Ravi Pratap Singh

Bio: Ravi Pratap Singh is an academic researcher from Dr. B. R. Ambedkar National Institute of Technology Jalandhar. The author has contributed to research in topics: Superconductivity & Magnetization. The author has an hindex of 28, co-authored 208 publications receiving 2575 citations. Previous affiliations of Ravi Pratap Singh include University of Warwick & Tata Institute of Fundamental Research.

Controlling Bulk Conductivity in Topological Insulators: Key Role of Anti‐Site Defects

Abstract: Intrinsic topological insulators are realized by alloying Bi2Te3 with Bi2Se3. Angle-resolved photoemission and bulk transport measurements reveal that the Fermi level is readily tuned into the bulk bandgap. First-principles calculations of the native defect landscape highlight the key role of anti-site defects for achieving this, and predict optimal growth conditions to realize maximally resistive topological insulators.

Significant applications of virtual reality for COVID-19 pandemic.

Abstract: Background and aims During COVID-19 pandemic, researchers are using innovative technologies for fast-tracking the development to end this menace. Virtual Reality (VR) also offers an imperative role for fighting this pandemic, through audiovisual-based virtual communication. Methods A brief study on Virtual Reality and its applications for the COVID-19 pandemic is carried out by employing keywords as Virtual reality or VR and COVID-19 from the databases of SCOPUS, Google Scholar, PubMed, Web of science Academia and ResearchGate. Results VR is beneficial for remote sites for exploring telemedicine, planning, treatment, and controlling of the infections by providing proper awareness to the people regarding this disease. Conclusions VR technology develops a platform to reduce the face to face interaction of doctors with the infected COVID-19 patients. Through live video streaming, it helps to improve surveillance systems on the ongoing situation.

Internet of Medical Things (IoMT) for orthopaedic in COVID-19 pandemic: Roles, challenges, and applications.

Abstract: Internet of Medical Things (IoMT) is an innovative mean of amalgamating medical devices and their applications to connect with the healthcare information technology systems by using networking technologies. We have explored the possibilities of confronting the ongoing COVID-19 pandemic by implementing the IoMT approach while offering treatment to orthopaedic patients. The data sharing, report monitoring, patients tracking, information gathering and analysis, hygiene medical care, etc. are the various cloud and connected network-based services of IoMT. It can completely change the working layout of the healthcare facilities while treating orthopaedic patients with a superior level of care and more satisfaction, especially during this pandemic COVID-19 lockdown. Remote-location healthcare has also become feasible with the proposed IoMT approach.

Detection of time-reversal symmetry breaking in the noncentrosymmetric superconductor Re6Zr using muon-spin spectroscopy.

Abstract: We have investigated the superconducting state of the noncentrosymmetric compound Re6Zr using magnetization, heat capacity, and muon-spin relaxation or rotation (μSR) measurements. Re6Zr has a superconducting transition temperature, Tc=6.75±0.05 K. Transverse-field μSR experiments, used to probe the superfluid density, suggest an s-wave character for the superconducting gap. However, zero and longitudinal-field μSR data reveal the presence of spontaneous static magnetic fields below Tc indicating that time-reversal symmetry is broken in the superconducting state and an unconventional pairing mechanism. An analysis of the pairing symmetries identifies the ground states compatible with time-reversal symmetry breaking.

Blockchain technology applications for Industry 4.0: A literature-based review

Abstract: Industry 4.0 involves innovations with upcoming digital technologies, and blockchain is one of them. Blockchain can be incorporated to improve security, privacy, and data transparency both for small and large enterprises. Industry 4.0 is a synthesis of the new production methods that allow manufacturers to achieve their target more rapidly. Research has been conducted on various Industry 4.0 technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, and Blockchain, and how they could create significant interruptions in recent years. These technologies provide various possibilities in the world of manufacturing and supply chain. Blockchain is a technology that has gained much recognition and can enhance the manufacturing and supply chain environment. Various fields now have fascinating insights into the advantages of blockchain. Several research articles on “Blockchain” and “Industry 4.0″ from Google Scholar, Scopus, and other relevant sources are identified and reviewed for this study. This paper discusses the major potential of Blockchain in Industry 4.0. Various drivers, enablers, and associated capabilities of Blockchain technology for Industry 4.0 are discussed for insights. Different Industry 4.0 spheres/sub-domains for Blockchain technology realisation are also discussed. Finally, we have identified and studied fourteen significant applications of Blockchain in Industry 4.0. It is a range of new developments and hope for immense opportunities that are changing Industry 4.0. This technology would work to achieve amplified outcomes and work individually to enhance the process.

Machine learning

Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Phd by thesis

Abstract: Deposits of clastic carbonate-dominated (calciclastic) sedimentary slope systems in the rock record have been identified mostly as linearly-consistent carbonate apron deposits, even though most ancient clastic carbonate slope deposits fit the submarine fan systems better. Calciclastic submarine fans are consequently rarely described and are poorly understood. Subsequently, very little is known especially in mud-dominated calciclastic submarine fan systems. Presented in this study are a sedimentological core and petrographic characterisation of samples from eleven boreholes from the Lower Carboniferous of Bowland Basin (Northwest England) that reveals a >250 m thick calciturbidite complex deposited in a calciclastic submarine fan setting. Seven facies are recognised from core and thin section characterisation and are grouped into three carbonate turbidite sequences. They include: 1) Calciturbidites, comprising mostly of highto low-density, wavy-laminated bioclast-rich facies; 2) low-density densite mudstones which are characterised by planar laminated and unlaminated muddominated facies; and 3) Calcidebrites which are muddy or hyper-concentrated debrisflow deposits occurring as poorly-sorted, chaotic, mud-supported floatstones. These