碳酸盐岩岩溶储层多井评价方法及地质应用

多井评价是建立在单井精细解释对比分析基础上的预测储层及含油气性平面分布规律的技术方法,而常规测井资料无法区分碳酸盐岩岩溶储层,因而利用多井评价结果确定岩溶发育程度在平面上的分布规律就显得十分重要。为此,在岩心标定成像测井的基础上,对四川盆地高石梯—磨溪地区15口井的成像测井岩溶发育特征进行分析,建立了中二叠统茅口组岩溶发育各分带的标准成像图版,利用交会图及直方图分析各分带的常规测井响应特征,在此基础上形成了电成像测井刻度常规测井识别岩溶发育带的新方法。研究结果表明:①高石梯—磨溪地区茅口组岩溶带自上而下可划分为风化壳残积带、垂直渗流岩溶带、水平潜流岩溶带以及受岩溶作用较弱的基岩;②风化壳残积带在成像测井图像显示为"暗—亮—暗"条带状模式,垂直渗流岩溶带为垂直线状与暗色斑状组合模式,水平潜流岩溶带为水平线状—层状与斑状组合模式,基岩整体显示为亮色块状模式偶见线状或斑状特征;③有效储层主要发育在垂直渗流带和水平潜流带的顶部;④该区茅口组岩溶发育主要受裂缝发育控制,而裂缝发育又与断层关系密切。结论认为,该新方法对碳酸盐岩岩溶储层的多井评价具有普遍适用性,为四川盆地中二叠统风险探井的部署提供了技术支撑。     

自然循环工况下离心泵阻力特性的实验研究与数值模拟

停转后泵的阻力特性对自然循环流量影响明显。为研究低流量自然循环工况下离心泵的阻力特性，设计了实验台架，对离心泵的正向压降、反向压降和损失系数进行了测量，实验雷诺数为2.0×10⁴～1.5×10⁵。实验表明：相同雷诺数下，反向压降明显高于正向压降；雷诺数大于8×10⁴时，损失系数基本保持不变，而低雷诺数下损失系数随雷诺数的降低有增大的趋势；基于实验得到了损失系数的经验关系式。采用CFD方法对离心泵的内部流场进行了模拟，计算表明：CFD方法能较好地预测损失系数，RNG k-ε模型与实验值的相对误差不超过10%；离心泵的压力损失主要集中在叶轮、导叶等结构的交界区域；正向与反向流动的流场分布存在显著区别；低雷诺数下局部流动更加不稳定。     

重力注水过程流动不稳定现象关键影响因素研究

重力驱动注水过程中由于流量较小以及蒸汽的积聚可能导致流动不稳定现象的发生，对核反应堆安全运行具有重要的影响。通过实验研究的方法，搭建了重力注水模拟实验装置，研究了不同蒸汽出口形阻、高位储水箱水位和加热棒初始温度下流动不稳定现象的变化规律。结果表明，重力驱动注水过程流动不稳定现象包含冷却水初次注入阶段、注入水逐出阶段和冷却水再注入阶段等。在一定冷却水初始温度、冷却水入口形阻以及系统压力下，蒸汽排出速度以及实验本体内筒顶部的聚集情况取决于蒸汽出口形阻，减小蒸汽出口形阻可加快蒸汽排放速度，压力峰峰值降低、振荡周期变长，有利于系统稳定；提高高位储水箱水位加快了冷却水注入速率，增加了加热棒被淹没率，降低了流动不稳定现象的发生次数和持续时间；随加热棒初始温度的升高，冷却水流量出现了波动向停滞的转变，流动不稳定现象发生的次数增加且持续时间加长。     

冷链条件下气调包装酱卤鸭肉制品货架期预测模型的建立

目的 以气调包装酱卤鸭肉制品为研究对象,在冷链温度范围内建立一套准确、高效的货架期预测模型。方法 利用选择性培养基测定不同温度下产品各微生物数量,确定4~25℃条件下产品优势腐败菌。对乳酸菌数量与感官评定值进行了回归分析确定最小腐败量Ns。分别采用修正的Gompertz方程和平方根方程建立一、二级模型,并通过预测值与实测值对比验证模型的可靠性。结果 确定了4~25℃条件下产品优势腐败菌为乳酸菌,最小腐败量Ns=6.14(lg(cfu /g))。一、二级模型拟合度均良好,三种温度下模型预测值与实际值间的差异均在30％左右,波动幅度在10%以内。结论 实现了对4~25℃内任何时间点产品剩余货架期的预测,为冷链条件下气调包装酱卤鸭肉制品品质的变化提供了理论指导。     

Human mobility synthesis using matrix and tensor factorizations

Human mobility prediction is of great advantage in route planning and schedule management. However, mobility data is a high-dimensional dataset in which multi-context prediction is difficult in a single model. Mobility data can usually be expressed as a home event, a work event, a shopping event and a traveling event. Previous works have only been able to learn and predict one type of mobility event and then integrate them. As the tensor model has a strong ability to describe high-dimensional information, we propose an algorithm to predict human mobility in tensors of location context data. Using the tensor decomposition method, we extract human mobility patterns with multiple expressions and then synthesize the future mobility event based on mobility patterns. The experiment is based on real-world location data and the results show that the tensor decomposition method has the highest accuracy in terms of prediction error among the three methods. The results also prove the feasibility of our multi-context prediction model.     

悬浮于润滑油中的颗粒运动分析及其对油膜压力的影响

对含有固体颗粒的局部润滑流域建立格子Boltzmann(LBM)离散模型,分析固体颗粒在润滑油中的动力学特性;考虑颗粒形状的影响,推导计入单个固体颗粒运动的润滑方程,并分析得到油膜压力;将油膜流动特性与颗粒动力学计算相结合,分析不同形状的颗粒运动对于油膜压力的影响。分析发现,当颗粒进入润滑油后,经过很短的瞬时颗粒就会达到一个瞬态稳定的状态,无论颗粒在油膜厚度方向的初始位置位于两壁面之间的中线上侧还是下侧,颗粒都会向中线位置移动;当颗粒速度为0时对于油膜压力的影响较大,随着颗粒速度逐渐增大,颗粒对于油膜压力的影响逐渐减小;当颗粒的宽度在油膜厚度方向相同时,长宽比越大的颗粒对于油膜压力的影响也越大;当颗粒长轴相等时,颗粒在油膜厚度方向的宽度越大,则其对于油膜压力的影响也越大,即颗粒形状对于油膜流动的阻碍能力越强,则其对于油膜压力的影响越大。     

Identification of Host Cellular Protein Substrates of SARS-COV-2 Main Protease

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease-19 (COVID-19) being associated with severe pneumonia. Like with other viruses, the interaction of SARS-CoV-2 with host cell proteins is necessary for successful replication, and cleavage of cellular targets by the viral protease also may contribute to the pathogenesis, but knowledge about the human proteins that are processed by the main protease (3CLpro) of SARS-CoV-2 is still limited. We tested the prediction potentials of two different in silico methods for the identification of SARS-CoV-2 3CLpro cleavage sites in human proteins. Short stretches of homologous host-pathogen protein sequences (SSHHPS) that are present in SARS-CoV-2 polyprotein and human proteins were identified using BLAST analysis, and the NetCorona 1.0 webserver was used to successfully predict cleavage sites, although this method was primarily developed for SARS-CoV. Human C-terminal-binding protein 1 (CTBP1) was found to be cleaved in vitro by SARS-CoV-2 3CLpro, the existence of the cleavage site was proved experimentally by using a His₆-MBP-mEYFP recombinant substrate containing the predicted target sequence. Our results highlight both potentials and limitations of the tested algorithms. The identification of candidate host substrates of 3CLpro may help better develop an understanding of the molecular mechanisms behind the replication and pathogenesis of SARS-CoV-2.     

Extended preservation of raw beef and pork meat by hyperbaric storage at room temperature

Hyperbaric storage (HS) was evaluated as a new food preservation methodology at room temperature (RT) for beef and pork meat, both minced and in pieces, and compared to refrigeration (RF) storage. The meat samples were stored at 50, 75 and 100 MPa and variable RT up to 60 days. HS at 75 and 100 MPa could not only inhibit microbial growth but also inactivate microorganisms. Regarding physicochemical analyses, an overall equal to better pH maintenance in HS samples was achieved, and similar colour differences between HS and RF were observed. Generally, similarities in moisture content and drip loss between HS (mostly 75 and 100 MPa) and RF were detected (tendency for lower values in the former and higher values in the latter for the higher pressure level). Protein solubility revealed a decrease of sarcoplasmic protein values during storage with a pressure level dependency in some samples.     

Three‐dimensional color prediction modeling of single‐ and double‐layered woven fabrics

In this research, the three‐dimensional structural and colorimetric modeling of three‐dimensional woven fabrics was conducted for accurate color predictions. One‐hundred forty single‐ and double‐layered woven samples in a wide range of colors were produced. With the consideration of their three‐dimensional structural parameters, three‐dimensional color prediction models, K/S‐, R‐, and L*a*b*‐based models, were developed through the optimization of previous two‐dimensional models which have been reported to be the three most accurate models for single‐layered woven structures. The accuracy of the new three‐dimensional models was evaluated by calculating the color differences ΔL*, ΔC*, Δh°, and ΔE_CMC(2:1) between the measured and the predicted colors of the samples, and then the error values were compared to those of the two‐dimensional models. As a result, there has been an overall improvement in color predictions of all models with a decrease in ΔE_CMC(2:1) from 10.30 to 5.25 units on average after the three‐dimensional modeling.