快堆伪裂变产物数据的计算

从微观评价核数据库出发 ,计算给出了快堆谱的伪裂变产物数据 ,包括一群的截面数据和多群的全套中子数据 ,裂变核有2 3 5U和2 3 9Pu。截面数据有全截面、弹性散射、非弹散射、(n ,γ)、(n ,2n)截面 ,多群全套中子数据还包括次级中子角分布和能谱。数据以ENDF B -6格式给出 ,同时还分析讨论了数据误差和可靠性 ,数据可用于实际的快堆计算     

Measurements of gamma-ray emission probabilities of 241, 243Am and 239Np

Gamma-ray emission probabilities of ^{241, 243}Am and ²³⁹Np have been precisely measured with gamma- and alpha-ray spectroscopic methods. The activities of the samples were determined by measuring alpha particles using a Si semiconductor detector. Gamma rays emitted from the samples were measured with a planar type High-Purity Germanium (HPGe) detector. An efficiency curve of the HPGe detector was derived with uncertainties from 0.7% to 2.5% by combining measured efficiencies and Monte Carlo simulation. The gamma-ray emission probabilities for the major gamma rays of these nuclides were determined with uncertainties less than 1.2%.     

Using differential evolution for fine tuning naïve Bayesian classifiers and its application for text classification

The Naive Bayes (NB) learning algorithm is simple and effective in many domains including text classification. However, its performance depends on the accuracy of the estimated conditional probability terms. Sometimes these terms are hard to be accurately estimated especially when the training data is scarce. This work transforms the probability estimation problem into an optimization problem, and exploits three metaheuristic approaches to solve it. These approaches are Genetic Algorithms (GA), Simulated Annealing (SA), and Differential Evolution (DE). We also propose a novel DE algorithm that uses multi-parent mutation and crossover operations (MPDE) and three different methods to select the final solution. We create an initial population by manipulating the solution generated by a method used for fine tuning the NB. We evaluate the proposed methods by using their resulted solutions to build NB classifiers and compare their results with the results of obtained from classical NB and Fine-Tuning Naïve Bayesian (FTNB) algorithm, using 53 UCI benchmark data sets. We name these obtained classifiers NBGA, NBSA, NBDE, and NB-MPDE respectively. We also evaluate the performance NB-MPDE for text-classification using 18 text-classification data sets, and compare its results with the results of obtained from FTNB, BNB, and MNB. The experimental results show that using DE in general and the proposed MPDE algorithm in particular are more convenient for fine-tuning NB than all other methods, including the other two metaheuristic methods (GA, and SA). They also indicate that NB-MPDE achieves superiority over classical NB, FTNB, NBDE, NBGA, NBSA, MNB, and BNB.     

Identification of potent inhibitors of DNA methyltransferase 1 (DNMT1) through a pharmacophore-based virtual screening approach

DNA methylation is an epigenetic change that results in the addition of a methyl group at the carbon-5 position of cytosine residues. DNA methyltransferase (DNMT) inhibitors can suppress tumour growth and have significant therapeutic value. However, the established inhibitors are limited in their application due to their substantial cytotoxicity. Additionally, the standard drugs for DNMT inhibition are non-selective cytosine analogues with considerable cytotoxic side-effects. In the present study, we have designed a workflow by integrating various ligand-based and structure-based approaches to discover new agents active against DNMT1. We have derived a pharmacophore model with the help of available DNMT1 inhibitors. Utilising this model, we performed the virtual screening of Maybridge chemical library and the identified hits were then subsequently filtered based on the Naïve Bayesian classification model. The molecules that have returned from this classification model were subjected to ensemble based docking. We have selected 10 molecules for the biological assay by inspecting the interactions portrayed by these molecules. Three out of the ten tested compounds have shown DNMT1 inhibitory activity. These compounds were also found to demonstrate potential inhibition of cellular proliferation in human breast cancer MDA-MB-231 cells. In the present study, we have utilized a multi-step virtual screening protocol to identify inhibitors of DNMT1, which offers a starting point to develop more potent DNMT1 inhibitors as anti-cancer agents.     

Storage devices for heat exchangers with phase change

Heat exchangers with phase-change achieve minimal dissipation when there is only a small temperature difference between the inlet and the outlet on the side of the sensible heat transfer medium. However, this does not usually occur in applications where these heat exchangers are typically used. In order to overcome this issue, an innovative prototype heat pump was realised. The heat pump was equipped with switchable storage devices to adapt the high temperature difference of the application to small temperature differences in the condenser. This way, the dissipation in the condenser was minimised, which led to COP increases by reducing the required mean pressure in the condenser. The use of storage devices resulted in measured efficiency improvements of 10%–50% in the prototype. With the described set-up, it is possible to approach the maximal thermodynamically possible COP, which makes an adaptation of the theoretical assessment of heat pumps necessary.     

Total cross section of solid mesitylene, toluene and a mixture of them at thermal neutron energies

The total neutron cross sections of mesitylene, toluene and a solution 3:2 by volume of mesitylene and toluene were measured at the electron LINAC based pulsed neutron source of Centro Atómico Bariloche. Measurements were performed at 180 K, 120 K and 31.6 K for mesitylene and at 120 K and 31.6 K for toluene and a solution 3:2 by volume of mesitylene and toluene. The systems are potential moderator materials to be considered in the design of a cold neutron source due to their high resistance to radiation and the richness in low-energy excitations of their frequency spectra, that lead to produce an enhanced cold neutron flux.     

Floristic diversity with particular reference to endemic,rare or endangered flora in forest formations of Saïda (Algeria)

An evaluation and inventory study on the natural vegetation cover and its diversity was carried out on the forest vegetation of the district of Saïda. The floristic diversity of our study area revealed the existence of 397 taxa belonging to 64 families and 271 genera. The most important families are Asteraceae (42 genera), Fabaceae (25 genera), Brassicaceae (22 genera) and Poaceae (22 genera). The category of rare taxa represents 13% of the studied flora (52 taxa). In our study area, we have counted 35 endemic regional species of which 19 species are endemic Algerian-Moroccan, and 3 species are strictly endemic to Algeria. Among the taxa analysed, 02 are included in the IUCN Red List, and 12 are on the Algerian list of protected non-cultivated plant species.     

我国部分省份环境气溶胶中239+240 Pu的测定

采用阴离子交换树脂分离结合α谱仪测量的方法对我国7个省份环境气溶胶中^239+240Pu含量进行测定,实验全程回收率为60.8%～94.6%,对气溶胶中^239+240Pu的最小可探测限为0.008 μBq/m³。普通环境气溶胶采样量需要达到30 000 m³以上,在应急情况等特殊环境气溶胶采样量为10 000 m³可满足要求。一般情况下气溶胶中²¹⁰Po含量比^239+240Pu高4～6个数量级,在分析和测量中要关注²¹⁰Po对^239+240Pu测量的影响。测量结果显示,我国普通省份环境气溶胶中^239+240Pu含量为0.009～0.099 μBq/m³,与美国、韩国和西班牙等国家空气气溶胶中的Pu含量处于同一水平。     

Detection of motor imagery EEG signals employing Naïve Bayes based learning process

The objective of this study is to develop a reliable and robust analysis system that can automatically detect motor imagery (MI) based electroencephalogram (EEG) signals for the development of brain–computer interface (BCI) systems. The detection of MI tasks provides an important basis for designing a communication way between brain and computer in creating devices for people with motor disabilities. This paper presents a synthesis approach based on optimum allocation system and Naive Bayes (NB) algorithm for detecting mental states based on EEG signals. In this study, an optimal allocation (OA) is introduced to discover the most effective representatives with minimal variability from a large number of MI based EEG data and the NB classifier is employed on the extracted features for discriminating the MI signals. The feasibility and effectiveness of the proposed method is demonstrated by analyzing the results and its success on two public benchmark datasets. The results indicate that the proposed approach outperforms the most recently reported five methods and achieves 0.64–20.90% improvement on average accuracy. The performances of this proposed approach implies that it can be reliably used to detect EEG based MI activity and can be a promising avenue for EEG based BCI applications.