Islamic Ethics And Commitment Among Muslim Nurses In Indonesia

Ethical principles are among the topics that are widely emphasised in the Islamic society. Ethics is a set of values, do’s and don’ts that can play an important role in the effective management of organisations. If employees of organisations, especially medical staff, are working in the atmosphere of Islamic ethics, they show functional behaviours in line with the goals and missions of organisation. Due to the direct relationship and treatment of nurses with recipients of medical services, nurses’ behaviours have significant impact on the quality of services provided by medical centres. Therefore, the purpose of this study is to explore the relationship between Islamic ethics and commitment of 1100 Muslim nurses in Indonesia in 2021. This study was performed by descriptive-analytical correlational method. Data were collected using Islamic ethics and organisational commitment questionnaires and measured by Pearson correlation coefficient in Statistical Package for the Social Sciences (SPSS) and structural equation modelling analysis (SEM) in linear structural relationships (LISREL). The results indicate that Islamic ethics have significant and positive relationship with nurses’ commitment as p = 0.542 and t = 5.63

Multiple machine learning models for prediction of CO2 solubility in potassium and sodium based amino acid salt solutions

In this work, we developed artificial intelligence-based models for prediction and correlation of CO2 solubility in amino acid solutions for the purpose of CO2 capture. The models were used to correlate the process parameters to the CO2 loading in the solvent. Indeed, CO2 loading/-solubility in the solvent was considered as the sole model’s output. The studied solvent in this work were potassium and sodium-based amino acid salt solutions. For the predictions, we tried three potential models, including Multi-layer Perceptron (MLP), Decision Tree (DT), and AdaBoostDT. In order to discover the ideal hyperparameters for each model, we ran the method multiple times to find out the best model. R2 scores for all three models exceeded 0.9 after optimization confirming the great prediction capabilities for all models. AdaBoost-DT indicated the highest R2 Score of 0.998. With an R2 of 0.98, Decision Tree was the second most accurate one, followed by MLP with an R2 of 0.9

Solving a Two-Level Location Problem with Nonlinear Costs and Limited Capacity: Application of Two-Phase Recursive Algorithm Based on Scatter Search

This study examines the issue of distribution network design in the supply chain system. There are many production factories and distribution warehouses in this issue. The most efficient strategy for distributing the product from the factory to the warehouse and from the warehouse to the customer is determined by solving this model. This model combines location problems with and without capacity limits to study a particular location problem. In this system, the cost of production and maintenance of the product in the factory and warehouse is a function of its output. This increases capacity without additional costs, and ultimately does not lose customers. This algorithm is a population-based, innovative method that systematically combines answers to obtain the most accurate answer considering quality and diversity. A two-phase recursive algorithm based on a scattered object has been developed to solve this model. Numerical results show the efficiency and effectiveness of this two-phase algorithm for problems of different sizes

Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations

Development of machine learning methods in hybrid energy storage systems in electric vehicles

The hybrid energy storage systems are a practical tool to solve the issues in single energy storage systems in terms of specific power supply and high specific energy. These systems are especially applicable in electric and hybrid vehicles. Applying a dynamic and coherent strategy plays a key role in managing a hybrid energy storage system. The data obtained while driving and information collected from energy storage systems can be used to analyze the performance of the provided energy management method. Most existing energy management models follow predetermined rules that are unsuitable for vehicles moving in different modes and conditions. Therefore, it is so advantageous to provide an energy management system that can learn from the environment and the driving cycle and send the needed data to a control system for optimal management. In this research, the machine learning method and its application in increasing the efficiency of a hybrid energy storage management system are applied. In this regard, the energy management system is designed based on machine learning methods so that the system can learn to take the necessary actions in different situations directly and without the use of predicted select and run the predefined rules. The advantage of this method is accurate and effective control with high efficiency through direct interaction with the environment around the system. The numerical results show that the proposed machine learning method can achieve the least mean square error in all strategies