Mori-Tanaka-based statistical methodology to compute the effective Young modulus of polymer matrix nano-composites considering the experimental quantification of nanotubes dispersion and alignment degree

This paper presents a Mori-Tanaka-based statistical methodology to predict the effective Young modulus of carbon nanotubes (CNTs)-reinforced composites considering three variables: weight content, reinforcement dispersion and orientation. Last two variables are quantified by two parameters, namely, free-path distance between nano-reinforcements and orientation angle regarding the loading direction. To validate the present methodology, samples of multi-walled CNTs (MWCNTs)-reinforced polyvinyl alcohol (PVA)-matrix composite were manufactured by mixing solution. The MWCNT/PVA Young modulus was measured by nano-indentation, while the MWCNTs Young modulus was quantified by micro-Raman spectroscopy. Both stretched and unstretched composite specimens were fabricated. Transmission electron microscopy (TEM) and in-plane image analysis were used to obtain fitting coefficients of log-normal frequency distribution functions for the free-path distance and orientation angle. It was evidenced that numerical results fit well to measured values of effective Young modulus of MWCNTs and MWCNT/PVA, with exception of some particular cases where significant differences were found. Microstructural heterogeneities, cluster formation, polymer chains alignment, errors associated with the dispersion, orientation and mechanical characterization procedures, as well as idealization and statistical errors, were identified as possible causes of these differences. Finally, using the proposed methodology and the dispersion and orientation distribution functions experimentally obtained, the effective Young modulus is estimated for three kinds of thermoplastic matrices (polyvinyl alcohol, polyethylene ketone, and ultra-high molecular weight polyethylene) with different kinds of nanotubes (single wall, double wall, and multi-walled), at different weight contents, finding the superior mechanical performance for double-walled CNTs-reinforced composites and the lower one for multi-walled CNTs-reinforced ones.

Evaluation of Smartphone’s Embedded Sensors Through Applications: A Case Study of Gyroscope and Accelerometer

Smartphones are used for many daily activities like tele-communication, gaming, web browsing, fitness and health monitoring and traditional office working. Smartphones are equipped with built-in sensors to be able to perform these activities. It is well known that the sensors affect the resolution of the smartphone applications which is very vital in life critical applications (LCA). In this paper, two main sensors, the gyroscope and accelerometer have been studied. All commercial smartphones contain these two sensors and support functions related to them. These two sensors have direct link with the physical measurements which feed the fitness and health applications. A fitness application has been selected and ran under Android and iOS operating systems in two different popular smartphones: Samsung Note5 and iPhone7s smartphones. Statistical methodology has been applied to analysis the data and evaluate the performance of the sensors. The results show that commercial smartphones are not reliable devices for motion-related measurements and they can only be used for general purpose monitoring but not in life critical applications.

Statistical Forecasting : Third Wave of COVID-19-With an Application to India

The pandemic due to the SARS-CoV-2 virus impacted the entire world in different waves. An important question that arise after witnessing the first and second waves of COVID-19 is - Will the third wave also arrive and if yes, then when. Various types of methodologies are being used to explore the arrival of third wave. A statistical methodology based on the fitting of mixture of Gaussian distributions is explored in this paper and the aim is to forecast the third wave using the data on the first two waves of pandemic. Utilizing the data of different countries that are already facing the third wave, modelling of their daily cases data and predicting the impact and timeline for the third wave in India is attempted in this paper. The Gaussian mixture model based on algorithm for clustering is used to estimate the parameters.

Financial Behavior of the Russian Population: Regional Differences

The introductory part of the article specifies the aim and objectives of the study, reflecting some of the important outcomes of the interregional differentiation in the financial behavior of the population of individual Russian territories (in more detail – regions of the Far Eastern Federal District, FEFD).The main section of the article identifies interregional differences in the financial behavior of the population of the considered regions of the country, including the FEFD territories, based on statistical methodology and using official statistics. The authors conducted a comparative interregional analysis of the structure of consumer spending, average per capita deposits, and public debt on loans granted by credit institutions for the period 2018–2020. Specific statistics show that the FEFD regions are experiencing a negative trend of accelerating the debt of the population to credit institutions.The article concludes with outcomes of an interregional comparative analysis of the considered characteristics, reflecting the emerging negative trends in the financial behavior of the population, showing that parts of the eastern part of the country lag markedly behind the general trend in social and economic development.

Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK

Invasive pests pose a great threat to forest, woodland and urban tree ecosystems. The oak processionary moth (OPM) is a destructive pest of oak trees, first reported in the UK in 2006. Despite great efforts to contain the outbreak within the original infested area of South-East England, OPM continues to spread. Here we analyse data of the numbers of OPM nests removed each year from two parks in London between 2013 and 2020. Using a state-of-the-art Bayesian inference scheme we estimate the parameters for a stochastic compartmental SIR (susceptible, infested, removed) model with a time varying infestation rate to describe the spread of OPM. We find that the infestation rate and subsequent basic reproduction number have remained constant since 2013 (with R_0 between one and two). This shows further controls must be taken to reduce R_0 below one and stop the advance of OPM into other areas of England. Our findings demonstrate the applicability of the SIR model to describing OPM spread and show that further controls are needed to reduce the infestation rate. The proposed statistical methodology is a powerful tool to explore the nature of a time varying infestation rate, applicable to other partially observed time series epidemic data.spread and show that further controls are needed to reduce the infestation rate. The proposed statistical methodology is a powerful tool to explore the nature of a time varying infestation rate, applicable to other partially observed time series epidemic data.

Development of the it sector in Russia: drivers and stimulation methods

The development of the digital economy is directly linked to advances in the information technology sector. The information technology sector refers to a set of high-tech computer services. The article shows the place of this sector in the provision of high-tech services according to international and Russian statistical methodology. It has been substantiated that the information technology sector has a significant cumulative development effect. The IT sector refers to a set of high-tech computer services. The drivers of the information technology sector development have been identified and the government’s methods of stimulating it have been analysed. Particular attention has been paid to global external shocks to the industry, namely sanctions and the COVID-19 pandemic. The short term specifics of the information technology sector drivers have been highlighted. The role of the state as a facilitator of methods to stimulate industry development has been shown.

On studying the relationship between personality and mental disorder - Suggestions for further study

<p>Links between traits and mental disorders have been postulated off and on for centuries, but since a recent revival of interest within psychology, there has been a consistent and expanding field of research concerned with studying what is now widely accepted as the ‘personality- psychopathology relationship’. This thesis explores that field of research, considering what has led to its stagnation and apparent difficulty in reaching robust and useful conclusions. In doing so, I provide an overview and critique of the study of the personality-psychopathology relationship. Several limitations of recent research are identified and explored, specifically concerning its focus of inquiry, statistical methodology and conceptual foundations. Throughout the thesis, I discuss the appropriate scientific method(s) and necessary conceptual considerations involved in studying the relationship between personality and mental disorder, and make some suggestions about what is required for robust and reliable research in this area.</p>

ProteinUnet2 for Fast Protein Secondary Structure Prediction: A Step Towards Proper Evaluation

Background: The importance of protein secondary structure (SS) prediction is widely known, its solution enables learning about the role of a protein in organisms. As the experimental methods are expensive and sometimes impossible, many SS predictors, mainly based on different machine learning methods have been proposed for many years. SS prediction as the imbalanced classification problem should not be judged by the commonly used Q3/Q8 metrics. Moreover, as the benchmark datasets are not random samples, the classical statistical null hypothesis testing based on the Neyman-Pearson approach is not appropriate. Also, the state-of-the-art predictors have usually relatively long prediction times.Results: We present a new deep network ProteinUnet2 for SS prediction which is based on U-Net convolutional architecture. We also propose a new statistical methodology for prediction performance assessment based on the significance from Fisher-Pitman permutation tests accompanied by practical significance measured by Cohen’s effect size. Through an extensive evaluation study, we report the performance of ProteinUnet2 in comparison with two state-of-the-art methods SAINT and SPOT-1D on benchmark datasets TEST2016, TEST2018, and CASP12. Conclusions: Our results suggest that ProteinUnet2 has much shorter prediction times while maintaining (or outperforming) the mentioned predictors. We strongly believe that our proposed statistical methodology will be adopted and used (and even expanded) by the research community.

On studying the relationship between personality and mental disorder - Suggestions for further study

<p>Links between traits and mental disorders have been postulated off and on for centuries, but since a recent revival of interest within psychology, there has been a consistent and expanding field of research concerned with studying what is now widely accepted as the ‘personality- psychopathology relationship’. This thesis explores that field of research, considering what has led to its stagnation and apparent difficulty in reaching robust and useful conclusions. In doing so, I provide an overview and critique of the study of the personality-psychopathology relationship. Several limitations of recent research are identified and explored, specifically concerning its focus of inquiry, statistical methodology and conceptual foundations. Throughout the thesis, I discuss the appropriate scientific method(s) and necessary conceptual considerations involved in studying the relationship between personality and mental disorder, and make some suggestions about what is required for robust and reliable research in this area.</p>