Influence of exhaust gas heating and L/D ratios on the discharge efficiencies for an activated carbon natural gas storage system

A transient 2D axi-symmetric and lumped parameter (LP) model with constant outflow conditions have been developed to study the discharge capacity of an activated carbon bed. The predicted discharge times and variations in bed pressure and temperature are in good agreement with experimental results obtained from a 1.82 l adsorbed natural gas (ANG) storage system. Under ambient air conditions, a maximum temperature drop of 29.5 K and 45.5 K are predicted at the bed center for discharge rates of 1.0 l min(-1) and 5.0 l min(-1) respectively. The corresponding discharge efficiencies are 77% and 71.5% respectively with discharge efficiencies improving with decreasing outflow rates. Increasing the LID ratio from 1.9 to 7.8 had only a marginal increase in the discharge efficiency. Forced convection (exhaust gas) heating had a significant effect on the discharge efficiency, leading to efficiencies as high as 92.8% at a discharge of 1.0 l min(-1) and 88.7% at 5 l min(-1). Our study shows that the LP model can be reliably used to obtain discharge times due to the uniform pressure distributions in the bed. Temperature predictions with the LP model were more accurate at ambient conditions and higher discharge rates, due to greater uniformity in bed temperatures. For the low thermal conductivity carbon porous beds, our study shows that exhaust gas heating can be used as an effective and convenient strategy to improve the discharge characteristics and performance of an ANG system. (C) 2013 Elsevier Ltd. All rights reserved.

Heterogeneity in Dye-TiO2 Interactions Dictate Charge Transfer Efficiencies for Diketopyrrolopyrrole-Based Polymer Sensitized Solar Cells

Power conversion efficiency of a solar cell is a complex parameter which usually hides the molecular details of the charge generation process. For rationally tailoring the overall device efficiency of the dye-sensitized solar cell, detailed molecular understanding of photoinduced reactions at the dye-TiO2 interface has to be achieved. Recently, near-IR absorbing diketopyrrolopyrrole-based (DPP) low bandgap polymeric dyes with enhanced photostabilities have been used for TiO2 sensitization with moderate efficiencies. To improve the reported device performances, a critical analysis of the polymerTiO(2) interaction and electron transfer dynamics is imperative. Employing a combination of time-resolved optical measurements complemented by low temperature EPR and steady-state Raman spectroscopy on polymerTiO(2) conjugates, we provide direct evidence for photoinduced electron injection from the TDPP-BBT polymer singlet state into TiO2 through the C-O group of the DPP-core. A detailed excited state description of the electron transfer process in films reveals instrument response function (IRF) limited (<110 fs) charge injection from a minor polymer fraction followed by a picosecond recombination. The major fraction of photoexcited polymers, however, does not show injection indicating pronounced ground state heterogeneity induced due to nonspecific polymerTiO(2) interactions. Our work therefore underscores the importance of gathering molecular-level insight into the competitive pathways of ultrafast charge generation along with probing the chemical heterogeneity at the nanoscale within the polymerTiO2 films for optimizing photovoltaic device efficiencies.

Is the cellular initiation of translation an exclusive property of the initiator tRNAs?

Translation of mRNAs is the primary function of the ribosomal machinery. Although cells allow for a certain level of translational errors/mistranslation (which may well be a strategic need), maintenance of the fidelity of translation is vital for the cellular function and fitness. The P-site bound initiator tRNA selects the start codon in an mRNA and specifies the reading frame. A direct P-site binding of the initiator tRNA is a function of its special structural features, ribosomal elements, and the initiation factors. A highly conserved feature of the 3 consecutive G:C base pairs (3GC pairs) in the anticodon stem of the initiator tRNAs is vital in directing it to the P-site. Mutations in the 3GC pairs diminish/abolish initiation under normal physiological conditions. Using molecular genetics approaches, we have identified conditions that allow initiation with the mutant tRNAs in Escherichia coli. During our studies, we have uncovered a novel phenomenon of in vivo initiation by elongator tRNAs. Here, we recapitulate how the cellular abundance of the initiator tRNA, and nucleoside modifications in rRNA are connected with the tRNA selection in the P-site. We then discuss our recent finding of how a conserved feature in the mRNA, the Shine-Dalgarno sequence, influences tRNA selection in the P-site.

Translation Induction on Indian Language Corpora Using Translingual Themes from Other Languages

Identifying translations from comparable corpora is a well-known problem with several applications, e.g. dictionary creation in resource-scarce languages. Scarcity of high quality corpora, especially in Indian languages, makes this problem hard, e.g. state-of-the-art techniques achieve a mean reciprocal rank (MRR) of 0.66 for English-Italian, and a mere 0.187 for Telugu-Kannada. There exist comparable corpora in many Indian languages with other ``auxiliary'' languages. We observe that translations have many topically related words in common in the auxiliary language. To model this, we define the notion of a translingual theme, a set of topically related words from auxiliary language corpora, and present a probabilistic framework for translation induction. Extensive experiments on 35 comparable corpora using English and French as auxiliary languages show that this approach can yield dramatic improvements in performance (e.g. MRR improves by 124% to 0.419 for Telugu-Kannada). A user study on WikiTSu, a system for cross-lingual Wikipedia title suggestion that uses our approach, shows a 20% improvement in the quality of titles suggested.

Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.

Improving speech transcription for Mandarin-english translation

This paper describes the development of the CU-HTK Mandarin Speech-To-Text (STT) system and assesses its performance as part of a transcription-translation pipeline which converts broadcast Mandarin audio into English text. Recent improvements to the STT system are described and these give Character Error Rate (CER) gains of 14.3% absolute for a Broadcast Conversation (BC) task and 5.1% absolute for a Broadcast News (BN) task. The output of these STT systems is then post-processed, so that it consists of sentence-like segments, and translated into English text using a Statistical Machine Translation (SMT) system. The performance of the transcription-translation pipeline is evaluated using the Translation Edit Rate (TER) and BLEU metrics. It is shown that improving both the STT system and the post-STT segmentations can lower the TER scores by up to 5.3% absolute and increase the BLEU scores by up to 2.7% absolute. © 2007 IEEE.