Dr. Neeraj Kumar
@gyanvihar.org
Professor and Mechanical/Engineering & Technology
Suresh Gyan Vihar University
Neeraj Kumar, he is presently working as Professor in Mechanical & Heading Gyan Vihar School of Engineering & Technology . He has specialization in Manufacturing System Engineering and having research interest in the development of composite material & machining parameter optimization techniques. He has published 137 International & National research papers inclusive 40 Scopus indexed papers, 4 books, 2 Patents and he has supervised 15 M.Tech and 13 Ph.D. thesis. He has total 17+ years’ experience of teaching & industry.
EDUCATION
Ph.D (Mechanical)
M.Tech (Manufacturing System Engineering)
B.E (Mechanical)
RESEARCH, TEACHING, or OTHER INTERESTS
Mechanical Engineering, Industrial and Manufacturing Engineering, Engineering, General Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
P. A. Thakare, Neeraj Kumar, V. B. Ugale, Jayant Giri, Neeraj Sunheriya, and Hamad A. Al-Lohedan
AIP Publishing
In this work, four varieties of hybrid Fiber-Reinforced Polymer (FRP) panels made of kevlar-29 and natural fibers are studied. All panels have kevlar-29 face sheets and natural fiber core, such as jute, flax, sisal, and hemp. This research focuses on the behavior of these hybrid FRP panels under flexural and impact loading so that the panels can be explored for the structural/semi-structural members of army shelters, portable helipad, and roofing panels in high-altitude areas. Natural fibers are chemically treated with NaOH to improve hydrophobicity. The panels are vacuum bagged, the fiber volume fraction is 0.39, and the thickness is close to 4 mm. Three-point flexural loading using the universal testing machine and low-velocity impact loading up to 24 J under drop weight impact test setup is carried out to characterize the panels. Damage area, delamination, permanent deformation, indentation depth, energy absorbed, flexural strength, and modulus are measured. The hybrid flax/kevlar panel and hemp/kevlar panel, each resist impact with permanent deformation less than 0.5 mm up to 24 J. Without significant face sheet or core fiber breakage, the delamination is spread over a small radial distance of 18.5 and 24.5 mm, respectively. Interface matrix breakage causes delamination. The load vs deflection curve is almost linear under flexural loading, and specimens failed under compression at 240 MPa. The numerical simulation is also done using ANSYS and LS-DYNA for detailed study.
Vikas, Kavita, K S Golda, T K Ghosh, A Jhingan, P Sugathan, A Chatterjee, B R Behera, Ashok Kumar, Rakesh Kumar,et al.
IOP Publishing
Abstract We have measured the fission fragment mass-angle and mass-total kinetic energy (TKE) distributions for the neutron-deficient 190Pt compound nucleus (CN) populated via 12C + 178Hf reaction, at around and above barrier energies. No mass-angle correlation was observed in the fission of 190Pt signifying the absence of quasi-fission events in the studied reaction. The observed mass-TKE distributions have expected triangular shape and TKE distributions are well described with the single Gaussian fits, and mean TKE shows parabolic dependence on fragment mass as predicted based on liquid drop fission behaviour. The widths of measured TKE distributions agree well with the observed systematics for CN fission in this mass region. Though the CN is relatively neutron deficient, these observations suggest a clear picture of true CN fission behaviour for the chosen reaction in the studied energy domain.
Amit Tiwari, Neeraj Kumar, and M.K. Banerjee
Elsevier BV
Imtiyaz Khan, Neeraj Kumar, Mahavir Choudhary, Sunil Kumar, and Tej Singh
Elsevier BV
Nitin Sharma, Kushagra Khanna, Neeraj Kumar, Ritu Karwasra, Ashok Kumar Janakiraman, and Mogana Sundari Rajagopal
Mary Ann Liebert Inc
An alternative to oral administration for the delivery of therapeutic substances is the topical route, which frequently has comparable efficacy but may have a better tolerability profile. Gamma scintigraphy is a noninvasive technique that involves the application of radioactive substances to conduct biodistribution studies of therapeutic substances delivered through various routes. Nimesulide (NSD) was radiolabeled with technetium pertechnetate (Technetium99m [99mTc]) and this radiolabeled drug complex (99mTc-NSD) was used to prepare a topical gel formulation. The permeation of the radiolabeled drug from the topical gel was determined by gamma scintigraphy on human volunteers. The region of interest was calculated for the quantification of permeated radiolabeled drugs. This was observed that the mean percentage permeation of 99mTc-NSD was found to be 0.32 ± 0.22 to 36.37 ± 2.86 at 5 and 240 min. It was demonstrated that gamma scintigraphy may be a noninvasive and reliable technique for the determination of drug permeation through topical routes.
Neeraj Kumar, Madan Mohan Tripathi, Saket Gupta, Majed A. Alotaibi, Hasmat Malik, and Asyraf Afthanorhan
MDPI AG
This paper seeks to investigate the impact analysis of wind energy on electricity prices in an integrated renewable energy market, using regression models. This is especially important as wind energy is hard to predict and its integration into electricity markets is still in an early stage. Price forecasting has been performed with consideration of wind energy generation to optimize energy portfolio investment and create an efficient energy-trading landscape. It provides an insight into future market trends which allow traders to price their products competitively and manage their risks within the volatile market. Through the analysis of an available dataset from the Austrian electricity market, it was found that the Decision Tree (DT) regression model performed better than the Linear Regression (LR), Random Forest (RF), and Least Absolute Shrinkage Selector Operator (LASSO) models. The accuracy of the model was evaluated using the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The MAE values considering wind energy generation and without wind energy generation for the Decision Tree model were found to be lowest (2.08 and 2.20, respectively) among all proposed models for the available dataset. The increasing deployment of wind energy in the European grid has led to a drop in prices and helped in achieving energy security and sustainability.
N. Saneesh, Divya Arora, A. Chatterjee, Neeraj Kumar, Anamika Parihari, Chandra Kumar, I. Ahmed, S. Kumar, Mohit Kumar, Akhil Jhingan,et al.
American Physical Society (APS)
Anuj, S Kumar, Naveen Kumar, Neelam Rajput, K Rojeeta Devi, Neeraj Kumar, C V Ahmad, Akashrup Banerjee, Aman Rohilla, C K Gupta,et al.
IOP Publishing
Abstract Excited states in the 84Sr nucleus were investigated via the 76Ge(12C,4n)84Sr reaction at a beam energy of 58 MeV. The de-excited γ-rays were detected using the Indian National Gamma Array (INGA) spectrometer at Inter-University Accelerator Center, New Delhi. Directional Correlation from Oriented (DCO) states ratio and the polarization asymmetry (Δ) measurements were done to confirm the spin-parity of the low-lying states. Eight new γ-ray transitions were placed in the level scheme of 84Sr. The systematic behaviour of energy staggering S(I) of the γ-band (Band 1 and Band 2) was compared with the γ-bands in the mass A ≈ 80 region and the nuclei of other mass regions with similar behaviour (odd-I down). The E1 character is confirmed for strong γ-ray transitions connecting Band 3 to the Yrast band. Comparison of new results such as B(E1)/B(E2) ratio, frequency ratio ω −/ω + and energy displacement ΔE in 84Sr with those of 72Se, 150Sm, 152Gd, 220Ra and 224Th nuclei suggests the presence of octupole correlations in 84Sr.
Neeraj Kumar, Peter H. Gann, Stephanie M. McGregor, and Amit Sethi
Springer Science and Business Media LLC
Abstract Purpose PAM50 profiling assigns each breast cancer to a single intrinsic subtype based on a bulk tissue sample. However, individual cancers may show evidence of admixture with an alternate subtype that could affect prognosis and treatment response. We developed a method to model subtype admixture using whole transcriptome data and associated it with tumor, molecular, and survival characteristics for Luminal A (LumA) samples. Methods We combined TCGA and METABRIC cohorts and obtained transcriptome, molecular, and clinical data, which yielded 11,379 gene transcripts in common and 1,178 cases assigned to LumA. We used semi-supervised non-negative matrix factorization (ssNMF) to compute the subtype admixture proportions of the four major subtypes—pLumA, pLumB, pHER2, and pBasal—for each case and measured associations with tumor characteristics, molecular features, and survival. Results Luminal A cases in the lowest versus highest quartile for pLumA transcriptomic proportion had a 27% higher prevalence of stage > 1, nearly a threefold higher prevalence of TP53 mutation, and a hazard ratio of 2.08 for overall mortality. We found positive associations between pHER2 and HER2 positivity by IHC or FISH; between pLumB and PR negativity; and between pBasal and younger age, node positivity, TP53 mutation, and EGFR expression. Predominant basal admixture, in contrast to predominant LumB or HER2 admixture, was not associated with shorter survival. Conclusion Bulk sampling for genomic analyses provides an opportunity to expose intratumor heterogeneity, as reflected by subtype admixture. Our results elucidate the striking extent of diversity among LumA cancers and suggest that determining the extent and type of admixture holds promise for refining individualized therapy. LumA cancers with a high degree of basal admixture appear to have distinct biological characteristics that warrant further study.
Sumit Mishra and Neeraj Kumar
Springer Science and Business Media LLC
Sasmita Nayak, Neeraj Kumar, and B. B. Choudhury
Springer Nature Singapore
Rachna Pathak, Arnav Wadhwa, Poras Khetarpal, and Neeraj Kumar
Informa UK Limited
Considering the escalating rates of exhaustion of non-renewable energy resources, coupled with the harmful environmental side effects of harnessing them (e.g. damage to public health via air pollution), the need for a near-complete transition to renewable energy production seems inevitable. In recent times, renewable energy production has seen a strong support from investors, governmental initiatives, and industries across the world. Globally installed wind power capacity has seen an increase of 345.24% over the past decade. This increase brings along a need for robust power production management systems having a potential for predicting wind turbine power outputs primarily based on real-time input wind velocities. We propose and compare five optimized robust regression models for forecasting the wind power generated through turbines based on wind velocity vector components, out of which the Extreme Gradient Boosting regression model provided the best results. The forecasted output of our model can be compared with a city’s daily average threshold power requirement in order to make informed decisions about either shutting down an appropriate number of turbines to avoid excessive power production and wastage, or to compensate forecasted shortcomings in production on less windy days via alternative energy generation methodologies.
Ruchika Verma, Neeraj Kumar, Abhijeet Patil, Nikhil Cherian Kurian, Swapnil Rane, and Amit Sethi
Institute of Electrical and Electronics Engineers (IEEE)
We had released MoNuSAC2020 as one of the largest publicly available, manually annotated, curated, multi-class, and multi-instance medical image segmentation datasets. Based on this dataset, we had organized a challenge at the International Symposium on Biomedical Imaging (ISBI) 2020. Along with the challenge participants, we had published an article summarizing the results and findings of the challenge (Verma et al., 2021). Foucart et al. (2022) in their "Analysis of the MoNuSAC 2020 challenge evaluation and results: metric implementation errors" have pointed ways in which the computation of the segmentation performance metric for the challenge can be corrected or improved. After a careful examination of their analysis, we have found a small bug in our code and an erroneous column-header swap in one of our result tables. Here, we present our response to their analysis, and issue an errata. After fixing the bug the challenge rankings remain largely unaffected. On the other hand, two of Foucart et al.'s other suggestions are good for future consideration, but it is not clear that those should be immediately implemented. We thank Foucart et al. for their detailed analysis to help us fix the two errors.
Neeraj Kumar, Sumit Mishra, Tanmay Baweja, Ashutosh Dubey, and Abhishek Dhiman
Springer Nature Singapore
Neeraj Kumar, Apoorva Jain, Shalini Sati, Kushagra Kapoor, and Pratham Garg
Springer Nature Singapore
Neeraj Kumar and M.M. Tripathi
IOS Press
Penetration of renewable energy resources into grid is necessary to meet the elevated demand of electricity. In view of this penetration of solar and wind power increasing immensely across the globe. Solar energy is widely expanding in terms of generation and capacity addition due its better predictability over wind energy. Electricity pricing is one of the important aspects for power system planning and it felicitates information for the electricity bidder for accurate electricity generation and resource allocation. The important task is to forecast the electricity price accurately in grid interactive environment. This task is tedious in renewable integrated market due to intermittency issue. In this paper, investigation has been done on the effect of solar energy generation on electricity price forecasting. Different state of the art Machine learning (ML) models have been applied and compared with LSTM model for electricity price forecasting and the evaluation of the impact of solar energy generation on electricity price has been done. During the investigation it was found from the results that the LSTM model outperform all other models and impact of solar energy generation on electricity price is evaluated using forecasting metrics. The forecasted electricity price considering the factor of solar energy generation was lower as compared with the forecast without solar energy generation. The reliability test of the MAPE values has been performed by calculating confidence interval for proposed model.
Neeraj Kumar and M. M. Tripathi
Springer Science and Business Media LLC
Neeraj Kumar and M. M. Tripathi
The Development scenario for renewable energy across the globe is changing rapidly in terms of capacity addition and grid interconnection. The impact of wind energy on electricity price is significant and it is an important task for power system planners to forecast the price in light of its variability. The impact of wind energy penetration on electricity price using Support Vector Regression (SVR) and Deep Neural Network (DNN) has been investigated for the Austria Electricity market. From the evaluation metrics calculation, it is observed that the DNN model performs better over SVR for the available dataset. The MAPE Value for DNN model was found 5.384 for the available dataset.
Neeraj Kumar, Shashi Verma, Shabnam Mohsina, Jhilam Sadhukhan, K. Rojeeta Devi, A. Banerjee, N. Saneesh, M. Kumar, Ruchi Mahajan, Meenu Thakur,et al.
Elsevier BV
Akhil Jhingan, N. Saneesh, M. Kumar, Ruchi Mahajan, Meenu Thakur, Gurpreet Kaur, K. Kapoor, Neeraj Kumar, M. Shareef, R. Dubey,et al.
AIP Publishing
Characteristics and performance of a time of flight (TOF) spectrometer developed for performing fission mass distribution studies are presented. The spectrometer contains two TOF arms based on multi-wire proportional counters (MWPCs). Each arm has two MWPCs to form a start–stop detection system for TOF measurements. The start detector has an active area of 4 × 4 cm2. The stop detector is a two-dimensional position sensitive MWPC with an active area of 16 × 11 cm2. Salient features of the MWPCs are the use of reduced sub-millimeter wire pitches of 0.635 and 0.317 mm in the electrodes along with the use of gold plated tungsten wires of diameters 10 and 20 µm. A delay line for position electrodes is prepared using chip inductors and capacitors. Ten different configurations of MWPC were investigated for the start detector, which involved the use of three and four electrode geometries, use of different wire pitches, and use of aluminized mylar for timing electrodes. Performance results close to micro-channel plate detectors have been observed with some designs of MWPC, displaying rise times better than 2 ns with an estimated inherent time resolution of ∼100 ps FWHM. A position resolution of ∼1 mm (FWHM) has been observed. Design features of the MWPCs and their test performance results are described in this article.
S. Naresh Ram, Sukumar Mishra, Neeraj Kumar, Somara Lakra, N. Nallarasan, and D. Ravishankar
IEEE
Power System operator's real time decisions depend on available Supervisory Control and Data Acquisition (SCADA) measurement systems. Under external disturbances like failure in Fiber Optic (FO) cables, measurement transducers etc., an operator may get inaccurate data displayed. To ensure reliable operation of the power grid under such cases (including unknown network topology), this paper proposes an interpretable data-driven model Soft Lookup False data Matching (SLFM) through Dynamic Time Warping (DTW) for estimating missing/suspected SCADA data values. Numerical experiments conducted on one of the utilities of the Northern Region of the Indian Grid with real-time data of SCADA and Meter of PoC (Point of Connection) feeders for four different cases and compared the results with LSTM (Long Short Term Memory) Autoencoder model. The experimental results demonstrated the better performance of the proposed model.
Ayush Kumar, Neeraj Kumar, Bharat Singh, Aditya Chaudhary, Karan Dikshit, and Akash Sharma
Springer Singapore
Akash Sharma, Neeraj Kumar, Ayush Kumar, Karan Dikshit, Kusum Tharani, and Bharat Singh
IOS Press
In modern day Psychiatric analysis, Epileptic Seizures are considered as one of the most dreadful disorders of the human brain that drastically affects the neurological activity of the brain for a short duration of time. Thus, seizure detection before its actual occurrence is quintessential to ensure that the right kind of preventive treatment is given to the patient. The predictive analysis is carried out in the preictal state of the Epileptic Seizure that corresponds to the state that commences a couple of minutes before the onset of the seizure. In this paper, the average value of prediction time is restricted to 23.4 minutes for a total of 23 subjects. This paper intends to compare the accuracy of three different predictive models, namely – Logistic Regression, Decision Trees and XGBoost Classifier based on the study of Electroencephalogram (EEG) signals and determine which model has the highest rate of detection of Epileptic Seizure.
P. V. Laveen, E. Prasad, N. Madhavan, A. K. Nasirov, J. Gehlot, S. Nath, G. Mandaglio, G. Giardina, A. M. Vinodkumar, M. Shareef,et al.
American Physical Society (APS)
The fusion evaporation residue (ER) excitation function has been measured for $^{35,37}\\mathrm{Cl}+^{181}\\mathrm{Ta}$ reactions at energies above the Coulomb barrier. The measurements were performed using the HYbrid Recoil mass Analyzer at IUAC, New Delhi. Comparable ER cross sections have been observed in both reactions and there is no isotopic dependence. Measured ER cross sections were compared with theoretical calculations employing the dinuclear system model at projectile and target nuclei interaction and statistical model for the deexcitation of the formed compound nucleus. Larger ER cross sections at the complete deexcitation cascade of the formed compound nucleus are noticed in both reactions at higher excitation energies $({E}^{*}g80 \\mathrm{MeV})$ over the calculated results. Fusion probability varies from $95%$ to $40%$ in the excitation energy range of the study. No appreciable difference in the fusion probability is noticed in the two reactions. Comparison of our results with other reactions populating $^{216}\\mathrm{Th}$ shows a very strong entrance channel dependence.