Lumped Parameter Thermal Network Modelling for Thermal Characterization and Protection of Traction Motors in Electric Vehicle Application

This thesis investigates thermal modelling of traction motors for thermal characterization and protection in electric vehicle application. The requirements for traction motor characteristics include high power density; high torque at low speed for starting and climbing; high power at high speed for cruising; wide speed range; a fast torque response; high efficiency over wide torque and speed ranges and high reliability. High torque and power density requirements in traction motors mean increasing current and consequently, higher temperature rise in the motor. When the temperature of the winding and magnet in traction motors exceed permissible thermal limit frequently due to lack of proper understanding and managing of the thermal conditions it will have a short-term and a long term impacts on the motor operation. In the short-term, it will never be able to produce required torque and power for standard driving conditions of electric vehicle. In the long-term, it will have the detrimental effects on the life of insulation material and consequently, it will cause permanent insulation breakdown and on the other hand, demagnetization due to higher temperature will cause a permanent damage to the motor. Hence, it is extremely important to predict temperature rise in the motor accurately and regulate liquid cooling accordingly so that the motor does not fail to produce required torque and power for any driving conditions. This research work proposes a higher order lumped parameter thermal network (LPTN) model to determine a comprehensive thermal characterization of the traction motors. Such characterization predicts the temperature of the winding, magnet and other parts of the motor. The proposed model is capable of taking inputs dynamically of motor operating parameters in electric vehicle and generate a motor loss model that feeds loss results into LPTN thermal model to predict motor temperature. The proposed model investigates cooling requirements to the motor so that the motor continues to produce the rated torque and power. The LPTN model results are validated through thermal tests on a copper rotor induction motor (CRIM) and an interior permanent magnet synchronous motor (IPMSM) in the laborator

Influence of Bulk and Surface Interactions from Thick, Porous, Soil-based Substrates on the Spreading Behavior of Different Viscosity Oils

Crude oils and motor oils are commonly identified in oil spills on land. Controlling and understanding their flow both across and into land is of paramount importance to minimize spread and subsequent damage to the ecosystem. Spreading kinetics and surface energy studies were conducted with these oils over several realistic soil-based matrixes, consisting of topsoil (silt-dominant), sand, clay, and moisture. Spreading area through a 1.3 cm deep matrix was reduced with increased moisture content, densely packed matrixes, and higher viscosity oils. Initial contact angle (CA) measurements for all oils was typically lower on clay matrixes due to its sheet-like structure and high absorption capabilities. Individual droplet penetration took longer at lower MC in direct contradiction to bulk kinetics studies, suggesting different spreading behavior across the surface border. Low viscosity oils recorded the highest lateral spreads, and incomplete wetting profiles were identified for most conditions tested. Importantly, dimensionless profiles of droplet diameter and CA with time did not conform to universal behavior, with statistically significant influences of matrix heterogeneity, oil viscosity, and ill-controlled surface roughness identified. Flow regimes of oil droplets instead conformed to vertical spreading through thick matrixes, and a delayed lateral spreading that occurred quite late into the total penetration time of the droplet. These findings, obtained from studying realistic soil-based matrixes, draws new conclusions regarding the important influences of matrix thickness, variable porosity, and chemical heterogeneity on fluid flow behavior. This new knowledge will assist in the development of future containment efforts surrounding oil spills

Designing of highly effective complementary and mismatch siRNAs for silencing a gene

In past, numerous methods have been developed for predicting efficacy of short interfering RNA (siRNA). However these methods have been developed for predicting efficacy of fully complementary siRNA against a gene. Best of author's knowledge no method has been developed for predicting efficacy of mismatch siRNA against a gene. In this study, a systematic attempt has been made to identify highly effective complementary as well as mismatch siRNAs for silencing a gene. Support vector machine (SVM) based models have been developed for predicting efficacy of siRNAs using composition, binary and hybrid pattern siRNAs. We achieved maximum correlation 0.67 between predicted and actual efficacy of siRNAs using hybrid model. All models were trained and tested on a dataset of 2182 siRNAs and performance was evaluated using five-fold cross validation techniques. The performance of our method desiRm is comparable to other well-known methods. In this study, first time attempt has been made to design mutant siRNAs (mismatch siRNAs). In this approach we mutated a given siRNA on all possible sites/positions with all possible nucleotides. Efficacy of each mutated siRNA is predicted using our method desiRm. It is well known from literature that mismatches between siRNA and target affects the silencing efficacy. Thus we have incorporated the rules derived from base mismatches experimental data to find out over all efficacy of mutated or mismatch siRNAs. Finally we developed a webserver, desiRm (http://www.imtech.res.in/raghava/desirm/) for designing highly effective siRNA for silencing a gene. This tool will be helpful to design siRNA to degrade disease isoform of heterozygous single nucleotide polymorphism gene without depleting the wild type protein

Inset Fed Rectangular Patch Antenna Design for ISM Band Applications

Wireless communication systems rely on efficient and compact antennas to transmit and receive signals. Microstrip patch antennas have gained popularity due to their small size, low profile, and ease of fabrication. In this study, an inset fed microstrip rectangular patch antenna using a partial ground plane is designed for ISM band applications in the frequency range of 2.4 - 2.4835 GHz. The antenna is made on a low-cost FR4 substrate with a dielectric constant of 4.3 and a thickness of 1.6 mm. The dimensions of the antenna is 28.35 × 37.58  mm2. The antenna is fed by an inset feedline, which provides a compact and efficient feeding mechanism. The design of the antenna is carried out using CST Microwave Studio software. The performance of the antenna is evaluated based on various parameters such as return loss, bandwidth, VSWR, directivity, gain, and radiation pattern. The simulation results indicate that the proposed antenna achieves a return loss (S11) of -27.339 dB, a bandwidth of 0.01478 GHz (147.8 MHz), and a VSWR of 1.09. Additionally, the antenna provides a gain of 2.97 dBi, a directivity of 4.7 dBi, and an efficiency of -1.726 dB (67.20%). Overall, this design meets the requirements of decreased antenna size, lightweight, low profile, cost-effectiveness, simple manufacturing, and good performance for ISM band applications

A Missing Link Between Job Autonomy and Unethical Behavior

The purpose of this paper is to theoretically address a surprising omission in literature by proposing a cognitive mechanism that sanctions individual-level unethical behaviors. This secondary literature-based qualitative study fills a theoretical gap by employing an extensive review of substantive empirical and theoretical literature of the last 15 years. However, those who consider their moral identity necessary for their self-concept are less likely to behave unethically. This proposed process, along with the path suggested by previous studies, in which individuals are having job autonomy feel unconstrained by rules before engaging in unethical behaviors. So, it proposes an underlying cognitive mechanism between job autonomy and unethical behavior. This study implies that it clarifies job autonomy’s role in promoting the negative outcome of employees’ unethical behaviors and informs organizational policymakers about the importance of satisfying the need for job autonomy.JEL Classification: D23, M12, M51, O15How to Cite:Ahmed, A., Shamsi, A. F., & Aziz, M. (2020). A Missing Link Between Job Autonomy and Unethical Behavior. Etikonomi: Jurnal Ekonomi, 19(1), 95 – 118. https://doi.org/10.15408/etk.v19i1.12391

Studies of viral and cellular proteins involved in herpes simplex virus type-1 egress

The egress pathway of herpes simplex virus-1 (HSV-1) is a complicated process mediated by co-ordinated activity of several virus glycoproteins. The virions are first assembled and enveloped at trans-Golgi-network (TGN) or endosome membranes and then travel through a guided pathway that is directed towards the cell adherent points for secretion. Once secreted the vast majority of virions remain associated with the extracellular membrane of cells and very few free virions are released into the culture medium (<1%). The mechanisms that mediate both the targeted secretion of newly assembled virions at cell contact points and post-secretion attachment of virions with the extracellular surface of cells are poorly understood, and were the topics of this research. In this thesis, an HSV-1 passage mutant of increased virion secretion phenotype had been studied. Genome sequencing of the mutant virus identified mutations in three viral envelope proteins. Study of recombinant viruses that were constructed based on those three mutations revealed that a single amino acid change in glycoprotein I (gI) of glycine to arginine at residue 39 is responsible for the increased release of virus. The result suggests the principal effect of this mutation is to modify the secretory pathway used by virions during their release from infected cells. Data also suggests a role of gC in the attachment of virions to the extracellular surface of cells after egress. In the context of HSV-1 envelopment and egress glycoprotein E (gE), which forms a heterodimeric complex with gI (gE/gI), is known to be important. The gE/gI complex has been shown to interact with many tegument proteins and have a redundant role in secondary envelopment. The gE/gI complex has been also proposed to colocalise with various cellular components and sort the nascent virions to cell contact points. However, there is little understanding of the cellular proteins that gE/gI interact with, or the mechanisms that mediate targeted secretion of virions. This research has identified a novel interactome of gE/gI by mass-spectrometric analysis utilising stable isotope labelling with amino acids in cell culture (SILAC) medium. Among the cellular interactome obtained, Nipsnap1 was validated by co-precipitation assays from both infected and transfected cells, and furthermore using cell free systems, suggesting gE and Nipsnap1 directly interact. Nipsnap1 and its homologue Nipsnap2 have been proposed to contribute in vesicle transport and membrane fusion in cells. Using CRISPR-Cas9 technology these proteins were knocked out in a keratinocyte cell line (HaCaT) to investigate their role in HSV-1 egress. However, little or no effect on HSV-1 egress could be observed upon loss of either or both of these proteins suggesting the biological significance of gE-Nipsnap1 interaction may not be directly linked to any egress function of gE/gI. Two further interesting ‘hits’ from the gE/gI interactome were interferon-induced transmembrane protein type-2 (IFITM2), a virus restriction factor, and Myoferlin that has a putative role in endocytic vesicle recycling. This study could validate gE-Myoferlin interaction and co-localisation in infected or transfected cells however, functional significance of this interaction remains to be determined. Overall, the research of this thesis has provided a better understanding of the role of the gE/gI complex in HSV-1 egress and investigated the role of some interesting cellular proteins in the context of virion egress.1. Commonwealth Scholarship Commission in the UK, 2. Cambridge Commonwealth, European & International Trust, 3. Department of Pathology, Cambridge Universit

Prediction of guide strand of microRNAs from its sequence and secondary structure

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are produced by the sequential processing of a long hairpin RNA transcript by Drosha and Dicer, an RNase III enzymes, and form transitory small RNA duplexes. One strand of the duplex, which incorporates into RNA-induced silencing complex (RISC) and silences the gene expression is called guide strand, or miRNA; while the other strand of duplex is degraded and called the passenger strand, or miRNA*. Predicting the guide strand of miRNA is important for better understanding the RNA interference pathways.</p> <p>Results</p> <p>This paper describes support vector machine (SVM) models developed for predicting the guide strands of miRNAs. All models were trained and tested on a dataset consisting of 329 miRNA and 329 miRNA* pairs using five fold cross validation technique. Firstly, models were developed using mono-, di-, and tri-nucleotide composition of miRNA strands and achieved the highest accuracies of 0.588, 0.638 and 0.596 respectively. Secondly, models were developed using split nucleotide composition and achieved maximum accuracies of 0.553, 0.641 and 0.602 for mono-, di-, and tri-nucleotide respectively. Thirdly, models were developed using binary pattern and achieved the highest accuracy of 0.708. Furthermore, when integrating the secondary structure features with binary pattern, an accuracy of 0.719 was seen. Finally, hybrid models were developed by combining various features and achieved maximum accuracy of 0.799 with sensitivity 0.781 and specificity 0.818. Moreover, the performance of this model was tested on an independent dataset that achieved an accuracy of 0.80. In addition, we also compared the performance of our method with various siRNA-designing methods on miRNA and siRNA datasets.</p> <p>Conclusion</p> <p>In this study, first time a method has been developed to predict guide miRNA strands, of miRNA duplex. This study demonstrates that guide and passenger strand of miRNA precursors can be distinguished using their nucleotide sequence and secondary structure. This method will be useful in understanding microRNA processing and can be implemented in RNA silencing technology to improve the biological and clinical research. A web server has been developed based on SVM models described in this study <url>http://crdd.osdd.net:8081/RISCbinder/</url>.</p

Wireless Power Transfer Techniquies : A Review

The invention of various wireless technologies are great revolution in the field of communication. Wireless technology can be used for transmission of electric power wirelessly from one end to other end. This technology will reduce the losses incurred in power transmission through wires. This paper presents the inclusive review and detailed analysis of different techniques used for wireless power transmission. In this paper, we have compared the different techniques of wireless power transmission. Advantages and disadvantages of different techniques are discussed in this paper with the other wireless power transfer (WPT) technologies

Prediction of polyadenylation signals in human DNA sequences using nucleotide frequencies

The polyadenylation signal plays a key role in determining the site for addition of a polyadenylated tail to nascent mRNA and its mutation(s) are reported in many diseases. Thus, identifying poly(A) sites is important for understanding the regulation and stability of mRNA. In this study, Support Vector Machine (SVM) models have been developed for predicting poly(A) signals in a DNA sequence using 100 nucleotides, each upstream and downstream of this signal. Here, we introduced a novel split nucleotide frequency technique, and the models thus developed achieved maximum Matthews correlation coefficients (MCC) of 0.58, 0.69, 0.70 and 0.69 using mononucleotide, dinucleotide, trinucleotide, and tetranucleotide frequencies, respectively. Finally, a hybrid model developed using a combination of dinucleotide, 2nd order dinucleotide and tetranucleotide frequencies, achieved a maximum MCC of 0.72. Moreover, for independent datasets this model achieved a precision ranging from 75.8-95.7% with a sensitivity of 57%, which is better than any other known methods

Mining Functional Elements in Messenger RNAs: Overview, Challenges, and Perspectives

Eukaryotic messenger RNA (mRNA) contains not only protein-coding regions but also a plethora of functional cis-elements that influence or coordinate a number of regulatory aspects of gene expression, such as mRNA stability, splicing forms, and translation rates. Understanding the rules that apply to each of these element types (e.g., whether the element is defined by primary or higher-order structure) allows for the discovery of novel mechanisms of gene expression as well as the design of transcripts with controlled expression. Bioinformatics plays a major role in creating databases and finding non-evident patterns governing each type of eukaryotic functional element. Much of what we currently know about mRNA regulatory elements in eukaryotes is derived from microorganism and animal systems, with the particularities of plant systems lagging behind. In this review, we provide a general introduction to the most well-known eukaryotic mRNA regulatory motifs (splicing regulatory elements, internal ribosome entry sites, iron-responsive elements, AU-rich elements, zipcodes, and polyadenylation signals) and describe available bioinformatics resources (databases and analysis tools) to analyze eukaryotic transcripts in search of functional elements, focusing on recent trends in bioinformatics methods and tool development. We also discuss future directions in the development of better computational tools based upon current knowledge of these functional elements. Improved computational tools would advance our understanding of the processes underlying gene regulations. We encourage plant bioinformaticians to turn their attention to this subject to help identify novel mechanisms of gene expression regulation using RNA motifs that have potentially evolved or diverged in plant species