Fabrication of NixCo3-xS4 hollow nanosphere as wideband electromagnetic absorber at thin matched thickness

Nowadays, transition metal sulfide (TMS), especially for spinel crystal structure (A_xB_3-xS₄), have been proved to be a promising electromagnetic (EM) absorber if been used to deal with the severe electromagnetic pollution. However, EM performance degradation and absorption layer thickness-decreasing at present remains a big challenge, owning to the poor EM attenuation ability. To overcome this barrier, herein we reported a Ni_xCo_3-xS₄ (x = 0, 0.3, 0.6, 1.0) absorber with hollow sphere structure to realize a good EM performance with a thinner matched thickness (<1.5 mm). The average sizes of these Ni_xCo_3-xS₄ distributed in 450–550 nm. The dielectric loss ability (ε'') can be boosted by tuning the molar ratio of Ni/Co, which attributes to EM performance. Additionally, hollow structure would lead to the electromagnetic multi-reflection, also benefited to EM performance. The results demonstrated that the maximum qualified absorption bandwidth (f_E) of 3.8 GHz can be achieved for the Ni_0·3Co_2·7S₄ sample when specimen thickness only equals to 1.3 mm.     

基于k-shell的复杂网络最短路径近似算法

复杂网络最短路径经典算法的处理效率较低，不适用于大规模复杂网络，而现有近似算法通用性有限，且计算准确率不理想，不能满足规模日益扩大的复杂网络中的最短路径计算需求。针对于此，提出基于[k]-shell的复杂网络最短路径近似算法。算法利用节点的[k]-shell值进行网络划分并引导搜索路径，利用超点聚合处理[k]-shell子网来降低路径搜索中节点和连边的规模，通过在路径搜索过程使用双向搜索树方法提高算法的计算效率和准确率。实验结果表明，算法通用性较好，在现实与仿真大规模复杂网络中均具有较高的计算效率和准确率。     

Photomontage in the Present Perfect Continuous

In its first instance as art practice among the historical avant-garde, photomontage was considered indispensable for its claim to intervene in perceptual processes, stimulating a critical mode of apprehension that would redirect the viewer away from conventions of aesthetic experience and towards a lived reception of art with pronounced relevance to the sociopolitical landscape. The effect was understood as structural, that is, activated not so much by direct political content, but by the stark and shocking effects of juxtaposition. By this measure, one challenge to contemporary photomontage is clear: in a postindustrial and postdigital visual landscape dominated by the structural fragmentation of the attention economy, the ‘simultaneity of the radically disparate’ (as Peter Bürger put it) might no longer present as heterodoxy but rather threaten to sink into invisibility. Yet with the migration off-screen of the effects of electronic media, a new urgency around moving photomontage structures into physical, public space is rising in contemporary practices. Shannon Ebner’s multi-part project A Hudson Yard (2014–15) is emblematic of the new ways in which artists are manipulating photomontage as a form of fully sensory experience that gives the medium room to play critically in both virtual and material space. By constructing subtle interruptions of naturalised commercial space, A Hudson Yard activates a détournement of instrumentalised language, using structures of juxtaposition to divert the discursive surfaces of public space away from consumption and towards what could be called a public poesis.     

A gradient boosting decision tree approach for insider trading identification: An empirical model evaluation of China stock market

Insider trading is a kind of criminal behavior in stock market by using nonpublic information. In recent years, it has become the major illegal activity in China’s stock market. In this study, a combination approach of GBDT (Gradient Boosting Decision Tree) and DE (Differential Evolution) is proposed to identify insider trading activities by using data of relevant indicators. First, insider trading samples occurred from year 2007 to 2017 and corresponding non-insider trading samples are collected. Next, the proposed method is trained by the GBDT, and initial parameters of the GBDT are optimized by the DE. Finally, out-of-samples are classified by the trained GBDT–DE model and its performances are evaluated. The experiment results show that our proposed method performed the best for insider trading identification under time window length of ninety days, indicating the relevant indicators under 90-days time window length are relatively more useful. Additionally, under all three time window lengths, relative importance result shows that several indicators are consistently crucial for insider trading identification. Furthermore, the proposed approach significantly outperforms other benchmark methods, demonstrating that it could be applied as an intelligent system to improve identification accuracy and efficiency for insider trading regulation in China stock market.     

2.5 GHz城市环境电磁态势的计算与补偿

复杂城市空间的电磁态势分布可以通过电磁场数值计算的方法进行预测。文章使用矩量法(MOM)结合一致性几何绕射理论(UTD)的方法对2.5 GHz频率下某小区环境中的电磁态势进行了仿真计算以及实际测量。场景中的树木结构在此频率下显示出比较强烈的波动性,对电磁仿真计算结果产生了不可忽略的影响,文中将其对仿真计算结果的影响进行了单独建模仿真,给出其产生影响的经验值,对仿真结果进行修正。修正结果与测量结果对比证明该经验值在该频率下可以有效补偿计算结果的缺失。     

Design structure matrix (DSM) method application to issue of modeling and analyzing the fault tree of a wind energy asset

The perpetual energy production of a wind farm could be accomplished (under proper weather conditions) if no failures occurred. But even the best possible design, manufacturing, and maintenance of a system cannot eliminate the failure possibility. In order to understand and minimize the system failures, the most crucial components of the wind turbines, which are prone to failures, should be identified. Moreover, it is essential to determine and classify the criticality of the system failures according to the impact of these failure events on wind turbine safety. The present study is processing the failure data from a wind farm and uses the Fault Tree Analysis as a baseline for applying the Design Structure Matrix technique to reveal the failure and risk interactions between wind turbine subsystems. Based on the analysis performed and by introducing new importance measures, the “readiness to fail” of a subsystem in conjunction with the “failure riskiness” can determine the “failure criticality.” The value of the failure criticality can define the frame within which interventions could be done. The arising interventions could be applied either to the whole system or could be focused in specified pairs of wind turbine subsystems. In conclusion, the method analyzed in the present research can be effectively applied by the wind turbine manufacturers and the wind farm operators as an operation framework, which can lead to a limited (as possible) design‐out maintenance cost, failures' minimization, and safety maximization for the whole wind turbine system.     

Controllable Fe/HCS catalysts in the Fischer-Tropsch synthesis: Effects of crystallization time

The Fischer–Tropsch synthesis (FTS) continues to be an attractive alternative for producing a broad range of fuels and chemicals through the conversion of syngas (H₂ and CO), which can be derived from various sources, such as coal, natural gas, and biomass. Among iron carbides, Fe₂C, as an active phase, has barely been studied due to its thermodynamic instability. Here, we fabricated a series of Fe₂C embedded in hollow carbon sphere (HCS) catalysts. By varying the crystallization time, the shell thickness of the HCS was manipulated, which significantly influenced the catalytic performance in the FTS. To investigate the relationship between the geometric structure of the HCS and the physic-chemical properties of Fe species, transmission electron microscopy, X-ray diffraction, N₂ physical adsorption, X-ray photoelectron spectroscopy, hydrogen temperature-programmed reduction, Raman spectroscopy, and Mössbauer spectroscopy techniques were employed to characterize the catalysts before and after the reaction. Evidently, a suitable thickness of the carbon layer was beneficial for enhancing the catalytic activity in the FTS due to its high porosity, appropriate electronic environment, and relatively high Fe₂C content.     

太阳能及空气源热泵复合供热系统故障诊断研究与应用

为确保复杂太阳能热水系统的稳定运行,有必要采集实时运行数据从中获取故障特征信号,通过专家系统进行自动故障诊断。采用故障树分析方法,以太阳能热利用系统典型故障为例构建故障树,求解最小割集,并以此为基础建立诊断知识库;运用正向推理结合人工辅助决策机制进行故障诊断和定位,建立故障诊断专家系统。开发故障诊断软件,并在工程中进行测试,验证故障辨识的准确性。     

Predicting Prostephanus truncatus (Horn) (Coleoptera: Bostrichidae) populations and associated grain damage in smallholder farmers’ maize stores: A machine learning approach

Prostephanus truncatus is a notorious pest of stored-maize grain and its spread throughout sub-Saharan Africa has led to increased levels of grain storage losses. The current study developed models to predict the level of P. truncatus infestation and associated damage of maize grain in smallholder farmer stores. Data were gathered from grain storage trials conducted in Hwedza and Mbire districts of Zimbabwe and correlated with weather data for each site. Insect counts of P. truncatus and other common stored grain insect pests had a strong correlation with time of year with highest recorded numbers from January to May. Correlation analysis showed insect-generated grain dust from boring and feeding activity to be the best indicator of P. truncatus presence in stores (r = 0.70), while a moderate correlation (r = 0.48) was found between P. truncatus numbers and storage insect parasitic wasps, and grain damage levels significantly correlated with the presence of Tribolium castaneum (r = 0.60). Models were developed for predicting P. truncatus infestation and grain damage using parameter selection algorithms and decision-tree machine learning algorithms with 10-fold cross-validation. The P. truncatus population size prediction model performance was weak (r = 0.43) due to the complicated sampling and detection of the pest and eight-week long period between sampling events. The grain damage prediction model had a stronger correlation coefficient (r = 0.93) and is a good estimator for in situ stored grain insect damage. The models were developed for use under southern African climatic conditions and can be improved with more input data to create more precise models for building decision-support tools for smallholder maize-based production systems.