Indian Institute of Technology Roorkee

Bilal, Pant M., Zaheer H., Garcia-Hernandez L., Abraham A.

Since its inception in 1995, Differential Evolution (DE) has emerged as one of the most frequently used algorithms for solving complex optimization problems. Its flexibility and versatility have prompted several customized variants of DE for solving a variety of real life and test problems. The present study, surveys the near 25 years of existence of DE. In this extensive survey, 283 research articles have been covered and the journey of DE is shown through its basic aspects like population generation, mutation schemes, crossover schemes, variation in parameters and hybridized variants along with various successful applications of DE. This study also provides some key bibliometric indicators like highly cited papers having citations more than 500, publication trend since 1996, journal citations etc. The main aim of the present document is to serve as an extended summary of 25 years of existence of DE, intended for dissemination to interested parties. It is expected that the present survey would generate interest among the new users towards the philosophy of DE and would also guide the experience researchers.

Parvate S., Dixit P., Chattopadhyay S.

Biomimetic nanosurfaces with distinct wettability and versatility have found special enthusiasm in both fundamental research and industrial applications. With the advent of nanotechnology, it is doable to acclimate surface architecture and surface chemistry to attain superhydrophobicity. The uniqueness of superhydrophobic surfaces arises from various phenomenal advances, and its progress is expected to continue for decades in the future. In this Review Article, we discuss recent progress made in defining physical aspects of numerical modeling, experimental practices adopted, and applications of superhydrophobic surfaces. First, we revisit various classical models of superhydrophobicity and recent theoretical advances achieved related to the wetting phenomena. Subsequently, we emphasize various precursors and advance fabrication strategies adopted to fabricate superhydrophobic surfaces. In the following section, we take up various potential applications and appropriate working principles to explain wettability phenomena. Finally, some general conclusions are drawn along with proposed guidelines for designing robust superhydrophobic coatings.

Verma S., Pant M., Snasel V.

This paper provides an extensive review of the popular multi-objective optimization algorithm NSGA-II for selected combinatorial optimization problems viz. assignment problem, allocation problem, travelling salesman problem, vehicle routing problem, scheduling problem, and knapsack problem. It is identified that based on the manner in which NSGA-II has been implemented for solving the aforementioned group of problems, there can be three categories: Conventional NSGA-II, where the authors have implemented the basic version of NSGA-II, without making any changes in the operators; the second one is Modified NSGA-II, where the researchers have implemented NSGA-II after making some changes into it and finally, Hybrid NSGA-II variants, where the researchers have hybridized the conventional and modified NSGA-II with some other technique. The article analyses the modifications in NSGA-II and also discusses the various performance assessment techniques used by the researchers, i.e., test instances, performance metrics, statistical tests, case studies, benchmarking with other state-of-the-art algorithms. Additionally, the paper also provides a brief bibliometric analysis based on the work done in this study.

Amano H., Collazo R., Santi C.D., Einfeldt S., Funato M., Glaab J., Hagedorn S., Hirano A., Hirayama H., Ishii R., Kashima Y., Kawakami Y., Kirste R., Kneissl M., Martin R., et. al.

Solid state UV emitters have many advantages over conventional UV sources. The (Al,In,Ga)N material system is best suited to produce LEDs and laser diodes from 400 nm down to 210 nm—due to its large and tuneable direct band gap, n- and p-doping capability up to the largest bandgap material AlN and a growth and fabrication technology compatible with the current visible InGaN-based LED production. However AlGaN based UV-emitters still suffer from numerous challenges compared to their visible counterparts that become most obvious by consideration of their light output power, operation voltage and long term stability. Most of these challenges are related to the large bandgap of the materials. However, the development since the first realization of UV electroluminescence in the 1970s shows that an improvement in understanding and technology allows the performance of UV emitters to be pushed far beyond the current state. One example is the very recent realization of edge emitting laser diodes emitting in the UVC at 271.8 nm and in the UVB spectral range at 298 nm. This roadmap summarizes the current state of the art for the most important aspects of UV emitters, their challenges and provides an outlook for future developments.

Sinha A., Dolz J.

Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of information, where similar low-level features are extracted multiple times at multiple scales. Second, long-range feature dependencies are not efficiently modeled, resulting in non-optimal discriminative feature representations associated with each semantic class. In this paper we attempt to overcome these limitations with the proposed architecture, by capturing richer contextual dependencies based on the use of guided self-attention mechanisms. This approach is able to integrate local features with their corresponding global dependencies, as well as highlight interdependent channel maps in an adaptive manner. Further, the additional loss between different modules guides the attention mechanisms to neglect irrelevant information and focus on more discriminant regions of the image by emphasizing relevant feature associations. We evaluate the proposed model in the context of semantic segmentation on three different datasets: abdominal organs, cardiovascular structures and brain tumors. A series of ablation experiments support the importance of these attention modules in the proposed architecture. In addition, compared to other state-of-the-art segmentation networks our model yields better segmentation performance, increasing the accuracy of the predictions while reducing the standard deviation. This demonstrates the efficiency of our approach to generate precise and reliable automatic segmentations of medical images. Our code is made publicly available at: https://github.com/sinAshish/Multi-Scale-Attention.

Bhatti G., Mohan H., Raja Singh R.

Worldwide, transportation accounts for 18% of global carbon dioxide emissions (as of 2019). In order to battle the impending threat of climate change, consumers and industry must adopt sustainable transport that complies with the United Nations Sustainable Development Goals of increased energy efficiency and reduced greenhouse gas emissions. To fulfil these objectives, a new class of vehicles has recently emerged, smart electric vehicles, which is forecasted to reduce carbon dioxide emissions up to 43% as compared to diesel engine vehicles. However, to bring these vehicles to the mainstream, supporting architecture is needed to optimize them in a sustainable manner. One such novel architecture is Digital Twin Technology, which is a virtual mapping technology, extending from it, capable of investigating the lifecycle of multisystem bodies in a digital environment. In recent years, digital twin technology is becoming an underpinning area of research globally. As a result, novel individual research covering digital twin implementation on various aspects of smart vehicles has transpired in research and industrial studies, consequently allowing digital twin technology to evolve over the years. This work aims to bridge the gap between individual research to provide a comprehensive review from a technically-informed and academically neutral standpoint. Conceptual groundwork of digital twin technology is built systematically for the reader, to allow insight into its inception and evolution. The study sifts the digital twin domain for contributions in smart vehicle systems, exploring its potential and contemporaneous challenges to realization. The study then proceeds to review recent research and commercial projects for innovation within this domain. To the knowledge of the authors, this is the first extensive review of the application of digital twin technology in smart electric vehicles. The review has been systematically classified into specific domains within the smart vehicle system such as autonomous navigation control, advanced driver assistance systems, vehicle health monitoring, battery management systems, vehicle power electronics, and electrical power drive systems. An in-depth discussion of each vehicle subsystem is undertaken to present this review as an eclectic panorama of the smart vehicle system. This review further facilitates appreciation of the role of digital twin technology within each classification from a holistic technical perspective. Finally, the work ends with an inspection of the techno-socio-economic impact of digital twin technology that will revolutionize mainstream vehicle technology and the obstacles for further development. • In-depth systematic review and synopsis of recent advancements of digital twin technology in smart electric vehicles. • Detailed workflow for constructing a complex digital twin of electric vehicle systems. • An overview of the factors affecting the realization of digital twin technology in smart electric vehicles. • The benefits of digital twin technology and the significant challenges to its growth. • A discussion of the potential for the technology as well as the implications it may have in the future.

Yadav R., Chaudhary J.K., Jain N., Chaudhary P.K., Khanra S., Dhamija P., Sharma A., Kumar A., Handu S.

Coronavirus belongs to the family of Coronaviridae, comprising single-stranded, positive-sense RNA genome (+ ssRNA) of around 26 to 32 kilobases, and has been known to cause infection to a myriad of mammalian hosts, such as humans, cats, bats, civets, dogs, and camels with varied consequences in terms of death and debilitation. Strikingly, novel coronavirus (2019-nCoV), later renamed as severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), and found to be the causative agent of coronavirus disease-19 (COVID-19), shows 88% of sequence identity with bat-SL-CoVZC45 and bat-SL-CoVZXC21, 79% with SARS-CoV and 50% with MERS-CoV, respectively. Despite key amino acid residual variability, there is an incredible structural similarity between the receptor binding domain (RBD) of spike protein (S) of SARS-CoV-2 and SARS-CoV. During infection, spike protein of SARS-CoV-2 compared to SARS-CoV displays 10–20 times greater affinity for its cognate host cell receptor, angiotensin-converting enzyme 2 (ACE2), leading proteolytic cleavage of S protein by transmembrane protease serine 2 (TMPRSS2). Following cellular entry, the ORF-1a and ORF-1ab, located downstream to 5′ end of + ssRNA genome, undergo translation, thereby forming two large polyproteins, pp1a and pp1ab. These polyproteins, following protease-induced cleavage and molecular assembly, form functional viral RNA polymerase, also referred to as replicase. Thereafter, uninterrupted orchestrated replication-transcription molecular events lead to the synthesis of multiple nested sets of subgenomic mRNAs (sgRNAs), which are finally translated to several structural and accessory proteins participating in structure formation and various molecular functions of virus, respectively. These multiple structural proteins assemble and encapsulate genomic RNA (gRNA), resulting in numerous viral progenies, which eventually exit the host cell, and spread infection to rest of the body. In this review, we primarily focus on genomic organization, structural and non-structural protein components, and potential prospective molecular targets for development of therapeutic drugs, convalescent plasm therapy, and a myriad of potential vaccines to tackle SARS-CoV-2 infection.

Amran M., Debbarma S., Ozbakkaloglu T.

• Significant exposure to fly ash possesses a risk to human health and environment. • The large use of fly ash in making GPC can reduce the CO 2 emissions and provide cost-benefits. • NaOH, KOH, and Na 2 SiO 3 are most used activator in fly ash-based GPC. • Durability of fly ash-based GPC is mainly affected by the fineness of fly ash particles. • The long-term durability properties of FA-GPC have a great resistance to aggressive environments. Since fly ash (FA) is widely available across the globe, which is used to produce the next generation green concrete for the modern construction. The FA is a pozzolanic material with high alumina and silica content that gives a cementitious property in the presence of water. Thereby, FA-based geopolymer concrete (FA-GPC) seems to be a superlative option over the conventional concrete, to be developed. Using cost-effective and commonly obtainable FA as mineral fillers in concrete brings plentiful advantages to reduce the consumption of ordinary Portland cement (OPC), eliminate the disposal of FA in landfills, and decrease the CO 2 emissions, which all contribute toward a clean environment. With FA, a durable FA-GPC is a comparatively new, revolutionary and sustainable engineered composite material with several benefits, including the high early strength and improved durability properties (such as reduced permeability) against aggressive environments. Production of sustainable and greener concretes has become a major interest in the construction industry globally. This paper reviews the clean production, factors affecting the durability of FA-GPC, and the human health and environmental impacts of FA disposal. This review also aims to provide a critical review on the long-term durability properties and the behavior of FA-GPC composites, in addition to synopsize the research development trends to generate comprehensive insights into the potential applications of FA as a sustainable and ecofriendly construction material, for making greener concrete composite, towards industrialize environmentally buildings today.

Chumak A.V., Kabos P., Wu M., Abert C., Adelmann C., Adeyeye A.O., Akerman J., Aliev F.G., Anane A., Awad A., Back C.H., Barman A., Bauer G.E., Becherer M., Beginin E.N., et. al.

Magnonics is a field of science that addresses the physical properties of spin waves and utilizes them for data processing. Scalability down to atomic dimensions, operations in the GHz-to-THz frequency range, utilization of nonlinear and nonreciprocal phenomena, and compatibility with CMOS are just a few of many advantages offered by magnons. Although magnonics is still primarily positioned in the academic domain, the scientific and technological challenges of the field are being extensively investigated, and many proof-of-concept prototypes have already been realized in laboratories. This roadmap is a product of the collective work of many authors that covers versatile spin-wave computing approaches, conceptual building blocks, and underlying physical phenomena. In particular, the roadmap discusses the computation operations with Boolean digital data, unconventional approaches like neuromorphic computing, and the progress towards magnon-based quantum computing. The article is organized as a collection of sub-sections grouped into seven large thematic sections. Each sub-section is prepared by one or a group of authors and concludes with a brief description of the current challenges and the outlook of the further development of the research directions.

Tiwari A., Srivastava S., Pant M.

The past few years have witnessed a significant increase in medical cases related to brain tumors, making it the 10th most common form of tumor affecting children and adults alike. However, it is also one of the most curable forms of tumors if detected well on time. Consequently scientists and researchers have been working towards developing sophisticated techniques and methods for identifying the form and stage of tumor. Magnetic Resonance Imaging (MRI) and Computer Tomography (CT) are two methods widely used for resectioning and examining the abnormalities in terms of shape, size or location of brain tissues which in turn help in detecting the tumors. MRI, due to its advantages over CT scan, discussed later in the paper, is preferred more by the doctors. The way towards sectioning tumor from MRI picture of a brain cerebrum is one of the profoundly engaged regions in the network of medical science as MRI is non-invasive imaging. This paper provides a systematic literature survey of techniques for brain tumor segmentation and classification of abnormality and normality from MRI images based on different methods including deep learning techniques, metaheuristic techniques and hybridization of these two. It includes presentation and quantitative investigation used in conventional segmentation and classification techniques.