Band alignment in organic light emitting diodes - On the track of thickness dependent onset voltage shifts

In this paper we report on an unclear effect in the IV characteristics of organic light emitting diodes (OLEDs). When the thickness of the emitter layer based on a modified phenyl carbozole triplet host material (TH) in the device is increased, a significant shift of the onset voltage to higher values can be noticed. The voltage shift is not observed if the TH is substituted by an isomer with only minor variation of the molecular structure. In a previous publication we could already show that an electric interface field is necessary to describe the onset voltage behaviour. To find the origin of this interface field in the present publication the two isomers are characterized and the band alignment at the interfaces to the emitter layer is investigated using photoelectron spectroscopy. The interface energy diagrams have been measured on stepwise prepared model interfaces. A further simplification of the bipolar to a hole only device stack proofs, that band bending at the hole injecting interface to the TH layer is the origin of the interface field. In contrast an entire flat band situation is measured in case of the device using the other isomer showing no onset voltage shift.     

Performance optimization of integrated resilience engineering and lean production principles

This paper conducts performance assessment from integrated resilience engineering (IRE) and lean production points of view. This is the first study that evaluates the impact of integrated resilience engineering (IRE) on lean production principles. Second, this study considers integrated impact of lean production by a unique intelligent algorithm. The proposed algorithm is composed of radial basis function (RBF), multi-layer perceptron (MLP) and adaptive neuro-fuzzy inference system (ANFIS). Moreover, the algorithm is capable of handling both crisp and fuzzy data due to the existence of intelligent approach. The proposed algorithm is equipped with verification and validation mechanism through conventional regression, statistical methods and data envelopment analysis. To demonstrate the applicability of the study, a real-world pipe manufacturer is considered as our case study. The results showed that “pull system” and “fault tolerant” among lean and IRE factors, respectively have been implemented inappropriately, while other factors are either suitably executed or ineffective.     

Quantile autoregression neural network model with applications to evaluating value at risk

We develop a new quantile autoregression neural network (QARNN) model based on an artificial neural network architecture. The proposed QARNN model is flexible and can be used to explore potential nonlinear relationships among quantiles in time series data. By optimizing an approximate error function and standard gradient based optimization algorithms, QARNN outputs conditional quantile functions recursively. The utility of our new model is illustrated by Monte Carlo simulation studies and empirical analyses of three real stock indices from the Hong Kong Hang Seng Index (HSI), the US S&P500 Index (S&P500) and the Financial Times Stock Exchange 100 Index (FTSE100).     

Introduction of affinity set and its application in data-mining example of delayed diagnosis

At least 44,000 people die in hospitals each year as a result of medical errors, and these deaths are becoming the eighth-leading cause of death in the United States. Thus, medical providers have the responsibility to pay attention for reducing avoidable medical errors and improve patient safety as best as they can. It requires the rapid evaluation and prioritisation of life threatening injuries in the primary survey followed by a detailed secondary survey in the emergency room. However, time is always valuable and limited such that some important vital signs may be delayed and ignored. This research explores delayed diagnosis problem and uses the affinity set by Topology concept to classify/focus on key attributes causing delayed diagnosis (missed injury) in order to reduce error risk. Results interestingly indicate that when a patient can breathe normally, but his (or her) blood-pressure or pulse is abnormal, a high probability of delayed diagnosis exists. This affinity work also compares the performance with the model of rough set (Rosetta), neural network, support vector machine and logistic regression. And our affinity model shows its advantage by prediction accuracy and explanation power.     

基于FFT雷达图的气体传感器温度调制模式

通过快速傅里叶变换提取特征参数,建立气体传感器动态响应的雷达图,并在此基础上研究了6种周期、4种温度幅值的正弦波、三角波、锯齿波以及矩形波等共96种温度调制模式下的微热板式气体传感器对3种易燃易爆气体的响应,分析温度幅值、周期和波形对3种气体的辨识情况影响,确立了一种基于FFT雷达图的温度调制模式优化选择的方法。     

二次采样随机共振的工程应用研究

研究了小参数随机共振(SR)理论的克莱默斯(Kramers)逃逸速率和罗伦兹(Lorentzian)分布的噪声频谱特性，得出了只有在噪声能量集中的低频区域才能产生随机共振的论点。在此基础上，通过二次采样频率变换思想，成功地将小参数随机共振扩展应用到大参数的随机共振，从而实现了从强噪声中检测出弱信号的目的。大参数随机共振谱特性及其信噪比的进一步定量分析表明，提高采样频率可把弱信号移入噪声能量集中的低频区域，有利于产生可辨识的随机共振谱峰。电机故障的监测与诊断实例证明了该方法在实际应用中的有效性。     

集群服务器文件动态分配的改进算法

在分析MMPacking算法的基础上,提出了一种改进的文件分配算法。在按照MMPacking算法分配文件时,根据节点的文件累积需求度去完成文件的分配或复制,考虑了节点的剩余能力。文件分配在所有服务器节点中周期性地进行,每进行了一轮文件分配后,都要从第1个节点开始新的一轮分配。在开始新一轮分配前,服务器节点要按照服务器的剩余能力重新进行降序排列。在每轮分配中,每分配一次文件到某个服务器节点后,都要检测当前节点服务器的剩余能力是否大于下一节点的剩余能力,如果满足条件,则将重新开始新的一轮文件分配。改进后的算法降低了由于客户需求或服务器配置变化所要支出的额外成本,有效地达到了负载均衡的目的。仿真结果表明,改进后的算法优于MMPacking 算法。     

The effect of pore necking on Knudsen diffusivity and collision frequency of gas molecules with pore walls

Straight pores whose walls were concave (for “Swiss cheese” type solids), convex (for “cannonball” type), or cylindrical (for capillary type) were simulated by computer. The pores having concave and convex walls were represented as an assemblage of spherical elements since any pore could be described merely by listing the radii and coordinates of the elements. Using a Monte Carlo method, molecular trajectories of gas molecules inside the pores were computed in the Knudsen regime to obtain the diffusivities, collision numbers and collision densities. These parameters were strongly dependent on the pore wall configuration. An equation was presented which enabled the prediction of Knudsen diffusivity from the mean pore size d̄ and (d_n/d_w)² (where d_n and d_w are the diameters of narrow and wide sections inside the pores, respectively). The average number of collisions per molecule J̄ was also correlated quite well with the ratio of geometric diffusion length to mean pore size L₀/d̄ for each type and was in the order capillary type > Swiss cheese type > cannonball type. It was found that the pore of capillary type had the preferred wall configuration among these three types from the view point of diffusivity and collision numbers of molecules. The results obtained here will have application to the design of micro- and macro-pore structures of supported catalysts.     

On Weakly Semicommutative Rings

A ring R is said to be weakly scmicommutative if for any a, b ∈ R,ab ＝ 0 implies aRb (∈) Nil(R), where Nil(R) is the set of all nilpotcnt elements in R.In this note, we clarify the relationship betveen weakly semicommutative rings and NI-rings by proving that the notion of a weakly semicommutative ring is a proper generalization of NI-rings. We say that a ring R is weakly 2-primal if the set of nilpotent elements in R coincides with its Levitzki radical, and prove that if R is a weakly 2-primal ring which satisfies α-condition for an endomorphism α of R (that is, ab ＝ 0 (→) aαα(b) ＝ 0 where a, b ∈ R) then the skew polynomial ring R[x; α]is a weakly 2-primal ring, and that if R is a ring and I is an ideal of R such that I and R/I are both weakly semicommutative then R is weakly semicommutative.Those extend the main results of Liang et al. 2007 (Taiwanese J. Math., 11(5)(2007),1359-1368) considerably. Moreover, several new results about weakly semicommutative rings and NI-rings are included.