In this research, we derive equations for solving for the stationary points of the DLPNO-CCSD Lagrangian, in the t1-transformed formalism introduced earlier and as currently implemented in the Psi4 quantum chemistry software package. These lambda equations in the local pair natural orbital basis allow for the evaluation of CCSD(T)Λ energetics with linear-scaling computational effort, also known as the asymmetric triples correction. This DLPNO-CCSD(T)Λ method allows for accurate triples contributions to be computed for larger molecules, especially in cases that CCSD(T) is known to be insufficient, such as with multireference systems and bond-breaking systems. We showcase the accuracy of our code on reaction energies, barrier heights, and noncovalent interaction energies. Also showcased are the capabilities of our code by evaluating DLPNO-CCSD(T)Λ energetics on large noncovalent dimers up to 112 atoms, as well as a rhodium catalyst complex containing 66 atoms.
Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at predetermined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. IIW-GEEs have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We show that currently available methods can be biased for situations in which the health outcome of interest may influence a subject's dropout from the study. We have extended the IIW-GEE framework to adjust for informative dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for.
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This study presents a numerical analysis of the combined influence of SWCNT-H₂O nanofluid concentration and core topology on thermal performance, using ANSYS Fluent and the Finite Volume Method (FVM). Five core materials-Polyurethane (PU), Unplasticized Polyvinyl Chloride (UPVC), Extruded Polystyrene (XPS), Expanded Polystyrene (EPS), and Glass Wool (GW)-were analyzed under laminar flow and constant heat flux conditions ranging from 35 to 350 kW/m². The continuity, momentum, and energy equations were discretized using a second-order upwind scheme, with convergence thresholds of 10⁻⁷ for the continuity and momentum equations and 10⁻⁹ for the energy equation. This study presents a coupled multi-physics numerical analysis of SWCNT-H₂O nanofluid-cooled sandwich panels, simultaneously evaluating thermal transport, hydraulic behavior, and moisture diffusion within a unified finite volume framework. Unlike conventional investigations focusing on isolated thermo-hydraulic or hygrothermal effects, the present work quantifies the interacting mechanisms governing convective heat-transfer enhancement, conductive insulation resistance, and moisture stability across multiple core materials. Furthermore, entropy generation and Bejan number analyses are incorporated to reveal thermodynamic trade-offs and irreversibility characteristics, providing deeper insight into performance optimization. The study reveals a non-intuitive trade-off: materials providing higher thermal insulation do not always deliver superior thermo-hydraulic efficiency under nanofluid cooling, highlighting the competing roles of conduction resistance and convective enhancement. The increase in pressure drop between φ = 1.5% and φ = 4.5% remained below 12% in all cases, which is modest compared to the 39.8-55.4% improvement in heat transfer. However, this finding is specific to the single-phase homogeneous nanofluid model employed; real SWCNT-water nanofluids at 4.5% concentration would likely exhibit larger viscosity increases and higher pressure penalties. Overall, the findings confirm that SWCNT-H₂O nanofluids significantly enhance thermal conductivity and convective heat transfer, particularly at higher Reynolds numbers (Re = 200-600). Among the tested materials, the UPVC panel exhibited the highest thermo-hydraulic efficiency-surpassing the other cores by up to 42.7%-whereas PU proved to be the most cost-effective option, offering a balanced combination of thermal and structural performance.
This study uses data from the National Health and Nutrition Examination Survey (NHANES) to evaluate the extent of cardiovascular risk reclassification from the 2013 pooled cohort equations (PCEs) to the 2023 Predicting Risk of Cardiovascular EVENTs (PREVENT-ASCVD) equations.
Pyrolysis is a relatively mature process for recycling plastic waste, yet predictive kinetic models remain elusive due to the enormous number of radical reaction pathways, intermediates, and products. Starting from elementary hydrogen abstraction, random and chain-end β-scission reactions, we construct continuum population balance equations (PBEs) and discrete species balance equations (SBEs) to model polypropylene (PP) pyrolysis for realistic initial molecular weight distributions (MWDs) and reactor time and length scales. The framework accounts for the separate but coupled MWDs of alkanes, α-olefins, and α,ω-olefins polymers, along with the amounts of volatile alkanes and olefins generated during the reaction. We parameterized the model using rate constants from prior ab initio calculations. The model predicts the evolution of the polymer MWD, the degree of double-bond functionality, the number of scission events, and the amounts of volatile alkane and alkene products as a function of time. The predictions agree with experimental MWD data and resolve prior questions about the double-bond functionality (f) of volatile and nonvolatile products. We discuss how the model can help design processes to obtain products of the desired molecular weight and functionality.
Augmented renal clearance (ARC) is a critical clinical phenomenon characterized by altered renal excretory function in patients. Although ARC has garnered growing clinical and research attention in recent years, notable gaps remain in its investigation. Inadequate recognition of ARC-related risks may lead to underappreciation of this clinically significant condition in practice. This narrative review synthesizes current evidence on ARC. Current evidence on ARC was narratively reviewed, including pathophysiological hypotheses, diagnostic criteria, predictive models, drug-dosing implications and emerging monitoring techniques. Compared with earlier summaries, we place particular emphasis on recent changes in kidney-function assessment for drug dosing, including the distinction between measured glomerular filtration rate (GFR), measured creatinine clearance (CrCl), Cockcroft-Gault estimated CrCl, and contemporary estimated GFR equations. We discuss the limitations of predictive equations in unstable kidney function, the potential role of repeated short-interval CrCl, bedside hemodynamic assessment, standardized experimental models and multi-omics approaches. ARC should be regarded as a dynamic pharmacokinetic phenotype requiring timely recognition, drug-specific interpretation, therapeutic drug monitoring and direct or repeated assessment of renal clearance in high-risk patients.
Constructal design of the NACA (National Advisory Committee for Aeronautics) airfoil was to reduce the drag force and increase the lift force for search and rescue (SAR) missions. This series of NACA airfoils is defined by a 4-digit NACA mptt, which indicates the camber, the location of maximum camber, and the thickness. Three degrees of freedom can be used to optimize aircraft airfoils using constructal theory. The CFD (Computational Fluid Dynamics) 2-D simulation was conducted in ANSYS FLUENT version (15), and the SST k-ω model turbulence equations were used to solve the incompressible Reynolds-averaged Navier-Stokes (RANS) equations. Simulations are performed using MATLAB's Neural Network ANN (Artificial Neural Network), which enables a robust surrogate-based algorithm for the design study. The highest Cl/Cd ratio indicates the trade-off between lift and drag. An airfoil with a high Cl/Cd (lift-to-drag coefficient) ratio produces more lift than drag, thereby enhancing aerodynamic performance through comparative analysis of three degrees of freedom (m, p, t) at low Reynolds numbers ([Formula: see text] and [Formula: see text]). NACA 4412 makes a better Cl/Cd ratio, 1.38 at 4[Formula: see text]-12[Formula: see text], than NACA 4418, about 26% higher at the same AoA 4[Formula: see text]-12[Formula: see text]. An increase in Reynolds number led to higher Cl/Cd ratios, 1.38 at angles of attack (AoAs) of [Formula: see text], compared to NACA 4418, about 26% higher at the same AoAs of [Formula: see text]. across all profiles, indicating higher aerodynamic efficiency. It is particularly applicable to search-and-rescue UAVs, which can operate at varying speeds and altitudes depending on mission requirements. The ANN model has determined the optimal AoA and Reynolds number that maximizes the Cl/Cd ratio of the NACA 4412 airfoil. The CFD results are validated against ANN results, which are based on experimental results used to train the artificial intelligence algorithm to predict ANN results from the present study.
In this article, a novel homotopic reinforcement learning (RL) framework is proposed to address the distributed cluster consensus problem in continuous-time multiagent systems (MASs). For the first time, the investigated issue is formulated as a zero-sum differential game using a proposed minmax game policy, in which a local regulation error is introduced to characterize the deviation of each agent from its assigned leader. Based on this formulation, a set of group game algebraic Riccati equations is derived to obtain the optimal control law. To overcome reliance on known system models, these equations are solved using a data-driven homotopic policy-iteration scheme that leverages online state and input information. In contrast to conventional learning schemes, the proposed approach embeds a homotopic process that relaxes the requirement for an admissible initial policy. Rigorous stability and convergence analyses are provided, and the effectiveness of the proposed method is further demonstrated through theoretical analysis and numerical simulations.
Simulating intracellular biochemical reactions remains a significant challenge in mathematical modeling because of the complex interactions among diverse molecular species. The natural number simulation (NNS) framework offers a dynamic approach to simulating these reactions using a novel algorithm based on reaction equations. In this study, we developed a computational cell model incorporating mitochondria to examine key metabolic processes, including glucose uptake, glycolysis, the tricarboxylic acid cycle, and ATP synthesis via the electron transport chain. Substrate transport mediated by membrane proteins, such as pyruvate and nicotinamide adenine dinucleotide transporters, and the electron transport chain, was replicated using simplified reaction equations. The simulation results showed that, with appropriately chosen rate constants, the ATP production rate reached approximately 155 molecules s- 1 per ATP synthase. Sensitivity analysis indicated that the number of mitochondrial phosphate transporters and the rate of phosphate transport into mitochondria strongly influence ATP production. The model also showed that intermittent glucose supply has a minimal impact on ATP production and that the framework is capable of incorporating the effects of deuterium-containing water on ATP synthesis. This framework provides a foundation for future efforts in simulating more detailed metabolic pathways and integrating experimental data.
Accurate and temporally consistent cardiac motion tracking from cine MRI is essential for functional assessment and disease analysis. However, existing learning-based methods are typically formulated in discrete time and struggle to capture long-range temporal dependencies, often leading to accumulated errors and physically inconsistent motion. In this paper, we propose a unified unsupervised framework for cardiac motion tracking based on latent neural ordinary differential equations (ODEs). A frame-aware encoder extracts motion-sensitive features with temporal embeddings, which are evolved in a compact latent space via a neural ODE to model continuous-time deformation dynamics. To capture complex temporal variations in an efficient manner, we incorporate a conditional MLP-based dynamics module and further perform bidirectional forward-backward evolution within a unified framework. The evolved latent representations are decoded into inter-frame deformation fields, and a bidirectional Lagrangian regularization is introduced to enforce long-term temporal consistency and motion reversibility across the cardiac cycle. Extensive experiments on the ACDC and M&Ms datasets demonstrate that the proposed method achieves state-of-the-art performance, producing temporally consistent and physiologically plausible motion fields with a lightweight and efficient architecture.
Mobile health (mHealth) technologies are increasingly integrated into perioperative care to enhance patient engagement and communication. Prior studies in surgical and medical specialties suggest that mHealth platforms may be associated with reductions in hospital stay and readmissions; however, evidence supporting their impact in otolaryngology, particularly in sinonasal surgery, remains limited. The aim of this study was to evaluate the association between perioperative enrollment in CareSense, a patient-facing mHealth platform, and postoperative health care utilization outcomes, including hospital readmissions, emergency department (ED) visits, and length of stay (LOS), among adults undergoing sinonasal surgery. This is a retrospective cohort study performed at a single tertiary care academic medical center between May 2021 and January 2024. All adult patients (≥18 years) who underwent sinonasal surgery with two fellowship-trained rhinologists during the study period were included. CareSense was offered to all patients at the time of surgical scheduling, and enrollment was voluntary. Patients were categorized into CareSense participants and nonparticipants. Primary outcomes were all-cause hospital readmissions and ED visits within 30, 60, and 90 days following surgery. Secondary outcomes included the length of hospital stay among readmitted patients. Clinical, demographic, and outcome data were obtained through retrospective electronic health record review. Univariate analyses compared outcomes between groups, and multivariable logistic regression using generalized estimating equations was performed to estimate the association between CareSense participation and outcomes while adjusting for age, sex, hypertension, and diabetes. A total of 1135 patients were included, of whom 340 (30%) enrolled in CareSense and 795 (70%) did not. Compared with nonparticipants, CareSense participants had lower adjusted odds ratio (OR) for readmission for any cause at 30 days (OR 0.24, 95% CI 0.08-0.75; P=.007), 60 days (OR 0.40, 95% CI 0.19-0.83; P=.01), and 90 days (OR 0.54, 95% CI 0.29-0.99; P=.04). Among patients who were readmitted, mean LOS was shorter in the CareSense group than in the nonparticipating group (0.17 vs 1.68 d; P<.001). The majority of readmissions in both cohorts were unrelated to complications of the index sinonasal procedure. This study demonstrates the benefit of CareSense in lowering postoperative readmission rates and LOS for sinonasal surgery patients, illustrating the role of medical health technology in improving patient care and quality outcomes. Perioperative enrollment in a patient-facing mHealth platform was associated with lower postoperative health care utilization and shorter hospital length of stay following sinonasal surgery. Given the voluntary nature of enrollment and the observational design, these findings should be interpreted as observation findings and hypothesis-generating for prospective studies to more definitively assess the causal impact of mHealth interventions and to identify which components of digital perioperative care most effectively improve outcomes in otolaryngologic surgery.
Return-to-work varies among previously employed patients after critical illness. Social determinants of health (SDH) may play a role in this variation. We compared patient characteristics and explored the association between SDH and long-term joblessness among patients who experienced severe COVID-19. A secondary analysis of a prospective nationwide database. The COVID-19 Recovery Study II (CORES II) study, a nationwide prospective observational study in Japan that examined long-term symptoms and social status over two years following hospitalization. We included adults hospitalized with severe COVID-19 between April 1 and September 30, 2021, who were employed before hospitalization. SDH included education history and pre-hospitalization employment status. The primary outcome was joblessness at the first- and second-year follow-ups. We used generalized estimating equations for the main analysis to examine longitudinal associations between SDH and joblessness, with the SDH variables entered into the model as separate categorical variables.Of 3297 hospitalized patients, 337 met inclusion criteria. At the first-year follow-up, 275 had returned to work, whereas 62 (18.4%) remained jobless. At the second-year follow-up, 242 (82.3 %) had returned to work, and 52 (17.7%) remained jobless. Patients who were jobless at the first-year follow-up showed more persistent symptoms and greater physical and mental impairment compared with those who returned to work. Among SDH factors, non-permanent employment was associated with joblessness over the 2-year follow-up period (odds ratio 2.35; 95% CI [1.26-4.40]) compared with permanent employment. Education history was not associated with joblessness at the first- or second-year follow-ups. Non-permanent employment was associated with joblessness during the pandemic after hospitalization for severe COVID-19. Pre-hospitalization employment status may be relevant to long-term social recovery after critical illness.
In children with epilepsy associated with global developmental delay (GDD), around 10-20% experience refractory epilepsy, often requiring multiple antiseizure drugs (ASDs). These ASDs have narrow therapeutic indices. Therapeutic drug monitoring is crucial for optimizing ASD therapy. In this case, an infant with GDD with TBC1D24 mutation and refractory seizures was admitted with uncontrolled seizures despite multiple ASDs. The patient's serum levels of valproic acid, carbamazepine, and phenytoin were subtherapeutic. Simulations were performed by solving the pharmacokinetic ordinary differential equations numerically to optimize the dose. The modifications were made for valproate (240 mg every 4 h) and carbamazepine (100 mg thrice a day) to achieve therapeutic concentrations. Serum drug levels, after dose adjustment, were within the therapeutic range. The patient experienced no further seizure episodes. This case highlights the importance of individualized pharmacokinetic simulation in optimizing drug doses instead of empirical dose adjustments.
Atherosclerotic cardiovascular disease (ASCVD) prevention relies on risk estimation. Yet among adults labeled high risk by the Pooled Cohort Equations (≥20%), coronary heart disease (CHD) event rates vary, with some experiencing few events, raising the possibility of a resilient phenotype. This study aimed to investigate whether a coronary artery calcium (CAC) score of zero identifies a resilient lower-risk group among adults with high calculated ASCVD risk and compare their risk factor profiles with those with non-zero CAC. We analyzed participants in the Multi-Ethnic Study of Atherosclerosis with Pooled Cohort Equations-estimated ASCVD risk ≥20% and CAC data. Baseline characteristics were compared using standardized mean differences. Ten-year CHD events were analyzed using Kaplan-Meier methods, with HRs for CAC = 0 vs CAC >0 estimated using Cox models adjusted for age, sex, and race/ethnicity. Effect modification by age and sex was assessed. Among 1,608 adults (mean age 73.2 years, 61.4% men) with ASCVD risk ≥20%, 359 (22.3%) had CAC = 0. Risk factors were similar between CAC groups, with standardized mean differences <0.20 for blood pressure, lipids, diabetes, and body mass index. Over 10 years, hard CHD incidence rates were 3.52 and 13.46 per 1,000 person-years for CAC = 0 and CAC >0, respectively. CAC = 0 was associated with lower CHD risk (HR: 0.27; 95% CI: 0.15-0.49). A significant CAC × age interaction (P = 0.023) indicated stronger protection at older ages. Nearly one-quarter of adults labeled high ASCVD risk had CAC = 0 and accrued few events despite similar risk factor burden. CAC = 0 may mark a resilient phenotype warranting targeted mechanistic investigation.
Cardiovascular disease is the leading cause of death in rheumatoid arthritis (RA). By replacing C-reactive protein (CRP) with gamma-glutamyl transferase (GGT), the DAS28-GGT was developed to integrate both inflammatory disease activity and cardiovascular (CV) risk into a single score. We evaluated its ability to classify CV risk in RA patients initiating Janus kinase inhibitors (JAKi) and compared its performance with DAS28-CRP. This cross-sectional study used data from the MAJIK-SFR observational registry. RA patients initiating a JAKi with available GGT were included. CV risk was estimated using the SCORE2 and Framingham equations. The discriminative performance of the DAS28-GGT was assessed using ROC curves and compared with the DeLong test. DAS28-GGT discrimination across DAS28-CRP disease activity categories was assessed. We included 185 RA patients (72% women, mean age 58 ± 13 years). Mean DAS28-CRP was 4.07 ± 1.27 and mean DAS28-GGT 8.88 ± 1.83. According to SCORE2, 54.3% patients were at low, 38.9% at moderate, and 6.8% at high CV risk and 34.8%, 44.7%, and 20.5%, according to Framingham, respectively. DAS28-GGT better discriminated patients with at least moderate CV risk than DAS28-CRP (SCORE2 AUC 0.59 vs 0.52, p <0.05; Framingham AUC 0.63 vs 0.53, p <0.01). DAS28-GGT significantly discriminated DAS28-CRP remission, low, and high disease activity (AUCs 0.91, 0.70, and 0.83 respectively). This study supports the DAS28-GGT as a simple and practical tool to capture both cardiovascular risk and inflammatory activity in RA patients initiating JAKi. Prospective studies are needed to confirm its value for predicting cardiovascular events.
To (1) examine health- and work-related outcomes among partners of cancer survivors and (2) explore associated factors of these outcomes. In the STEPS prospective cohort study 225 employed partners of cancer survivors in the Netherlands completed questionnaires at baseline, and after 6 and 12 months, on work participation, health-related quality of life (HRQoL), and medical, lifestyle, health, and work factors. Generalized estimating equations and time-lagged regression analyses were used to examine changes over time and associations with measured factors. Partners worked on average 31.1 h/week at baseline, which remained stable over the 12-month follow-up. Employment status (i.e., whether participants were employed versus not) slightly declined from 91 to 86%. Around 30% were sick listed at least once during the 6 months prior to baseline, which did not change over time. Physical HRQoL declined over time (beta [95% confidence interval]: - 1.65 [- 2.96 to - 0.35]). Mental HRQoL remained below population norms (e.g., on average 45.4 (12.6) at baseline), but did not statistically change over time. Multivariate analyses revealed that factors such as income, educational level, financial necessity to work, breadwinner status, work tasks, work ability, need for recovery and survivors' disease and treatment characteristics were associated with partners' work and health outcomes. Although work participation remained stable during our study period, partners of cancer survivors experienced a substantial burden regarding their work and health. Interventions should focus on supporting partners at risk, addressing financial strain, helping them balance work and caregiving demands to sustain health and work participation.
Delays in completion of metabolic and bariatric surgery (MBS) remain common and may adversely affect postoperative cardiometabolic recovery. This study examined whether delayed MBS completion was associated with differences in postoperative cardiometabolic outcomes over 24 months. This prospective longitudinal cohort study included 413 adults referred to MBS from academic and community-based programs in Texas. Of these, 172 participants completed MBS within the study period and comprised the analytic sample for the present analysis. Repeated measures of weight and cardiometabolic biomarkers were collected before MBS and 6, 12- and 24-months post-MBS. Generalized estimating equations were used to evaluate associations between cardiometabolic biomarkers (anthropometrics, HbA1c, lipids), timing of MBS completion (early completion: ≤3 months after referral; delayed completion: >3 to 15 months after referral), adjusting for covariates. Among 172 adults who completed MBS, 66 completed surgery within 3 months of referral, and 106 completed surgery > 3 to 15 months post-referral. Among early completers, the median time from referral to surgery was 2.0 months (IQR 0.7-4.3), compared with 5.6 months (IQR 3.2-9.6) among delayed completers. Participants identified as 39.2% Non-Hispanic White, 39.0% Non-Hispanic Black, and 17.6% Hispanic. After multivariable adjustment, delayed MBS completers had higher total cholesterol (β = 14.52, 95% CI = 1.70 to 27.33, p = 0.026), higher non-HDL cholesterol (β = 16.96, 95% CI = 5.31 to 28.61, p = 0.004), higher cholesterol/HDL ratio (β = 0.43, 95% CI = 0.06 to 0.81, p = 0.024), and higher hemoglobin A1c levels (β = 0.36, 95% CI = 0.07 to 0.65, p = 0.016) compared to early completers post-MBS. Triglyceride levels were reduced significantly at 6 months (p < 0.001), 12 months (p = 0.002), and 24 months (p < 0.001). HDL levels increased significantly at 24 months (p < 0.001), whereas non-HDL cholesterol levels (p = 0.014) and cholesterol/HDL ratio (p = 0.001) reduced significantly at 24 months only. Weight, glucose, and HbA1c levels also decreased significantly over 24 months post-MBS (p < 0.05). A significant sex-specific effect was observed with women demonstrating higher levels of total cholesterol (p < 0.001), HDL (p < 0.001), LDL (p = 0.001), and non-HDL cholesterol (p = 0.001) compared to men. Delayed completion of MBS was associated with less favorable postoperative cardiometabolic profiles, including higher total cholesterol, non-HDL cholesterol, cholesterol/HDL ratio, and HbA1c levels over 24 months following surgery. These findings support efforts to reduce delays in access to MBS and optimize timely surgical care.
This research aims to provide a precise and computationally efficient solution for two important systems in mathematical modeling and environmental science: the fractional nonlinear coupled Burgers' system and the fractional dynamics of the coupled plankton-oxygen model in (1 + 1) dimensions. Despite the increasing complexity of these systems, existing numerical methods often struggle with the high computational costs of non-local operators. To address this, we propose a robust hybrid framework integrating the Fractional Differential Quadrature Method (FDQM) with a Newton-Raphson (NR) iterative procedure.These systems are critical for understanding various physical processes, including turbulent fluid dynamics and the biological interactions of oxygen and plankton in aquatic environments. The models use fractional derivatives, which provide greater flexibility in capturing memory and hereditary features, making them more suitable for correctly simulating real-world processes than traditional integer-order models. The proposed work solves these problems using a generalized Liouville-Caputo fractional-order model mixed with versions of the differential quadrature technique (DQM), which allows effective handling of complex boundary conditions and spatial derivatives. The nonlinearity in these equations is handled using Newton-Raphson's iterative approach, which ensures the solutions' stability and convergence. The proposed system was implemented in MATLAB, and a complete parametric analysis was carried out to investigate how various parameters, including the fractional-order derivative, the oxygen generation rate, and the maximum per capita growth rate of phytoplankton, influence model outputs. This study not only proves the suggested techniques' accuracy, convergence, and efficiency but also sheds light on their sensitivity to important parameters, making them more applicable to real-world circumstances. The findings of this work are expected to contribute to better modeling methodologies for complex systems, ultimately benefiting academics in domains ranging from environmental science to fluid dynamics.