Evolution mechanism of active groups and thermal effects of Chinese lignite in low-temperature oxidation

Abstract

The evolution of active groups at low temperature was examined using Chinese lignite by infrared technology and X-ray photoelectron spectroscopy (XPS). The results showed that the hydroxyl, aliphatic ether, methylene, and methyl groups played important roles in the low-temperature oxidation of lignite below 200?°C. Carbonyl and carboxyl groups were important intermediates. Thus, a multi-step evolution mechanism involving the hydroxyl, aliphatic ether groups, and alkane was reasoned to describe the low-temperature oxidation of lignite. In addition, according to the oxidation kinetics experiment and the evolution laws of the active groups, the ratios of the reaction lines were determined considering the accuracy of thermal effects. The thermal effects and the heat release intensities of each temperature interval were obtained based on the evolution mechanism and the reaction ratios. The shortest spontaneous combustion period of lignite was calculated and compared with the experimental value, which proved that the reasoned evolution mechanism of the active groups and the calculations of the thermal effects were reliable.     

基于HECC的门限群签密研究

分析了已有的门限群签密方案,针对现有方案中抗合谋攻击及身份追查等不足的问题,在(t,n)门限签密的基础上,基于HECC提出了一种可抗合谋攻击、身份可追查的高安全性门限群签密方案。该方案在可抵御合谋攻击的同时,可分辨攻击是来自于群成员还是外部,并可针对群成员的欺诈行为进行身份追查。该方案的安全性是建立在超椭圆曲线离散对数问题的难解性上,在保障安全的同时,也具备了HECC的诸多优点。     

基于顾客退货的风险规避型双渠道供应链最优策略

在随机市场需求条件下,考虑风险规避型双渠道供应链中存在顾客退货时的最优决策问题,决策变量为两个渠道的商品售价和制造商批发价。以集中决策时的最优决策作为比较标杆,重点利用均值—方差方法分析了分散决策时风险规避型制造商和零售商的最优决策。结合灵敏度分析和数值试验,讨论了分散决策下的风险规避度,以及市场需求波动和顾客退货率对最优决策的影响。结果表明,高退货率不利于零售商、制造商及整个供应链的收益;风险规避度对收益的影响不是一成不变的,与需求波动大小有关;分散决策虽然引起了双边际效应,但制造商风险规避度的增加将降低双边际效应的负面影响。     

Hierarchical power management of a system with autonomously power-managed components using reinforcement learning

This paper presents a hierarchical dynamic power management (DPM) framework based on reinforcement learning (RL) technique, which aims at power savings in a computer system with multiple I/O devices running a number of heterogeneous applications. The proposed framework interacts with the CPU scheduler to perform effective application-level scheduling, thereby enabling further power savings. Moreover, it considers non-stationary workloads and differentiates between the service request generation rates of various software application. The online adaptive DPM technique consists of two layers: component-level local power manager and system-level global power manager. The component-level PM policy is pre-specified and fixed whereas the system-level PM employs temporal difference learning on semi-Markov decision process as the model-free RL technique, and it is specifically optimized for a heterogeneous application pool. Experiments show that the proposed approach considerably enhances power savings while maintaining good performance levels. In comparison with other reference systems, the proposed RL-based DPM approach, further enhances power savings, performs well under various workloads, can simultaneously consider power and performance, and achieves wide and deep power-performance tradeoff curves. Experiments conducted with multiple service providers confirm that up to 63% maximum energy saving per service provider can be achieved.     

基于人工智能算法的投诉用户质差小区定位及预测的方法研究

目前LTE及GSM的质差小区投诉定位主要是依靠用户投诉的地址回放分析附近小区是否存在性能、告警上的裂变,如无则安排人员现场测试信号强度、干扰等。为了解决自动定位投诉质差小区,创新性提出以数据驱动来实现业务规则自动化,充分考虑用户投诉的时间和问题、网络交互信令、无线性能指标和告警等海量数据,运用人工智能技术中的逻辑回归及决策树算法训练出“2G语音、VoLTE语音及数据业务的投诉质差小区定界模型、质差小区预测模型及问题网元”,更快速,更智能,更准确输出语音及数据业务投诉原因、质差小区及质差原因,并对潜在质差小区进行预测,对无线规划、基站建设、故障处理、网络改造等方面的工作也具有积极指导意义。     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.     

Improving the homogeneity and properties of ferrous powder mixes by a novel powder mixing process

Fe-2Cu-0.8C powder mixes were prepared by a wet mixing and spray-drying process using iron, copper, graphite and zinc stearate as main raw materials, and stearic acid, alcohol wax and amide wax as binders. The properties of the as-prepared binder treated powder mixes were investigated. The bonding efficiency of the binders was tested by measure the retention of graphite and copper in the powder mixes after dusting resistance test. The test results show that the wet mixing and spray-drying process can realise the bonding of different particles and improve processing properties of the powder mixes. A flow rate of 30.6?s/50?g and an apparent density of 3.18?g/cm³ were obtained for powder mixes with alcohol wax as binder. The results of dusting resistance test demonstrated that alcohol wax and amide wax have better bonding effect for Cu and graphite powders, respectively, which was reflected by the difference value of electronegativity between Fe, Cu, C elements and functional groups. Compared with mechanically mixing powder mixes, using binder treated powder mixes can also reduce the friction coefficient during compaction by 0.012–0.026. The addition of different binders also has a great influence on the properties of sintered parts.     

Prioritizing the elective surgery patient admission in a Chinese public tertiary hospital using the hesitant fuzzy linguistic ORESTE method

In public health systems around the world, there are not enough medical resources to provide elective (e.g., scheduled or non-emergency) services for all patients immediately. One feasible solution is to prioritize patients by taking into account a variety of factors, such as disease severity, waiting time, and disease types. This is a typical Multiple Criteria Decision Making (MCDM) problem. To solve this problem, in this paper, we first conduct an investigation on the admission process, and obtain 16 indicators affecting patients’ admission, which form a criteria system. Since there is much vague and uncertain information which can be depicted by the hesitant fuzzy linguistic term set effectively for these indicators, we then apply a powerful MCDM method, named the hesitant fuzzy linguistic ORESTE, to prioritize the elective surgery patient admission in a Chinese public tertiary hospital, the West China Hospital. Robust results are obtained by performing a sensitivity analysis with six scenarios. We also compare the results with those derived by other HFL-MCDM methods. It is illustrated that the hesitant fuzzy linguistic ORESTE can help hospitals flexibly manage the patient admissions.     

共享交通的时空轨迹检索与群体发现

为解决共享交通下的共乘用户群体发现效率低、准确率不高问题，依据R-树原理建立GeoOD-Tree索引，并在此基础上提出以最大化共乘率为目标的群体发现策略。首先，对原始时空轨迹数据进行特征提取与标定处理，挖掘有效出行起讫点（OD）轨迹；其次，针对用户起讫点轨迹的特征，建立GeoOD-Tree索引进行有效的存储管理；最后，给出以最大化共乘行程为目标的群体发现模型，并运用K最近邻（KNN）查询对搜索空间剪枝压缩，提高群体发现效率。采用西安市近12000辆出租车营运轨迹数据，选取动态时间规整（DTW）等典型算法与所提算法在查询效率与准确率上进行性能对比分析。与DTW算法相比，所提算法的准确率提高了10.12%，查询效率提高了约15倍。实验结果表明提出的群体发现策略能有效提高共乘用户群体发现的准确率和效率，可有效提升共乘出行方式的出行率。     

A hybrid knowledge and ensemble classification approach for prediction of venous thromboembolism

Clinical narratives such as progress summaries, lab reports, surgical reports, and other narrative texts contain key biomarkers about a patient's health. Evidence-based preventive medicine needs accurate semantic and sentiment analysis to extract and classify medical features as the input to appropriate machine learning classifiers. However, the traditional approach of using single classifiers is limited by the need for dimensionality reduction techniques, statistical feature correlation, a faster learning rate, and the lack of consideration of the semantic relations among features. Hence, extracting semantic and sentiment-based features from clinical text and combining multiple classifiers to create an ensemble intelligent system overcomes many limitations and provides a more robust prediction outcome. The selection of an appropriate approach and its interparameter dependency becomes key for the success of the ensemble method. This paper proposes a hybrid knowledge and ensemble learning framework for prediction of venous thromboembolism (VTE) diagnosis consisting of the following components: a VTE ontology, semantic extraction and sentiment assessment of risk factor framework, and an ensemble classifier. Therefore, a component-based analysis approach was adopted for evaluation using a data set of 250 clinical narratives where knowledge and ensemble achieved the following results with and without semantic extraction and sentiment assessment of risk factor, respectively: a precision of 81.8% and 62.9%, a recall of 81.8% and 57.6%, an F measure of 81.8% and 53.8%, and a receiving operating characteristic of 80.1% and 58.5% in identifying cases of VTE.