Day Time Sleepiness and Quality of Life Predicts Perceived Stress among Youth

The study examined the predictor of perceived stress among youth. The variables used in the current study were day time sleepiness, quality of life and perceived stress. The sample consists of 150 youth (81Male & 69 Female) with in the age range of 18-24 years. The Epworth Sleepiness Scale (ESS), World Health Organization Quality of Life (WHO-BRIEF), and Perceived Stress Scale (PSS) were used. The data were interpreted after obtaining the descriptive statistics i.e. Mean and SD, Pearson’s Product Moment correlation and step wise multiple regression analysis. The findings indicated that day time sleepiness and quality of life have a significant positive relation with perceived stress among youth. The step wise regression analysis found day time sleepiness and psychological health related quality of life are the predictors of perceived stress. Both the variable accounts for 39% of total variance in perceived stress among youth

A Deep Learning Approach towards Cold Start Problem in Movie Recommendation System

Recommendation systems play an important role for e-commerce websites to make profits. It has a variety of applications in different domains. There are three types of categories in which recommendation systems are classified i.e. content based, collaborative and hybrid systems. These systems suffer when a redundant amount of information is not available to provide recommendations. This problem is known as the cold start problem. In this digital era, it is possible to collect meta information about a user and provide rich recommendations. Various approaches such as social media analysis, graph networks have been proposed to solve this problem. But they lack personalization and generate irrelevant recommendations affecting the system performance. The objective of this work is to resolve new user cold start problem in movie recommendation systems using a deep learning approach that utilizes demographic attributes to cluster similar users. This embedding is given to the deep neural network to generate the recommendations. From the analysis done, we verify the effectiveness of our approach.

Mill tailings based composites as paste backfill in mines of U-bearing dolomitic limestone ore

This paper elaborates on the development of paste backfill using mill tailings generated during the processing of a uranium ore deposit hosted in dolomitic limestone. The tailings have been characterized in terms of the physical, chemical and mineralogical properties. Time-dependent rheological behaviors and geotechnical properties of cemented paste backfill (CPB) are also determined. The studies show that the mill tailing has the potential to form paste and the CPB has adequate strength to provide support to mine pillars, roofs, and walls. Keywords: Mining engineering, Uranium ore deposit, Tailings, Cemented paste backfill (CPB), Rheology, Compressive strengt

Feasibility Study of Railway Line in Hilly Region using GIS

Aim of this study is to demonstrate the potential of GIS and multi-criteria decision approach (MCDA) as a tool for the selection of alignment of rail route in Hilly region. In the present study, Geographic information system (GIS) and multi-criteria decision approach (MCDA) is used to plan the alignment of railway route from Bhuntar to Kullu in Himachal Pradesh (HP), India. The various factors which affect the alignment of rail route are identified in this study. The factors considered in this study are topography, land-use, distance from power line, drainage pattern and road network of the area. The Analytical Hierarchy Process (AHP) is used to determine the ranking of the relative parameters. The determined ranking is used in pair-wise comparison to find the weights of these relative parameters. The maps with weighted parameters are overlaid and resultant map is created in GIS for route finding, station location and alignment of railway. The final map shows the cumulative effect of all the factors which affect the rail route alignment. The final map is divided into four parts based on feasibility index. It is found that 55% area is highly feasible for route alignment while 25% of the total area comes under category of low feasibility

Coupled Electrochemical-Thermal Simulations and Validation of Minichannel Cold-Plate Water-Cooled Prismatic 20 Ah LiFePO₄ Battery

This paper discusses the quantitative validation carried out on a prismatic 20 Ah LiFePO4 battery sandwiched between two minichannel cold-plates with distributed flow having a single U-turn. A two-way coupled electrochemical-thermal simulations are performed at different discharge rates (1–4 C) and coolant inlet temperatures (15–35 °C). The predicted battery voltage response at room temperature (22 °C) and the performance of the Battery Thermal Management System (BTMS) in terms of the battery surface temperatures (maximum temperature, Tmax and temperature difference, ΔT) have been analyzed. Additionally, temperature variation at ten different locations on the battery surface is studied during the discharge process. The predicted temperatures are compared with the measured data and found to be in close agreement. Differences between the predicted and measured temperatures are attributed to the assumption of uniform heat generation by the Li-ion model (P2D), the accuracy of electrochemical property input data, and the accuracy of the measuring tools used. Overall, it is suggested that the Li-ion model can be used to design the efficient BTMS at the cell level