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This research paper reports the findings of the first comprehensive survey of senior executives in Iran’s teaching hospitals. It is based on an analysis identifying the continuing professional development (CPD) needs of the total population of the two senior levels of teaching hospitals management‐presidents of physician‐managers and administrative‐managers. Four key areas of management knowledge were selected as the focus of the need identification: operational; financial; human resource; and organization change. The findings reveal a pent up demand for introductory level, formal knowledge in all four areas of management theory, to complement and extend the practical experience they have acquired in managing the complex environment of teaching hospitals. The paper goes on to propose a curriculum design and overall framework of provision to meet these genuinely felt CPD needs.

The purpose of the study is to assess manufacturing firm performance indicators using a reasonably comprehensive integrated BSC-Game model to empirically determine the importance of the perspectives and indicators under evaluation and the best combination of indicators.

After identification of manufacturing indicators of the firm, the Shapley value index is used as the solution of the cooperative game to determine the importance of the perspectives and indicators under evaluation and the best combination of indicators to facilitate the achievement of target goals.

The game theory approach is used as a technique to rank BSC perspectives of the firm's manufacturing performance. The results reveal that the customers' perspective receives the highest ranking. The knowledge sharing, new technology, customer satisfaction and sale profitability are considered as the best combination of BSC indicators.

The integrated BSC-Game framework, approach and outcomes can be beneficial to practitioners and researchers who aim to select the proper indicators’ policy in manufacturing performance field.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

Petroleum hydrocarbons are naturally occurring flammable fossil fuels used as conventional energy sources. It has carcinogenic, mutagenic properties and is considered a hazardous pollutant. Soil contaminated with petroleum hydrocarbons adversely affects the properties of soil. This paper aim to remove pollutants from the environment is an urgent need of the hour to maintain the proper functioning of soil ecosystems.

The ability of micro-organisms to degrade petroleum hydrocarbons makes it possible to use these microorganisms to clean the environment from petroleum pollution. For preparing this review, research papers and review articles related to petroleum hydrocarbons degradation by micro-organisms were collected from journals and various search engines.

Various physical and chemical methods are used for remediation of petroleum hydrocarbons contaminants. However, these methods have several disadvantages. This paper will discuss a novel understanding of petroleum hydrocarbons degradation and how micro-organisms help in petroleum-contaminated soil restoration. Bioremediation is recognized as the most environment-friendly technique for remediation. The research studies demonstrated that bacterial consortium have high biodegradation rate of petroleum hydrocarbons ranging from 83% to 89%.

Proper management of petroleum hydrocarbons pollutants from the environment is necessary because of their toxicity effects on human and environmental health.

This paper discussed novel mechanisms adopted by bacteria for biodegradation of petroleum hydrocarbons, aerobic and anaerobic biodegradation pathways, genes and enzymes involved in petroleum hydrocarbons biodegradation.

In response to scholarly calls, this study aims to explore the persuasive predictors including (job security, autonomy, work-life balance, culture and climate, career growth, leadership style and support, training and development and reward, recognition and pay) of talent retention in Indian PSUs by emphasising the commonalities and differences between different category of employees from different cohorts based on their contribution in the prime function of the business.

A self-administered questionnaire was used to collect data from 472 employees from selected organisations through Multistage sampling technique and Simple random sampling technique. Data were analysed using tests such as logistic regression, MANOVA, post hoc Tukey in SPSS 20.

Job security, autonomy, career growth and culture and climate are the persuasive predictors of talent retention in Indian PSUs. However, there is a significant interaction effect of age and job standard on job security. Job security and perceived employability do not relate to age for core category employees rather possess identical perception than other employee category. Findings indicate that identifying the position to be retained is more viable than a common retention strategy.

This study contributes to the existing knowledge of talent retention by exploring novel insights in comprehending the commonalities and differences of different employee category at all level from different cohorts, sharing different responsibilities in the prime function of the business that has largely been ignored. The study can succour in improving performance and cost-effective retention strategy.