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The coronavirus disease 2019 (COVID-19) pandemic has become a global humanitarian challenge. This scourge has impacted people from all walks of life as well as every economic sector and activity, from travel to automotives, hotels to banking, and supply chain to retail. The pandemic has affected not only physical and mental health but also financial health. Studies have examined the pandemic's economic impact, but very few have examined its impact on personal finances. Efforts to contain the pandemic's spread, such as lockdowns, have resulted in suspended business operations throughout the world that have intensified joblessness. To prepare and protect people from such unforeseen situations, financial education and planning are necessary. We attempt to expand the evidence on this issue by applying a structural equation modelling approach to identify the mediating role of financial literacy programs in preparing and protecting household wealth against sudden worldwide setbacks. The research design is descriptive and exploratory using snowball sampling technique. The data was collected through an internet survey. In total, 400 survey responses were obtained. After testing the measurement model for key validity dimensions, the hypothesised causal relationships are examined in several path models. The results indicated that coronavirus awareness exerts a direct or indirect influence on the financial health of individuals through financial literacy. We conclude that financial literacy has a full mediating effect on the personal finance of individuals during the COVID-19 pandemic. The findings not only contributed to the need and understanding of financial literacy but also have managerial implications. Financial literacy programs provide investment advice and suggestions which are actionable and also work to help individuals to come out stronger in terms of knowledge and skill set when the COVID-19 crisis passes.

Environmental impact and changes are becoming essential in textile and yarn industries, where reliable measurement of parameters related to processing harmful substances needs to be examined. Such findings can be cumulated using smart assessment like life cycle analysis. The ecological impact category, supply chain, and climate-changing factors were considered for the necessary assessment.

This paper applies the Life Cycle Assessment technique in the textile and yarn industry to estimate critical environmental potentials. The critical input for the fabric and yarn industry was put in the GaBi software model to estimate various environmental potentials.

Global warming potential, electricity, and raw cotton consumption in the fabric and yarn industry were critical concerns where attention should be focused on minimizing environmental potentials from cradle to gate assessment.

This qualitative study is made via the industry case-wise inputs and outputs, which can vary with demographic conditions. Some machine and human constraints have not been implemented in modelling life cycle model for smart simulation. Smart simulation helps in linking different parameters and simulates their combined effects on the product life cycle.

This modelling approach will help access pollution constituents in different supply chain production processes and optimize them simultaneously.

The raw data used in this analysis are collected from an Indian small scale textile industry. In the textile fabrication industry, earlier assessments were carried out in cotton generation, impact of PET, cradle to grave assessment of textile products and garment processing only. In this research the smart model is drawn to consider each input parameter of yarn and textile fabric to determine the criticality of each input in this assessment. This article mainly talks about life cycle and circular supply assessment applied to first time for both cotton to yarn processing and yarn to fabric industry for necessary estimation of environment potentials.

The successive waves of the Covid-19 SARS-II pandemic and the attendant lockdown imposed by the governments worldwide drove the economic activities to a halt. Offices and factories closed, production of goods and services declined and supply chains got severely disrupted. Many companies were embattled with the grim reality of shrinkage of aggregate demand, first due to supply shock and later due to loss of jobs and wages. Amidst all this, the handling and shipping of commodities became extremely complex. As the pandemic shifted consumer preference in favour of digital platforms, more and more fast-moving consumer goods (FMCG) companies were confronted with multiple strategies and choices of an appropriate distribution channel to ensure smooth delivery of raw materials and products. The present study aims to study this shift and its implications in the Indian context.

A mix-method approach, integrating quantitative and qualitative analysis, is employed to investigate the factors influencing the selection of distribution channels amongst general trade, modern trade, e-commerce and hyperlocal for FMCG companies in India. The first phase of the study uses exploratory factor analysis (EFA), followed by the application of analytical hierarchy process (AHP) approach in a fuzzy environment to realise the priority weights and ranking of the identified factors. Finally, sensitivity analysis is performed to confirm the robustness of the fuzzy analytical hierarchy process (FAHP) outcomes.

The study revealed that modern trade has emerged as the most favoured channel in the post-pandemic Indian economy. It has the potential to disrupt general trade. The study also revealed that the hyperlocal delivery model is not economically viable, and the partnership of FMCG companies with these applications is at best a short-term solution. However, it must be submitted that due to its sheer capability to ensure quick deliveries within a confined geographic area, hyperlocal delivery will gain momentum with the advancement of technology.

This study can be seen as the first attempt to investigate the issues related to the selection of the distribution channels in the FMCG sector of India using multi-criteria decision-making technique (MCDM).

This study aims to present a scientometric analysis of publications related to “Augmented Reality.” In today’s Information Technology-driven era, augmented reality (A.R.) has evolved as a new immersive data source for developing knowledge combining authentic and digital images. Consequently, extensive research is going on “Augmented Reality” to discover its potential in knowledge development.

The paper analyses and emphasizes the bibliographic data of Scopus articles with a suitable search query. The study was done concerning the chronological growth of research publications, highly cited publications, productive countries, prominent journals, prolific authors and institutions, author and country co-authorship network analysis and keywords analysis. The analysis was conducted by using open-source tools such as VOSViewer, Biblioshiny and Gephi.

The study reveals that a maximum number of publications on research in “Augmented Reality” are in the form of conference proceedings and articles. A.R., Virtual reality and A.R. application are the keywords with maximum number of occurrences. A significant number of publications are done in the USA, followed by Germany in the year 2020.

This study provides a precise idea of work done in different countries, a network of co-authorship between authors and countries, publication and citation impact of authors, journals, institutions and countries, year-wise progression and trending “augmented reality” topics research. This investigation will be advantageous for researchers and stakeholders to obtain rigorous bibliographic knowledge on literature related to the topic and work accordingly for R&D activities.

The purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.

The paper presents a simulation‐based genetic algorithm approach for the job shop scheduling problem. In total, three cases have been considered to access the performance of the job shop, with an objective to minimise mean tardiness and makespan. A restart scheme is embedded into regular genetic algorithm in order to avoid premature convergence.

Simulation‐based genetic algorithm can be used for job shop scheduling problems. Moreover, a restart scheme embedded into a regular genetic algorithm results in improvement in the fitness value. Single process plans selected on the basis of minimum production time criterion results in improved shop performance, as compared to single process plans selected randomly. Moreover, availability of multiple process plans during scheduling improves system performance measures.

The paper presents a simulation‐based genetic algorithm approach for job shop scheduling problem, with and without restart scheme. In this paper the effect of multiple process plans over single process plans, as well as criterion for selection of single process plans, are studied. The findings should be taken into account while designing scheduling systems for job shop environments.

Diabetes is a chronic disease and the major types of diabetes are type 1 and type 2. On aging, people with diabetes tend to have long-term problems in hypertension, coronary artery disease, obesity, and nerves. Given the increasing number of complications in recent years, by 2040, 624 million people will have diabetes worldwide and l in 8 adults will have diabetes in the future. Machine learning (ML) is evolving rapidly, many aspects of medical learning use ML. In this study, tension-type headaches (TTH) were associated with diabetes using SPSS, Pearson correlation, and ANOVA tests. Data were collected from Delhi NCR Hospital. It contains 30 diabetic subjects. The purpose of this study was to correlate diabetes analysis from TTH and other diseases using the latest technologies to analyze the Internet of Things and Big Data and Stress Correlation (TTH) on human health. The authors used Pearson correlation to correlate study variables and see if there was any effect between them. There was an important relationship between the percent variable, the total number of individuals, the number of individuals, and the minimum variable. The age (field) of the number of individuals to one of the total number of individuals showed a strong correlation (1.000) with a significant value of p (1.000). Overall, cases of TTH increased with age in men and do not follow the pattern of change in diabetes with age, but in cases of TTH, patterns of headaches such as diabetes increase to age 60 and then tend to decrease.

The purpose of this paper is to examine the purchase behavior of consumers towards green products by adapting and extending the theory of planned behavior with the inclusion of three pertinent environmental factors posited to reflect environmental consciousness in the form of environmental concern, environmental knowledge and environmental values.

The data was collected from 410 consumers at shopping malls with retail stores selling green and non-green products in a developing country using cluster sampling and analyzed using covariance-based structural equation modeling.

The findings of this study indicate that environmental factors reflecting environmental consciousness positively influence consumers’ attitude towards purchasing green products, wherein consumers’ environmental values have a stronger influence than their environmental concern and environmental knowledge. The findings also reveal that subjective norm, attitude and perceived behavioral control toward purchasing green products positively shape green purchase intention. The same positive effect is also witnessed between green purchase intention and behavior. However, perceived behavioral control towards purchasing green products had no significant influence on green purchase behavior.

This study suggests that green marketers should promote environmental consciousness among consumers to influence and shape their planned behavior towards green purchases. This could be done by prioritizing efforts and investments in inculcating environmental values, followed by enhancing environmental knowledge and finally inducing environmental concern among consumers. Green marketers can also leverage subjective norm and perceptions of behavioral control toward purchasing green products to reinforce green purchase intention, which, in turn, strengthens green purchase behavior. This green marketing strategy should also be useful to address the intention–behavior gap as seen through the null effect of perceived behavioral control on purchase behavior toward green products when this strategy is present.

This study contributes to theoretical generalizability by reaffirming the continued relevance of the theory of planned behavior in settings concerning the environment (e.g. green purchases), and theoretical extension by augmenting environmental concern, environmental knowledge and environmental values with the theory of planned behavior, resulting in an environmentally conscious theory of planned behavior. The latter is significant and noteworthy, as this study broadens the conceptualization and operationalization of environmental consciousness from a unidimensional to a multidimensional construct.

This paper aims to deal with the human activity recognition using human gait pattern. The paper has considered the experiment results of seven different activities: normal walk, jogging, walking on toe, walking on heel, upstairs, downstairs and sit-ups.

In this current research, the data is collected for different activities using tri-axial inertial measurement unit (IMU) sensor enabled with three-axis accelerometer to capture the spatial data, three-axis gyroscopes to capture the orientation around axis and 3° magnetometer. It was wirelessly connected to the receiver. The IMU sensor is placed at the centre of mass position of each subject. The data is collected for 30 subjects including 11 females and 19 males of different age groups between 10 and 45 years. The captured data is pre-processed using different filters and cubic spline techniques. After processing, the data are labelled into seven activities. For data acquisition, a Python-based GUI has been designed to analyse and display the processed data. The data is further classified using four different deep learning model: deep neural network, bidirectional-long short-term memory (BLSTM), convolution neural network (CNN) and CNN-LSTM. The model classification accuracy of different classifiers is reported to be 58%, 84%, 86% and 90%.

The activities recognition using gait was obtained in an open environment. All data is collected using an IMU sensor enabled with gyroscope, accelerometer and magnetometer in both offline and real-time activity recognition using gait. Both sensors showed their usefulness in empirical capability to capture a precised data during all seven activities. The inverse kinematics algorithm is solved to calculate the joint angle from spatial data for all six joints hip, knee, ankle of left and right leg.

This work helps to recognize the walking activity using gait pattern analysis. Further, it helps to understand the different joint angle patterns during different activities. A system is designed for real-time analysis of human walking activity using gait. A standalone real-time system has been designed and realized for analysis of these seven different activities.

The data is collected through IMU sensors for seven activities with equal timestamp without noise and data loss using wirelessly. The setup is useful for the data collection in an open environment outside the laboratory environment for activity recognition. The paper also presents the analysis of all seven different activity trajectories patterns.

Non-performing assets (NPAs) are classified as loans and advances which are in default, either refund of principal or interest payments are not duly met. This not only leads to dishonour of loan agreement from the recipients' point of view but also huge NPAs result macroeconomic instability and economic crisis. The financial crisis may create hindrances towards achievement of sustainable development of an economy. Keeping NPA in balance sheet portrays lacunae in management of the lender. The non-recovery of interest and principal reduces the lender's operating cash flow, which upsets the budget and drops the earnings. Statutory provisions, set aside to cover probable losses, reduce the income further. When the non-recovery is determined to be definite in nature, they are written off against earnings of the lending institution. Thus, presence of NPAs in balance sheet gives a distress signal to the stakeholders of the lending institution. Under this consideration, the present study will look upon some of these issues related to NPA management in Indian banking sector. The main objective of this study is to discuss the nexus between the NPA of Indian scheduled banks for priority sector, non-priority sector and public sector and the gross domestic product (GDP) of Indian economy for the time period 2005–2020. To study this objective, the ratio analysis and the trend analysis of NPA of three sectors and GDP of Indian economy over the given time frame have been done. Finally, some policy prescriptions regarding achievement of sustainable development after taking into account NPA management of an economy have also been proposed.

This paper aims to study the weak form of efficiency of Indian capital market during the period of global financial crisis in the form of random walk.

The study considered daily closing prices of S&P CNX Nifty, BSE, CNX100, S&P CNX 500 from April 1, 2005 to March 31, 2010. The data source is the equity market segment of NSE and BSE. Both parametric and nonparametric tests (“ex‐posts” in nature) are applied for the purpose of testing weak‐form efficiency. The parametric tests include Augmented Dickey‐Fuller (ADF) unit root tests and nonparametric tests include Phillips‐Perron (PP) unit root tests and Run test. ADF tests use a parametric autoregressive structure to capture serial correlation and PP tests use non‐parametric corrections based on estimates of the long‐run variance of ΔY_t.

The results suggested that the Indian stock market was efficient in its weak form during the period of recession. It means that investors should not be able to consistently earn abnormal gains by analysing the historical prices. Hence one should not be able to make a profit from using something that everybody else knows.

The study reports that all the stocks in these selected indices are fundamentally strong and their prices are not influenced largely by historical prices and other relevant factors which came from industry and any other information that is publically available. Thus it can be concluded that the Indian stock market was informationally efficient and no investor can usurp any privileged information to make abnormal profits.

Where past studies have examined the weak‐form of efficiency of various markets and the effect of globalisation and global financial crisis on the various sectors of developing and emerging economies, this paper attempts to study the weak form of efficiency of the Indian capital market in the period of recession in the form of random walk.