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The purpose of the study is to developed the effect of Nusselt number on impeller diameter in agitated vessel, which is beneficial to find out the heat transfer coefficient in the process industry. A comparison has been done between the experimental and calculated Nusselt numbers with standard deviation found to be 8.03 per cent.

For studying the effect of impeller diameter on Nusselt Number, the heat transfer measurements were made with three different impellers of diameter. Although the diameter of impeller, D_a shows its effect in Reynolds number, an attempt has been made to find the relationship between the impeller diameter and Nusselt number. A correlation between (N_Nuj/N^″_Pra^1/3 N^″_Rea^2/3) vs D_a/D_T and (N_Nuoc/N^″_Pra^1/3 N^″_Rea^2/3) vs D_a/D_c in which data of three fluids [1, 2 and 4 per cent carboxy methyl cellulose solution of A type (CMC-A) solutions] have been plotted.

The heat transfer data for agitated Newtonian and non-Newtonian fluids have been successfully correlated by using the viscosity of the fluid evaluated at the impeller tip assuming a cylinder of diameter equal to that of impeller rotating in an infinite fluid. Data of 1, 2 and 4 per cent CMC-A, for three impeller diameters, have been correlated by equations. Using the above concepts of Reynolds and Prandtl numbers, Nusselt Numbers and D_a/D_T, it is also possible to correlate the available published data for other non-Newtonian fluids obtained with different impeller geometries.

A set up was made for studying the effect of impeller diameter, the heat transfer measurements were made with three impellers of diameter 7.5, 12.7 and 18.35 cm respectively. Although the diameter of impeller, Da shows its effect is Reynolds number, an attempt has been made to find the effect of D_a/D_T ratio on Nusselt number.

Chronic diseases are considered as one of the serious concerns and threats to public health across the globe. Diseases such as chronic diabetes mellitus (CDM), cardio vasculardisease (CVD) and chronic kidney disease (CKD) are major chronic diseases responsible for millions of death. Each of these diseases is considered as a risk factor for the other two diseases. Therefore, noteworthy attention is being paid to reduce the risk of these diseases. A gigantic amount of medical data is generated in digital form from smart healthcare appliances in the current era. Although numerous machine learning (ML) algorithms are proposed for the early prediction of chronic diseases, these algorithmic models are neither generalized nor adaptive when the model is imposed on new disease datasets. Hence, these algorithms have to process a huge amount of disease data iteratively until the model converges. This limitation may make it difficult for ML models to fit and produce imprecise results. A single algorithm may not yield accurate results. Nonetheless, an ensemble of classifiers built from multiple models, that works based on a voting principle has been successfully applied to solve many classification tasks. The purpose of this paper is to make early prediction of chronic diseases using hybrid generative regression based deep intelligence network (HGRDIN) model.

In the proposed paper generative regression (GR) model is used in combination with deep neural network (DNN) for the early prediction of chronic disease. The GR model will obtain prior knowledge about the labelled data by analyzing the correlation between features and class labels. Hence, the weight assignment process of DNN is influenced by the relationship between attributes rather than random assignment. The knowledge obtained through these processes is passed as input to the DNN network for further prediction. Since the inference about the input data instances is drawn at the DNN through the GR model, the model is named as hybrid generative regression-based deep intelligence network (HGRDIN).

The credibility of the implemented approach is rigorously validated using various parameters such as accuracy, precision, recall, F score and area under the curve (AUC) score. During the training phase, the proposed algorithm is constantly regularized using the elastic net regularization technique and also hyper-tuned using the various parameters such as momentum and learning rate to minimize the misprediction rate. The experimental results illustrate that the proposed approach predicted the chronic disease with a minimal error by avoiding the possible overfitting and local minima problems. The result obtained with the proposed approach is also compared with the various traditional approaches.

Usually, the diagnostic data are multi-dimension in nature where the performance of the ML algorithm will degrade due to the data overfitting, curse of dimensionality issues. The result obtained through the experiment has achieved an average accuracy of 95%. Hence, analysis can be made further to improve predictive accuracy by overcoming the curse of dimensionality issues.

The proposed ML model can mimic the behavior of the doctor's brain. These algorithms have the capability to replace clinical tasks. The accurate result obtained through the innovative algorithms can free the physician from the mundane care and practices so that the physician can focus more on the complex issues.

Utilizing the proposed predictive model at the decision-making level for the early prediction of the disease is considered as a promising change towards the healthcare sector. The global burden of chronic disease can be reduced at an exceptional level through these approaches.

In the proposed HGRDIN model, the concept of transfer learning approach is used where the knowledge acquired through the GR process is applied on DNN that identified the possible relationship between the dependent and independent feature variables by mapping the chronic data instances to its corresponding target class before it is being passed as input to the DNN network. Hence, the result of the experiments illustrated that the proposed approach obtained superior performance in terms of various validation parameters than the existing conventional techniques.

In highly competitive environments, sustainability positioning is crucial for firms, as they are evaluated based on their sustainable practices. This study aims to draw on the legitimacy and information asymmetry theories to explore attributes that impact business-to-business (B2B) sustainability positioning in emerging economies, such as India, within the service industry.

This study used a mixed-methods exploratory research design to examine the attributes affecting sustainability positioning. In the first phase of the study, a qualitative research method was used to explore the attributes influencing B2B sustainability positioning. In the second phase, this study used these factors to develop a structural model.

A variety of attributes was critical in assessing the sustainability positioning of B2B firms. This study identified a number of factors that explain the attributes affecting sustainability positioning in B2B markets. Some of them included environmental consciousness and external assurance.

This study significantly contributes to the theoretical discourse on sustainable practices in B2B businesses in multiple ways. First, it provides empirical data on the relationship between firms’ environmental consciousness and sustainability positioning in the B2B context, thereby adding to and expanding the current literature on this topic. Second, this study investigates the impact of external assurance on B2B firms’ sustainability positioning and shows how it can enhance credibility, transparency and accountability. Finally, it analyzes sustainable positioning in the service sector, specifically in India, thereby contributing to the body of knowledge on this topic.

The real challenge in the solution of contact problems is the lack of an optimal adaptive scheme. As the contact zone is a priori unknown, successive refinement and iterative method are necessary to obtain a high-accuracy solution. The purpose of this paper is to provide an optimal adaptive scheme based on second-generation finite element wavelets for the solution of non-linear variational inequality of the contact problem.

To generate an elementary multi-resolution mesh, the authors used hierarchical bases (HB) composed of Lagrange finite element interpolation functions. These HB functions are customized using second-generation wavelet techniques for a fast convergence rate. At each step of the algorithm, the active set method along with mesh adaptation is used for solving the constrained minimization problem of contact case. Wavelet coefficients-based error indicators are used, and computation is focused on mesh zones with a high error indication. The authors take advantage of the wavelet transform to develop a parameter-free adaptive scheme to generate an appropriate and optimal mesh.

Adaptive wavelet Galerkin scheme (AWGS), a newly developed method for multi-scale mesh adaptivity in this work, is a combination of the second-generation wavelet transform and finite element method and significantly improves the accuracy of the results without approximating an additional problem of error estimation equations. A comparative study is performed taking a solution on a highly refined mesh and results are generated using AWGS.

The proposed adaptive technique can be utilized in the simulation of mechanical and biomechanical structures where multiple bodies come into contact with each other. The algorithm of the method is easy to implement and found to be successful in producing a sufficiently accurate solution with relatively less number of mesh nodes.

Although many error estimation techniques have been developed over the past several years to solve contact problems adaptively, because of boundary non-linearity development, a reliable error estimator needs further investigation. The present study attempts to resolve this problem without having to recompute the entire solution on a new mesh.

Currency usage either in the physical or electronic marketplace through chip-based or magnetic strip-based plastic card becoming the vulnerable point for the handlers. Proper education and awareness can only thrive when concrete fraud detection techniques are being suggested together with potential mitigation possibilities. The purpose of this research study is tendering in the same direction with a suitable plan of action in developing the authentication strength metric to give weightage marks for authentication techniques.

In this research study, a qualitative in-depth exploration approach is being adapted for a better description, interpretation, conceptualization for attaining exhaustive insights into specific notions. A concrete method of observation is being adopted to study various time boxed reports on plastic card fraud and its possible impacts. Content and narrative analysis are being followed to interpret more qualitative and less quantitative story about existing fraud detection techniques. Moreover, an authentication strength metric is being developed on the basis of time, cost and human interactions.

The archived data narrated in various published research articles represent the local and global environment and the need for plastic card money. It gives the breathing sense and capabilities in the marketplace. The authentication strength metric gives a supporting hand for more solidification of the authentication technique with respect to the time, cost and human ease.

The research study is well controlled and sufficient interpretive. The empirical representation of authentication technique and fraud detection technique identification and suggestive mitigation gives this research study an implication view for the imbibing research youths. An application and metric based pathway of this research study provides a smoother way to tackle futuristic issues and challenges.

This research study represents comprehensive knowledge about the causes of the notion of plastic card fraud. The authentication strength metric represents the novelty of a research study which produced on the basis of rigorous documentary and classified research analysis. The creativity of the research study is rendering the profound and thoughtful reflection of the novel dimension in the same domain.

The purpose of this paper is to investigate the relationship between diversity management (i.e. diversity-focused human resource (HR) practices related to recruitment and selection, training and development, performance appraisal and compensation) and perceived firm performance, and the mediating effect of procedural justice.

Primary data based on 400 respondents of 162 organizations operating in India were analysed using statistical tools such as factor analysis, analysis of variance (ANOVA) and multiple regression analysis.

Multiple regression analysis indicated that diversity-focused HR practices had a positive association with perceived firm performance. Further, it was found that procedural justice played a partially mediating in the relationship between diversity-focused HR practices and perceived firm performance.

This paper relied on self-report surveys for data collection, and there laid a possibility of common method variance in the result findings. Hence, future studies should collect data from multiple sources by using multiple methods (e.g. interviews, surveys, peer reports, etc.).

The first implication highlights that senior management’s support is a prerequisite to execute justice-based diversity management processes, which in turn aid in harvesting the true potentials of diversity. Second, organizations should adopt an egalitarian approach while formulating and implementing diversity management initiatives to accentuate the fair and just perceptibility of procedures among employees.

This study sheds new light on the effects of diversity-focused HR practices on firm performance (perceived) in Indian context.

This paper presents a cross-sectional study that assessed the impact of the COVID-19 pandemic on rural migrants in Bihar. The primary objective of this study was to evaluate the overall impact of the pandemic on migrants and examine their livelihoods, with a focus on identifying measures that can mitigate the economic consequences.

This study used a telephonic survey to collect primary data from 419 respondents. Descriptive statistics were used to analyze the data, and three indices were constructed: fear and worries, trust and prevention.

The findings provide insights into the psychological well-being of migrant workers and highlight the challenges they face in sustaining their livelihoods amidst the pandemic. This study concludes by suggesting potential measures to alleviate the economic impact and enhance the resilience of this vulnerable population.

This study may be limited by the representativeness of the sample as well as the potential for social desirability bias. The study may also be limited by the reliability and validity of the measures used to capture the fear and worries, trust and prevention indices.

Numerous studies have examined the impact of the COVID-19 pandemic on rural migrants. However, there are limited studies that estimate the impact of the proposed study based on the challenges faced by rural migrants in Bihar during the pandemic.

The purpose of this study was to create a theoretical model by combining the technology acceptance model (TAM) with the theory of technology readiness (TR) and then empirically test it to identify the key factors influencing older citizens’ intention to adopt and use mobile health (m-health), which has emerged as a tool to facilitate health-care rights for all.

The convenience sampling method was used to collect data from 465 respondents aged 60 and up from the Delhi-National capital region of India using a questionnaire survey method. The data collected for this study were analyzed using partial least squares structural equation modeling using SmartPLS 3.0.

The study’s findings indicate that all TR components influence perceived usefulness (PU) and ease of use. The exception is discomfort, which does not affect perceived ease of use (PEOU). Furthermore, PU and PEOU influenced the older citizen’s attitude toward m-health, and attitude influenced their intention to use m-health applications.

To the best of the authors’ knowledge, this is the first study to apply the TAM in combination with TR index to examine the acceptability of m-health consulting by the older citizen in an emerging economy like India.

The purpose of this paper is to check the effects of empowering leadership on job performance of employees through the mediation of psychological empowerment.

Primary data were gathered from 418 Indian banking employees. Statistical techniques like factor analysis, multiple regressions and bootstrapping via PROCESS were used to analyze the data.

Initially, the results of multiple regression analysis revealed that empowering leadership behaviors have positive influence on psychological empowerment and job performance of subordinates. Further, bootstrap analysis revealed that the individual dimensions of psychological empowerment serially mediate the effects of empowering leadership behaviors on subordinates’ job performance.

The study indicates that the empowering leadership style is not applicable to Western culture only, but it is also very effective in high power distance cultures like of India. The major limitation of the study is that the data have been gathered from a single source. This may lead to common method variance.

In banking context, empowering leadership can improve the performance of employees by means of psychological empowerment. Training should be provided to organizational leaders in banks with special focus on facilitating the empowering behaviors among them.

The research studies on measuring the effects of empowering leadership on job performance through mediation of psychological empowerment are very limited.