Transformer Diagnosis Using UV-Spectrophotometer

A power transformer is most significant and extremely important element in the power utility industry. Transformers are key-stone in transmission and distribution system. The failure of power transformer causes the interruption of power supply, huge financial loss. Failure coming without warning is responsible for large economic losses and unscheduled outage. In the absence of transformers components monitoring and diagnostics, the failure risk always remain high. The System abnormalities such as over loading, frequent switching, sever weather condition and poor maintains normally contribute to accelerated aging. The lack of proper monitoring and diagnostics leads to high risk of failure. Diagnostics and proper monitoring plays key role in the life expectancy of proper transformer. Mineral oil in transformer is the inseparable component of the dielectric insulation system. In this paper a new diagnostic technique for power transformer diagnostics known as uv spectrophotometry for transformer oil analysis has been discussed. This paper presents basic information about uv spectrophotometer, sampling and testing of transformer oil. Based on the spectrophotometer results and their subsequent analysis condition of transformer oil can be predicte

Neuromorphic In-Memory Computing Framework using Memtransistor Cross-bar based Support Vector Machines

This paper presents a novel framework for designing support vector machines (SVMs), which does not impose restriction on the SVM kernel to be positive-definite and allows the user to define memory constraint in terms of fixed template vectors. This makes the framework scalable and enables its implementation for low-power, high-density and memory constrained embedded application. An efficient hardware implementation of the same is also discussed, which utilizes novel low power memtransistor based cross-bar architecture, and is robust to device mismatch and randomness. We used memtransistor measurement data, and showed that the designed SVMs can achieve classification accuracy comparable to traditional SVMs on both synthetic and real-world benchmark datasets. This framework would be beneficial for design of SVM based wake-up systems for internet of things (IoTs) and edge devices where memtransistors can be used to optimize system's energy-efficiency and perform in-memory matrix-vector multiplication (MVM).Comment: 4 pages, 5 figures, MWSCAS 201

Breathogenomics: A Computational Architecture for Screening, Early Diagnosis and Genotyping of Lung Cancer

The genome sequences of some genes have been implicated to carry various mutations that lead to the initiation and advancement of lung cancer. In addition, it has been scientifically established that anytime we breathe out, chemicals called Volatile Organic Compounds (VOCs) are released from the breath. Hundreds of such VOCs have been uniquely identified from samples of breathe collected from lung cancer patients, which make them viable as chemical biomarkers for lung cancer. Based on the foregoing scientific breakthroughs, we developed breathogenomics, a computational architecture for screening, early diagnosis and genotyping of lung cancer victims anchored on the analysis of exhaled breath and mutational profiles of genomic biomarkers. The architecture contains two important sub-modules. At the first sub-module, the exhaled breadths of smokers or persons that are at risk of lung cancer are collected and appropriate computational algorithms are employed to determine the presence of any of the VOC biomarkers. Next, a patient with any VOC biomarker in the exhaled breath proceeds to the second sub-module, which contains appropriate computational models for the detection of mutated genes. Once mutations are detected in any of the biomarker genes found in a given patient, such patient is recommended for targeted therapy to promptly curtail the progression of the mutations to advanced stages. The breathogenomics architecture serves as a generic template for the development of clinical equipment for breath and genomic based screening, early diagnosis and genotyping of lung cancer. In this paper, we report the preliminary result obtained from the prototype that we are currently developing based on the architecture. Constructing a lung cancer early diagnosis/screening system based on the prototype when fully developed will hopefully minimize the current spate of deaths as a result of late diagnosis of the disease

Algorithm for solutions of nonlinear equations of strongly monotone type and applications to convex minimization and variational inequality problems

Real-life problems are governed by equations which are nonlinear in nature. Nonlinear equations occur in modeling problems, such as minimizing costs in industries and minimizing risks in businesses. A technique which does not involve the assumption of existence of a real constant whose calculation is unclear is used to obtain a strong convergence result for nonlinear equations of (p, {\eta})-strongly monotone type, where {\eta} > 0, p > 1. An example is presented for the nonlinear equations of (p, {\eta})-strongly monotone type. As a consequence of the main result, the solutions of convex minimization and variational inequality problems are obtained. This solution has applications in other fields such as engineering, physics, biology, chemistry, economics, and game theory.Comment: 11 page

Secondary Postpartum Hemorrhage due to Spontaneous Uterine Artery Rupture after Normal Vaginal Delivery Managed by Selective Arterial Embolization

Secondary postpartum hemorrhage due to an intraperitoneal bleed following a vaginal delivery is extremely rare. We present a case of spontaneous rupture of the uterine artery following a normal vaginal delivery with a delayed presentation, which resulted in significant morbidity. This case discusses the presentation and management of this rare obstetrical emergency. The report also discusses the role of selective arterial embolization in management of secondary postpartum hemorrhage

Heavy Quarkonia in a Potential Model: Binding Energy, Decay Width, and Survival Probability

Recently a lot of progress has been made in deriving the heavy quark potential within a QCD medium. In this article we have considered heavy quarkonium in a hot quark gluon plasma phase. The heavy-quark potential has been modeled properly for short as well as long distances. The potential at long distances is modeled as a QCD string which is screened at the same scale as the Coloumb field. We have numerically solved the 1+1-dimensional Schrodinger equation for this potential and obtained the eigen wavefunction and binding energy for the $1S$ and $2S$ states of charmonium and bottomonium. Further, we have calculated the decay width and dissociation temperature of quarkonium states in the QCD plasma. Finally, we have used our recently proposed unified model with these new values of decay widths to calculate the survival probability of the various quarkonium states with respect to centrality at relativistic heavy ion collider (RHIC) and large hadron collider (LHC) energies. This study provides a unified, consistent and comprehensive description of spectroscopic properties of various quarkonium states at finite temperatures along with their nuclear modification factor at different collision energies.Comment: 26 page, 25 Figures, 1 Tabl

Importance of carotid intimal medial thickness measurement in patients with coronary artery disease

Background: Prevalence of CAD in urban India is about double that of rural India and about 4-fold higher than in United States. Mortality related to CAD is high in Indian Population. Early diagnosis can prevent the CAD related morbidity and mortality. Aims and objectives is to study and compare the CIMT among the patients with CAD and asymptomatic control group.Methods: Hundred patients with CAD were studied for the CIMT and compared with age and sex matched asymptomatic control subjects in Department of Medicine of G. R. Medical College, Gwalior for one year from 2012 to 2013. Details on history, risk factors and presenting symptoms were recorded for all. High resolution B mode ultrasonography was performed to assess CIMT of carotid arteries.Results: CAD was more prevalent among males (78%) having mean age of 56.82±8.91 years. Majority of CAD patients had dyslipidemia (42%) followed by hypertension (21%), diabetes (13%) and smoking (17%). Majority of the CAD patients had chest pain (98%) followed by breathlessness (54%) and sweating (12%) as the most common presenting symptom. Mean CIMT was significantly more among the CAD patients (0.76±0.34) as compared to those without it (0.63±0.22) (p<0.001).Conclusions: CIMT was found to be more in CAD as compared to asymptomatic control subjects. CIMT can be an important tool for assessing CAD and atherosclerosis

Nitric acid scavenging by mineral and biomass burning aerosols

The abundance of gas phase nitric acid in the upper troposphere is overestimated by global chemistry-transport models, especially during the spring and summer seasons. Recent aircraft data obtained over the central US show that mineral aerosols were abundant in the upper troposphere during spring. Chemical reactions on mineral dust may provide an important sink for nitric acid. In regions where the mineral dust abundance is low in the upper troposphere similar HNO3 removal processes may occur on biomass burning aerosols. We propose that mineral and biomass burning aerosols may provide an important global sink for gas phase nitric acid, particularly during spring and summer when aerosol composition in the upper troposphere may be greatly affected by dust storms from east Asia or tropical biomass burning plumes

Experimental Investigation of Frequency Chaos Game Representation for In Silico and Accurate Classification of Viral Pathogens from Genomic Sequences

This paper presents an experimental investigation to determine the efficacy and the appropriate order of Frequency Chaos Game Representation (FCGR) for accurate and in silico classification of pathogenic viruses. For this study, we curated genomic sequences of selected viral pathogens from the virus pathogen database and analysis resource corpus. The viral genomes were encoded using the first to seventh order FCGRs so as to produce training and testing genomic data features. Thereafter, four different kernels of naïve Bayes classifier were experimentally trained and tested with the generated FCGR genomic features. The performance result with the highest average classification accuracy of 98% was returned by the third and fourth order FCGRs. However, due to consideration for memory utilization, computational efficiency vis-à-vis classification accuracy, the third order FCGR is deemed suitable for accurate classification of viral pathogens from genome sequences. This provides a promising foundation for developing genomic based diagnostic toolkit that could be used to promptly address the global incidence of epidemics from pathogenic viruses