Enhancing decisions with life cycle analysis for risk management

Although corporate financial distress is an infrequent occurrence, it has an extremely debilitating effect on the stability of a firm when it does occur. For this reason, an accurate risk assessment mechanism is needed in numerous industry sectors, particularly in financial institutions and banking. Based on corporation life cycle theory and risk management, this study develops a risk pre-warning model, namely the RSVMDT model, to eliminate serious financial punching and to examine the effectiveness of transparency and the full disclosure index (TFDI) during each life cycle stage. The RSVMDT model includes three techniques: random forest (RF), support vector machines (SVMs), and decision trees (DTs). The RF is used to determine the essential attributes of firms and therefore decrease the computational complexity of financial analysis and improve the classification accuracy. The SVM is employed as a classifier to identify corporations in financial distress. Finally, the DT is utilized as a rule generator that allows decision makers to adjust the financial structures of firms at specific life cycle stages. Together, these three techniques can increase the probability of corporate survival in a highly competitive environment. Additionally, the study further evaluates the importance of the TFDI during a turbulent economy. The public sectors can benefit from this evaluation by formulating future policies based on the rules derived from the developed RSVMDT model.     

Morphology of craters at hypervelocity impact in isotropic composites with inclusions

Results of a study of hypervelocity impact in model disperse-reinforced composites with an epoxy or aluminum matrix with metallic (Al and Pb) or ceramic (SiO₂) inclusions are reported. The goal of the present study is to find materials that possess a higher resistance to penetration of a high-velocity projectile compared with materials of separate components. This resistance is characterized by the ratio of the depth of a crater in a sufficiently thick target to the diameter of a spherical projectile. For two composites studied, we show that in impact of a steel particle with a velocity ranging from3 to11km/sec, the crater depth is approximately one projectile diameter smaller than that for lead or aluminum targets. Lavrent’ev Institute of Hydrodynamics, Siberian Division, Russian Academy of Sciences, Novosibirsk 630090. Translated from Fizika Goreniya i Vzryva, Vol. 33, No. 3, pp. 139–151, May–June, 1997.     

A group decision classifier with particle swarm optimization and decision tree for analyzing achievements in mathematics and science

Group decision making is a multi-criteria decision-making method applied in many fields. However, the use of group decision-making techniques in multi-class classification problems and rule generation is not explored widely. This investigation developed a group decision classifier with particle swarm optimization (PSO) and decision tree (GDCPSODT) for analyzing students’ mathematic and scientific achievements, which is a multi-class classification problem involving rule generation. The PSO technique is employed to determine weights of condition attributes; the decision tree is used to generate rules. To demonstrate the performance of the developed GDCPSODT model, other classifiers such as the Bayesian classifier, the k-nearest neighbor (KNN) classifier, the back propagation neural networks classifier with particle swarm optimization (BPNNPSO) and the radial basis function neural networks classifier with PSO (RBFNNPSO) are used to cope with the same data. Experimental results indicated the testing accuracy of GDCPSODT is higher than the other four classifiers. Furthermore, rules and some improvement directions of academic achievements are provided by the GDCPSODT model. Therefore, the GDCPSODT model is a feasible and promising alternative for analyzing student-related mathematic and scientific achievement data.     

High-Resolution Electron Microscopy Observations of Stacking Faults in β-SiC

Structural images of the stacking faults in β-SiC were obtained with a high-resolution electron microscope. Stacking faults initially present in β-SiC powder particles were eliminated as grain growth proceeded at elevated temperatures.     

Stearic acid unlike shorter-chain saturated fatty acids is poorly utilized for triacylglycerol synthesis and β-oxidation in cultured rat hepatocytes

Utilization of stearate as compared to various saturated fatty acids for cholesterol and lipid synthesis and β-oxidation was determined in primary culture of rat hepatocytes. At 0.5 mmol/L in the medium, stearate (18:0) adequately solubilized by albumin was less inhibitory to cholesterol synthesis from [2-¹⁴C] acetate than myristate (14:0) and palmitate (16:0) (68% vs. 91 and 88% inhibition, respectively). The rate of incorporation into cholesterol from [1-¹⁴C] stearate (3.0±0.6 nmol/mg protein/4 h) was 37-, 1.8-, and 7.8-fold of that from myristate, palmitate, and oleate, respectively. Conversely, the rate of [1-¹⁴C] stearate incorporation into total glycerolipids was 88–90% lower than that of labeled palmitate, myristate, and oleate. The rate of [1-¹⁴C] stearate incorporation into triacylglycerol (3.6±0.4 nmol/mg protein/4 h) was 6–8% of that from myristate, palmitate, oleate, and linoleate. The rate of stearate incorporation into phospholipids was the lowest among tested fatty acids, whereas the rate of mono- and diacylglycerol synthesis was the highest with stearate treatment. The rate of β-oxidation as measured by CO₂ and acid soluble metabolite production was also the lowest with [1-¹⁴C] stearate treatment at 22.7 nmol/mg protein/4 h, which was 35–40% of those from other [1-¹⁴C] labeled fatty acids. A greater proportion of stearate than other fatty acids taken up by the hepatocytes remained free and was not metabolized. Clearly, stearate as compared to shorter-chain saturated fatty acids was less efficiently oxidized and esterified to triacylglycerol in cultured rat hepatocytes.     

Multipath effects on vector tracking algorithm for GNSS signal

The vector tracking algorithm uses a single extended Kalman filter （EKF） to predict the time-delays and the Doppler deviations of the GNSS signal, while also estimating the user＇s position, velocity, and clock state. In this paper, the effects of multipath on the tracking performance of the vector delay / frequency lock loop （VDFLL） is studied for better application in the multipath environment. The error expressions of the measurements are given in theory. The tracking error caused by multipath is reduced by VDFLL, which is proved by the tracking error of VDFLL through a new iterating method. The theoretical analysis is verified by the Monte Carlo simulation.     

基于运行数据的短期风电功率预测误差互补特性探析

随着风电装机容量的不断增加,风电场、风电场集群功率预测误差对调度运行的影响越来越大.当前风电预测误差特性研究主要针对单个风电场,无法满足当前电网调度运行需求.基于此,文中首先统计分析了大量风电场的分布特性,总结了不同规模风电场集群预测误差分布特性的变化规律,探索了集群预测误差概率分布的最佳拟合模型.然后,针对预测误差分布对未来的指导作用,提出了误差分布持续性的评价指标.最后,对风电预测误差研究的作用进行了较为全面的分析,并指出以风电场站集群为单位开展电网调度更具有工程适用性.     

面向新能源消纳能力评估的年负荷序列建模及场景生成方法

全年负荷序列是开展中国省级电网新能源消纳能力评估的基础,文中提出了基于聚类分析和马尔可夫链技术的年负荷序列建模和场景生成方法。首先,通过自组织映射对历史负荷数据进行典型日聚类分析,并采用离散马尔可夫链描述不同典型日之间的状态转移特性。针对每类典型日,采用核密度估计和t-Copula函数构建日负荷特性指标的联合概率分布模型。然后,通过马尔可夫链蒙特卡洛随机抽样生成每日的典型日状态和日负荷特性指标。最后,通过构建日负荷序列优化模型,实现每日负荷序列的优化重构,直至生成全年负荷序列场景。算例基于中国某省级电网全年负荷数据进行测试,并利用所生成的负荷序列场景开展未来年度新能源消纳能力的评估,结果验证了所提方法的有效性及实用性。     

Bare finger 3D air‐touch system with embedded multiwavelength optical sensor arrays for mobile 3D displays

A camera‐free 3D air‐touch system was proposed. Hovering, air swiping, and 3D gestures for further interaction with the floated 3D images on the mobile display were demonstrated. By embedding multiwavelength optical sensors into the display pixels and adding angular‐scanning illuminators with multiwavelength on the edge of the display, the flat panel can sense images reflected by a bare finger from different heights. In addition, three axis (x, y, z) information of the reflected image of the fingertip can be calculated. Finally, the proposed 3D air‐touch system was successfully demonstrated on a 4‐inch mobile 3D display.     

直流±250 kV海底电缆J型管段载流量提升研究北大核心CSCD

J型管作为新型电力系统中新能源海上风电送出系统海底电缆的保护装置,其存在会阻碍海缆对外散热,制约整条风电送出线路的载流量。为了提高J型管段海缆载流量,文中建立了典型J型管段流体—温度耦合数值模型,仿真分析了环境风速、太阳辐射、环境温度对直流±250 kV海底电缆封闭J型管系统温度场的影响规律,确定J型管载流量瓶颈位置,提出了一种J型管外壁开孔配合平台开孔引入外界风的J型管载流量提升方法,验证了仿真模型与算法的正确性。结果表明与全封闭J型管系统相比,J型管外壁开孔配合平台开孔能有效降低海缆温度,环境风速较低为1 m/s时载流量可有效提升13.98%。研究结果为指导J型管设计提升J型管段海缆载流量提供了新的解决方案。