Energy and Exergy Analysis of a Coal Fired Power Plant

In this paper, energy and exergy analysis has been conducted on a subcritical coal fired power plant of Wisconsin Power and Light Company, USA to investigate the steam cycle energy and exergy efficiency. The cycle is analyzed by developing a mathematical model using its operating and design parameters. The analysis is performed using EES (Engineering Equation Solver). The energy analysis shows that major share of energy loss occurs in condenser i.e. 72% of total cycle energy loss, whereas, exergy analysis shows that 83.09% total exergy destruction of cycle occurs in boiler.Furthermore, the simulation results are compared with actual with an absolute error of 3.1%. Additionally, the parametric study is performed to examine the effects of various operating parameters such as main steam pressure and temperature, condenser pressure, terminal and drain cooler temperature difference on net power output, energy andexergy efficiency of cycle. The parametric study shows that the plant has maximum energy and exergy efficiencies at steam pressure of 2500psi, condenser pressure of 1.0psi and main steam temperature of 1100oF. Furthermore, these parameters do not seem to change energy and exergy efficiencies significantly

UBI-XGB: IDENTIFICATION OF UBIQUITIN PROTEINS USING MACHINE LEARNING MODEL

A recent line of research has focused on Ubiquitination, a pervasive and proteasome-mediated protein degradation that controls apoptosis and is crucial in the breakdown of proteins and the development of cell disorders, is a major factor.  The turnover of proteins and ubiquitination are two related processes. We predict ubiquitination sites; these attributes are lastly fed into the extreme gradient boosting (XGBoost) classifier. We develop reliable predictors computational tool using experimental identification of protein ubiquitination sites is typically labor- and time-intensive. First, we encoded protein sequence features into matrix data using Dipeptide Deviation from Expected Mean (DDE) features encoding techniques. We also proposed 2nd features extraction model named dipeptide composition (DPC) model. It is vital to develop reliable predictors since experimental identification of protein ubiquitination sites is typically labor- and time-intensive. In this paper, we proposed computational method as named Ubipro-XGBoost, a multi-view feature-based technique for predicting ubiquitination sites. Recent developments in proteomic technology have sparked renewed interest in the identification of ubiquitination sites in a number of human disorders, which have been studied experimentally and clinically.  When more experimentally verified ubiquitination sites appear, we developed a predictive algorithm that can locate lysine ubiquitination sites in large-scale proteome data. This paper introduces Ubipro-XGBoost, a machine learning method. Ubipro-XGBoost had an AUC (area under the Receiver Operating Characteristic curve) of 0.914% accuracy, 0.836% Sensitivity, 0.992% Specificity, and 0.839% MCC on a 5-fold cross validation based on DPC model, and 2nd 0.909% accuracy, 0.839% Sensitivity, 0.979% Specificity, and 0. 0.829% MCC on a 5-fold cross validation based on DDE model. The findings demonstrate that the suggested technique, Ubipro-XGBoost, outperforms conventional ubiquitination prediction methods and offers fresh advice for ubiquitination site identification

Frontiers in Psychology

The growth in social media use has given rise to concerns about the impacts it may have on users' psychological well-being. This paper's main objective is to shed light on the effect of social media use on psychological well-being. Building on contributions from various fields in the literature, it provides a more comprehensive study of the phenomenon by considering a set of mediators, including social capital types (i.e., bonding social capital and bridging social capital), social isolation, and smartphone addiction. The paper includes a quantitative study of 940 social media users from Mexico, using structural equation modeling (SEM) to test the proposed hypotheses. The findings point to an overall positive indirect impact of social media usage on psychological well-being, mainly due to the positive effect of bonding and bridging social capital. The empirical model's explanatory power is 45.1%. This paper provides empirical evidence and robust statistical analysis that demonstrates both positive and negative effects coexist, helping to reconcile the inconsistencies found so far in the literature.Frontiers in Psychologyhttps://doi.org/10.3389/fpsyg.2021.67876