Search Results (1,095)

We seek to identify the most relevant benefits offered by Medicare Advantage Health Plans that are attractive to members and that drive membership and market share. We explore plans operating in a single county in New Jersey between 2018 and 2023. A dataset of benefits from publicly available data sources was created and the variance inflation factor was applied to identify the correlation between the extracted features, avoiding multicollinearity and overparameterization problems. We categorized the variable market share and used it as a multinomial response variable with three categories: less than 0.3%, 0.3% to 1.5%, and over 1.5%. Categories were chosen to achieve approximately uniform distribution of plans (47, 60 and 65, respectively). A multinomial Lasso model using 5-fold cross validation tunes the penalty parameter and reduces overfitting by dropping some features from the model, thus increasing interpretability. For each category, important variables vary. Certain brands drive market share, as do PPO plans and prescription drug coverage. Benefits, particularly ancillary benefits that are not part of CMS’s required benefits, appear to have little influence, while financial terms such as deductibles, copays and out-of-pocket limits are associated with higher market share. Finally, we evaluated the multinomial Lasso model on a held-out test set. The model achieved an overall classification accuracy of 0.76, meaning that 76% of plans were correctly classified into the low, medium or high market share categories. Full article

Background: Attention-Deficit/Hyperactivity Disorder (ADHD) affects an estimated 6% of adults in the United States and contributes to a significant economic burden. Telehealth has emerged as a vital tool in the management of ADHD, offering improved access to care, especially for individuals in underserved communities. Despite its growing role, there remain critical gaps in understanding how social determinants of health (SDOH) are associated with disparities in telehealth utilization for ADHD treatment. Objectives and Methods: This study analyzed data from the National Center for Health Statistics (NCHS) Rapid Surveys System (RSS) Round 2: ADHD (October–November 2023), a nationally fielded survey of U.S. adults. Respondents were classified into three groups: never diagnosed, previously diagnosed, and currently diagnosed with ADHD. The study aimed to (1) compare the distribution of SDOH across ADHD status groups and the general adult population to identify factors associated with ADHD diagnosis; (2) assess the homogeneity of SDOH distributions across ADHD groups; (3) evaluate telehealth utilization among adults currently diagnosed with ADHD; and (4) examine the relationship between SDOH and telehealth use for ADHD treatment. Multivariable logistic regression (MVLR) served as a benchmark model, while machine learning (ML) models—including regularized linear regression, support vector machine (SVM), random forest (RF), LightGBM, multilayer perceptron (MLP), and Few-Shot Learning (FSL)—were trained to identify key predictors. Results: A total of 7009 survey responses were analyzed: 124 had a past diagnosis, 444 were currently diagnosed, and the remainder had never been diagnosed with ADHD, corresponding to a current ADHD prevalence of 6.3%. Adults with current ADHD were more likely to be male, single, younger, white, non-homeowners, and frequent users of online health resources. They also reported lower education, income, and financial security. About 70% used telehealth for counseling and prescriptions; insurance covered telehealth visits for 82.32% of users, yet 38.76% reported no coverage of ADHD-related diagnostic or treatment costs. Nineteen SDOH elements across four domains—demographic, socioeconomic, neighborhood/built environment, and healthcare access—were identified as predictors. ML models outperformed MVLR, with SVM and FSL achieving the highest F1 (both 0.63), and FSL the highest recall (0.69). Age, race, marital status, difficulty paying bills, home ownership, education, and household size were the most consistently important variables. Limitations: This study is limited by a cross-sectional design, reliance on self-reported ADHD diagnoses, and a lack of genetic or family-history measures. Additionally, the omission of complex sampling weights limits the national representativeness of these findings. Finally, the small effective sample size poses risks of model overfitting, and the generalizability of the models could not be externally validated due to the unavailability of comparable independent datasets. Conclusions: Despite widespread internet access, disparities in telehealth use for ADHD persist. Among 19 SDOH predictors, age (aOR = 0.56), difficulty paying medical bills (aOR = 2.52), and race (aOR = 1.37) were significantly associated with telehealth use, and all ML models outperformed the MVLR benchmark, though bootstrap CIs overlapped. Future research should incorporate inclusive data collection and stratified modeling to better represent disadvantaged populations and inform equitable access strategies. Full article

Food–medication interactions can occur when medicines are taken with foods, drinks, herbs, or supplements that influence drug absorption, exposure, or activity. Screening these combinations is challenging because the available evidence is imbalanced, prescription text may be recognized incorrectly, unsupported inputs may produce unreliable predictions, and altered software artifacts may change the recommendation presented to the user. iMediFood-Shield addresses these concerns through an evidence-first edge-AI framework that combines structured diet–drug interaction evidence, prescription-assisted medication confirmation, coverage-aware rejection, calibrated five-class prediction, false-safe-aware confidence gating, and software-based tamper-evident verification. The DDID preparation process began with 23,950 evidence records and produced 16,644 canonical medication–food/herb pairs, including 16,165 single-effect model-eligible pairs and 479 multi-effect conflict pairs. A leakage-free 70%–15%–15% split was applied after canonicalization, and the deployed lookup was restricted to training-supported and conflict records. On the operational locked-test AI branch of 2259 supported unseen pairs, the final calibrated LinearSVC with the validation-selected MedSafe-GATE threshold of 0.65 achieved 91.72% accuracy, 80.57% balanced accuracy, and a macro F1-score of 0.8359. The gate reduced calibrated false-safe predictions from 55 to 28, corresponding to a 49.09% reduction and a final false-safe rate of 1.35% among interaction-bearing AI-branch pairs. RxOCR-Guard achieved 94.67% candidate recall and 100.00% candidate precision on a controlled synthetic prescription benchmark, while mandatory user confirmation was retained because top-1 candidate accuracy was 51.33%. The unchanged baseline and all ten adverse software-bundle conditions produced the expected verification outcomes for artifact-modification, missing-file, key-mismatch, manifest-alteration, and rollback cases. Raspberry Pi deployment reproduced all 2259 reference predictions without mismatch, completed covered AI inference in 1.737 ms on average, and verified the protected software bundle in 80.249 ms on average. These results show that iMediFood-Shield can combine evidence-grounded screening, conservative AI decision control, prescription confirmation, and software-integrity verification within a resource-constrained edge research prototype. Full article

In recent years, interest in artificial intelligence has grown significantly, particularly in the development of advanced computational models for supply chain decision-making. Inventory management is one of the areas in which intelligent algorithms can support demand forecasting, replenishment, stock control, and operational optimization under uncertainty. This study presents an updated systematic literature review of intelligent algorithms applied to inventory management. The review followed PRISMA 2020 guidelines and combined database searches in Scopus, ScienceDirect, Web of Science, IEEE Xplore, SpringerLink, Taylor & Francis, and complementary manual searching. The original search covering January 2020 to December 2024 was updated in July 2026 to include studies published or available online up to June 2026. After applying strict eligibility criteria, 37 primary studies with quantitative evidence were included. The updated corpus confirms the predominance of deep learning, reinforcement learning, and hybrid intelligent models, while also showing the recent emergence of Transformer-based, graph neural network, multi-agent reinforcement learning, and prescriptive analytics approaches. The most frequent application areas were inventory control, inventory optimization, replenishment decision-making, and demand forecasting. Reported improvements were mainly associated with cost efficiency, service level, stockout reduction, and system performance; however, the magnitude of improvement varied across algorithms, data sources, sectors, and simulation or real-world settings. Overall, intelligent algorithms represent a relevant tool for improving inventory management, but their adoption requires careful validation, transparent reporting, and alignment with the operational context. Full article

Background: Emergency aeromedical services, including helicopter emergency medical service (HEMS) and medical emergency evacuation (MEDEVAC), are critical to time-sensitive care. Dispatch decisions are complex and consequential, determining whether, which, and under what conditions to deploy aeromedical resources. This study reviews modelling approaches for emergency aeromedical dispatch. Methods: Following PRISMA, studies between 2003 and 2026 (June) were screened, yielding 42 studies. Models were classified as predictive and learning-based, sequential decision, and prescriptive optimisation-based, with solution techniques, operational applications, and policy contexts analysed. Results: Markov decision process and approximate dynamic programming models dominate the sequential decision literature, particularly in military MEDEVAC. Prescriptive models support resource allocation, base location, coverage planning, and dispatch optimisation, while predictive and AI/ML-based approaches remain limited but emerging. Key challenges include computational complexity, data uncertainty, policy fragmentation, and ethical concerns. Sequential models reflect dispatch’s dynamic, stochastic nature, where current deployments constrain future resource availability. Priority-aware policies outperform closest-unit rules, but limited real-world validation hinders adoption. Conclusions: This review maps methods and provides evidence-based guidance for researchers and dispatch organisations selecting decision support models. Future opportunities include AI-assisted dispatch, hybrid predictive–prescriptive modelling, real-time adaptive algorithms, sustainability-oriented optimisation, improved helicopter landing zone identification, and standardised ethical and regulatory frameworks. Full article

Background: This study evaluated vaccine prescription conversion and identified associated factors in Henan Province, China, during 2025. Methods: The study was conducted in Lingbao City, Henan Province. All vaccine prescriptions issued in 2025 were collected, and vaccination status was linked through the Henan Provincial Immunization Information Management System. Subgroup analyses were stratified by vaccine type (NIP vs. non-NIP). Univariate analyses used χ², Mann–Whitney U, and Kruskal–Wallis H tests. Multivariable analyses employed Firth-corrected logistic regression for conversion behavior, and mixed-effects models with random intercepts for patient ID for conversion timeliness, analyzed using a two-part model. All analyses were performed using R software (version 4.5.3). Results: Among 50,887 prescriptions, the overall conversion rate was 13.8%. NIP vaccines had a higher conversion rate than non-NIP vaccines (76.8% vs. 12.5%, p < 0.001) and a higher same-day conversion rate (13.9% vs. 7.4%; χ² = 59.585, p < 0.001), but longer delays among non-same-day conversions (23 vs. 4 days). Manual prescriptions were associated with significantly higher conversion in both NIP (OR = 3.913) and non-NIP (OR = 37.720) subgroups. Vaccination clinics were associated with a nearly 20-fold higher odds (OR = 19.386). Interaction analyses showed manual prescriptions were more strongly associated with conversion and shorter delays in older adults, and had stronger associations with conversion for non-NIP vaccines (χ² = 22.275, p < 0.001). Vaccination clinics were most strongly associated with conversion in children (ΔP = 0.130); however, the improvement in same-day conversion among older adults was relatively smaller, suggesting additional barriers beyond clinic availability may exist in this population. Conclusions: Manual prescriptions and vaccination clinics were associated with higher vaccine prescription conversion, with particularly pronounced effects on non-NIP vaccines. Older adults exhibited the lowest conversion rates. In parallel with promoting electronic prescriptions, co-issuing paper prescriptions is recommended, particularly for non-NIP vaccines and older adults. Full article

Tool-level attacks on Large Language Model (LLM) agents—poisoned tool descriptions, prompt injection, and capability misrepresentation—are universally effective, yet no existing defense provides comprehensive protection. We propose Architectural Intent Collapse (AIC), a formal framework capturing the systematic loss of communicative intent when text from heterogeneous sources is flattened into a single context window. Grounded as a novel instantiation of the Confused Deputy Problem, AIC reveals that the missing boundary is not permission but intent: the architecture cannot distinguish descriptive statements from prescriptive commands. We formalize AIC via an architectural collapse operator, introduce Intent Separation Degree (ISD) as a measurable metric, and develop a mechanism-based taxonomy of five intent-disguise attack types, including two previously undescribed (Conditional Latency and Inference Inducement). Experiments across 25 framework–model combinations (employing GPT-4o, Claude-4-Sonnet, Gemini-2.5-Pro, DeepSeek-V3, and Qwen3-32B as LLM backends) confirm that ISD degrades with description verbosity, strongly predicts defense effectiveness ($r=-0.97$), and is uniformly low across all current frameworks. Three root-cause defense principles are derived; one retains substantial protection against adaptive attackers. This research is useful for agent framework designers, security practitioners, and researchers seeking a principled understanding of why tool-level attacks succeed and how architectural defenses can address their root cause. Full article

Large Language Models (LLMs) are increasingly integrated into agentic workflows that require extended reasoning, persistent state management, coordinated tool use, and controlled execution. As this operational scope expands, a central question emerges: whether probabilistic generation alone can reliably support coherent behavior across interacting system components. This paper addresses that question through a structural diagnostic review of contemporary agentic systems. Starting from LLM-based tutoring as an analytically demanding entry point and extending toward structurally related agent architectures, the paper draws on a five-phase review of $N=145$ research records. The analysis is organized through the Agentic Structure Taxonomy (AST), which structures the literature across four dimensions: Cognition, Interaction, Orchestration, and Governance. The review identifies five recurrent empirical problem patterns and uses them as abductive diagnostic cues for formulating seven cross-dimensional transition gaps that capture recurrent discontinuities at the boundaries between reasoning, state, control, and execution. From these gaps, fourteen structural constraints are derived across three control domains: state isolation, control alignment, and execution governance. These constraints are interpreted not as prescriptive design mandates, but as analytically derived conditions associated with reducing error propagation across subsystem transitions. The paper argues that reliability in agentic systems is shaped not only by model performance or prompt design, but also by whether the boundaries linking probabilistic reasoning to persistent state, orchestration, and execution are governed by explicit structural conditions. Full article

To meet the requirements of differentiated pesticide application between soybean and maize strips in soybean–maize strip intercropping systems, this study developed a strip-specific variable-rate spraying system based on real-time prescription map interpretation and spatiotemporal nozzle matching with delay compensation. A simulated prescription map with predefined application-rate levels was generated using ArcMap and converted into a binary data structure suitable for embedded-controller access. Based on high-precision RTK-BDS positioning information, a local field coordinate transformation model was established and combined with SRAM-based memory preloading to achieve rapid prescription matrix addressing. To reduce boundary misalignment caused by positioning offset, actuator response lag, and hydraulic delay during dynamic field operations, a nozzle spatial position prediction model and a forward delay-compensation control algorithm were developed. Results from the strip-specific variable-rate spraying tests based on the simulated prescription map showed that, under the tested conditions, the mean boundary offset decreased from 0.69 m to 0.29 m after delay compensation. The mean flow-rate control accuracy for both soybean and maize strips exceeded 95% across different application-rate levels, while the coefficients of variation of flow rate were below 6%. These results indicate that, under the tested conditions, the developed system was able to perform real-time prescription map interpretation, target application-rate matching, and strip-specific variable-rate control, demonstrating its technical feasibility for variable-rate spraying in soybean–maize strip intercropping. Full article

With the increasing frequency of extreme-heat events, there is an urgent need to identify the key drivers of the urban thermal environment at fine spatial scales. Focusing on urban blocks within Beijing’s Fifth Ring Road, this study integrates Landsat 8 imagery acquired from March 2020 to February 2021 with multi-source data on land cover, buildings, population, pollution, and topography. LightGBM–SHAP, a theory-informed directed acyclic graph (DAG), CausalForestDML, and cross-fitted g-computation were employed to investigate predictive associations, Q25–Q75 average total treatment effects, and block-level responses to prespecified urban-morphology intervention scenarios for seasonal land surface temperature (LST). The within-season SHAP analyses consistently placed building height (BH) and building density (BD) among the relatively important predictors, whereas the predictive patterns of the normalized difference vegetation index (NDVI) and the proportion of impervious surfaces (ID) were more season-specific. The autumn and winter models also assigned relatively high within-season importance to the digital elevation model (DEM), PM_2.5, and CO₂ emission proxy. Because the four seasonal models differed in predictive performance and LST distributions, these cross-seasonal patterns were interpreted qualitatively rather than as direct comparisons of absolute SHAP values or rank positions. Causal-effect estimation further indicated that, under the primary DAG and identification assumptions, the Q25–Q75 point estimates were positive for BD and negative for BH in all four seasons. In summer, the Q25–Q75 effects of NDVI and ID were −0.772 and +1.548 °C, respectively. Intervention-scenario analysis further indicated that the estimated responses varied across blocks and seasons, emphasizing the importance of considering baseline urban conditions and common support when interpreting potential planning effects. Additional spatially blocked validation yielded lower predictive performance than random validation, while significant positive residual spatial autocorrelation remained in all four seasons. These findings may inform the local evaluation of season- and context-specific surface-temperature mitigation strategies within the observed-support range, but they should not be interpreted as universal planning prescriptions. Full article

Objective: Siweixizangmaoru decoction (SXD), a classical Tibetan prescription documented in the medical text Four Medical Tantras, has shown therapeutic activity in experimental rheumatoid arthritis (RA). However, its candidate active constituents and underlying mechanisms remain unclear. Methods: In this study, serum-absorbed constituents following SXD administration were profiled using ultra-high-performance liquid chromatography coupled with quadrupole-Orbitrap high-resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS), and network pharmacology was employed to identify core targets and candidate active constituents of SXD against RA. Candidate active constituents were further screened using cell-based assays, and the selected constituents were quantified using HPLC fingerprint analysis. Both in vivo (CIA rat model) and in vitro (RAW264.7 pyroptosis model) systems were used to investigate the pharmacological effects and mechanisms of SXD. Results: A total of 11 absorbed constituents were identified in the serum of SXD-treated rats, and four preliminary candidate active constituents, including chebulagic acid, kaempferol, gentiopicroside, and berberine, were screened. The combined in vivo and in vitro results showed that SXD attenuated RA progression and reduced inflammatory cytokine levels in rat serum and cell culture supernatants. At the molecular level, SXD increased Pol β expression and reduced DNA damage markers, cytosolic dsDNA accumulation, and cGAS/STING-, NF-κB-, and NLRP3-associated signaling. Pol β knockdown partially reduced the effects of SXD. Conclusions: These findings support the partial involvement of Pol β-associated processes in the anti-arthritic effects of SXD. Full article

Liquefaction and dynamic separation of moisture-sensitive solid bulk cargoes may remain latent for much of a voyage and then manifest as a sustained heel, leaving a comparatively short interval for emergency action. This study develops an exploratory reliability-based analysis of accident chronology using a source-traceable registry of 35 casualties and incidents. Eighteen cases provided post-heel information suitable for the principal emergency time reserve analysis; the observations comprised exact, approximate, reconstructed, interval-censored, and right-censored times. Descriptive statistics calculated from the selected central values and censoring bounds yielded a mean emergency time reserve TR of 200.99 min, a median of 192.20 min, and a range of 67.50–335.10 min. In likelihood-based fitting that retained censoring, the Weibull model achieved the lowest AIC (212.51) and BIC (215.18), with Kolmogorov–Smirnov D = 0.097 (p = 0.989). The fitted lower-tail quantiles were Q10 = 105.40 min and Q25 = 147.99 min, substantially shorter than the descriptive mean. Robustness was examined using nonparametric estimators, Akaike-weighted model averaging, source-confidence weighting, leave-one-out analysis, and alternative interval assumptions. The contribution is a reproducible framework for converting heterogeneous casualty narratives into uncertainty-qualified lower-tail time-reserve evidence and non-prescriptive bridge–team decision support. The framework is not a physical stability model and cannot replace ship-specific GM/GZ calculations, approved loading and stability information, or the master’s judgement. Full article

In make-to-order (MTO) manufacturing, customer-side issues involving payment, documentation, logistics, or site readiness may postpone shipment after order acceptance, increasing finished-goods inventory, capital tie-up, and capacity inefficiency. Although prior studies mainly address in-production delays or pre-order demand uncertainty, Customer-Induced Shipment Postponement (CISP) and its integration into production planning remain comparatively underexamined. This study proposes a predictive–prescriptive decision-support framework that uses ERP data from a real MTO manufacturer to predict shipment-timing outcomes associated with CISP and transfers the resulting predictions into an offline linear-programming-based production-planning model balancing capacity and inventory. Following CRISP-DM, 18,506 order records from 2017 to 2020 were analyzed, and eight machine-learning methods were evaluated across four scenarios combining Full and Parsimonious feature sets with three-class and binary target structures. Models were evaluated using an ex-post cost framework, with conventional classification metrics retained as diagnostic indicators. The selected model was interpreted using SHapley Additive exPlanations (SHAP). Under the stated evaluation assumptions, integrating predictions into the offline LP-based production plan reduced total planning cost by 22.85% relative to the no-prediction baseline. This suggests that the proposed framework can generate measurable planning value. Full article

Industrial IoT maintenance often stops at health prediction, leaving maintenance, rescheduling, safety, and communication to separate decision processes. This study presents MIRA, a safety-constrained graph-based multi-agent reinforcement learning architecture for joint prescriptive maintenance, production rescheduling, and event-triggered communication. Machine condition was estimated from CNC milling data using temporal convolutional models; because predictive uncertainty failed a predefined validation gate, the controller used deterministic health estimates. Evaluation covered five controllers, six simulated scenarios, and 1800 matched episodes. Relative to Graph-MAPPO, MIRA reduced operational cost by 9.38%, weighted tardiness by 28.10%, unexpected failures by 17.39%, message count by 84.98%, and transmitted data by 83.83%, while increasing on-time completion by 23.55%, without a detectable difference in corrected critical-message recall. Across the three independently trained seeds, failures, safety violations, and message count favored MIRA consistently, whereas cost and tardiness favored MIRA in two seeds. Disabling the execution shield increased safety violations from 0 to 3.56 per episode. Post-training variation in the projected-health safe-start threshold from 0.124 to 0.132 produced no safety violations and only small changes in aggregate operational outcomes. Cross-domain health transfer to PHM 2010 failed without adaptation. The results support simulator-level decision coordination, while broader replication, variable-size deployment, and factory validation remain necessary. Full article

Background: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dual incretin therapies have significantly advanced obesity management by facilitating clinically significant weight loss and enhancing cardiometabolic outcomes. Nevertheless, their growing application calls for careful consideration of nutritional and behavioural factors, including decreased dietary intake, gastrointestinal symptoms, loss of lean mass, micronutrient deficiencies, and potential weight regain following treatment cessation. Methods: This narrative review synthesises evidence from clinical trials, observational studies, reviews, professional guidelines, and MBS nutrition protocols to evaluate the role of structured dietetic care in adults receiving GLP-1 RAs or related incretin-based therapies for weight management. MBS pathways provide a comparative framework for investigating how established principles of nutritional assessment, monitoring, and dietetic follow-up may inform pharmacological treatments of obesity. Results: The literature indicates that pharmacological weight-loss treatments should not be administered solely as prescription-based treatments. Structured dietetic care can facilitate nutritional assessment, personalised dietary interventions, the management of gastrointestinal symptoms, protein optimisation, behavioural counselling, physical activity support, and long-term weight maintenance. MBS pathways illustrate the role of dietitians in pre-, peri-, and post-major weight-loss interventions through assessment, guidance on supplementation, dietary progression, biochemical monitoring, and long-term maintenance. Although these principles require adaptation rather than direct application to GLP-1 RA pathways, they offer a valuable system for integrating dietitians into comprehensive obesity pharmacotherapy. Conclusions: Structured dietetic care should be regarded as a fundamental component of the pharmacological management of obesity, especially as GLP-1 RAs and incretin-based therapies become more prevalent. Dietitians are well positioned to help optimise nutritional adequacy, preserve lean mass, manage gastrointestinal symptoms, and support sustained weight maintenance. Future research should investigate effective models for delivering dietetic care alongside incretin-based therapies, including strategies to identify patients who need more intensive support. Full article