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Introduction: OpenAI's ChatGPT, a Large Language Model (LLM), is a powerful tool across domains, designed for text and code generation, fostering collaboration, especially in public health. Investigating the role of this advanced LLM chatbot in assisting public health practitioners in shaping disease transmission models to inform infection control strategies, marks a new era in infectious disease epidemiology research. This study used a case study to illustrate how ChatGPT collaborates with a public health practitioner in co-designing a mathematical transmission model. Methods: Using natural conversation, the practitioner initiated a dialogue involving an iterative process of code generation, refinement, and debugging with ChatGPT to develop a model to fit 10 days of prevalence data to estimate two key epidemiological parameters: i) basic reproductive number (Ro) and ii) final epidemic size. Verification and validation processes are conducted to ensure the accuracy and functionality of the final model. Results: ChatGPT developed a validated transmission model which replicated the epidemic curve and gave estimates of Ro of 4.19 (95 % CI: 4.13- 4.26) and a final epidemic size of 98.3 % of the population within 60 days. It highlighted the advantages of using maximum likelihood estimation with Poisson distribution over least squares method. Conclusion: Integration of LLM in medical research accelerates model development, reducing technical barriers for health practitioners, democratizing access to advanced modeling and potentially enhancing pandemic preparedness globally, particularly in resource-constrained populations.

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During silicon crystal growth, oxygen, a well-known major impurity, affects the final silicon wafer's mechanical and electrical properties. This study focused on regulation of discharge of different concentrations of oxygen from the quartz crucible into the silicon melt while considering the crucible angular speed and the friction at the melt-crucible interface. The three-dimensional transient governing equations for heat transfer, fluid flow, and impurity transportation in the Czochralski (CZ) puller were solved numerically. The oxygen solvation equation representing the crucible to silicon melt was modified to evaluate the accuracy of oxygen concentration calculations during the CZ process. Experimental measurements using the Fourier-transform infrared (FTIR) technique were used to confirm the simulation results. The results demonstrate that the crucible angular speed affects the oxygen concentration near the crucible wall and therefore in the silicon ingot. The proposed modifications for evaluating oxygen concentration offer a more comprehensive understanding of the oxygen dynamics during the CZ crystal growth.

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Medium entropy alloy (MEA) is a hot spot in the field of material research in recent years. At present, the most widely used processing method of MEAs is "casting-rolling-heat treatment", and the preparation of CoCrNi MEA by laser additive manufacturing (LAM) is still in primary stage. In this study, CoCrNi MEAs were fabricated with different scanning speeds by laser additive manufacturing, and the influence of scanning speed on its mechanical properties was investigated. The results show that higher scanning speed can significantly improve the mechanical properties of CoCrNi MEA. Compared with the low-speed laser additive manufacturing (LSLAM) MEA, the tensile strength of high-speed laser additive manufacturing (HSLAM) MEA is increased by 5.6 % and the fracture strain is increased by 60 %, which is mainly due to the entanglement of 1/6<112> and 1/2<110> dislocations and the defect structure at the grain boundary. LAM is a promising technology that can promote the development and application of MEAs in industry.

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The effects of varying nanoparticle size; polyethylene glycol (PEG) molecule length, type, and density; and functional groups for drug delivery systems are investigated computationally. A molecular dynamics (MD) study in the framework of a Monte Carlo simulated annealing scheme is done on gold nanoparticles (Au NPs) for sizes of 2.6 nm, 3.4 nm and 6.8 nm. The bonding of PEG molecules is investigated, and the binding energy (BE) is analysed as a reference to chemisorption and physisorption of the molecules. To investigate the frontier molecular orbitals and molecular electrostatic potentials, density functional theory (DFT) simulations are also performed for various PEG lengths and functional groups (FGs). The study reports on three conclusions: firstly, reducing the Au NP size leads to coordination number (CN) loss as the number of lowly coordinated atoms increases with decreasing particle size. Secondly, the stability of the Au-PEG system is independent of length beyond [Formula: see text]. And due to PEG high steric repulsion, the number of these molecules that can be physically adsorbed to the surface is limited. And thirdly, the FGs can be grouped into electron-withdrawing group (-NTA, Biotin, COOH) and electron-donating group (-NH2, OH). In future work, we will study how these conclusions influence the Au drug delivery system toxicity and cellular uptake.

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An integrated simulation framework consisting of the 3D finite element method and 3D cellular automaton method is presented for simulating the multi-track and multi-layer selective laser melting (SLM) process. The framework takes account of all the major multi-physics phenomena in the SLM process, including the initial grain structure, the growth kinetics, the laser scanning strategy, the laser-powder and laser-matter interactions, the melt flow, and the powder-to-liquid-to-solid transformations. The feasibility of the proposed framework is demonstrated by simulating the evolution of the epitaxy grain structure of Inconel 718 (IN718) during a 15-layer SLM process performed using a bi-directional 67° rotation scanning strategy and various SLM process parameters. The simulation results are found to be in good agreement with the experimental observations obtained in the present study and in the literature. In particular, a strong (001) texture is observed in the final component, which indicates that the grains with a preferred <001> orientation win the competitive epitaxy grain growth process. In addition, the size and shape of the IN718 grains are governed primarily by the cooling rate, where the cooling rate is determined in turn by the SLM parameters and the build height. Overall, the results show that the proposed framework provides an accurate approach for predicting the final microstructures of SLM components, and therefore, it can play an important role in optimizing the SLM processing parameters in such a way as to produce components with the desired mechanical properties.

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The endemic threat of methicillin-resistant Staphylococcus aureus (MRSA) in nursing homes poses a serious and escalating challenge to public health administration in infection control. Nursing homes are considered as major reservoirs for MRSA colonization, with considerable high levels of colonization prevalence. We employed a computation model to evaluate effects of three intervention scenarios on MRSA colonization prevalence rate in nursing homes. Simulations were conducted using a deterministic compartmental model featuring heterogeneous contact matrix between residents and health-care workers (HCWs). Contact parameters were derived from a nursing home survey. Three intervention scenarios were simulated: (1) hand-hygiene compliance by HCWs, (2) screening-and-isolation upon admission, and (3) implementing both interventions at the same time. For every 10% reduction in average contamination duration in HCWs, the estimated average reduction in prevalence rate was 1.29 percentage point compared with the prevalence rate before the intervention was implemented. Screening-and-isolation intervention resulted in an average reduction of 19.04 percentage point in prevalence rate (S.D. = 1.58; 95% CI = 18.90-19.18). In intervention scenario 3, synergistic effects were observed when implementing hand-hygiene compliance by HCWs and screening-and-isolation together. Our results provide evidence showing that implementing multiple interventions together has a synergistic effect on colonization prevalence reduction.

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OBJECTIVE: Identification of a cost-effective treatment strategy is an unmet need in Crohn's disease (CD). Here we consider the patient outcomes and cost impact of pan-intestinal video capsule endoscopy (PVCE) in the English National Health Service (NHS). DESIGN: An analysis of a protocolized CD care pathway, informed by guidelines and expert consensus, was performed in Microsoft Excel. Population, efficacy and safety data of treatments and monitoring modalities were identified using a structured PubMed review with English data prioritized. Costs were taken from the NHS and Payer Provided Services (PSS) 2016-17 tariffs for England and otherwise literature. Analysis was via a discrete-individual simulation with discounting at 3.5% per annum. SETTING: NHS provider and PSS perspective. PARTICIPANTS: 4000 simulated CD patients. INTERVENTIONS: PVCE versus colonoscopy ± magnetic resonance enterography (MRE). MAIN OUTCOME MEASURES: Costs in 2017 GBP and quality-adjusted life years (QALY). RESULTS: The mean, total 20-year cost per patient was £42 266 with colonoscopy ± MRE and £38 043 with PVCE. PVCE incurred higher costs during the first 2 years due to higher treatment uptake. From year 3 onwards, costs were reduced due to fewer surgeries. Patients accrued 10.67 QALY with colonoscopy ± MRE and 10.96 with PVCE. PVCE dominated (less cost and higher QALY) colonoscopy ± MRE and was likely (>74%) to be considered cost-effective by the NHS. Results were similar if a lifetime time horizon was used. CONCLUSIONS: PVCE is likely to be a cost-effective alternative to colonoscopy ± MRE for CD surveillance. Switching to PVCE resulted in lower treatment costs and gave patients better quality of life.

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An overall model describing the dynamic behavior of fed-batch E. coli processes for protein production has been built, calibrated and validated. Using a macroscopic approach, the model consists of three interconnected blocks allowing simulation of biomass, inducer and protein concentration profiles with time. The model incorporates calculation of the extra and intracellular inducer concentration, as well as repressor-inducer dynamics leading to a successful prediction of the product concentration. The parameters of the model were estimated using experimental data of a rhamnulose-1-phosphate aldolase-producer strain, grown under a wide range of experimental conditions. After validation, the model has successfully predicted the behavior of different strains producing two different proteins: fructose-6-phosphate aldolase and ω-transaminase. In summary, the presented approach represents a powerful tool for E. coli production process simulation and control.

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This paper proposes two new measures to assess performance of surgical practice based on observed mortality: reliability, measured as the area under the ROC curve and a living score, the sum of individual risk among surviving patients, divided by the total number of patients. A Monte Carlo simulation of surgeons' practice was used for conceptual validation and an analysis of a real-world hospital department was used for managerial validation. We modelled surgical practice as a bivariate distribution function of risk and final state. We sampled 250 distributions, varying the maximum risk each surgeon faced, the distribution of risk among dead patients, the mortality rate and the number of surgeries performed yearly. We applied the measures developed to a Portuguese cardiothoracic department. We found that the joint use of the reliability and living score measures overcomes the limitations of risk adjusted mortality rates, as it enables a different valuation of deaths, according to their risk levels. Reliability favours surgeons with casualties, predominantly, in high values of risk and penalizes surgeons with deaths in relatively low levels of risk. The living score is positively influenced by the maximum risk for which a surgeon yields surviving patients. These measures enable a deeper understanding of surgical practice and, as risk adjusted mortality rates, they rely only on mortality and risk scores data. The case study revealed that the performance of the department analysed could be improved with enhanced policies of risk management, involving the assignment of surgeries based on surgeon's reliability and living score.

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Since its introduction, in the early 1970s, large eddy simulations (LES) have advanced considerably, and their application is transitioning from the academic environment to industry. Several landmark developments can be identified over the past 40 years, such as the wall-resolved simulations of wall-bounded flows, the development of advanced models for the unresolved scales that adapt to the local flow conditions and the hybridization of LES with the solution of the Reynolds-averaged Navier-Stokes equations. Thanks to these advancements, LES is now in widespread use in the academic community and is an option available in most commercial flow-solvers. This paper will try to predict what algorithmic and modelling advancements are needed to make it even more robust and inexpensive, and which areas show the most promise.

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In this paper we propose a new bottom-up approach to cellular computing, in which computational chemical processes are encapsulated within liposomes. This "liposome logic" approach (also called vesicle computing) makes use of supra-molecular chemistry constructs, e.g. protocells, chells, etc. as minimal cellular platforms to which logical functionality can be added. Modeling and simulations feature prominently in "top-down" synthetic biology, particularly in the specification, design and implementation of logic circuits through bacterial genome reengineering. The second contribution in this paper is the demonstration of a novel set of tools for the specification, modelling and analysis of "bottom-up" liposome logic. In particular, simulation and modelling techniques are used to analyse some example liposome logic designs, ranging from relatively simple NOT gates and NAND gates to SR-Latches, D Flip-Flops all the way to 3 bit ripple counters. The approach we propose consists of specifying, by means of P systems, gene regulatory network-like systems operating inside proto-membranes. This P systems specification can be automatically translated and executed through a multiscaled pipeline composed of dissipative particle dynamics (DPD) simulator and Gillespie's stochastic simulation algorithm (SSA). Finally, model selection and analysis can be performed through a model checking phase. This is the first paper we are aware of that brings to bear formal specifications, DPD, SSA and model checking to the problem of modeling target computational functionality in protocells. Potential chemical routes for the laboratory implementation of these simulations are also discussed thus for the first time suggesting a potentially realistic physiochemical implementation for membrane computing from the bottom-up.