This study aims to contribute to the literature on organizational sustainability and explore the issues, challenges, and prospects of the Bangladesh pharmaceutical industry in terms of sustainability. Organizations around the world are becoming more and more concerned about their ability to reduce or prevent the adverse effects of their actions. Sustainability, socially responsible human resource management (SRHRM), green human resource management (GHRM), characteristics of stakeholders, voluntary green behavior, innovative behavior, and government roles are the main areas of concern. Research and existing literature are explored to have a clear grasp of several organizational sustainability components. This study also shows how organizations understand and implement organizational sustainability concerns and difficulties and how to identify whether essential systemic components are still unaddressed. It's difficult to balance social, economic, and environmental responsibility with sustainability. Enhance SRHRM and GHRM by examining stakeholder characteristics and the government's involvement, combined with voluntary green and creative behavior, to create a positive image for society, the economy, and the environment. This research explores the issues, challenges, and prospects of Bangladesh's pharmaceutical industry's organizational sustainability. In contrast to the age of industry 4.0, artificial intelligence and machine learning concerns about human resource management, stakeholder characteristics, government role, and employee behavior contribute to the organizational sustainability of the pharmaceutical sector. The study presents first insights on the issues, challenges, and prospects of Bangladesh's pharmaceutical industry from the perspective of organizational sustainability. Sustainability, employee green and innovative behavior, SRHRM, GHRM, stakeholder characteristics, and government roles are drawing the attention of organizations globally. They are taking more and more responsibility for how their actions affect the environment, society, and the economy.

The bullwhip effect happens whenever the demand order fluctuations in the supply chain (SC) escalate as they are transferred up the SC. In fact, a small change in point-of-sale demand may be interpreted by SC participants as a much bigger variability in demand. This looks like a cracking a whip, where a small flick of the wrist may yield a large motion at the end of the whip. Misstate data from one side of a SC to the other part may yield substantial sloppiness. This includes increase in inventories, shortage of cash flow, weak customer satisfaction, etc. Enterprises may efficiently reduce the bullwhip effect by completely learning its root causes. This paper presents an overview on the concept and recent development of the bullwhip effect.

The aim of the study was to examine the impact of logistics drivers on the operational performance of large manufacturing industries in Ethiopia. To this end, the researchers employed an explanatory research design with a mixed approach. To select the case industries the researchers used the census sampling technique and stratified sampling technique for respondents’ selection. Both primary and secondary data were accessed about the issue to analyze. After the data collection process, the study used a partial least square structural equation model (PLS-SEM) to find the relationships between the hypothesized constructs. In this study, inferential analysis was made with the support of smart PLS software. The result of the finding depicts that the product of R2 Value specifies that 69.1% of enhancement in an endogenous variable (i.e., Operational performance) is caused by the joint results from the exogenous variable (i.e. Transportation, Inventory, and facility Management). The finding also shows that there are raw materials and currency problems that made the industries produce below their capacity.

The capital market, as one of the main components of the financial markets, plays an important role in the economic development of countries. As financial markets become more globalized through the free flow of capital and international trade, price fluctuations in financial assets also affect other assets and markets. Due to the high impact of foreign exchange, gold, and oil markets on other financial markets, this study examined the impact of these markets on the Tehran Stock Exchange market from April 2015 to March 2021. In this regard, US dollar, ounces of gold, and crude oil are used as independent variables, and Tehran Price Index (TEPIX) is considered as a dependent variable. The results of the final model indicate that the prices of dollars, gold, and oil had significant effects on the total price index of the Tehran Stock Exchange.

The capital market is one of the most critical pillars of financial markets, which plays an important role in countries' economies. With the increasing development of international trade and the free flow of capital, financial asset prices spread to other assets. The extend is increasing with the expansion of communication networks as well as the close connection of financial markets to each other. Therefore, the state of a market is not only specific to itself but also affects other financial markets. Global oil, gold, and basic metal prices, including palladium, significantly influence the American financial markets. Due to the importance of the Nasdaq index for investors' investment decisions, this study analyzes the impact of oil, gold, and palladium metal prices on the Nasdaq index monthly between 2016 and 2021. Based on the final model, the oil and gold markets significantly impact the Nasdaq index, while palladium has a less significant impact.

To calculate the change in the values of efficiency and productivity of public hospitals in Greece during the period 2015 to 2019. The calculation of the efficiency values includes the technical, pure and scale efficiency using the window-DEA method and the productivity change using the Malmquist index. The source of the data used to calculate the change in the efficiency of Greek public hospitals is the statistical databases of the Greek Ministry of Health that have resulted from the collection of data through Information Systems in combination with data provided by the Greek Statistical Authority (ELSTAT). The design of the study was based on the realization of a Window DEA study and the calculation of the Malmquist index with its components. The study was designed to measure the change in efficiency and productivity but over a relatively long period of time. The data were obtained from the databases of both financial and operational data of Greek Public Hospitals held by the Greek Ministry of Health and located on the Ministry’s website. Also, additional data were requested and obtained from ELSTAT. The data were examined and those which were appropriate for the conduct of the study were selected. The technical efficiency of Greek hospitals follows a slightly upward trend with ups and downs. Their pure efficiency follows a steady course with ups and downs. Scale efficiency is on an upward course. Productivity exhibits an overall negligible change. The research’s fluctuation of the inputs and outputs determines the change in the values of efficiency and, in combination with technological change, of productivity. During the period under study, the best placement of Greek hospitals on the scale is achieved in terms of their size. Hospital management cannot achieve better utilization of resources. A parallel increase of some of the inputs and outputs prevents increase in the values of technical and pure efficiency. The change in efficiency constrains the change in productivity.

Emergency medical services (EMS) stations reduce mortality and irreparable damage from injuries through the timely treatment of patients. After performing the initial measures at the scene of the accident, if necessary, they transfer the patient to the hospital. In such cases, the goal is to save human lives. Thus, suggestions and solutions that can improve the performance of these centers are very welcome. One of the most important parameters in providing high-quality EMS is the timing of these services. Therefore, the location of these centers plays a key role in diminishing the response time to demand. In that regard, the location of these centers in cities, especially large and densely populated cities, is very important. In this study, in order to answer the mentioned questions, a linear mathematical model based on the maximum expected coverage model is presented. In this model, by considering the relative coverage conditions, the best locations in the city, as well as the coverage of demand points and distance traveled by the vehicles will be obtained. Furthermore, robust optimization (RO) is used to provide better situations for the operation of the model. Finally, according to the results, it is found that the proposed model has a better resolution time than nonlinear models and is also able to solve cases with high input data. The proposed model is implemented in District 10 of Tehran, Iran.

The purpose of this study is to analyze the effect of green innovation (GI) on environmental performance (EI), the effect of green supply chain management (GSCM) on GI, and finally the effect of GI on environmental performance (EP) in the digital era. The study uses quantitative research methods and descriptive statistics. The sampling technique used is non-probability sampling with the type of purposive sampling. Respondents used in this study were 190 employees of small and medium enterprises (SMEs). Data was obtained by distributing online questionnaires through social media. The analytical technique used in this research is based on descriptive analysis and Structural Equation Model (SEM) using Partial Least Square (PLS). The results of this study indicate that GSCM practices have a significant positive effect on EI, GSCM practices have a significant positive effect on GI, GI has a significant positive effect on EP and, finally, GI mediates the relationship between GSCM and EP.

This study aims to explain the partial and simultaneous effect of green innovation, competitive advantage and empowerment on marketing performance. Quantitative research method is used through data collection from online surveys with a sample of 130 respondents. Green innovation variable with innovation indicators in processes and products, competitive advantage variable with unique product indicators, flexibility, customer relationships and empowerment variables are measured from the dimensions of market development, knowledge and facilities. The marketing performance variable is expressed in three dimensions, namely sales, growth, and market share. The measurement of respondents' answers to the questions in the questionnaire adopts a Likert scale consisting of seven alternative answers. Furthermore, the results of the questionnaire are analyzed using multiple linear regression with the help of the SPSS program. The results of the research indicate that competitive advantage and empowerment have a positive and significant effect on marketing performance, however, green innovation has a negative and insignificant effect on marketing performance. Simultaneously there is a positive and significant effect of green innovation, excellence competitiveness and empowerment on SME marketing performance.

Liver disease indicates inflammatory condition of the liver, liver cirrhosis, cancer, or an overload of toxic substances. A liver transplant may reinstate and extend life if a patient has severe liver disease. In the last few years, machine learning (ML) based diagnosis systems have played a vital role in assessing liver patients which eventually leads to proper treatment and saves human life. In this study, we try to predict liver patients by adopting a hybrid feature extraction method to enhance the performance of the ML algorithm. Medical data frequently exhibits non-linear patterns and class imbalances. This is undesirable for the majority of ML algorithms and degrades performance. Here, we present a hybrid feature space that combines t-SNE, Isomap nonlinear features, and kernel principal components that can explain 90% of the variation in the data as a solution to this issue. Before feeding the ML model, data preprocessing techniques including class balancing, identifying outliers, and impute missing values are used. A simulation study and ensemble learning also conducted to justify the proposed prediction performances. Our suggested hybrid non-linear feature exhibits a 2-20 % improvement over existing studies and the ensemble classifier achieved an ideal and outstanding accuracy of 91.33 %.