Purpose: The purpose of this study is to explore the most significant profitability determinants of the manufacturing companies in Indonesia. Design/Methodology/Approach: Several independent variables examined for their influence on profitability were working capital, firm size, firm growth, capital structure, and non-debt tax shields. The sample of this study were manufacturing firms listed on the Indonesia Stock Exchange from 2010 to 2017. The number of samples were 350 manufacturing companies. Findings: The results of this study indicate that working capital, firm size and firm growth were positively related to profitability. Meanwhile, capital structure and non-debt tax shield did not affect profitability. The findings of this study were consistent with the pecking order theory and the financial agency theory. Practical implications: This study implies that managers need to adjust their investment needs with the profitability that has been achieved and the total assets of the company, and to maximize the value of the company by managing current assets so that the rate of the return on marginal investment is equal to or greater than the cost of capital used to finance the current assets. Furthermore, financial managers must be able to determine essential investment objectives by maximizing the use of assets and fixed assets which are expected to make the company to enjoy the sales growth in the future. Originality/Value: Although this study organically builds upon recent studies about the firms' profitability, it conducted in the new administrative setting in Indonesia, which is the Widodo's administration. Widodo's administration supports the manufacturing industry to be able to compete globally.

Previous studies have shown that cryptocurrencies could hedge equities. However, most of those studies did not take into account the recent cryptocurrencies bubbles in 2018 and domestic currencies. Therefore, this research aimed to study whether the hedge effectiveness of cryptocurrencies still exists. This research used five cryptocurrencies (bitcoin, ethereum, monero, ripple, and litecoin), equity indices (Indonesia, Malaysia, Vietnam, Thailand, and the Philippines), and iShares ETF MSCI World (developed world). Commodities-based hedging using iShares S&P GSCI Commodity-Indexed Trust was also analyzed as a comparison. The asymmetric generalized dynamic conditional correlation (AG-DCC) GARCH showed that one cryptocurrency could not significantly and consistently hedge equities while five equally weighted cryptocurrencies could marginally hedge equities. Meanwhile, the classical minimum variance model also showed that the hedge effectiveness of cryptocurrencies was insignificantly positive. Equity traders could add cryptocurrencies into portfolios when the purpose was to maximize the Sharpe ratio instead of hedging. Overall, commodities were the better hedge for Southeast Asia emerging markets.

Magnetorheological (MR) materials are a group of smart materials used in new technologies with controlled reliability. The development of these materials is expanding, starting from MR fluids, elastomers, grease, and gel. This large number of material types further expands the various applications of MR materials as a creative technology to support performance enhancement. For example, MR fluid is used to improve the performance of shock absorbers such as vehicle suspension, the damping of building structures, and polishing of the workpiece. MR elastomers are used for engine mounting, insulation base, and many other applications with intelligent material properties such as stiffness controllability. However, there are still complexities in the practical implementation of the control system beyond reliability. Many previous studies have focused on the performance improvement and reliability of MR materials as smart materials for application devices and systems. In this review article, the specific discussion related to vibration control strategies in MR material-based systems was thoroughly investigated. To discuss this point, many MR applications including transportation system and vibration isolation were adopted using different types of control strategies. Many different control strategies that have been used for MR applications such as fuzzy logic control, optimal control, and skyhook control are discussed in-depth in terms of the inherent control characteristics of merits and demerits.

Lithium-ion batteries play a critical role in the reliability and safety of a system. Battery health monitoring and remaining useful life (RUL) prediction are needed to prevent catastrophic failure of the battery. The aim of this research is to develop a data-driven method to monitor the batteries state of health and predict their RUL by using the battery capacity degradation data. This paper also investigated the effect of prediction starting point to the RUL prediction error. One of the data-driven method drawbacks is the need of a large amount of data to obtain accurate prediction. This paper proposed a method to generate a series of degradation data that follow the Gaussian distribution based on limited battery capacity degradation data. The prognostic model was constructed from the new data using least square support vector machine (LSSVM) regression. The remaining useful life prediction was carried out by extrapolating the model until reach the end of life threshold. The method was applied to three differences lithium-ion batteries capacity data. The results showed that the proposed method has good performance. The method can predict the lithium-ion batteries RUL with a small error, and the optimal RUL starting point was found at the point where the battery has experienced the highest capacity recovery due to the self-recharge phenomenon.

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