Search results for: Nishant Jainm

Authors: Shreyas Kotian, Nishant Jainm, Nachiket Methekar

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The increase in energy demands in various industrial sectors has called for devices small in size with high heat transfer rates. Microchannel heat exchangers (MCHX) have thus been studied and applied in various fields such as thermal engineering, aerospace engineering and nanoscale heat transfer. They have been a case of investigation due to their augmented thermal characteristics and low-pressure drop. The goal of the current investigation is to analyze the thermohydraulic performance of the heat exchanger analytically. Studies are done for various inlet conditions and flow conditions. At Thi of 90°C, the effectiveness increased by about 22% for an increase in Re from 1000 to 5000 of the cold fluid. It was also observed that at Re = 5000 for the hot fluid, the heat recovered by the hot fluid increases by about 69% for an increase in inlet temperature of the hot fluid from 50°C to 70°C.

Keywords: theoretical analysis, performance parameters, microchannel heat exchanger, Reynolds number

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Authors: Nishant Singh, Christian Huyck, Vaibhav Gandhi, Alexander Jones

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A robotic arm and hand controlled by simulated neurons is presented. The robot makes use of a biological neuron simulator using a point neural model. The neurons and synapses are organised to create a finite state automaton including neural inputs from sensors, and outputs to effectors. The robot performs a simple pick-and-place task. This work is a proof of concept study for a longer term approach. It is hoped that further work will lead to more effective and flexible robots. As another benefit, it is hoped that further work will also lead to a better understanding of human and other animal neural processing, particularly for physical motion. This is a multidisciplinary approach combining cognitive neuroscience, robotics, and psychology.

Keywords: cell assembly, force sensitive resistor, robot, spiking neuron

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Authors: Nishtha Madaan, Prateek Chaudhury, Nishant Kumar, Srikanta Bedathur

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In modern NLP applications, word embeddings are a crucial backbone that can be readily shared across a number of tasks. However, as the text distributions change and word semantics evolve over time, the downstream applications using the embeddings can suffer if the word representations do not conform to the data drift. Thus, maintaining word embeddings to be consistent with the underlying data distribution is a key problem. In this work, we tackle this problem and propose TransDrift, a transformer-based prediction model for word embeddings. Leveraging the flexibility of the transformer, our model accurately learns the dynamics of the embedding drift and predicts future embedding. In experiments, we compare with existing methods and show that our model makes significantly more accurate predictions of the word embedding than the baselines. Crucially, by applying the predicted embeddings as a backbone for downstream classification tasks, we show that our embeddings lead to superior performance compared to the previous methods.

Keywords: NLP applications, transformers, Word2vec, drift, word embeddings

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Authors: Aayushi Patidar, Nishant Parihar

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One of the principal causes of failure in existing vehicle brokerage solutions is that they require the introduction of a single trusted third party to whom transport offers and requirements are sent, and which solves the scheduling problem. Advances in planning and scheduling could be utilized to address the scalability issues inherent here, but such refinements do not address the key need to decentralize decision-making. This is not to say that matchmaking of potential transport suppliers to consumers is not essential, but information from such a service should inform rather than determining the transport options for customers. The approach that is proposed, is the use of intelligent commuters that act within the system and to identify options open to users, weighing the evidence for desirability of each option given a model of the user’s priorities, and to drive dialogue among commuters in aiding users to solve their individual (or collective) transport goals. Existing research in commuter support for transport resource management has typically been focused on the provider. Our vision is to explore both the efficient use of limited transport resources and also to support the passengers in the transportation flexibility & integration among various modes in India.

Keywords: flexibility, integration, service design, technology

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Authors: Nishant Parashar, Sawan S. Sinha, Balaji Srinivasan

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We perform an investigation of the unclosed terms in the evolution equation of the velocity gradient tensor (VGT) in compressible decaying turbulent flow. Velocity gradients in a compressible turbulent flow field influence several important nonlinear turbulent processes like cascading and intermittency. In an attempt to understand the dynamics of the velocity gradients various researchers have tried to model the unclosed terms in the evolution equation of the VGT. The existing models proposed for these unclosed terms have limited applicability. This is mainly attributable to the complex structure of the higher order gradient terms appearing in the evolution equation of VGT. We investigate these higher order gradients using the data from direct numerical simulation (DNS) of compressible decaying isotropic turbulent flow. The gas kinetic method aided with weighted essentially non-oscillatory scheme (WENO) based flow- reconstruction is employed to generate DNS data. By applying neural-network to the DNS data, we map the structure of the unclosed higher order gradient terms in the evolution of the equation of the VGT with VGT itself. We validate our findings by performing alignment based study of the unclosed higher order gradient terms obtained using the neural network with the strain rate eigenvectors.

Keywords: compressible turbulence, neural network, velocity gradient tensor, direct numerical simulation

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Authors: Nishant Rodrigues, Nicole Spanedda, Chilukuri K. Mohan, Arindam Chakraborty

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A crucial objective in quantum chemistry is the computation of the energy levels of chemical systems. This task requires electron-repulsion integrals as inputs, and the steep computational cost of evaluating these integrals poses a major numerical challenge in efficient implementation of quantum chemical software. This work presents a moment-based machine-learning approach for the efficient evaluation of electron-repulsion integrals. These integrals were approximated using linear combinations of a small number of moments. Machine learning algorithms were applied to estimate the coefficients in the linear combination. A random forest approach was used to identify promising features using a recursive feature elimination approach, which performed best for learning the sign of each coefficient but not the magnitude. A neural network with two hidden layers were then used to learn the coefficient magnitudes along with an iterative feature masking approach to perform input vector compression, identifying a small subset of orbitals whose coefficients are sufficient for the quantum state energy computation. Finally, a small ensemble of neural networks (with a median rule for decision fusion) was shown to improve results when compared to a single network.

Keywords: quantum energy calculations, atomic orbitals, electron-repulsion integrals, ensemble machine learning, random forests, neural networks, feature extraction

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Authors: Anjuri Srivastava, Akhouri Nishant Bhanu

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Soil is a natural material with a distinctive form. It is regarded to be a natural source of nutrients and minerals for plants. It meets many of our needs through the crops, trees, and inhabited places that have grown on or underneath it. Productive and rich soil plays a crucial role in both its wealth and well-being. If any external substance changes the soil's composition, it directly impacts the plant that was grown in that soil. If the soil is deficient in one or more essential components, fly ash can be utilized as fertilizer by incorporating it into the soil. This can also increase the porosity of the soil. Fly ash has a sufficient concentration of essential components to promote the growth of plants. The high concentration of elements in fly ash, including C, Na, K, Fe, and Zn, increases crop yields. Hazardous compounds harm plant life as soon as they get into the soil. The US Environmental Protection Agency and other regulatory agencies have found it as non-hazardous. By employing fly ash as a potential fertilizer supplement for degraded soils, the problem of disposing of solid waste can be partially handled. Fly ash's rapid growth can slow down mineralization because it contains a higher proportion of harmful heavy metals. The chemical characteristics, inclusion ratio, and composting process of fly ash have a significant impact on the fly ash compost’s potential to improve soil nutrition. Research institutions and regulatory agencies have been thoroughly investigating fly ash for a long time. Guard cells on plant leaves that accumulate fly ash trigger the regulatory system. Fly ash increases both chemical and physical damage at certain humidity levels. The lengthy sowing period is caused by the high levels of fly ash in the soil, which also slows down seedling germination and growth. For the sake of human health, it is crucial to consider the bioaccumulation of dangerous heavy metals and their necessary concentrations in plant tissues and soil.

Keywords: soil, fly ash, plant, fertilizer, composts

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Authors: Nishant Shrivastava, D. K. Sehgal

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In Finite Element Technique nodal stresses are calculated through displacement as nodes. In this process, the displacement calculated at nodes is sufficiently good enough but stresses calculated at nodes are not sufficiently accurate. Therefore, the accuracy in the stress computation in FEM models based on the displacement technique is obviously matter of concern for computational time in shape optimization of engineering problems. In the present work same is focused to find out unique points within the element as well as the boundary of the element so, that good accuracy in stress computation can be achieved. Generally, major optimal stress points are located in domain of the element some points have been also located at boundary of the element where stresses are fairly accurate as compared to nodal values. Then, it is subsequently concluded that there is an existence of unique points within the element, where stresses have higher accuracy than other points in the elements. Therefore, it is main aim is to evolve a generalized procedure for the determination of the optimal stress location inside the element as well as at the boundaries of the element and verify the same with results from numerical experimentation. The results of quadratic 9 noded serendipity elements are presented and the location of distinct optimal stress points is determined inside the element, as well as at the boundaries. The theoretical results indicate various optimal stress locations are in local coordinates at origin and at a distance of 0.577 in both directions from origin. Also, at the boundaries optimal stress locations are at the midpoints of the element boundary and the locations are at a distance of 0.577 from the origin in both directions. The above findings were verified through experimentation and findings were authenticated. For numerical experimentation five engineering problems were identified and the numerical results of 9-noded element were compared to those obtained by using the same order of 25-noded quadratic Lagrangian elements, which are considered as standard. Then root mean square errors are plotted with respect to various locations within the elements as well as the boundaries and conclusions were drawn. After numerical verification it is noted that in a 9-noded element, origin and locations at a distance of 0.577 from origin in both directions are the best sampling points for the stresses. It was also noted that stresses calculated within line at boundary enclosed by 0.577 midpoints are also very good and the error found is very less. When sampling points move away from these points, then it causes line zone error to increase rapidly. Thus, it is established that there are unique points at boundary of element where stresses are accurate, which can be utilized in solving various engineering problems and are also useful in shape optimizations.

Keywords: finite elements, Lagrangian, optimal stress location, serendipity

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Authors: Makda Aamir, Sood Aayushi, Syed Omar, Nihan Khuld, Iskander Peter, Ijaz Naeem, Sharma Nishant

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Introduction:Glycogen Storage Disease Type-1 (GSD-1) is a rare phenomenon primarily affecting the liver and kidney. Excessive accumulation of glycogen and fat in liver, kidney, and intestinal mucosa is noted in patients with deficiency of Glucose-6-phosphatase deficiency. Patients with GSD-1 have a wide spectrum of symptoms, including hepatomegaly, hypoglycemia, lactic acidemia, hyperlipidemia, hyperuricemia, and growth retardation. Age of onset, rate of disease progression and its severity is variable in this disease.Case:An 18-year-old male with GSD-1a, Von Gierke’s disease, hyperuricemia, and hypertension presented to the hospital with nausea and vomiting. The patient followed an hourly cornstarch regimen during the day and overnight through infusion via a PEG tube. The complaints started at work, where he was unable to tolerate oral cornstarch. He washemodynamically stable on arrival. ABG showed pH 7.372, PaCO2 30.3, and PaO2 92.2. WBC 16.80, K+ 5.8, HCO3 13, BUN 28, Cr 2.2, Glucose 60, AST 115, ALT 128, Cholesterol 352, Triglycerides >1000, Uric Acid 10.6, Lactic Acid 11.8 which trended down to 8.0. CT abdomen showed hepatomegaly and fatty infiltration with the PEG tube in place.He was admitted to the ICU and started on D5NS for hypoglycemia and lactic acidosis. Per request by the patient’s pediatrician, he was transitioned to IV D10/0.45NS at 110mL/Hr to maintain blood glucose above 75 mg/L. Frequent accuchecks were done till he could tolerate his dietary regimen with cornstarch. Lactic acid downtrend to 2.9, and accuchecks ranged between 100-110. Cr improved to 1.3, and his home medications (Allopurinol and Lisinopril) were resumed. He was discharged in stable condition with plans for further genetic therapy work up.Discussion:Mainstay therapy for Von Gierke’s Disease is the prevention of metabolic derangements for which dietary and lifestyle changes are recommended. A low fructose and sucrose diet is recommended by limiting the intake of galactose and lactose to one serving per day. Hypoglycemia treatment in such patients is two-fold, utilizing both quick and stable release sources. Cornstarch has been one such therapy since the 1980s; its slow digestion provides a steady release of glucose over a longer period of time as compared with other sources of carbohydrates. Dosing guidelines vary from age to age and person to person, but it is highly recommended to check BG levels frequently to maintain a BG > 70 mg/dL. Associated high levels of triglycerides and cholesterol can be treated with statins, fibrates, etc. Conclusion:The management of hypoglycemia in GSD 1 disease presents various obstacles which could prove to be fatal. Due to the deficiency of G6P, treatment with a specialized hypoglycemic regimen is warranted. A D10 ½ NS infusion can be used to maintain blood sugar levels as well as correct metabolic or lactate imbalances. Infusion should be gradually weaned off after the patient can tolerate oral feeds as this can help prevent the risk of hypoglycemia and other derangements. Further research is needed in regards to these patients for more sustainable regimens.

Keywords: von gierke, glycogen storage disease, hypoglycemia, genetic disease

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Authors: Shailendra Kumar, Lokesh Kashyap, Puneet Khanna, Nishant Patel, Rakesh Kumar, Arshad Ayub, Kelika Prakash, Yudhyavir Singh, Krithikabrindha V.

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Introduction: Spinal anesthesia is considered a standard anesthesia technique for caesarean delivery. The incidence of spinal hypotension during caesarean delivery is 70 -80%. Spinal hypotension may cause cerebral hypoperfusion in the mother, but physiologically cerebral autoregulatory mechanisms accordingly prevent cerebral hypoxia. Cerebral blood flow remains constant in the 50-150 mmHg of Cerebral Perfusion Pressure (CPP) range. Near-infrared spectroscopy (NIRS) is a non-invasive technology that is used to detect Cerebral Desaturation Events (CDEs) immediately compared to other conventional intraoperative monitoring techniques. Objective: The primary aim of the study is to correlate the change in cerebral oxygen saturation using NIRS with respect to a fall in mean blood pressure after spinal anaesthesia and to find out the effects of spinal hypotension on neonatal APGAR score, neonatal acid-base variations, and presence of Postoperative Delirium (POD). Methodology: NIRS sensors were attached to the forehead of all the patients, and their baseline readings of cerebral oxygenation on the right and left frontal regions and mean blood pressure were noted. Subarachnoid block was given with hyperbaric 0.5% bupivacaine plus fentanyl, the dose being determined by the individual anaesthesiologist. Co-loading of IV crystalloid solutions was given to the patient. Blood pressure reading and cerebral saturation were recorded every 1 minute till 30min. Hypotension was a fall in MAP less than 20% of the baseline values. Patients going for hypotension were treated with an IV Bolus of phenylephrine/ephedrine. Umbilical cord blood samples were taken for blood gas analysis, and neonatal APGAR was noted by a neonatologist. Study design: A prospective observational study conducted in a population of Thirty ASA 2 and 3 parturients scheduled for lower segment caesarean section (LSCS). Results: Mean fall in regional cerebral saturation is 28.48 ± 14.7% with respect to the mean fall in blood pressure 38.92 ± 8.44 mm Hg. The correlation coefficient between fall in saturation and fall in mean blood pressure is 0.057, and p-value {0.7} after subarachnoid block. A fall in regional cerebral saturation occurred 2±1 min before a fall in mean blood pressure. Twenty-nine out of thirty patients required vasopressors during hypotension. The first dose of vasopressor requirement is needed at 6.02±2 min after the block. The mean APGAR score was 7.86 and 9.74 at 1 and 5 min of birth, respectively, and the mean umbilical arterial pH of 7.3±0.1. According to DRS-98 (Delirium Rating Scale), the mean delirium rating score on postoperative day 1 and day 2 were 0.1 and 0.7, respectively. Discussion: There was a fall in regional cerebral oxygen saturation, which started before with respect to a significant fall in mean blood pressure readings but was statistically not significant. Maximal fall in blood pressure requiring vasopressors occurs within 10 min of SAB. Neonatal APGAR scores and acid-base variations were in the normal range with maternal hypotension, and there was no incidence of postoperative delirium in patients with post-spinal hypotension.

Keywords: cerebral oxygenation, LSCS, NIRS, spinal hypotension

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Authors: Ayesha Selwyn, Annapoorni Chandrashekar, Kumar Ashwarya, Nishant Baghel

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Project-based learning (PBL) activities are typically implemented in technology-enabled schools by highly trained teachers. In rural India, students have limited access to technology and quality education. Implementing typical PBL activities is challenging. This study details how Pratham Education Foundation’s Hybrid Learning model was used to implement two PBL activities related to music in 900 remote Indian villages with 46,000 students aged 10-14. The activities were completed by 69% of groups that submitted a total of 15,000 videos (completed projects). Pratham’s H-Learning model reaches 100,000 students aged 3-14 in 900 Indian villages. The community-driven model engages students in 20,000 self-organized groups outside of school. The students are guided by 6,000 youth volunteers and 100 facilitators. The students partake in learning activities across subjects with the support of community stakeholders and offline digital content on shared Android tablets. A training and implementation toolkit for PBL activities is designed by subject experts. This toolkit is essential in ensuring efficient implementation of activities as facilitators aren’t highly skilled and have limited access to training resources. The toolkit details the activity at three levels of student engagement - enrollment, participation, and completion. The subject experts train project leaders and facilitators who train youth volunteers. Volunteers need to be trained on how to execute the activity and guide students. The training is focused on building the volunteers’ capacity to enable students to solve problems, rather than developing the volunteers’ subject-related knowledge. This structure ensures that continuous intervention of subject matter experts isn’t required, and the onus of judging creativity skills is put on community members. 46,000 students in the H-Learning program were engaged in two PBL activities related to Music from April-June 2019. For one activity, students had to conduct a “musical survey” in their village by designing a survey and shooting and editing a video. This activity aimed to develop students’ information retrieval, data gathering, teamwork, communication, project management, and creativity skills. It also aimed to identify talent and document local folk music. The second activity, “Pratham Idol”, was a singing competition. Students participated in performing, producing, and editing videos. This activity aimed to develop students’ teamwork and creative skills and give students a creative outlet. Students showcased their completed projects at village fairs wherein a panel of community members evaluated the videos. The shortlisted videos from all villages were further evaluated by experts who identified students and adults to participate in advanced music workshops. The H-Learning framework enables students in low resource settings to engage in PBL and develop relevant skills by leveraging community support and using video creation as a tool. In rural India, students do not have access to high-quality education or infrastructure. Therefore designing activities that can be implemented by community members after limited training is essential. The subject experts have minimal intervention once the activity is initiated, which significantly reduces the cost of implementation and allows the activity to be implemented at a massive scale.

Keywords: community supported learning, project-based learning, self-organized learning, education technology

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