Lipase‐catalysed esterification of oleic acid and ethanol in a continuous packed bed reactor,using supercritical CO2 as solvent: approximation of system kinetics

This work investigated the immobilised lipase kinetics of esterification of oleic acid and ethanol. The reaction was conducted under supercritical conditions (13 × 10⁶ Pa and 40 °C) using carbon dioxide as solvent in a continuous packed bed (plug flow) reactor. Biocatalyst Lypozyme^TM IM60, which is lipase from Rhizomucor miehei (EC.3.1.1.3), immobilised on Duolite (anionic exchange resin) was used as biocatalyst. Kinetically, with regard to oleic acid, the reaction was successfully modelled by the Michaelis–Menten mechanism. The reaction rate constants K_m and V_max were evaluated. Furthermore, it was found to undergo competitive inhibition by ethanol, and the inhibition constant K_i was evaluated. © 2000 Society of Chemical Industry     

A Homozygous Deep Intronic Variant Causes Von Willebrand Factor Deficiency and Lack of Endothelial-Specific Secretory Organelles,Weibel–Palade Bodies

A type 3 von Willebrand disease (VWD) index patient (IP) remains mutation-negative after completion of the conventional diagnostic analysis, including multiplex ligation-dependent probe amplification and sequencing of the promoter, exons, and flanking intronic regions of the VWF gene (VWF). In this study, we intended to elucidate causative mutation through next-generation sequencing (NGS) of the whole VWF (including complete intronic region), mRNA analysis, and study of the patient-derived endothelial colony-forming cells (ECFCs). The NGS revealed a variant in the intronic region of VWF (997 + 118 T > G in intron 8), for the first time. The bioinformatics assessments (e.g., SpliceAl) predicted this variant creates a new donor splice site (ss), which could outcompete the consensus 5′ donor ss at exon/intron 8. This would lead to an aberrant mRNA that contains a premature stop codon, targeting it to nonsense-mediated mRNA decay. The subsequent quantitative real-time PCR confirmed the virtual absence of VWF mRNA in IP ECFCs. Additionally, the IP ECFCs demonstrated a considerable reduction in VWF secretion (~6% of healthy donors), and they were devoid of endothelial-specific secretory organelles, Weibel–Palade bodies. Our findings underline the potential of NGS in conjunction with RNA analysis and patient-derived cell studies for genetic diagnosis of mutation-negative type 3 VWD patients.     

Block Reservation Time Division Multiple Access (BRTDMA) Protocol for a High Capacity Wireless Network

Next generation high capacity wireless networks need to support various types of traffic, including voice, video and data, each of which have different Quality of Service (QoS) requirements for successful transmission. This paper presents an advanced reservation packet access protocol BRTDMA (Block Reservation Time Division Multiple Access) that can accommodate voice and data traffic with equal efficiency in a wireless network. The proposed BRTDMA protocol has been designed to operate in a dynamic fashion by allocating resources according to the QoS criteria of voice and data traffic. Most of the existing reservation protocols offers reservation to voice traffic while data packets are transmitted using contention mode. In this paper we propose a block reservation technique to reserve transmission slots for data traffic for a short duration, which minimizes the speech packet loss and reduce the end-to-end delay for wireless data traffic. The optimum block reservation length for data traffic has been studied in a cellular mobile radio environment using a simulation model. Simulation results show that the BRTDMA protocol offers higher traffic capacity than standard PRMA protocol for integrated voice and data traffic and offers flexibility in accommodating multimedia traffic.     

Effects of Rice Husk Filler on the Mechanical and Thermal Properties of Liquid Natural Rubber Compatibilized High-Density Polyethylene/Natural Rubber Blends

Natural rubber/high-density polyethylene (NR/HDPE) blend with rice husk (RH) filler and liquid natural rubber (LNR) as the compatibilizer was prepared using an internal mixer at 140°C and 50 rpm. The reinforcing effect and compatibilizing performance of the added reagents in the composites were evaluated from the mechanical and thermal properties, and blend homogeneity. The tensile and impact strength decreased with RH loadings in the matrix, while the tensile modulus and hardness showed an opposite trend. The weak filler–matrix interaction, resulting in poor filler dispersion and large agglomerated particle size, caused those properties to decrease. However, the mechanical properties of the composites improved with the addition of NR or LNR into the matrix. The dissolution effect caused interactions between the phases, leading to an improvement in the compatibility in the blend. Changes in morphology resulted in the shift of T _g of the amorphous part of NR to higher temperatures, as observed in differential mechanical analysis (DMA) thermograms. Scanning electron microscopy (SEM) micrographs of the fractured surface had also revealed the good RH–matrix interaction and, thus, the dispersion of particles in samples with added LNR.     

The K-L Expansion as an Effective Feature Ordering Technique for Limited Training Sample Size

An effective feature ordering technique is experimentally studied in cases where the number of training samples is limited in classifying multivariate two-class normal distributions. Several experimental results on the Hughes phenomenon using this ordering technique are presented, particularly in situations where the number of training samples is only slightly higher than the number of dimensions.     

Ozone Depletion Identification in Stratosphere Through Faster Region-Based Convolutional Neural Network

The concept of classification through deep learning is to build a model that skillfully separates closely-related images dataset into different classes because of diminutive but continuous variations that took place in physical systems over time and effect substantially. This study has made ozone depletion identification through classification using Faster Region-Based Convolutional Neural Network (F-RCNN). The main advantage of F-RCNN is to accumulate the bounding boxes on images to differentiate the depleted and non-depleted regions. Furthermore, image classification’s primary goal is to accurately predict each minutely varied case’s targeted classes in the dataset based on ozone saturation. The permanent changes in climate are of serious concern. The leading causes beyond these destructive variations are ozone layer depletion, greenhouse gas release, deforestation, pollution, water resources contamination, and UV radiation. This research focuses on the prediction by identifying the ozone layer depletion because it causes many health issues, e.g., skin cancer, damage to marine life, crops damage, and impacts on living being’s immune systems. We have tried to classify the ozone images dataset into two major classes, depleted and non-depleted regions, to extract the required persuading features through F-RCNN. Furthermore, CNN has been used for feature extraction in the existing literature, and those extricated diverse RoIs are passed on to the CNN for grouping purposes. It is difficult to manage and differentiate those RoIs after grouping that negatively affects the gathered results. The classification outcomes through F-RCNN approach are proficient and demonstrate that general accuracy lies between 91% to 93% in identifying climate variation through ozone concentration classification, whether the region in the image under consideration is depleted or non-depleted. Our proposed model presented 93% accuracy, and it outperforms the prevailing techniques.     

Electrical Tree Image Segmentation Using Hybrid Multi Scale Line TrackingAlgorithm

Electrical trees are an aging mechanism most associated with partial discharge (PD) activities in crosslinked polyethylene (XLPE) insulation of high-voltage (HV) cables. Characterization of electrical tree structures gained considerable attention from researchers since a deep understanding of the tree morphology is required to develop new insulation material. Two-dimensional (2D) optical microscopy is primarily used to examine tree structures and propagation shapes with image segmentation methods. However, since electrical trees can emerge in different shapes such as bush-type or branch-type, treeing images are complicated to segment due to manifestation of convoluted tree branches, leading to a high misclassification rate during segmentation. Therefore, this study proposed a new method for segmenting 2D electrical tree images based on the multi-scale line tracking algorithm (MSLTA) by integrating batch processing method. The proposed method, h-MSLTA aims to provide accurate segmentation of electrical tree images obtained over a period of tree propagation observation under optical microscopy. The initial phase involves XLPE sample preparation and treeing image acquisition under real-time microscopy observation. The treeing images are then sampled and binarized in pre-processing. In the next phase, segmentation of tree structures is performed using the h-MSLTA by utilizing batch processing in multiple instances of treeing duration. Finally, the comparative investigation has been conducted using standard performance assessment metrics, including accuracy, sensitivity, specificity, Dice coefficient and Matthew’s correlation coefficient (MCC). Based on segmentation performance evaluation against several established segmentation methods, h-MSLTA achieved better results of 95.43% accuracy, 97.28% specificity, 69.43% sensitivity rate with 23.38% and 24.16% average improvement in Dice coefficient and MCC score respectively over the original algorithm. In addition, h-MSLTA produced accurate measurement results of global tree parameters of length and width in comparison with the ground truth image. These results indicated that the proposed method had a solid performance in terms of segmenting electrical tree branches in 2D treeing images compared to other established techniques.     

Hybrid Renewable Energy Resources Management for Optimal Energy Operation in Nano-Grid

Renewable energy resources are deemed a potential energy production source due to their cost efficiency and harmless reaction to the environment, unlike non-renewable energy resources. However, they often fail to meet energy requirements in unfavorable weather conditions. The concept of Hybrid renewable energy resources addresses this issue by integrating both renewable and non-renewable energy resources to meet the required energy load. In this paper, an intelligent cost optimization algorithm is proposed to maximize the use of renewable energy resources and minimum utilization of non-renewable energy resources to meet the energy requirement for a nanogrid infrastructure. An actual data set comprising information about the load and demand of utility grids is used to evaluate the performance of the proposed nanogrid energy management system. The objective function is formulated to manage the nanogrid operation and implemented using a variant of Particle Swarm Optimization (PSO) named recurrent PSO (rPSO). Firstly, rPSO algorithm minimizes the installation cost for nanogrid. Thereafter, the proposed NEMS ensures cost efficiency for the post-installation period by providing a daily operational plan and optimizing renewable resources. State-of-the-art optimization models, including Genetic Algorithm (GA), bat and different Mathematical Programming Language (AMPL) solvers, are used to evaluate the model. The study's outcomes suggest that the proposed work significantly reduces the use of diesel generators and fosters the use of renewable energy resources and beneficiates the eco-friendly environment.     

WSM: a novel algorithm for subgraph matching in large weighted graphs

Given an undirected/directed large weighted data graph and a similar smaller weighted pattern graph, the problem of weighted subgraph matching is to find a mapping of the nodes in the pattern graph to a subset of nodes in the data graph such that the sum of edge weight differences is minimum. Biological interaction networks such as protein-protein interaction networks and molecular pathways are often modeled as weighted graphs in order to account for the high false positive rate occurring intrinsically during the detection process of the interactions. Nonetheless, complex biological problems such as disease gene prioritization and conserved phylogenetic tree construction largely depend on the similarity calculation among the networks. Although several existing methods provide efficient methods for graph and subgraph similarity measurement, they produce nonintuitive results due to the underlying unweighted graph model assumption. Moreover, very few algorithms exist for weighted graph matching that are applicable with the restriction that the data and pattern graph sizes are equal. In this paper, we introduce a novel algorithm for weighted subgraph matching which can effectively be applied to directed/undirected weighted subgraph matching. Experimental results demonstrate the superiority and relative scalability of the algorithm over available state of the art methods.