Use Of Computational Models Research Articles

Prediction of Sudden Cardiac Death: Looking Beyond Ejection Fraction

Sudden cardiac death (SCD) is a major public health burden accounting for 15-20% of global mortality. Contemporary guidelines for SCD prevention are centered around the presence of low left ventricular ejection fraction, although the majority of SCD accrues in those not meeting contemporary criteria for SCD prevention. The goal of this review is to elaborate on the contemporary landscape of SCD prediction tools and further highlight gaps and opportunities in SCD prediction and prevention. There have been considerable advancements in both non-invasive and invasive measures for SCD risk prediction including clinical morbidities, electrocardiographic measures, cardiac imaging (nuclear, magnetic resonance, computed tomography), serum biomarkers, genetics, and invasively assessed electrophysiological characteristics. Novel methodological approaches including application of machine learning, incorporation of competing risk, and use of computational modeling have underscored a future of personalized risk prediction. SCD remains a vital public health challenge. Emerging methods highlight opportunities to improve SCD prediction in the majority of those at risk who do not meet contemporary criteria for SCD prevention therapies. Future efforts will need to focus on easily deployed, multi-parametric risk models that enrich for SCD risk and not for competing mortality.

The Effectiveness of Students’ Use of Computer Modeling in Learning Engineering Mathematics

The aim of this study is to explore the effectiveness of teaching mathematics using computer modeling. It was conducted for a five-grade engineering unit by modeling concepts, terminology, and issues related to engineering in collaboration between groups of students and teachers from several primary schools. in Amman, Jordan, in the academic year 2021/2022. The working group consisted of thirty students. An action research model was used to conduct this study. Photographs, videos, teacher records, graded grading model, focus group discussions, and researcher's record were used to collect data. Through percentage statistics and content analysis, the obtained qualitative data were analyzed. And concluded that the positive impact of students' use of computer modeling in learning aeronautical engineering mathematics and motivating students towards studying mathematics and increasing study skills and the ability to represent and model to clarify any issue in mathematics in space engineering.

Stylized versus voxel phantoms: quantification of internal organ chord length distances

Dosimetric calculations, whether for radiation protection or nuclear medicine applications, are greatly influenced by the use of computational models of humans, called anthropomorphic phantoms. As anatomical models of phantoms have evolved and expanded, thus has the need for quantifying differences among each of these representations that yield variations in organ dose coefficients, whether from external radiation sources or internal emitters. This work represents an extension of previous efforts to quantify the differences in organ positioning within the body between a stylized and voxel phantom series. Where prior work focused on the organ depth distribution vis-à-vis the surface of the phantom models, the work described here quantifies the intra-organ and inter-organ distributions through calculation of the mean chord lengths. The revised Oak Ridge National Laboratory stylized phantom series and the University of Florida/National Cancer Institute voxel phantom series including a newborn, 1-, 5-, 10- and 15 year old, and adult phantoms were compared. Organ distances in the stylized phantoms were computed using a ray-tracing technique available through Monte Carlo radiation transport simulations in MCNP6. Organ distances in the voxel phantom were found using phantom matrix manipulation. Quantification of differences in organ chord lengths between the phantom series displayed that the organs of the stylized phantom series are typically situated farther away from one another than within the voxel phantom series. The impact of this work was to characterize the intra-organ and inter-organ distributions to explain the variations in updated internal dose coefficient quantities (i.e. specific absorbed fractions) while providing relevant data defining the spatial and volumetric organ distributions in the phantoms for use in subsequent internal dosimetric computations, with prospective relevance to patient-specific individualized dosimetry, as well as informing machine learning definition of organs using these reference models.

Some Topological Approaches To Analyzing The Efficiency Of Municipalities

The evaluation of the effectiveness of the activity of the heads of the municipality is included in the list of the most acute topics and the most discussed problems in the field of local self-government. The established approach to the construction of efficiency coefficients largely does not meet modern requirements for working with information, namely socio-economic data of municipalities. It is necessary to consider the possibility of using other, more intuitive ways of analyzing and comparing the results of the activities of municipalities. One such approach is the topological approach. The use of integral indicators used to solve the problem of simplifying calculations in manual mode, whereas with the beginning of widespread digitalization of workplaces in municipalities, more complex calculations became available that can be used to process data from multifactor models. The transition to digital data processing allows us to apply approaches that are characterized by large-dimensional input data. In addition, the data represent different semantic fields, which makes them heterogeneous. Topological approaches to data mining realize the possibility of visual comparison of categorical and numerical data of different semantics, which allows us to solve a complex problem and answer the question of the effectiveness of the municipal administration. While maintaining the achievement of the goal, it was possible to make the assessment not only intuitive, but also to increase the accuracy of determining a comprehensive efficiency assessment, since geometric averages were removed from the basic formula established by the current legislation. The proposed approach makes it possible not only to switch to more accurate performance indicators to identify the most advanced practices of state and municipal management, but also to take a step to solve the problem of using artificial intelligence technologies, such as artificial neural networks to build predictive models. In addition, it will allow more efficient use of computer models of reasoning for data mining purposes.

Stem cell biology needs a theory.

Stem cell biologists are increasingly making use of computational models to decipher their data. However, there is sometimes uncertainty about what makes a "good" model. The purpose of this commentary is to argue for closer integration of experiment and theory in stem cell research and propose guidelines for good theory.

Этапы создания транспортных моделей и их построение в PTV Visum

The article analyzes various aspects of the use of computer modeling for the development of transport infrastructure. The levels of modeling, the main stages of preparation and creation of transport models of systems are considered. The overview of specialized software for modeling transport processes is given. The advantages of the PTV Visum software product, which is widely used in transport planning and analysis to build accurate and detailed models of transport systems, are specified.

The dynamic brain-network model of PTSD

The brain is a highly complex system whose functioning is critical for our interaction with the world. Neural elements, from single cells to brain systems, constantly fluctuate in their dynamics, accompanying the plethora of possible exchanges between our environment and ourselves. However, sometimes things go awry. An unfortunate example is post-traumatic stress disorder (PTSD), a debilitating clinical condition that can appear after exposure to a threatening life event. In this work, using complexity as a framework, we aim to introduce the dynamic brain network model of PTSD. We hope this model will allow the generation of novel specific hypotheses concerning brain organization and dynamics in PTSD research. We first introduce how the network framework complements the localizationist approach centered in specific brain regions or subsets of brain regions, with a whole brain approach considering brain regions' dynamic relationships. Then, we review key concepts in network neuroscience, focusing on the importance of the network topology and dynamics to understand the organizational principles of the brain, that is, functional segregation and integration. In the third part, we apply this knowledge to describe the possible trajectories conducting a brain system to present PTSD alterations. Accordingly, we introduce the Dynamic Brain Network Model (DBNM) of PTSD, a concrete framework built on the network approach and resilience theory to study the transition of a brain network from state 1 (e.g., before the traumatic event) to state 2 (e.g., after the traumatic event). To conclude, we provide a summary of metrics for quantifying elements on the DBNM and its potential use in computational models of PTSD.

Computational Models for Biomarker Discovery.

Alzheimer's disease (AD) is a prevalent and debilitating neurodegenerative disorder characterized by progressive cognitive decline. Early diagnosis and accurate prediction of disease progression are critical for developing effective therapeutic interventions. In recent years, computational models have emerged as powerful tools for biomarker discovery and disease prediction in Alzheimer's and other neurodegenerative diseases. This paper explores the use of computational models, particularly machine learning techniques, in analyzing large volumes of data and identifying patterns related to disease progression. The significance of early diagnosis, the challenges in classifying patients at the mild cognitive impairment (MCI) stage, and the potential of computational models to improve diagnostic accuracy are examined. Furthermore, the importance of incorporating diverse biomarkers, including genetic, molecular, and neuroimaging indicators, to enhance the predictive capabilities of these models is highlighted. The paper also presents case studies on the application of computational models in simulating disease progression, analyzing neurodegenerative cascades, and predicting the future development of Alzheimer's. Overall, computational models for biomarker discovery offer promising opportunities to advance our understanding of Alzheimer's disease, facilitate early diagnosis, and guide the development of targeted therapeutic strategies.

Computer modeling of physical phenomena

This article presents aspects of the use of Information Technology in physics lessons, in particular, the work of students with computer models. This work helps to increase students ' interest in studying the subject and the level of thinking. Topic relevance the use of computer models of various physical processes and phenomena contributes to the effectiveness of teaching physics to students, allowing them to understand the mechanisms of physical phenomena, often invisible to the human eye, and in some cases impractical and impossible in life. In order to identify the advantages of computer modeling, studies were conducted on two groups of 9th grade students (Control and experimental), their general understanding in the use of computer modeling was studied, and the role of computer modeling in understanding the concepts of speed and acceleration in body movement was determined. The two groups were applied the same traditional type of training on this topic, and the peculiarity was that the experimental group used computer simulations as a complement. Based on the results presented in the studies, it is shown that students working with the help of computer modeling scored significantly higher scores in their tasks. After analyzing the results, it is confirmed that computer modeling can be used as an additional teaching tool that helps to increase the level of thinking of students, as well as to develop systemic ideas about phenomena in physics. In the course of research using modeling, on average, 6 out of 10 students achieved good results. The importance of teaching using computer models, organizing the student's interaction with the model to participate in active research activities is revealed. It was also found that computer models can be used both in distance learning and in traditional learning, including with network support. The result of the research is that the training of physics teachers in computer modeling can be used internationally in the training of future physics teachers, enhancing their intellectual abilities, contributing to the performance of mental operations and the development of intelligence in general.

Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration

With the advancements of computer architectures, the use of computational models proliferates to solve complex problems in many scientific applications such as nuclear physics and climate research. However, the potential of such models is often hindered because they tend to be computationally expensive and consequently ill-fitting for uncertainty quantification. Furthermore, they are usually not calibrated with real-time observations. We develop a computationally efficient algorithm based on variational Bayes inference (VBI) for calibration of computer models with Gaussian processes. Unfortunately, the standard fast-to-compute gradient estimates based on subsampling are biased under the calibration framework due to the conditionally dependent data which diminishes the efficiency of VBI. In this work, we adopt a pairwise decomposition of the data likelihood using vine copulas that separate the information on dependence structure in data from their marginal distributions and leads to computationally efficient gradient estimates that are unbiased and thus scalable calibration. We provide an empirical evidence for the computational scalability of our methodology together with average case analysis and describe all the necessary details for an efficient implementation of the proposed algorithm. We also demonstrate the opportunities given by our method for practitioners on a real data example through calibration of the Liquid Drop Model of nuclear binding energies.

A Guided Tutorial on Modelling Human Event-Related Potentials with Recurrent Neural Networks

In cognitive neuroscience research, computational models of event-related potentials (ERP) can provide a means of developing explanatory hypotheses for the observed waveforms. However, researchers trained in cognitive neurosciences may face technical challenges in implementing these models. This paper provides a tutorial on developing recurrent neural network (RNN) models of ERP waveforms in order to facilitate broader use of computational models in ERP research. To exemplify the RNN model usage, the P3 component evoked by target and non-target visual events, measured at channel Pz, is examined. Input representations of experimental events and corresponding ERP labels are used to optimize the RNN in a supervised learning paradigm. Linking one input representation with multiple ERP waveform labels, then optimizing the RNN to minimize mean-squared-error loss, causes the RNN output to approximate the grand-average ERP waveform. Behavior of the RNN can then be evaluated as a model of the computational principles underlying ERP generation. Aside from fitting such a model, the current tutorial will also demonstrate how to classify hidden units of the RNN by their temporal responses and characterize them using principal component analysis. Statistical hypothesis testing can also be applied to these data. This paper focuses on presenting the modelling approach and subsequent analysis of model outputs in a how-to format, using publicly available data and shared code. While relatively less emphasis is placed on specific interpretations of P3 response generation, the results initiate some interesting discussion points.

Evaluating the Haemodynamic Performance of Endografts for Complex Aortic Arch Repair.

Thoracic endovascular aortic repair (TEVAR) of aortic aneurysms and dissections involving the arch has evolved over the last two decades. Compared to conventional surgical methods, endovascular repair offers a less invasive treatment option with lower risk and faster recovery. Endografts used in TEVAR vary in design depending on the procedure and application. Novel endografts (e.g., branched stent-graft) were developed to ensure perfusion of blood to the supra-aortic vessels, but their haemodynamic performance and long-term durability have not been adequately studied. This review focuses on the use of computational modelling to study haemodynamics in commercially available endografts designed for complex aortic arch repair. First, we summarise the currently adopted workflow for computational fluid dynamics (CFD) modelling, including geometry reconstruction, boundary conditions, flow models, and haemodynamic metrics of interest. This is followed by a review of recently (2010-present) published CFD studies on complex aortic arch repair, using both idealized and patient-specific models. Finally, we introduce some of the promising techniques that can be potentially applied to predict post-operative outcomes.

From evolutionary ecosystem simulations to computational models of human behavior.

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence.

Revision as an essential step in modeling to support predicting, observing, and explaining cellular respiration system dynamics

ABSTRACT Comprehensive understanding of complex biological systems necessitates the use of computational models because they facilitate visualisation and interrogation of system dynamics and data-driven analysis. Computational model-based (CMB) activities have demonstrated effectiveness in improving students’ understanding and their ability to use and reason with models. To maximise the effectiveness of computational modelling, this study examined an improved cognitive scaffolding and its impact on student learning of cellular respiration. This scaffolding proposes the predict-observe-revise-explain (PORE) sequence of tasks that explicitly challenge students to revise their predictions and computational models to resolve cognitive conflict. Based on revision work in a CMB activity, a sample of n = 362 undergraduate biology students were categorised into three groups – not expected to revise (NR, n = 109), required-revised (RR, n = 179), and required-did not revise (RDNR, n = 74). Students’ performance in predict, revise, and explain tasks were significantly associated with post-test performance. RR students were more than twice as likely to demonstrate a positive learning gain in the post-test (odds ratio = 2.47) compared to RDNR students. While science education has implicitly acknowledged revision as a critical cognitive process in modelling, this study presents evidence that making revision an explicit cognitive task in a CMB activity supports student learning of a complex biological system.

Cerebrospinal fluid dynamics along the optic nerve.

The cerebrospinal fluid (CSF) plays an important role in delivering nutrients and eliminating the metabolic wastes of the central nervous system. An interrupted CSF flow could cause disorders of the brain and eyes such as Alzheimer's disease and glaucoma. This review provides an overview of the anatomy and flow pathways of the CSF system with an emphasis on the optic nerve. Imaging technologies used for visualizing the CSF dynamics and the anatomic structures associated with CSF circulation have been highlighted. Recent advances in the use of computational models to predict CSF flow patterns have been introduced. Open questions and potential mechanisms underlying CSF circulation at the optic nerves have also been discussed.

Application and Prospect Analysis of Artificial Intelligence in the Field of Physical Education.

The development of AI technology has a significant impact on every sector of business. Artificial intelligence uses this technology to reduce the amount of work required, duplicate work, and increase the accuracy of work by modelling human behaviour and thought. On the basis of a thorough analysis of artificial intelligence technology, the technology is applied to teaching, specialised training, and specialised testing in response to the current issues in the field of physical education and the business that can be improved through auxiliary tools. The paper discusses how artificial intelligence technology can be used in physical education. After that, it examines how physical education is currently taught in classrooms and discusses how artificial intelligence is affecting this field. Following a thorough demonstration of the aforementioned material, the commonly used artificial intelligence technologies are introduced in turn, along with the methodology used in physical education. The use of computer models in physical education is then explained, and future analysis is conducted on the basis of this information.

Acute retigabine-induced effects on myelinated motor axons in amyotrophic lateral sclerosis.

Altered motor neuron excitability in patients with amyotrophic lateral sclerosis (ALS) has been suggested to be an early pathophysiological mechanism associated with motor neuron death. Compounds that affect membrane excitability may therefore have disease‐modifying effects. Through which mechanism(s), these compounds modulate membrane excitability is mostly provided by preclinical studies, yet remains challenging to verify in clinical studies. Here, we investigated how retigabine affects human myelinated motor axons by applying computational modeling to interpret the complex excitability changes in a recent trial involving 18 ALS patients. Compared to baseline, the post‐dose excitability differences were modeled well by a hyperpolarizing shift of the half‐activation potential of slow potassium (K+)‐channels (till 2 mV). These findings verify that retigabine targets slow K+‐channel gating and highlight the usefulness of computational models. Further developments of this approach may facilitate the identification of early target engagement and ultimately aid selecting responders leading to more personalized treatment strategies.

METHODOLOGICAL FUNDAMENTALS OF USING PHET-SIMULATIONS IN THE PROCESS OF LEARNING PHYSICS

The article considers didactic and methodological possibilities of using PhET Interactive Simulations in the educational process. The application of electronic resources in the organization of educational and cognitive activities of students in the study of physics is proposed. Features and conditions of using computer models during the organization of the educational process are analyzed. The aim of the article is to consider the methodological possibilities and analyze the effectiveness of the use of PhET-Simulations in the study of physics. The following methods were used in the research process: theoretical – analysis, comparison, systematization and generalization of educational and methodological, popular science and applied sources on the research problem.
 It is argued that computer models are an effective tool for testing homework, explaining new and consolidating the previously learned material as homework or for independent work, and they play a special role during a demonstration experiment or laboratory workshop. Models become especially relevant when a real physical experiment is impossible. Examples of qualitative and quantitative tasks of different levels of complexity from the section “Mechanics” are given; performing tasks of this type contributes to better mastering of the theoretical material. It is substantiated that the use of computer modeling at lessons of natural and mathematical disciplines stimulates educational and scientific-cognitive, research activities of students, activates creative activity and has a positive effect on success, expands understanding of physical phenomena and processes occurring in the environment; enable students to understand natural phenomena at the highest level. The use of information and communication technologies in the educational process is based on changes in student learning activities and radical modernization of teacher activities, provides the formation of both key and subject competences of students.

Influence of surface roughness on the transient interfacial phenomena in laser impact welding

Presented is an investigation into laser impact welding (LIW) wherein experimentally measured surface profiles of the flyer and target foils are incorporated to study their effects on the transient physical phenomena that occur during this rapid, collision-based joining process. During LIW, thermal response, plastic strains, and shear stresses evolve over a sub-microsecond timescale, which necessitates the use of computational modeling to predict the influence that surface roughness has on the material response and resulting joint morphology. White light interferometry is used to experimentally characterize the surface profiles of an aluminum 1100 flyer foil and a stainless steel 304 target foil. The profiles are mapped to material volumes within a plane strain, thermomechanical simulation employing an Eulerian framework. A spatially and temporally varying laser-induced plasma pressure is applied to the flyer foil, and the resulting transient phenomena are analyzed at timeframes ranging from initial contact through complete weld formation. To reveal effects of the rough foil surfaces, results from the same computational model using smooth foil surfaces are also obtained. When the rough surface profiles are included, it is found that significant differences in the thermal response and plastic strains are observed, and these differences are hypothesized to arise from the effects of discontinuous surface contact along the collision path. The incorporation of rough surface asperities also reveals two distinct regions prior to the weld initiation point; the first where elastic rebound occurs at a negligible collision angle, leaving interfacial voids, and the second where superficial shear deformation flattens the surfaces without mutual ablation and jetting. The work represents the first study in which measured surface profiles of the flyer and target foils are incorporated into a computational model of laser impact welding.

Design and use of computational models for contrast-enhanced spectral mammography

Introduction: Contrast-enhanced spectral mammography (CESM) is a new technique for cancer investigations. The application of this technique to screen the breast would result in contrast improvement of the different breast lesions. This can be effectively ensured by the use of phantoms. Computational phantoms are one of the ways to investigate the characteristics of the received image and thus to evaluate the whole technique. Aim: The aim of this study is to validate and analyse the design of three different computational phantoms for CESM. Materials and Methods: An in-house software tool was used to create three computational phantoms, consisting of iodinated inserts for the simulation of the CESM procedure. The inserts in the phantoms, modelled from Omnipaque, have a radius of 8–10 mm and varying height. The three phantoms are made of polymethyl methacrylate (PMMA), with different shape and composition. One of the phantoms is characterised by a heterogeneous background. For each phantom two x-ray radiographs were generated, one at x-ray energy of 20keV and one at 34 keV. The images were processed to obtain a recombined iodine image, which shows the iodine contrast agent and suppresses the surrounding background tissue. Results: Simulated spectral images demonstrated a great improvement of the image quality compared to low-energy images of the phantoms. The simulations with the inhomogeneous model revealed that the heterogeneous background has been successively depressed while improving the visibility of the iodine inserts. Conclusion: The heterogeneous breast phantom might be used as a reference tool for information about the needed iodine concentration which needs to be inserted during the procedure to obtain significant enhancement in the suspicious area.