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.     

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。     

Exploring the Cold-Adaptation Mechanism of Serine Hydroxymethyltransferase by Comparative Molecular Dynamics Simulations

Cold-adapted enzymes feature a lower thermostability and higher catalytic activity compared to their warm-active homologues, which are considered as a consequence of increased flexibility of their molecular structures. The complexity of the (thermo)stability-flexibility-activity relationship makes it difficult to define the strategies and formulate a general theory for enzyme cold adaptation. Here, the psychrophilic serine hydroxymethyltransferase (pSHMT) from Psychromonas ingrahamii and its mesophilic counterpart, mSHMT from Escherichia coli, were subjected to μs-scale multiple-replica molecular dynamics (MD) simulations to explore the cold-adaptation mechanism of the dimeric SHMT. The comparative analyses of MD trajectories reveal that pSHMT exhibits larger structural fluctuations and inter-monomer positional movements, a higher global flexibility, and considerably enhanced local flexibility involving the surface loops and active sites. The largest-amplitude motion mode of pSHMT describes the trends of inter-monomer dissociation and enlargement of the active-site cavity, whereas that of mSHMT characterizes the opposite trends. Based on the comparison of the calculated structural parameters and constructed free energy landscapes (FELs) between the two enzymes, we discuss in-depth the physicochemical principles underlying the stability-flexibility-activity relationships and conclude that (i) pSHMT adopts the global-flexibility mechanism to adapt to the cold environment and, (ii) optimizing the protein-solvent interactions and loosening the inter-monomer association are the main strategies for pSHMT to enhance its flexibility.     

An analytical method for the analysis of trihalomethanes in ambient air using solid‐phase microextraction gas chromatography‐mass spectrometry: An application to indoor swimming pool complexes

A simple method for the collection and analysis of the four brominated and chlorinated trihalomethanes (THMs) in air samples is described. Ambient air samples were collected in pre‐prepared glass vials, with THM analysis performed using solid‐phase microextraction gas chromatography‐mass spectrometry, where the need for chemical reagents is minimized. Analytical parameters, including oven temperature program, solvent volume, incubation time, vial agitation, extraction time and temperature, as well as desorption time and temperature, were evaluated to ensure optimal method performance. The developed method allows for point‐in‐time quantification (compared to an average concentration measured over extended periods of time), with detection limits between 0.7 to 2.6 µg/m³. Excellent linearity (r² > 0.99), repeatability (3% to 11% RSD), and reproducibility (3% to 16% RSD) were demonstrated over a concentration range from 2 to 5000 µg/m³. The method was validated for the analysis of THMs in indoor swimming pool air and was used to investigate the occurrence of THMs in the air above 15 indoor swimming pools. This is the first study to report the occurrence of THMs in swimming pool air in Australia, and concentrations higher than those previously reported in other countries were measured.     

Hydrogen carriers: Production,transmission, decomposition,and storage

Recognizing the potential role of liquid hydrogen carriers in overcoming the inherent limitations in transporting and storing gaseous and liquid hydrogen, a complete production and use scenario is postulated and analyzed for perspective one-way and two-way carriers. The carriers, methanol, ammonia and toluene/MCH (methylcyclohexane), are produced at commercially viable scales in a central location, transmitted by rail or pipelines for 2000 miles, and decomposed near city gates to generate fuel-cell quality hydrogen for distribution to refueling stations. In terms of the levelized cost of H₂ distributed to the stations, methanol is less expensive to produce ($1.22/kg-H₂) than MCH ($1.35/kg-H₂) or ammonia ($2.20/kg-H₂). Levelized train transmission cost is smaller for methanol ($0.63/kg-H₂) than ammonia ($1.29/kg-H₂) or toluene/MCH system ($2.07/kg-H₂). Levelized decomposition cost is smaller for ammonia ($0.30–1.06/kg-H₂) than MCH ($0.54–1.22/kg-H₂) or methanol ($0.43–1.12/kg-H₂). Over the complete range of demand investigated, 10–350 tpd-H₂, the levelized cost of H₂ distributed to stations is aligned as methanol « ammonia ~ MCH. With pipelines at much larger scale, 6000 tpd-H₂, the levelized cost decreases by ~1 $/kg-H₂ for ammonia and MCH and much less for methanol. Methanol is a particularly attractive low-risk carrier in the transition phase with lower than 50-tpd H₂ demand.     

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.     

Key-dependent side-channel cube attack on CRAFT

CRAFT is a tweakable block cipher introduced in 2019 that aims to provide strong protection against differential fault analysis. In this paper, we show that CRAFT is vulnerable to side-channel cube attacks. We apply side-channel cube attacks to CRAFT with the Hamming weight leakage assumption. We found that the first half of the secret key can be recovered from the Hamming weight leakage after the first round. Next, using the recovered key bits, we continue our attack to recover the second half of the secret key. We show that the set of equations that are solvable varies depending on the value of the key bits. Our result shows that 99.90% of the key space can be fully recovered within a practical time.