ANALISIS KELAYAKAN USAHA AGROINDUSTRI KOPI LUWAK DI KECAMATAN BALIK BUKIT KABUPATEN LAMPUNG BARAT

The study aimed to analyze the financial feasibility of luwak coffee agroindustry. The research was conducted at Balik Bukit district of West Lampung Regency. This location was selected purposively. The research used primary and secondary data. The research samples were 2 agro industries which were chosen purposively. The data was collected in Oktober to November 2012. The financial feasibility was analyzed by NPV, IRR, Net B/C, Gross B/C, Payback Period, BEP and sensitivity. The results showed that small and micro agroindustries of luwak coffee at Balik Bukit District of West Lampung Regency were financially feasible and profitable to be developed, the increase of cost and decrease of product’s price influenced the luwak coffee agro industries. Keywords: financial feasibility, macro agroindustry, luwak coffee, small agroindustr

DAYA SAING JAGUNG DI KECAMATAN SEKAMPUNG UDIK KABUPATEN LAMPUNG TIMUR

This research aims to analyze the competitiveness of corn and the effect of input-output price changes on the competitiveness of corn farming in Sekampung Udik District, East Lampung Regency. This research was held in Sidorejo Village, Sekampung Udik District, East Lampung Regency. The respondents of this research were 6 farmers implementing intensive farming management, taken using purposive sampling. The competitiveness was analyzed using PAM (Policy Analysis Matrix). The results showed that corn farming in Sekampung Udik District, East Lampung Regency was competitive (PCR = 0.3499 and DRC = 0.2944). The decreasing of corn price by 26%, the increasing of fertilizer price (urea = 33.33%, SP-36 = 29.03%, Phonska = 31.43%, and KCl = 25%), the increasing of seed price and landrate by 4.28%, caused a decrease in the competitiveness of corn farming. The competitiveness of corn farming in Sekampung Udik District, East Lampung Regency was sensitive to the decreasing of corn price by 26%.Key words: competitiveness, corn, PAM, sensitivit

The effect of conventional and microwave heating techniques on transesterification of waste cooking oil to biodiesel

This research is focused on the effect of processing parameters such as molar ratio of sample to solvent (1:3 - 1:15), catalyst loading (0.5 - 2.5 wt %), temperature (40 - 80 C) and time of reaction ( 5 - 180 min) on the transesterification yield of waste cooking oil (WCO) in conventional thermal heating and microwave heating technique s . The analysis carried out revealed that the microwave assisted transesterification produced a comparable yield to conventional heating transesterification with ~ 5 times faster in heating up the reaction mixture to a reaction temperature and reduced ~ 90% of the reaction time required . This study concludes that microwave assisted transesterification , which is a green technology, may have great potential in reducing the processing time compared to conventional thermal heating transesterification

The zCOSMOS 10k-Bright Spectroscopic Sample

We present spectroscopic redshifts of a large sample of galaxies with I_(AB) < 22.5 in the COSMOS field, measured from spectra of 10,644 objects that have been obtained in the first two years of observations in the zCOSMOS-bright redshift survey. These include a statistically complete subset of 10,109 objects. The average accuracy of individual redshifts is 110 km s^(–1), independent of redshift. The reliability of individual redshifts is described by a Confidence Class that has been empirically calibrated through repeat spectroscopic observations of over 600 galaxies. There is very good agreement between spectroscopic and photometric redshifts for the most secure Confidence Classes. For the less secure Confidence Classes, there is a good correspondence between the fraction of objects with a consistent photometric redshift and the spectroscopic repeatability, suggesting that the photometric redshifts can be used to indicate which of the less secure spectroscopic redshifts are likely right and which are probably wrong, and to give an indication of the nature of objects for which we failed to determine a redshift. Using this approach, we can construct a spectroscopic sample that is 99% reliable and which is 88% complete in the sample as a whole, and 95% complete in the redshift range 0.5 < z < 0.8. The luminosity and mass completeness levels of the zCOSMOS-bright sample of galaxies is also discussed

Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation Networks

One of the key goals of Informetrics is to identify citation-based popular articles among so many other aspects, such as determining popular research topics, identifying influential scholars, and predicting hot trends in science. These can be achieved by applying network science approaches to scientific networks and formulating the problem as a popular (most-cited) node ranking task. To rank the papers based on their future citation gain. In this work a deep learning based framework is proposed. Which helps in automatic node level feature extraction and can make node level prediction in dynamic graphs such as citation networks. To achieve this we have learned global ranking preserve d dimensional node embedding. We have only considered temporal features, which makes it suitable for generalisation to other networks. Although our model can consider node level explicit features also. Further we have given novel cost function which can be easily solve ranking problem for dynamic graphs using probabilistic regression method. Which can be easily optimised. Another novelty of our work is that our model can be trained using different snapshots of the graph and different time. Further trained model can be used to make future prediction. The proposed model has been tested on an arXiv paper citation network using six standard information retrieval-based metrics. The results show that our proposed model outperforms, on average, other state-of-the-art static models as well as dynamic node ranking models. The outcome of this research study leads to informed data-driven decision-making in science, such as the allocation and distribution of research funds and investment in strategic research centers. When considering past time window size as 10 months and making prediction after 10 months our proposed model&#x2019;s performance on various ranking based evaluation metrics are as follows: AUC-0.974, Kendal&#x2019;s rank correlation tau-0.455, Precision- 0.643, Novelty-0.0456, Temporal novelty-0.375 and on NDCG-0.949. Our model is able to make long term trend prediction with just training on short time window