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.

Globally, factors influencing the residential real estate market are of great importance. This study examines the influence of theory of planned behavior constructs as well as introduces uninterrupted electricity supply as electricity availability construct in place of the locational attributes on intention to invest in residential real estate. In addition to the mediation effects of attitude and perceived behavioral control on the relationships between electricity availability and intention to invest in real estate property. The data for this study were obtained from the residents of government-reserved areas in Akure, Ondo State, Nigeria. Based on random sampling techniques, a total of 132 questionnaires were useful for this study. The results established the indirect-only mediation and the no-effect non-mediation for both paths via attitude and perceived behavioral control respectively. Among other results, the study concluded that attitude is a reliable antecedent of behavioral intention in making a decision to invest in residential real estate in the study area.

The FMCG sector in developing nations is still not prepared to withstand any disruption brought on by the worldwide pandemic. In order to adapt to the new normal, businesses must make both micro and broad changes to their supply chain strategy. The goal of this research is to create plans to minimize any disruptions caused by upcoming pandemics. To restore the broken supply chain, a number of new implications and adjustments to the current attributes were proposed in the areas of sourcing, manufacturing, and distribution. Finding the fundamental drivers that are frequently impacted by the disturbance is part of the technique. The afflicted locations were the focus of the models' development. The ideas work as preventative measures intended to thwart the disturbance when and if it happens. In order to assess the model's viability, the Key Performance Indicators (KPI) value was ultimately retrieved with the aid of 25 industry experts. These suggestions may result in improved transparency, real-time monitoring, cost effectiveness, and responsiveness, among other benefits. Our analysis indicates that the KPI scores for procurement, production, and distribution are 92.86%, 82.14%, and 87.50%, respectively. The models' total viability is 87.50%. The most recent Covid-19 pandemic has provided us with a vivid illustration of what could go wrong in such circumstances. In both pandemic and non-pandemic conditions, the adaptation of stated suggestions at the aspect of sourcing, production, and distribution might result in a significant shift to organization-wide activities.

Climate change and global warming have led to many risks and changes for the Earth, including the increase in natural fires, floods, air pollution, unusual seasons, etc. The increase in the trend of global warming may bring many countries underwater. For instance, the populous Asian nation of Bangladesh is most vulnerable to rising sea levels. It is estimated that a rise of just one meter in sea level is enough to submerge 30,000 square kilometres of the coastal areas of Bangladesh and displace 15 million people. Therefore, it is crucial to determine the effects of different factors on global warming and take possible actions to reduce the damage. Among various factors, natural fires are believed to be responsible for up to 20% of greenhouse gas production in the world. The source of 80% of fresh water in the United States is believed to be forest lands, which means the effect of natural fires can be disastrous not only for drinking water but also for aquatic habitats. In this paper, we present a survey on the impacts of forest fires on global warming.

The evolving technology has pushed machine learning techniques to replace human smartness. A machine learning model is capable of learning and replicating like our brain. This approach of data-driven model is implemented to predict the cutting force in machining of Ti6Al4V. Titanium alloys are commonly used in high strength applications due to their excellent properties. These same properties make the machining of the titanium alloy complicated. An attempt has been made for finding the cutting force under minimum quantity lubrication (MQL). MQL is a sustainable manufacturing-based lubrication system. Taguchi’s approach was used to attain a full factorial design for combination of different parameters. Accordingly, a neural network (NN) model was developed which was capable of predicting cutting forces based on the trained model. The proposed model could be implemented to find optimal parameters in shortest duration, thereby eliminating the need for experimental computations.

Land subsidence in Iran is one of the environmental issues of recent years in Iran, which is mostly due to shortage of the groundwater resources. Iran is considered as the country with the most land subsidence. The amount of land subsidence in Iran is believed to be far more than the average of developed countries. Presently, over 300 plains of Iran are suffering from land subsidence and in some plains of Iran, the conditions have passed the subsidence and the ground has entered the critical stage of creating sinkholes. This paper presents an overview of the current status and challenging issues of land subsidence in Iran.