利用正多项式响应曲面模型实现模拟电路参数自动生成

提出一种基于仿真的模拟电路参数自动生成方法，通过利用模拟电路性能仿真数值结果生成描述电路性能与电路参数之间关系的正多项式响应曲面模型（polynomial response surface models），再利用若干性能曲面模型协同求出满足全部性能要求的模拟电路的参数配置．这种方法的本质是将电路参数化问题转化为几何规划（geometric program）问题，为线性或非线性电路生成达到Spice器件仿真级精度的性能正多项式响应曲面．文中提出的正多项式响应曲面模型的待求参数包括正实数系数和任意实数指数，其回归分析过程中如果模型无法满足精度要求，可通过自动修改模型的多项式结构最终获得理想结果．最后以运算放大器电路为例，生成精确描述电路性能的正多项式响应曲面模型，并通过若干正多项式响应曲面模型得到满足性能要求的参数配置．

Immunoprotective analysis of VhhP2, a Vibrio harveyi vaccine candidate

VhhP2 is an Outer membrane protein identified in a pathogenic Vibrio harveyi strain, T4, isolated from diseased fish. When used as a Subunit Vaccine, purified recombinant VhhP2 affords high level of protection upon Japanese flounder against V harveyi challenge. Vaccination with VhhP2 induced the expression of a number of immune-related genes, especially those encoding immunoglobulin M (IgM) and major histocompatibility complex (MHC) II alpha. A VhhP2 surface display system, in the form of the fish commensal strain FIR harboring the vhhP2-expressing plasmid pJVP, was constructed. PF3/pJVP is able to produce and present recombinant VhhP2 on cell surface. Vaccination of fish with live PF3/pJVP via intraperitoneal injection elicited Strong immunoprotection. Vaccination of fish orally with live PF3/pJVP embedded in alginate microspheres also induced effective immunoprotection. In addition, a VhhP2-based surface display system was created, in which VhhP2 serves as a carrier for the Surface delivery of a heterologous Edwardsiella tarda immunogen, Et18, that is fused in-frame to VhhP2. DH5 alpha/pJVP18, which expresses and surface-displays the VhhP2-Et18 chimera, proved to be an effective vaccine that call protect fish against infections by V. harveyi and E. tarda to the extents comparable to those produced by vaccination with purified recombinant VhhP2 and Et18, respectively. These data suggest that VhhP2 may be applied as a vaccine and a vaccine carrier against infections by V. harveyi and other pathogens such as F. tarda. (C) 2009 Elsevier Ltd. All rights reserved.

Electrochemical biosensor based on microfabricated electrode arrays for life sciences applications

In developing a biosensor, the utmost important aspects that need to be emphasized are the specificity and selectivity of the transducer. These two vital prerequisites are of paramount in ensuring a robust and reliable biosensor. Improvements in electrochemical sensors can be achieved by using microelectrodes and to modify the electrode surface (using chemical or biological recognition layers to improve the sensitivity and selectivity). The fabrication and characterisations of silicon-based and glass-based gold microelectrode arrays with various geometries (band and disc) and dimension (ranging from 10 μm-100 nm) were reported. It was found that silicon-based transducers of 10 μm gold microelectrode array exhibited the most stable and reproducible electrochemical measurements hence this dimension was selected for further study. Chemical electrodeposition on both 10 μm microband and microdisc were found viable by electro-assisted self-assembled sol-gel silica film and nanoporous-gold electrodeposition respectively. The fabrication and characterisations of on-chip electrochemical cell was also reported with a fixed diameter/width dimension and interspacing variation. With this regard, the 10 μm microelectrode array with interspacing distance of 100 μm exhibited the best electrochemical response. Surface functionalisations on single chip of planar gold macroelectrodes were also studied for the immobilisation of histidine-tagged protein and antibody. Imaging techniques such as atomic force microscopy, fluorescent microscopy or scanning electron microscope were employed to complement the electrochemical characterisations. The long-chain thiol of self-assembled monolayer with NTA-metal ligand coordination was selected for the histidine-tagged protein while silanisation technique was selected for the antibody immobilisation. The final part of the thesis described the development of a T-2 labelless immunosensor using impedimetric approach. Good antibody calibration curve was obtained for both 10 μm microband and 10 μm microdisc array. For the establishment of the T-2/HT-2 toxin calibration curve, it was found that larger microdisc array dimension was required to produce better calibration curve. The calibration curves established in buffer solution show that the microelectrode arrays were sensitive and able to detect levels of T-2/HT-2 toxin as low as 25 ppb (25 μg kg-1) with a limit of quantitation of 4.89 ppb for a 10 μm microband array and 1.53 ppb for the 40 μm microdisc array.

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Optimization and finite element analysis for reliable electronic packaging

This paper demonstrates a modeling and design approach that couples computational mechanics techniques with numerical optimisation and statistical models for virtual prototyping and testing in different application areas concerning reliability of eletronic packages. The integrated software modules provide a design engineer in the electronic manufacturing sector with fast design and process solutions by optimizing key parameters and taking into account complexity of certain operational conditions. The integrated modeling framework is obtained by coupling the multi-phsyics finite element framework - PHYSICA - with the numerical optimisation tool - VisualDOC into a fully automated design tool for solutions of electronic packaging problems. Response Surface Modeling Methodolgy and Design of Experiments statistical tools plus numerical optimisaiton techniques are demonstrated as a part of the modeling framework. Two different problems are discussed and solved using the integrated numerical FEM-Optimisation tool. First, an example of thermal management of an electronic package on a board is illustrated. Location of the device is optimized to ensure reduced junction temperature and stress in the die subject to certain cooling air profile and other heat dissipating active components. In the second example thermo-mechanical simulations of solder creep deformations are presented to predict flip-chip reliability and subsequently used to optimise the life-time of solder interconnects under thermal cycling.

Reliability analysis for power electronics modules

This paper discusses the reliability of power electronics modules. The approach taken combines numerical modeling techniques with experimentation and accelerated testing to identify failure modes and mechanisms for the power module structure and most importantly the root cause of a potential failure. The paper details results for two types of failure (i) wire bond fatigue and (ii) substrate delamination. Finite element method modeling techniques have been used to predict the stress distribution within the module structures. A response surface optimisation approach has been employed to enable the optimal design and parameter sensitivity to be determined. The response surface is used by a Monte Carlo method to determine the effects of uncertainty in the design.

Thermal-mechanical modelling of power electronic module packaging

In this paper the reliability of the isolation substrate and chip mountdown solder interconnect of power modules under thermal-mechanical loading has been analysed using a numerical modelling approach. The damage indicators such as the peel stress and the accumulated plastic work density in solder interconnect are calculated for a range of geometrical design parameters, and the effects of these parameters on the reliability are studied by using a combination of the finite element analysis (FEA) method and optimisation techniques. The sensitivities of the reliability of the isolation substrate and solder interconnect to the changes of the design parameters are obtained and optimal designs are studied using response surface approximation and gradient optimization method

Optimizing the reliability of power electronics module isolation substrates

Optimal design of a power electronics module isolation substrate is assessed using a combination of finite element structural mechanics analysis and response surface optimisation technique. Primary failure modes in power electronics modules include the loss of structural integrity in the ceramic substrate materials due to stresses induced through thermal cycling. Analysis of the influence of ceramic substrate design parameters is undertaken using a design of experiments approach. Finite element analysis is used to determine the stress distribution for each design, and the results are used to construct a quadratic response surface function. A particle swarm optimisation algorithm is then used to determine the optimal substrate design. Analysis of response surface function gradients is used to perform sensitivity analysis and develop isolation substrate design rules. The influence of design uncertainties introduced through manufacturing tolerances is assessed using a Monte-Carlo algorithm, resulting in a stress distribution histogram. The probability of failure caused by the violation of design constraints has been analyzed. Six geometric design parameters are considered in this work and the most important design parameters have been identified. Overall analysis results can be used to enhance the design and reliability of the component.

A prognostic assessment method for power electronics modules

This paper describes a prognostic method which combines the physics of failure models with probability reasoning algorithm. The measured real time data (temperature vs. time) was used as the loading profile for the PoF simulations. The response surface equation of the accumulated plastic strain in the solder interconnect in terms of two variables (average temperature, and temperature amplitude) was constructed. This response surface equation was incorporated into the lifetime model of solder interconnect, and therefore the remaining life time of the solder component under current loading condition was predicted. The predictions from PoF models were also used to calculate the conditional probability table for a Bayesian Network, which was used to take into account of the impacts of the health observations of each product in lifetime prediction. The prognostic prediction in the end was expressed as the probability for the product to survive the expected future usage. As a demonstration, this method was applied to an IGBT power module used for aircraft applications.

Combined Effects Of Body Size Food Concentration And Season On Physiology Of Mytilus-Edulis

Multivariate experiments are used to study the effects of body size, food concentration, and season on the oxygen consumption, ammonia excretion, food assimilation efficiency and filtration rate of Mytilus edulis adults. Food concentrations and season affect both the intercept and the slope of the allometric equation describing oxygen uptake as a function of body size. Multiple regression and response surface techniques are used to describe and illustrate the complex relationship between metabolic rate, ration, season and the body size of M. edulis. Filtration rate has a relatively low weight exponent Q> = 038) and the intercept for the allometric equation is not significantly affected by food concentration, season or acclimation temperatures between 5 and 20 °C. Food assimilation efficiency declines exponentially with increasing food concentration and is dependent on body size at high food levels. The rate of ammonia excretion shows a similar seasonal cycle to that of oxygen consumption. They are both minimal in the autumn/winter and reach a maximum in the spring /summer.

A probabilistic modelling scheme for analysis of long-term failure of cemented femoral joint replacements

Reliable prediction of long-term medical device performance using computer simulation requires consideration of variability in surgical procedure, as well as patient-specific factors. However, even deterministic simulation of long-term failure processes for such devices is time and resource consuming so that including variability can lead to excessive time to achieve useful predictions. This study investigates the use of an accelerated probabilistic framework for predicting the likely performance envelope of a device and applies it to femoral prosthesis loosening in cemented hip arthroplasty.
A creep and fatigue damage failure model for bone cement, in conjunction with an interfacial fatigue model for the implant–cement interface, was used to simulate loosening of a prosthesis within a cement mantle. A deterministic set of trial simulations was used to account for variability of a set of surgical and patient factors, and a response surface method was used to perform and accelerate a Monte Carlo simulation to achieve an estimate of the likely range of prosthesis loosening. The proposed framework was used to conceptually investigate the influence of prosthesis selection and surgical placement on prosthesis migration.
Results demonstrate that the response surface method is capable of dramatically reducing the time to achieve convergence in mean and variance of predicted response variables. A critical requirement for realistic predictions is the size and quality of the initial training dataset used to generate the response surface and further work is required to determine the recommendations for a minimum number of initial trials. Results of this conceptual application predicted that loosening was sensitive to the implant size and femoral width. Furthermore, different rankings of implant performance were predicted when only individual simulations (e.g. an average condition) were used to rank implants, compared with when stochastic simulations were used. In conclusion, the proposed framework provides a viable approach to predicting realistic ranges of loosening behaviour for orthopaedic implants in reduced timeframes compared with conventional Monte Carlo simulations.

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

Development of chitosan packaging films with carvacrol and grape seed extract as antimicrobial and antioxidant agents

In order to produce packaging films with a broad spectrum of action on microorganisms, the effect of two antimicrobial (AM) to be included in the films, carvacrol and GSE were studied separately on different microorganisms. Carvacrol was more effective against the grampositive bacteria than against the gram-negative bacterium. GSE was not effective against yeast. Subsequently, a search for optimal combinations of carvacrol, GSE and the addition of chitosan (as a third component with film forming properties) was carried out. Response surface analysis showed several synergetic effects and three optimal AM combinations (OAMC) were obtained for each microorganism. The experimental validation confirmed that the optimal solutions found can successfully predict the response for each microorganism. The optimization of mixtures of the three components, but this time, using the same concentration for all microorganisms, was also studied to obtain an OAMC with wide spectrum of activity. The results of the response surface analysis showed several synergistic effects for all microorganisms. Three OAMC, OAMC-1, OAMC-2, OAMC-3, were found to be the optimal mixtures for all microorganisms. The radical scavenging activity (RSA) of the different agents was then compared with a standard antioxidant (AOX) BHT, at different concentrations; as also at the OAMC. The RSA increased in the following order: chitosan

Analysis and Optimization of Machining Process Using Evolutionary Algorithms

To ensure quality of machined products at minimum machining costs and maximum machining effectiveness, it is very important to select optimum parameters when metal cutting machine tools are employed. Traditionally, the experience of the operator plays a major role in the selection of optimum metal cutting conditions. However, attaining optimum values each time by even a skilled operator is difficult. The non-linear nature of the machining process has compelled engineers to search for more effective methods to attain optimization. The design objective preceding most engineering design activities is simply to minimize the cost of production or to maximize the production efficiency. The main aim of research work reported here is to build robust optimization algorithms by exploiting ideas that nature has to offer from its backyard and using it to solve real world optimization problems in manufacturing processes.In this thesis, after conducting an exhaustive literature review, several optimization techniques used in various manufacturing processes have been identified. The selection of optimal cutting parameters, like depth of cut, feed and speed is a very important issue for every machining process. Experiments have been designed using Taguchi technique and dry turning of SS420 has been performed on Kirlosker turn master 35 lathe. Analysis using S/N and ANOVA were performed to find the optimum level and percentage of contribution of each parameter. By using S/N analysis the optimum machining parameters from the experimentation is obtained.Optimization algorithms begin with one or more design solutions supplied by the user and then iteratively check new design solutions, relative search spaces in order to achieve the true optimum solution. A mathematical model has been developed using response surface analysis for surface roughness and the model was validated using published results from literature.Methodologies in optimization such as Simulated annealing (SA), Particle Swarm Optimization (PSO), Conventional Genetic Algorithm (CGA) and Improved Genetic Algorithm (IGA) are applied to optimize machining parameters while dry turning of SS420 material. All the above algorithms were tested for their efficiency, robustness and accuracy and observe how they often outperform conventional optimization method applied to difficult real world problems. The SA, PSO, CGA and IGA codes were developed using MATLAB. For each evolutionary algorithmic method, optimum cutting conditions are provided to achieve better surface finish.The computational results using SA clearly demonstrated that the proposed solution procedure is quite capable in solving such complicated problems effectively and efficiently. Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. From the results it has been observed that PSO provides better results and also more computationally efficient.Based on the results obtained using CGA and IGA for the optimization of machining process, the proposed IGA provides better results than the conventional GA. The improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. Finally, a comparison among these algorithms were made for the specific example of dry turning of SS 420 material and arriving at optimum machining parameters of feed, cutting speed, depth of cut and tool nose radius for minimum surface roughness as the criterion. To summarize, the research work fills in conspicuous gaps between research prototypes and industry requirements, by simulating evolutionary procedures seen in nature that optimize its own systems.

Lipase from marine Aspergillus awamori BTMFW032: Production, partial purification and application in oil effluent treatment

Marine fungus BTMFW032, isolated from seawater and identified as Aspergillus awamori, was observed to produce an extracellular lipase, which could reduce 92% fat and oil content in the effluent laden with oil. In this study, medium for lipase production under submerged fermentation was optimized statistically employing response surface method toward maximal enzyme production. Medium with soyabean meal- 0.77% (w/v); (NH4)2SO4-0.1 M; KH2PO4-0.05 M; rice bran oil-2% (v/v); CaCl2-0.05 M; PEG 6000-0.05% (w/v); NaCl-1% (w/v); inoculum-1% (v/v); pH 3.0; incubation temperature 35 8C and incubation period-five days were identified as optimal conditions for maximal lipase production. The time course experiment under optimized condition, after statistical modeling, indicated that enzyme production commenced after 36 hours of incubation and reached a maximum after 96 hours (495.0 U/ml), whereas maximal specific activity of enzyme was recorded at 108 hours (1164.63 U/mg protein). After optimization an overall 4.6- fold increase in lipase production was achieved. Partial purification by (NH4)2SO4 precipitation and ion exchange chromatography resulted in 33.7% final yield. The lipase was noted to have a molecular mass of 90 kDa and optimal activity at pH 7 and 40 8C. Results indicated the scope for potential application of this marine fungal lipase in bioremediation.