汉江上游干支流沉积物细菌群落多样性和影响因素

为了解汉江上游干支流沉积物细菌多样性以及确定性过程和随机性过程在沉积物细菌群落构建过程中的相对重要性,基于Illumina高通量测序技术,分析了环境因子对细菌群落组成的影响,采用非度量多维尺度(NMDS)排序探究了季节之间沉积物细菌群落的差异,并结合中性群落模型和标准化随机率量化了确定性过程和随机过程对群落构建的影响。结果表明:汉江上游及其支流细菌群落主要由变形菌门(Proteobacteria)、拟杆菌门(Bacteroidetes)、蓝藻门(Cyanophyta)、浮霉菌门(Planctomycetes)和酸杆菌门(Acidobacteria)等组成；细菌群落在不同季节有显著差异；地理距离和环境因子对细菌群落结构影响较小,确定性过程并未在细菌群落组成中起到主导作用；随机过程很大程度上影响了群落在秋季和春季的组成,是沉积物细菌群落构建的主导因素。     

基于双应力加速寿命试验的关节轴承寿命预测与可靠性分析

自润滑关节轴承寿命主要是由衬垫的磨损性能决定的,但衬垫的磨损一般是非线性的,使得寿命难以预测。采用同时改变载荷和摆频2种应力的方法进行加速寿命试验,建立以pv值、磨损量退化数据为输入参数,寿命值为输出参数的灰色神经网络预测模型。经验证该预测模型对关节轴承寿命预测的最大误差仅为7.33%,平均误差仅为3.892%。对不同加速应力下自润滑关节轴承可靠性进行评估,结果表明,关节轴承的可靠性在L_(10)(可靠度为90%时的寿命)之前下降趋势缓慢,然后迅速下降,pv值越大可靠性下降越迅速;随着pv值的增大,关节轴承寿命近似呈指数下降,经验证可用逆幂率加速模型反映二者关系。     

一种灰度化混合法在集装箱箱号识别中的运用

研究一种基于机器视觉的集装箱箱号识别方法。对于集装箱彩色图像预处理过程中的灰度化方法，传统的灰度化算法不能有效弥补图像中污损或其他信息缺失的部分，因此，本文提出使用主成分分析法（PCA）结合贝叶斯阈值估计灰度变化率的混合法对图像的灰度化进行优化，可以在判断图像中某一点灰度值与周围相邻像素点的灰度值的变化率后，弥补缺失信息，有效确定边缘特征，从而使后续的字符识别准确率大大提高。最后使用该算法模型设计实现一套用于港口集装箱的智能检测系统。经过Matlab实验验证，在对50幅港口集装箱箱号图像的识别中，通过使用本文提出的混合灰度化方法，与普通的均值法和加权平均法的灰度化方法相比，准确率更高，其中单一字符准确率可达96%，箱号准确率可达92%。     

300M超高强钢在中性盐雾环境中的腐蚀行为及机制

摘要：为了研究300M超高强钢在中性盐雾环境中的腐蚀行为及腐蚀机制，采用失重法，宏观、微观腐蚀形貌分析，三维表面轮廓分析及电化学分析的研究方法，来表征腐蚀实验现象并进行分析。结果表明：300M超高强钢在中性盐雾环境中的腐蚀产物为FeOOH、Fe2O3、Fe(OH)3和Fe3O4；腐蚀速率随着腐蚀时间逐渐降低，腐蚀后期（72h）腐蚀速率降低50%；腐蚀初期以点蚀为主，点蚀坑通过横向扩展，逐渐发展为后期的均匀腐蚀，腐蚀表面形貌呈沟壑状；外腐蚀层对基体的保护能力很弱，Cr元素在锈层靠近基体的一侧偏聚使内腐蚀层具有一定的抗腐蚀性。     

热镀锌渣中浸渣硫酸浸出锌的工艺研究

以锌含量为17.22%的热镀锌渣中浸渣为原料,在水热反应釜中以硫酸为浸出剂浸出其中锌。结果表明,最佳浸出条件为: 硫酸体积浓度15%、浸出温度180 ℃、液固比6∶1、浸出时间8 h,此条件下锌浸出率可达到99.71%; 浸渣主要成分为硫酸铝盐和硫酸钙。     

煤粉锅炉脱硫系统中除尘器压差过高原因及解决方案

高倍率灰钙循环烟气脱硫技术在神东矿区的煤粉锅炉环保改造项目中已投入建设并成功运行,脱硫率可达95%以上。在NGD系统的运行调试期,布袋除尘器压差的数值及其上升速度均较改造前有大幅提高,影响了锅炉系统的正常运行并降低了布袋除尘器的运行效率。为了解决此问题,先分析滤料透气性降低而导致布袋除尘器压差上升的根本原因,再结合现场调研情况对NGD系统工况下的烟气含湿量、烟气温度、烟气灰浓度、除尘器本体的问题以及上游锅炉系统问题逐一进行对比和分析,并找出问题产生的各主要因素。NGD系统中布袋除尘器差压升高的原因包括多方面,为了使NGD系统的运行更加稳定,需要使除尘器适应NGD系统的工况并对系统中部分设备进行改造。在未来NGD系统的设计中,布袋除尘器需按高浓度烟气设计,且针对煤粉工业锅炉的运行、维护特性应加入对烟气中含水量的监测及对布袋除尘器的相应保护措施。     

Bimetallic CuCo2S4 Nanozymes with Enhanced Peroxidase Activity at Neutral pH for Combating Burn Infections

Peroxidase-mimicking nanozymes that can generate toxic hydroxyl radicals (^.OH) hold great promise as antibacterial alternatives. However, most of them display optimal performance under strongly acidic conditions (pH 3–4), and are thus not feasible for many medical uses, including burn infections with a wound pH close to neutral. Herein, we report a copper-based nanozyme (CuCo₂S₄) that exhibits intrinsic peroxidase-like activity and can convert H₂O₂ into ^.OH at neutral pH. In particular, bimetallic CuCo₂S₄ nanoparticles (NPs) exhibited enhanced peroxidase-like activity and antibacterial capacity, superior to that of the corresponding monometallic CuS and CoS NPs. The CuCo₂S₄ nanozymes possessed excellent ability to kill various bacteria, including methicillin-resistant Staphylococcus aureus (MRSA). Furthermore, this CuCo₂S₄ nanozymes could effectively disrupt MRSA biofilms in vitro and accelerate MRSA-infected burn healing in vivo. This work provides a new peroxidase mimic to combat bacteria in neutral pH milieu and this CuCo₂S₄ nanozyme could be a promising antibacterial agent for the treatment of burn infections.     

Reduction of Escherichia coli O157:H7 and Salmonella Typhimurium DT 104 on fresh produce using an automated washer with near neutral electrolyzed (NEO) water and ultrasound

Automated produce washers can be a useful processing aid when treating fresh produce contaminated with pathogens. The use of near neutral pH electrolyzed (NEO) water as a wash or sanitizing solution has been shown to lead to significant reductions of Escherichia coli O157:H7 and Salmonella on fresh produce. To further enhance reported pathogen reductions, the effects of a combined NEO water (155 mg/L free chlorine, pH 6.5) and ultrasound wash protocol on lettuce and tomatoes inoculated with E. coli O157:H7 and S. Typhimurium DT 104 were studied. The effects of the pH of NEO water and washer agitation on pathogen reductions were also assessed. Inoculated tomatoes and lettuce leaves were treated with either chilled deionized water or NEO water, with or without 20 kHz ultrasound (130 W and 210 W). Tomatoes were treated for 1, 3 and 5 min while lettuce was treated for 5, 10 and 15 min. Ultrasound significantly increased the oxidation-reduction potential (ORP) of NEO water (p < 0.05) but did not affect the pH and free chlorine concentration (p > 0.05). Increased washing time and higher ultrasonic power led to significantly greater reductions of both pathogens on produce items (p < 0.05). NEO water combined with 210 W ultrasonication for 15 min led to 4.4 and 4.3 log reductions of E. coli O157:H7 and S. Typhimurium on lettuce, respectively, while 210 W ultrasound for 5 min completely inactivated both pathogens on tomatoes. Both pathogens were completely inactivated in NEO water solutions, suggesting that its use presents little chance of cross-contamination.     

Asymptotic stability of general linear methods for systems of linear neutral delay differential-algebraic equations

This paper is concerned with numerical stability of general linear methods (GLMs) for a system of linear neutral delay differential-algebraic equations. A sufficient and necessary condition for asymptotic stability of GLMs solving such system is derived. Based on this main result, we further investigate the asymptotic stability of linear multistep methods, Runge–Kutta methods, and block θ-methods, respectively. Numerical experiments confirm our theoretical result.     

Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms

In pharmaceutical development, it is very useful to exploit the knowledge of the causal relationship between product quality and critical material attributes (CMA) in developing new formulations and products, and optimizing manufacturing processes. With the big data captured in the pharmaceutical industry, computational intelligence (CI) models could potentially be used to identify critical quality attributes (CQA), CMA and critical process parameters (CPP). The objective of this study was to develop computational intelligence models for pharmaceutical tabletting processes, for which bio-inspired feature selection algorithms were developed and implemented for optimisation while artificial neural network (ANN) was employed to predict the tablet characteristics such as porosity and tensile strength. Various pharmaceutical excipients (MCC PH 101, MCC PH 102, MCC DG, Mannitol Pearlitol 200SD, Lactose, and binary mixtures) were considered. Granules were also produced with dry granulation using roll compaction. The feed powders and granules were then compressed at various compression pressures to produce tablets with different porosities, and the corresponding tensile strengths were measured. For the CI modelling, the efficiency of seven bio-inspired optimization algorithms were explored: grey wolf optimization (GWO), bat optimization (BAT), cuckoo search (CS), flower pollination algorithm (FPA), genetic algorithm (GA), particle swarm optimization (PSO), and social spider optimization (SSO). Two-thirds of the experimental dataset was randomly chosen as the training set, and the remaining was used to validate the model prediction. The model efficiency was evaluated in terms of the average reduction (representing the fraction of selected input variables) and the mean square error (MSE). It was found that the CI models can well predict the tablet characteristics (i.e. porosity and tensile strength). It was also shown that the GWO algorithm was the most accurate in predicting porosity. While the most accurate prediction for the tensile strength was achieved using the SSO algorithm. In terms of the average reduction, the GA algorithm resulted in the highest reduction of inputs (i.e. 60%) for predicting both the porosity and the tensile strength.