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Because the US Medical Licensing Examination Step 1 (Step 1) has transitioned to pass/fail scoring, US Medical Licensing Examination Step 2 has emerged as a primary standardized metric in residency selection. In response, many medical schools have implemented hard deadlines for Step 2 completion before Electronic Residency Application Service (ERAS) submission. Although intended to streamline the application process and ensure timely examination completion, such mandates may unintentionally heighten student stress, jeopardize academic performance, and impair Match outcomes. This perspective offers a medical student's insight into the psychological and professional consequences of Step 2 deadlines and suggests policy recommendations to better align institutional goals with student wellness and success.
Fission-powered space flight, a 60-year dream, would supercharge Solar System exploration.
Anomia is a primary feature of aphasia that negatively impacts quality of life. Although current anomia treatments improve word retrieval, long-term retention and generalisation of trained words to discourse-level communication are rarely measured. Treatment that produces lasting naming gains and generalises to real-world use is one of the top priorities of people living with aphasia. Here, we report the protocol for a randomised clinical trial that investigates individualised anomia treatment through adaptive naming deadlines to achieve 'desirable difficulty' to promote learning retention and generalisation. We implement a within-subject sequential, crossover design in which 30 participants with chronic post-stroke aphasia will complete three anomia treatment conditions in randomised order: (1) an adaptive condition where the naming deadline (ie, amount of time the participant is given to name the item) dynamically adjusts between 1.5 and 10 s based on ongoing participant performance and (2) a static Effort-Maximised condition where there is a fixed 10-second naming deadline for all treatment sessions and (3) a static Accuracy-Maximised condition where items are presented immediately in auditory and orthographic form and are repeated by the participant. In each condition, participants are treated on 40 unique non-overlapping words across eight treatment sessions. Before and after each condition, participants complete naming probes and discourse probes. Treatment outcomes from the adaptive treatment will be tested against the two static conditions using linear mixed-effects modelling. Our primary outcome is performance on noun picture naming at 3 months post-treatment. We evaluate production of treated words in discourse probes as a secondary analysis. We predict that our novel, adaptive naming treatment will produce more successful outcomes compared with the static treatment conditions. The Institutional Review Board of the University of Pittsburgh approved the trial protocol (Study 21120130). Following study completion, results will be disseminated in peer-reviewed journals. If hypothesised results are observed, the adaptive treatment will be a novel, empirically based intervention for long-term retention of anomia treatment gains, positively impacting the lives and recovery of individuals living with aphasia. NCT05653440.
Targeting the younger generation and growing appeal among older users, video games have become an important and timely source of entertainment for teenagers and preteens. The study aims to assess the relationship between Jordanian schoolchildren's academic success and online gaming addiction. A sample of 458 children, whose ages were 9 to 17, were randomly chosen from five private schools to participate in this cross-sectional survey, which was carried out in Jordan between May and July 2024. Pre-teens (9-12 years old) and adolescents (13-17 years old) were the two groups of participants. The Internet Gaming Disorder Scale-Short-Form (IGDS9-SF) in Arabic was used to measure the severity of gaming. Self-reported academic achievement, gaming habits, and sociodemographic information were all covered via a standardized questionnaire. The t-test and chi-square test were used to analyse group differences; p < 0.05 was deemed statistically significant. The findings indicated that adolescents' excessive gaming was more severe than that of preteens. Teens reported a poor correlation with their academic performance, were more likely to use gaming apps during class, and had trouble focusing. Teens, who made up 63.5% of the participants, reported a higher perceived negative impact of gaming applications on their overall GPA that did not reach statistical significance. The overall effect of gaming on meeting deadlines and exam preparation did not differ significantly between age groups. Internet gaming scores were high among teenagers (p < 0.05). Furthermore, compared to pre-teens, teenagers reported using the internet for gaming more frequently in class (p= 0.049). Teens also claimed that using gaming applications negatively affected their ability to focus in class (p<0.05). The findings showed that excessive usage of gaming apps in class has a more detrimental effect on students' ability to concentrate, but it has no significant effect on missing deadlines or preparing for exams. These findings highlight the significance of keeping an eye on gaming behaviour in order to reduce the negative effects of IGD on concentration in the classroom. Video game disorder is quite prevalent among Jordanian private school students.These results underscore the need for more long-term studies using objective academic indicators and larger samples to elucidate the educational implications of internet gaming in this population. They also show that attention during class and difficulty focusing during lessons are regarded as potential areas of concern in the context of intensive gaming.
The Critical Assessment of Functional Annotation (CAFA) is a community effort held to understand the field of computational protein function prediction. Every three years, since 2010, the organizers initiate an experiment to collect function predictions on a large set of proteins and then evaluate the performance of predicting methods on a subset of proteins that have accumulated experimental annotations between the submission deadline and the evaluation time. CAFA provides an independent and rigorous assessment of the current state of the art, thus leveling the playing field, highlighting successes, revealing bottlenecks, and offering a forum for the exchange of ideas in protein science. Here, we report the results of the fourth CAFA experiment (CAFA4). CAFA4 featured the participation of 148 methods from 70 research groups on a total of 46,205 unique proteins over a 5-year annotation accumulation phase, the longest in any CAFA. In a comparison across CAFA2-CAFA4 methods, the prediction of Gene Ontology (GO) terms has clearly improved across all three GO aspects and traditional evaluation settings. While not achieving the first rank, several CAFA2 and CAFA3 methods featured in the top ten methods in many evaluations, suggesting that earlier methods still hold relevance. The performance is weaker in the newly introduced "partial knowledge" evaluation category (proteins with experimental annotations before submission deadline that gained additional annotations in the same GO aspect during the annotation accumulation phase), highlighting the need for a new class of methods. The rankings of the methods were stable over the years in traditional evaluation settings, but less so in the new partial knowledge evaluation. Overall, the field continues to progress with some influx of new participants. Sustained efforts will be necessary to substantially advance it.
Humans often align their respiration with external events, which may optimize neural resources for perception and action. This may adjust neurophysiological processes related to neural excitation, attention, or arousal to optimize task performance. However, it remains unclear whether this alignment is a passive entrainment to a task's overall rhythm or an active process selectively aligning respiration more to highly demanding events. We tested this by recording respiration during three visual discrimination experiments that manipulated the importance of individual trials by imposing response deadlines or manipulating trial value and difficulty. We found that participants align their respiration more consistently for trials with short deadlines or trials presenting high-value and high-difficulty. This demonstrates that respiratory alignment is dynamically modulated on a trial-by-trial basis according to anticipated effort or task demands. Hence, respiration serves as an active tool to strategically allocate cognitive resources for sensory-motor challenges.
With the rapid development of Internet of Vehicles (IoV) applications, the demand for serving computation-intensive and delay-sensitive tasks, which are executed in a dynamic mobility environment, continues to grow, while the embedded computing power carried by vehicles remains limited, and they are facing strict requirements in terms of latency. In vehicular edge computing, to offload computation to the nearby roadside units (RSUs) and to enable centralized learning-based offloading, mobility, task, channel, and resource information at the whole system needs to be gathered at a central learner, leading to a higher communication overhead and raw data exposure. This study introduces a privacy-aware federated deep reinforcement learning (FDRL) framework for vehicular edge computing task offloading with RSU assistance. The novelty of the proposed framework does not lie in the common usage of federated learning and deep reinforcement learning (DRL), but rather in the compactness of four coupled mechanisms: generation of a hybrid action representation of federated binary offloading decision and continuous resource allocation for RSUs, a personalized federated aggregation mechanism for non-IID vehicular observations collected on the RSU, a task-criticality-aware deadline reliability model with class-dependent violation penalties, and a handover-aware multi-RSU model that incorporates signaling delay, service-context transfer delay, and processing/authentication delay. In the proposed framework, the model parameters of the local SAC policies are provided to the federated coordinator rather than the locally observed information, such as raw vehicular trajectories, which can instead be used for local training of the SAC-based policies. Controlled simulation experiments are conducted to compare the proposed method with both local execution and edge-offloading methods, two centralized DRL baselines (DQN and DDPG), and three federated DRL baselines (FedAvg-DQN, centralized-SAC, and federated-MADRL). The results indicate that under the adopted simulation settings, the proposed FDRL framework achieves competitive and/or better system cost, delay, energy, deadline-violation performance, and communication overhead compared with other schemes. This privacy usefulness really means having less raw data exposed when federated training is used, and does not mean any formal privacy guarantee against inference attacks against model updates.
The first iteration of Good Practice for Conference Abstracts and Presentations (GPCAP), published in 2019, set the baseline for general recommendations and expectations for scientific conference abstracts and presentations. While individual conference guidelines must be followed, these recommendations aim to provide principles and best practice for pharmaceutical company-sponsored research. The purpose of conferences is the prompt communication of new data for dissemination and discussion within the context of short deadlines and relevant audiences. These updated recommendations aim to provide support for all individuals with a vested interest in the communication of scientific data at conferences. To provide principles and best practice covering the preparation and presentation of pharmaceutical-supported conference material. Feedback from the previous iteration, interviews with experts, and general revisions have been incorporated into these updated recommendations, aligning with Good Publication Practice (GPP) 2022. New sections have been added to cover topics that have risen in prominence since the first iteration: patient engagement, accessibility, and inclusivity; artificial intelligence; and enhanced content. Other sections cover authorship, copyright, citations, and encores, and have been updated accordingly. Conferences remain the key arena for communication and discussion of scientific data in real time as new developments within the scientific field continue to evolve rapidly. These updated recommendations provide principle-based, practical, insights-driven recommendations and suggestions on how to submit and present company-sponsored research with high standards and a commitment to consistency, transparency, and integrity for the scientific community. The aim is to make conferences the best they can be for all interested parties, within the context of pharmaceutical company-sponsored research.
Exam anxiety is a multidimensional construct combining physiological reactions and affective responses that can hinder academic performance. Academic stress reflects students' perceived pressure related to workload, deadlines, and self-evaluation. Achievement motivation refers to students' drive to attain optimal performance. This study evaluated academic anxiety, perceived stress, and achievement motivation before and after examinations and examined whether artificial intelligence can effectively assess students' psychological states. A cross-sectional, repeated-measures design was used. Academic institution students completed an online questionnaire assessing exam anxiety, perceived academic stress, and achievement motivation before and after examinations. Data were analysed using SPSS for statistical modelling. In parallel, a fuzzy logic system (FLS) was developed to model students' psychological states and estimate exam anxiety and achievement motivation in relation to perceived stress. Outputs from SPSS and FLS were compared to evaluate concordance. SPSS analysis showed a significant interaction between perceived stress and achievement motivation prior to examinations (b = 0.02, 95% CI: 0.01-0.02, p < 0.001). This moderating effect was not observed after examinations (b = 0.00, 95% CI: -0.01-0.01, p = 0.554). The FLS results were consistent with conventional statistical findings, demonstrating strong agreement in identifying levels of exam anxiety and the role of achievement motivation before exams. Achievement motivation moderates the relationship between perceived stress and exam anxiety only in the pre-examination period, highlighting the temporal nature of this interaction. The alignment between SPSS and FLS outcomes suggests that artificial intelligence, particularly fuzzy logic systems, can efficiently evaluate students' academic exam anxiety. These findings support the potential use of AI-based tools for psychological state assessment in educational settings, especially for early identification of students at risk of heightened exam anxiety.
This study aims to systematically search, screen, evaluate, and summarize the best evidence related to the prevention and management of febrile seizures in children, with the goal of providing evidence-based guidance for clinical practice in this field. A comprehensive search was conducted across multiple databases, including UpToDate, Joanna Briggs Institute (JBI), Guidelines International Network, Agency for Healthcare Research and Quality, Scottish Intercollegiate Guidelines Network, National Institute for Health and Care Excellence, Registered Nurses Association of Ontario, the Cochrane Library, Embase, PubMed, Web of Science, China National Knowledge Internet (CNKI), SinoMed, and WanFang database. The search deadline was from database establishment to July, 2025. Two researchers independently evaluated the quality of the included literature and extracted and summarized the evidence. Twenty literature sources were included, comprising 3 clinical decision, 7 guidelines, 4 expert consensus, 1 best practice, and 5 systematic reviews. Through integration of these sources, 29 best evidence statements were ultimately developed, covering 6 major themes: Clinical assessment and diagnosis, Indications of hospitalization, risk assessment, non-pharmacological treatment, pharmacological treatment, as well as caregiver support and follow-up. This study summarizes the best evidence for the prevention and management of febrile seizures in children. In clinical application, healthcare professionals should make professional judgment and select evidence based on clinical circumstances and the preferences of the child, thereby providing a more scientifically grounded basis for clinical nursing practice. This study was based on the evidence summary reporting specifications of the Fudan University Center for the Evidence-based Nursing, the register name is "Evidence summary for prevention and management of febrile seizures in children", the registration number is "ES20246803".
Stakeholder engagement improves the quality, clinical relevance, and contextual fit of healthcare interventions. However, there is limited work characterizing how researchers approach stakeholder engagement in practice, and which organizational supports they most value. The objective of this study is to describe how experienced researchers engaged multilevel stakeholders in implementation studies, and the benefits, challenges, and needs they encountered. We identified Veterans Affairs (VA) healthcare system researchers with histories of stakeholder-engaged implementation research through a combination of literature review and snowball sampling. We conducted semi-structured telephone interviews with 28 participants in 2016. Two research team members analyzed interview data using a hybrid inductive-deductive approach. Participants described how multilevel stakeholder input substantively improved their research. Common engagement approaches varied in intensity and included interviews and focus groups, advisory committees, and stakeholder membership on research teams. Although participants expressed a desire-even a moral imperative-to increase stakeholder engagement, their efforts were limited by organizational constraints. Participants suggested that VA can support stakeholder engagement in research by reconceptualizing funding mechanisms, providing resources, and fostering an organizational culture that aligns with engagement principles. Experienced researchers perceived multilevel stakeholder engagement as both critical for implementation research and difficult to achieve. Findings highlight that stakeholder engagement is shaped not only by researcher commitment and skill but also by organizational constraints, incentives, and power structures. Results offer actionable suggestions for improving alignment between engagement activities, professional incentives, and organizational infrastructure that may strengthen stakeholder-engaged implementation research in VA and other healthcare settings. Engaging stakeholders, such as patients, providers, and administrators, in healthcare research can improve the relevance and quality of research processes and findings. However, little is known about how institutions limit or support meaningful stakeholder engagement in research. We interviewed 28 researchers at Veterans Affairs (VA) healthcare facilities about how they engaged stakeholders in their studies, and the benefits and needs they experienced. Participants shared that stakeholder input strengthened their research, and many wanted to deepen stakeholder involvement in their work. They also stated that stakeholder engagement is time-consuming, complex, and sometimes difficult to balance with organizational pressures (e.g. related to funding, deadlines, and promotion). Participants suggested that VA can support stakeholder engagement in research by aligning funding mechanisms, resources, and organizational culture with engagement principles. These findings can improve stakeholder engagement research in VA and other healthcare organizations.
Background/Objectives: Oral health promotion in early childhood depends strongly on caregivers' preventive care at home and educational centers. The aim of this study was to investigate socioeconomic, educational, cultural, and oral health factors associated with caregivers' decisions to decline their children's participation in school-based oral health promotion programs. Methods: Caregivers who did not authorize their children's participation in the project were identified through school records and contacted using available information (name, telephone number, and email address). Participants were selected through simple random sampling. Results: Among the 58 caregivers included in the study, the main reasons reported were failure to return the consent form or missing the deadline (36.2%), considering the child too young (19.0%), already receiving private dental care (13.8%), not understanding the consent form (13.8%), not having received the document (10.3%), lack of trust in the professional (3.4%), and other reasons (3.4%). Higher income was significantly associated with higher educational level (p = 0.002), increased toothbrushing frequency (p = 0.007), shorter time since the last dental visit (p < 0.001), and lower levels of embarrassment related to oral health (p < 0.001). Additionally, lower-income caregivers were more likely to seek dental care only in the presence of problems (p = 0.046), while higher-income families were more likely to report private dental care as a reason for non-authorization (p < 0.001). Conclusions: These findings associate socioeconomic and educational inequalities with adverse effects on family oral health among parents, by reducing the frequency of preventive dental examinations and daily oral hygiene practices; and among children, by limiting authorization to participate in school-based oral health promotion programs. This evidence underscores that successful promotion of children's oral health in educational settings requires addressing social disparities while strengthening caregivers' knowledge and motivation to support participation.
Retinal ganglion cells (RGCs) are vulnerable to excitotoxic damage mediated by excessive NMDA receptor activation and calcium overload. Extracellular magnesium (Mg2+) blocks NMDA receptors in a voltage-dependent manner, offering potential neuroprotection. However, the optimal Mg2+ concentrations and timing for effective intervention remain poorly defined. We developed a conductance-based computational model of an RGC incorporating Hodgkin-Huxley dynamics, AMPA and NMDA receptor-mediated synaptic transmission, and intracellular calcium dynamics. We systematically varied Mg2+ concentration (0.2-2.5 mM) and stimulation frequency (10-100 Hz) to identify therapeutic windows balancing neuroprotection with function preservation. At physiological frequencies (10-60 Hz), elevated Mg2+ reduced calcium (Ca2+) accumulation by 50-85% without affecting spike output. At excitotoxic frequencies (80 Hz), a narrow therapeutic window of 1.6-2.0 mM was identified, lying within a broader 1.4-2.0 mM spike-loss plateau (20% loss), where calcium additionally fell below the toxicity threshold while spike output was preserved. Intervention timing analysis revealed that Mg2+ protection efficacy is maximal with pre-treatment or immediate intervention (100%), and declines steeply with delay-reflecting the rapid early rise in Ca2+ rather than a fixed biological deadline (≥50% protection requires intervention within 0.2 s in our abrupt-onset protocol; ∼11% by 0.5 s). Re-analysis in terms of normalized Ca2+ progress revealed that the critical constraint for ≥50% protection is intervention before ∼35% of peak Ca2+ accumulation-a state-based threshold reflecting relative phase sensitivity that generalizes across timescales. Sensitivity analyses confirmed robustness of the therapeutic window across physiologically plausible parameter ranges, and numerical validation demonstrated accuracy of the computational approach. These findings demonstrate that Mg2+-mediated neuroprotection is highly dependent on both concentration and timing, with implications for therapeutic strategies targeting glutamate excitotoxicity in glaucoma and retinal ischemia.
BACKGROUND: With the surge in scientific publication over the years, there is an exponential rise in predatory publishing. The aim of the current study was to assess predatory journals’ awareness among clinical and non-clinical researchers. METHODS: A cross-sectional study was conducted in a tertiary care hospital, affiliated university and the research center. Both males/females, physicians, professors, researchers, students, working in a hospital, university or a research center were enrolled. The primary outcome was the awareness of predatory journals, and to identify the predictors of awareness. The estimated sample size was 369. The survey consisted of 33 questions divided into five sections: (i) General information (ii) Research publications (iii) Awareness of predatory journals (iv) Open access (v) Journal selection. The questions were either yes/no or 5-point Likert scale. An electronic survey design was used for data collection. Variables were compared by aware and unaware groups using chi-square test. Logistic regression analysis was utilized to identify the predictors of awareness. Statistical analyses were performed using SAS version 9.4. RESULTS: Of the total 328 respondents, females were 178(54.4%), near half had bachelors/master’s degree 156(48.3%), 117(35%) were physicians, 37(11%) researchers, 17(5%) professors, and 38(12%) students. Half of the participants 167(50.9%) were aware of the term ‘predatory medical journals. Only 56(17%) reported knowing how to spot a predatory journal. 15(4.5%) have reported publishing in a predatory journal due to lack of knowledge (6/15; 40%), to meet a deadline (3/15; 20%), suggested by a colleague (3/15; 20%), or due to (1/15; 6.6%) fast publication process. Reported sources of information were; colleagues 141(42%), attending a course 78(24%), and spam emails 73(22%). 69(21%) respondents reported using Clarivate Master Journal List, 54(16%) SCImago Journal & Country Rank list, 48(15%) use DOAJ. 282(85%) didn’t hear of the educational website Think.Check.Submit. Age (p = 0.036), gender (p = 0.0007), qualification (p = 0.0002), profession (p = 0.01), working in a research center (p = 0.005), and professional training (p = 0.0006) were significantly different by predatory journal awareness. CONCLUSION: Publishing in a predatory medical journal is a global threat to the scientific community and academic integrity. Efficient steps need to be taken in order to address predatory journals’ invasion, and raising awareness.
A monitoring campaign was carried out in a Mediterranean agricultural district by transplantation of thalli of the lichen Evernia prunastri to 32 stations to evaluate the distribution of pesticides, the development of drift events, the dimension of the area, and the level of exposure in the Cantinella village. Spearman correlation coefficients and PCA performed on the database stations × pesticides revealed that they were sprayed as mixtures and mostly bioaccumulated in the northern part. Spirotetramat and Hexythiazox exhibited the highest levels, consistent with the pests, being the former sprayed to contrast Tetranychus urticae, the most diffused species, and the latter used as a multi-spectrum pesticide. Based on the Calabria Region treatment schedule and covariance between pesticide spatial variation, we believe that Hexythiazox and Acetamiprid treatment has gone beyond prescribed deadline. Drift events were associated with the detection of pesticides in Cantinella and zones managed with organic agriculture criteria and to the significant correlation between the concentration patterns and wind flows. All the pesticides were detected inside Cantinella stations pointing to a potential co-exposure of the inhabitants to them. Spirotetramat concentration was 80% higher than that of the outside stations. The green-cement cover ratio was strongly inadequate for reducing atmospheric pollution, with a significant spatial variation (chi-square test) in green areas (northern side: 14%, southern side: 24%) associated with the percentage of single and total pesticide loads (northern side: 25%, southern side: 7%). Our data suggest that widespread drift, caused also by over-spraying, can damage the agricultural economy and promote pesticide inhalation by residents, especially when urban characteristics increase exposure.
Electronic medical records (EMRs) have become foundational to healthcare, improving communication, data access, and patient outcomes. However, increasing reliance on EMR's has increased vulnerabitlity during downtime. In paediatric cardiac care, where patients require highly specialized, multidisciplinary treatment, the absence of a functional EMR significantly disrupts documentation workflows and threatens the accuracy of data submitted to national cardiac registries including the Paediatric Cardiac Critical Care Consortium and the Society of Thoracic Surgeons. This article examines the impact of an unexpected 27-day complete EMR downtime followed by a 10-day partial downtime, where a data abstraction team manually managed data for 123 unique cardiac patient encounters totalling 762 patient days at a paediatric heart centre and the response of the cardiac data abstraction team. We describe how the team adapted its abstraction process during the downtime, used collaborative strategies, enhanced paper tracking, and proactively communicated to maintain data integrity. Efforts were grounded in a deep understanding of paediatric registry metrics and submission requirements. Despite significant workflow disruptions, the team was able to preserve data accuracy and meet registry deadlines by identifying documentation gaps, supplementing data from paper records, and coordinating with frontline providers. The event revealed key vulnerabilities in downtime preparedness but also demonstrated the value of dedicated data abstractors in ensuring continuity of quality reporting. Downtime events highlight the critical role of data abstractors and the need for institutional planning and registry-level guidance. Developing robust downtime protocols and embedding abstraction-aware workflows can mitigate documentation risks and protect data quality, ultimately supporting improved outcomes for paediatric cardiac patients.
Medicaid managed care serves 86 million beneficiaries at $376 billion annually, yet evidence that managed care delivery improves outcomes remains inconclusive, with nearly 40% of Medicaid acute care visits attributable to ambulatory care-sensitive conditions. State procurement processes create principal-agent relationships where managed care organizations (MCOs) make performance commitments before contract award, but no research has systematically evaluated whether MCO commitments align with state priorities. We assembled 265 procurement documents from 32 states (2017-2024), extracting 1666 text files. Using retrieval-augmented generation (a method that grounds AI outputs in retrieved document passages) with large language model analysis, we extracted 372 283 performance claims classified into 6 thematic domains. Two doctoral-level coders validated a stratified random sample (Cohen's kappa: 0.86, 95% CI: 0.81-0.91). We calculated per-file normalized concordance ratios (MCO claims per document divided by state requirements per document, controlling for document volume) and conducted stratified analyses by document type. MCOs systematically overemphasized technology claims (normalized concordance ratio: 53.9, median: 28.2) and health equity claims (ratio: 22.0, median: 10.3) relative to state requirements, while underemphasizing chronic disease management (ratio: 15.8) and workforce development (ratio: 14.2). Post-pandemic increases in health equity (10.3-fold) and technology claims were driven primarily by state contract and scoring documents rather than MCO proposals. Only 6 states had sufficient paired data for concordance analysis. Medicaid procurement documents reveal systematic thematic divergence between MCO performance claims and state priorities. The observed patterns may reflect strategic positioning, normal negotiation dynamics, or evolving state procurement frameworks. Given that Medicaid beneficiary outcomes remain poor despite widespread managed care adoption, procurement reforms requiring measurable, time-bound performance commitments could improve alignment between organizational claims and care delivery.Short Article Summary - Layperson TermsWhen states hire insurance companies to manage Medicaid health coverage for low-income Americans, the companies make extensive promises about what services they will provide, but this study found they systematically overpromise on politically popular topics like technology and health equity while undercommitting to everyday care like managing diabetes or having enough doctors in their network. States should require insurance companies to back up their promises with specific numbers and deadlines rather than accepting vague commitments that sound good but don't translate into better care for the ~80 million Americans who depend on Medicaid.
In the context of smart manufacturing, with the widespread deployment of Industrial Internet of Things (IoT) devices, a large number of computation tasks that are highly sensitive to latency and have strict deadlines have emerged, requiring real-time processing. Effectively offloading tasks to address the issues of increased latency and task dropouts caused by dynamic changes in edge node load has become a key challenge in the cloud-edge-end collaborative environment of smart manufacturing. To tackle the complex issues of unknown edge node loads and dynamic system state changes, this paper proposes a distributed algorithm based on deep reinforcement learning, utilizing convolutional neural networks (CNN) and the Informer architecture. The proposed algorithm leverages CNN to extract local features of edge node loads while utilizing Informer's self-attention mechanism to capture long-term load variation trends, thereby effectively handling the uncertainty and dynamics inherent in node loads. Furthermore, by integrating the Dueling Deep Q-Network (DQN) and Double DQN techniques, the algorithm achieves a precise approximation of the state-action value function, further enhancing its capability to perceive system temporal characteristics and adapt to heterogeneous tasks. Each mobile device can independently make task offloading decisions and scheduling strategies based on its observations, enabling dynamic task allocation and optimization of execution order. Simulation results show that, compared to various existing algorithms, the proposed method reduces task dropout rates by 82.3-94% and average latency by 28-39.2%. Experimental results validate the significant advantages of this method in intelligent manufacturing scenarios with high load and latency-sensitive tasks.
In accordance with Article 31 of Regulation (EC) No 178/2002, the European Commission mandated EFSA to issue a statement concerning confirmatory data that were not submitted by the set deadline by the applicant following Article 12 MRL reviews under Regulation (EC) No 396/2005 for the active substances dicloran, fenazaquin and tebufenozide. EFSA assessed the availability and completeness of the confirmatory information and prepared a statement providing a conclusion on whether the existing tentative maximum residue levels (MRLs) are supported by data. The statement also included indications to risk managers whether the tentative MRLs currently established by Regulation (EC) No 396/2005 could be maintained. Before finalisation, the statement was circulated to Member States for consultation via a written procedure.
This paper introduces a resilient distributed model predictive control (RDMPC) framework for coordinating energy management across networked microgrids with demand response integration. The coordination mechanism employs an alternating direction method of multipliers (ADMM)-based distributed MPC formulation that maintains tie-line reciprocity via a shared consensus schedule; standard ADMM convergence results apply under reliable communication for the convex quadratic-program relaxation. Safe operation under communication impairments and early termination is achieved by executing the reciprocal consensus tie-line setpoints and performing a local feasibility-repair step with physically interpretable slack variables (load shedding and spillage), providing anytime feasibility while (under reliable communication) optimality improves with additional ADMM iterations. Communication failure resilience is achieved by treating tie-line mismatch as bounded disturbances and applying two-sided reserve margins (upward and downward) through constraint tightening, ensuring feasibility for any mismatch within the assumed bounds when sufficient reserve headroom exists. Demand response is incorporated using distinct models for shiftable loads (energy-by-deadline) and curtailable loads (penalized reduction). Evaluation on a five-microgrid benchmark under packet loss, burst outages, and topology changes confirms feasible reciprocal execution under loss; relative performance versus naive distributed MPC (B2) is seed-dependent, and DR ablation shows large degradations (about [Formula: see text] energy not served (ENS) and [Formula: see text] cost increases) when flexibility is removed.