The impact of low-density polyethylene (LDPE) microplastics (<100 mm; P100-A P100-B, P100-C, 100 e200 mm; P200, 200e500 mm; P500) on Acropora formosa was investigated. This study investigated the bleaching and necrosis extent of A. formosa caused by LDPE contamination via laboratory assay. The staghorn coral ingested the microplastics, resulting in bleaching and necrosis that concomitantly occurred with the release of zooxanthellae. P100-A experimentation was the worst case, showing bleaching by day 2 (10.8 ± 2.2%) and continued bleaching to 93.6% ± 2.0 by day 14 followed by 5.9 ± 2.5% necrosis. The overall results confirmed that the LDPE concentration impacts coral health. We highlighted that microplastics have been ingested and partially egested. Their presence showed either a direct or indirect impact on coral polyps via direct interaction or through photosynthesis perturbation due to microplastics that cover the coral surface.

Prior research identifies free cash flow (FCF) as one source of agency problems between managers and shareholders. Managers of firms with high FCF and of low growth opportunity tend to invest in marginal or even negative NPV project and use income increasing discretionary accruals to camouflage the effects of non-wealth-maximizing investments. Therefore, the objective of this study is to assess the value relevance of earnings and book value and the effect of agency problem caused by FCF, on the value relevance of earnings and book value. As predicted, results show that earnings and book value are value relevant and agency problem caused by FCF, reduces the value relevance of earnings and book value. However, the effect is not stable across sample years Firms with FCF agency problem do not have lower earnings (book value) coefficient than other firms in year 2002 (2004). Investigation into specific event that may have driven the difference in result is subject to further research.

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Optimizing a huge oilfield with hundreds of wells would involve thorough analysis. Here, the performance of a subset of those wells might be a critical factor. Therefore, it is important to obtain accurate information on that subset.Obtaining accurate information on an individual well is usually done via a proper well test analysis. Although performing a well test is relatively expensive, it can provide important information such as the skin, reservoir pressure (i.e. BHP), permeability, and nearby faults.In this study, we would like emphasize on the measurement of BHP. This emphasis is chosen due to the fact that the BHP from a subset of all wells can be a contributing factor towards the field oil recovery factor. Hence, the objective of this study is to find that subset of wells based on uncertainty studies conducted via reservoir simulations. Here, the uncertainty study involves performing 100 Latin Hypercube Monte Carlo (LHMC) sampling of the reservoir simulation input parameters. For each of the 100 runs, the uncertain input parameters and simulated BHP values/trends for each well are recorded. Thus, a dataset consists of the input parameters, simulated BHP for each well, and the field oil recovery factor as columns will be generated.The random forest (RF) algorithm was implemented in order to find the subset of wells mentioned. RF algorithm is simply a collection of decision trees (DT), where the results generated by RF were obtained by using a voting mechanism among the trees involved. Several trees were sampled from the generated forest, and reveal interactions between the monitored BHP value/trend with other parameters. Combination of LHMC and RF can also be seen a global approach to sensitivity analysis, since RF can reveal the rank of importance of the RF's independent variables based on a collection of uncertainty runs.In the exploration and production (E&P) applications, DT has been used extensively; appraising, real option valuation selecting an artificial lift method, predicting permeabilities and detecting fracture corridors . However, to the best of the authors' knowledge, RF had never been implemented in any E&P activities, particularly in strategic well test planning.

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This paper examines how trade and profit-loss sharing (PLS) financing as well as non performing financing (NPF) affect theprofitability ofIslamic banking in Indonesia. The profitability is measured by return on assets. The sample selection method is purposive sampling and obtained four Islamic banks are being sampled, that is Bank Syariah Mega Indonesia, Bank Syariah Mandiri, Bank Muamalat Indonesia, and BRI Syariah in 11 periods of observation.Multiple regression is used toanalyzethe data. The resultshows thattrade financingand NPF have positivelly affect,while profit-loss sharing financing has negativelly affect on theprofitability ofIslamic banks.

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The purpose of this study is to provide empirical evidence of the effect of non profit sharing finance, profit sharing finance and credit risk on financial performance with the integration of intellectual capital as a moderating variable. The population is all Islamic banking registered in OJK for the period 2015-2018 with a total of 192 observations. These are analyzed using hierarchical regression techniques, multiple linear regression tests and moderated regression analysis (MRA) tests, with e-views 9 software. The results show that non profit sharing financing, profit sharing financing and intellectual capital have a positive and significant effect to financial performance on Islamic banking. While credit risk has a significant negative effect to financial performance of Islamic banking. Furthermore showed that intellectual capital significantly moderates the relationship of non profit sharing finance and profit sharing finance to financial performance Islamic banks. However intellectual capital can’t be moderates the relationship of credit risk and financial performance.