Search results for: Anup Barai

Authors: Romeo Malik, Yashraj Tripathy, Anup Barai

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Transportation safety is a major concern for vehicle electrification on a large-scale. The failure cost of lithium-ion batteries is substantial and is significantly impacted by higher liability and replacement cost. With continuous advancement on the material front in terms of higher energy density, upgrading safety characteristics are becoming more crucial for broader integration of lithium-ion batteries. Understanding and impeding thermal runaway is the prime issue for battery safety researchers. In this study, a comprehensive comparison of thermal runaway mechanisms for two different cathode types, Li(Ni₀.₃Co₀.₃Mn₀.₃)O₂ and Li(Ni₀.₈Co₀.₁₅Al₀.₀₅)O₂ is explored. Both the chemistries were studied for different states of charge, and the various abuse scenarios that lead to thermal runaway is investigated. Abuse tests include mechanical abuse, electrical abuse, and thermal abuse. Batteries undergo thermal runaway due to a series of combustible reactions taking place internally; this is observed as multiple jets of flame reaching temperatures of the order of 1000ºC. The physicochemical characterisation was performed on cells, prior to and after abuse. Battery’s state of charge and chemistry have a significant effect on the flame temperature profiles which is otherwise quantified as heat released. Majority of the failures during transportation is due to these external short circuit. Finally, a mitigation approach is proposed to impede the thermal runaway hazard. Transporting lithium-ion batteries under low states of charge is proposed as a way forward. Batteries at low states of charge have demonstrated minimal heat release under thermal runaway reducing the risk of secondary hazards such as thermal runaway propagation.

Keywords: battery reliability, lithium-ion batteries, thermal runaway characterisation, tomography

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Authors: Anup Kumar Keshri, Arvind Agarwal

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Carbon nanotube (CNT) reinforced aluminum oxide (Al2O3) composite coating was synthesized on the steel substrate using plasma spraying technique. Three different compositions of coating such as Al2O3, Al2O¬3-4 wt. % CNT and Al2O3-8 wt. % CNT were synthesized and the fracture strength was determined using the four point bend test. Uniform dispersion of CNTs over Al2O3 powder particle was successfully achieved. With increasing CNT content, porosity in the coating showed decreasing trend and hence contributed towards enhanced mechanical properties such as hardness (~12% increased) and elastic modulus (~34 % increased). Fracture strength of the coating was found to be increasing with the CNT additions. By reinforcement of 8 wt. % of CNT, fracture strength increased by ~2.5 times. The improvement in fracture strength of Al2O3-CNT coating was attributed to three competitive phenomena viz. (i) lower porosity (ii) higher hardness and elastic modulus (iii) CNT bridging between splats.

Keywords: aluminum oxide, carbon nanotube, fracture strength, plasma spraying

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Authors: Jagdeep Nayak, Biswajit Paul, Anup Gupta

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Characterization of coal refuses to fly ash, and steel slag from steel industries have been performed to develop a mixture of both these materials to enhance strength properties of their utilization in other sectors like mine fill, construction work, etc. A large amount of Linz-Donawitz (LD) slag and fly ash waste are generated from steel and thermal power industries respectively. Management of these wastes is problematic, and their reutilization may provide a sustainable waste management option. LD slag and fly ash mixed in different proportions were tested to analyse the micro structural improvement and hardening rate of the matrix. Mixing of activators such as sodium hydroxide and potassium silicate with silica-alumina of LD slag-fly ash mixture, geopolymeric structure were found to be developed. The effect of geo-polymerization behaviour and subsequent structural rearrangement has been studied using compressibility; shear strength and permeability tests followed by micro-graphical analysis. Densification in the mixture was observed along with an improvement of geotechnical properties due to the addition of LD slag. Due to suitable strength characteristics of these two waste materials as mixture, it can be used in the various construction field or may be used as a filling material in mine voids.

Keywords: LD slag, fly-ash, geopolymer, strength property, compressibility

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Authors: Savita, Pargam Vashishtha, Govind Gupta, Ankush Vij, Anup Thakur

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The ligand field dependent visible as well as NIR emission of the Cr³+dopant in spinel hosts has attracted immense attention in tuning the color emitted by the material. In this research, Mg1-xCrxAl₂O₄(x=0.5, 1, 3, 5, and 10 mol%) nanocrystals have been synthesizedby solution combustion method. The synthesized nanocrystals possessed a single phase cubic structure. The strong absorption by host lattice defects (antisite defects, F centres) andd-d transitions of Cr³+ ions lead to radiative emission in the visible and NIR region, respectively. The red-NIR emission in photoluminescence spectra inferred the octahedral symmetry of Cr³+ ions and anticipated the site distortion by the presence ofCr³+ clusters and antisite defects in the vicinity of Cr³+ ions. The thermoluminescence response of UV and γ-irradiated Cr doped MgAl2O4 samples revealed the formation of various shallow and deep defects with doping Cr³+ions. The induced structural cation disorder with an increase in doping concentration caused photoluminescence quenching beyond 3 mol% Cr³+ doping. The color tuning exhibited by Cr doped MgAl₂O₄ nanocrystals by varying Cr³+ ion concentration and excitation wavelength find its applicability in solid state lighting.

Keywords: antisite defects, cation disorder, color tuning, combustion synthesis

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Authors: Riaz A. Agha, Ishani Barai, Shivanchan Rajmohan, Seon Lee, Mohammed O. Anwar, Alex J. Fowler, Dennis P. Orgill, Douglas G. Altman

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Introduction: Medical knowledge is growing fast. Evidence-based medicine works best if the evidence is reported well. Past studies have shown reporting quality to be lacking in the field of surgery. Reporting guidelines are an important tool for authors to optimize the reporting of their research. The objective of this study was to analyse the frequency and strength of recommendation for such reporting guidelines within surgical journals. Methods: A systematic review of the 198 journals within the Journal Citation Report 2014 (surgery category) published by Thomson Reuters was undertaken. The online guide for authors for each journal was screened by two independent groups and results were compared. Data regarding the presence and strength of recommendation to use reporting guidelines was extracted. Results: 193 journals were included (as five appeared twice having changed their name). These had a median impact factor of 1.526 (range 0.047 to 8.327), with a median of 145 articles published per journal (range 29-659), with 34,036 articles published in total over the two-year window 2012-2013. The majority (62%) of surgical journals made no mention of reporting guidelines within their guidelines for authors. Of the journals (38%) that did mention them, only 14% (10/73) required the use of all relevant reporting guidelines. The most frequently mentioned reporting guideline was CONSORT (46 journals). Conclusion: The mention of reporting guidelines within the guide for authors of surgical journals needs improvement. Authors, reviewers and editors should work to ensure that research is reported in line with the relevant reporting guidelines. Journals should consider hard-wiring adherence to them. This will allow peer-reviewers to focus on what is present, not what is missing, raising the level of scholarly discourse between authors and the scientific community and reducing frustration amongst readers.

Keywords: CONSORT, guide for authors, PRISMA, reporting guidelines, journal impact factor, citation analysis

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Authors: Yongjiang Zhang, Kallol Barai, Umesh R. Hodeghatta, Trang Tran, Vikas Dhiman

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Detecting water stress on crops early and accurately is crucial to minimize its impact. This study aims to measure water stress in wild blueberry crops non-destructively by analyzing proximal hyperspectral data. The data collection took place in the summer growing season of 2022. A drought experiment was conducted on wild blueberries in the randomized block design in the greenhouse, incorporating various genotypes and irrigation treatments. Hyperspectral data ( spectral range: 400-1000 nm) using a handheld spectroradiometer and leaf water potential data using a pressure chamber were collected from wild blueberry plants. Machine learning techniques, including multiple regression analysis and random forest models, were employed to predict leaf water potential (MPa). We explored the optimal wavelength bands for simple differences (RY1-R Y2), simple ratios (RY1/RY2), and normalized differences (|RY1-R Y2|/ (RY1-R Y2)). NDWI ((R857 - R1241)/(R857 + R1241)), SD (R2188 – R2245), and SR (R1752 / R1756) emerged as top predictors for predicting leaf water potential, significantly contributing to the highest model performance. The base learner models achieved an R-squared value of approximately 0.81, indicating their capacity to explain 81% of the variance. Research is underway to develop a neural vegetation index (NVI) that automates the process of index development by searching for specific wavelengths in the space ratio of linear functions of reflectance. The NVI framework could work across species and predict different physiological parameters.

Keywords: hyperspectral reflectance, water potential, spectral indices, machine learning, wild blueberries, optimal bands

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Authors: Shalu Jain, Anju Bansal, Anup Kumar, Sunita Saxena

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Objectives: The novel TMPRSS2:ERG gene fusion is a common somatic event in prostate cancer that in some studies is linked with a more aggressive disease phenotype. Thus, this study aims to determine whether clinical variables are associated with the presence of TMPRSS2:ERG-fusion gene transcript in Indian patients of prostate cancer. Methods: We evaluated the clinical variables with presence and absence of TMPRSS2:ERG gene fusion in prostate cancer and BPH association of clinical patients. Patients referred for prostate biopsy because of abnormal DRE or/and elevated sPSA were enrolled for this prospective clinical study. TMPRSS2:ERG mRNA copies in samples were quantified using a Taqman chemistry by real time PCR assay in prostate biopsy samples (N=42). The T2:ERG assay detects the gene fusion mRNA isoform TMPRSS2 exon1 to ERG exon4. Results: Histopathology report has confirmed 25 cases as prostate cancer adenocarcinoma (PCa) and 17 patients as benign prostate hyperplasia (BPH). Out of 25 PCa cases, 16 (64%) were T2: ERG fusion positive. All 17 BPH controls were fusion negative. The T2:ERG fusion transcript was exclusively specific for prostate cancer as no case of BPH was detected having T2:ERG fusion, showing 100% specificity. The positive predictive value of fusion marker for prostate cancer is thus 100% and the negative predictive value is 65.3%. The T2:ERG fusion marker is significantly associated with clinical variables like no. of positive cores in prostate biopsy, Gleason score, serum PSA, perineural invasion, perivascular invasion and periprostatic fat involvement. Conclusions: Prostate cancer is a heterogeneous disease that may be defined by molecular subtypes such as the TMPRSS2:ERG fusion. In the present prospective study, the T2:ERG quantitative assay demonstrated high specificity for predicting biopsy outcome; sensitivity was similar to the prevalence of T2:ERG gene fusions in prostate tumors. These data suggest that further improvement in diagnostic accuracy could be achieved using a nomogram that combines T2:ERG with other markers and risk factors for prostate cancer.

Keywords: prostate cancer, genetic rearrangement, TMPRSS2:ERG fusion, clinical variables

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Authors: Anup Anand Deshmukh, Catherine Soladie, Renaud Seguier

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Emotion plays a key role in many applications like healthcare, to gather patients’ emotional behavior. Unlike typical ASR (Automated Speech Recognition) problems which focus on 'what was said', it is equally important to understand 'how it was said.' There are certain emotions which are given more importance due to their effectiveness in understanding human feelings. In this paper, we propose an approach that models human stress from audio signals. The research challenge in speech emotion detection is finding the appropriate set of acoustic features corresponding to an emotion. Another difficulty lies in defining the very meaning of emotion and being able to categorize it in a precise manner. Supervised Machine Learning models, including state of the art Deep Learning classification methods, rely on the availability of clean and labelled data. One of the problems in affective computation is the limited amount of annotated data. The existing labelled emotions datasets are highly subjective to the perception of the annotator. We address the first issue of feature selection by exploiting the use of traditional MFCC (Mel-Frequency Cepstral Coefficients) features in Convolutional Neural Network. Our proposed Emo-CNN (Emotion-CNN) architecture treats speech representations in a manner similar to how CNN’s treat images in a vision problem. Our experiments show that Emo-CNN consistently and significantly outperforms the popular existing methods over multiple datasets. It achieves 90.2% categorical accuracy on the Emo-DB dataset. We claim that Emo-CNN is robust to speaker variations and environmental distortions. The proposed approach achieves 85.5% speaker-dependant categorical accuracy for SAVEE (Surrey Audio-Visual Expressed Emotion) dataset, beating the existing CNN based approach by 10.2%. To tackle the second problem of subjectivity in stress labels, we use Lovheim’s cube, which is a 3-dimensional projection of emotions. Monoamine neurotransmitters are a type of chemical messengers in the brain that transmits signals on perceiving emotions. The cube aims at explaining the relationship between these neurotransmitters and the positions of emotions in 3D space. The learnt emotion representations from the Emo-CNN are mapped to the cube using three component PCA (Principal Component Analysis) which is then used to model human stress. This proposed approach not only circumvents the need for labelled stress data but also complies with the psychological theory of emotions given by Lovheim’s cube. We believe that this work is the first step towards creating a connection between Artificial Intelligence and the chemistry of human emotions.

Keywords: deep learning, brain chemistry, emotion perception, Lovheim's cube

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Authors: Pratap Chandra Pati, Prabina Rajib, Parama Barai

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The traditional finance theory neglects the role of sentiment factor in asset pricing. However, the behavioral approach to asset-pricing based on noise trader model and limit to arbitrage includes investor sentiment as a priced risk factor in the assist pricing model. Investor sentiment affects stock more that are vulnerable to speculation, hard to value and risky to arbitrage. It includes small stocks, high volatility stocks, growth stocks, distressed stocks, young stocks and non-dividend-paying stocks. Since the introduction of Chicago Board Options Exchange (CBOE) volatility index (VIX) in 1993, it is used as a measure of future volatility in the stock market and also as a measure of investor sentiment. CBOE VIX index, in particular, is often referred to as the ‘investors’ fear gauge’ by public media and prior literature. The upward spikes in the volatility index are associated with bouts of market turmoil and uncertainty. High levels of the volatility index indicate fear, anxiety and pessimistic expectations of investors about the stock market. On the contrary, low levels of the volatility index reflect confident and optimistic attitude of investors. Based on the above discussions, we investigate whether market-wide fear levels measured volatility index is priced factor in the standard asset pricing model for the Indian stock market. First, we investigate the performance and validity of Fama and French three-factor model and Carhart four-factor model in the Indian stock market. Second, we explore whether India volatility index as a proxy for fearful market-based sentiment indicators affect the cross section of stock returns after controlling for well-established risk factors such as market excess return, size, book-to-market, and momentum. Asset pricing tests are performed using monthly data on CNX 500 index constituent stocks listed on the National stock exchange of India Limited (NSE) over the sample period that extends from January 2008 to March 2017. To examine whether India volatility index, as an indicator of fear sentiment, is a priced risk factor, changes in India VIX is included as an explanatory variable in the Fama-French three-factor model as well as Carhart four-factor model. For the empirical testing, we use three different sets of test portfolios used as the dependent variable in the in asset pricing regressions. The first portfolio set is the 4x4 sorts on the size and B/M ratio. The second portfolio set is the 4x4 sort on the size and sensitivity beta of change in IVIX. The third portfolio set is the 2x3x2 independent triple-sorting on size, B/M and sensitivity beta of change in IVIX. We find evidence that size, value and momentum factors continue to exist in Indian stock market. However, VIX index does not constitute a priced risk factor in the cross-section of returns. The inseparability of volatility and jump risk in the VIX is a possible explanation of the current findings in the study.

Keywords: India VIX, Fama-French model, Carhart four-factor model, asset pricing

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Authors: Jasmeet Kaur, Anup Singh, Shashikant Iyengar, Arun Kumar, Ira Sahay

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Latent autoimmune diabetes in adults (LADA) is an irreversible autoimmune disease that affects insulin production. LADA is characterized by the production of Glutamic acid decarboxylase (GAD) antibodies, which is similar to type 1 diabetes. Individuals with LADA may eventually develop overt diabetes and require insulin. In this condition, the pancreas produces little or no insulin, which is a hormone used by the body to allow glucose to enter cells and produce energy. While type 1 diabetes was traditionally associated with children and teenagers, its prevalence has increased in adults as well. LADA is frequently misdiagnosed as type 2 diabetes, especially in adulthood when type 2 diabetes is more common. LADA develops in adulthood, usually after age 30. Managing LADA involves metabolic control with exogenous insulin and prolonging the life of surviving beta cells, thereby slowing the disease's progression. This case study examines the impact of approximately 3 months of low-carbohydrate dietary intervention in a 42-year-old woman with LADA who was initially misdiagnosed as having type 2 diabetes. Her c-peptide was 0.13 and her HbA1c was 9.3% when this trial began. Low-carbohydrate interventions have been shown to improve blood sugar levels, including fasting, post-meal, and random blood sugar levels, as well as haemoglobin levels, blood pressure, energy levels, sleep quality, and satiety levels. The use of low-carbohydrate dietary intervention significantly reduces both hypo- and hyperglycaemia events. During the 3 months of the study, there were 2 to 3 hyperglycaemic events owing to physical stress and a single hypoglycaemic event. Low-carbohydrate dietary therapies lessen insulin dose inaccuracy, which explains why there were fewer hyperglycaemic and hypoglycaemic events. In three months, the glycated haemoglobin (HbA1c) level was reduced from 9.3% to 6.3%. These improvements occur without the need for caloric restriction or physical activity. Stress management was crucial aspect of the treatment plan as stress-induced neuroendocrine hormones can cause immunological dysregulation. Additionally, supplements that support immune system and reduce inflammation were used as part of the treatment during the trial. Long-term studies are needed to track disease development and corroborate the claim that such dietary treatments can prolong the honeymoon phase in LADA. Various factors can contribute to additional autoimmune attacks, so measuring c-peptide is crucial on a regular basis to determine whether insulin levels need to be adjusted.

Keywords: autoimmune, diabetes, LADA, low_carb, nutrition

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Authors: Nejoud Alostad, Anup Bora, Prashant Dhote

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Kuwait Oil Company (KOC) has been producing from its major reservoirs that are well defined and highly productive and of superior reservoir quality. These reservoirs are maturing and priority is shifting towards difficult reservoir to meet future production requirements. This paper discusses the results of the detailed integrated study for one of the satellite complex field discovered in the early 1960s. Following acquisition of new 3D seismic data in 1998 and re-processing work in the year 2006, an integrated G&G study was undertaken to review Lower Cretaceous prospectivity of this reservoir. Nine wells have been drilled in the area, till date with only three wells showing hydrocarbons in two formations. The average oil density is around 300API (American Petroleum Institute), and average porosity and water saturation of the reservoir is about 23% and 26%, respectively. The area is dissected by a number of NW-SE trending faults. Structurally, the area consists of horsts and grabens bounded by these faults and hence compartmentalized. The Wara/Burgan formation consists of discrete, dirty sands with clean channel sand complexes. There is a dramatic change in Upper Wara distributary channel facies, and reservoir quality of Wara and Burgan section varies with change of facies over the area. So predicting reservoir facies and its quality out of sparse well data is a major challenge for delineating the prospective area. To characterize the reservoir of Wara/Burgan formation, an integrated workflow involving seismic, well, petro-physical, reservoir and production engineering data has been used. Porosity and water saturation models are prepared and analyzed to predict reservoir quality of Wara and Burgan 3rd sand upper reservoirs. Subsequently, boundary conditions are defined for reservoir and non-reservoir facies by integrating facies, porosity and water saturation. Based on the detailed analyses of volumetric parameters, potential volumes of stock-tank oil initially in place (STOIIP) and gas initially in place (GIIP) were documented after running several probablistic sensitivity analysis using Montecalro simulation method. Sensitivity analysis on probabilistic models of reservoir horizons, petro-physical properties, and oil-water contacts and their effect on reserve clearly shows some alteration in the reservoir geometry. All these parameters have significant effect on the oil in place. This study has helped to identify uncertainty and risks of this prospect particularly and company is planning to develop this area with drilling of new wells.

Keywords: original oil-in-place, sensitivity, uncertainty, sandstone, reservoir modeling, Monte-Carlo simulation

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Authors: Shashikant Iyengar, Jasmeet Kaur, Anup Singh, Arun Kumar, Ira Sahay

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Insulin resistance (IR) is a condition that occurs when cells in the body become less responsive to insulin, leading to higher levels of both insulin and glucose in the blood. This condition is linked to metabolic syndromes, including diabetes. It is crucial to address IR promptly after diagnosis to prevent long-term complications associated with high insulin and high blood glucose. This four-month case study highlights the importance of treating the underlying condition to manage diabetes effectively. Insulin is essential for regulating blood sugar levels by facilitating the uptake of glucose into cells for energy or storage. In IR individuals, cells are less efficient at taking up glucose from the blood resulting in elevated blood glucose levels. As a result of IR, beta cells produce more insulin to make up for the body's inability to use insulin effectively. This leads to high insulin levels, a condition known as hyperinsulinemia, which further impairs glucose metabolism and can contribute to various chronic diseases. In addition to regulating blood glucose, insulin has anti-catabolic effects, preventing the breakdown of molecules in the body, such as inhibiting glycogen breakdown in the liver, inhibiting gluconeogenesis, and inhibiting lipolysis. If a person is insulin-sensitive or metabolically healthy, an optimal level of insulin prevents fat cells from releasing fat and promotes the storage of glucose and fat in the body. Thus optimal insulin levels are crucial for maintaining energy balance and plays a key role in metabolic processes. During the four-month study, researchers looked at the impact of a low-carb dietary (LCD) intervention on two male individuals (A & B) who had Type-2 diabetes. Althoughvneither of these individuals were obese, they were both slightly overweight and had abdominal fat deposits. Before the trial began, important markers such as fasting blood glucose (FBG), triglycerides (TG), high-density lipoprotein (HDL) cholesterol, and Hba1c were measured. These markers are essential in defining metabolic health, their individual values and variability are integral in deciphering metabolic health. The ratio of TG to HDL is used as a surrogate marker for IR. This ratio has a high correlation with the prevalence of metabolic syndrome and with IR itself. It is a convenient measure because it can be calculated from a standard lipid profile and does not require more complex tests. In this four-month trial, an improvement in insulin sensitivity was observed through the ratio of TG/HDL, which, in turn, improves fasting blood glucose levels and HbA1c. For subject A, HbA1c dropped from 13 to 6.28, and for subject B, it dropped from 9.4 to 5.7. During the trial, neither of the subjects were taking any diabetic medications. The significant improvements in their health markers, such as better glucose control, along with an increase in energy levels, demonstrate that incorporating LCD interventions can effectively manage diabetes.

Keywords: metabolic disorder, insulin resistance, type-2 diabetes, low-carb nutrition

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