Concurrent multiresponse non-linear screening: Robust profiling of webpage performance

Profiling engineered data with robust mining methods continues attracting attention in knowledge engineering systems. The purpose of this article is to propose a simple technique that deals with non-linear multi-factorial multi-characteristic screening suitable for knowledge discovery studies. The method is designed to proactively seek and quantify significant information content in engineered mini-datasets. This is achieved by deploying replicated fractional-factorial sampling schemes. Compiled multi-response data are converted to a single master-response effectuated by a series of distribution-free transformations and multi-compressed data fusions. The resulting amalgamated master response is deciphered by non-linear multi-factorial stealth stochastics intended for saturated schemes. The stealth properties of our method target processing datasets which might be overwhelmed by a lack of knowledge about the nature of reference distributions at play. Stealth features are triggered to overcome restrictions regarding the data normality conformance, the effect sparsity assumption and the inherent collapse of the ‘unexplainable error’ connotation in saturated arrays. The technique is showcased by profiling four ordinary controlling factors that influence webpage content performance by collecting data from a commercial browser monitoring service on a large scale web host. The examined effects are: (1) the number of Cascading Style Sheets files, (2) the number of JavaScript files, (3) the number of Image files, and (4) the Domain Name System Aliasing. The webpage performance level was screened against three popular characteristics: (1) the time to first visual, (2) the total loading time, and (3) the customer satisfaction. Our robust multi-response data mining technique is elucidated for a ten-replicate run study dictated by an L₉(3⁴) orthogonal array scheme where any uncontrolled noise embedded contribution has not been necessarily excluded.     

SIMS as a subnanometer probe: A new tool for chemical profile analysis of grafted molecules

The complexity of modern engineered surfaces requires the development of very powerful methods to analyze and characterize them. We demonstrate that it is possible to obtain chemical information about the skeleton of organic molecules constituting SAMs grafted on a silicon surface by using a new type of SIMS method. A profile can be achieved by the investigation of the temporal variation of secondary ion intensities that correspond to the fractional parts of the molecule constituting the SAMs. The equivalent ablation rate is less than 0.5 nm/min.     

Quantitative Depth Profiling of K-Doped Fullerene Films Using XPS and SIMS

 Phenomena accompanying electrochemical doping of solid fullerene films with potassium were studied by sputter ion depth profiling (XPS and SIMS). The potassium distribution was determined, and artifacts associated with possible damage of the layer composition caused by ion impact were investigated and discussed. To compare the charge transfer while reductive doping is taking place at fullerene/solution interface with doping from gas phase, model layers were prepared and doped by potassium under UHV conditions. It was found that sputtering by Ar⁺ primary ions yields both accurate information on the alkaline metal distribution and on its concentration. Sputtering by O⁺ ions led to an enrichment of potassium, apparently due to the reactivity of oxygen with the fullerene matrix. It is shown that the reductive doping starts at the fullerene/solution interface. The concentration of potassium in the doped films was found to be lower than expected from the charge transferred during the electrochemical reduction. Other phase transformations such as hydrogenation are discussed. Received March 4, 2002; accepted July 26, 2002     

Extension of multivariate regression trees to interval data. Application to electricity load profiling

Summary  Several data can be presented as interval curves where intervals reflect a within variability. In particular, this representation is well adapted for load profiles, which depict the electricity consumption of a class of customers. Electricity load profiling consists in assigning a daily load curve to a customer based on their characteristics such as energy requirement. Within the load profiling scope, this paper investigates the extension of multivariate regression trees to the case of interval dependent (or response) variables. The tree method aims at setting up simultaneously load profiles and their assignment rules based on independent variables. The extension of multivariate regression trees to interval responses is detailed and a global approach is defined. It consists in a first stage of a dimension reduction of the interval response variables. Thereafter, the extension of the tree method is applied to the first principal interval components. Outputs are the classes of the interval curves where each class is characterized both by an interval load profile (e.g. the class prototype) and an assignment rule based on the independent variables.     

Recoil spectrometry: Ion accelerator based elemental characterisation of surface layers

Recoil Spectrometry covers a group of techniques that are very similar to the well known Rutherford backscattering Spectrometry technique, but with the important difference that one measures the recoiling target atom rather than the projectile ion. This makes it possible to determine both the identity of the recoil and its depth of origin from its energy and velocity, using a suitable detector system. The incident ion is typically high-energy (30–100MeV)³⁵C1,⁸¹Br or¹²⁷I. Low concentrations of light elements such as C, O and N can be profiled in a heavy matrix such as Fe or GaAs. Here we present an overview of mass and energy dispersive recoil Spectrometry and illustrate its successful use in some typical applications.     

Evaluation of the sputtering rate variation in SIMS ultra‐shallow depth profiling using multiple short‐period delta layers

We have developed multiple short‐period delta layers as a reference material for SIMS ultra‐shallow depth profiling. Boron nitride delta layers and silicon spacer layers were sputter‐deposited alternately, with a silicon spacer thickness of 1–5 nm. These delta‐doped layers were used to measure the sputtering rate change in the initial stage of oxygen ion bombardment. A significant variation of sputtering rate was observed in the initial 3 nm or less. The sputtering rate in the initial 3 nm was estimated to be about four times larger than the steady‐state value for 1000 eV oxygen ions. Copyright © 2003 John Wiley & Sons, Ltd.     

Oxygen diffusion in SrCe<Subscript>0.95</Subscript>Yb<Subscript>0.05</Subscript>O<Subscript>3−δ</Subscript>

¹⁸O/¹⁶O isotope exchange depth profiling (IEDP) combined with secondary ion mass spectrometry (SIMS) has been used to measure the oxygen tracer diffusivity of SrCe_0.95Yb_0.05O_3– between 800 °C and 500 °C at a nominal pressure of 200 mbar. The values of D* (oxygen tracer diffusion coefficient) and k (surface exchange coefficient) increase steadily with increasing temperature, and the activation energies are 1.13 eV and 0.96 eV, respectively. Oxygen ion conductivities have been calculated using the Nernst–Einstein equation. The transport number for oxide ions at 769 °C, the highest temperature studied, is only ~0.05. Moreover, SrCe_0.95Yb_0.05O_3– has been studied using impedance spectroscopy under dry O₂, wet O₂ and wet H₂ (N₂/10% H₂) atmospheres, over the range 850–300 °C. Above ~550 °C, SrCe_0.95Yb_0.05O_3– shows higher conductivity in dry O₂ than in wet O₂ or wet H₂; below that temperature the results obtained for the three atmospheres are comparable. Dry O₂ shows the highest activation energy (0.77 eV); the activation energies for wet O₂ and wet H₂ are identical (0.62 eV).Abbreviations HTPC high-temperature proton conductor - IEDP isotope exchange depth profiling - SIMS secondary ion mass spectrometryPresented at the OSSEP Workshop Ionic and Mixed Conductors: Methods and Processes, Aveiro, Portugal, 10–12 April 2003     

计及位置不确定的喷雾机喷杆系统仿形姿态渐近跟踪控制北大核心CSCD

针对喷雾机喷杆仿形系统中同时存在负载变化、未建模不确定项、物理参数摄动以及外部干扰等问题,提出了一种基于小波网络逼近的具有自适应性和鲁棒性的反步控制方法.首先,将含有不确定、未知和非线性项的喷杆仿形系统建立为完整的数学模型,将其等价转化为具有严格反馈的状态空间形式;其次,采用设计的小波基元去构造神经网络,在满足最优误差有界条件下逼近反步法中虚拟等效控制部分,选取自适应更新律估计系统中存在的未知参数,引入鲁棒补偿项减小复合干扰对系统的不利影响,降低了输入指令信号的阶次要求;最后,通过构造合适的Lyapunov函数,应用稳定性理论证明了闭环系统位置跟踪误差渐近收敛到原点.仿真结果表明,所提控制方法可实现喷雾机喷杆位置姿态快速升降机动调整,有效地增强了喷杆系统的鲁棒稳定性和控制精度.     

An affinity-based probe for the proteomic profiling of aspartic proteases

We have developed an affinity-based probe for the proteomic profiling of aspartic proteases. Our probe was shown to be selective towards aspartic proteases over other proteins. It was also shown that the strategy may be used to label selectively aspartic proteases in the presence of a large excess of other proteins, thus making it useful for future proteome profiling experiments.