Decision Tree is widely applied in many areas, such as classification and recognition. Traditional information entropy and Pearson’s correlation coefficient are often applied as measures of splitting rules to find the best splitting attribute. However, these methods can not handle uncertainty, since the relation between attributes and the degree of disorder of attributes can not be measured by them. Motivated by the idea of Deng Entropy, it can measure the uncertain degree of Basic Belief Assignment (BBA) in terms of uncertain problems. In this paper, Deng entropy is used as a measure of splitting rules to construct an evidential decision tree for fuzzy dataset classification. Compared to traditional combination rules used for combination of BBAs, the evidential decision tree can be applied to classification directly, which efficiently reduces the complexity of the algorithm. In addition, the experiments are conducted on iris dataset to build an evidential decision tree that achieves the goal of more accurate classification.
Objective. To explore risks underlying traditional Chinese medicine (TCM) injection-related adverse drug reactions (ADRs) in Chinese children, and to discuss the implications of postmarketing reevaluation studies. Methods. We identified potential cases of exposure to TCM injections for children (<18 years of age) and adults (18 years and upwards) from database of ADRs. First, the associations between TCM injection-related ADRs and three administration routes (i.e., intravenous or intramuscular administration, oral administration, and external use) and the imbalance of TCM injection-related ADRs between the paediatric and adult populations were tested using the Chi-square (χ2) test. Second, the proportional reporting ratio (PPR) was applied to identify statistically significant associations between drugs and anaphylactic shock in the paediatric population. Results. The χ2 test revealed that the highest frequency of paediatric ADRs was due to 5 types of herbal injections (i.e., Shuanghuanglian (SHL), Yuxingcao (YXC), Qingkailing (QKL), Xiyanping (XYP), and Reduning (RDN) herbal injections) (P<0.000), and the reports of ADRs attributed to the XYP and RDN herbal injections in children accounted for a greater proportion than the reports for adults (P<0.000). The PPR identified 5 types of herbal injections-anaphylactic shock pairs (i.e., the SHL, XYP, QKL, YXC, and Fufang Danshen herbal injections) that met the minimum criteria (i.e., a PPR of at least 2 and χ2 of at least 4 and three or more cases), which suggested that TCM injections were significantly associated with anaphylactic shock. Conclusions. TCM injections pose graver risks to the paediatric population than the adult population. To achieve optimal benefits and minimal risk to children treated with TCM injections, we suggest reevaluating the effectiveness and safety, monitoring the risks, and promoting rational use of TCM injections in Chinese children.
Quantum neural networks (QNNs) have emerged as a leading strategy to establish applications in machine learning, chemistry, and optimization. While the applications of QNN have been widely investigated, its theoretical foundation remains less understood. In this paper, we formulate a theoretical framework for the expressive ability of data re-uploading quantum neural networks that consist of interleaved encoding circuit blocks and trainable circuit blocks. First, we prove that single-qubit quantum neural networks can approximate any univariate function by mapping the model to a partial Fourier series. Beyond previous works' understanding of existence, we in particular establish the exact correlations between the parameters of the trainable gates and the working Fourier coefficients, by exploring connections to quantum signal processing. Second, we discuss the limitations of singlequbit native QNNs on approximating multivariate functions by analyzing the frequency spectrum and the flexibility of Fourier coefficients. We further demonstrate the expressivity and limitations of single-qubit native QNNs via numerical experiments. As applications, we introduce natural extensions to multi-qubit quantum neural networks, which exhibit the capability of classifying real-world multi-dimensional data. We believe these results would improve our understanding of QNNs and provide a helpful guideline for designing powerful QNNs for machine learning tasks.
The purpose of this work is to develop a low-cost and high-performance catalyst for the selective catalytic hydrogenation of acetylene to ethylene. Non-precious metals Cu and Ni were selected as active ingredients for this study. Using ZSM-12 as a carrier, Cu-Ni bimetallic catalysts of CuNix/ZSM-12 (x = 5, 7, 9, 11) with different Ni/Cu ratios were prepared by incipient wetness impregnation method. The total Cu and Ni loading were 2 wt%. Under the optimal reaction conditions, the acetylene conversion was 100%, and the ethylene selectivity was 82.48%. The CuNi7/ZSM-12 prepared in this work exhibits good performance in the semi-hydrogenation of acetylene to ethylene with low cost and has potential for industrial application.
To obtain the production of propylene oxide (PO) with high purity, a coupled azeotropic and extractive separation process is proposed to remove the impurities such as aldehyde, methanol, methyl formate, 2-methylpentane, water, propylene glycol (PG), and so forth from crude PO. n-Butane is screened as an azeotropic entrainer for the disposal of the light impurities, while n-octane is employed for PO purification. For the PG removal, the process of withdrawing the PG and n-octane azeotropic mixture from the side of the solvent recovery column and then separating them through liquid–liquid phase split is designed. The simulation and the total annual cost (TAC) analysis are performed by Aspen Plus with the non-random-two-liquid thermodynamic model. The key design variables such as the ratio of solvents to crude PO, the theoretical plate number, the feed stage, and so forth are analyzed and determined based on the minimal TAC method, in which the capital, energy, and raw material costs are comprehensively considered. The result that is verified through the pilot experiment shows that the purity of the PO product exceeds 99.99 % in mass and the impurity contents of formaldehyde and water are lower than the national standard value of China (GB/T 14491-2015).
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