Heat and mass transfer characteristics of radiative hybrid nanofluid flow over a stretching sheet with chemical reaction

Unsteady magnetohydrodynamic heat and mass transfer analysis of hybrid nanoliquid flow over a stretching surface with chemical reaction, suction, slip effects, and thermal radiation is analyzed in this study. A combination of alumina (Al₂O₃) and titanium oxide (TiO₂) nanoparticles are taken as hybrid nanoparticles and water is considered as the basefluid. Using the similarity transformation method, the governing equations are changed into a system of ordinary differential equations. These equations together with boundary conditions are numerically evaluated by using the Finite element method. The influence of various pertinent parameters on the profiles of fluids concentration, temperature, and velocity is calculated and the outcomes are plotted through graphs. The values of nondimensional rates of heat transfer, mass transfer, and velocity are also analyzed and the results are depicted in tables. Temperature sketches of hybrid nanoliquid intensified in both the steady and unsteady cases as the volume fraction of both nanoparticles rises.     

Physicochemical stability of antilisterial proteins from P. polymyxa Kp10 as potential food biopreservative

Listeria monocytogenes has continuously become a significant threat to consumers worldwide. The use of chemical-derived preservatives that are commonly associated with safety and nutritional issues has prompted the use of natural-based preservatives as a better alternative. Many bacterial strains including Paenibacillus polymyxa Kp10 have been reported to produce various antimicrobial proteins and compounds that are considered more natural. However, their stability in various physicochemical conditions should be examined before being applied in various types of food. In this study, the stability of four previously identified antilisterial proteins in P. polymyxa Kp10 upon exposure to several physicochemical conditions was examined. More than 80% residual antilisterial activity is conserved upon heat and proteinase K treatment. But, sensitivity to 24 h trypsin digestion has been observed. P1 and P2 proteins (histone-like DNA binding proteins HU) were sensitive to alkaline pH (pH 10-12) as compared with other proteins. More than 70% and 97% residual antilisterial activity were recovered after incubation in raw beef homogenates and simulated meat gravy model, respectively. However, the antilisterial activity of most proteins was highly compromised in chicken and salmon meat homogenates, and UHT cow milk. Inoculation of these proteins into Listeria-contaminated simulated meat gravy showed that all proteins exerted a bactericidal action against L. monocytogenes. P1 and P2 shared almost similar antilisterial activity rates, while P4 was the most potent antilisterial protein. The findings in this study could provide important preliminary data for future applications of these proteins as preservative in food products especially beef-based meat products.     

Utilization and impacts of hydrogen in the ironmaking processes: A review from lab-scale basics to industrial practices

Hydrogen (H₂) energy is a promising candidate to replace carbon monoxide (CO) as a reductant for iron oxide reduction in revolutionary ironmaking industrial processes, and numerous studies have been conducted to intensively study the utilization and impact of H₂ in ironmaking processes. Therefore, this review first collects and compares the H₂-assisted reduction mechanism and kinetics. The impacts of H₂ on the reduction accompanying behaviors, such as the disintegration, swelling, sticking, softening, and melting of iron ores, are then summarized. Third, the performance of H₂ predicted by either mass and heat balance models or numerical simulation models in various ironmaking processes is highlighted. Finally, the different applications of hydrogen-rich materials in blast furnace and non-blast furnace ironmaking processes are further compared to illuminate H₂ utilization before our outlook on the use of H₂ in the ironmaking industry.     

Large lakes in climate models: A Great Lakes case study on the usability of CMIP5

Large lakes have an impact on regional weather. In addition, they can be both sensitive to and influence regional climate changes. In the climate models that are used to investigate future climate changes, lakes are greatly simplified and sometimes absent. At the regional scale, this can have strong implications for the quality of the model information about the future. Through our work with climate information users in the Laurentian Great Lakes region, we have found that basic credibility of the information requires the underlying climate models simulate lake-atmosphere-land interactions. We are not aware of efforts within the scientific community to make known how individual large lakes are represented in models and how those representations translate to the quality of the data for particular regions. We share our framework for identifying how the Laurentian Great Lakes are represented in the Coupled Model Intercomparison Project (CMIP) version 5 climate models. We found that most CMIP5 models do not simulate the Great Lakes in a way that captures their impact on the regional climate, which is a credibility issue for their projections. We provide a perspective on the usability of CMIP5 for practitioners in the Great Lakes region and offer recommendations for alternative options.     

Soft sensing of water depth in combined sewers using LSTM neural networks with missing observations

Information and communication technologies combined with in-situ sensors are increasingly being used in the management of urban drainage systems. The large amount of data collected in these systems can be used to train a data-driven soft sensor, which can supplement the physical sensor. Artificial Neural Networks have long been used for time series forecasting given their ability to recognize patterns in the data. Long Short-Term Memory (LSTM) neural networks are equipped with memory gates to help them learn time dependencies in a data series and have been proven to outperform other type of networks in predicting water levels in urban drainage systems. When used for soft sensing, neural networks typically receive antecedent observations as input, as these are good predictors of the current value. However, the antecedent observations may be missing due to transmission errors or deemed anomalous due to errors that are not easily explained. This study quantifies and compares the predictive accuracy of LSTM networks in scenarios of limited or missing antecedent observations. We applied these scenarios to an 11-month observation series from a combined sewer overflow chamber in Copenhagen, Denmark. We observed that i) LSTM predictions generally displayed large variability across training runs, which may be reduced by improving the selection of hyperparameters (non-trainable parameters); ii) when the most recent observations were known, adding information on the past did not improve the prediction accuracy; iii) when gaps were introduced in the antecedent water depth observations, LSTM networks were capable of compensating for the missing information with the other available input features (time of the day and rainfall intensity); iv) LSTM networks trained without antecedent water depth observations yielded larger prediction errors, but still comparable with other scenarios and captured both dry and wet weather behaviors. Therefore, we concluded that LSTM neural network may be trained to act as soft sensors in urban drainage systems even when observations from the physical sensors are missing.     

Does thermal control improve visual satisfaction? Interactions between occupants’ self-perceived control,visual, thermal,and overall satisfaction

Occupants’ satisfaction had been researched independently related to thermal and visual stimuli for many decades showing among others the influence of self-perceived control. Few studies revealed interactions between thermal and visual stimuli affecting occupant satisfaction. In addition, studies including interactions between thermal and visual stimuli are lacking different control scenarios. This study focused on the effects of thermal and visual factors, their interaction, seasonal influences, and the degree of self-perceived control on overall, thermal, and visual satisfaction. A repeated-measures laboratory study with 61 participants running over two years and a total of 986 participant sessions was conducted. Mixed model analyses with overall satisfaction as outcome variable revealed that thermal satisfaction and visual satisfaction are the most important predictors for overall satisfaction with the indoor environment. Self-perceived thermal control served as moderator between thermal satisfaction and overall satisfaction. Season had slight influence on overall satisfaction. Random effects explained the highest amount of variance, indicating that intra- and interindividual differences in the ratings of satisfaction are more prevalent than study condition. Future building design and operation plans aiming at a high level of occupant satisfaction should consider personal control opportunities and take into account the moderating effect of control opportunities in multimodal interactions.     

基于声发射和高斯混合模型的灰岩破裂特征识别研究

通过单轴压缩条件下灰岩破裂过程的声发射试验研究,利用高斯混合模型(GMM)对加载过程声发射信号波形特征进行深入分析,探索性识别灰岩破裂失稳过程的裂纹模式及其前兆特征。分析结果表明,灰岩在单轴加载过程中先后主要存在张拉和剪切两种破裂模式。其中,张拉破裂的声发射信号波形特征在AF-RA坐标空间呈现低A_F值、高R_A值分布;剪切破裂的声发射信号波形特征在AF-RA坐标空间呈现低R_A值、高A_F值,且随着应力的增加分布中心向A_F轴靠拢。GMM分析结果揭示了灰岩在整个应力加载过程中以张拉裂纹为主,在加载前中期几乎全为张拉裂纹,临近破坏阶段过渡到剪切破坏为主。剪切裂纹所占比例的最大值出现在(0.8～0.9)σ_c阶段,也是AF-RA坐标轴分布呈现最大A_F值时。研究结果可为预测早期灰岩破裂失稳提供参考,同时为深入研究识别岩石破裂失稳前兆信号特征提供了一种分析方法。     

Resonance Assignments of Lowly Populated and Unstable Enzyme Intermediate Complex under Real-Time Conditions

Unstable and low-abundance protein complexes represent a large family of transient protein complexes that are difficult to characterize, even by means of high-resolution NMR spectroscopy. A method to assign the NMR signals of these unstable complexes through a combination of selective isotope labeling of amino acids in a protein and site-specific labeling the protein with a paramagnetic tag is presented herein. By using this method, the resonances of unstable thioester intermediate complex (lifetime <5 h and highest concentration ≈20 μm ) generated by Staphylococcus aureus sortase A and its peptide substrate under a real-time reaction have been assigned.