Isobaric vapor−liquid equilibrium (VLE) data for the binary system methanol + dimethyl carbonate as well as the VLE data for the ternary systems methanol + dimethyl carbonate +1-ethyl-3-methylimidazolium trifluoromethanesulfonate ([EMIM][OTf]) and methanol + dimethyl carbonate + 1butyl-3-methylimidazolium trifluoromethanesulfonate ([BMIM][OTf]) at 101.3 kPa have been obtained with a modified Othmer still. The results indicated that both [EMIM][OTf] and [BMIM][OTf] produced crossover effects. Due to the difference of polarity of the two ILs, [BMIM][OTf] eliminated the azeotropic point at mole fraction about 10%, whereas [EMIM][OTf] only pulled down the azeotropic point. The measured VLE data were fitted using the NRTL model with a good consistency.
This paper studies the security of 7-round ARIA-192 against multiple impossible differentials cryptanalysis. We propose six special 4-round impossible differentials which have the same input difference and different output difference with the maximum number of nonzero common bytes. Based on these differentials, we construct six attack trails including the maximum number of common subkey bytes. Under such circumstances, we utilize an efficient sieving process to improve the efficiency of eliminating common subkeys; therefore, both data and time complexities are reduced. Furthermore, we also present an efficient algorithm to recover the master key via guess-and-determine technique. Taking advantage of the above advances, we have obtained the best result so far for impossible differential cryptanalysis of ARIA-192, with time, data, and memory complexities being 2 189.8 7-round ARIA encryptions, 2 116.6 chosen plaintexts, and 2 139.3 bytes, respectively.
Most of the currently mature methods that are used globally for population spatialization are researched on a single level, and are dependent on the spatial relationship between population and land covers (city, road, water area, etc.), resulting in difficulties in data acquisition and an inability to identify precise features on the different levels. This paper proposes a multi-level population spatialization method on the different administrative levels with the support of China’s first national geoinformation survey, and then considers several approaches to verify the results of the multi-level method. This paper aims to establish a multi-level population spatialization method that is suitable for the administrative division of districts and streets. It is assumed that the same residential house has the same population density on the district level. Based on this assumption, the least squares regression model is used to obtain the optimized prediction model and accurate population space prediction results by dynamically segmenting and aggregating house categories.In addition, it is assumed that the distribution of the population is relatively regular in communities that are spatially close to each other, and that the population densities on the street level are similar, so the average population density is assessed by optimizing the community and surrounding residential houses on the street level. Finally, the scientificalness and rationality of the proposed method is proved by spatial autocorrelation analysis, overlay analysis, cross-validation analysis and accuracy assessment methods.
Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.
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