Hydrogen refuelling stations in the Netherlands: An intercomparison of quantitative risk assessments used for permitting

As of 2003, 15 hydrogen refuelling stations (HRSs) have been deployed in the Netherlands. To become established, the HRS has to go through a permitting procedure. An important document of the permitting dossier is the quantitative risk assessment (QRA) as it assesses the risks of the HRS associated to people and buildings in the vicinity of the HRS. In the Netherlands, a generic prescribed approach exists on how to perform a QRA, however specific guidelines for HRSs do not exist. An intercomparison among the QRAs of permitted HRSs has revealed significant inconsistencies on various aspects of the QRA: namely the inclusion of HRS sub-systems and components, the HRS sub-system and component considerations as predefined components, the application of failure scenarios, the determination of failure frequencies, the application of input parameters, the consideration of preventive and mitigation measures as well as information provided regarding the HRS surroundings and the societal risk. It is therefore recommended to develop specific QRA guidelines for HRSs.     

基于计算机控制的新型鸡精组分建模研究

以鲜鸡肉粉(富钙型)、味精、食盐、白砂糖、淀粉为主要原料,根据谷氨酸钠、呈味核苷酸二钠、总氮、水分含量的要求,利用计算机数学建模法计算出鸡精中各原料的组分。选出8组较优的方案制成鸡精,上述4种物质的含量与模型非常接近,其中第1组方案的营养价值和感官评价均最好,配方为鲜鸡肉粉14%、味精22%、食盐36%、白砂糖7%、淀粉5%。实验结果表明基于计算机控制的新型鸡精组分建模准确,具有指导意义。     

A datamining approach to classify,select and predict the formation enthalpy for intermetallic compound hydrides

In this paper, two techniques of datamining tools were adopted, a principal component analysis (PCA) and artificial neural network (ANN). A PCA to classify, select and identify several combinations between transition element A and B (B = Ti, Zr, Hf, Sc, Y, La and Th) and ANN to predict ΔH for ternary hydrides. Based on the datasets selected from different works, a principal component analysis (PCA) has been applied to select, classify and identify around 76 possible combinations between transition metal elements A and B. The results showed that the clustering of combinations A-B are significantly influenced by the atomic parameters of element A, such atomic radius (R_A), Pauling's electronegativity (χ_A) and atomic electron density (Z_A/R_A³). From 76 combinations, 55 systems which have χ_A ≥ 1.5, Z_A/R_A³>1.28 and R_A < 1.46 Å are categorized as group 1. On the other hand, 21 systems which have χ_A < 1.5, Z_A/R_A³ < 1.28, and R_A > 1.46 Å are categorized as group 2. From the first group, 46 different combinations are identified and have a negative ΔH, within 18 well-known promising binary alloys of hydrogen storage.An (6-15-1) architecture of artificial neural network (ANN) has been developed to estimate the ΔH for the other ternary hydrides selected from different published works. The performance indices such as relative error, coefficient of determination (R²) and mean square error (MSE) were used to control the performance of obtained results. In addition to this, the ΔH obtained from ANN model were compared with those experimental data and theoretical results available in the literature.     

Gender difference in color preference across cultures: An archetypal pattern modulated by a female cultural stereotype

A gender difference in color preference among British participants has been repeatedly reported, in which both males and females show a preference for blue‐green colors, while females express an additional preference for pink‐purple colors. To investigate the robustness of gender difference in color preference in a different culture, we tested 81 young adult Indians from a school of design and compared them to 80 young British students in Psychology. The 35‐item International Personality Item Pool (IPIP) and Bem Sex Role Inventory (BSRI) questionnaires were also administered to explore possible links between personality traits, gender schemata, and color preferences. Results confirmed a gender difference in both cultures; participants collectively expressed a preference for cool over warm colors, while in addition females showed a preference for pink colors, with a warm bias for Indian females and a cool bias for British females. While these results extend gender difference to Indian culture and support the universality of an underlying pattern they also reveal a culture‐specific contribution essentially observed in females. In British participants, color preference was correlated exclusively with BSRI scores in females and overwhelmingly with IPIP scores in males; this gender‐specific pattern of correlation was not replicated in the Indian sample. Results point to an archetypal pattern of gender difference in color preference with a remarkable cross‐cultural similarity in men and a subtle but significant cultural difference in women whose origin is yet to be explained.     

预制构件厂精益设计方案评价研究

作为新兴产业，预制构件厂的设计和运营是实现装配式建筑构件高效率、高质量生产和配送的关键前提要素。而精益设计可以帮助预制构件厂从源头消除浪费、提高生产效率，所以对预制构件厂构建精益设计方案并且评价实施后的效果无疑是一个重点问题。针对以上问题，本文从节约资金能力、生产能力、交付能力、持续改进能力等4个视角构建出了预制构件厂精益设计评价指标体系，采用了AHP-DEMATEL法确定了指标的权重，并用改进TOPSIS法得到最终结果，最后以某预制构件厂为例验证了精益设计评价指标体系的合理性和可行性。     

Trajectories of knowledge economy in SSA and MENA countries

In the first critical assessment of knowledge economy dynamic paths in Africa and the Middle East, but for a few exceptions, we find overwhelming support for diminishing cross-country disparities in knowledge-based economy dimensions. The paper employs all the four components of the World Bank's Knowledge Economy Index (KEI): economic incentives, innovation, education, and information infrastructure. The main finding suggests that sub-Saharan African (SSA) and the Middle East and North African (MENA) countries with low levels of KE dynamics and catching-up their counterparts of higher KE levels. We provide the speeds of integration and time necessary to achieve full (100%) integration. Policy implications are also discussed.     

果实喷施脱落酸对黑虎香葡萄酒品质的影响

黑虎香葡萄属于鲜食、酿酒兼用品种，香气独特、抗逆性强。对转色期黑虎香葡萄果实喷施不同浓度脱落酸（ABA），研究其对葡萄酒品质的影响。结果表明，喷施500 mg/L ABA处理效果最好，显著提高黑虎香葡萄酒酒精度、干浸出物、花色苷和总酚含量，显著降低酸含量（P＜0.05）；显著增加黑虎香葡萄酒萜烯类、醇类和醛酮类等香气物质含量（P＜0.05），含量影响较大的香气成分为乙酸乙酯、乙酸苯乙酯、苯乙醇、壬醇、戊二酮、香叶基丙酮、乙缩醛、香茅醇和香叶醇。     

Preparation and properties of fluorinated silicon two‐component polyurethane hydrophobic coatings

A polyurethane (PU) hydrophobic coating was prepared by the two‐component method, polycarbonate diol and isophorone diisocyanate becoming a two‐phase composition. The PU films with hydrophobic surface were prepared by establishing a rough structure on the surface of silica (SiO₂) modified with silane coupling agents (γ‐(2,3‐epoxypropoxy)propytrimethoxysilane (KH560) and (heptadecafluoro‐1,1,2,2‐tetradecyl)trimethoxysilane (FAS)). First, the surface of SiO₂ was covered by a layer of hydrophobic methyl and fluorocarbon (C–F) groups. Then, the SiO₂ and modified SiO₂ were obtained by the introduction of KH560 and FAS with the silanol reaction by ultrasonic stirring. The effect of SiO₂ and modified SiO₂ on the structure and hydrophobic properties of PU was investigated by a series of test instruments. The results showed that the introduction of SiO₂ and modified SiO₂ was beneficial for increasing the roughness of the PU coating surface; the roughness of FAS/SiO₂‐PU could reach up to 14.790 nm, four times better than pure PU. A hydrophobic modified PU coating with water contact angle 123° was fabricated by using the hydrophobic C–F group FAS as a low surface energy material and establishing a micro rough structure on the surface of PU. Moreover, PU modified with KH560 and FAS can reduce the glass transition temperature (T_g) of soft segments, resulting in improvement of micro‐phase separation. © 2020 Society of Chemical Industry     

Novel preparation approach with a 2-step process for spherical particles with high drug loading and controlled size distribution using melt granulation: MALCORE®

A novel approach for preparing drug-containing particles (DCPs) with controlled size distribution and high drug loading was developed using melt granulation. This approach comprises two steps. First, melting component adsorbed particles (MAs) were prepared by mixing and heating the melting components with a porous carrier using a high shear granulator. Second, DCPs were prepared by layering the drug on MAs using a fluidized bed rotor granulator. The time taken for both steps was within 30 min. Adding the polymer in the second step remarkably increased the viscosity of the mixture of melting components and the polymer. Therefore, DCPs could be successfully loaded with a high amount of drug (70% w/w). The particle size distribution of the DCPs was narrow, and it depended on that of the MAs. The flowability of the DCPs was excellent, and the sphericity was close to 1. A unique particle formulation mechanism was suggested based on the observation of DCPs using scanning electron microscopy. The manufacturing time and DCP characteristics were not affected by the manufacturing scale. In conclusion, we have successfully developed a highly efficient novel approach for preparing optimal DCPs through melt granulation, named “Melt Adsorption and Layering with Porosity Core” (MALCORE®).     

Design of data feature-driven 1D/2D convolutional neural networks classifier for recycling black plastic wastes through laser spectroscopy

In this study, two types of convolutional neural network (CNN) classifiers are designed to handle the problem of classifying black plastic wastes. In particular, the black plastic wastes have the property of absorbing laser light coming from spectrometer. Therefore, the classification of black plastic wastes remains still a challenging problem compared to classifying other colored plastic wastes using existing spectroscopy (i.e., NIR). When it comes the classification problem of black plastic wastes, effective classification techniques by the laser spectroscopy of Fourier Transform-Infrared Radiation (FT-IR) with Attenuated Total Reflectance (ATR) and Raman to analyze the classification problem of black plastic wastes are introduced. Due to the strong ability of extracting spatial features and remarkable performance in image classification, 1D and 2D CNN through data features are designed as classifiers. The technique of chemical peak points selection is considered to reduce data redundancy. Furthermore, through the selection of data features based on the extracted 1D data with peak points is introduced. Experimental results demonstrate that 2DCNN classifier designed with the help of 2D data feature selection as well as 1DCNN classifier shows the best performance compared with other reported methods for classifying black plastic wastes.