Information on aboveground biomass of mangrove forest at Rawa Aopa Watumohai National (RAWN) Park is fundamental for the management of mangroves (Lumnitzera racemosa Willd.) in the park. Allometric relationships of partial or whole L. racemosa trees were examined using independent variables D30, DBH, and DB or quadratics D30 2 H, DBH 2 H, and DB 2 H from different individuals of L. racemosa trees. Aboveground biomass was estimated by allometric equations and tree census data. Results showed that the best fit for allometric models of stem biomass (WS) and leaf biomass (WL) used the independent variable of DBH 2 H, while the best fit for branch biomass (WB) used quadratic DB 2 H. The quadratic DBH 2 H is the most reliable parameter for estimating aboveground biomass of L. racemosa trees. Aboveground biomass of L. racemosa varied among stands, ranging from 26.23 to 191.01 ton ha ¡1. Biomass density of L. racemosa forest varied from 4.07 to 29.80 ton ha ¡1 m ¡1. The high aboveground biomass of L. racemosa indicates its high blue carbon stock, which maintains the productivity of nearby coastal areas. Therefore, the results of this study can help the sustainable management of mangroves in the RAWN Park and surrounding areas.
ARIMA method is often used in time series data forecasting. However, when an outlier occurs or there is an observation value that is far from a set of data, forecasting with this method will provide a large residual so that the normality assumption is not met. One method developed from the ARIMA Method that overcomes outliers, is called the ARIMA Additive Outlier Method (ARIMA AO). The purpose of this study is to forecast time series data containing outliers with the ARIMA AO Method. This method can reduce the presence of outliers with iterative procedures. The data used is data on the number of foreign tourists visiting the Port of Tanjung Priok. The results obtained are that the model has a normal distribution residual and forecasting accuracy value with MSE and MAPE is smaller than the ARIMA.
Relation of signals appears in several aspects, from traveling waves to the dynamics of stocks and exchange rates. For travelling waves, the relation has been applied to predict the incoming waves provided that the signals at several points upstream are already known. Can similar idea be applied in dynamics of stocks or exchange rates? On the other hand, signals of waves or the dynamics of stocks and exchange rates often consist of the so called ripples or noises. The signals are proposed to be represented in a form of temporal density functions (t-pdf's). The 'moving' average of the t-pdf is the trend of the signal, where the noise of ripples is filtered. The temporal variance represents the characteristic of the noise. In this paper, a relation of temporal probability density functions is considered. A method to obtain a linear relation of two signals is proposed. The application is in the need for the dynamics of stocks and exchange rates especially when the prediction of the dynamics on itself is almost impossible and the prediction of the relation among the detailed signals is difficult.
In this paper, the controllability of linear descriptor systems is investigated by using a direct approach. This approach is based on viewing the system as a whole. In general method, the system is first decomposed into two subsystems in the canonical form. Then, these subsystems are dealt with separately. However, in this paper, we developed the system generally not viewed separately. Then, we derived the structure of controllable subspaces for linear descriptor systems. From these results, we can state the definition of a controllable matrix and then we investigated the properties of the linear descriptor systems related to the controllability matrices by using geometric term.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.