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The purpose of this paper is to fabricate acrylonitrile-butadiene-styrene (ABS)/high impact polystyrene (HIPS) based multi-material geometries using a low cost polymer printer. At the same time, efforts have been made to investigate the mechanical characteristics of the obtained prints and to perform the optimization using the Taguchi-Grey (TGRA) method.

Initially, the feedstock materials were in-house fabricated in the form of filament wires, workable with fused filament fabrication (FFF) technique, of 1.75 ± 0.1 mm diameter by using a single screw extruder. Multi-material structures were fabricated using variable parameters (such as: raster angles, layer height, fill density and solid layers) and the experimentation was conducted as per Taguchi L18 array. Mechanical responses obtained by performing tensile, impact and bending test were studied in response to input variables and ultimately optimized settings were obtained, for individual as well as multiple parameters). Scanning electron microscopy (SEM) analysis was performed to analyze the fractured surfaces.

The Signal/Noise (S/N) plots for the quality characteristics highlighted that selected input parameters significantly influenced the obtained values for tensile strength, impact strength and flexural strength. Micrographs of the fractured specimens showed the occurrence of brittle fracture with higher levels of perimeter, infill density and solid layers. The extent of delamination was also increased under the bending load and further increased by increasing solid layers.

The results of the study strongly advocated the utility of fabricated multi-materials structures in automotive, aerospace and other manufacturing industries.

This work represents the fabrication, testing and analysis of polymer-based multi-material structures for engineering applications.

The purpose of this paper is to study the corrosion inhibition tendency of cigarette waste (water extracts of cigarette butts, WECB) on an iron surface in an acid medium.

The electrochemical impedance spectroscopy and polarization techniques were used to analyze the performance of WECB on the iron working electrode. Electrochemical polarization curves were used to determine the intensity of the metal corrosion, specifically to see the effectiveness of the anodic and cathodic reactions in the corrosive medium having WECB. Moreover, the electrochemical impedance of WECB with electrode was analyzed qualitatively. The electrochemical data that relate isotherm adsorption of WECB with iron were analyzed; furthermore, the scanning electron microscope was used to analyze morphology change during the corrosion inhibition.

After analyzing the impedance data, it is seen that there exists a single capacitive semicircle at the higher frequency range corresponding to a one-time constant in the Bode-phase plot. In the polarization curves studies (Tafel slopes), the current densities of both cathodic and anodic branches are greatly affected in the presence of WECB in the corrosive medium, suggesting that WECB performs as a mixed inhibitor. The free energy data and Temkin adsorption isotherm process show that the adsorption process of WECB on the metal surface follows a physisorption. Furthermore, the WECB-coated metal surface analyzed by scanning electron microscopy confirms the corrosion inhibition of WECB in the acid medium.

An in-depth characterization of the corroded scales is recommended to endorse the results of this study.

There may be some people who may challenge that the research may encourage smoking; however, if taken positively, the research offers a very cost-effective and eco-friendly solution to tackle the cigarette waste.

Idea of the present work is to reuse the WECB as corrosion inhibitors for the metal surface, as this waste contains large amount of nicotine, which exhibits corrosion inhibition properties. The present work deals with the study of corrosion inhibition properties of WECB on the iron surface in acid medium. The findings of this study can be very useful from scientific, as well as industrial application point of view. Moreover, the research is important as there is no proper recycling process for this waste so as to maintain a clean environment.

The purpose of this paper is to investigate the dynamic relationship among Gold, Crude oil, Indian Rupee-US Dollar and Stock market-Sensex (gold, oil, dollar and stock market (GODS)) in the pre-crisis, the crisis and the post-crisis periods in the Indian context.

The authors use Johansen’s cointegration technique, Vector Error Correction Model (VECM), Vector Auto Regression, VEC Granger Causality/Block Exogeneity Wald Test, and Granger Causality and Toda Yamamoto modified Granger causality to study long-run relationship and causality.

Johansen’s cointegration test results indicate that there is a long-run equilibrium relationship among the variables in the pre-crisis and the crisis periods but not in post-crisis period. VECM results report that none of four models of the variables show long-run causality in the pre-crisis period. During the crisis period, both crude oil and Sensex models show long-run causality. However, in some cases, results indicate short-run causality. The authors find one-way causality from USD and Sensex to crude oil, and from gold and Sensex to USD. Thus, the authors conclude that the relationship among GODS is dynamic across global financial crisis.

The research findings of this study are vital to the large group of stakeholders and participants of gold, crude oil, US dollar and stock market in emerging economies like India. The results are useful to importers, exporters, government, policy makers, corporate houses, retail investors, portfolio managers, commodity traders, treasury and fund managers, other commercial traders, etc.

This study is one of its kinds as it investigates the relationship among GODS in India in different sub-periods like before, during and after the global financial crisis of 2008. None of the studies compare phase-wise relationship among GODS in the Indian context. The study contributes to the economic theory and the body of knowledge. It highlights the need to revisit the economic theory to explain the interplay mechanism among GODS.

The purpose of this study to investigate the effects and possible future prediction of COVID-19. The dataset considered in this study to investigate the effects and possible future prediction of COVID-19 is constrained as follows: age, gender, systolic blood pressure, HDL-cholesterol, diabetes and its medication, does the patient suffered from heart disease or took anti-cough agent food or sensitive to cough related issues and any other chronic kidney disease, physical contact with foreign returns and social distance for the prediction of the risk of COVID-19.

This work implemented a meta-heuristic algorithm on the aforementioned dataset for possible analysis of the risk of being infected with COVID-19. The authors proposed a simple yet effective Risk Prediction through Nature Inspired Hybrid Particle Swarm Optimization and Sine Cosine Algorithm (HPSOSCA), particle swarm optimization (PSO), and sine cosine algorithm (SCA) algorithms.

The simulated results on different cases discussed in the dataset section reveal which category of individuals may happen to have the disease and of what level. The experimental results reveal that the proposed model can predict the percentage of risk with an overall accuracy of 88.63%, sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%) and Gmean (88.12%) with 41 and 146 true positive and negative, 18 and 6 false positive and negative cases, respectively. The proposed model provides a quite stable prediction of risk for COVID-19 on different categories of individuals.

The work for the very first time developed a novel HPSOSCA model based on PSO and SCA for the prediction of COVID-19 disease. The convergence rate of the proposed model is too high as compared to the literature. It also produces a better accuracy in a computationally efficient fashion. The obtained outputs are as follows: accuracy (88.63%), sensitivity (87.23%), specificity (89.02%), precision (69.49%), recall (87.23%), f_measure (77.36%), Gmean (88.12%), Tp (41), Tn (146), Fb (18) and Fn (06). The recommendations to reduce disease outbreaks are as follow: to control this epidemic in various regions, it is important to appropriately manage patients suspected of having the disease, immediately identify and isolate the source of infection, cut off the transmission route and prevent viral transmission from these potential patients or virus carriers.

This study analyses the impact of the Covid-19 on stock market performance of BRICS nations together. BRICS countries comprise almost 30% of the global GDP and around 50% of the world’s economic growth. As BRICS nations have gained the attraction as financial investment destinations, their financial markets have apparently been as potential opportunities for foreign portfolio investors. While there is extensive research on the impact of the Covid-19 pandemic on individual economies and global financial markets, this paper is among the first to systematically investigate the dynamic connectedness of these emerging economies during the pandemic using the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach.

We categorise our data into two distinct periods: the pre-Covid period spanning from January 1, 2018, to March 10, 2020, and the Covid crisis period extending from March 11, 2020, to June 4, 2021. To achieve our research objectives, we employ the Time-Varying Parameter Vector Autoregressions (TVP-VAR) approach to assess dynamic connectedness.

Our findings reveal that among the BRICS nations, Brazil and South Africa serve as net transmitters of shocks, while China and India act as net receivers of shocks during the Covid crisis. However, the total connectedness index (TCI) has exhibited a notable increase throughout this crisis period. This paper makes several notable contributions to the academic literature by offering a unique focus on BRICS economies during the Covid-19 pandemic, providing practical insights for stakeholders, emphasising the importance of risk management and investment strategy, exploring diversification implications and introducing advanced methodology for analysing interconnected financial markets.

The results have important implications for the investors, the hedge funds, portfolio managers and the policymakers in BRICS stock markets. The investors, investment houses, portfolio managers and policymakers can develop investment strategies and policies in the light of the findings of this study to cope up the future pandemic crisis.

This study is one of its kind that examines the dynamic connectedness of BRICS with recently developed TVP-VAR approach across pandemic crisis.

Artificial neural network (ANN) is a powerful technique to forecast the time series data such as the stock market. Therefore, this study aims to predict the Indian stock market closing price using ANNs.

The input variables identified from the literature are some macroeconomic variables and a global stock market factor. The study uses an ANN with Scaled Conjugate Gradient Algorithm (SCG) to forecast the Bombay Stock Exchange (BSE) Sensex.

The empirical findings reveal that the ANN model is able to achieve 93% accuracy in predicting the BSE Sensex closing prices. Moreover, the results indicate that the Morgan Stanley Capital International world index is the most important variable and the index of industrial production is the least important in predicting Sensex.

The findings of the study have implications for the investors of all categories such as foreign institutional investors, domestic institutional investors and investment houses.

The novelty of this study lies in the fact that there are hardly any studies that use ANN to forecast the Indian stock market using macroeconomic indicators.

Proper and effective control through audit is necessary in the case of public enterprises as the funds invested in them do not belong to those who manage the affairs of these enterprises. That is why it is important that their financial operations are subjected to severe scrutiny. Examines the problems of audit control of public enterprises, which would indicate an insight into the legal framework of audit accountability and the deviations emerging there from in actual practice. Government auditors do not have sufficient appreciation of the commercial nature of the public enterprises, and too detailed and continuous audit dampens the initiative of enterprising managers, forcing them to adopt a more cautious approach and restricting the scope of delegation of powers. In some cases the Comptroller and Auditor‐General is the sole auditor, and in other cases he performs the superimposed audit in addition to the audit by the professional auditor. Therefore, finds that audit control over public enterprises varies from case to case, and the CAG should interpret this power according to the need of situation, and there should be external efficiency audit for public enterprises.

Inventory models are quantitative ways of calculating low-cost operating systems. These models can be either deterministic or stochastic. A deterministic model hypothesizes variable quantities like demand and lead time, as certain. However, various types of research have revealed that the value of demand and lead time is still ambiguous and vary unanimously. The main purpose of this research piece is to reduce the uncertainties in such a dynamic environment of Industry 4.0.

The current study tackles the multiperiod single-item inventory lot-size problem with varying demands. The three lot sizing policies – Lot for Lot, Silver–Meal heuristic and Wagner–Whitin algorithm – are reviewed and analyzed. The suggested machine learning (ML)–based technique implies the criteria, when and which of these inventory models (with varying demands and safety stock) are best fit (or suitable) for economical production.

When demand surpasses a predicted value, variance in demand comes into the picture. So the current work considers these things and formulates the proper lot size, which can fix this dynamic situation. To deduce sufficient lot size, all three considered stochastic models are explored exclusively, as per respective protocols, and have been analyzed collectively through suitable regression analysis. Further, the ML-based Classification And Regression Tree (CART) algorithm is used strategically to predict which model would be economical (or have the least inventory cost) with continuously varying demand and other inventory attributes.

The ML-based CART algorithm has rarely been seen to provide logical assistance to inventory practitioners in making wise-decision, while selecting inventory control models in dynamic batch-type production systems.

“Machine learning (ML)” in business aids in increasing company scalability and boosting company operations for businesses all over the world. “Artificial intelligence (AI)” technologies and several “ML” algorithms have grown in prominence in the business analytics sector. In the era of a huge quantum of data being generated by the virtue of the integration of the various software with the business operations, the relevance of “ML” is continuously increasing. As a result, companies may now profit from knowing how companies may use “ML” and incorporating it into their own operations. “ML” derives useful results from the data to address very dynamic and difficult social and business problems. ML helps in establishing a system that learns automatically and produces results in less time and effort, allowing machines to discover. ML is developing at a breakneck pace, fuelled mostly by new computer technology to competitive advantages during the COVID pandemic.

For firms all around the world, “ML” in business aids in expanding scalability and boosting operations. In the field of business analytics, artificial intelligence (AI) and machine learning (ML) algorithms have become increasingly popular. The importance of “ML” is growing in an era when a massive amount of data is generated as a result of the integration of various applications with company activities. As a result, businesses can now benefit from understanding how other businesses are using “ML” and adopting it into their own operations. In order to handle very dynamic and demanding societal and business challenges, machine learning (ML) extracts valuable results from data. Machine learning (ML) aids in the development of a system that learns automatically and generates outcomes with less time and effort, allowing machines to discover. ML is progressing at a dizzying pace, fueled primarily by new computer technology and used to gain competitive advantages during the COVID pandemic.

According to a new study published by the Accenture Institute for High Performance, “AI” might double yearly economic growth rates in several wealthy nations by 2035. With broad AI deployment, the yearly growth rate in the USA increased from 2.6% to 4.6%, resulting in an extra $8.3tn. In the UK, AI may contribute $814bn to the economy, raising the yearly growth rate from 2.5% to 3.9%. The authors are already in a business period when huge technological development is assisting us in addressing a variety of difficulties to achieve maximum development. AI technology has enormous developmental consequences. In addition, big data analytics is helping to make AI more enterprise ready. Future developments in “ML” cannot be understated. Machines will very certainly eventually be smarter than humans in practically every way.

The introduction of AI into the market has enabled small businesses to use tried-and-true strategies for achieving greater business objectives. AI is continually offering a competitive advantage to start-ups, whilst large corporations provide a platform for building novel solutions. AI has become an integral component of reality, from functioning as a robot in a production unit to self-driving automobiles and voice activated resources in complex medical procedures. As a consequence, solving the difficulties highlighted below and finding out how to collaborate with robots will be a constant problem for the human species (Sujaya and Bhaskar, 2021).