Production of Biogas with Two-Stage Fermentation of Cow Dung-Palm Oil Mill Effluent

In this research, biogas is produced from Palm Oil Mill Effluent (POME) by fermentation of cow dung using a stirred reactor and purified by various CO2 and H2S removal techniques. The variables in this study were: composition of cow dung (55%, 60%, 65%, 70%, 75%, 80% w/w), amino acid composition (0.5%, 1%, 1.5% w/w) and length of fermentation time (2, 6, 10, 14, 16 days). The fixed variables were stirring speed (100 rpm), temperature (30oC) and reactor volume (100 L). This research also investigated the effect of using a lime packed reactor on the purity of methane gas. From the results of first stage of fermentation, it was found that the optimum composition of cow dung-POME was at 60% and the fermentation time was 14 days. In the second stage of fermentation using optimum results at first stage compared to fermentation of cow dung without POME, the results of measuring the gas pressure produced in 60% cow dung-POME fermentation were 17.5 Psig greater than fermentation of cow dung without POME of 15 Psig

Reducing model bias in a deep learning classifier using domain adversarial neural networks in the MINERvA experiment

We present a simulation-based study using deep convolutional neural networks (DCNNs) to identify neutrino interaction vertices in the MINERvA passive targets region, and illustrate the application of domain adversarial neural networks (DANNs) in this context. DANNs are designed to be trained in one domain (simulated data) but tested in a second domain (physics data) and utilize unlabeled data from the second domain so that during training only features which are unable to discriminate between the domains are promoted. MINERvA is a neutrino-nucleus scattering experiment using the NuMI beamline at Fermilab. $A$-dependent cross sections are an important part of the physics program, and these measurements require vertex finding in complicated events. To illustrate the impact of the DANN we used a modified set of simulation in place of physics data during the training of the DANN and then used the label of the modified simulation during the evaluation of the DANN. We find that deep learning based methods offer significant advantages over our prior track-based reconstruction for the task of vertex finding, and that DANNs are able to improve the performance of deep networks by leveraging available unlabeled data and by mitigating network performance degradation rooted in biases in the physics models used for training.Comment: 41 page

De novo assembly and transcriptome analysis of five major tissues of Jatropha curcas L. using GS FLX titanium platform of 454 pyrosequencing

<p>Abstract</p> <p>Background</p> <p><it>Jatropha curcas </it>L. is an important non-edible oilseed crop with promising future in biodiesel production. However, factors like oil yield, oil composition, toxic compounds in oil cake, pests and diseases limit its commercial potential. Well established genetic engineering methods using cloned genes could be used to address these limitations. Earlier, 10,983 unigenes from Sanger sequencing of ESTs, and 3,484 unique assembled transcripts from 454 pyrosequencing of uncloned cDNAs were reported. In order to expedite the process of gene discovery, we have undertaken 454 pyrosequencing of normalized cDNAs prepared from roots, mature leaves, flowers, developing seeds, and embryos of <it>J. curcas</it>.</p> <p>Results</p> <p>From 383,918 raw reads, we obtained 381,957 quality-filtered and trimmed reads that are suitable for the assembly of transcript sequences. <it>De novo </it>contig assembly of these reads generated 17,457 assembled transcripts (contigs) and 54,002 singletons. Average length of the assembled transcripts was 916 bp. About 30% of the transcripts were longer than 1000 bases, and the size of the longest transcript was 7,173 bases. BLASTX analysis revealed that 2,589 of these transcripts are full-length. The assembled transcripts were validated by RT-PCR analysis of 28 transcripts. The results showed that the transcripts were correctly assembled and represent actively expressed genes. KEGG pathway mapping showed that 2,320 transcripts are related to major biochemical pathways including the oil biosynthesis pathway. Overall, the current study reports 14,327 new assembled transcripts which included 2589 full-length transcripts and 27 transcripts that are directly involved in oil biosynthesis.</p> <p>Conclusion</p> <p>The large number of transcripts reported in the current study together with existing ESTs and transcript sequences will serve as an invaluable genetic resource for crop improvement in jatropha. Sequence information of those genes that are involved in oil biosynthesis could be used for metabolic engineering of jatropha to increase oil content, and to modify oil composition.</p

Firms cash management, adjustment cost and its impact on firms’ speed of adjustment-A cross country analysis

We investigate the firms’ specific attributes that determine the difference in speed of adjustment (SOA) towards the cash holdings target in the Scandinavian countries: Denmark, Norway and Sweden. We examine whether Scandinavian firms maintain an optimal level of cash holdings and determine if the active cash holdings management is associated with the firms’ higher SOA and lower adjustment costs. Our findings substantiate that a higher level of off-target cost induces professional managers to rebalance their cash level towards the optimal balance of cash holdings. Our results reveal that Scandinavian firms accelerate SOA towards cash targets primarily for the precautionary motive. Moreover, our results show that SOA is heterogeneous across Scandinavian firms based on adjustment cost and deviate cash holdings towards the target mainly with the support of internal financing. Furthermore, our empirical findings show that the SOA of Norwegian firms is significantly higher than the Danish and Swedish firms