Electrocatalytic Oxidative Dehydrogenation of Propane in Solid Oxide Electrolysis Cells

Electrocatalytic oxidative dehydrogenation (ODH) of propane is a promising alternative method to steam cracking for propane production, due to its environmentally-friendly nature and lower operating temperature requirement. It also outperforms traditional catalytic dehydrogenation processes because the thermodynamic limitations are overcome, thereby enhancing propylene yields. In this study, the performance of dual phase composite perovskites consisting of electron and oxide ion-conducting strontium-doped lanthanum manganite (LSM) and proton-conducting ytterbium-doped strontium ceria (SCY) as the anode in a solid oxide electrolysis cell (SOEC) was investigated. The catalytic materials were characterized and analyzed via X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), di use re ectance infrared Fourier transform spectroscopy (DRIFT), X-ray absorption near edge ne structure (XANES), and temperature-programmed reaction/ reduction (TPrxn/TPR). Propane ODH was carried out at 600°C in an SOEC with varied ratio of LSM and SCY in the anode. It was found that the selectivity towards proplyene was 24% for any anode composition. However, the alkene/COx selectivity increased with increasing SCY composition, indicating the presence of SCY could reduce the propane deep oxidation. It was observed that although pure LSM without the addition of SCY exhibited the highest conversion, it produced the most COx, possibly attributable to the reactive nature of LSM with propane. This study certi ed the feasibility of using electrocatalytic propane ODH to resolve the propylene supply and demand mismatch, but further investigations and development are needed to meet the standards of industrial-scale applications.No embargoAcademic Major: Chemical Engineerin

The Implications of Diverse Applications and Scalable Data Sets in Benchmarking Big Data Systems

Now we live in an era of big data, and big data applications are becoming more and more pervasive. How to benchmark data center computer systems running big data applications (in short big data systems) is a hot topic. In this paper, we focus on measuring the performance impacts of diverse applications and scalable volumes of data sets on big data systems. For four typical data analysis applications---an important class of big data applications, we find two major results through experiments: first, the data scale has a significant impact on the performance of big data systems, so we must provide scalable volumes of data sets in big data benchmarks. Second, for the four applications, even all of them use the simple algorithms, the performance trends are different with increasing data scales, and hence we must consider not only variety of data sets but also variety of applications in benchmarking big data systems.Comment: 16 pages, 3 figure

Inferring Economic Condition Uncertainty from Electricity Big Data

Inferring the uncertainties in economic conditions are of significant importance for both decision makers as well as market players. In this paper, we propose a novel method based on Hidden Markov Model (HMM) to construct the Economic Condition Uncertainty (ECU) index that can be used to infer the economic condition uncertainties. The ECU index is a dimensionless index ranges between zero and one, this makes it to be comparable among sectors, regions and periods. We use the daily electricity consumption data of nearly 20 thousand firms in Shanghai from 2018 to 2020 to construct the ECU indexes. Results show that all ECU indexes, no matter at sectoral level or regional level, successfully captured the negative impacts of COVID-19 on Shanghai's economic conditions. Besides, the ECU indexes also presented the heterogeneities in different districts as well as in different sectors. This reflects the facts that changes in uncertainties of economic conditions are mainly related to regional economic structures and targeted regulation policies faced by sectors. The ECU index can also be easily extended to measure uncertainties of economic conditions in different fields which has great potentials in the future

A Randomized Phase I/II Trial to Compare Weekly Usage with Triple Weekly Usage of Paclitaxel in Concurrent Radiochemotherapy for Patients with Locally Advanced Non-small Cell Lung Cancer

Background and objective Although the guidelines of the National Comprehensive Cancer Network of USA recommend that the standard therapy for locally advanced non-small cell lung cancer (LANSCLC) is concurrent chemoradiotherapy. There is ongoing controversy about the treatment regimen which combines chemotherapy concurrently with radiotherapy. The aim of this study is to compare weekly usage with triple weekly usage of paclitaxel in concurrent radiochemotherapy for patients with LANSCLC, and to obtain the best paclitaxel regimen in the concurrent radiochemotherapy. Methods From April 2006 to April 2009, some LANSCLC patients in multicenter were randomly divided into weekly usage (45 mg/m2, 1 times/week, a total of 270 mg/m2 in six weeks) and triple weekly usage (15 mg/m2, 3 times/week, a total of 270 mg/m2 in six weeks) group of paclitaxel by a random number table. All patients were treated with 3D radiotherapy, and 95% planning target volume (PTV) received a prescription dose of (60-70) Gy/(30-35)times/(6-7)weeks, (1.8-2.0) Gy/fraction. Then the side effects, response and overall survival rate were compared between two groups of patients. Results Thirty-eight LANSCLC patients were enrolled. Weekly usage and triple weekly usage group were 20 and 18 patients, respectively. In the triple weekly usage group, the side effects were 12 patients had radiation esophagitis of I-II degree, 1 patient had radiation esophagitis of III degree, 2 patients had radiation pneumonitis of I degree, 1 patient had radiation pneumonitis of II degree, 1 patient had radiation pneumonitis of III degree and died of respiratory failure, 2 patients developed weight loss of I degree. In the weekly usage group, the side effects were 11 patients had radiation esophagitis of I-III degree, 6 patients had radiation pneumonitis of II-III degree, 2 patients developed weight loss of I degree, 6 patients developed leucopenia of III-IV degree. The response rate of two groups was 88.8% and 50.0%, respectively (P=0.026). 1-year survival rate of two groups was 79% and 67%, respectively (P=0.607). Conclusion Although the preliminary results did not show the merits of survival in triple weekly usage, but preliminary results show that triple weekly usage was more safe and effective than weekly usage of paclitaxel in concurrent radiochemotherapy for patients with LANSCLC

Explainable Topic-Enhanced Argument Mining from Heterogeneous Sources

Given a controversial target such as ``nuclear energy'', argument mining aims to identify the argumentative text from heterogeneous sources. Current approaches focus on exploring better ways of integrating the target-associated semantic information with the argumentative text. Despite their empirical successes, two issues remain unsolved: (i) a target is represented by a word or a phrase, which is insufficient to cover a diverse set of target-related subtopics; (ii) the sentence-level topic information within an argument, which we believe is crucial for argument mining, is ignored. To tackle the above issues, we propose a novel explainable topic-enhanced argument mining approach. Specifically, with the use of the neural topic model and the language model, the target information is augmented by explainable topic representations. Moreover, the sentence-level topic information within the argument is captured by minimizing the distance between its latent topic distribution and its semantic representation through mutual learning. Experiments have been conducted on the benchmark dataset in both the in-target setting and the cross-target setting. Results demonstrate the superiority of the proposed model against the state-of-the-art baselines.Comment: 10 pages, 3 figure

Imaging through multimode fibres with physical prior

Imaging through perturbed multimode fibres based on deep learning has been widely researched. However, existing methods mainly use target-speckle pairs in different configurations. It is challenging to reconstruct targets without trained networks. In this paper, we propose a physics-assisted, unsupervised, learning-based fibre imaging scheme. The role of the physical prior is to simplify the mapping relationship between the speckle pattern and the target image, thereby reducing the computational complexity. The unsupervised network learns target features according to the optimized direction provided by the physical prior. Therefore, the reconstruction process of the online learning only requires a few speckle patterns and unpaired targets. The proposed scheme also increases the generalization ability of the learning-based method in perturbed multimode fibres. Our scheme has the potential to extend the application of multimode fibre imaging

Molecular cloning of a novel <em>bioH</em> gene from an environmental metagenome encoding a carboxylesterase with exceptional tolerance to organic solvents

BACKGROUND: BioH is one of the key enzymes to produce the precursor pimeloyl-ACP to initiate biotin biosynthesis de novo in bacteria. To date, very few bioH genes have been characterized. In this study, we cloned and identified a novel bioH gene, bioHx, from an environmental metagenome by a functional metagenomic approach. The bioHx gene, encoding an enzyme that is capable of hydrolysis of p-nitrophenyl esters of fatty acids, was expressed in Escherichia coli BL21 using the pET expression system. The biochemical property of the purified BioHx protein was also investigated. RESULTS: Screening of an unamplified metagenomic library with a tributyrin-containing medium led to the isolation of a clone exhibiting lipolytic activity. This clone carried a 4,570-bp DNA fragment encoding for six genes, designated bioF, bioHx, fabG, bioC, orf5 and sdh, four of which were implicated in the de novo biotin biosynthesis. The bioHx gene encodes a protein of 259 aa with a calculated molecular mass of 28.60 kDa, displaying 24-39% amino acid sequence identity to a few characterized bacterial BioH enzymes. It contains a pentapeptide motif (Gly(76)-Trp(77)-Ser(78)-Met(79)-Gly(80)) and a catalytic triad (Ser(78)-His(230)-Asp(202)), both of which are characteristic for lipolytic enzymes. BioHx was expressed as a recombinant protein and characterized. The purified BioHx protein displayed carboxylesterase activity, and it was most active on p-nitrophenyl esters of fatty acids substrate with a short acyl chain (C4). Comparing BioHx with other known BioH proteins revealed interesting diversity in their sensitivity to ionic and nonionic detergents and organic solvents, and BioHx exhibited exceptional resistance to organic solvents, being the most tolerant one amongst all known BioH enzymes. This ascribed BioHx as a novel carboxylesterase with a strong potential in industrial applications. CONCLUSIONS: This study constituted the first investigation of a novel bioHx gene in a biotin biosynthetic gene cluster cloned from an environmental metagenome. The bioHx gene was successfully cloned, expressed and characterized. The results demonstrated that BioHx is a novel carboxylesterase, displaying distinct biochemical properties with strong application potential in industry. Our results also provided the evidence for the effectiveness of functional metagenomic approach for identifying novel bioH genes from complex ecosystem