Improving the Robustness of Particle Size Analysis by Multivariate Statistical Process Control

The robustness of online particle size analysis in wet processes is improved by applying data based modeling methods to the control of the sample preparation and measurement sequence of the particle size analyzer. The aim is to find a more accurate and reliable method of determining the end of the particle size integration period using multivariate statistical process control (MSPC). The studied approach is tested on analyzers installed at two mineral processing plant sites and validated using two validation tests. Research shows that the proposed method works with two very different slurry types. The main advantage of the adapted approach is that there are no adjustable parameters that have to be set by the user.     

Gene Network Analysis of Alzheimer’s Disease Based on Network and Statistical Methods

Gene network associated with Alzheimer’s disease (AD) is constructed from multiple data sources by considering gene co-expression and other factors. The AD gene network is divided into modules by Cluster one, Markov Clustering (MCL), Community Clustering (Glay) and Molecular Complex Detection (MCODE). Then these division methods are evaluated by network structure entropy, and optimal division method, MCODE. Through functional enrichment analysis, the functional module is identified. Furthermore, we use network topology properties to predict essential genes. In addition, the logical regression algorithm under Bayesian framework is used to predict essential genes of AD. Based on network pharmacology, four kinds of AD’s herb-active compounds-active compound targets network and AD common core network are visualized, then the better herbs and herb compounds of AD are selected through enrichment analysis.     

基于油液光谱Wiener过程的综合传动装置失效预测

原子发射光谱是分析油液中微小磨损颗粒元素浓度的重要方法。作为一种非直接测量方法,油液光谱数据是车辆综合传动装置可靠性评估中的系统性能劣化的重要监测指标,可用于系统失效评估与剩余寿命预测。针对油液光谱数据这类型的一元劣化失效,随机过程尤其是Wiener过程模型具有良好的计算分析性质,在基于性能劣化的可靠性分析中应用日趋广泛。通过对车辆综合传动装置运行中的实时采样,共取得50个油液光谱样本。采用其中三种指示元素的线性回归方程来计算综合传动装置运行中每个瞬时的特征值与均值。基于正漂移Wiener过程,建立了综合传动装置的劣化失效预测模型,并基于R语言环境进行了随机微分方程的仿真与求解。得到了油液光谱中的Fe,Cu和Mo元素含量增长趋势的预测结果以及三种指示元素各自的首中时间。经比较,劣化失效周期的预测值较之条件维护时间延长了27 Mh(15.9%)。维护时间的延长,能够有效的减少全寿命周期内的维护次数,并最终降低维护成本。研究结果表明,该方法适用于综合传动装置的磨损与失效预测、全寿命周期费用与维护计划的优化。同时,也可推广至其他复杂机械系统的失效预测与评价等相关领域。     

Analysis on International Competitiveness of Service Trade in the Guangdong–Hong Kong–Macao Greater Bay Area Based on Using the Entropy and Gray Correlation Methods

Based on the analysis and measurement of the overall situation, import and export structure and international competitiveness of the various sectors of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area, with the help of MATLAB and Gray System Modeling software, the synergy degree model was established to quantitatively analyze the synergy level of service trade in the Greater Bay Area with the help of grey correlation analysis method and entropy weight method. The results show that the overall development trend of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area is good. The service trade industries in different regions are highly complementary and have a high degree of correlation. The potential for the coordinated development of internal service trade is excellent, and the overall situation of service trade in the Greater Bay Area is in a stage of transition from a moderate level of synergy to a high level of synergy. The Greater Bay Area can achieve industrial synergy by accelerating industrial integration and green transformation, establishing a coordinated development mechanism, sharing market platform, strengthening personnel security, and further enhancing the international competitiveness of service trade. The established model better reflects the current coordination of service trade in the Guangdong–Hong Kong–Macao Greater Bay Area and has good applicability. In the future, more economic, technological, geographic, and policy data and information can be comprehensively used to study the spatial pattern, evolution rules, and mechanisms of coordinated development in the broader area.     

Ground State,Magnetization Process and Bipartite Quantum Entanglement of a Spin-1/2 Ising–Heisenberg Model on Planar Lattices of Interconnected Trigonal Bipyramids

The ground state, magnetization scenario and the local bipartite quantum entanglement of a mixed spin-1/2 Ising–Heisenberg model in a magnetic field on planar lattices formed by identical corner-sharing bipyramidal plaquettes is examined by combining the exact analytical concept of generalized decoration-iteration mapping transformations with Monte Carlo simulations utilizing the Metropolis algorithm. The ground-state phase diagram of the model involves six different phases, namely, the standard ferrimagnetic phase, fully saturated phase, two unique quantum ferrimagnetic phases, and two macroscopically degenerate quantum ferrimagnetic phases with two chiral degrees of freedom of the Heisenberg triangular clusters. The diversity of ground-state spin arrangement is manifested themselves in seven different magnetization scenarios with one, two or three fractional plateaus whose values are determined by the number of corner-sharing plaquettes. The low-temperature values of the concurrence demonstrate that the bipartite quantum entanglement of the Heisenberg spins in quantum ferrimagnetic phases is field independent, but twice as strong if the Heisenberg spin arrangement is unique as it is two-fold degenerate.