Mechanism and Prevention of Agglomeration/Defluidization during Fluidized-Bed Reduction of Iron Ore

The mechanisms of agglomeration and defluidization and fluidization characteristic of iron oxide particles were investigated based on the theory of surface diffusion, interface reaction, surface nano/microeffect, and phase transformation. Moreover, a mathematical model was developed to predict the high-temperature defluidization behavior by the force-balance and plastic-viscous flow mechanism, and the fluidization phase diagram was obtained. On these bases, a control method of defluidization and its inhibition mechanism were proposed. As a result, the theoretical system of agglomeration/defluidization in the gas-solid fluidization was developed, and thus afforded theory support and technological bases for the solution of defluidization in industrial fluidized-bed reactors

Changes in nonlinearity and stability of streamflow recession characteristics under climate warming in a large glaciated basin of the Tibetan Plateau

The accelerated climate warming in the Tibetan Plateau after 1997 has profound consequences in hydrology, geography, and social wellbeing. In hydrology, the change in streamflow as a result of changes in dynamic water storage that originated from glacier melt and permafrost thawing in the warming climate directly affects the available water resources for societies of the most populated nations in the world. In this study, annual streamflow recession characteristics are analyzed using daily climate and hydrological data during 1980–2015 in the Yarlung Zangbo River basin (YRB) of the southern Tibetan Plateau. The recession characteristics are examined in terms of dQ=dt DaQb and the response/ sensitivity of streamflow to changes in groundwater storage. Major results show that climate warming has significantly increased the nonlinearity of the response (b) and streamflow stability [log.a/] in most subbasins of the YRB. These changes in the recession characteristics are attributed to the opposite effects of increases in the available water storage and recession timescale on the recession. Climate warming has increased subbasin water storage considerably due to more recharge from accelerated glacier melting and permafrost thawing after 1997. Meanwhile, the enlarged storage lengthens recession timescales and thereby decreases the sensitivity of discharge to storage. In the recession period when recharge diminished, increased evaporation and the decreased buffering effect of frost soils under warmer temperatures accelerate the initial recession of streamflow. By contrast, enlarged storage and lengthened recession timescales slow down the recession. While reservoir regulations in some basins have helped reduce and even reverse some of these climate warming effects, this short-term remedy can only function before the solid water storage is exhausted should the climate warming continue

Self-Normalized Importance Sampling for Neural Language Modeling

To mitigate the problem of having to traverse over the full vocabulary in the softmax normalization of a neural language model, sampling-based training criteria are proposed and investigated in the context of large vocabulary word-based neural language models. These training criteria typically enjoy the benefit of faster training and testing, at a cost of slightly degraded performance in terms of perplexity and almost no visible drop in word error rate. While noise contrastive estimation is one of the most popular choices, recently we show that other sampling-based criteria can also perform well, as long as an extra correction step is done, where the intended class posterior probability is recovered from the raw model outputs. In this work, we propose self-normalized importance sampling. Compared to our previous work, the criteria considered in this work are self-normalized and there is no need to further conduct a correction step. Through self-normalized language model training as well as lattice rescoring experiments, we show that our proposed self-normalized importance sampling is competitive in both research-oriented and production-oriented automatic speech recognition tasks.Comment: Accepted at INTERSPEECH 202

Inhibition of highly pathogenic PRRSV replication in MARC-145 cells by artificial microRNAs

<p>Abstract</p> <p>Background</p> <p>Highly pathogenic porcine reproductive and respiratory syndrome (HP-PRRS) has caused large economic losses in swine industry in recent years. However, current antiviral strategy could not effectively prevent and control this disease. In this research, five artificial microRNAs (amiRNAs) respectively targeted towards ORF5 (amirGP5-243, -370) and ORF6 (amirM-82, -217,-263) were designed and incorporated into a miRNA-based vector that mimics the backbone of murine miR-155 and permits high expression of amiRNAs in a GFP fused form mediated by RNA Pol II promoter CMV.</p> <p>Results</p> <p>It was found that amirGP5-370 could effectively inhibit H-PRRSV replication. The amirM-263-M-263, which was a dual pre-amiRNA expression cassette where two amirM-263s were chained, showed stronger virus inhibitory effects than single amirM-263. H-PRRSV replication was inhibited up to 120 hours in the MARC-145 cells which were stably transduced by recombinant lentiviruses (Lenti-amirGP5-370, -amirM-263-M-263). Additionally, efficacious dose of amirGP5-370 and amirM-263 expression did not trigger the innate interferon response.</p> <p>Conclusions</p> <p>Our study is the first attempt to suppress H-PRRSV replication in MARC-145 cells through vector-based and lentiviral mediated amiRNAs targeting GP5 or M proteins coding sequences of PRRSV, which indicated that artificial microRNAs and recombinant lentiviruses might be applied to be a new potent anti-PRRSV strategy.</p

Inhibition of HSP90 attenuates porcine reproductive and respiratory syndrome virus production in vitro

BACKGROUND: Porcine reproductive and respiratory syndrome virus (PRRSV) infection leads to substantial economic losses to the swine industry worldwide. However, no effective countermeasures exist to combat this virus infection so far. The most common antiviral strategy relies on directly inhibiting viral proteins. However, this strategy invariably leads to the emergence of drug resistance due to the error-prone nature of viral ploymerase. Targeting cellular proteins required for viral infection for developing new generation of antivirals is gaining concern. Recently, heat shock protein 90 (HSP90) was found to be an important host factor for the replication of multiple viruses and the inhibition of HSP90 showed significant antiviral effects. It is thought that the inhibition of HSP90 could be a promising broad-range antiviral approach. However, the effects of HSP90 inhibition on PRRSV infection have not been evaluated. In the current research, we tried to inhibit HSP90 and test whether the inhibition affect PRRSV infection. METHODS: We inhibit the function of HSP90 with two inhibitors, geldanamycin (GA) and 17- allylamono-demethoxygeldanamycin (17-AAG), and down-regulated the expression of endogenous HSP90 with specific small-interfering RNAs (siRNAs). Cell viability was measured with alamarBlue. The protein level of viral N was determined by western blotting and indirect immunofluorescence (IFA). Besides, IFA was employed to examine the level of viral double-stranded RNA (dsRNA). The viral RNA copy number and the level of IFN-β mRNA were determined by quantitative real-time PCR (qRT-PCR). RESULTS: Our results indicated that both HSP90 inhibitors showed strong anti-PRRSV activity. They could reduce viral production by preventing the viral RNA synthesis. These inhibitory effects were not due to the activation of innate interferon response. In addition, we observed that individual knockdown targeting HSP90α or HSP90β did not show dramatic inhibitory effect. Combined knockdown of these two isoforms was required to reduce viral infection. CONCLUSIONS: Our results shed light on the possibility of developing potential therapeutics targeting HSP90 against PRRSV infection

TOC interpretation of lithofacies-based categorical regression model: A case study of the Yanchang formation shale in the Ordos basin, NW China

In this paper, taking the shale of Chang 7-Chang 9 oil formation in Yanchang Formation in the southeastern Ordos Basin as an example, through the study of shale heterogeneity characteristics, starting from the preprocessing of supervision data set, a logging interpretation method of total organic carbon content (TOC) on the lithofacies-based Categorical regression model (LBCRM) is proposed. It is show that: 1) Based on core observation, and Differences of sedimentation and structure, five lithofacies developed in the Yanchang Formation: shale shale facies, siltstone/ultrafine sandstone facies, tuff facies, argillaceous shale facies with silty lamina and argillaceous shale facies with tuff lamina. 2) The strong heterogeneity of shale makes it difficult to accurately explain the TOC distribution of shale intervals in the application of model-based interpretation methods. The LBCRM interpretation method based on the understanding of shale heterogeneity can effectively reduce the influence of formation factors other than TOC on the prediction accuracy by studying the characteristics of shale heterogeneity and constructing a TOC interpretation model for each lithofacies category. At the same time, the degree of unbalanced distribution of data is reduced, so that the data mining algorithm achieves better prediction effect. 3) The interpretability of lithofacies logging ensures the wellsite application based on the classification and regression model of lithofacies. Compared with the traditional homogeneous regression model, the prediction performance has been greatly improved, TOC segment prediction is more accurate. 4) The LBCRM method based on shale heterogeneity can better understand the reasons for the deviation of the traditional model-based interpretation method. After being combined with the latter, it can make logging data provide more useful information