Thapar Institute of Engineering & Technology

Due to the proliferation of ICT during the last few decades, there is an exponential increase in the usage of various smart applications such as smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, energy management etc. However, for all the aforementioned applications, security and privacy are major concerns keeping in view of the usage of the open channel, i.e., Internet for data transfer. Although many security solutions and standards have been proposed over the years to enhance the security levels of aforementioned smart applications, but the existing solutions are either based upon the centralized architecture (having single point of failure) or having high computation and communication costs. Moreover, most of the existing security solutions have focussed only on few aspects and fail to address scalability, robustness, data storage, network latency, auditability, immutability, and traceability. To handle the aforementioned issues, blockchain technology can be one of the solutions. Motivated from these facts, in this paper, we present a systematic review of various blockchain-based solutions and their applicability in various Industry 4.0-based applications. Our contributions in this paper are in four fold. Firstly, we explored the current state-of-the-art solutions in the blockchain technology for the smart applications. Then, we illustrated the reference architecture used for the blockchain applicability in various Industry 4.0 applications. Then, merits and demerits of the traditional security solutions are also discussed in comparison to their countermeasures. Finally, we provided a comparison of existing blockchain-based security solutions using various parameters to provide deep insights to the readers about its applicability in various applications.

Microbially induced calcium carbonate precipitation (MICCP) is a naturally occurring biological process in which microbes produce inorganic materials as part of their basic metabolic activities. This technology has been widely explored and promising with potential in various technical applications. In the present review, the detailed mechanism of production of calcium carbonate biominerals by ureolytic bacteria has been discussed along with role of bacteria and the sectors where these biominerals are being used. The applications of bacterially produced carbonate biominerals for improving the durability of buildings, remediation of environment (water and soil), sequestration of atmospheric CO2 filler material in rubbers and plastics etc. are discussed. The study also sheds light on benefits of bacterial biominerals over traditional agents and also the issues that lie in the path of successful commercialization of the technology of microbially induced calcium carbonate precipitation from lab to field scale.

The objective of this article is to extend and present an idea related to weighted aggregated operators from fuzzy to Pythagorean fuzzy sets (PFSs). The main feature of the PFS is to relax the condition that the sum of the degree of membership functions is less than one with the square sum of the degree of membership functions is less than one. Under these environments, aggregator operators, namely, Pythagorean fuzzy Einstein weighted averaging (PFEWA), Pythagorean fuzzy Einstein ordered weighted averaging (PFEOWA), generalized Pythagorean fuzzy Einstein weighted averaging (GPFEWA), and generalized Pythagorean fuzzy Einstein ordered weighted averaging (GPFEOWA), are proposed in this article. Some desirable properties corresponding to it have also been investigated. Furthermore, these operators are applied to decision-making problems in which experts provide their preferences in the Pythagorean fuzzy environment to show the validity, practicality, and effectiveness of the new approach. Finally, a systematic comparison between the existing work and the proposed work has been given.

The industrial revolution has been the main cause ever since tremendous technological advancement was observed. The ubiquitous deployment of recent information and communication technologies (ICT), namely Artificial Intelligence (AI), Internet of Things (IoT), and Blockchain technology, is hastening the world’s industrial and technological transformation. This technical aggrandizement enhances the working culture and has a favorable impact on the workplace, as per the progressivist perspective. The breakneck pace of technological advancement, as well as AI, has enabled humans to replace manual labor in various industries. As being a domain of science and technology, AI develops machines and programs for computers that are intelligent and can accomplish tasks that would normally require human intelligence abilities. This paper mainly explores the frontiers of artificial intelligence and its applications in various fields. The AI Frontiers promulgate methodical concepts that are peer-reviewed cutting-edge research on the disruptive technological revolution of Artificial Intelligence. Additionally, some key viewpoints in the field of AI have been listed along with the main frontiers, including Machine Learning (ML), Deep Learning (DL), Fuzzy Logic (FL), Natural Language Processor (NLP), and Genetic Algorithm (GA). Furthermore, this paper discussed some common AI applications and a briefing about the current scenario in the worldwide market for artificial intelligence.

Abstract Biological removal of dyes from effluents of textile and dyestuff manufacturing industry offers some distinct advantages over the commonly used chemicals and physicochemical methods. These include possible mineralization of the dyes to harmless inorganic compounds like carbon dioxide and water, and formation of a lesser quantity of relatively harmless sludge. Removal of dyes from these wastewaters has been reviewed with respect to biological decolorization as well as complete biodegradation of the dye molecules. Emerging techniques with reference to biological treatment of these wastewaters have been discussed under aerobic, anaerobic, and combined anaerobic–aerobic systems. Advantages and limitations of different biological methods have been highlighted, and future studies to establish these techniques for their applications on industrial scale have been suggested. Keywords: bioaugmentationbiodegradationdecolorizationsynthetic effluenttextile and dyestuff effluenttextile dyestriphenylmethane and azo dyes

Bioactive glass and glass-ceramics are used in bone repair applications and are being developed for tissue engineering applications. Bioactive glasses/Bioglass are very attractive materials for producing scaffolds devoted to bone regeneration due to their versatile properties, which can be properly designed depending on their composition. An important feature of bioactive glasses, which enables them to work for applications in bone tissue engineering, is their ability to enhance revascularization, osteoblast adhesion, enzyme activity and differentiation of mesenchymal stem cells as well as osteoprogenitor cells. An extensive amount of research work has been carried out to develop silicate, borate/borosilicate bioactive glasses and phosphate glasses. Along with this, some metallic glasses have also been investigated for biomedical and technological applications in tissue engineering. Many trace elements have also been incorporated in the glass network to obtain the desired properties, which have beneficial effects on bone remodeling and/or associated angiogenesis. The motivation of this review is to provide an overview of the general requirements, composition, structure-property relationship with hydroxyapatite formation and future perspectives of bioglasses.Attention has also been given to developments of metallic glasses and doped bioglasses along with the techniques used for their fabrication.

The pulp and paper industry processes huge quantities of lignocellulosic biomass every year. The technology for pulp manufacture is highly diverse, and numerous opportunities exist for the application of microbial enzymes. Historically, enzymes have found some uses in the paper industry, but these have been mainly confined to areas such as modifications of raw starch. However, a wide range of applications in the pulp and paper industry have now been identified. The use of enzymes in the pulp and paper industry has grown rapidly since the mid 1980s. While many applications of enzymes in the pulp and paper industry are still in the research and development stage, several applications have found their way into the mills in an unprecedented short period of time. Currently the most important application of enzymes is in the prebleaching of kraft pulp. Xylanase enzymes have been found to be most effective for that purpose. Xylanase prebleaching technology is now in use at several mills worldwide. This technology has been successfully transferred to full industrial scale in just a few years. The enzymatic pitch control method using lipase was put into practice in a large-scale paper-making process as a routine operation in the early 1990s and was the first case in the world in which an enzyme was successfully applied in the actual paper-making process. Improvement of pulp drainage with enzymes is practiced routinely at mill scale. Enzymatic deinking has also been successfully applied during mill trials and can be expected to expand in application as increasing amounts of newsprint must be deinked and recycled. The University of Georgia has recently opened a pilot plant for deinking of recycled paper. Pulp bleaching with a laccase mediator system has reached pilot plant stage and is expected to be commercialized soon. Enzymatic debarking, enzymatic beating, and reduction of vessel picking with enzymes are still in the R&D stage but hold great promise for reducing energy. Other enzymatic applications, i.e., removal of shives and slime, retting of flax fibers, and selective removal of xylan, are also expected to have a profound impact on the future technology of the pulp and paper-making process.

Nontechnical losses, particularly due to electrical theft, have been a major concern in power system industries for a long time. Large-scale consumption of electricity in a fraudulent manner may imbalance the demand-supply gap to a great extent. Thus, there arises the need to develop a scheme that can detect these thefts precisely in the complex power networks. So, keeping focus on these points, this paper proposes a comprehensive top-down scheme based on decision tree (DT) and support vector machine (SVM). Unlike existing schemes, the proposed scheme is capable enough to precisely detect and locate real-time electricity theft at every level in power transmission and distribution (T&D). The proposed scheme is based on the combination of DT and SVM classifiers for rigorous analysis of gathered electricity consumption data. In other words, the proposed scheme can be viewed as a two-level data processing and analysis approach, since the data processed by DT are fed as an input to the SVM classifier. Furthermore, the obtained results indicate that the proposed scheme reduces false positives to a great extent and is practical enough to be implemented in real-time scenarios.

Poly(lactic acid), a bio‐degradable polymer, has been studied extensively during the past 15 years. This paper presents a review on poly(lactic acid) (PLA) with focus on its stereochemistry, synthesis via ring‐opening polymerization, reaction kinetics and thermodynamics, synthesis of low molecular weight polymer, a continuous process for production of PLA from lactic acid, and blends. The different polymerization mechanisms, which have been proposed in the literature, are also summarized. Various catalyst systems, solvents, and reaction temperature and time give products of an entire range of molecular weights, ranging from a few thousand to over a million. Modeling and simulation of the ring‐opening polymerization of PLA is also discussed.

In machine learning, the data imbalance imposes challenges to perform data analytics in almost all areas of real-world research. The raw primary data often suffers from the skewed perspective of data distribution of one class over the other as in the case of computer vision, information security, marketing, and medical science. The goal of this article is to present a comparative analysis of the approaches from the reference of data pre-processing, algorithmic and hybrid paradigms for contemporary imbalance data analysis techniques, and their comparative study in lieu of different data distribution and their application areas.

Vehicular technology has recently gained increasing popularity, and autonomous driving is a hot topic. To achieve safe and reliable intelligent transportation systems, accurate positioning technologies need to be built to factor in the different types of uncertainties such as pedestrian behavior, random objects, and types of roads and their settings. In this work, we look into the other domains and technologies required to build an autonomous vehicle and conduct a relevant literature analysis. In this work, we look into the current state of research and development in environment detection, pedestrian detection, path planning, motion control, and vehicle cybersecurity for autonomous vehicles. We aim to study the different proposed technologies and compare their approaches. For a car to become fully autonomous, these technologies need to be accurate enough to gain public trust and show immense accuracy in their approach to solving these problems. Public trust and perception of auto vehicles are also explored in this paper. By discussing the opportunities as well as the obstacles of autonomous driving technology, we aim to shed light on future possibilities.

This is a tutorial on use of external-electric-fields (EEFs) as effectors of chemical change. The tutorial instructs readers how to conceptualize and design electric-field effects on bonds, structures, and reactions. Most effects can be comprehended as the field-induced stabilization of ionic structures. Thus, orienting the field along the "bond axis" will facilitate bond breaking. Similarly, orienting the field along the "reaction axis", the direction in which "electron pairs transform" from reactants- to products-like, will catalyse the reaction. Flipping the field's orientation along the reaction-axis will cause inhibition. Orienting the field off-reaction-axis will control stereo-selectivity and remove forbidden-orbital mixing. Two-directional fields may control both reactivity and selectivity. Increasing the field strength for concerted reactions (e.g., Diels-Alder's) will cause mechanistic-switchover to stepwise mechanisms with ionic intermediates. Examples of bond breaking and control of reactivity/selectivity and mechanisms are presented and analysed from the "ionic perspective". The tutorial projects the unity of EEF effects, "giving insight and numbers".

Emotion recognition technology through analyzing the EEG signal is currently an essential concept in Artificial Intelligence and holds great potential in emotional health care, human-computer interaction, multimedia content recommendation, etc. Though there have been several works devoted to reviewing EEG-based emotion recognition, the content of these reviews needs to be updated. In addition, those works are either fragmented in content or only focus on specific techniques adopted in this area but neglect the holistic perspective of the entire technical routes. Hence, in this paper, we review from the perspective of researchers who try to take the first step on this topic. We review the recent representative works in the EEG-based emotion recognition research and provide a tutorial to guide the researchers to start from the beginning. The scientific basis of EEG-based emotion recognition in the psychological and physiological levels is introduced. Further, we categorize these reviewed works into different technical routes and illustrate the theoretical basis and the research motivation, which will help the readers better understand why those techniques are studied and employed. At last, existing challenges and future investigations are also discussed in this paper, which guides the researchers to decide potential future research directions.

Chapter Objectives✓ To learn about the concepts of data mining.✓ To understand the need for, and the applications of data mining✓ To differentiate between data mining and machine learning✓ To understand the process of data mining.✓ To understand the difference between data mining and machine learning.

Internet of Things (IoT) is a network of all devices that can be accessed through the Internet. These devices can be remotely accessed and controlled using existing network infrastructure, thus allowing a direct integration of computing systems with the physical world. This also reduces human involvement along with improving accuracy and efficiency, resulting in economic benefit. The devices in IoT facilitate the day-to-day life of people. However, the IoT has an enormous threat to security and privacy due to its heterogeneous and dynamic nature. Authentication is one of the most challenging security requirements in the IoT environment, where a user (external party) can directly access information from the devices, provided the mutual authentication between user and devices happens. In this paper, we present a new signature-based authenticated key establishment scheme for the IoT environment. The proposed scheme is tested for security with the help of the widely used Burrows-Abadi-Needham logic, informal security analysis, and also the formal security verification using the broadly accepted automated validation of Internet security protocols and applications tool. The proposed scheme is also implemented using the widely accepted NS2 simulator, and the simulation results demonstrate the practicability of the scheme. Finally, the proposed scheme provides more functionality features, and its computational and communication costs are also comparable with other existing approaches.

Advances in wireless communications, embedded systems, and integrated circuit technologies have enabled the wireless body area network (WBAN) to become a promising networking paradigm. Over the last decade, as an important part of the Internet of Things, we have witnessed WBANs playing an increasing role in modern medical systems because of its capabilities to collect real-time biomedical data through intelligent medical sensors in or around the patients' body and send the collected data to remote medical personnel for clinical diagnostics. WBANs not only bring us conveniences but also bring along the challenge of keeping data's confidentiality and preserving patients' privacy. In the past few years, several anonymous authentication (AA) schemes for WBANs were proposed to enhance security by protecting patients' identities and by encrypting medical data. However, many of these schemes are not secure enough. First, we review the most recent AA scheme for WBANs and point out that it is not secure for medical applications by proposing an impersonation attack. After that, we propose a new AA scheme for WBANs and prove that it is provably secure. Our detailed analysis results demonstrate that our proposed AA scheme not only overcomes the security weaknesses in previous schemes but also has the same computation costs at a client side.

The application of artificial intelligence is machine learning which is one of the current topics in the computer field as well as for the new COVID-19 pandemic. Researchers have given a lot of input to enhance the precision of machine learning algorithms and lot of work is carried out rapidly to enhance the intelligence of machines. Learning, a natural process in human behaviour that also becomes a vital part of machines as well. Besides this, another concept of deep learning comes to play its major role. Deep neural network (deep learning) is a subgroup of machine learning. Deep learning had been analysed and implemented in various applications and had shown remarkable results thus this field needs wider exploration which can be helpful for further real-world applications. The main objective of this paper is to provide insight survey for machine learning along with deep learning applications in various domains. Also, some applications with new normal COVID-19 blues. A review on already present applications and currently going on applications in several domains, for machine learning along with deep neural learning are exemplified.

In recent years, the research in generic Internet of Things (IoT) attracts a lot of practical applications including smart home, smart city, smart grid, industrial Internet, connected healthcare, smart retail, smart supply chain and smart farming. The hierarchical IoT network (HIoTN) is a special kind of the generic IoT network, which is composed of the different nodes, such as the gateway node, cluster head nodes, and sensing nodes organized in a hierarchy. In HIoTN, there is a need, where a user can directly access the real-time data from the sensing nodes for a particular application in generic IoT networking environment. This paper emphasizes on the design of a new secure lightweight three-factor remote user authentication scheme for HIoTNs, called the user authenticated key management protocol (UAKMP). The three factors used in UAKMP are the user smart card, password, and personal biometrics. The security of the scheme is thoroughly analyzed under the formal security in the widely accepted real-or-random model, the informal security as well as the formal security verification using the widely accepted automated validation of Internet security protocols and applications tool. UAKMP offers several functionality features including offline sensing node registration, freely password and biometric update facility, user anonymity, and sensing node anonymity compared to other related existing schemes. In addition, UAKMP is also comparable in computation and communication costs as compared to other existing schemes.

Natural processes, such as weathering, faults, land subsidence, earthquakes, and human activities, create fractures and fissures in concrete structures that can reduce the service life of the structures. A novel strategy to restore or remediate such structures is biomineralization of calcium carbonate using microbes, such as those in the genus of the Bacillus species. The present study investigated the effects of Bacillus sp. CT-5, isolated from cement, on compressive strength and water-absorption tests. The results showed a 36% increase in compressive strength of cement mortar with the addition of bacterial cells. Treated cubes absorbed six times less water than control cubes as a result of microbial calcite deposition. The current work demonstrated that production of “microbial concrete” by Bacillus sp. on constructed facilities could enhance the durability of building materials.

Nature-inspired algorithms are becoming popular among researchers due to their simplicity and flexibility. The nature-inspired metaheuristic algorithms are analysed in terms of their key features like their diversity and adaptation, exploration and exploitation, and attractions and diffusion mechanisms. The success and challenges concerning these algorithms are based on their parameter tuning and parameter control. A comparatively new algorithm motivated by the social hierarchy and hunting behavior of grey wolves is Grey Wolf Optimizer (GWO), which is a very successful algorithm for solving real mechanical and optical engineering problems. In the original GWO, half of the iterations are devoted to exploration and the other half are dedicated to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, a modified GWO (mGWO) is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm. Simulations based on benchmark problems and WSN clustering problem demonstrate the effectiveness, efficiency, and stability of mGWO compared with the basic GWO and some well-known algorithms.