Sigit and Rachel Shannon Twigivanya
This paper examines Malaysia's perception of China following the Asian Financial Crisis. The Asian Financial Crisis, which occurred in 1997, resulted in a contraction in…
Abstract
This paper examines Malaysia's perception of China following the Asian Financial Crisis. The Asian Financial Crisis, which occurred in 1997, resulted in a contraction in Malaysia's GDP, which resulted in increased unemployment in Malaysia. China is a rising economy. Several bilateral visits and trade missions meet both states to achieve an advantageous economic position. Malaysia's decision to rely on China despite historical events that had sparked tensions between the two countries. Despite Malaysia's economic downturn, the country is taking swift action to address the issue. During the crisis, Malaysia viewed Western countries as irresponsible and allowed the situation to deteriorate, which later became the reason for Malaysia's relationship with China. The crisis, however, has influenced Malaysian Chinese businesses to improve their foreign policy and bilateral relations. This paper contends that Malaysia recognizes the importance of its bilateral relationship with China in stabilizing its economic development and social activity following the crisis.
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Djordje Cica, Branislav Sredanovic, Sasa Tesic and Davorin Kramar
Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with…
Abstract
Sustainable manufacturing is one of the most important and most challenging issues in present industrial scenario. With the intention of diminish negative effects associated with cutting fluids, the machining industries are continuously developing technologies and systems for cooling/lubricating of the cutting zone while maintaining machining efficiency. In the present study, three regression based machine learning techniques, namely, polynomial regression (PR), support vector regression (SVR) and Gaussian process regression (GPR) were developed to predict machining force, cutting power and cutting pressure in the turning of AISI 1045. In the development of predictive models, machining parameters of cutting speed, depth of cut and feed rate were considered as control factors. Since cooling/lubricating techniques significantly affects the machining performance, prediction model development of quality characteristics was performed under minimum quantity lubrication (MQL) and high-pressure coolant (HPC) cutting conditions. The prediction accuracy of developed models was evaluated by statistical error analyzing methods. Results of regressions based machine learning techniques were also compared with probably one of the most frequently used machine learning method, namely artificial neural networks (ANN). Finally, a metaheuristic approach based on a neural network algorithm was utilized to perform an efficient multi-objective optimization of process parameters for both cutting environment.