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 %.

In this paper, various classical time series forecasting methods were compared to determine the forecasting method with the highest accuracy in predicting demand of the 50cl product of a bottled water supply chain. The classical time series forecasting methods compared are the moving average, weighted moving average, exponential smoothing, adjusted exponential smoothing, linear trend line, Holt’s model, and Winter’s model. These methods were evaluated to determine the method with the least Mean Absolute Deviation (MAD) value and hence the highest forecasting accuracy. From the results, the weighted moving average forecasting method had the lowest MAD value of 1,987, making it the forecasting method with the highest accuracy for predicting the 50cl bottled water demand. While the exponential smoothing forecasting method had the highest MAD value of 2,483, making it the forecasting method with the least accuracy for predicting the 50cl bottled water demand. This research provides a procedure for aiding supply chain analysts in implementing demand forecasting using classical time series forecasting models.