Designing a Graphene Coating-Based Supercapacitor with Lithium Ion Electrolyte: An Experimental and Computational Study via Multiscale Modeling

Graphene electrodes are investigated for electrochemical double layer capacitors (EDLCs) with lithium ion electrolyte, the focus being the effect of the pore size distribution (PSD) of electrode with respect to the solvated and desolvated electrolyte ions. Two graphene electrode coatings are examined: a low specific surface area (SSA) xGNP-750 coating and a high SSA coating based on a-MWGO (activated microwave expanded graphene oxide). The study comprises an experimental and a computer modeling part. The experimental part includes fabrication, material characterization and electrochemical testing of an EDLC with xGNP-750 coating electrodes and electrolyte 1M LiPF6 in EC:DMC. The computational part includes simulations of the galvanostatic charge-discharge of each EDLC type, based on a continuum ion transport model taking into account the PSD of electrodes, as well as molecular modeling to determine the parameters of the solvated and desolvated electrolyte ions and their adsorption energies with each type of electrode pore surface material. Predictions, in agreement with the experimental data, yield a specific electrode capacitance of 110 F g−1 for xGNP-750 coating electrodes in electrolyte 1M LiPF6 in EC:DMC, which is three times higher than that of the high SSA a-MWGO coating electrodes in the same lithium ion electrolyte.

Python for gene expression

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

Machine Learning Approach to Estimate the Diffusion Coefficient of CO2 in Hydrocarbons

Diffusion of the gas into the liquids is a critical part in understanding multiphase systems and engineering applications associated with these multiphase systems. The study couples multiphase pendant drop experiments and computational modelling to calculate the CO2 diffusion coefficient in n-decane. Experiments were carried out at a varied range of pressure and temperature 25–45°C and 25–65 bar. During the experiments, the change in the volume of the hydrocarbon drop due to CO2 diffusion was dynamically measured, and numerical model was developed which used the experimental data to estimate the diffusion coefficient. The current study brings in the capability of machine learning as a replacement of the computational part for prediction of the diffusion coefficient of the process. The feasibility of various machine learning models such as Gradient boosting, Gaussian Process Regression (GPR), k-NN, Decision tree etc. are checked. Firstly different algorithms were trained on the dataset and finally evaluated on the test dataset, using various statistical metrics). Finally, the most accurate algorithm is used as a surrogate model for predicting the diffusion coefficient. The chosen ML algorithm was fairly accurate in predicting the diffusion coefficient with a maximum inaccuracy of 7.5%. Therefore, ML may then be employed as an alternative to experiments and numerical methods. A case study is performed to demonstrate the proposed methodology.

INTERACTIVE APPLICATION DEVELOPMENT BASED ON THE JOINT USE OF THE HASKELL FUNCTIONAL PROGRAMMING LANGUAGE AND Qt FRAMEWORK

The Haskell language allows you to efficiently implement computational algorithms. Higher-order functions, composition of functions, lazy calculations allow you to create functional programs with a clear structure that most fully reflect the logic of the algorithm being implemented. This allows you to get programs that are more compact and more flexible to make changes. Also, the use of pure functions allows you to avoid errors associated with the influence of the “side effect”. The I / O operations required for user interaction use special constructs and functions with a “side effect”, which violates the functional purity of the Haskell language. The approach considered in the article involves the allocation of two parts in an interactive application: interface (front-end) and computing (back-end), interacting with each other by exchanging parameters. The interface part of the application organizes the interaction with the user through the graphical user interface, and the computational part provides the algorithm implementation. To implement front-end is proposed to use the Qt Creator framework with C++ language, and to implement the back-end – the Haskell language. Using a shared address space (files) for data exchange minimizes the impact of the “outside world” on the Haskell program. We also consider a possible way of the software implementation of this approach and example of its applying to develop an application for solve the k-shortest path problem in a graph.

Parallel Delay Multiply and Sum Algorithm for Microwave Medical Imaging Using Spark Big Data Framework

Microwave imaging systems are currently being investigated for breast cancer, brain stroke and neurodegenerative disease detection due to their low cost, portable and wearable nature. At present, commonly used radar-based algorithms for microwave imaging are based on the delay and sum algorithm. These algorithms use ultra-wideband signals to reconstruct a 2D image of the targeted object or region. Delay multiply and sum is an extended version of the delay and sum algorithm. However, it is computationally expensive and time-consuming. In this paper, the delay multiply and sum algorithm is parallelised using a big data framework. The algorithm uses the Spark MapReduce programming model to improve its efficiency. The most computational part of the algorithm is pixel value calculation, where signals need to be multiplied in pairs and summed. The proposed algorithm broadcasts the input data and executes it in parallel in a distributed manner. The Spark-based parallel algorithm is compared with sequential and Python multiprocessing library implementation. The experimental results on both a standalone machine and a high-performance cluster show that Spark significantly accelerates the image reconstruction process without affecting its accuracy.

Ecotoxicity Assessment of the Industrial Explosives Explosion Products

The statistics is presented in the article concerning the industrial explosives consumption in the world and in the Russian Federation, based on which it is possible to estimate the scale of emission of toxic gaseous explosion products into the environment. The mechanism of formation of gaseous explosion products depending on the oxygen balance of the mixed ammonium nitrate explosive is briefly outlined. In the methodological part of the work, an overview is made related to some computational methods for determining the qualitative and quantitative compositions of gaseous explosion products of industrial explosives based on ammonium nitrate. The main regularities of the influence of physical and physicochemical factors surrounding the charge of an industrial explosive on the formation of toxic gaseous explosion products are described. Computational part of the work is a description of the calculation parameters and the used software packages Shock and Detonation and Real, and the tabulated results of the calculations in comparison with the experimental data obtained from the literature sources. When discussing the results, it is shown that using theoretical calculation methods, only an approximate estimate (with an error of more than 32 %) of the qualitative and quantitative compositions of gaseous explosion products is possible. Doubt is expressed about the conversion coefficient of the concentration of nitrogen oxides to the conditional carbon monoxide-6.5, which is adopted for use in the calculations without substantiating the reasons. It is concluded that only computational methods for determining the composition of gaseous explosion products are available, the results of which are far from reality, and experimental methods are only partially close to the real conditions, respectively, it is required to develop new computational methods. It is noted that the prospect for the development of this topic is the creation of a new computational software package based on the experimental data.

Feature selection and classification approaches in gene expression of breast cancer

<abstract> <p>DNA microarray technology with biological data-set can monitor the expression levels of thousands of genes simultaneously. Microarray data analysis is important in phenotype classification of diseases. In this work, the computational part basically predicts the tendency towards mortality using different classification techniques by identifying features from the high dimensional dataset. We have analyzed the breast cancer transcriptional genomic data of 1554 transcripts captured over from 272 samples. This work presents effective methods for gene classification using Logistic Regression (LR), Random Forest (RF), Decision Tree (DT) and constructs a classifier with an upgraded rate of accuracy than all features together. The performance of these underlying methods are also compared with dimension reduction method, namely, Principal Component Analysis (PCA). The methods of feature reduction with RF, LR and decision tree (DT) provide better performance than PCA. It is observed that both techniques LR and RF identify TYMP, ERS1, C-MYB and TUBA1a genes. But some features corresponding to the genes such as ARID4B, DNMT3A, TOX3, RGS17 and PNLIP are uniquely pointed out by LR method which are leading to a significant role in breast cancer. The simulation is based on <italic>R</italic>-software.</p> </abstract>

A review of heat pump implementation specifics in residential sector energy efficiency

Heat pump systems become very promising equipment saving energy and improving overall energy efficiency and environmental protection in the residential sector. This review paper provides an update on current specifics of heat pump implementation in Russian residential sector. The paper also reviews major technical and supporting measures promoting broad distribution of the heat pump technology. In a computational part of this paper, heat pump refrigerant evaporation temperature and its influence on fuel consumption were analyzed for a heat pump system arrangement option with parallel evaporators.

Three-point bending test of pantographic blocks: numerical and experimental investigation

The equilibrium forms of pantographic blocks in a three-point bending test are investigated via both experiments and numerical simulations. In the computational part, the corresponding minimization problem is solved with a deformation energy derived by homogenization within a class of admissible solutions. To evaluate the numerical simulations, series of measurements have been carried out with a suitable experimental setup guided by the acquired theoretical knowledge. The observed experimental issues have been resolved to give a robust comparison between the numerical and experimental results. Promising agreement between theoretical predictions and experimental results is demonstrated for the planar deformation of pantographic blocks.

Assessment of Nano-Indentation Method in Mechanical Characterization of Heterogeneous Nanocomposite Materials Using Experimental and Computational Approaches

This study investigates the capacity of the nano-indentation method in the mechanical characterization of a heterogeneous dental restorative nanocomposite using experimental and computational approaches. In this respect, Filtek Z350 XT was selected as a nano-particle reinforced polymer nanocomposite with a specific range of the particle size (50 nm to 4 µm), within the range of indenter contact area of the nano-indentation experiment. A Sufficient number of nano-indentation tests were performed in various locations of the nanocomposite to extract the hardness and elastic modulus properties. A hybrid computational-experimental approach was developed to examine the extracted properties by linking the internal behaviour and the global response of the nanocomposite. In the computational part, several representative models of the nanocomposite were created in a finite element environment to simulate the mechanism of elastic-plastic deformation of the nanocomposite under Berkovich indenter. Dispersed values of hardness and elastic modulus were obtained through the experiment with 26.8 and 48.5 percent average errors, respectively, in comparison to the nanocomposite properties, respectively. A disordered shape was predicted for plastic deformation of the equilateral indentation mark, representing the interaction of the particles and matrix, which caused the experiment results reflect the local behaviour of the nanocomposite instead of the real material properties.