workload configuration, structured dataset aggregation, duplicate validation, and timing-oriented metric extraction. The evaluation compares a standard shared-pipeline execution model with an nMPRA-inspired preserved-context mode under identical scheduler and workload conditions. The campaign includes CoreMark, Dhrystone, and a deterministic synthetic RT-Control workload, 2–32 concurrent tasks, 50 k–1 M cycle observation windows, cache-disabled and cache-enabled configurations, and four-stage and five-stage pipeline organizations, resulting in 864 validated configurations. Results show that increasing task concurrency amplifies timing variability and deadline pressure. Preserved-context execution reduces switching-induced disturbance and delays or reduces higher-pressure timing behavior in several trajectories. Under the five-stage cache-disabled RT-Control configuration at N = 32, it reduces the deadline miss ratio from 3.74% to 2.21%, corresponding to a 41.1% relative reduction, with the clearest benefits observed for Dhrystone and RT-Control at intermediate–high task counts. Full article

Background: Dynapenic abdominal obesity (DAO), the coexistence of low muscle strength and abdominal obesity, may accelerate disability. However, its longitudinal association with disability remains unclear. In this study, we examined the relationship between DAO and disability in middle-aged and older adults. Methods: A systematic review and random-effects meta-analysis of prospective cohort studies was conducted to examine longitudinal associations following the PRISMA 2020 guidelines (PROSPERO: CRD42024609352). Comprehensive database searches were conducted in PubMed, Embase, MEDLINE (Ovid), CINAHL (EBSCOhost), and the Cochrane Library from database inception through 31 January 2025. To ensure that the review reflected the most recent evidence prior to manuscript submission, an updated search using the same search strategy and eligibility criteria was conducted on 31 May 2026. When multiple effect estimates were reported, the most fully adjusted estimates were selected. Because odds ratios (ORs) and hazard ratios (HRs) are different effect measures and are not directly comparable, they were analyzed separately. A random-effects model was used for quantitative synthesis. Results: Five cohort studies (n = 33,670; mean follow-up: 5.3 years) were included. Of these, four studies reported adjusted ORs and one reported an HR. DAO was associated with significantly higher odds of disability (pooled OR = 2.13, 95% CI: 1.74–2.60, I² = 0%). Subgroup analyses demonstrated significant associations across predominant population types and age groups, with ORs ranging from 2.09 to 2.18. Conclusions: DAO is associated with increased odds of future disability across population types and age groups. These findings highlight the importance of early identification of DAO and suggest that interventions targeting muscle strength and abdominal obesity represent promising strategies for disability prevention; however, their effectiveness should be confirmed in future prospective intervention studies. Full article

Emerging technologies are reshaping labor market dynamics and redefining the competencies required for employability. However, their benefits remain unevenly distributed, particularly among socially vulnerable populations such as people with visual impairments. This study examined the relationship between emerging technologies and employability among individuals with visual disabilities in a city in northern Peru, within the broader framework of inclusive development and equal opportunity. A quantitative, non-experimental, cross-sectional design was employed with a sample of 132 participants. Data were collected through a Likert-type questionnaire measuring indicators associated with emerging technologies and employability. To capture the structural interdependencies between both domains, the study used psychometric network analysis based on ordinal correlations and EBICglasso estimation. The resulting network comprised 17 nodes and 89 edges, with a predominance of cross-domain associations between technology and employability indicators. The nodes with the strongest expected influence were EM5, TE7, and TE9, suggesting that these indicators occupied relatively central positions within the estimated exploratory network. Taken together, the results suggest preliminary associations between employability-related indicators and technology-related indicators, particularly those linked to technological access, skills development, autonomy, and innovation. Given the exploratory nature of the instrument, the cross-sectional design, and the sample size, these findings should be interpreted as preliminary. In this context, psychometric network analysis is best understood as a complementary exploratory approach that helps identify conditional associations between indicators, while further psychometric confirmation of the instrument remains necessary. Full article