Indonesian Institute of Sciences

About: Indonesian Institute of Sciences is a facility organization based out in Jakarta, Indonesia. It is known for research contribution in the topics: Population & Biology. The organization has 4795 authors who have published 10544 publications receiving 76990 citations. The organization is also known as: Indonesian Institute of Sciences Cibinong, Indonesia.

Persistence of lowland rainforest birds in a recently logged area in central Java

Abstract: Summary In 2001 and 2002, we assessed avian richness in Linggoasri (central Java) where almost all lowland rainforest had been cleared 3 years previously. Bird surveys were carried out in a selectively-logged forest, two regenerating forests and one pine Pinus merkusii plantation. A comparison with a bird list complied at least 3 years prior to logging in Linggoasri showed that 10 lowland bird species may have been extirpated from this area following logging. However, further surveys will be needed to validate these extirpations. A comparison with a list of lowland forest birds of Java shows that only 37% of these occurred in Linggoasri. From the lowland avifauna of Java, sole frugivores and insectivores were less likely to be present in Linggoasri. Smaller, generalist-feeding lowland species, endemic to Java, were more likely to be present in this area. Persisting lowland bird species, however, did not appear to be seriously affected physiologically by habitat degradation, showing no significant loss of body condition or reduction in survival and reproduction. There is an urgent need to preserve the few remaining lowland forest patches of Java and, given the alarming rate of deforestation throughout Southeast Asia, it is critical to conserve adequately large primary lowland forests (e.g. >5,000 ha) if we are to preserve existing lowland avifauna.

Battery state of charge estimation by using a combination of Coulomb Counting and dynamic model with adjusted gain

Abstract: Majority of electric vehicles depend on batteries that require a Battery Management System (BMS) Increasing accuracy of State of charge (SoC) estimation was crucial The uncomplicated method for SoC estimation is Coulomb Counting (CC), however it has drawback in information of previous SoC initial value and tolerate with error propagation Dynamic model equipped with integral controller can increase accuracy of the CC method In this paper adjusted gain of the integral controller is proposed to further improve performance of SOC estimation The Li-ion battery was modelled using first order RC battery model Least square method is used to determine parameter of battery model The simulation results show that the combination of CC and dynamic model with fixed integral gain could increase accuracy of the CC algorithm approximately to 87%, and the mixed algorithm with adjusted integral gain could further increase it to around 89%