Significant spatial co-distribution pattern discovery

Given instances (spatial points) of different spatial features (categories), significant spatial co-distribution pattern discovery aims to find subsets of spatial features whose spatial distributions are statistically significantly similar to each other. Discovering significant spatial co-distribution patterns is important for many application domains such as identifying spatial associations between diseases and risk factors in spatial epidemiology. Previous methods mostly associated spatial features whose instances are frequently located together; however, this does not necessarily indicate a similarity in the spatial distributions between different features. Thus, this paper defines the significant spatial co-distribution pattern discovery problem and subsequently develops a novel method to solve it effectively. First, we propose a new measure, dissimilarity index, to quantify the difference between spatial distributions of different features under the spatial neighbor relation and then employ it in a distribution clustering method to detect candidate spatial co-distribution patterns. To further remove spurious patterns that occur accidentally, the validity of each candidate spatial co-distribution pattern is verified through a significance test under the null hypothesis that spatial distributions of different features are independent of each other. To model the null hypothesis, a distribution shift-correction method is presented by randomizing the relationships between different features and maintaining spatial structure of each feature (e.g., spatial auto-correlation). Comparisons with baseline methods using synthetic datasets demonstrate the effectiveness of the proposed method. A case study identifying co-morbidities in central Colorado is also presented to illustrate the real-world applicability of the proposed method.     

High strength and high modulus carbon fibers

Carbon fibers have been processed from gel spun polyacrylonitrile copolymer on a continuous carbonization line at Georgia Tech (GT) with a tensile strength in the range of 5.5–5.8 GPa, and tensile modulus in the range of 354–375 GPa. This combination of strength and modulus is the highest for any continuous fiber reported to date, and the gel spinning route provides a pathway for further improvements in strength and modulus for mass production of carbon fibers. At short gauge length, fiber tensile strength was as high as 12.1 GPa, which is the highest value ever reported for a PAN based carbon fiber. Structure analysis shows random flaws of about 2 nm size, which results in limiting tensile strength of higher than 20 GPa. Inter-planar turbostratic graphite shear modulus in high strength carbon fibers is 30 GPa, while in graphite the corresponding value is only 4 GPa.     

Ni2V2O7 dandelion microsphere for a high-performance electrocatalyst in water splitting

Water splitting is an effective way to produce hydrogen to solve the energy crisis problem, and inorganic metal compounds are widely used in electrocatalysis field due to efficient hydrogen evolution reaction (HER). Herein, we synthesize Ni₂V₂O₇ dandelion microsphere from nickel nitrate and vanadium pentoxide by “one-step hydrothermal” way, which exhibits large specific surface area of 102.74 m² g⁻¹. The as-prepared Ni₂V₂O₇ microsphere shows good electrocatalysis performances including OER overpotential of 358 mV and good stability, as well as HER overpotential of 195 mV. Furthermore, the Ni₂V₂O₇ microsphere electrode is assembled to Ni₂V₂O₇ microsphere//Ni₂V₂O₇ microsphere system, showing the water splitting voltage of 1.50 V at 10 mA cm⁻² by two-electrode method, which is much lower than those of commercial RuO₂//Pt/C system and most of spinel oxides electrocatalysts. Our work opens up a new and facile avenue for fabricating inorganic microsphere electrocatalyst in hydrogen production field.     

Mitigating Hydrological Risk with Energy Derivatives

The variability of river inflows affects the energy production of hydropower generators and may result in reductions in revenues that can be financially disruptive for these producers. Recent climatic changes have highlighted the risks involved in hydroelectriciy production in Brazil. In this paper, we propose a different approach to formulating a collar derivative, namely an Inverted Collar, to mitigate hydrological risk considering the particularities of Brazil's energy regulatory environment. In addition, we propose a customized collar-by-difference as a variation of the collar model. The effect of these derivatives is analyzed considering electricity market price and power generation uncertainty for a typical hydro generator. The results suggest that these derivatives are effective tools to manage hydrological risk during period of great climatic volatility, such as the height of the drought period experienced by Brazil in 2016. The results also indicate that our models outperform traditional commercial hedging commonly practiced by hydropower producers in the country.     

A structural approach for modelling the hierarchical dynamic process of Web workload in a large-scale campus network

A new structural approach based on hidden Markov model is proposed to describe the hierarchical nature of dynamic process of Web workload. The proposed approach includes two latent Markov chains and one observable process. One of the latent Markov chains is called macro-state process which is used to describe the large-scale trends of Web workload. The remaining latent Markov chain is called sub-state process which is used to describe the small-scale fluctuations that are happening within the duration of a given macro-state. An efficient parameter re-estimation algorithm and a workload simulation algorithm are derived for the proposed discrete model. Experiments based on a real workload of a large-scale campus network are implemented to validate the proposed model.     

捷联式惯性导航系统初始对准扰动补偿控制算法

提出了一种捷联惯性导航系统初始对准扰动补偿二级控制器的设计方法。选取载体的飞行姿态、速度与位置增量建立了扰动补偿控制的状态空间数学模型,基于Backstepping控制方法,通过引入虚拟反馈控制变量设计了非线性控制器,保证大初始扰动条件下系统状态稳定收敛到系统的平衡点集。在平衡点集,设计了H2最优状态反馈控制律,以保证系统状态在平衡点集的渐进收敛。仿真和系统试验结果表明,这种二级补偿控制方法能有效抑制初始对准扰动误差,导航解算精度能够满足工程实际需要。     

基于LVQ神经网络的城市快速路事件自动检测算法

基于具有自组织功能的学习矢量量化(LVQ)神经网络设计了城市快速路异常事件的自动检测算法,提出分车道检测的构想。研究了原始数据筛选、输入向量模式、神经元个数及检测时段等参数的选择。基于小波分析技术对原始数据的高频噪声进行滤波,引入长车流量作为输入参数,并对比了引入前后的检测效果。选用加利福尼亚算法作为评价的参考依据,对其执行过程和门限值的选择进行了研究。     

基于顺路原则及位置预估的动态调度策略

针对市内货物配送和收集这一典型的集送货问题,在利用"混合禁忌搜索算法"进行静态调度求解的基础上,提出基于"顺路原则"的局部调整策略实现集送货问题的动态调度计算.计算过程中采用模糊推理方法选择局部调整范围,同时采用车辆位置预估方法消除计算、传输和执行延迟带来的车辆位置的变化对优化结果的影响.以40个遍布于北京的客户构成的集送货问题为例,验证了动态调度策略的有效性.     

Voltage Decay of Li-Rich Layered Oxides: Mechanism,Modification Strategies,and Perspectives

Li-rich layered oxides (LLOs) have been considered as the most promising cathode materials for achieving high energy density Li-ion batteries. However, they suffer from continuous voltage decay during cycling, which seriously shortens the lifespan of the battery in practical applications. This review comprehensively elaborates and summarizes the state-of-the-art of the research in this field. It is started from the proposed mechanism of voltage decay that refers to the phase transition, microscopic defects, and oxygen redox or release. Furthermore, several strategies to mitigate the voltage decay of LLOs from different scales, such as surface modification, elemental doping, regulation of components, control of defect, and morphology design are summarized. Finally, a systematic outlook on the real root of voltage decay is provided, and more importantly, a potential solution to voltage recovery from electrochemistry. Based on this progress, some effective strategies with multiple scales will be feasible to create the conditions for their commercialization in the future.     

Geometry evolution of rail weld irregularity and the effect on wheel-rail dynamic interaction in heavy haul railways

Rail welded joints are important structural connections in the formation of continuous welded rail (CWR). Due to the difference in the stiffness and material at rail welded joints, rail damage and geometric degradation will occur and evolve under repeated train loads, which will significantly influence the wheel-rail dynamic interaction. In this paper, geometric measurements of the rail surface are made at the single flush welding zone by a tracking test on Datong-Qinhangdao heavy haul railway line in China. Fractal geometry theory is applied to calculate the fractal dimension and describe the geometric evolution of rail weld irregularities. Then, the vehicle-track coupled dynamic model is used to investigate the effect of the geometric evolution of rail weld on the wheel-rail dynamic interactions both in the time and frequency domains. The calculation results show that rail weld irregularities have fractal characteristics due to the fractal dimension of measured geometries mostly larger than 1.1. The geometry evolution of rail welded irregularity has great effects on the wheel-rail dynamic interactions in the time domain, but has little effects in the frequency domain. Fractal geometry theory can be used to describe the geometry evolution of rail weld irregularity and the effect on the wheel-rail dynamic interactions. The results can provide novel ideas for the evaluation and evaluation of the rail weld irregularity.