Abstract: 

This study focussed on the separation of Ni(II) ions from aqueous solution using the liquid-liquid extraction technique using different concentrations of isobutyl methyl ketone (IBMK) and tributyl phosphate (TBP) on NiCl2.6H2O solutions with varying pH, while 1M H2SO4 was employed as a stripping agent. The separated layers were analysed using Atomic Absorption Spectrometer (AAS). The result showed that variation of pH has significant effects on both the extraction and stripping processes. It was observed that % extraction increased as the pH increased. And increased concentration enhanced the efficiency of liquid-liquid extraction. A better stripping process was attained using H2SO4 as a stripping agent. And an increase in the concentration of IBMK and TBP led to increasing extraction efficiency.  

Keywords:  Tributyl Phosphate; Extractants; Stripping 

Abstract: 

     This paper aims to determine the level of the company's comprehensive plan and the effective contribution to the development of the comprehensive plan to meet the economic, social, and cultural challenges witnessed by business organizations. The descriptive aspect was relied upon by a questionnaire for data collection. The study finding is the application of aggregate production planning needs to follow innovative and unconventional methods of manufacturing and production in the presence of contemporary environmental challenges. 

Keywords: Aggregate production planning (APP). Business environment.  

Abstract: 

Educational research is largely based on observational studies. The possibility of demonstrating causal relationships in such studies is under debate. However, several methods of causal analysis for such data have been developed over the past twenty years. The present research aims to identify causal relationships between the six criteria defining the summary indicator of the quality of education systems (ISQ) and its final score in 2018. For this purpose, causal Bayesian networks are used and, more specifically, directed acyclic graphs that allow the identification of causalities.

Keywords:  Bayesian networks, synthetic indicator of the quality of education systems "ISQ", directed acyclic graph, causality. 

Abstract: 

This research aims to investigate the direct effect of recycling on the values of several mechanical properties of polyethylene terephthalate (PET). Virgin PET grains have been used depending on the molecular weight. The properties were determined for three recycling stages as well as the virgin stage. The work has included the manufacturing of the specimens, specifying the procedure to get the required recycling stage, and measuring the properties which are: tensile strength, impact and hardness. The data have been analyzed to obtain reliable forms for the variation of these properties due to the number of recycling. The results showed that the degree of crystallinity was the highest for the virgin specimens and then declined gradually by recycling. This behavior is happened due to the increase in crosslinking which decreases the packed lamella and affects consequently on the mechanical properties. Generally, there is a decrease in the tensile strength, impact and hardness by 34, 38 and 10%, respectively.

Keywords:  Crystallinity; recycling, polyethylene terephthalate; plastics; sustainability. 

Abstract: 

Text mining (TM) is most widely used to find patterns from various text documents. Cyber-bullying is the term that is used to abuse a person online or offline platform. Nowadays cyber-bullying becomes more dangerous to people who are using social networking sites (SNS). Cyber-bullying is of many types such as text messaging, morphed images, morphed videos, etc. It is a very difficult task to prevent this type of abuse of the person in online SNS. Finding accurate text mining patterns gives better results in detecting cyber-bullying on any platform. Cyber-bullying is developed with the online SNS to send defamatory statements or orally bully other persons or by using the online platform to abuse in front of SNS users. Deep Learning (DL) is one of the significant domains which are used to extract and learn the quality features dynamically from the low-level text inclusions. In this scenario, Convolutional neural networks (CNN) are used for training the text data, images, and videos. CNN is a very powerful approach to training on these types of data and achieved better text classification. In this paper, an Ensemble model is introduced with the integration of Term Frequency (TF)-Inverse document frequency (IDF) and Deep Neural Network (DNN) with advanced feature-extracting techniques to classify the bullying text, images, and videos. The proposed approach also focused on reducing the training time and memory usage which helps the classification improvement.

Keywords:  Convolutional neural networks (CNN), Text mining (TM), Term Frequency (TF)-Inverse document frequency (IDF), Deep Neural Network (DNN).