National Institute of Technology Tiruchirappalli

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

Simulated systems are used for modelling and analysis of systems for which an analytical solution is either not accessible or difficult to achieve. Simulation is a highly flexible and adaptable discipline within computer science. Because it is simpler than conventional approaches, which are often challenging, simulation is also chosen as a method of system analysis. Because of this, simulation is an area with extensive application and demand, making it interesting and beneficial to have a chapter dedicated to researching simulation with a case study of modelling a Queuing system.

Abstract The initial hype and fanfare from the Meta Platforms view of how the metaverse could be brought to life has evolved into an ongoing discussion of not only the metaverse's impact on users and organizations but also the societal and cultural implications of widespread usage. The potential of consumer interaction with brands within the metaverse has engendered significant debate within the marketing‐focused discourse on the key challenges and transformative opportunities for marketers. Drawing on insights from expert contributors, this study examines the marketing implications of the hypothetical widespread adoption of the metaverse. We identify new research directions and propose a new framework offering valuable contributions for academia, practice, and policy makers. Our future research agenda culminates in a checklist for researchers which clarifies how the metaverse can be beneficial to digital marketing and advertising, branding, services, value creation, and consumer wellbeing.

Nucleation, the initial process in vapor condensation, crystal nucleation, melting, and boiling, is the localized emergence of a distinct thermodynamic phase at the nanoscale that macroscopically grows in size with the attachment of growth units. These phase changes are the result of atomistic events driven by thermal fluctuations. The occurrence of atomistic level events with the length scales on the order of 10–10 m and time scales of 10–13 S equivalent to the vibrational frequency of atoms makes the nucleation a very complicated phenomenon to study. Even though abundant literature is available about fundamental aspects of nucleation, the knowledge on these phenomena is far from complete. The classical pathway to nucleation which was once considered to have general applicability to all nucleating systems is gradually giving way to a nonclassical pathway which is now considered as a dominating mechanism in solution crystallization and other systems. In this review, an attempt is made to compare underlying physical principles involved in various nucleating systems and their theoretical treatment based on classical nucleation theory, and other important theories such as a density functional approach and diffuse interface theory. The limitations of classical theory, the gradual evolution of a nonclassical two-step pathway to nucleation, and the questions that have to be addressed in the future are discussed systematically.

Color removal from wastewater has been a matter of concern, both in the aesthetic sense and health point of view. Color removal from textile effluents on a continuous industrial scale has been given much attention in the last few years, not only because of its potential toxicity, but also mainly due to its visibility problem. There have been various promising techniques for the removal of dyes from wastewater. However, the effectiveness of adsorption for dye removal from wastewater has made it an ideal alternative to other expensive treatment methods. In this review, an extensive list of sorbent literature has been compiled. The review evaluates different agricultural waste materials as low-cost adsorbents for the removal of dyes from wastewater. The review also outlines some of the fundamental principles of dye adsorption on to adsorbents.

Partial shading of PV arrays reduces the energy yield of PV systems and the arrays exhibit multiple peaks in the P-V characteristics. The losses due to partial shading are not proportional to the shaded area but depend on the shading pattern, array configuration and the physical location of shaded modules in the array. This paper presents a technique to configure the modules in the array so as to enhance the generated power from the array under partial shading conditions. In this approach, the physical location of the modules in a Total Cross Tied (TCT) connected PV array are arranged based on the Su Do Ku puzzle pattern so as to distribute the shading effect over the entire array. Further, this arrangement of modules is done without altering the electrical connection of the modules in the array. The Su Do Ku arrangement reduces the effect of shading of modules in any row thereby enhancing the generated PV power. The performance of the system is investigated for different shading patterns and the results show that positioning the modules of the array according to “Su Do Ku” puzzle pattern yields improved performance under partially shaded conditions.

Fused deposition modelling is the most significant technique in additive manufacturing (AM) that refers to the process where successive layers of material are deposited in a computer-controlled environment to create a three-dimensional object. The main limitations of using fused deposition modelling (FDM) process in the industrial applications are the narrow range of available materials and parts fabricated by FDM are used only as demonstration or conceptual parts rather than as functional parts. Recently, researchers have studied many ways in order to increase the range of materials available for the FDM process which resulted in the increase in the scope of FDM in various manufacturing sectors. Most of the research are focussed on the composite materials such as metal matrix composites, ceramic composites, natural fibre-reinforced composites and polymer matrix composites. This article intends to review the research carried out so far in developing samples using different composite materials and optimising their process parameters for FDM in order to improve different mechanical properties and other desired properties of the FDM components.

Noble metal (gold or silver) nanoparticles or patterned films are typically used as substrates for surface-enhanced Raman spectroscopy (SERS). Two-dimensional (2D) carbides and nitrides (MXenes) exhibit unique electronic and optical properties, including metallic conductivity and plasmon resonance in the visible or near-infrared range, making them promising candidates for a wide variety of applications. Herein, we show that 2D titanium carbide, Ti3C2Tx, enhances Raman signal from organic dyes on a substrate and in solution. As a proof of concept, MXene SERS substrates were manufactured by spray-coating and used to detect several common dyes, with calculated enhancement factors reaching ∼106. Titanium carbide MXene demonstrates SERS effect in aqueous colloidal solutions, suggesting the potential for biomedical or environmental applications, where MXene can selectively enhance positively charged molecules.

Manufacturing simulation is an encouraging research area in resent decade. Creation or development of better simulation tool or technique is one of the major intension in manufacturing simulation. In resent research most of the manufacturing processes are simulated successfully. But some processes are not yet simulated effectively, especially automatic air conditioning (AC) system or refrigeration system. The automatic AC system for the passenger vehicle are not yet effectively simulated. Hence in this paper a machine learning technique is adopted for the effective prediction of parameter of automatic AC system. The proposed system uses k-nearest neighbour technique for the prediction of parameter will less error and high accuracy. The proposed system is implemented using MATLAB and its performance is compared with the support vector machine and ANN in terms of mean square error and accuracy. The proposed technique out-performs the conventional technique and suggest that the k-nearest neighbour become the most suitable technique for the modelling and performance analysis of automatic AC system.

Dielectric capacitors offer high-power density and ultrafast discharging times as compared to electrochemical capacitors and batteries, making them potential candidates for pulsed power technologies (PPT). However, low energy density in different dielectric materials such as linear dielectrics (LDs), ferroelectrics (FEs), and anti-ferroelectric (AFEs) owing to their low polarization, large hysteresis loss and low breakdown strength, respectively, limits their real time applications. Thus, achieving a material with high dielectric constant, large dielectric breakdown strength and slim hysteresis is imperative to obtain superior energy performance. In this context, relaxor ferroelectrics (RFEs) emerged as the most promising solution for energy storage capacitors. This review starts with a brief introduction of different energy storage devices and current advances of dielectric capacitors in PPT. The latest developments on lead-free RFEs including bismuth alkali titanate based, barium titanate based, alkaline niobite based perovskites both in ceramics and thin films are comprehensively discussed. Further, we highlight the different strategies used to enhance their energy storage performance to meet the requirements of the energy storage world. We also provide future guidelines in this field and therefore, this article opens a window for the current advancement in the energy storage properties of RFEs in a systematic way.

For the maximum utilization of solar energy, photovoltaic (PV) power generation systems are operated at the maximum power point (MPP) under varying atmospheric conditions, and MPP tracking (MPPT) is generally achieved using several conventional methods. However, when partial shading occurs in a PV system, the resultant power-voltage (P-V) curve exhibits multiple peaks and traditional methods that need not guarantee convergence to true MPP always. This paper proposes an artificial bee colony (ABC) algorithm for global MPP (GMPP) tracking under conditions of in-homogenous insolation. The formulation of the problem, application of the ABC algorithm, and the results are analyzed in this paper. The numerical simulations carried out on two different PV configurations under different shading patterns strongly suggest that the proposed method is far superior to existing MPPT alternatives. Experimental results are also provided to validate the new dispensation.

This paper reports the development of a maximum power-point tracking (MPPT) method for photovoltaic (PV) systems under partially shaded conditions using firefly algorithm. The major advantages of the proposed method are simple computational steps, faster convergence, and its implementation on a low-cost microcontroller. The proposed scheme is studied for two different configurations of PV arrays under partial shaded conditions and its tracking performance is compared with traditional perturb and observe (P&O) method and particle swarm optimization (PSO) method under identical conditions. The improved performance of the algorithm in terms of tracking efficiency and tracking speed is validated through simulation and experimental studies.

Pure ZnO and Neodymium (Nd) doped ZnO nanoparticles (NPs) were synthesized by the co-precipitation method. The synthesized nanoparticles retained the wurtzite hexagonal structure. From FESEM studies, ZnO and Nd doped ZnO NPs showed nanorod and nanoflower like morphology respectively. The FT-IR spectra confirmed the Zn-O stretching bands at 422 and 451 cm(-1) for ZnO and Nd doped ZnO NPs respectively. From the UV-VIS spectroscopic measurement, the excitonic peaks were found around 373 nm and 380 nm for the respective samples. The photoluminescence measurements revealed that the broad emission was composed of ten different bands due to zinc vacancies, oxygen vacancies and surface defects. The antibacterial studies performed against extended spectrum β-lactamases (ESBLs) producing strains of Escherichia coli and Klebsiella pneumoniae showed that the Nd doped ZnO NPs possessed a greater antibacterial effect than the pure ZnO NPs. From confocal laser scanning microscopic (CLSM) analysis, the apoptotic nature of the cells was confirmed by the cell shrinkage, disorganization of cell wall and cell membrane and dead cell of the bacteria. SEM analysis revealed the existence of bacterial loss of viability due to an impairment of cell membrane integrity, which was highly consistent with the damage of cell walls.

An experimental investigation was conducted in a single cylinder constant speed diesel engine to establish the effects of Carbon Nanotubes (CNT) with the Jatropha Methyl Esters (JME) emulsion fuel. The JME was produced from the Jatropha oil by transesterification process, and subsequently the JME emulsion fuel was prepared in the proportion of 93% of JME, 5% of water and 2% of surfactants (by volume) with a hydrophilic–lipophilic balance of 10. The Carbon Nanotubes are blended with the JME emulsion fuel in the various dosages systematically. The whole investigation was conducted in the diesel engine using the following fuels: neat JME, neat JME emulsion fuel and CNT blended JME emulsion fuels accordingly. The experimental results revealed an appreciable enhancement in the brake thermal efficiency for the CNT blended JME emulsion fuels compared to that of neat JME and neat JME emulsion fuel. At the full load, the brake thermal efficiency for the JME fuel observed was 24.80%, whereas it was 26.34% and 28.45% for the JME2S5W and JME2S5W100CNT fuels respectively. Further, due to the combined effects of micro-explosion and secondary atomization phenomena associated with the CNT blended JME emulsion fuels, the level of harmful pollutants in the exhaust gases (such as NOx and smoke) was drastically reduced when compared to that of neat JME. At the full load, the magnitude of NOx and smoke opacity for the neat JME was 1282 ppm and 69%, whereas it was 910 ppm and 49% for the JME2S5W100CNT fuel respectively.

Researchers and Practitioners have been interested in the field of impulse buying for the past sixty years (Clover,1950; Stern, 1962; Rook, 1987; Peck and Childers, 2006; Chang et.al, 2011). The purpose of this paper is toprovide a detailed account of the impulse buying behavior by compiling the various research works literature inthe field of Retailing and Consumer Behavior. It gives a broad overview of the impulse buying construct and thevarious behavior related aspects. A wide range of journal databases and books were referred to review the worksof various researchers. The content analysis of the various research works led to the classification of literatureinto different factors influencing impulse buying and further development of research framework. The multipleaspects of the subject are categorized for future research works in the area of impulse buying with thesuggestions. The paper will be useful for marketing practitioners and researchers towards comprehensiveunderstanding of the consumer’s impulsiveness.

Nanocrystalline tin oxide (SnO2) powders with different grain size were prepared by chemical precipitation method. The reaction was carried out by varying the period of hydrolysis and the as-prepared samples were annealed at different temperatures. The samples were characterized using X-ray powder diffractometer and transmission electron microscopy. The microstrain and crystallite size were calculated for all the samples by using Williamson-Hall (W-H) models namely, isotropic strain model (ISM), anisotropic strain model (ASM) and uniform deformation energy density model (UDEDM). The morphology and particle size were determined using TEM micrographs. The directional dependant young’s modulus was modified as an equation relating elastic compliances (sij) and Miller indices of the lattice plane (hkl) for tetragonal crystal system and also the equation for elastic compliance in terms of stiffness constants was derived. The changes in crystallite size and microstrain due to lattice defects were observed while varying the hydrolysis time and the annealing temperature. The dependence of crystallite size on lattice strain was studied. The results were correlated with the available studies on electrical properties using impedance spectroscopy.

The agriculture sector has an immense potential to improve the requirement of food and supplies healthy and nutritious food. Crop insect detection is a challenging task for farmers as a significant portion of the crops are damaged, and the quality is degraded due to the pest attack. Traditional insect identification has the drawback of requiring well-trained taxonomists to identify insects based on morphological features accurately. Experiments were conducted for classification on nine and 24 insect classes of Wang and Xie dataset using the shape features and applying machine learning techniques such as artificial neural networks (ANN), support vector machine (SVM), k-nearest neighbors (KNN), naive bayes (NB) and convolutional neural network (CNN) model. This paper presents the insect pest detection algorithm that consists of foreground extraction and contour identification to detect the insects for Wang, Xie, Deng, and IP102 datasets in a highly complex background. The 9-fold cross-validation was applied to improve the performance of the classification models. The highest classification rate of 91.5% and 90% was achieved for nine and 24 class insects using the CNN model. The detection performance was accomplished with less computation time for Wang, Xie, Deng, and IP102 datasets using insect pest detection algorithm. The comparison results with the state-of-the-art classification algorithms exhibited considerable improvement in classification accuracy, computation time performance while apply more efficiently in field crops to recognize the insects. The results of classification accuracy are used to recognize the crop insects in the early stages and reduce the time to enhance the crop yield and crop quality in agriculture.

The perturb and observe (P&O) algorithm is a simple and efficient technique, and is one of the most commonly employed maximum power point (MPP) tracking (MPPT) schemes for photovoltaic (PV) power-generation systems. However, under partially shaded conditions (PSCs), P&O method miserably fails to recognize global MPP (GMPP) and gets trapped in one of the local MPPs (LMPPs). This paper proposes ant-colony-based search in the initial stages of tracking followed by P&O method. In such a hybrid approach, the global search ability of ant-colony optimization (ACO) and local search capability of P&O method are integrated to yield faster and efficient convergence. A theoretical analysis of the static and dynamic convergence behavior of the proposed algorithm is presented together with computed and measured results.

The concentration of heavy metals was studied in the soil samples collected around the municipal solid waste (MSW) open dumpsite, Ariyamangalam, Tiruchirappalli, Tamilnadu to understand the heavy metal contamination due to leachate migration from an open dumping site. The dump site receives approximately 400–470 tonnes of municipal solid waste. Solid waste characterization was carried out for the fresh and old municipal solid waste to know the basic composition of solid waste which is dumped in the dumping site. The heavy metal concentration in the municipal solid waste fine fraction and soil samples were analyzed. The heavy metal concentration in the collected soil sample was found in the following order: Mn > Pb > Cu > Cd. The presence of heavy metals in soil sample indicates that there is appreciable contamination of the soil by leachate migration from an open dumping site. However, these pollutants species will continuously migrated and attenuated through the soil strata and after certain period of time they might contaminate the groundwater system if there is no action to be taken to prevent this phenomenon.

The interactions of human and bovine serum albumins (HSA and BSA) with various drugs and nanomaterials receive great attention in the recent years owing to their significant impact in the biomedical field. Although there are various techniques available for studying such interactions, fluorescence spectroscopy is the most appealing one due to its high sensitivity and straightforwardness. Detailed information about the interactions of drugs and nanomaterials with serum can be deducted from a mass of information accumulated by the fluorescence quenching studies. The present review emphasizes the interaction of various nanomaterials, antibiotics, anticancer drugs, anti-inflammatory agents, dyes, flavonoids, and certain noxious materials with HSA and BSA. In particular, we focus on the interactions of serum albumin with nanomaterials having different size and stabilizing agents with various receptors. This review helps in understanding the structural features of drugs/nanomaterials crucial for not only their affinity for serum albumin but also their optimum pharmacological activities.