The purpose of this paper is to understand how transfer pricing law affects transfer pricing decision making in foreign direct investment enterprises in Vietnam. The study also suggests further research on the determinants over transfer pricing decision making of these enterprises to have better solutions to deal with transfer mispricing in Vietnam. A quantitative research method involving self-administered closed ended questionnaires is extended to manage Directors/Chief Executive Officers, Tax Managers/ Directors, Chief Finance Officers or Heads of Finance from foreign direct investment enterprises in Vietnam. Findings indicate a strong probability of a relationship between transfer pricing law and the transfer pricing decision making of foreign direct investment enterprises in Vietnam. Therefore, there is a need for government to have specific law on transfer pricing. Good transfer pricing database is al-so expected; punishment should be reasonable; transfer pricing audit process should be stronger; transfer pricing trainings should be introduced more to the enterprises. It is be-lieved that the implementation of suitable transfer pricing law in Vietnam will assist the government better deal with transfer pricing abuse in foreign direct investment enterprises. The findings support Vietnamese policy makers to improve the law on transfer pricing. Nevertheless, tax officials and accounting representatives can have indepth knowledge with regard to transfer pricing decision making which the outcome of this study aspires as guidance for better understanding the aspects of transfer pricing decision making while doing business in Vietnam.

This paper presents a scheme of implementation of the protocol for diagnosing stakeholders of educational space, which aims to meet the needs and interests of both contractors and the university, and makes an efficiency assessment in achieving the goals of the university. The ranking of the social status of the stakeholders in the association is obtained, which demonstrates the importance of each stakeholder in the collaboration. The relationships between university and stakeholders, the degree of communication of stakeholders with each other, the attitude of each stakeholder towards the group of stakeholders as a whole and the total activity of group interaction based on the calculation of sociometric indicators are determined. Finally, the effectiveness of the level of involvement of stakeholders in the activities of the University is also proved.

Mobile payment is considered as a technology innovation which is being exploited and fur-ther expanded in both developed and developing or emerging countries. It is considered as an alternative method to cash payment method. This study is employed with the objective of exploring how Cambodian user behavior intention is affected by perceived transaction convenience, perceived transaction speed, social influences, and facilitating condition. Each fac-tor is measured by different items and there are 38 items developed in this study. The data is collected by distributing questionnaires to Cambodian users and 329 questionnaires are collected successfully.

Root cause failure analysis (RCFA) is a structured process to acknowledge cause and effect relationship of failure or unfavorable events in the organization in order to prevent repetition or reduction of failures. There are various tools for RCFA like interviewing, fault tree analysis (FTA), 5 whys, failure mode and effects analysis (FMEA), Pareto analysis and storytelling method with different strengths and weaknesses. In this paper, an integrated process model is developed using Reality Charting, FMEA and DEMATEL to understand and implement RCFA effectively. The proposed process model has eight main steps. The presented model in a case study for the UTD-20 engine is implemented and thus, in addition to the use of supporting research in model development, real data as well as the approval of the UTD-20 analysis team members are assisted to validate the proposed model.

With the inception of high-speed internet data services and ever-growing technical advancement in manufacturing technology, the integration of production systems and the internet of things to produce different types of jobs via cloud computing has become possible. The internet-enabled advanced automatic production systems can be referred to as the cyber-physical production systems (CPPS). The use of CPPS via cloud computing and the internet of things (IoT) can offer high productivity and high flexibility for the production of jobs in a dynamic production environment with varying specifications. The aim of this paper is to present a generalized synergic framework between different production facilities locating at different geographical locations to realize an energy-saving and efficient cyber-physical production system for the production of different types of jobs in the context of the industry 4.0 and beyond. In addition to the above, the study also identifies a need to address large scale multi-objective optimization issues to make the best decisions for different combinatorial production scenarios by using CPPS that are functioning in smart production facilities at different geographical locations.

Building Maintenance plays an important role throughout the building lifecycle from devis-ing conceptual plans to the end. Due to the high cost of building maintenance and the direct impact of maintenance effectiveness on the quality of life of building occupants, special attention must be devoted. One of the most important issues in this field is building maintenance contractor selection. This issue becomes even more critical in public buildings, such as hospitals, offices, and military centers. The purpose of this study is to present a method that can be used to select the contractor in such a way that the response robustness is high and the employed method is the most accurate one among other similar methods. To do this, the contractors are ranked by 7 multi-criteria decision-making methods. Then, the Spearman correlation coefficients are obtained for each pair of methods. When there is a significant difference between the outcomes of the methods, the output of each method is compared with the output of the Artificial Neural Network (ANN) model. The method with the least difference with the neural network output is taken as the superior method. After selecting the best method, a robustness analysis is performed on it to verify the stability of the answer. The proposed model is implemented on a real case study. Statistical analysis shows that the implementation of this method has increased the satisfaction of the residents.

Computer TCP/IP networks are becoming critical in all aspects of life. As computer networks continue to improve, the levels of redundancy continue to increase. Modern network redun-dancy features can be complex and expensive. This leads to misconfiguration of the redun-dancy features. Monitoring everything is not always practical. Some redundancy features are easy to detect while others are more difficult. It is common for redundancy features to fail or contribute to a failure scenario. Incorrectly configured redundancy will lead to network downtime when the network is supposed to be redundant. This presents a false sense of se-curity to the network operators and administrators. This research will present two scenarios that are commonly left unmonitored and look at a practical way to deploy solutions to these two scenarios in such a way that the network uptime can be improved. Implementing a practical approach to monitor and mitigate these types of failures allows costs spent on re-dundancy to increase uptime, and thus increase overall quality that is critical to a modern digital company.

The computer numerical control (CNC) machines are chiefly used for the production of jobs with high accuracy and high speed. The CNC machining centers perform the machining operations according to the given program instructions which are commonly programmed by a CNC programmer. In this paper, a procedure to develop an automatic CNC program for machining of different types of holes by using different machine learning algorithms is developed. The machine learning algorithms namely support vector machine (SVM) and restricted boltzmann machine algorithm (RBM) with deep belief network (DBN) are used for the au-tomatic development of CNC machining programs of different types of holes. Initially, the position and other parameters of machining operations are identified and thereafter the CNC machining program is developed by using the MATLAB application. The automatically de-veloped CNC programs are tested on a CNC simulator. It is found that the application of RBM machine learning algorithm with DBN outperforms the SVM machine learning algo-rithm for the development of automatic CNC machining program for the machining of blind holes, through holes, counterbores and countersink operations.

Service quality is basically a comparison between expectations and the perceptions of the customer. The interrelationship between various aspects of information technology (IT) adoption and other basic characteristics of service quality is complex and dependent on the expected service composition. Reliability, conformance, durability and serviceability are taken as functions of service quality; whereas Tenure, utility and vendor are taken as functions of Information Technology. Substantial literature has examined the concept of service quality, its dimensions, and measurement methods. Statistical analysis of quality parameters has been performed and validation is done with different Artificial Neural Network (ANNs) architecture with different hidden layers. The model proposed in this study is designed to evaluate and improve service quality within a comprehensive framework.

The main objective of this research article is to select the best mobile model among various alternatives available on the market. For this analysis 10 alternative models from different brands are selected from different online shopping website having different specifications and ranging from low budget to medium budget in terms of price. For this selection purposes two multiple criteria decision making tools (MCDM) has been adopted i.e. Complex Propor-tional Assessment (COPRAS) and Additive Ratio Assessment (ARAS). The selection process is done based on four important criteria i.e. price, internal storage, RAM and brand. The weightages of the criteria are calculated by using Analytic Hierarchy Process (AHP) and these weightages are further used in COPRAS and ARAS methods. Individual COPRAS and ARAS method is applied for the selection of the best mobile and the preference ranking order of the models are also proposed by each process. The proposed ranking order by both the methods are compared and it is found that the outcome results are more or less the same using both techniques but there is a slight change in ranking of the middle-order alternatives. Both processes give model 1 and model 4 as the best and the worst models respectively among 10 alternatives.