Life cycle analysis of zero waste management for Jonggol green city

A Zero waste management is believed to be one of methods to gain sustainability in urban areas. Take advantages of resources as enough as the needs and process it until the last part to be wasted is a contribution to take care the environment for the next generation. Reduce, reuse, and recycle are three simplesactivities which are until nowadays consideredas the back bone of zero waste. Jonggolgreen city is a new urban area in Indonesia with a 100 ha of surface area zoned as education tourism area. It is an independent area with pure natural resources of water, air, and land to be managed and protected. It is planned as green city through zero waste management since2013. In this preliminary period, a monitoring tool is being prepared by applying a Life Cycle Analysis (LCA) for urban areas [1]. This paper will present an explanatory assessment ofthe zero waste management for Jonggolgreen city. The existing situation will be examined through LCA and afterwards,the new program and the proposed green design to gain the next level of zero waste will be discussed. The purpose is to track the persistence of the commitment and the perception of the necessary innovationsin order to achieve the ideal behavior level of LCA.

Ergosterol as a rapid measurement of Ganoderma rot of oil palm

Palm oil (PO) is a very important commodity for many countries and especially Indonesia and Malaysia who are the predominant producers. PO is used in ca. 30% of supermarket foods, cosmetics, cooking and as biodiesel. The growth of oil palms in plantations is controversial as the production methods contribute to climate change and cause environmental damage [1]. The plant is subjected to a devastating disease in these two countries caused by the white rot fungus Ganoderma. There are no satisfactory methods to diagnose the disease in the plant as they are too slow and/or inaccurate. The lipid compound ergosterol is unique to fungi and is used to measure growth especially in solid substrates. We report here on the use of ergosterol to measure the growth of Ganoderma in oil palms using HPLC and TLC methods [2]. The method is rapid and correlates well with other methods and is capable of being used on-site, hence improving the speed of analysis and allowing remedial action. Climate change will affect the health of OP [1] and rapid detection methods will be increasingly required to control the disease. [1] Paterson, RRM, Kumar, L, Taylor, S, Lima N. Future climate effects on suitability for growth of oil palms in Malaysia and Indonesia. Scientific Reports, 5, 2015, 14457. [2] Muniroh, MS, Sariah M, Zainal Abidin, MA, Lima, N, Paterson, RRM. Rapid detection of Ganoderma-infected oil palms by microwave ergosterol extraction with HPLC and TLC. Journal of Microbiological Methods, 100, 2014, 143–147.

Indústria Transformadora em Timor-Leste: uma proposta para um setor industrial

Dissertação de mestrado em Engenharia Industrial

Consumption, wealth, stock and housing returns: evidence from emerging markets

We test the predictive ability of the transitory deviations of consumption from its common trend with aggregate wealth and labour income, cay, for both future equity and housing risk premia in emerging market economies. Using quarterly data for 31 markets, our country-level evidence shows that forecasting power of cay vis-à-vis stock returns is high for Brazil, China, Colombia, Israel, Korea, Latvia and Malaysia. As for housing returns, the empirical evidence suggests that financial and housing assets are perceived as complements in the case of Chile, Russia, South Africa and Thailand, and as substitutes in Argentina, Brazil, Hong Kong, Indonesia, Korea, Malaysia, Mexico and Taiwan. Using a panel econometric framework, we find that the cross-country heterogeneity observed in asset return predictability does not accrue to regional location, but can be attributed to differences in the degree of equity market development and in the level of income.

The Data Mining applied for the prediction of highway roughness due to overloaded trucks

Currently, the quality of the Indonesian national road network is inadequate due to several constraints, including overcapacity and overloaded trucks. The high deterioration rate of the road infrastructure in developing countries along with major budgetary restrictions and high growth in traffic have led to an emerging need for improving the performance of the highway maintenance system. However, the high number of intervening factors and their complex effects require advanced tools to successfully solve this problem. The high learning capabilities of Data Mining (DM) are a powerful solution to this problem. In the past, these tools have been successfully applied to solve complex and multi-dimensional problems in various scientific fields. Therefore, it is expected that DM can be used to analyze the large amount of data regarding the pavement and traffic, identify the relationship between variables, and provide information regarding the prediction of the data. In this paper, we present a new approach to predict the International Roughness Index (IRI) of pavement based on DM techniques. DM was used to analyze the initial IRI data, including age, Equivalent Single Axle Load (ESAL), crack, potholes, rutting, and long cracks. This model was developed and verified using data from an Integrated Indonesia Road Management System (IIRMS) that was measured with the National Association of Australian State Road Authorities (NAASRA) roughness meter. The results of the proposed approach are compared with the IIRMS analytical model adapted to the IRI, and the advantages of the new approach are highlighted. We show that the novel data-driven model is able to learn (with high accuracy) the complex relationships between the IRI and the contributing factors of overloaded trucks

Potencial geopolítico da língua portuguesa para Timor-Leste

Dissertação de mestrado em Relações Internacionais