Development Of Computational Models Research Articles

Clinically practical pharmacometrics computer model to evaluate and personalize pharmacotherapy in pediatric rare diseases: application to Graves' disease.

Graves' disease (GD) with onset in childhood or adolescence is a rare disease (ORPHA:525731). Current pharmacotherapeutic approaches use antithyroid drugs, such as carbimazole, as monotherapy or in combination with thyroxine hormone substitutes, such as levothyroxine, as block-and-replace therapy to normalize thyroid function and improve patients' quality of life. However, in the context of fluctuating disease activity, especially during puberty, a considerable proportion of pediatric patients with GD is suffering from thyroid hormone concentrations outside the therapeutic reference ranges. Our main goal was to develop a clinically practical pharmacometrics computer model that characterizes and predicts individual disease activity in children with various severity of GD under pharmacotherapy. Retrospectively collected clinical data from children and adolescents with GD under up to two years of treatment at four different pediatric hospitals in Switzerland were analyzed. Development of the pharmacometrics computer model is based on the non-linear mixed effects approach accounting for inter-individual variability and incorporating individual patient characteristics. Disease severity groups were defined based on free thyroxine (FT4) measurements at diagnosis. Data from 44 children with GD (75% female, median age 11 years, 62% receiving monotherapy) were analyzed. FT4 measurements were collected in 13, 15, and 16 pediatric patients with mild, moderate, or severe GD, with a median FT4 at diagnosis of 59.9 pmol/l (IQR 48.4, 76.8), and a total of 494 FT4 measurements during a median follow-up of 1.89 years (IQR 1.69, 1.97). We observed no notable difference between severity groups in terms of patient characteristics, daily carbimazole starting doses, and patient years. The final pharmacometrics computer model was developed based on FT4 measurements and on carbimazole or on carbimazole and levothyroxine doses involving two clinically relevant covariate effects: age at diagnosis and disease severity. We present a tailored pharmacometrics computer model that is able to describe individual FT4 dynamics under both, carbimazole monotherapy and carbimazole/levothyroxine block-and-replace therapy accounting for inter-individual disease progression and treatment response in children and adolescents with GD. Such clinically practical and predictive computer model has the potential to facilitate and enhance personalized pharmacotherapy in pediatric GD, reducing over- and underdosing and avoiding negative short- and long-term consequences. Prospective randomized validation trials are warranted to further validate and fine-tune computer-supported personalized dosing in pediatric GD and other rare pediatric diseases.

Computational model for the semi‐analytical assessment of the free‐edge effect in composite laminated shells

AbstractThe development of efficient computational models for the accurate prediction of the state variables in general composite laminated shells undergoing uniform edge loadings is a major challenge, especially when stress concentration phenomena such as the free‐edge effect have to be considered. This paper addresses this issue by introducing a higher‐order semi‐analytical approach for the assessment of the three‐dimensional stress field in circular cylindrical composite shells with arbitrary layups subjected to a uniform bending moment. The presented semi‐analytical approach combines a closed‐form analytical plane‐strain solution with a higher‐order layerwise approach, and the governing equations are derived by virtue of the principle of minimum elastic potential. The resulting system of coupled ordinary differential equations is then solved by means of the state‐space approach and the free constants are determined by evaluating the boundary conditions at the traction‐free edges. A comparison of the numerical results of the presented semi‐analytical method with finite element simulations for various composite laminates indicates that the developed method works with high accuracy, although being extremely efficient in terms of computational resources.

The Benefits and Challenges of Antibiotics-Non-Steroidal Anti-Inflammatory Drugs Non-Covalent Reaction.

Recently, non-covalent reactions have emerged as approaches to improve the physicochemical properties of active pharmaceutical ingredients (API), including antibiotics and non-steroidal anti-inflammatory drugs (NSAIDs). This review aimed to present and discuss the non-covalent reaction products of antibiotics, including salt and neutral multi-component solid forms, by framing their substituents and molar ratios, manufacturing techniques, characterization methods, benefits, potency changes, and toxicity, and is completed with an analysis of the development of computational models used in this field. Based on the data, NSAIDs are the most-developed drugs in multi-component system preparations, followed by antibiotics, i.e., antituberculosis and fluoroquinolones. They have reacted with inorganic elements, excipients, nutraceuticals, natural products, and other drugs. However, in terms of treatments for common infections, fluoroquinolones are more frequently used. Generally, NSAIDs are acquired on an over-the-counter basis, causing inappropriate medication. In addition, the pKa differences between the two groups of medicine offer the potential for them to react non-covalently. Hence, this review highlights fluoroquinolone-NSAID multi-component solid systems, which offer some benefits. These systems can increase patient compliance and promote the appropriate monitoring of drug usage; the dual drug multi-component solids have been proven to improve the physicochemical properties of one or both components, especially in terms of solubility and stability. In addition, some reports show an enhancement of the antibiotic activity of the products. However, it is important to consider the possibility of activity changes, interaction, and toxicity when using drug combinations. Hence, these aspects also are discussed in this review. Finally, we present computational modeling, which has been utilized broadly to support multi-component system designs, including coformer screening, preparation methods, and structural modeling, as well as to predict physicochemical properties, potency, and toxicity. This integrated review is expected to be useful for further antibiotic-NSAID multi-component system development.

Neurophysiological determinants of occupational stress and burnout

IntroductionResearch results show that one of the greatest health challenges of the 21st century, especially in developed countries, is becoming the fight against the effects of living too fast, including the fight against occupational stress and burnout.
 Aim of the study
 The purpose of this article is to elucidate the neurophysiological determinants of occupational stress and burnout, including ocupational, including through the path of research review and the development of computational models based on artificial intelligence.
 Materials and methods
 A literature search was conducted in six bibliographic databases: PubMed, EBSCO, PEDro, Web of Science, Scopus and Google Scholar. Articles were searched in English using the following keywords: occupational stress, burnout, marker, electroencephalography, EEG, magnetic resonance imaging, MRI, fMRI, computed tomography, CT, positron emission tomography, PET, computational model, machine learning, artificial intelligence, virtual patient, digital twin and similar. Neurophysiological determinants of occupational stress and burnout as far as computational models of occupational stress and burnout were analysed and discussed.
 Results
 The best currently observed neurophysiological markers of occupational stress and burnout may currently be a combination of EEG analysis (alpha power (IAF, PAF), P300, ERP (VPP and EPN)), diagnostic PET imaging (ACC, insular cortex and hippocampus) and monitoring changes in cortisol, prolactin, adrenocorticotropic hormone (ACTH), corticotropin-releasing hormone (CRH) and thyroid hormones, as well as plasma BDNF levels. In addition, ERPs (LPPs) are a marker significantly differentiating burnout from depression.
 Conclusions
 The combination of traditional clinimetric tests, the aforementioned neurophysiological tests and AI-based big data analysis will provide new classifiers, highly accurate results and new diagnostic methods.

Algorithms for Сonstructing Matrixes of Routes of Pipeline Networks by Using the Method of Graph Theory

Использование средств информационных технологий и уровня автоматизации процессов управления значительно повышают роль технического прогресса и решают поставленные задачи Республикой Узбекистан по обеспечению целевым продуктом населению. Вместе с тем возможности современных компьютерных систем и развитие математических методов моделирования используются далеко не полностью из-за неформализации многих задач, а также несовершенства или отсутствия ряда алгоритмов, обеспечивающих возможность произвести анализ функционирования объекта, обработать поступающую информацию и принять соответствующие решения по оптимальному управлению многосвязных систем. В связи с этим разработка и исследование компьютерных моделей, вычислительных алгоритмов и на их основе создание объектно-ориентированных программных средств в этом направлении представляется актуальной проблемой дальнейшего развития автоматизации научных исследований для различных предметных областей, в частности трубопроводных систем. Всякая реальная энергетическая сеть, в том числе и газовая, по своей топологической структуре эквивалентна некоторому графу. Провести расчет и анализ несогласованности исходной информации сетей с более одним кольцом затруднительно. В этих случаях необходимо обратиться к законам Кирхгофа и соответствующим замыкающим соотношениям. Тем не менее, топология сети отличается разнообразием, чему свидетельствуют, в частности, схемы городских сетей теплоснабжения. В таких условиях основная тяжесть гидравлического или экономического расчета должна концентрироваться на стадии формирования топологии сети, для чего используются теории графов. В данной статье рассматриваются алгоритмы построения и оптимизации функционирования газопроводных многоконтурных сетей методом теории графов и программное обеспечение для оптимального управления, анализа и синтеза трубопроводных систем. Таким образом, разработанные вычислительные алгоритмы и компьютерная программа расчета можно применять для оптимизации функционирования газопроводных сетей, когда сеть имеет сложную лучистую и многокольцевую структуру. The use of information technology tools and the level of automation of management processes significantly increase the role of technical progress and solve the tasks set by the Republic of Uzbekistan to provide the population with the target product. At the same time, the capabilities of modern computer systems and the development of mathematical modeling methods are far from being fully used due to the informalization of many tasks, as well as the imperfection or absence of a number of algorithms that make it possible to analyze the functioning of an object, process incoming information and make appropriate decisions on the optimal control of multiply connected systems. . In this regard, the development and research of computer models, computational algorithms and, on their basis, the creation of object-oriented software in this direction seems to be an urgent problem for the further development of automation of scientific research for various subject areas, in particular pipeline systems. Any real energy network, including a gas network, is equivalent in its topological structure to a certain graph. It is difficult to calculate and analyze the inconsistency of the initial information of networks with more than one ring. In these cases, it is necessary to turn to Kirchhoff’s laws and the corresponding closing relations. However, the network topology is diverse, as evidenced, in particular, by the schemes of urban heating networks. In such conditions, the main burden of hydraulic or economic calculation should be concentrated on the stage of network topology formation, for which graph theories are used. This article discusses algorithms for constructing and optimizing the functioning of gas pipeline multiloop networks using the graph theory method and software for optimal control, analysis and synthesis of pipeline systems. Thus, the developed computational algorithms and a computer calculation program can be used to optimize the operation of gas pipeline networks when the network has a complex radiant and multi-ring structure.

A system theory based digital model for predicting the cumulative fluid balance course in intensive care patients.

Background: Surgical interventions can cause severe fluid imbalances in patients undergoing cardiac surgery, affecting length of hospital stay and survival. Therefore, appropriate management of daily fluid goals is a key element of postoperative intensive care in these patients. Because fluid balance is influenced by a complex interplay of patient-, surgery- and intensive care unit (ICU)-specific factors, fluid prediction is difficult and often inaccurate. Methods: A novel system theory based digital model for cumulative fluid balance (CFB) prediction is presented using recorded patient fluid data as the sole parameter source by applying the concept of a transfer function. Using a retrospective dataset of n = 618 cardiac intensive care patients, patient-individual models were created and evaluated. RMSE analyses and error calculations were performed for reasonable combinations of model estimation periods and clinically relevant prediction horizons for CFB. Results: Our models have shown that a clinically relevant time horizon for CFB prediction with the combination of 48h estimation time and 8-16h prediction time achieves high accuracy. With an 8-h prediction time, nearly 50% of CFB predictions are within ±0.5 L, and 77% are still within the clinically acceptable range of ±1.0L. Conclusion: Our study has provided a promising proof of principle and may form the basis for further efforts in the development of computational models for fluid prediction that do not require large datasets for training and validation, as is the case with machine learning or AI-based models. The adaptive transfer function approach allows estimation of CFB course on a dynamically changing patient fluid balance system by simulating the response to the current fluid management regime, providing a useful digital tool for clinicians in daily intensive care.

Development of computational intelligence models for assessment of drug nanonization using green chemistry technique: Improvement of drug solubility

Determination of solubility via theoretical approaches was carried out in this study. Because of its importance to the expansion of the pharmaceutical industry, this study models Lenalidomide solubility in supercritical carbon dioxide using multiple tree-based techniques which are of machine learning nature. These parameters are molded based on temperature and pressure input features due to the significant variability of drug solubility with the temperature and pressure. The experimental data have been collected and inputted the models to train them and used the data for testing the machine learning models. The results are useful for production of nanomedicine with enhanced solubility in solvents. Decision Tree (DT), Extra Trees (ET), and Gradient Boosting (GB) models are used and optimized using SCA algorithm to obtain more robust models for prediction of the drug solubility in the solvent. So, the developed models are called SCA-DT, SCA-ET, and SCA-GB in this study and have R2-scores of 0.932, 0.951, and 0.997, respectively. The SCA-DT model has an RMSE error rate of 0.0948, this rate is 0.0822 for SCA-ET, and 0.0203 for SCA-GB. So, the SCA-GB is introduced as the best model of this research for prediction of Lenalidomide solubility in the solvent.

MARSY: a multitask deep-learning framework for prediction of drug combination synergy scores.

Combination therapies have emerged as a treatment strategy for cancers to reduce the probability of drug resistance and to improve outcomes. Large databases curating the results of many drug screening studies on preclinical cancer cell lines have been developed, capturing the synergistic and antagonistic effects of combination of drugs in different cell lines. However, due to the high cost of drug screening experiments and the sheer size of possible drug combinations, these databases are quite sparse. This necessitates the development of transductive computational models to accurately impute these missing values. Here, we developed MARSY, a deep-learning multitask model that incorporates information on the gene expression profile of cancer cell lines, as well as the differential expression signature induced by each drug to predict drug-pair synergy scores. By utilizing two encoders to capture the interplay between the drug pairs, as well as the drug pairs and cell lines, and by adding auxiliary tasks in the predictor, MARSY learns latent embeddings that improve the prediction performance compared to state-of-the-art and traditional machine-learning models. Using MARSY, we then predicted the synergy scores of 133722 new drug-pair cell line combinations, which we have made available to the community as part of this study. Moreover, we validated various insights obtained from these novel predictions using independent studies, confirming the ability of MARSY in making accurate novel predictions. An implementation of the algorithms in Python and cleaned input datasets are provided in https://github.com/Emad-COMBINE-lab/MARSY.

Production and transport of turbulent kinetic energy from premixed flames subjected to mean pressure gradients

The influence of mean favorable pressure gradients on the production and transport of turbulent kinetic energy is experimentally investigated in a premixed bluff-body combustor. The combustor wall geometry is modified into converging, straight, or diverging configurations to experimentally alter the mean pressure gradient in the reacting flow field. For each configuration, turbulent kinetic energy production and transport dynamics are characterized with high-speed particle image velocimetry and simultaneous CH* chemiluminescence imaging. For all cases, the premixed reaction increases the turbulent kinetic energy and integral length scales in the flow. However, increasing the magnitude of the mean favorable pressure gradient increases the turbulent kinetic energy produced by the flame. The source of increased turbulent fluctuations from reactants to products is first investigated with a steady flow energy analysis, and is accompanied with a Reynolds decomposed investigation of the kinetic energy in the flow. As the mean pressure gradient increases, the turbulent kinetic energy in the flow increases due to stronger magnitudes of flame-generated turbulence in the form of baroclinic torque. The evolution of the flame-generated turbulence is examined by decomposing the transport equation of turbulent kinetic energy. Increasing the mean favorable pressure gradient augments the largest magnitude transport terms, and the budget of turbulent kinetic energy is primarily driven by pressure gradient work and viscous dissipation. The results here differ from previous direct numerical simulation (DNS) studies, and should ultimately motivate additional research to aid the development of computational models which can better predict turbulent flame-flow dynamics in confined environments with mean pressure gradients.

Honey De-crystallization by radio frequency heating for process efficiency: Computational monitoring combined with time domain nuclear magnetic resonance

Industrial de-crystallization of honey is carried out by using hot water or air at 60 °C or higher temperatures with the cost of energy and longer process times. The objectives of this study were to apply RF processing for de-crystallization and monitor this process with time-domain nuclear magnetic resonance (TD-NMR) data and a computational model. For this purpose, crystallized honey samples were processed in a 10 kW - 27.12 MHz free-running oscillator RF system, and experimental temperature data were used to validate the developed computational model. TD-NMR experiments were performed to monitor the de-crystallization, and the obtained kinetics data were coupled with temperature increase to compute the crystal content changes through the process. The RF de-crystallization led to a significant decrease (>70% compared to the conventional processes) in process time for industrial scale processing demonstrating its sustainability.

Development of a novel computational fluid dynamics-based model for a solar photovoltaic/thermal collector-assisted domestic hot water system with sensible heat storage

This study proposes a novel numerical modelling approach to investigate the dynamic performance of a solar photovoltaic/thermal domestic hot water system. A three-dimensional numerical model of a flat-box solar collector developed in ANSYS-Fluent is coupled with a reduced one-dimensional model of all other system components. Coupling these models allows for a more accurate evaluation of the system’s overall performance as consideration is given to both the spatial variations inside the collectors, and the overall system’s temporal response. The city of Ottawa, Canada is used as the case study location and a monthly comparison is made between scenarios comprising two different working fluids (i.e. air and a water-ethylene glycol solution) and two photovoltaic/thermal collector design alternatives (i.e. with and without fins). To assess the performance of each scenario, the solar fraction, electricity fraction, and utilization factor are computed. Results show that the solar fraction is greater in the air-based system than in the water-based system for all months of the year, and values as high as 90.1% and 84.3% are obtained, respectively. Similarly, the addition of fins to these systems is shown to improve the solar fraction on an annual basis by 7.4%, and 1.4%, respectively. Similar trends are observed for the utilization factor, which indicates that the air-based system with fins is the most effective system with regards to utilizing incident solar radiation for meeting simultaneous heat and power loads. The annual average electricity fraction, on the other hand, shows little variation between scenarios on a month-by-month basis, which leads to the conclusion that adding fins and/or changing the working fluid has a negligible effect on the electrical performance of the system.

Development of a Numerical Model of the Aerodynamic Interaction of a High-Speed Train, Air Environment and Infrastructure Facilities

The design of high-speed railway lines (HSR) requires mandatory consideration of loads from the aerodynamic interaction of a moving train, the air environment and infrastructure facilities, acting both on structures and facilities, and on the train itself. Software systems of computational fluid dynamics are most expedient to determine the nature and intensity of the load. To find the optimal approach to modelling the processes of aerodynamic interaction between a moving high-speed train and the air environment, as well as to assess the degree of validity of the simulation, a series of calculations were performed in the ANSYS CFX software environment using various approaches to the construction of calculation models (the sliding grid method and the immersed solid method). An analysis of the results of the performed calculations makes it possible to determine the area of rational application of the considered approaches in the development of computational models of aerodynamic interaction. To verify the developed calculation models, experimental measurements of the aerodynamic impact of Sapsan high-speed electric train on the air environment were performed. Also, the developed models were verified based on the results of similar international experimental studies. Comparison of the results of numerical simulation and experimental measurements allows us to conclude that the developed computational models are sufficiently valid and can be further applied.

Development and Verification of Novel Building Integrated Thermal Storage System Models

In electrical grids with a high renewable percentage, weather conditions have a greater impact on power generation. This can lead to the overproduction of electricity during periods of substantial wind power generation, resulting in shutoffs of wind turbines. To reduce such shutoffs and to bridge periods of lower electricity production, three thermal energy storage systems (TESs) have been developed for space heating and domestic hot water. These include a water-based thermal system (WBTS), a thermally activated building system (TABS), and a high-temperature stone storage system (HTSS). The paper explains the development of computer models used to simulate the systems and their successful verification using field measurements. Target values to cover about 90% of building heating demand with excess electricity were found to be achievable, with performance ratios depending on storage size, particularly for WBTS and HTSS. The TABS’ storage capacity is limited by building geometry and the available inner ceilings and walls.

Computational models of autonomic regulation in gastric motility: Progress, challenges, and future directions.

The stomach is extensively innervated by the vagus nerve and the enteric nervous system. The mechanisms through which this innervation affects gastric motility are being unraveled, motivating the first concerted steps towards the incorporation autonomic regulation into computational models of gastric motility. Computational modeling has been valuable in advancing clinical treatment of other organs, such as the heart. However, to date, computational models of gastric motility have made simplifying assumptions about the link between gastric electrophysiology and motility. Advances in experimental neuroscience mean that these assumptions can be reviewed, and detailed models of autonomic regulation can be incorporated into computational models. This review covers these advances, as well as a vision for the utility of computational models of gastric motility. Diseases of the nervous system, such as Parkinson's disease, can originate from the brain-gut axis and result in pathological gastric motility. Computational models are a valuable tool for understanding the mechanisms of disease and how treatment may affect gastric motility. This review also covers recent advances in experimental neuroscience that are fundamental to the development of physiology-driven computational models. A vision for the future of computational modeling of gastric motility is proposed and modeling approaches employed for existing mathematical models of autonomic regulation of other gastrointestinal organs and other organ systems are discussed.

Review of the Digital Twin Technology Applications for Electrical Equipment Lifecycle Management

Digital twin is one of the emerging technologies for the digital transformation of the power industry. Many existing studies claim that the widespread application of digital twins will shift the industry to a principally new level of development. This article provides an extensive overview of the industrial application experience of digital twin technologies for solving the problems of modern power systems with a particular focus on the task of high-voltage power equipment lifecycle management. The latter task contours one of the most promising areas for the application of the digital twins in the power industry since it requires deep analysis of the technological processes dynamics and the development of physical, mathematical and computer models that cover all the potential benefits of the digital twin technology. At the moment, there is a lack of reliable data on the problems of assessing and predicting the technical state of high-voltage power equipment. The use of digital twin technology in modern power systems will allow for aggregating data from a variety of real objects and will allow the automatization of collecting and processing of big data by implementing artificial intelligence methods, which will ultimately make it possible to manage the life cycle of the power equipment. The article puts to scrutiny the industrial experience of digital twins creation, considering the technical solutions suggested by the largest manufacturers of electrical equipment. A classification of digital twins, examples and main features of their application in the power industry, including the problem of managing the life cycle of high-voltage electrical equipment, are considered and discussed.

Development of numerical model-based machine learning algorithms for different healing stages of distal radius fracture healing

Background and objectivesEarly therapeutic exercises are vital for the healing of distal radius fractures (DRFs) treated with the volar locking plate. However, current development of rehabilitation plans using computational simulation is normally time-consuming and requires high computational power. Thus, there is a clear need for developing machine learning (ML) based algorithms that are easy for end-users to implement in daily clinical practice. The purpose of the present study is to develop optimal ML algorithms for designing effective DRF physiotherapy programs at different stages of healing. MethodFirst, a three-dimensional computational model for the healing of DRF was developed by integrating mechano-regulated cell differentiation, tissue formation and angiogenesis. The model is capable of predicting time-dependant healing outcomes based on different physiologically relevant loading conditions, fracture geometries, gap sizes, and healing time. After being validated using available clinical data, the developed computational model was implemented to generate a total of 3600 clinical data for training the ML models. Finally, the optimal ML algorithm for each healing stage was identified. ResultsThe selection of the optimal ML algorithm depends on the healing stage. The results from this study show that cubic support vector machine (SVM) has the best performance in predicting the healing outcomes at the early stage of healing, while trilayered ANN outperforms other ML algorithms in the late stage of healing. The outcomes from the developed optimal ML algorithms indicate that Smith fractures with medium gap sizes could enhance the healing of DRF by inducing larger cartilaginous callus, while Colles fractures with large gap sizes may lead to delayed healing by bringing excessive fibrous tissues. ConclusionsML represents a promising approach for developing efficient and effective patient-specific rehabilitation strategies. However, ML algorithms at different healing stages need to be carefully chosen before being implemented in clinical applications.

EEG-Based BCIs on Motor Imagery Paradigm Using Wearable Technologies: A Systematic Review.

In recent decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of brain-computer interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on motor imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims to systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

The transmission mechanism theory of disease dynamics: Its aims, assumptions and limitations.

Most of the progress in the development of single scale mathematical and computational models for the study of infectious disease dynamics which now span over a century is build on a body of knowledge that has been developed to address particular single scale descriptions of infectious disease dynamics based on understanding disease transmission process. Although this single scale understanding of infectious disease dynamics is now founded on a body of knowledge with a long history, dating back to over a century now, that knowledge has not yet been formalized into a scientific theory. In this article, we formalize this accumulated body of knowledge into a scientific theory called the transmission mechanism theory of disease dynamics which states that at every scale of organization of an infectious disease system, disease dynamics is determined by transmission as the main dynamic disease process. Therefore, the transmission mechanism theory of disease dynamics can be seen as formalizing knowledge that has been inherent in the study of infectious disease dynamics using single scale mathematical and computational models for over a century now. The objective of this article is to summarize this existing knowledge about single scale modelling of infectious dynamics by means of a scientific theory called the transmission mechanism theory of disease dynamics and highlight its aims, assumptions and limitations.

Computational analysis of the thermo-hydrodynamic transport processes during substrate re-melting in laser powder bed fusion of AlSi10Mg

As a precursor to the development of a full-scale computational model for analysis of the thermal-hydrodynamic transport phenomena during laser powder bed fusion of AlSi10Mg, a model for substrate re-melting has been developed. Material evaporation has also been included in the model as a medium for energy loss. While the melt pool temperatures and dimensions reflected a typical relationship with the process parameters described in the literature, the results agreed very well with the in-house experimental data too. The study also focused on unveiling the individual effects of the melt pool hydrodynamics and material evaporation on the melt pool, and it was observed that the former had very little effect on the results compared to the latter. Parametric analysis for the melting mode identification and melt track stability analysis revealed that the melting happened in conduction mode even at the highest linear energy density and remained stable throughout the parameter window. Both findings were proved true from the experimental observation.

Trends in the 40 + -year development of advanced structural acoustic and vibration computational modeling

Over the course of the past 40-45 years of computational modeling in the fields of structures, structural acoustics, and vibrations, there have been extreme advancements made in numerous areas that have greatly increased and improved the size, accuracy, efficiency, and complexity of numerical solutions possible. Most of this progress has been made through a combination of improvements in the distinct areas of computer processing approaches/speed and available memory, primarily evolutionary enhancements in detailed numerical material and continuum mechanics models, development of entirely new physics-based numerical vibroacoustic modeling approaches, etc. This paper presents a historical review of the detailed progress made in computational structural acoustics and vibration from the “early days” in the 1980s through current state-of-the-art advanced numerical approaches, much of which would not have been dreamed possible back four decades ago! While most of the examples utilized in this talk are pulled from the speaker’s specific technical area of undersea vehicle/ system structural acoustics, the overall trends presented are certainly represented across the broad spectrum of vibro-acoustics modeling applications.