Visvesvaraya Technological University

In the present scenario, there has been a rapid attention in research and development in the natural fiber composite field due to its better formability, abundant, renewable, cost-effective and eco-friendly features. This paper exhibits an outline on natural fibers and its composites utilized as a part of different commercial and engineering applications. In this review, many articles were related to applications of natural fiber reinforced polymer composites. It helps to provide details about the potential use of natural fibers and its composite materials, mechanical and physical properties and some of their applications in engineering sectors.

Fault diagnosis in electronic circuits is an emerging area of research, where fully automated diagnosis systems are being developed for the investigation of the circuits. Developing test methods for the diagnosis of faults in analog circuits is still a complex task. Consequently, a technique for the fault diagnosis in analog circuits is designed by proposing a new optimization algorithm, named, rider optimization algorithm (ROA). The development of ROA is based on a group of riders, racing toward a target location. Moreover, a classifier, termed RideNN, is developed by including the proposed algorithm as the training algorithm for the neural network (NN). RideNN, along with the orthogonal transformation and Bhattacharyya coefficient, is applied for the fault diagnosis of analog circuits. The proposed technique is experimented using three basic circuits, such as triangular wave generator (TWG), low noise bipolar transistor amplifier (BTA), and differentiator (DIF) and an application circuit, solar power converter (SPC). The performance is evaluated using two evaluation metrics, namely, accuracy (ACC) and false alarm ratio (FAR). The analysis results show that the proposed technique attains an ACC of 99.9% in TWG, 99.9% in BTA, 99% in DIF, and 95% in SPC without noise.

Nanofluids have gained significant popularity in the field of sustainable and renewable energy systems. The heat transfer capacity of the working fluid has a huge impact on the efficiency of the renewable energy system. The addition of a small amount of high thermal conductivity solid nanoparticles to a base fluid improves heat transfer. Even though a large amount of research data is available in the literature, some results are contradictory. Many influencing factors, as well as nonlinearity and refutations, make nanofluid research highly challenging and obstruct its potentially valuable uses. On the other hand, data-driven machine learning techniques would be very useful in nanofluid research for forecasting thermophysical features and heat transfer rate, identifying the most influential factors, and assessing the efficiencies of different renewable energy systems. The primary aim of this review study is to look at the features and applications of different machine learning techniques employed in the nanofluid-based renewable energy system, as well as to reveal new developments in machine learning research. A variety of modern machine learning algorithms for nanofluid-based heat transfer studies in renewable and sustainable energy systems are examined, along with their advantages and disadvantages. Artificial neural networks-based model prediction using contemporary commercial software is simple to develop and the most popular. The prognostic capacity may be further improved by combining a marine predator algorithm, genetic algorithm, swarm intelligence optimization, and other intelligent optimization approaches. In addition to the well-known neural networks and fuzzy- and gene-based machine learning techniques, newer ensemble machine learning techniques such as Boosted regression techniques, K-means, K-nearest neighbor (KNN), CatBoost, and XGBoost are gaining popularity due to their improved architectures and adaptabilities to diverse data types. The regularly used neural networks and fuzzy-based algorithms are mostly black-box methods, with the user having little or no understanding of how they function. This is the reason for concern, and ethical artificial intelligence is required.

In past decades researchers found many difficulties while providing environmentally supportive materials for product making. Natural fibers possess many advantages over synthetic fibers such as ease of availability, bio-degradability, low cost, lesser density, minimal health hazards, and eco-friendly in nature. Natural fiber reinforced polymer composites are the new innovative class of sustainable materials having good mechanical properties for practical applications. Further to increase the performance of a composites, researchers pointed out that using filler materials essentially increases the mechanical properties and in turn minimizes the organic contents in the composite laminates. This literature outlines the effect of natural fillers on the fiber-reinforced polymer matrix composites and also discusses on physical, chemical, thermal, and mechanical properties in terms of XRD, FTIR and SEM characterization. The main motive of this article is to discuss the different combinations of materials along with natural fillers and to assess their suitability for potential engineering applications.

Abstract In recent days, natural fiber reinforced polymer composite is more popular due to its extensive properties suitable for various potential applications. The attention towards natural fibers is because of low cost, biodegradable, recyclability, nonabrasive, combustible, lightweight, and nontoxic properties. However, there is a need for furthermore fundamental knowledge for the raw materials processing and fabrication of composite structures, which is still challenging in current days. Natural fiber sources exist all over the world, which is obtained from animals, plants and minerals. The quality of the natural fibers depends on the extraction methods and different processing techniques. These natural fibers surface characteristics could be enhanced by selecting suitable surface treatment and chemical treatment. These fiber treatments reduce the water intake percentage, improve the adhesive nature, and enhance the overall performance of resulting polymer composites. Among all the chemical treatments, alkaline treatment (NaOH) is the most preferred chemical treatment because of its effectiveness and its low cost. This review article proposes the natural fibers detailed classification, composition, structure, properties, and extraction methods, chemical and surface treatments. We also summarize the previous research work findings on the fibers treatment, properties of natural/natural hybrid polymer composites and natural/synthetic hybrid polymer composites with applications.

The growing demand for electrical energy and the impact of global warming leads to a paradigm shift in the power sector. This has led to the increased usage of renewable energy sources. Due to the intermittent nature of the renewable sources of energy, devices capable of storing electrical energy are required to increase its reliability. The most common means of storing electrical energy is battery systems. Battery usage is increasing in the modern days, since all mobile systems such as electric vehicles, smart phones, laptops, etc., rely on the energy stored within the device to operate. The increased penetration rate of the battery system requires accurate modelling of charging profiles to optimise performance. This paper presents an extensive study of various battery models such as electrochemical models, mathematical models, circuit-oriented models and combined models for different types of batteries. It also discusses the advantages and drawbacks of these types of modelling. With AI emerging and accelerating all over the world, there is a scope for researchers to explore its application in multiple fields. Hence, this work discusses the application of several machine learning and meta heuristic algorithms for battery management systems. This work details the charging and discharging characteristics using the black box and grey box techniques for modelling the lithium-ion battery. The approaches, advantages and disadvantages of black box and grey box type battery modelling are analysed. In addition, analysis has been carried out for extracting parameters of a lithium-ion battery model using evolutionary algorithms.

Environmental friendly products are getting much attention nowadays because of their availability in abundance and their usability in many engineering applications. Natural fibers possess many unique properties which make them easily replaceable to man-made fibers. This review is envisioned to present the various extraction and chemical treatment methods and also focused on providing the information of the fibers in terms of their physical and chemical properties.

Increasing environmental concerns, along with the potential declination of the crude worldwide reserves, have made the human beings to utilize more regenerable resources to substitute for the design and development of more new products. This has made us to use the synthetic and natural fibers to develop innovative products. However, more eco-friendly properties of natural fibers have made them to be preferable over the synthetic fibers. To make efficient use of these fibers, it is essential to know the behavioral characteristics of these fibers. So, in this review II paper, an effort has been made to discuss the various characterization analysis studies, like Fourier transform-infrared spectra spectral analysis, X-ray and thermogravimetric methods carried out by various researchers.

Abstract Graphene has attracted wide consideration in recent years to the assembly of sensitive sensors and biosensors due to its unique and remarkable physical and electrochemical properties. Moreover, graphene, as an essential two‐dimensional carbon material with remarkably high quartz and electronic superiority, has also received significant research attention. This review presents the different synthesis techniques of graphene; graphene functionalized based electrochemical sensors and biosensors for various health care appellations. Further, were discussed on the basis of enhanced catalytic activity, improved detection limit in conjunction with sensitivity, and selectivity. Synergistic action of graphene and metal oxide nanostructure has contributed towards high activity as a biosensing material. The results with different sensors and biosensors for the detection of significant biomarkers such as protein sensor, electrochemical immune sensor, phytochrome sensor, cholesterol biosensor glucose, hydrogen peroxide, and nicotinamide adenine dinucleotide detection sensor etc., and highlighted the use of graphene and functionalized graphene in different sensing platforms. Finally, the challenges related to less aggregated graphene‐based electrochemical sensors and biosensors as well as future research directions are discussed.

Agriculture and its allied sectors are undoubtedly the largest providers of livelihoods in rural India. The agriculture sector is also a significant contributor factor to the country's Gross Domestic Product (GDP). Blessing to the country is the overwhelming size of the agricultural sector. However, regrettable is the yield per hectare of crops in comparison to international standards. This is one of the possible causes for a higher suicide rate among marginal farmers in India. This paper proposes a viable and user-friendly yield prediction system for the farmers. The proposed system provides connectivity to farmers via a mobile application. GPS helps to identify the user location. The user provides the area & soil type as input. Machine learning algorithms allow choosing the most profitable crop list or predicting the crop yield for a user-selected crop. To predict the crop yield, selected Machine Learning algorithms such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Random Forest (RF), Multivariate Linear Regression (MLR), and K-Nearest Neighbour (KNN) are used. Among them, the Random Forest showed the best results with 95% accuracy. Additionally, the system also suggests the best time to use the fertilizers to boost up the yield.

The reactivity of metallic and bimetallic nanoparticles of copper and silver towards <italic>in vitro</italic> study has been quantitatively investigated.

The objective of this study is to apply business intelligence in identifying potential customers by providing relevant and timely data to business entities in the Retail Industry. The data furnished is based on systematic study and scientific applications in analyzing sales history and purchasing behavior of the consumers. The curated and organized data as an outcome of this scientific study not only enhances business sales and profit, but also equips with intelligent insights in predicting consumer purchasing behavior and related patterns. In order to execute and apply the scientific approach using K-Means algorithm, the real time transactional and retail dataset are analyzed. Spread over a specific duration of business transactions, the dataset values and parameters provide an organized understanding of the customer buying patterns and behavior across various regions. This study is based on the RFM (Recency, Frequency and Monetary) model and deploys dataset segmentation principles using K-Means Algorithm. A variety of dataset clusters are validated based on the calculation of Silhouette Coefficient. The results thus obtained with regard to sales transactions are compared with various parameters like Sales Recency, Sales Frequency and Sales Volume.

The Internet was initially used to transfer data packets between users and data sources with a specific IP address. Due to advancements, the Internet is being used to share data among different small, resource constrained devices connected in billions to constitute the Internet of Things (IoT). A large amount of data from these devices imposes overhead on the IoT network. Hence, it is required to provide solutions for various network related problems in IoT including routing, energy conservation, congestion, heterogeneity, scalability, reliability, quality of service (QoS) and security to optimally make use of the available network. In this paper, a comprehensive survey on the network optimization in IoT is presented. The paper draws an attention towards the background of IoT and its distinction with other technologies, discussion on network optimization in IoT and algorithms classification. Finally, state-of-the-art-techniques for IoT in particular to network optimization are discussed based on the recent works and the review is concluded with open issues and challenges for network optimization in IoT. This paper not only reviews, compares and consolidates the recent related works, but also admires the author’s findings, solutions and discusses its usefulness towards network optimization in IoT. The uniqueness of this paper lies in the review of network optimization issues and challenges in IoT.

Prediction of Chronic Disorders in early stage is very vital. IoT facilitated remote health monitoring system has enormous benefits over customary health monitoring system. It is imperative to accumulate correct raw data in an efficient way; but more significant is to explore and mine the raw data to abstract more valued information such as correlations amongst things and services to afford web of things or Internet of services. In this Paper we have addressed the use of IoT in Healthcare system, challenges of IoT in Healthcare System and review on various works carried out on this research area with which a proposed methodology is been discussed.

Natural fiber-reinforced polymeric composites are gaining significant attention in engineering applications. The present investigation is an attempt to assess the hybridization effects of different laminate stacking sequence involving jute/kenaf/E-Glass woven fabric through study of physical and mechanical properties of nine different resulting composites. The composite laminates were fabricated using vacuum bagging method. The assessment of mechanical properties and study of fractured surfaces indicate significant improvement in tensile and flexural properties of jute/kenaf fabrics reinforced epoxy composites.

In recent years, there has been immense advancement in the development of nanobiosensors as these are a fundamental need of the hour that act as a potential candidate integrated with point-of-care-testing for several applications, such as healthcare, the environment, energy harvesting, electronics, and the food industry. Nanomaterials have an important part in efficiently sensing bioreceptors such as cells, enzymes, and antibodies to develop biosensors with high selectivity, peculiarity, and sensibility. It is virtually impossible in science and technology to perform any application without nanomaterials. Nanomaterials are distinguished from fine particles used for numerous applications as a result of being unique in properties such as electrical, thermal, chemical, optical, mechanical, and physical. The combination of nanostructured materials and biosensors is generally known as nanobiosensor technology. These miniaturized nanobiosensors are revolutionizing the healthcare domain for sensing, monitoring, and diagnosing pathogens, viruses, and bacteria. However, the conventional approach is time-consuming, expensive, laborious, and requires sophisticated instruments with skilled operators. Further, automating and integrating is quite a challenging process. Thus, there is a considerable demand for the development of nanobiosensors that can be used along with the POCT module for testing real samples. Additionally, with the advent of nano/biotechnology and the impact on designing portable ultrasensitive devices, it can be stated that it is probably one of the most capable ways of overcoming the aforementioned problems concerning the cumulative requirement for the development of a rapid, economical, and highly sensible device for analyzing applications within biomedical diagnostics, energy harvesting, the environment, food and water, agriculture, and the pharmaceutical industry.

Natural fibers are one of effective substitute for switching artificial fiber and concentrating to reinforce polymer matrixes due to their decomposable character. This study was implied to realize physico-chemical properties of bio fiber obtained from Heteropogon contortus (HC) plant. Heteropogon contortus fibers (HCFs) had cellulose (64.87 wt. %), hemicellulose (19.34 wt. %), lignin (13.56 wt. %), and low density (602 kg/m3). The chemical functional group of HCFs was established by Fourier transform infrared spectroscopy, thermal stability of the fiber up to 220°C discovered by thermogravimetric analysis. Further the assets of HCFs proved that it can act as an excellent reinforcement material as a bio composite. Finally, the tensile properties were carried out through single fiber tensile tests, such as tensile strength, tensile modulus and microfibrillar angle.

Abstract Carbon cloths are the important materials composed of woven carbon fibres having the diameters in the range of 5–10 μm. These materials have been investigated for innumerable applications such as supercapacitors (symmetric and asymmetric), batteries, solar cells, and catalysis. They are found to be the best supports as supercapacitive materials by providing high surface area, conductivity and flexibility compared to much widely used substrate materials such as Ni foam and 1D Fe nanowires. High conductivity and surface area of carbon cloths enable ion diffusion and cause decrease in charge transfer resistance, resulting in an increase of specific capacitance of specific electrodes. Several supercapacitive metal oxides, chalcogenides, phosphides, MXenes, carbon nanotubes, graphene, and conductive polymers have been incorporated into carbon cloths to improve their energy storage activity. Further modification of carbon cloth surface via oxidation, doping and by the growth of different nanostructures is also helpful as it increases the electroactive surface area necessary for electrochemical interaction. The present review mainly focuses on the development of flexible supercapacitors using carbon cloth‐based substrate materials. Such flexible supercapacitors can be further utilized for an uninterrupted and steady power supply to wearable and portable electronic devices.

This research has been carried out to find better hybrid natural/glass fiber-reinforced composites for engineering applications. This research work studied the impact and inter-laminar strength of E-glass with jute/kenaf woven fabric epoxy composites with the aim of evaluating the hybridization effects on different laminate stacking sequences made with jute, kenaf, and E-glass fabrics by the vacuum bagging method. All the laminates were prepared in 300 × 300 mm2 with a total of five plies maintained at 3 mm thickness, by varying the number and position of jute, kenaf, glass layers so as to obtain nine different stacking sequences. Among them, one group of all pure jute, pure kenaf, and pure E-glass laminates were also fabricated for comparison purpose. The specimen preparation and testing were carried out as per ASTM standards. From the results, it is shown that the properties of jute/kenaf fabrics-reinforced epoxy composites can be enhanced by hybridization with the addition of glass fabrics. The hybridization of jute/kenaf fabrics with E-glass fabrics provides a method to improve the mechanical impact and inter-laminar strength over pure natural fiber-reinforced composites. The hybrid laminate having E-glass and kenaf fiber plies as skin layers and jute fiber plies as core layers showed better properties compared to other laminates.

Preparation of nanofluid is of prime importance to obtain better thermal and physical properties. Different preparation parameters used in nanofluid preparation sometimes perform contrarily even if prepared with same nanoparticles and base fluid. Stability, thermal conductivity, and viscosity of the nanofluid are significantly affected by the cluster (agglomerate) size of nanoparticles in the base fluid which deteriorate thermal performance. In order to break the agglomerates and improve the dispersion of nanoparticles, ultrasonication is a more prevalent method. Nanofluids react differently for different sonication time and the reaction of the nanofluid with the change in sonication time varies for different nanofluids, which is dependent on various factors. In this regard, research works pertinent to the effect of ultrasonication on different properties of nanofluids are confined. In this paper, review of investigations carried out on experimentally evaluated ultrasonication effects on thermal properties and various physical properties of nanofluid. It is found that with an increased sonication time/energy, reduces the particle size and thus aids in obtaining a better dispersion leading to enhancement of stability, thermal conductivity and reducing viscosity. However, the longer ultrasonication duration was not found to be better in all cases where best performance was obtained for an optimum duration of ultrasonication.