Bituminous impurity in the elemental sulphur from the Mishraq deposit

The bituminous impurity incorporated with the elemental sulphur in the Mishraq deposit was separated by solvent extraction. The extract was further fractionated by column chromatography and the fractions were studied spectroscopically (i.r. u.v., and p.m.r.) The observation that the bituminous material is basically asphaltic (ca. 80% of the total weight, precipitated by straight-chain pentane) with no detection of light components, coupled with the p.m.r. results on carbon type distribution, may add support to the existing theories about the origin of elemental sulphur in the Mishraq region. Elemental analysis of bitumen samples isolated from Frasch sulphur when compared with those isolated from borehole samples using identical isolation techniques may indicate that a reaction between elemental sulphur and the bituminous impurity is taking place under the thermal conditions of the Frasch Process.     

Effect of dicumyl peroxide on biodegradable poly(lactic acid)/functionalized gum arabic based films

Blending of poly (lactic acid) (PLA)/functionalized gum arabic (FG) in presence of dicumyl peroxide (DCP) presents a simple process to produce film using melt extrusion (recycle time ~ 4 min, screw speed ~60 rpm) at 180°C with tailored characteristics. The FTIR investigation shows that the confirmation of grafting of PLA chains on FG through formation of new C─C linkage. Properties of fabricated films such as morphological, mechanical, UV barrier and contact angle are examined to develop film with improved interfacial interaction, increased toughness, UV–C blocking effect (~95%) and hydrophobicity (~14%). Polarized optical microscopy (POM) studies reveal that PLA/1FG with and without DCP has more crystal density as compared to PLA at 120°C. This melt extrusion permits straightforward, feasible bionanocomposite film and has great potential as a modification with DCP assists to overcome particular drawbacks of FG.     

An Improved Remote User Authentication Scheme Using Elliptic Curve Cryptography

Wireless Personal Communications - Internet of Things has drastically expanded the global network for information exchange, because thousands of communication devices are becoming part of the...     

Effect of Soya Protein Isolate as a Complementary Drying Aid of Maltodextrin on Spray Drying of Tamarind Pulp

The present study aimed to investigate the effect of adding soya protein isolate (SPI) as a complementary drying aid of maltodextrin (MD) on spray drying of tamarind pulp. Tamarind pulp powders were prepared by spray drying of tamarind pulp, adding 0, 5, 10, 15, and 20% of SPI together with maltodextrin as the drying aids. The results showed that the addition of SPI resulted in a significant increase in powder recovery from 9.35 to 70.04%, thereby reducing the requirement of higher levels of maltodextrin for the production of quality tamarind pulp powder. The powders were evaluated for moisture content, bulk density, particle size, morphological characteristics, cohesiveness, and glass transition temperature. With the addition of SPI in the feed material, the powder samples showed a significant variation in their properties. Addition of SPI decreased the cohesion index of the powders from 14.21 to 9.29 mm, thereby increasing the flowability of the powders. Surface morphology of the powders showed that the addition of SPI resulted in an indented and wrinkled particle surface, a characteristic feature of powders produced with protein as the drying aid.     

Crushing behaviour of composite hemispherical shells subjected to quasi-static axial compressive load

This paper examines the effects of composite constituents and geometry on the energy absorption capability of composite hemispherical shells. To examine the effects of matrix types on their energy absorption capability, glass fibre/epoxy and glass fibre/polyester hemispherical shells were fabricated. While glass fibre/epoxy and carbon fibre/epoxy hemispherical shells were fabricated to investigate the effect of fibre reinforcements. Effect of aspect ratio (R/t) was also examined and the results were presented. The results obtained showed that the energy absorption capability of the hemispherical shells significantly affected by the composite constituents as well as R/t ratio.     

2D MXene-Based Biosensing: A Review

MXene emerged as decent 2D material and has been exploited for numerous applications in the last decade. The remunerations of the ideal metallic conductivity, optical absorbance, mechanical stability, higher heterogeneous electron transfer rate, and good redox capability have made MXene a potential candidate for biosensing applications. The hydrophilic nature, biocompatibility, antifouling, and anti-toxicity properties have opened avenues for MXene to perform in vitro and in vivo analysis. In this review, the concept, operating principle, detailed mechanism, and characteristic properties are comprehensively assessed and compiled along with breakthroughs in MXene fabrication and conjugation strategies for the development of unique electrochemical and optical biosensors. Further, the current challenges are summarized and suggested future aspects. This review article is believed to shed some light on the development of MXene for biosensing and will open new opportunities for the future advanced translational application of MXene bioassays.     

A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning

Telecom industry relies on churn prediction models to retain their customers. These prediction models help in precise and right time recognition of future switching by a group of customers to other service providers. Retention not only contributes to the profit of an organization, but it is also important for upholding a position in the competitive market. In the past, numerous churn prediction models have been proposed, but the current models have a number of flaws that prevent them from being used in real-world large-scale telecom datasets. These schemes, fail to incorporate frequently changing requirements. Data sparsity, noisy data, and the imbalanced nature of the dataset are the other main challenges for an accurate prediction. In this paper, we propose a hybrid model, name as “A Hybrid System for Customer Churn Prediction and Retention Analysis via Supervised Learning (HCPRs)” that used Synthetic Minority Over-Sampling Technique (SMOTE) and Particle Swarm Optimization (PSO) to address the issue of imbalance class data and feature selection. Data cleaning and normalization has been done on big Orange dataset contains 15000 features along with 50000 entities. Substantial experiments are performed to test and validate the model on Random Forest (RF), Linear Regression (LR), Naïve Bayes (NB) and XG-Boost. Results show that the proposed model when used with XGBoost classifier, has greater Accuracy Under Curve (AUC) of 98% as compared with other methods.     

Threefold Optimized Forecasting of Electricity Consumption in Higher Education Institutions

Energy management benefits both consumers and utility companies alike. Utility companies remain interested in identifying and reducing energy waste and theft, whereas consumers’ interest remain in lowering their energy expenses. A large supply-demand gap of over 6 GW exists in Pakistan as reported in 2018. Reducing this gap from the supply side is an expensive and complex task. However, efficient energy management and distribution on demand side has potential to reduce this gap economically. Electricity load forecasting models are increasingly used by energy managers in taking real-time tactical decisions to ensure efficient use of resources. Advancement in Machine-learning (ML) technology has enabled accurate forecasting of electricity consumption. However, the impact of computation cost afforded by these ML models is often ignored in favour of accuracy. This study considers both accuracy and computation cost as concurrently significant factors because together they shape the technology environment as well as create economic impact. Thus, a three-fold optimized load forecasting model is proposed which includes (1) application specific parameters selection, (2) impact of different dataset granularities and (3) implementation of specific data preparation. It deploys and compares the widely used back-propagation Artificial Neural Network (ANN) and Random Forest (RF) models for the prediction of electricity consumption of buildings within a university. In addition to the temporal and historical power consumption date as input parameters, the study also embeds weather data as well as university operational calendars resulting in improved performance. The outcomes are indicative that the granularity i.e. the scale of details in data, and set of reduced and full input parameters impact performance accuracies differently for ANN and RF models. Experimental results show that overall RF model performed better both in terms of accuracy as well as computational time for a 1-min, 15-min and 1-h dataset granularities with the mean absolute percentage error (MAPE) of 2.42, 3.70 and 4.62 in 11.1 s, 1.14 s and 0.3 s respectively, thus well suited for a real-time energy monitoring application.     

3D experimental quantification of fabric and fabric evolution of sheared granular materials using synchrotron micro-computed tomography

Many experimental studies have demonstrated that mechanical response of granular materials is highly influenced by micro-structural fabric and its evolution. In the current literature, quantification of fabric and its evolution has been developed based on micro-structural observations using Discrete Element Method or 2D experiments with simple particle shapes. The emergence of X-ray computed tomography technique has made quantification of such experimental micro-structural properties possible using 3D high-resolution images. In this paper, synchrotron micro-computed tomography was used to acquire 3D images during in-situ conventional triaxial compression experiments on granular materials with different morphologies. 3D images were processed to quantify fabric and its evolution based on experimental measurements of contact normal vectors between particles. Overall, the directional distribution of contact normals exhibited the highest degree of isotropy at initial state (i.e., zero global axial strain). As compression progressed, contact normals evolved in the direction of loading until reaching a constant fabric when experiments approached the critical state condition. Further assessment of the influence of confining pressure, initial density state, and particle-level morphology on fabric and its evolution was formed. Results show that initial density state and applied confining pressure significantly influence the fabric-induced internal anisotropy of tested specimens at initial states. Relatively, a higher applied confining pressure and a looser initial density state resulted in a higher degree of fabric-induced internal anisotropy. Influence of particle-level morphology was also found to be significant particularly on fabric evolution.