Application Of Computational Models Research Articles

Applications of Computational and Statistical Models for Optimizing the Electrochemical Removal of Cephalexin Antibiotic from Water

One of the most serious effects of micropollutants in the environment is biological magnification, which causes adverse effects on humans and the ecosystem. Among all of the micro-pollutants, antibiotics are commonly present in the aquatic environment due to their wide use in treating or preventing various diseases and infections for humans, plants, and animals. Therefore, an aluminum-based electrocoagulation unit has been used in this study to remove cephalexin antibiotics, as a model of the antibiotics, from water. Computational and statistical models were used to optimize the effects of key parameters on the electrochemical removal of cephalexin, including the initial cephalexin concentration (15–55 mg/L), initial pH (3–11), electrolysis time (20–40 min), and electrode type (insulated and non-insulated). The response surface methodology-central composite design (RSM-CCD) was used to investigate the dependency of the studied variables, while the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) methods were applied for predicting the experimental training data. The results showed that the best experimental and predicted removals of cephalexin (CEX) were 88.21% and 93.87%, respectively, which were obtained at a pH of 6.14 and electrolysis time of 34.26 min. The results also showed that the ANFIS model predicts and interprets the experimental results better than the ANN and RSM-CCD models. Sensitivity analysis using the Garson method showed the comparative significance of the variables as follows: pH (30%) > electrode type (27%) > initial CEX concentration (24%) > electrolysis time (19%).

Recent Advances in Computational Modeling of Biomechanics and Biorheology of Red Blood Cells in Diabetes.

Diabetes mellitus, a metabolic disease characterized by chronically elevated blood glucose levels, affects about 29 million Americans and more than 422 million adults all over the world. Particularly, type 2 diabetes mellitus (T2DM) accounts for 90–95% of the cases of vascular disease and its prevalence is increasing due to the rising obesity rates in modern societies. Although multiple factors associated with diabetes, such as reduced red blood cell (RBC) deformability, enhanced RBC aggregation and adhesion to the endothelium, as well as elevated blood viscosity are thought to contribute to the hemodynamic impairment and vascular occlusion, clinical or experimental studies cannot directly quantify the contributions of these factors to the abnormal hematology in T2DM. Recently, computational modeling has been employed to dissect the impacts of the aberrant biomechanics of diabetic RBCs and their adverse effects on microcirculation. In this review, we summarize the recent advances in the developments and applications of computational models in investigating the abnormal properties of diabetic blood from the cellular level to the vascular level. We expect that this review will motivate and steer the development of new models in this area and shift the attention of the community from conventional laboratory studies to combined experimental and computational investigations, aiming to provide new inspirations for the development of advanced tools to improve our understanding of the pathogenesis and pathology of T2DM.

Development and Applicability of Computational Models in Educational Psychology:For Better Understanding of Theories, Constructs, and Educational Phenomena and Practice

This review addresses the development of computational models in educational psychology. First, we generally explained computational models and their background. Second, we searched academic articles relating to computational models in psychology and other fields. The search revealed that, on the one hand, international journals in behavioral or cognitive sciences include several articles relating to computational models, and the number is increasing in general psychology journals. On the other hand, domestic and international educational psychology journalsscarcely have relating papers. Third, we described the current challenges in educational psychology and how the ideas and analytical method on computational models would contribute to overcome these challenges.

Research progress on indoor environment of mushroom factory

Mushroom factory is an emerging production mode that is relied on facility and equipment by creating a suitable, steady, and uniform growing environment to accommodate the demand of edible fungus at each stage of growth. Therefore, an optimal range of key environmental factors (temperature, humidity, light, and CO2 concentrations) in mushroom facilities is crucial to achieving satisfactory production rates. This research aimed to provide an overview of recent progress on indoor environmental studies of mushroom cultivation facilities from three perspectives, 1) the development of environmental monitoring and controlling systems; 2) the application of computer modelling in addressing indoor environmental issues; 3) the refinement of mushroom facility design, including structure and ventilation scheme. With the aid of cutting-edge technologies, accomplishments have been made in developing smart farming systems that facilitate real-time recording of environmental parameters and automatic regulation of equipment, which helps the establishment of growth models for specific mushroom species. Computational Fluid Dynamics (CFD) modelling has been adopted by researchers to investigate indoor airflow patterns and assess the distribution of critical environmental parameters. Studies have been conducted to modify the design of mushroom facilities to improve the performance in structural stability, ventilation efficiency, and internal environmental condition. However, some existing problems still need further investigations, such as the lack of design guidelines, energy-saving strategies, and Artificial Intelligent (AI) quality control. Therefore, this overview was expected to provide constructive insights for future studies in addition to references of previous studies. Keywords: modern farming, mushroom factory, indoor environment control, computational fluid dynamics, remote sensing DOI: 10.25165/j.ijabe.20221501.6872 Citation: Chen L, Qian L, Zhang X, Li J Z, Zhang Z J, Chen X M. Research progress on indoor environment of mushroom factory. Int J Agric & Biol Eng, 2022; 15(1): 25–32.

Primer on Mathematical Modeling in C. elegans.

Recently, applications of mathematical and computational models to biological processes have helped investigators to systematically interpret data, test hypotheses built on experimental data, generate new hypotheses, and guide the design of new experiments, protocols, and synthetic biological systems. Availability of diverse quantitative data is a prerequisite for successful mathematical modeling. The ability to acquire high-quality quantitative data for a broad range of biological processes and perform precise perturbation makes C. elegans an ideal model system for such studies. In this primer, we examine the general procedure of modeling biological systems and demonstrate this process using the heat-shock response in C. elegans as a case study. Our goal is to facilitate the initial discussion between worm biologists and their potential collaborators from quantitative disciplines.

AMT 1.x: A toolbox for reproducible research in auditory modeling

The Auditory Modeling Toolbox (AMT) is a MATLAB/Octave toolbox for the development and application of computational auditory models with a particular focus on binaural hearing. The AMT aims for a consistent implementation of auditory models, well-structured in-code documentation, and inclusion of auditory data required to run the models. The motivation is to provide a toolbox able to reproduce the model predictions and allowing students and researchers to work with and to advance existing models. In the AMT, model implementations can be evaluated in two stages: by running so-called demonstrations, which are quick presentations of a model, and by starting so-called experiments aimed at reproducing results from the corresponding publications. Here, we describe the tools and mechanisms available within the framework of all AMT 1.x versions. The recently released AMT 1.1 includes over 60 models and is freely available as an open-source package fromhttps://www.amtoolbox.org.

2D CFD Modeling of Rapid Water Filling with Air Valves Using OpenFOAM

The rapid filling process in pressurized pipelines has been extensively studied using mathematical models. On the other hand, the application of computational fluid dynamics models has emerged during the last decade, which considers the development of CFD models that simulate the filling of pipes with entrapped air, and without air expulsion. Currently, studies of CFD models representing rapid filling in pipes with entrapped air and with air expulsion are scarce in the literature. In this paper, a two-dimensional model is developed using OpenFOAM software to evaluate the hydraulic performance of the rapid filling process in a hydraulic installation with an air valve, considering different air pocket sizes and pressure impulsion by means of a hydro-pneumatic tank. The two-dimensional CFD model captures the pressure evolution in the air pocket very well with respect to experimental and mathematical model results, and produces improved results with respect to existing mathematical models.

BEM evaluation of surface octahedral strains and internal strain gradients in 3D-printed scaffolds used for bone tissue regeneration

Most of the mechnoregulatory computational models appearing so far in tissue engineering for bone healing predictions, utilize as regulators for cell differentiation mainly the octahedral volume strains and the interstitial fluid velocity calculated at any point of the fractured bone area and controlled by empirical constants concerning these two parameters. Other stimuli like the electrical and chemical signaling of bone constituents are covered by those two regulatory fields. It is apparent that the application of the same mechnoregulatory computational models for bone healing predictions in scaffold-aided regeneration is questionable since the material of a scaffold disturbs the signaling pathways developed in the environment of bone fracture. Thus, the goal of the present work is to evaluate numerically two fields developed in the body of two different compressed scaffolds, which seem to be proper for facilitating cell sensing and improving cell viability and cell seeding efficiency. These two fields concern the surface octahedral strains that the cells attached to the scaffold can experience and the internal strain gradients that create electrical pathways due to flexoelectric phenomenon. Both fields are evaluated with the aid of the Boundary Element Method (BEM), which is ideal for evaluating with high accuracy surface strains and stresses as well as strain gradients appearing throughout the analyzed elastic domain.

CT-based fractional flow reserve: development and expanded application.

Computations of fractional flow reserve, based on CT coronary angiography and computational fluid dynamics (CT-based FFR) to assess the severity of coronary artery stenosis, was introduced around a decade ago and is now one of the most successful applications of computational fluid dynamic modelling in clinical practice. Although the mathematical modelling framework behind this approach and the clinical operational model vary, its clinical efficacy has been demonstrated well in general. In this review, technical elements behind CT-based FFR computation are summarised with some key assumptions and challenges. Examples of these challenges include the complexity of the model (such as blood viscosity and vessel wall compliance modelling), whose impact has been debated in the research. Efforts made to address the practical challenge of processing time are also reviewed. Then, further application areas—myocardial bridge, renal stenosis and lower limb stenosis—are discussed along with specific challenges expected in these areas.

Application of computational modeling to improve cornea transplant surgery

Application of computational modeling to improve cornea transplant surgery

Conceptual design and structural analysis of a Ground Support Equipment for handling and maintenance of Turboshaft engines

This paper describes the application of computational modeling and computer-aided design (CAD) for the conceptual design and structural analysis of the maintenance process of Klimov TV3-117 engines at an approved Maintenance, Repair and Overhaul (MRO) facility in Colombia. The main issue is that these engines are difficult to roll and manipulate due to their weight, which ranges between 250 and 350 kg, which causes time and cost overruns for the operator, and delays in the scheduled maintenance times. The solution proposed by this study is the conceptualization and structural feasibility of a prototype of ground support equipment for handling and maintenance of Turboshaft engines, implementation of which could save up to 100 person-hours, which translates into about USD 10,000, since the current process requires four specialists and two inspectors, whereas the modified process would only require one of each

Gut inference: A computational modelling approach

Neurocomputational theories have hypothesized that Bayesian inference underlies interoception, which has become a topic of recent experimental work in heartbeat perception. To extend this approach beyond cardiac interoception, we describe the application of a Bayesian computational model to a recently developed gastrointestinal interoception task completed by 40 healthy individuals undergoing simultaneous electroencephalogram (EEG) and peripheral physiological recording. We first present results that support the validity of this modelling approach. Second, we provide a test of, and confirmatory evidence supporting, the neural process theory associated with a particular Bayesian framework (active inference) that predicts specific relationships between computational parameters and event-related potentials in EEG. We also offer some exploratory evidence suggesting that computational parameters may influence the regulation of peripheral physiological states. We conclude that this computational approach offers promise as a tool for studying individual differences in gastrointestinal interoception.

Integrative Modelling of Gene Expression and Digital Phenotypes to Describe Senescence in Wheat.

Senescence is the final stage of leaf development and is critical for plants’ fitness as nutrient relocation from leaves to reproductive organs takes place. Although senescence is key in nutrient relocation and yield determination in cereal grain production, there is limited understanding of the genetic and molecular mechanisms that control it in major staple crops such as wheat. Senescence is a highly orchestrated continuum of interacting pathways throughout the lifecycle of a plant. Levels of gene expression, morphogenesis, and phenotypic development all play key roles. Yet, most studies focus on a short window immediately after anthesis. This approach clearly leaves out key components controlling the activation, development, and modulation of the senescence pathway before anthesis, as well as during the later developmental stages, during which grain development continues. Here, a computational multiscale modelling approach integrates multi-omics developmental data to attempt to simulate senescence at the molecular and plant level. To recreate the senescence process in wheat, core principles were borrowed from Arabidopsis Thaliana, a more widely researched plant model. The resulted model describes temporal gene regulatory networks and their effect on plant morphology leading to senescence. Digital phenotypes generated from images using a phenomics platform were used to capture the dynamics of plant development. This work provides the basis for the application of computational modelling to advance understanding of the complex biological trait senescence. This supports the development of a predictive framework enabling its prediction in changing or extreme environmental conditions, with a view to targeted selection for optimal lifecycle duration for improving resilience to climate change.

PROBLEMS IN THE FIELD OF MEDICAL INFORMATION REPORTING

Problems and errors in the application of computer technologies, Models and methods of organizing software development, the system of equations is solved by the method of a system of linear equations, and unknown phenomena are detected. The decision-making process often has to deal with problems that are often multidimensional in nature.

Computational models of atrial fibrillation: achievements, challenges, and perspectives for improving clinical care.

Despite significant advances in its detection, understanding and management, atrial fibrillation (AF) remains a highly prevalent cardiac arrhythmia with a major impact on morbidity and mortality of millions of patients. AF results from complex, dynamic interactions between risk factors and comorbidities that induce diverse atrial remodelling processes. Atrial remodelling increases AF vulnerability and persistence, while promoting disease progression. The variability in presentation and wide range of mechanisms involved in initiation, maintenance and progression of AF, as well as its associated adverse outcomes, make the early identification of causal factors modifiable with therapeutic interventions challenging, likely contributing to suboptimal efficacy of current AF management. Computational modelling facilitates the multilevel integration of multiple datasets and offers new opportunities for mechanistic understanding, risk prediction and personalized therapy. Mathematical simulations of cardiac electrophysiology have been around for 60 years and are being increasingly used to improve our understanding of AF mechanisms and guide AF therapy. This narrative review focuses on the emerging and future applications of computational modelling in AF management. We summarize clinical challenges that may benefit from computational modelling, provide an overview of the different in silico approaches that are available together with their notable achievements, and discuss the major limitations that hinder the routine clinical application of these approaches. Finally, future perspectives are addressed. With the rapid progress in electronic technologies including computing, clinical applications of computational modelling are advancing rapidly. We expect that their application will progressively increase in prominence, especially if their added value can be demonstrated in clinical trials.

Computing the Uncontrollable: Insights from Computational Modelling of Learning and Choice in Depression

The learned helplessness (LH) paradigm, developed in experimental animals, has had great influence on the development of models of mood and anxiety disorders. However, the insights from this paradigm have not always translated straightforwardly into human experimental work. In particular, instrumental contingency learning experiments yielded the contradictory finding of more accurate contingency knowledge in depressed individuals (“depressive realism”: DR). A growing literature involving the application of computational modelling applied to human learning studies suggest that possible sequelae of LH in humans may be manifest in one or more potential candidate reinforcement learning-based mechanisms. This analysis suggests that the DR effect is theoretically and empirically independent of LH, despite both LH and DR being related to depression. Overall, the review highlights recent developments within the complex literature of learning and choice in depression and proposes a variety of testable predictions which may serve to delineate more precisely the presence, and neural basis, of LH-like sequelae across social and non-social learning and choice paradigms in depressed patients.

Application of computational modeling for large wood dynamics with collisions on moveable channel beds

This study aims to analyze model reproducibility on large wood collisions on moveable channel beds. Flume experiments were conducted to observe the responses of moveable bed change with the behavior of large wood, including wood collisions. In the experiments, we employed time intervals of 5 and 9 s in wood supply, which modified the collision patterns. Subsequently, we conducted simulations based on the experimental conditions. Herein, to analyze the wood collision effect in the simulations, we employed the dashpot-spring model in a large wood dynamics model. Subsequently, we compared the simulations with and without wood collision and discussed the reproducibility of the model. The results of the simulations and experiments revealed that the wood pieces exhibited diverse behaviors, including floating, sliding, deposition, plane rotation, rolling, collision, remobilization, jam formation, and causing local bed changes. In particular, the results indicate that model reproducibility is dependent on wood collision because this can actively alter wood deposition patterns. We also described the piping and tunnel erosion processes linked to wood collision and moveable beds and suggested the corresponding, suitable computational treatment. This study presents an experimental and modeling methodology on wood collision and the interaction between bed change and large wood. Thus, useful information for solving numerical difficulties linked to large wood dynamics, including wood collisions, is provided.

Combined Theoretical and Experimental Investigation of Lewis Acid-Carbonyl Interactions for Metathesis.

The coordination of a carbonyl to a Lewis acid represents the first step in a wide range of catalytic transformations. In many reactions it is necessary for the Lewis acid to discriminate between starting material and product, and as a result, how these structures behave in solution must be characterized. Herein, we report the application of computational modeling to calculate properties of the solution interactions of acetone and benzaldehyde with FeCl3. Using these chemical models, we can predict spectral features in the carbonyl region of infrared (IR) spectroscopy. These simulated spectra are then directly compared to experimental spectra generated via titration-IR. We observe good agreement between theory and experiment, in that, between 0 and 1 equiv carbonyl with respect to FeCl3, a pairwise interaction dominates the spectra. When >1 equiv carbonyl is present, our theoretical model predicts two possible structures composed of 4:1 carbonyl to FeCl3, for acetone as well as benzaldehyde. When these predicted spectra are compared with titration-IR data, both structures contribute to the observed solution interactions. These findings suggest that the resting state of FeCl3-catalyzed carbonyl-based reactions employing simple substrates starts as a Lewis pair, but this structure is gradually consumed and becomes a highly ligated, catalytically less active Fe-centered complex as the reaction proceeds. An analytical model is proposed to quantify catalyst inhibition due to equilibrium between 1:1 and 4:1 carbonyl:Fe complexes.

Experimental and computational response of strip footing resting on prestressed geotextile-reinforced industrial waste

Geotechnical engineering practices involves the use of geosynthetic as one of the major construction materials for stabilizing terrains and these materials have been also proven to be technically efficient. In view of the above, a series of model load tests with variation in the depth of geotextile and prestressing force were carried out to study the strength and deformation characteristics of ferrochrome slag reinforced with single prestressed geotextile layer. The present study provides a sustainable replacements solution for industrial waste such as ferrochrome slag. It is also found that pretensioning of reinforcements is effective in comparison with simple reinforcement. The load settlement curves demonstrate that reinforcements and prestressing significantly reduce the settlement of a strip footing resting on geotextile-reinforced ferrochrome slag. Also, a pretensioning force of 9 kN/m is found to have the least settlement. Further, this study proposes the use of artificial neural network and extreme learning machine (ELM) to predict settlement using basic input parameters. Application of computational models provides an innovative solution for predicting the settlement of footing with ease and in a cost-efficient manner. The computational model concludes that the developed ELM models are efficient and effective in predicting the settlement of a footing and can be used as a robust tool for preliminary assessments.

Childbirth Computational Models: Characteristics and Applications.

The biomechanical process of childbirth is necessary to usher in new lives-but it can also result in trauma. This physically intense process can put both the mother and the child at risk of injuries and complications that have life-long impact. Computational models, as a powerful tool to simulate and explore complex phenomena, have been used to improve our understanding of childbirth processes and related injuries since the 1990s. The goal of this paper is to review and summarize the breadth and current state of the computational models of childbirth in the literature-focusing on those that investigate the mechanical process and effects. We first summarize the state of critical characteristics that have been included in computational models of childbirth (i.e., maternal anatomy, fetal anatomy, cardinal movements, and maternal soft tissue mechanical behavior). We then delve into the findings of the past studies of birth processes and mechanical injuries in an effort to bridge the gap between the theoretical, numerical assessment and the empirical, clinical observations and practices. These findings are from applications of childbirth computational models in four areas: (1) the process of childbirth itself, (2) maternal injuries, (3) fetal injuries, and (4) protective measures employed by clinicians during delivery. Finally, we identify some of the challenges that computational models still face and suggest future directions through which more biofidelic simulations of childbirth might be achieved, with the goal that advancing models may provide more efficient and accurate, patient-specific assessment to support future clinical decision-making.