Maulana Azad National Institute of Technology

Full list of authors: Price-Whelan, Adrian M.; Lim, Pey Lian; Earl, Nicholas; Starkman, Nathaniel; Bradley, Larry; Shupe, David L.; Patil, Aarya A.; Corrales, Lia; Brasseur, C. E.; Noethe, Maximilian; Donath, Axel; Tollerud, Erik; Morris, Brett M.; Ginsburg, Adam; Vaher, Eero; Weaver, Benjamin A.; Tocknell, James; Jamieson, William; van Kerkwijk, Marten H.; Robitaille, Thomas P.; Merry, Bruce; Bachetti, Matteo; Gunther, H. Moritz; Aldcroft, Thomas L.; Alvarado-Montes, Jaime A.; Archibald, Anne M.; Bodi, Attila; Bapat, Shreyas; Barentsen, Geert; Bazan, Juanjo; Biswas, Manish; Boquien, Mederic; Burke, D. J.; Cara, Daria; Cara, Mihai; Conroy, Kyle E.; Conseil, Simon; Craig, Matthew W.; Cross, Robert M.; Cruz, Kelle L.; D'Eugenio, Francesco; Dencheva, Nadia; Devillepoix, Hadrien A. R.; Dietrich, Jorg P.; Eigenbrot, Arthur Davis; Erben, Thomas; Ferreira, Leonardo; Foreman-Mackey, Daniel; Fox, Ryan; Freij, Nabil; Garg, Suyog; Geda, Robel; Glattly, Lauren; Gondhalekar, Yash; Gordon, Karl D.; Grant, David; Greenfield, Perry; Groener, Austen M.; Guest, Steve; Gurovich, Sebastian; Handberg, Rasmus; Hart, Akeem; Hatfield-Dodds, Zac; Homeier, Derek; Hosseinzadeh, Griffin; Jenness, Tim; Jones, Craig K.; Joseph, Prajwel; Kalmbach, J. Bryce; Karamehmetoglu, Emir; Kaluszynski, Mikolaj; Kelley, Michael S. P.; Kern, Nicholas; Kerzendorf, Wolfgang E.; Koch, Eric W.; Kulumani, Shankar; Lee, Antony; Ly, Chun; Ma, Zhiyuan; MacBride, Conor; Maljaars, Jakob M.; Muna, Demitri; Murphy, N. A.; Norman, Henrik; O'Steen, Richard; Oman, Kyle A.; Pacifici, Camilla; Pascual, Sergio; Pascual-Granado, J.; Patil, Rohit R.; Perren, Gabriel, I; Pickering, Timothy E.; Rastogi, Tanuj; Roulston, Benjamin R.; Ryan, Daniel F.; Rykoff, Eli S.; Sabater, Jose; Sakurikar, Parikshit; Salgado, Jesus; Sanghi, Aniket; Saunders, Nicholas; Savchenko, Volodymyr; Schwardt, Ludwig; Seifert-Eckert, Michael; Shih, Albert Y.; Jain, Anany Shrey; Shukla, Gyanendra; Sick, Jonathan; Simpson, Chris; Singanamalla, Sudheesh; Singer, Leo P.; Singhal, Jaladh; Sinha, Manodeep; Sipocz, Brigitta M.; Spitler, Lee R.; Stansby, David; Streicher, Ole; Sumak, Jani; Swinbank, John D.; Taranu, Dan S.; Tewary, Nikita; Tremblay, Grant R.; De Val-Borro, Miguel; Vasovic, Zlatan; Van Kooten, Samuel J.; Verma, Shresth; Cardoso, Jose Vinicius de Miranda; Williams, Peter K. G.; Wilson, Tom J.; Winkel, Benjamin; Wood-Vasey, W. M.; Xue, Rui; Yoachim, Peter; Zhang, Chen; Zonca, Andrea; Astropy Project Contributors; TARDIS Collaboration; Astropy Coordination Comm.--This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Multilevel inverters have created a new wave of interest in industry and research. While the classical topologies have proved to be a viable alternative in a wide range of high-power medium-voltage applications, there has been an active interest in the evolution of newer topologies. Reduction in overall part count as compared to the classical topologies has been an important objective in the recently introduced topologies. In this paper, some of the recently proposed multilevel inverter topologies with reduced power switch count are reviewed and analyzed. The paper will serve as an introduction and an update to these topologies, both in terms of the qualitative and quantitative parameters. Also, it takes into account the challenges which arise when an attempt is made to reduce the device count. Based on a detailed comparison of these topologies as presented in this paper, appropriate multilevel solution can be arrived at for a given application.

While training a model with data from a dataset, we have to think of an ideal way to do so. The training should be done in such a way that while the model has enough instances to train on, they should not over-fit the model and at the same time, it must be considered that if there are not enough instances to train on, the model would not be trained properly and would give poor results when used for testing. Accuracy is important when it comes to classification and one must always strive to achieve the highest accuracy, provided there is not trade off with inexcusable time. While working on small datasets, the ideal choices are k-fold cross-validation with large value of k (but smaller than number of instances) or leave-one-out cross-validation whereas while working on colossal datasets, the first thought is to use holdout validation, in general. This article studies the differences between the two validation schemes, analyzes the possibility of using k-fold cross-validation over hold-out validation even on large datasets. Experimentation was performed on four large datasets and results show that till a certain threshold, k-fold cross-validation with varying value of k with respect to number of instances can indeed be used over hold-out validation for quality classification.

This paper delves into the multifaceted concept of sustainability, covering its evolution, laws, principles, as well as the different domains and challenges related to achieving it in the modern world. Although capitalism, socialism, and communism have been utilized throughout history, their strengths and drawbacks have failed to address sustainable development comprehensively. Therefore, a holistic approach is necessary, which forms the basis for a new development model called sustainalism. This study proposes a new socio-economic theory of sustainalism that prioritizes quality of life, social equity, culture, world peace, social justice, and well-being. This paper outlines the six principles of sustainalism and identifies sustainalists as individuals who embrace these new concepts. This study also explores how to attain sustainalism in the modern world through a sustainable revolution, representing a step toward a sustainable era. In conclusion, this paper summarizes the key points and emphasizes the need for a new approach to sustainalism in the broader sense. The insights provided are valuable for further research on sustainalism and sustainability.

The growing energy demand in developing nations has triggered the issue of energy security. This has made essential to utilize the untapped potential of renewable resources. Grid connected PV systems have become the best alternatives in renewable energy at large scale. Performance analysis of these grid connected plants could help in designing, operating and maintenance of new grid connected systems. A 10 MW photovoltaic grid connected power plant commissioned at Ramagundam is one of the largest solar power plants with the site receiving a good average solar radiation of 4.97 kW h/m2/day and annual average temperature of about 27.3 degrees centigrade. The plant is designed to operate with a seasonal tilt. In this study the solar PV plant design aspects along with its annual performance is elaborated. The various types of power losses (temperature, internal network, power electronics, grid connected etc.) and performance ratio are also calculated. The performance results of the plant are also compared with the simulation values obtained from PV syst and PV-GIS software. The final yield (Y F) of plant ranged from 1.96 to 5.07 h/d, and annual performance ratio (PR) of 86.12%. It has 17.68% CUF with annual energy generation of 15 798.192 MW h/Annum.

The worsening energy crisis, growing environmental consciousness, and the detrimental consequences of climate change, prompted governments to reduce carbon footprints. One of the approaches involved is adopting green energy technology to charge electric vehicles (EVs). The US Department of Energy estimates that EVs may effectively use 60% of the input energy while driving, twice as much as traditional fossil fuel-based vehicles. Although EVs are tremendously efficient, the amount of greenhouse gas emissions they can reduce relies on the source of electricity needed to power them. To summarize the role of RE as a viable charging alternative, in this study, we analyze four essential elements of EV charging infrastructure, RE-enabled smart charging approaches, utility interest and associated challenges and opportunities. First, the existing RE sources employed for EV charging are discussed with their global adoption, advantages and drawbacks and the leading countries. Second, we presented a thorough investigation of energy storage technologies, charging systems, related power electronics, and smart grid integration to facilitate the adoption of RE in EVs. Third, we discussed in-depth the many industry-implemented smart charging approaches with RE in light of the most recent global trend in EV energy usage. Finally, given the inherent challenges associated with realizing the sustainable transition, we discuss the technological challenges and opportunities related to grid integration, renovation, standardization, maintenance, network security and resource optimization. The authors believe this manuscript will serve as an information cornerstone for all the involved parties and scientific communication to gain a deeper understanding and contribute.

Fungal diseases not only influence the economic importance of the plants and its products but also abate their ecological prominence. Mango tree, specifically the fruits and the leaves are highly affected by the fungal disease named as Anthracnose. The main aim of this paper is to develop an appropriate and effective method for diagnosis of the disease and its symptoms, therefore espousing a suitable system for an early and cost-effective solution of this problem. Over the last few years, due to their higher performance capability in terms of computation and accuracy, computer vision, and deep learning methodologies have gained popularity in assorted fungal diseases classification. Therefore, for this paper, a multilayer convolutional neural network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. This paper is validated on a real-time dataset captured at the Shri Mata Vaishno Devi University, Katra, J&K, India consists of 1070 images of the Mango tree leaves. The dataset contains both healthy and infected leaf images. The results envisage the higher classification accuracy of the proposed MCNN model when compared to the other state-of-the-art approaches.

This paper presents a multilevel inverter that has been conceptualized to reduce component count, particularly for a large number of output levels. It comprises floating input dc sources alternately connected in opposite polarities with one another through power switches. Each input dc level appears in the stepped load voltage either individually or in additive combinations with other input levels. This approach results in reduced number of power switches as compared to classical topologies. The working principle of the proposed topology is demonstrated with the help of a single-phase five-level inverter. The topology is investigated through simulations and validated experimentally on a laboratory prototype. An exhaustive comparison of the proposed topology is made against the classical cascaded H-bridge topology.

The efficiency of solar systems, in particular photovoltaic panels, is generally low. The output of the P.V. module is adversely affected by their surface rise in temperature. This increase is associated with the absorbed sunlight that is converted into heat, resulting in reduced power output, energy efficiency, performance and life of the panel. The use of cooling techniques can offer a potential solution to avoid excessive heating of P.V. panels and to reduce cell temperature. This paper presents details of various feasible cooling methods, including novel and advanced solutions for P.V. panels and indicates future trends of research. Different features and capability about each cooling techniques are presented, to provide better insight and valuable guidelines for researchers who intend to study, improve or optimise any type of cooling techniques of P·V. modules.

An azo dye, Acid Orange 7 [ p -(2-hydroxy-1 naphthylazo)benzene sulfonic acid] was removed by adsorption over two waste materials, namely, bottom ash, a power plant waste, and de-oiled soya, byproduct obtained during the processing of soybean in soya oil extraction mills. Both waste materials showed excellent adsorption abilities and can be treated as low-cost adsorbents. The adsorbents were characterized through IR spectroscopy and differential thermal analysis (DTA), and preliminary investigations were carried out by batch adsorption to examine the effects of pH, adsorbate concentration, sieve size, adsorbent dosage, contact time, and temperature. A plausible mechanism for the ongoing adsorption process and thermodynamic parameters were also obtained from Langmuir and Freundlich adsorption isotherm models. The kinetic measurements helped in determining the specific rate constant, confirming the applicability of the first-order rate expression. To identify whether the ongoing process is particle diffusion or film diffusion, the treatments given by Boyd et al. (Boyd, G. E.; Adamson, A. W.; Meyers, L. S. J. Am. Chem. Soc. 1947, 69, 2836) and Reichenberg (Reichenberg, D. J. Am. Chem. Soc. 1953, 75, 589) were employed. To assess the practical utility of the adsorbents, a fixed-bed column was designed, and necessary parameters were calculated by applying a mass-transfer kinetic approach. Experiments were also performed for the recovery of loaded dye through chemical regeneration of spent columns, and an estimate of the operating costs was also calculated.

Big data is a term used for very large data sets that have more varied and complex structure. These characteristics usually correlate with additional difficulties in storing, analyzing and applying further procedures or extracting results. Big data analytics is the term used to describe the process of researching massive amounts of complex data in order to reveal hidden patterns or identify secret correlations. However, there is an obvious contradiction between the security and privacy of big data and the widespread use of big data. This paper focuses on privacy and security concerns in big data, differentiates between privacy and security and privacy requirements in big data. This paper covers uses of privacy by taking existing methods such as HybrEx, k-anonymity, T-closeness and L-diversity and its implementation in business. There have been a number of privacy-preserving mechanisms developed for privacy protection at different stages (for example, data generation, data storage, and data processing) of a big data life cycle. The goal of this paper is to provide a major review of the privacy preservation mechanisms in big data and present the challenges for existing mechanisms. This paper also presents recent techniques of privacy preserving in big data like hiding a needle in a haystack, identity based anonymization, differential privacy, privacy-preserving big data publishing and fast anonymization of big data streams. This paper refer privacy and security aspects healthcare in big data. Comparative study between various recent techniques of big data privacy is also done as well.

Rose Bengal is a halogen-containing fluorescein water soluble dye, which is widely used for medical purposes. The dye, however, is highly toxic and can cause irritation, itching etc. to the human skin and eyes. It is therefore considered worthwhile to develop a systematic method for the removal of Rose Bengal by adsorption processes. The present investigation is devoted to batch as well as bulk removal of Rose Bengal. Under preliminary batch studies, adsorption isotherm measurements and kinetic studies were carried out, while for the bulk removal, a glass column was used as a fixed bed adsorber made up of Bottom Ash. Attempts were also made for the recovery of the dye from the exhausted Bottom Ash column by eluting dilute NaOH. The paper also presents a detailed procedure for activating Bottom Ash and its chemical and physical analysis. Rose Bengal showed a decrease in adsorption with increasing pH and conversely, increases in concentration, temperature, amount of adsorbent and sieve size increased the adsorption. Langmuir, Freundlich, Tempkin and D–R adsorption isotherm models were also verified, and on the basis of Langmuir constants thermodynamic parameters such as the Gibb's free energy, enthalpy and entropy of the adsorption were also calculated. A pseudo-second order process was found to operate during the adsorption. During column operations various parameters like fractional capacity of the column, mass flow rate, percentage saturation of column etc. were calculated. Desorption from the exhausted column gave almost 91% of dye recovery.

There are some specific constraints that hinder the development of solar and wind energy system in India. However, India has adequate sunshine and balanced wind speed. Hence there is greater opportunity for extension of solar and wind energy system in the Indian scenario along with enough future scope for these renewable sources through “Grid Parity”. The aim of this paper is to present in a coherent and integrated way the major constraints hampering the development of renewable energy in India.

An electrocardiogram (ECG) records the electrical signal from the heart to check for different heart conditions, but it is susceptible to noises. ECG signal denoising is a major pre‐processing step which attenuates the noises and accentuates the typical waves in ECG signals. Researchers over time have proposed numerous methods to correctly detect morphological anomalies. This study discusses the workflow, and design principles followed by these methods, and classify the state‐of‐the‐art methods into different categories for mutual comparison, and development of modern methods to denoise ECG. The performance of these methods is analysed on some benchmark metrics, viz., root‐mean‐square error, percentage‐root‐mean‐square difference, and signal‐to‐noise ratio improvement, thus comparing various ECG denoising techniques on MIT‐BIH databases, PTB, QT, and other databases. It is observed that Wavelet‐VBE, EMD‐MAF, GAN2, GSSSA, new MP‐EKF, DLSR, and AKF are most suitable for additive white Gaussian noise removal. For muscle artefacts removal, GAN1, new MP‐EKF, DLSR, and AKF perform comparatively well. For base‐line wander, and electrode motion artefacts removal, GAN1 is the best denoising option. For power‐line interference removal, DLSR and EWT perform well. Finally, FCN‐based DAE, DWT (Sym6) soft, MABWT (soft), CPSD sparsity, and UWT are promising ECG denoising methods for composite noise removal.

Abstract Photoexcited lead-free perovskite CH 3 NH 3 SnI 3 based solar cell device was simulated using a solar cell capacitance simulator. It was modeled to investigate its output characteristics under AM 1.5G illumination. Simulation efforts are focused on the thickness, acceptor concentration and defect density of absorber layer on photovoltaic properties of solar cell device. In addition, the impact of various metal contact work function was also investigated. The simulation results indicate that an absorber thickness of 500 nm is appropriate for a good photovoltaic cell. Oxidation of Sn 2+ into Sn 4+ was considered and it is found that the reduction of acceptor concentration of absorber layer significantly improves the device performance. Further, optimizing the defect density (10 14 cm −3 ) of the perovskite absorber layer, encouraging results of the J sc of 40.14 mA/cm 2 , V oc of 0.93 V, FF of 75.78% and PCE of 28.39% were achieved. Finally, an anode material with a high work function is necessary to get the device's better performance. The high-power conversion efficiency opens a new avenue for attaining clean energy.

Thermal degradation of waste plastics in an inert atmosphere has been regarded as a productive method, because this process can convert waste plastics into hydrocarbons that can be used either as fuels or as a source of chemicals. In this work, waste high-density polyethylene (HDPE) plastic was chosen as the material for pyrolysis. A simple pyrolysis reactor system has been used to pyrolyse waste HDPE with the objective of optimizing the liquid product yield at a temperature range of 400ºC to 550ºC. Results of pyrolysis experiments showed that, at a temperature of 450ºC and below, the major product of the pyrolysis was oily liquid which became a viscous liquid or waxy solid at temperatures above 475ºC. The yield of the liquid fraction obtained increased with the residence time for waste HDPE. The liquid fractions obtained were analyzed for composition using FTIR and GC-MS. The physical properties of the pyrolytic oil show the presence of a mixture of different fuel fractions such as gasoline, kerosene and diesel in the oil.

Abstract Biopsy is one of the most commonly used modality to identify breast cancer in women, where tissue is removed and studied by the pathologist under the microscope to look for abnormalities in tissue. This technique can be time‐consuming, error‐prone, and provides variable results depending on the expertise level of the pathologist. An automated and efficient approach not only aids in the diagnosis of breast cancer but also reduces human effort. In this paper, we develop an automated approach for the diagnosis of breast cancer tumors using histopathological images. In the proposed approach, we design a residual learning‐based 152‐layered convolutional neural network, named as ResHist for breast cancer histopathological image classification. ResHist model learns rich and discriminative features from the histopathological images and classifies histopathological images into benign and malignant classes. In addition, to enhance the performance of the developed model, we design a data augmentation technique, which is based on stain normalization, image patches generation, and affine transformation. The performance of the proposed approach is evaluated on publicly available BreaKHis dataset. The proposed ResHist model achieves an accuracy of 84.34% and an F1‐score of 90.49% for the classification of histopathological images. Also, this approach achieves an accuracy of 92.52% and F1‐score of 93.45% when data augmentation is employed. The proposed approach outperforms the existing methodologies in the classification of benign and malignant histopathological images. Furthermore, our experimental results demonstrate the superiority of our approach over the pre‐trained networks, namely AlexNet, VGG16, VGG19, GoogleNet, Inception‐v3, ResNet50, and ResNet152 for the classification of histopathological images.

At present aluminium-magnesium alloys are widely used in various engineering applications due to its light weight and superior properties. Joining is considered as one of the most complex phenomenon in various precision industries like aerospace, railway, automotive and marine structures because inflexible tolerances are required during different product assembly. The friction stir welding (FSW) of aluminium-magnesium of various grade has incited substantial scientific and industrial importance since it has a potency to transform the product with a good quality joint. The fabrication of such alloys is a challenging task through conventional fusion welding due to its various metallurgical concerns. Therefore, the present work is intended to summarize the recent progress in FSW of aluminium-magnesium alloys. Particular attention has been paid to microstructural evolution, phase transformation, recrystallization mechanism, material flow behaviour and how the process parameters influence the various mechanical properties and associated defects during FSW. Various experimental and numerical simulation results have been mentioned for weld property comparison. Finally, this work not only points out the prominent conclusions of the preceding research but also recommends the upcoming guidance concerning to fabrication of aluminium-magnesium alloys through FSW.

High entropy alloys (HEAs) for the first time were reported in 2004, however, the progress in this field was markedly realized since 2010 when a drastic upsurging demand of HEAs was observed owing to their unique properties and possibilities to form various compositions significantly grabbing the attention of academia and researchers. Initially, the main focus of researchers was on equiatomic HEAs which later paradigm shifted to non-equiatomic alloys. With progress in the research, four core effects were examined as the predominant factors enabling the distinguished properties. Amongst, mechanical alloying (MA) followed by the sintering route grabbed the attention of all the researchers dealing with the processing of HEAs. Therefore, the basic objective of this review is to critically discuss the mechanically alloyed HEAs, functional properties, and their applications. Subsequently, the existing challenges and their opportunities are also discussed which leads this review article towards uniqueness and novelty.

Power curve of a wind turbine depicts the relationship between output power and hub height wind speed and is an important characteristic of the turbine. Power curve aids in energy assessment, warranty formulations, and performance monitoring of the turbines. With the growth of wind industry, turbines are being installed in diverse climatic conditions, onshore and offshore, and in complex terrains causing significant departure of these curves from the warranted values. Accurate models of power curves can play an important role in improving the performance of wind energy based systems. This paper presents a detailed review of different approaches for modelling of the wind turbine power curve. The methodology of modelling depends upon the purpose of modelling, availability of data, and the desired accuracy. The objectives of modelling, various issues involved therein, and the standard procedure for power performance measurement with its limitations have therefore been discussed here. Modelling methods described here use data from manufacturers’ specifications and actual data from the wind farms. Classification of modelling methods, various modelling techniques available in the literature, model evaluation criteria, and application of soft computing methods for modelling are then reviewed in detail. The drawbacks of the existing methods and future scope of research are also identified.