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Workplace spirituality is presently a prominent research topic and is gaining recognition and importance among industry professionals and academicians. Workplace spirituality is defined as a sense of community, meaningful work and organizational values. The purpose of this research paper is to investigate the relationship between workplace spirituality and mental health, wherein employee engagement is considered as a mediator. Furthermore, this study examines the mediating role of employee engagement in the relationship between organizational justice and mental health.

Data were gathered from 344 information technology professionals working in India. Structural equation modelling was used to evaluate the model fit of workplace spirituality and its relationship to employee engagement, organizational justice and mental health.

The results revealed that workplace spirituality and organizational justice significantly and positively predict employee engagement, which is significantly related to employee mental health. The results also revealed that employee engagement significantly partially mediates the relationship between workplace spirituality and mental health as well as the relationship between organizational justice and mental health.

Results of research guide HR professionals, employee mental health concerns can be addressed by promoting workplace spirituality, improving employee engagement strategies and implementing organizational justice policies that are perceived to be fair. This study makes a significant contribution to the extant literature regarding mental health issues in the IT sector.

Findings of this research contribute to the area of human resource management and employee engagement. The current study fills a gap in the extant literature by investigating employee engagement intervening mechanism between organizational justice, workplace spirituality and mental health.

Employee well-being is increasingly relevant and crucial for organizational success. As work engagement and employee well-being affect employee performance, this area is the focus of increasing attention both from scholars and industry professionals. The main objective of the present research study is to investigate the mediating role of work engagement on the relationship between mindfulness at work, organizational justice and employee well-being.

The study was conducted on information technology (IT) employees in India, and 331 complete responses were collected for the data analysis. The cross-sectional data were collected through purposive sampling. Structural equation modeling (SEM) was applied to evaluate the proposed research hypotheses.

The findings support the convergent and discriminant validities of mindfulness, organizational justice, work engagement and employee well-being. The results indicate that mindfulness and organizational justice have an indirect relationship with employee well-being. In addition, the study demonstrates that work engagement significantly mediates the relationship between mindfulness and employee well-being as well as between organizational justice and employee well-being.

The findings will help organizations and human resources (HR) departments to understand the importance of work engagement and employee well-being in the workplace.

The mediating effect of work engagement between the workplace mindfulness–organizational justice relationship on employee well-being is addressed by drawing on conservation of resources (COR) and job demand–resource (JD–R) theories. Prior research has exclusively studied the relationship of employee well-being with either mindfulness or organizational justice. This research provides empirical insights regarding the fact that both mindfulness and organizational justice simultaneously have a relationship with employee well-being.

Businesses need to make quick decisions and adjustments to fulfill the growing online demand. Previous studies examined various factors affecting the online sales performance of products such as books, electronics and movies; however, they paid limited attention toward the local brand clothing products. The current study investigates the importance of different kinds of seller-generated and consumer-generated signals such as price, discount, product ratings, review volume, review sentiment, number of questions and interaction between some of these factors for predicting the sales performance of clothing products.

The multiple linear regressions has been employed to investigate the influence of various predictor variables on sales performance. The study also examines the importance of these predictor variables by using different machine learning models, including random forest (RF), neural networks and support vector regression (SVR).

The findings of the study emphasize the importance of price and discount rates offered on the product. The quantitative characteristics of reviews, such as review volume and average rating, have been found to be more important predictors than sentiment strengths. However, the sentiment strength of reviews with higher helpfulness scores plays a significant role in predicting sales performance.

The study highlights the varying importance of seller-based and consumer-based signals in predicting sales performance. It also investigates the interaction effect of these two kinds of signals. The consumer-generated signals have been further divided into two components based on social influence theory, and the interaction effects of these components have also been examined.

The purpose of this paper is to study the deformation of a rotating generalized thermoelastic medium with two temperatures under hydrostatic initial stress subjected to different types of sources.

The methodology applied here is the use of integral transforms to obtain the components of displacement, force stress, conductive temperature and temperature distribution in Laplace and Fourier domain. The general solution obtained is applied to a specific problem of a half‐space subjected to concentrated force, uniformly distributed force and a moving source. These components are then obtained in the physical domain by applying a numerical inversion method. Some particular cases are also discussed in the context of the problem. The results obtained are also presented graphically to show the effect of rotation and gravity.

The variations of all the quantities and for all the mediums are similar for concentrated force and distributed forces applied along the free surface of the solid. The values of these quantities are very close to each other for GTES and GTESWG. Deformation of a body depends on the nature of force applied as well as the type of boundary conditions. The variations of all the quantities are more uniform in nature when a force of constant magnitude moves along the surface of solid with some velocity.

Such types of problems in rotating media will find great applications in many dynamical systems and industries.

The purpose of this paper is to determine the use of BLA along with SiC as economical reinforcements to enhance the mechanical behavior of hybrid composite. The purpose of this research is the development of cost-effective aluminum hybrid metal matrix composites.

The present research work investigation evaluated the mechanical properties of Al-4.5%Cu alloy, Al-4.5Cu/10SiC, Al-4.5Cu/10SiC/2BLA and Al-4.5Cu/10SiC/4BLA composites by the Stir casting method. The fabricated composites were analyzed using optical microscopy (OM), scanning electron microscopy (SEM), and hardness and tensile test.

The microstructure modification with the addition of reinforcement particles in the matrix alloy and clear interface in between matrix and particles are observed. The density of the composite increased with the addition of SiC and decreased with the addition of BLA in comparison with that of matrix alloy. The hardness and tensile strength of the single-reinforced composite and hybrid composites improved with the addition of reinforcement particles. The strengthening of composites was due to load-bearing capacity of reinforcement particles over the matrix alloy and increased dislocation density of composites materials. The tensile failure mechanism of the composites is reveled with SEM analysis.

The papers reports the development of cost-effective and light weight aluminum hybrid composites with remarkable enhancement in the mechanical and tribological properties with the addition of BLA as economical reinforcement along with SiC.

The density, hardness and tensile values of fabricated aluminium composites were presented in this paper for the use in the engineering applications where the weight and cost are consider as a primary factors.

In present study, a full-scale testing of reinforced concrete (RC) frame sub-assemblage has been investigated under fire subsequent to simulated seismic loading. First part of the sequential loading consisted of a quasi-static cyclic lateral loading corresponds to life safety level of structural performance on the test frame. In the second part of the test, a compartment fire was ignited to the pre-damaged test frame for one hour duration simulating fire following earthquake (FFE) scenario. The results showed that the first cracking was observed at the end joints of the roof beams after the frame experienced a 30 mm cyclic lateral displacement. One hour heating and eleven hour cooling was tracked and temperatures were recorded. A knocking sound was heard from the fire compartment after 5 minutes of the fire ignition. An excessive degradation of the concrete material at a number of locations of the frame sub-assemblage was observed during visual inspection after the fire test. The Nondestructive tests (NDT) were also conducted to ascertain the damage in the RC frame at the various stages of loading. The test results developed an understanding of the behaviour of RC frame sub-assemblage in FFE.

Association rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.

The primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.

The experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.

Data mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

Nowadays, ample industries are fascinated to look for high strength and light weight materials for the development of robust parts. Because of light weight and high stiffness to weight ratio; usage of aluminum parts is growing rapidly, especially in automotive engineering. Process improvement of Al alloys and their grain structure refinement is the current area of interest in casting companies. In this research work, an investigation has been carried out to enhance the process improvement of die casting by optimization of various significant parameters and their refinement of grains by the effect of Nb-C novel grain refiner.

L27 orthogonal array (OA) has been considered to optimize the preferred casting input parameters such as molten metal temperature (°C), die temperature (°C), injection pressure (bar), Al-3.5Nb-1.5 C novel grain refiner and Ni alloying additions as key process parameters in order to increase the quality and efficiency of Al-9Si-3Cu aluminum alloy die casting by reducing the porosity formation.

It was observed that the porosity values have significantly decreased from 0.88% to 0.25% particularly at 0.1 wt.% of new grain refiner and 0.5 wt. % of Al-6Ni master alloy. As per the ANOVA results, it was observed that Al-3.5FeNb-1.5 C grain refiner (F value 2609.22), Al-6Ni alloying addition (F value 1329.13), molten metal temperature (F value 1002.43) and, injection pressure (F value 448.06) are the factors that significantly affects the porosity, whereas die temperature was found to be insignificant. The results show that new grain refiner is one the most significant factor among the other selected parameters. The contribution of the new grain refiner to the variation of mean casting porosity is around 57.74%. confidence interval (CI) has also been estimated as 0.013 for 95% consistency level to validate the predicted range of optimum casting porosity of aforesaid alloy.

To the best of the authors' knowledge, no study has been conducted in the past to investigate the combined effect of these die casting parameters and composition factors for the development of Al-Si robust cast parts. The paper represents original research and provides new information for the fabrication of die casting parts.

The International Journal of Organizational Analysis (IJOA) is a leading journal that has published high-quality research focused on various facets of organizational analysis since 1993. This paper aims to conduct a retrospective analysis of the IJOA journey from 2005 to 2020.

The data used in this study was extracted using the Scopus database. The bibliometric analysis, using several indicators, is adopted to reveal the major trends and themes of the journal. The mapping of bibliographic data is carried using VOSviewer and Biblioshiny.

The study findings indicate that IJOA has grown for publications and citations since its inception. Five significant research directions emerged, i.e. organizational diagnostics, organization citizenship behaviour, organizational commitment to employee retention, psychological capital and firm performance, based on cluster analysis of IJOA’s publications.

To the best of the authors’ knowledge, this is the first study to conduct a comprehensive bibliometric analysis of IJOA. The study presents the key themes and trends emerging from a leading journal, considered a high-quality journal, for researching various facets of organizational functioning by academicians, scholars and practitioners.

The rise in demand and high utilization of fuel causes severe environmental threat for the nations on the globe. Rapid burning potential of hydrogen produces enormous amount of thrust, and it is mainly owing to wide flame range and less onset of ignition.

The significant contribution of hydrogen as fuel has been explored by several researchers around the globe recently to use in aviation sector owing to its eco-friendly nature. Hydrogen is a safe and clean fuel, and it can be generated from several sources. The effects of addition on hydrogen on gas turbine on combustion characteristics and emission concentration level on atmosphere have been reviewed in this paper.

Incorporation of hydrogen is effective reducing nitrous oxide emission, high calorific value and flame less combustion. Addition of hydrogen to higher proportions enhances the combustion performance, minimizing the setbacks of conventional fuel and meets the specified standards on emission.

From the literature review, the comparative study on hydrogen with other fuel is explained. This paper concludes that addition of hydrogen in fuel enhances the performance of combustion on gas turbine engine along with significant reduction in emission levels.