In present study, a strategy for mechanical exfoliation of graphite to a few layered graphene (FLG) coupled with its mechanical infusion onto aluminium (Al6061) substrate through a solid-state friction surfacing (FS) methodology is developed and documented. Here, a graphite-rich and expendable FS tool fabricated through conventional powder metallurgy is used as a source of graphite. Later on, this expendable tool is utilized for impregnating graphite on the aluminium alloy surface through FS. The high frictional shear stress during FS results in the exfoliation of graphite to FLG which is confirmed by the characteristic wrinkled morphology through FE-SEM studies. Experimental results revealed successful impregnation of FLG flakes up to ∼165 μm depth on the surface of aluminium matrix. The effect of graphite flake size (60 mesh (max. 250 μm) and 325 mesh (max. 44 μm)) on specific wear rate and its ability to mechanically exfoliate into a FLG structure is also analysed. Dynamic recrystallization due to plastic deformation during FS results in∼90% grain size reduction relative to the as-received aluminium alloy (reference Al). Excellent interfacial bonding with mechanical intercalation between graphite flake and aluminium matrix is also observed through TEM studies. Intermetallic phase such as Al 4 C 3 is observed at the Al/graphite interface with larger flake size. The tribological properties are significantly improved with the graphite reinforcement leading to the decrease in coefficient of friction by ∼12.5 and 26.7% with graphite of 250 (FS250) and 44 μm (FS44) flake size respectively as compared to the reference Al. The least specific wear rate is observed for FS44 which is about ∼53.4 % and∼28.57 % less than the wear rate of reference Al and FS250, respectively. The microhardness, nano-hardness and XRD studies further corroborate the results.

Numerical investigations have been carried out for a postulated enclosure fire scenario instigated due to methanol pool ignition in a chemical cleaning facility. The pool fire under consideration is radiation-dominated and poses a risk to the nearby objects if appropriate safety requirements are not met. The objective of the current study was to numerically evaluate the postulated fire scenario and provide safety recommendations to prevent/minimize the hazard. To do this, the fire scenario was first modeled using the finite volume method (FVM) based solver to predict the fire characteristics and the resulting changes inside the enclosure. The FDS predicted temperatures were then used as input boundary conditions to conduct a three-dimensional heat transfer analysis using the finite element method (FEM). The coupled FVM–FEM simulation approach enabled detailed three-dimensional conjugate heat transfer analysis. The proposed FVM–FEM coupled approach to analyze the fire dynamics and heat transfer will be helpful to safety engineers in carrying out a more robust and reliable fire risk assessment.

Water management has always been a topic of serious discussion since infrastructure, rural, and industrial development flourished. Due to the depleting water resources, this is now even a bigger challenge. So, here is developed an IoT-based water management system where ultrasonic sensors are employed for predicting the depth of water in the tank and accordingly pumping the water to the sub tank of the apartment. In addition, the time series analysis Auto Regressive Integrative Moving Average (ARIMA) and Least Square Linear Regression (LSLR) algorithms were employed and compared for predicting the water demand for next six months based on the historical water consumption record of the main reservoir/tank. The information on the amount of water consumed from the main reservoir is pushed to the cloud and to the mobile application developed for utilities. The purpose is to access the water consumption pattern and predict water demand for the next six months from the cloud.

Abstract-Logical analysis of data is a methodology used for analyzing observations and detecting structural information which provides a solution to problems such as classification, marketing, feature selection development of pattern-based decision support systems, detection of inconsistencies in databases etc. There are a number of areas such as statistics, clustering, machine learning etc that are parallel to problems evaluated by LAD. However, it plays a significant role in the field of data classification through systematic identification of 'patterns' in the datasets There are a large number of real world data analysis applications such as economics, oil exploration, medical diagnosis etc. that can be formulated using Logical analysis of data. Although, in the recent years medical and related disciplines have been the focus of LAD and there have been a considerable number of medical problems to which LAD is successfully applied. The goal of this paper is to provide an overview and a comparative study between different works related to Logical analysis of data (LAD) in the field of data classification. It gives an insight of how Logical analysis of data (LAD) has been successfully applied to various data analysis applications particularly medical science and related disciplines. It provides a brief description of some useful work done in the past in the field of data classification to provide an accurate data classification using a small training set. It particularly gives an overview about the work done by P.L Hammer and Renato Bruni and Gianpiero Bianchi in the field of data classification. It explores about the characteristics and challenges involved with the past research work and also contributes significant variations to them to provide a fast and accurate data classification.

<p>Private Networks (also known as Non-Public Networks) bring significant benefits to Industry 4.0. These networks are typically deployed on-premises of the enterprises, and their isolation from the public (consumer) networks improves the crucial aspects of security and reliability. Despite the isolation, insider attacks can be mounted on these networks. This paper analyses such attacks using attack patterns from Common Attack Pattern Enumerations and Classifications (CAPEC) database. The analysis uses attack graphs, to combine individual domains, in the context of human, device, and network vulnerabilities. The attack graphs help identify paths, the cumulative impact on the system, and possible defense techniques, including security controls to mitigate the impact. Using three sample attack graphs in the context of standalone private 5G networks, this paper analyses possible security mechanisms and captures the difference among legacy enterprise networks (including Wi-Fi for limited mobility), public networks, and private networks.</p>