Effects of upholstery materials on the burning behavior of real‐scale residential upholstered furniture mock‐ups

Fire spread and growth on real‐scale four cushion mock‐ups of residential upholstered furniture (RUF) were investigated with the goal of identifying whether changes in five classes of materials (barrier, flexible polyurethane foam, polyester fiber wrap, upholstery fabric, and sewing thread), referred to as factors, resulted in statistically significant changes in burning behavior. A fractional factorial experimental design plus practical considerations yielded a test matrix with 20 material combinations. Experiments were repeated a minimum of two times. Measurements included fire spread rates derived from video recordings and heat release rates (HRRs). A total of 13 experimental parameters (3 based on the videos and 10 on the HRR results), referred to as responses, characterized the measurements. Statistical analyses based on Main Effects Plots (main effects) and Block Plots (main effects and factor interactions) were used. The results showed that three of the factors resulted in statistically significant effects on varying numbers of the 13 responses. The Barrier and Fabric factors had the strongest main effects with roughly comparable magnitudes. Foam was statistically significant for fewer of the responses and its overall strength was weaker than for Barrier and Fabric. No statistically significant main effects were identified for Wrap or Thread. Multiple two‐term interactions between factors were identified as being statistically significant. The Barrier*Fabric interaction resulted in the highest number of and strongest statistically significant effects. The existence of two‐term interactions means that it will be necessary to consider their effects in approaches designed to predict the burning behavior of RUF.     

The physicochemical and fire extinguishing performance of aqueous film-forming foams based on a class of short-chain fluorinated surfactants

Aqueous film-forming foams (AFFFs) are an important for fire extinguishing, and their key ingredient is fluorinated surfactant. In recent years, traditional long-chain fluorinated surfactants have been banned by most countries because of their persistence, bio-accumulation and toxicity. Therefore, increased attention has been paid to the research and development of short-chain fluorinated surfactants. As is well known, the introduction of hydrophilic or hydrophobic groups in a surfactant affects its surface activity, and therefore, the fire extinguishing performance of AFFFs. In this work, a series of short-chain fluorosurfactant-based AFFFs with different hydrophobic chain lengths were prepared. The physicochemical performance of mixed systems (fluorinated surfactant plus sodium hexanesulfonate), including surface activity, spreading ability, foam expansion, drainage time, and the fire extinguishing and burn-back performance of AFFFs were studied. The results show that the critical micelle concentration (CMC) and the surface tension (γ_CMC) at the CMC of mixed systems at 25°C are lower than 7.68 mmol/L and 16.51 mN/m, respectively. For mixed systems, the average spreading rate is more than 1.09 cm/s, the foam expansion is over 7.1, and the drainage time is greater than 3.28 min. The fire extinguishing time of AFFFs on fuels is less than 51 s while the burn-back time is more than 15.18 min. The results imply a potential application prospect of the short-chain fluorinated surfactants in AFFFs.     

基于改进残差特征蒸馏的轻量级超分辨率网络

基于深度学习的图像超分辨率算法通常采用递归的方式或参数共享的策略来减少网络参数，这将增加网络的深度，使得运行网络花费大量的时间，从而很难将模型部署到现实生活中。为了解决上述问题，本文设计一种轻量级超分辨率网络，对中间特征的关联性及重要性进行学习，且在重建部分结合高分辨率图像的特征信息。首先，引入层间注意力模块，通过考虑层与层之间的相关性，自适应地分配重要层次特征的权重。其次，使用增强重建模块提取高分辨率图像中更精细的特征信息，以此得到更加清晰的重建图片。通过大量的对比实验表明，本文设计的网络与其他轻量级模型相比，有更小的网络参数量，并且在重建精度和视觉效果上都有一定的提升。     

Using deep learning to combine static and dynamic power analyses of cryptographic circuits

Side-channel attacks have shown to be efficient tools in breaking cryptographic hardware. Many conventional algorithms have been proposed to perform side-channel attacks exploiting the dynamic power leakage. In recent years, with the development of processing technology, static power has emerged as a new potential source for side-channel leakage. Both types of power leakage have their advantages and disadvantages. In this work, we propose to use the deep neural network technique to combine the benefits of both static and dynamic power. This approach replaces the classifier in template attacks with our proposed long short-term memory network schemes. Hence, instead of deriving a specific probability density model for one particular type of power leakage, we gain the ability of combining different leakage sources using a structural algorithm. In this paper, we propose three schemes to combine the static and dynamic power leakage. The performance of these schemes is compared using simulated test circuits designed with a 45-nm library.     

Influence of thermal runaway in styrene–acrylonitrile bulk copolymerization revealed by computational fluid dynamics modeling

The computational fluid dynamics (CFD) and kinetic-based moment methods coupled approach is adopted to simulate the bulk copolymerization of styrene–acrylonitrile (SAN) in a stirred tank reactor. Numerical simulations are carried out to investigate the impacts of impeller speed, monomer ratio, initiator ratio, and initial reaction temperature on the copolymerization process and product properties. Particularly, the Chaos theory is selected as a criterion for evaluating the occurrence of the thermal runaway. The Flory's and Stockmayer's distributions are employed to calculate chain length distribution and copolymer composition distribution of copolymer. The simulation results highlight that the appearance of thermal runaway can be postponed by properly increasing the rotation speed, decreasing the initiator loadings, initial acrylonitrile contents and initial reactor temperature. Furthermore, significant differences exist in the product properties that predicted by the ideal and non-ideal models, which demonstrates that the temperature heterogeneity plays a crucial role in SAN copolymerization. This study could offer references for the safe operation and design of polymerization processes.     

Effect of comb-type copolymer on crystallization of paraffin from waxy oils and methyl ethyl ketone (MEK) -toluene dewaxing

In this research, maleic anhydride-α-octadecene copolymer and its derivative with phenylethylamine was synthesized and its effect on the crystallization of paraffins was investigated. This derivative, when added into second cut of vacuum gas oil and forth cut of vacuum gas oil, increases the size and improves aggregation of paraffin crystals observed by polarizing light microscopy, increases onset temperature and enthalpy of paraffin crystallization determined by differential scanning calorimetry, improves the dewaxing efficiency with dosage of 100?ppm explored by MEK-toluene dewaxing.     

Facial expression recognition of intercepted video sequences based on feature point movement trend and feature block texture variation

Facial Expression Recognition (FER) is an important subject of human–computer interaction and has long been a research area of great interest. Accurate Facial Expression Sequence Interception (FESI) and discriminative expression feature extraction are two enormous challenges for the video-based FER. This paper proposes a framework of FER for the intercepted video sequences by using feature point movement trend and feature block texture variation. Firstly, the feature points are marked by Active Appearance Model (AAM) and the most representative 24 of them are selected. Secondly, facial expression sequence is intercepted from the face video by determining two key frames whose emotional intensities are minimum and maximum, respectively. Thirdly, the trend curve which represents the Euclidean distance variations between any two selected feature points is fitted, and the slopes of specific points on the trend curve are calculated. Finally, combining Slope Set which is composed by the calculated slopes with the proposed Feature Block Texture Difference (FBTD) which refers to the texture variation of facial patch, the final expressional feature are formed and inputted to One-dimensional Convolution Neural Network (1DCNN) for FER. Five experiments are conducted in this research, and three average FER rates 95.2%, 96.5%, and 97% for Beihang University (BHU) facial expression database, MMI facial expression database, and the combination of two databases, respectively, have shown the significant advantages of the proposed method over the existing ones.     

Poly(L-lysine)-poly(ethylene glycol) layers with different structure and their influence on silica suspension stability

The effects of the structure of di- and triblock copolymers of poly(L-lysine) – LYS with poly(ethylene glycol) – PEG as well as the length of nonionic fragment in the LYS-PEG macromolecule on the copolymer chains conformation in the adsorption layer formed on the colloidal silica (SiO2) surface were examined. Spectrophotometry and turbidimetry were applied for the determination of copolymer adsorbed amounts and stability coefficients of silica aqueous suspensions. The electrokinetic parameters such as solid surface charge density and zeta potential were also estimated. The adsorption of LYS-PEG was proved to be the highest at pH 10 whereas the lowest adsorption on the solid surface was found for the triblock copolymer with long fragments of LYS at the same pH value.     

涤纶针织布的有机硅改性聚酯-聚醚共聚物整理

采用酯交换-缩聚反应,在催化剂与稳定剂的作用下,以对苯二甲酸二甲酯、乙二醇、聚乙二醇与自制聚醚硅油为原料合成了有机硅聚酯-聚醚共聚物整理剂。试验探究了聚醚硅油的用量对反应产物性能的影响,通过红外光谱表征了有机硅聚酯-聚醚共聚物的结构,并对其在涤纶针织物上的最佳应用条件进行了研究。结果表明:聚醚硅油用量占酯交换反应物质量分数为6%时,用其处理的针织物亲水性和手感较好;最佳使用工艺条件为浸轧法,质量浓度20 g/L,120℃焙烘60s。     

Design of energy‐efficient train speed profiles considering fixed‐block signaling system

In dense traffic railway networks, trains may often slow down or stop between stations owing to previous train delays. If preceding train trajectory can be predicted, energy‐efficient driving can be achieved by suppressing unnecessary speed changes. In this paper, we propose an algorithm to find energy‐efficient driving considering fixed‐block signaling (FBS) system by using dynamic programming (DP). DP is suitable for use because it can optimize the control inputs with discrete and state constraints. In this paper, we discuss energy‐efficient driving by considering a FBS system using some case studies of simulation. In the simulation, we examine a technique to drive an express train in an energy‐efficient way when the preceding local train is running toward the station with passing loops. The results show that the proposed method can derive complex speed profiles for energy‐efficient driving and the train can be operated with a maximum reduced energy consumption of 8.3%.