Application Of Computational Models Research Articles

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.

The VIth Congress of Biophysicists of Russia

An overview of the problems of modern biophysics, which were discussed at the VIth Congress of biophysicists of Russia, Sochi, September 2019 ( http://conf-2019.biophys.ru ), is given. The results of fundamental and applied research in the field of biophysics of single biomacromolecules and molecular complexes, membrane biophysics, cell biophysics, bionanotechnology were presented at the plenary, sectional, and poster sessions. Great attention was paid to issues on the application of information technology, mathematical and computer modeling, and bioinformatics methods as a necessary research tool at all levels of organization of living systems, from biomacromolecules to biogeocenoses, as well as to new experimental methods of biophysical research. Along with the fundamental problems of studying the physical mechanisms of the regulation of biological processes at the molecular, subcellular, and cellular levels, the application of biophysical research results in medicine and ecology was widely discussed. It was decided to hold the next Congress of biophysicists of Russia in 2023.

Effect of the Digital Transformation of Power System on Renewable Energy Utilization in China

Renewable energy is the development direction of power system of China. To further boost the development of China's power industry, the digital transformation of the power is underway. This study examined the panorama of digital transformation of power system of China. Through analyzing the effect of digital transformation on renewable energy utilization, three business applications were identified: short-term prediction of renewable energy output, renewable energy equipment maintenance, and intelligent power-grid maintenance. The implementation process and effect of the three business applications are analyzed by using the data covering 2012-2019 from three aspects: the short-term prediction accuracy of renewable energy output, the reduction of the fault restoration mean time for renewable energy equipment to the industry average, the reduction ratio of the mean time of power grid fault restoration to the industry average, and the rate of renewable energy utilization. The results indicated that digital transformation boosted the efficiency of renewable energy utilization. Based on the findings, the following suggestions are proposed for improving the digital transformation of the power industry and thereby promoting renewable energy utilization: 1) the first is to emphasize the scale efficiency of digital application in the early stage of digitization and renewable energy development, and emphasize the technical performance of a digital application in the middle and later stages of development; 2) the second is to enhance data accuracy, such as meteorological data, historical data for renewable energy power stations, power equipment status data, and strengthen the application of algorithms and computational models; 3) the third suggestion is to improve the overall digitalization rate of the power industry.

Развитие исследований по ИК-спектроскопии поверхности в Санкт-Петербургском университете

The paper describes the history of spectral studies of surface processes at St. Petersburg University from the first pre-war experiments of Terenin and Kasparov to the present day. The development of experimental technique and advances in the studies of surface acid and basic sites, functional groups, and adsorption complexes are considered. Spectroscopy in a wide temperature range enables us to study such phenomena as lateral interactions between adsorbed molecules and linkage isomerism, to obtain information about the mechanism of dark and photostimulated reactions. Attention is paid to the application of computer modeling and quantum chemistry to study processes on the surface.

Experimental and stochastic analysis of lyophilisation

The development of the freeze-drying processes through the use of a combination of targeted experiments and the application of multidimensional computational models is applied increasingly in pharmaceutical practice, especially for scale-up purposes. This study deals with the analysis of uncertainties in the data on material properties and model parameters, and their influence on the results delivered by advanced computational models of lyophilisation. As a means of uncertainty analysis, the Stochastic Collocation Method is applied, allowing the use of existing reliable deterministic models as black boxes in the stochastic computations. As a deterministic model, the lyophilisation model is used, based on the axisymmetric approximation of a vial, and the Boundary Element Method as a solver. Five parameters, covering material properties, process conditions and model constants, are selected for the sensitivity analysis simulation of the lyophilisation of an aqueous mannitol solution. The results show that during the initial stage of the primary drying heat transfer from the shelf is crucial, and that the uncertainties in the contact surface area and material properties of the vial play a more important role than the thermal properties of the drying material. When the temperature of the material reaches its distinct primary drying stage level the mass transfer through the porous cake becomes the most important, with great sensitivity to the Knudsen diffusivity in the porous cake. We observed uncertainties in the results for the primary drying time in the order of ±6%, and uncertainties in the results for temperatures of ±0.6°C in the frozen material and ±3°C in the porous cake. The uncertainty analysis proved to be a great help in determining the critical parameters in the heat and mass transfer during the important primary drying step, which led to a better definition of the numerical model for use in the context of design space determination.

The Significance of Computer Modeling and 3D Printing Technology for Facial Ectoprosthetics

The integration of information technologies in healthcare practice significantly changes the methods of diagnosis and treatment, the forms of interaction of doctors with patients and colleagues, the organization of treatment and recovery of health. The field of reconstruction of the auricle is still a huge challenge for facial plastic surgeons and requires at various techniques to find the best treatment for each patient. The paper describes the application of computer modeling and laser stereolithography technology in surgical practice for auricular surfaces ectoprosthetics. To improve the accuracy and quality of the surgical intervention the positioning of external prosthesis is applied with the aid of personal templates and computer navigation. The accuracy of ectoprosthesis positioning when using a plastic mask template was 0.3-0.4 mm, while computer navigation was 0.1 - 0.2 mm. Using personalized templates is a simpler and more intuitive way of positioning that does not require expensive computer navigation systems. This example of ectoprosthetics shows the possibilities of various reconstructions of facial organs, not only the ear, but also, for example, the nose, using computer modeling and 3d printing technologies

Analysis of adsorption isotherms of Ag+, Co+2, and Cu+2 onto zeolites using computational intelligence models

This manuscript discusses the application of computational intelligence models, such as Artificial Neural Network (ANN) and Adaptive Neuro-fuzzy Inference Systems (ANFIS), as alternatives for predicting the adsorption isotherms of metal ions on different zeolites. In particular, the adsorption of silver (Ag+), cobalt (Co+2), and copper (Cu+2) ions onto zeolites ZSM-5, ZHY, and Z4A was evaluated. The results showed that Z4A has better adsorption capacity for the three ions, followed by zeolite ZHY and ZSM-5. Furthermore, it was found that the Ag+ ion was more adsorbed compared to the Co+2 and Cu+2 ions; this can be related to the fact that Ag is a monovalent ion. In ANN and ANFIS, the equilibrium adsorption capacity for silver (Ag+), cobalt (Co+2), and copper (Cu+2) ions (target variables) were correlated with input variables, including temperature, Si/Al ratios of zeolites, molecular weights of metal ions and solution initial concentration. The 10-neuron hidden layer with Bayesian regularization backpropagation as a training function has shown better results for updating ANN weight and bias values. In contrast, the 16 fuzzy rule layer with the hybrid membership function has performed better for parameter training in ANFIS. ANN and ANFIS were able to predict all the adsorption systems with the advantage that they can take into consideration the adsorbent and adsorbate characteristics.

Human Simulation and Sustainability: Ontological, Epistemological, and Ethical Reflections

This article begins with a brief outline of recent advances in the application of computer modeling to sustainability research, identifying important gaps in coverage and associated limits in methodological capability, particularly in regard to taking account of the tangled human factors that are often impediments to a sustainable future. It then describes some of the ways in which a new transdisciplinary approach within “human simulation” can contribute to the further development of sustainability modeling, more effectively addressing such human factors through its emphasis on stakeholder, policy professional, and subject matter expert participation, and its focus on constructing more realistic cognitive architectures and artificial societies. Finally, the article offers philosophical reflections on some of the ontological, epistemological, and ethical issues raised at the intersection of sustainability research and social simulation, considered in light of the importance of human factors, including values and worldviews, in the modeling process. Based on this philosophical analysis, we encourage more explicit conversations about the value of naturalism and secularism in finding and facilitating effective and ethical strategies for sustainable development.

Research work in the Applied Cognitive Modelling Lab

Dr Junya Morita is based at the Applied Cognitive Modelling Laboratory (ACML) within the Department of Behavior Informatics at Shizuoka University in Japan. His team is conducting investigations that use computational models in an effort to improve our understanding of human minds and their inner workings. There are currently two directions of study underway at ACML. The first is concerned with theoretical studies of cognitive modelling, where the team try to construct models that explain human minds as computational and algorithmic levels. The second direction of study is the application of computational cognitive models. Morita and his team believe that there are fundamental values within the basic endeavours of cognitive science and are working to prove these values exist and are valid. Current topics of application include education, driving, entertainment, graphic design, language development, web navigation and mental illness.

Computational Insights into Industrial Chemistry

This brief Editorial is dedicated to announcing the Special Issue “Computational Insights into Industrial Chemistry”. The Special Issue covers the most recent progress in the rapidly growing field of computational chemistry, and the application of computer modeling in topics relevant to industrial chemistry (chemical industrial processes and materials, environmental effects caused by chemical industry activities, computer-aided design of catalysts, green chemistry, etc.).

The Application of Computational Modeling for the optimization of Bio Fuel Production Processes

The Application of Computational Modeling for the optimization of Bio Fuel Production Processes

Pre-Diagnosis of Chronic Diseases by the Application of Computational Intelligence Models

Computational intelligence models applied to medicine have become a growing area of research around the world. This article presents the results of a documentary research that allows identifying the most important computational intelligence models of the state of the art that are applied in the pre-diagnosis of diseases, as well as a study of the algorithms that represent each of these models. Also, through the use of the WEKA platform and the KEEL and UCI repositories, the performance exhibited by these pattern classifiers when applied in the pre-diagnosis of some important chronic diseases in the context of human health is studied. In particular, algorithms representing the most appreciated approaches in the area of intelligent pattern classification are tested. In the experimental part of this article, these computational intelligence algorithms were applied in databases of breast cancer, hyperthyroidism, and hypothyroidism. The results that were derived from the experiments show the superiority of the performance of the vector support machines, the 1-NN classifier and the Lernmatrix tau[9]. The performances achieved allow confirming, with a high degree of certainty, the usefulness of computational intelligence models in the pre-diagnosis of chronic diseases.

Facial nerve stimulation in a post-meningitic cochlear implant user: using computational modelling as a tool to probe mechanisms and progression of complications on a case-by-case basis

Facial nerve stimulation (FNS) is a side-effect of cochlear implantation that can result in severe discomfort for the user and essentially limits the optimal use of the implant. Three-dimensional cochlear implant modelling research has led to the progression from generic models to user-specific models with one of the intentions to develop model-based diagnostic tools. The objective of this study is to investigate the mechanisms that underlie the manifestation of FNS in the post-meningitic cochleae of a specific CI user through computational modelling. Bilateral models were created using a method previously developed for the construction of a three-dimensional user-specific volume conduction model of the cochlea and was expanded to include the facial nerve geometry. Reduced temporal bone density based on bone densitometry, cochlear duct ossification and degenerate auditory neural fibres were incorporated into a comprehensive FNS model. Auditory and facial nerve thresholds were predicted with the models showing good correspondence to perceptual thresholds and the user's FNS experience. Ossified cochlear ducts appear to aggravate the increase in thresholds caused by the otic capsule's decreased resistivity. This translational case study demonstrates the application of computational modelling as a clinical instrument in the assessment and management of complications with cochlear implantation.

A machine learning approach for magnetic resonance image-based mouse brain modeling and fast computation in controlled cortical impact.

Computational modeling of the brain is crucial for the study of traumatic brain injury. An anatomically accurate model with refined details could provide the most accurate computational results. However, computational models with fine mesh details could take prolonged computation time that impedes the clinical translation of the models. Therefore, a way to construct a model with low computational cost while maintaining a computational accuracy comparable with that of the high-fidelity model is desired. In this study, we constructed magnetic resonance (MR) image-based finite element (FE) models of a mouse brain for simulations of controlled cortical impact. The anatomical details were kept by mapping each image voxel to a corresponding FE mesh element. We constructed a super-resolution neural network that could produce computational results of a refined FE model with a mesh size of 70μm from a coarse FE model with a mesh size of 280μm. The peak signal-to-noise ratio of the reconstructed results was 33.26dB, while the computational speed was increased by 50-fold. This proof-of-concept study showed that using machine learning techniques, MR image-based computational modeling could be applied and evaluated in a timely fashion. This paved ways for fast FE modeling and computation based on MR images. Results also support the potential clinical applications of MR image-based computational modeling of the human brain in a variety of scenarios such as brain impact and intervention.Graphical abstract MR image-based FE models with different mesh sizes were generated for CCI. The training and testing data sets were computed with 5 different impact locations and 3 different impact velocities. High-resolution strain maps were estimated using a SR neural network with greatly reduced computational cost.

NECESSITY OF IMPLEMENTATING DATA SCIENCE COURSE IN ECONOMICS CURRICULA

The article describes the relevance and feasibility of implementation Data Science courses for leading economics majors: 051 Economics, 075 Marketing, 073 Management. Application of computer technology, mathematical methods and models, statistical analysis in the study process for economics students became routine long time ago, then why is Data Science linked mostly only to the faculties of information technologies? The specificity of economic professions requires the acquisition of skills in the work with large data sets, qualitative evaluation of statistics, predicting a large number of economic phenomena, so the economist of the future should be not only a specialist in the main subject area, but also a specialist in Big Data and Data Mining. The study outlines the underlying background for essential changes. The article analyzes relevant educational and professional programs, blocks of disciplines, providing qualitative assimilation of new information by students and acquisition of those abilities and skills that are needed by the modern specialist in the field of economy and will form student as a serious competitor in the labor market. It has been conducted the analysis of modern international commercial on-line courses, specifying the topics and aspects necessary for the future economics graduates. The logical scheme of Data Science specialties introduction which follows the relevant cycle of the existing disciplines of general and professional training is proposed. Mastering the knowledge of qualitative data analysis and tools for optimal work with them should be one of the main tasks of the methodological system of education and research at the faculties of economics. Modern educational technologies and scientific facilities of universities should help to expand the understanding and perception of the economist, marketologist, and manager profession, because the digital advertising, SMM, social networks, online applications, project management, State in a Smartphone, and other rapid transformations encourage to train not classic specialists, but universals who will be able to adapt quickly to the needs of the future.

Developments of Smart Drug-Delivery Systems Based on Magnetic Molecularly Imprinted Polymers for Targeted Cancer Therapy: A Short Review.

Cancer therapy is still a huge challenge, as especially chemotherapy shows several drawbacks like low specificity to tumor cells, rapid elimination of drugs, high toxicity and lack of aqueous solubility. The combination of molecular imprinting technology with magnetic nanoparticles provides a new class of smart hybrids, i.e., magnetic molecularly imprinted polymers (MMIPs) to overcome limitations in current cancer therapy. The application of these complexes is gaining more interest in therapy, due to their favorable properties, namely, the ability to be guided and to generate slight hyperthermia with an appropriate external magnetic field, alongside the high selectivity and loading capacity of imprinted polymers toward a template molecule. In cancer therapy, using the MMIPs as smart-drug-delivery robots can be a promising alternative to conventional direct administered chemotherapy, aiming to enhance drug accumulation/penetration into the tumors while fewer side effects on the other organs. Overview: In this review, we state the necessity of further studies to translate the anticancer drug-delivery systems into clinical applications with high efficiency. This work relates to the latest state of MMIPs as smart-drug-delivery systems aiming to be used in chemotherapy. The application of computational modeling toward selecting the optimum imprinting interaction partners is stated. The preparation methods employed in these works are summarized and their attainment in drug-loading capacity, release behavior and cytotoxicity toward cancer cells in the manner of in vitro and in vivo studies are stated. As an essential issue toward the development of a body-friendly system, the biocompatibility and toxicity of the developed drug-delivery systems are discussed. We conclude with the promising perspectives in this emerging field. Areas covered: Last ten years of publications (till June 2020) in magnetic molecularly imprinted polymeric nanoparticles for application as smart-drug-delivery systems in chemotherapy.

Computer modeling applications in Engineering Graphics lessons

Computer modeling applications in Engineering Graphics lessons