立柱加固型框架大楼的爆破拆除

采用定向控制爆破技术拆除立柱加固型7层框架大楼。为了彻底炸毁加固立柱,确保大楼顺利定向倾倒,通过多次爆破试验确定了炸药单耗、布孔参数和装药结构。为了控制大楼塌落时的触地震动,采取了秒差分区爆破、空中解体、铺垫缓冲层和开挖减震沟等技术措施。爆破过程中进行了震动监测。此外还介绍了起爆顺序、安全防护措施及爆破结果。     

Energy conversion performances during biomass air gasification process under microwave irradiation

Biomass gasification technology under microwave irradiation is a new and novel method, and the energy conversion performances during the process play a guiding role in improving the energy conversion efficiencies and developing the gasification simulation models. In order to improve the energy utilization efficiency of microwave biomass gasification system, this study investigated and presented the energy conversion performances during biomass gasification process under microwave irradiation, and these were materialized through detailing (a) the energy conversion performance in the microwave heating stage, and (b) the energy conversion performance in the microwave assisted biomass gasification stage. Different forms of energies in the biomass microwave gasification process were calculated by the method given in this study based on the experimental data. The results showed that the useful energy (energy in silicon carbide (SiC), 18.73 kJ) accounted for 31.22% of the total energy input (electrical energy, 60.00 kJ) in the heating stage, and the useful energy (energy in the products, 758.55 kJ) accounted for 63.41% of the total energy input (electrical and biomass energy, 1196.28 kJ) in the gasification stage. During the whole biomass gasification process under microwave irradiation, the useful energy output (energy in the products, 758.55 kJ) accounted for 60.38% of the total energy input (electrical and biomass energy, 1256.28 kJ), and the energy in the gas (523.40 kJ) product played a dominate role in product energy (758.55 kJ). The energy loss mainly included the heat loss in the gas flow (89.20 kJ), magnetron loss (191.80 kJ) and microwave dissipation loss (198.00 kJ), which accounted for 7.10%, 15.27% and 15.76% of the total energy, respectively. The contents detailed in this study not only presented the energy conversion performances during microwave assisted gasification process but also supplied important data for developing gasification simulation models.     

大规模多状态计算系统的容错性能分析

针对异构计算节点组成的大规模多状态计算系统的容错性能分析问题，提出了一种计算系统容错性能的评估方法。该方法采用自定义的两级容错性能形式化描述框架进行系统描述，通过构造多值决策图（Multi-value Decision Diagram，MDD）模型对系统进行容错性能建模，并基于构造的模型高效地计算出部件故障的条件下计算系统在特定性能水平上运行的概率，减少了计算的冗余性。实验结果表明，该方法在模型的大小和构建时间上均优于传统方法。该方法的提出将对系统操作员或程序设计者具有重要意义，使其确保系统适合预期应用。     

A self-adaptive synthetic over-sampling technique for imbalanced classification

Traditionally, in supervised machine learning, (a significant) part of the available data (usually 50%-80%) is used for training and the rest—for validation. In many problems, however, the data are highly imbalanced in regard to different classes or does not have good coverage of the feasible data space which, in turn, creates problems in validation and usage phase. In this paper, we propose a technique for synthesizing feasible and likely data to help balance the classes as well as to boost the performance in terms of confusion matrix as well as overall. The idea, in a nutshell, is to synthesize data samples in close vicinity to the actual data samples specifically for the less represented (minority) classes. This has also implications to the so-called fairness of machine learning. In this paper, we propose a specific method for synthesizing data in a way to balance the classes and boost the performance, especially of the minority classes. It is generic and can be applied to different base algorithms, for example, support vector machines, k-nearest neighbour classifiers deep neural, rule-based classifiers, decision trees, and so forth. The results demonstrated that (a) a significantly more balanced (and fair) classification results can be achieved and (b) that the overall performance as well as the performance per class measured by confusion matrix can be boosted. In addition, this approach can be very valuable for the cases when the number of actual available labelled data is small which itself is one of the problems of the contemporary machine learning.     

Platinum nanoparticles decorated on graphitic carbon nitride-ZIF-67 composite support: An electrocatalyst for the oxidation of butanol in fuel cell applications

In the present study, we report an eco-friendly and simple route to design and synthesize novel nanocomposite catalyst based on platinum nanoparticles anchored on binary support of graphitic carbon nitride (g-C₃N₄) and cobalt-metal-organic framework (ZIF-67). For this purpose, ZIF-67 was prepared by precipitation method and g-C₃N₄ was prepared through thermal polymerization method. Later, ZIF-67 and g-C₃N₄ were hybridized through sonication to get homogeneous g–C₃N₄–ZIF-67 nanocomposite support material. Platinum nanoparticles (PtNPs) were uniformly deposited on g–C₃N₄–ZIF-67 by an electrochemical method. The as-developed nanocatalyst was characterized by morphological, structural and electrochemical techniques. The electrocatalytic activity of PtNPs@g–C₃N₄–ZIF-67 nanocatalyst towards butanol oxidation was evaluated via CV, CA, LSV and EIS in an alkaline medium. Results revealed that the proposed catalyst showed greatly enhanced electrooxidation of butanol in terms of high magnificent current density, lower oxidation potential, excellent long-term stability, large surface area, low charge transfer resistance and less toxic ability. Enhanced catalytic performance of the proposed catalyst could be ascribed to the synergistic effect of g–C₃N₄–ZIF-67 nanocomposite and PtNPs. The PtNPs@g–C₃N₄–ZIF-67 catalyst holds promising potential applications to be used as an anodic electrocatalyst for the development of high-performance alkaline fuel cells.     

飞机撞击下高温气冷堆乏燃料厂房损伤研究

本文针对典型高温气冷堆乏燃料厂房在双发商用飞机撞击载荷下的响应及结构完整性开展研究，并探讨结构特性对撞击损伤的影响。对乏燃料厂房及飞机分别建立有限元模型，通过弹体-目标相互作用分析模拟了飞机撞击过程，综合IAEA与NRC的评价准则对乏燃料厂房在飞机撞击下的损伤程度进行评估。数值结果表明：厂房上对应于机身及发动机的撞击位置发生可接受的局部损伤；乏燃料贮存井墙体对于提高构筑物抗飞机撞击能力有重要作用。此外，构筑物外形对损伤有很大影响，圆柱形壳体的抗飞机撞击能力显著强于方形厂房，是核电厂厂房设计的优化方向之一。     

Temperature adaptability of the soluble lead flow battery using different solutions of fluoborate,perchlorate, methanesulfonate and trifluoromethanesulfonate

The evaluation of cell's weatherability is of practical interest. To further improve the soluble lead flow battery's weatherability, physiochemical properties of electrolytes containing fluoborate, perchlorate, methanesulfonate and trifluoromethanesulfonate are investigated from ?60 to 50 °C. Activities of CF₃SO₃H and HClO₄ are poor in trifluoromethanesulfonate and perchlorate solutions due to common anion effect. The solubility of lead salt can be improved by increasing temperature, but worsened by increasing acid's content. With the temperature increasing, the conductivity is enhanced, and the viscosity is lowered for four solutions. The same results have been found by increasing acid's content except for CF₃SO₃H. The high energy efficiency can be achieved for cells over ?40–0 °C using fluoborate and perchlorate solutions, 73.2% at ?40 °C and 78.1% at ?30 °C respectively. Over the temperature range of 20–50 °C, the cells with methanesulfonate and trifluoromethanesulfonate solutions have good performance, 77.4% and 73.7% at 50 °C respectively.     

Reliability of complex chemical engineering processes

This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab^® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.