This study is aimed to examine the effect of institutional ownership and capital structure on the company’s value. The population used in this study are all manufacturing companies listed in Indonesia Stock Exchange from 2010 until 2014. The main data used in this research is the financial data in 2010 until 2014. Completed and selected financial data this research are 32 companies so that the samples in this research using five periods are 160. For examining data, this research uses panel data regression model. After running chow test and Hausman test, the most suitable method for the regression is fixed effects method. This research shows that institutional ownership has positive influence significantly on the company’s value, capital structure has no influence significantly on the company’s value Keywords: institutional ownership, capital structure, the company’s value

Difficulty in understanding and applying physics concepts is a problem that is often encountered in learning. Therefore, problem-solving abilities are needed in physics learning. The ability to solve problems in physics learning is an ability that students must have to find solutions to a problem, especially in understanding and applying physics concepts. Problem-solving in physics learning is certainly better if teachers explain directly. However, in certain situations such as the Covid-19 pandemic, teachers cannot help students directly. In hybrid learning, face-to-face learning can still be done virtually. Research in the last 10 years publish through reputable journals in various countries in the word (Taiwan, Belgia, Jerman and Indonesia) were are analyzed for the purpose of this article with the help of NVIVO 12 Software. Results of the analysis from various articles found that hybrid learning is a learning model that can be used as an alternative to help students solve problems in physics learning. For this reason, hybrid learning needs to be given serious support for the current learning process and teachers need to be given special and continuous training in the use of this learning model the learning process can be carried out well even in difficult situations like today.

Background: Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) Delta variant (B.1.617.2) has been responsible for the current increase in Coronavirus disease 2019 (COVID-19) infectivity rate worldwide. We compared the impact of the Delta variant and non-Delta variant on the COVID-19 outcomes in patients from Yogyakarta and Central Java provinces, Indonesia.Methods: In this cross-sectional study, we ascertained 161 patients, 69 with the Delta variant and 92 with the non-Delta variant. The Illumina MiSeq next-generation sequencer was used to perform the whole-genome sequences of SARS-CoV-2.Results: The mean age of patients with the Delta variant and the non-Delta variant was 27.3 ± 20.0 and 43.0 ± 20.9 (p = 3 × 10−6). The patients with Delta variant consisted of 23 males and 46 females, while the patients with the non-Delta variant involved 56 males and 36 females (p = 0.001). The Ct value of the Delta variant (18.4 ± 2.9) was significantly lower than that of the non-Delta variant (19.5 ± 3.8) (p = 0.043). There was no significant difference in the hospitalization and mortality of patients with Delta and non-Delta variants (p = 0.80 and 0.29, respectively). None of the prognostic factors were associated with the hospitalization, except diabetes with an OR of 3.6 (95% CI = 1.02–12.5; p = 0.036). Moreover, the patients with the following factors have been associated with higher mortality rate than the patients without the factors: age ≥65 years, obesity, diabetes, hypertension, and cardiovascular disease with the OR of 11 (95% CI = 3.4–36; p = 8 × 10−5), 27 (95% CI = 6.1–118; p = 1 × 10−5), 15.6 (95% CI = 5.3–46; p = 6 × 10−7), 12 (95% CI = 4–35.3; p = 1.2 × 10−5), and 6.8 (95% CI = 2.1–22.1; p = 0.003), respectively. Multivariate analysis showed that age ≥65 years, obesity, diabetes, and hypertension were the strong prognostic factors for the mortality of COVID-19 patients with the OR of 3.6 (95% CI = 0.58–21.9; p = 0.028), 16.6 (95% CI = 2.5–107.1; p = 0.003), 5.5 (95% CI = 1.3–23.7; p = 0.021), and 5.8 (95% CI = 1.02–32.8; p = 0.047), respectively.Conclusions: We show that the patients infected by the SARS-CoV-2 Delta variant have a lower Ct value than the patients infected by the non-Delta variant, implying that the Delta variant has a higher viral load, which might cause a more transmissible virus among humans. However, the Delta variant does not affect the COVID-19 outcomes in our patients. Our study also confirms that older age and comorbidity increase the mortality rate of patients with COVID-19.

Problem statement: In many applications two or more dependent variables are observed at several values of the independent variables, such as at time points. The statistical problems are to estimate functions that model their dependences on the independent variables and to investigate relationships between these functions. Nonparametric regression model, especially smoothing splines provide powerful tools to model the functions which draw association of these variables. Approach: Penalized weighted least-squares was used to jointly estimate nonparametric functions from contemporaneously correlated data. We apply Generalized Maximum Likelihood (GML), Generalized Cross Validation (GCV) and leaving-out-one-pair Cross Validation (CV) for estimating the smoothing parameters, the weighting parameters and the correlation parameter Results: In this study we formulated the multi-response nonparametric regression model with unequal correlation of errors and give a theoretical method for both obtaining distribution of the response and estimating the nonparametric function in the model. We also estimate the smoothing parameters, the weighting parameters and the correlation parameter simultaneously by applying three methods GML, GCV and CV. Conclusion: Distribution of responses is normal. With multiple correlated responses it is better to estimate these functions jointly using the penalized weighted least-squares.

Banten Bay is categorized as a marine area that is busy with marine tourism activities, settlements and also industries. One potential impact of the condition is the occurrence of pollution from both industrial and domestic sources, erosion and sedimentation in the coastal environment. Samples were collected from 25 representative stations in April 2016. Chemical speciation of three heavy metals (Cu, Ni, and Zn) was studied using a modified sequential extraction procedure proposed by the European Standard, Measurements and Testing (SM&T) program, formerly the Community Bureau of Reference (BCR). The aims of this study are to determine geochemical speciation of 4 bounds of metal: acid-soluble, reducible, oxidizable and residual, and to assess their impacts in the sediments of Banten Bay, Indonesia. The result shows that the percentage of Copper (45.90-83.75%), Nickel (18.28-65.66%), and Zinc (30.45-79.51%) were mostly accumulated in residual fraction of the total concentrations. The Risk Assessment Code (RAC) reveals that about 0-7.07% of Copper and 1.11-24.35 % of Zinc at sites exist in exchangeable fraction and therefore, they are in low risk category. While 7.34-34.90 of Ni at sites exists in exchangeable fraction and therefore, it is in medium risk category to aquatic environment.