Methodological considerations in the study of genetic discrimination

The potential significance and dimensions of genetic discrimination have been described extensively in published literature, but epidemiological and verified case data are limited. Obtaining unbiased data from individuals about discrimination which has been based on erroneous or unjustifiable assumptions about their genetic predispositions poses unique challenges. Through review and discussion of research literature, we identify methodological considerations for collecting valid epidemiological data on genetic discrimination from individuals in the community; in particular, we consider issues which relate to sampling, selection and response. We identify issues to promote sound study design, with particular attention to verification of genetic discrimination, and highlight the importance of clinical and genetic knowledge of complex genotype-phenotype relationships.     

Rapid Learning-Based and Geologically Consistent History Matching

Various types of data become available at different stages of a reservoir’s life. The production data are integrated into the flow simulation models through a process referred to as history matching. The history-matching process is iterative, and it usually involves a large number of simulation runs. Hence, this process requires significant computational time. In most history-matching methods, the initial geological assumptions in the reservoir model are destroyed or significantly altered in the process. Furthermore, they do not account for the information obtained during the previous trials, and lack learning from the previous failures. In this paper, we introduce a new methodology that maintains the geological realism. The candidate realizations are selected through a learning-based history-matching (LHM) algorithm by which all the previously successful patterns are preserved and used to assist the construction of the next realizations. The various pieces of matching regions are assembled together to make a pool of the successful candidates. Such regions are then utilized for making an auxiliary dataset in a multiscale framework by which the next model is generated. To prevent from trapping in local minima, ideas from the genetic algorithm is adapted. The LHM algorithm can be applied to both categorical and continuous distributions. The LHM provides a conditional map by which the new production data are immediately incorporated into the existing reservoir models. We apply the LHM algorithm to various 2D and 3D examples with very complex binary and continuous properties. The algorithm is shown to produce history-matched models with significantly smaller CPU times.     

The challenge of validating genetic testing

Validation of genetic testing is a multidisciplinary task that involves medical/clinical geneticists, other medical specialists, legislative authorities and the public. It is an area in which many issues are unresolved. Validation should not focus on the laboratory process alone, but should also include the prerequisites for service delivery, such as education and staffing, data collection and banking and information transfer, as well as pre- and post-test genetic counselling in a cost-effective perspective. It must thus be seen as an ongoing process at laboratory, familial and societal levels which involves many different actors. The challenges for the near future are to continue to develop technical standards for the laboratory and counselling procedures, and, in collaboration among professionals, lay persons and state authorities, to define a legal framework for the actions of the various players. This chapter discusses some problems related to the validation process for genetic testing, broadly defined.     

SELECTION OF OBJECTIVE FUNCTIONS AND APPLICATION OF GENETIC ALGORITHMS IN DAMPING DESIGN OF PIPE SYSTEM

The vibration failure of pipe system of aeroengine seriously influences the safety of aircraft. Its damping design is determined by the selection of the design target, method and their feasibility. Five objective functions for the vibration design of a pipeline or pipe system are introduced, namely, the frequency, amplitude, transfer ratio, curvature and deformation energy as options for the optimization process. The genetic algorithms (GA) are adopted as the optimization method, in which the selection of the adaptive genetic operators and the method of implementation of the GA process are crucial. The optimization procedure for all the above objective functions is carried out using GA on the basis of finite element software-MSC/NASTRAN.The optimal solutions of these functions and the stress distribution on the structure are calculated and compared through an example, and their characteristics are analyzed. Finally we put forward two new objective functions, curvature and deformation energy for pipe system optimization. The calculations show that using the curvature as the objective function can reflect the case of minimal stress, and the optimization results using the deformation energy represent lesser and more uniform stress distribution. The calculation results and process showed that the genetic algorithms can effectively implement damping design of engine pipelines and satisfy the efficient engineering design requirement.     

Efficient synthesis of large-scale heat exchanger networks using monogenetic algorithm

A promising method for the efficient design of large-scale heat exchanger networks is the genetic algorithm. The heat exchanger network is divided into sub-networks which are optimized by a hybrid genetic algorithm. In additional steps these sub-networks are optimized by a monogenetic algorithm. An example consisting of 39 process streams was considered in detail. Significant improvements were made in reduction of total annual costs and the number of heat exchangers.     

遗传算法在炸药爆轰参数计算中的应用

为解决爆轰参数计算中自变量取值范围要求严格以及收敛性差等问题,在最小自由能法的基础上,引入遗传算法建立了炸药爆轰参数的计算方法,并利用典型单质炸药和混合炸药的实验结果对计算结果进行了验证。结果表明:采用该方法计算得到的单质和混合炸药的爆速、爆压与实验测试结果基本一致,误差在5%以内,满足炸药性能预估的要求;该方法人工干预弱,只需一次性确定少数主要组分物质的量的变化范围,可适应于多配方优化计算。     

Family communication about genetic risk: the little that is known

Although family communication is important in clinical genetics only a small number of studies have specifically explored the passing on of genetic knowledge to family members. In addition, many of these present exploratory or tentative findings based upon small sample sizes, or data collected only a short time after testing. Nevertheless, if health professionals are to develop effective strategies to help patients' deal with communication issues, we need to know more about what actually happens in families. The aim of this commentary is to identify factors which appear to influence whether patients share information about genetic risk with relatives who are unaware of that risk, with whom they share it and how they go about it. The paper draws upon evidence and thinking from the disciplines of psychology (including family therapy), sociology, medicine and genetic counselling. It is presented under the following headings: disease factors, individual factors, family factors and sociocultural factors. It concludes by highlighting a number of key issues which are relevant for health professionals.     

Genetics and global health care

OBJECTIVES: To examine an alternative model for funding genetic health care, on a global basis. METHODS: Internet-based national data on gross domestic product (GDP) per capita, health care funding, and public and private elements of health care costs. RESULTS: Wide variation in GDP per capita and in the proportion available for health care funding. Insufficient funds are available in developing countries. CONCLUSIONS: Health care provision for people with genetic disorders is unlikely to be fully funded unless a different approach to management costs is undertaken. Rare genetic disorders could be funded by an insurance model which may be more equitable and which could be developed to cover the total global health care costs of the genetic disorder.     

Economic considerations for health insurance coverage of emerging genetic tests

Public and private health insurance plans face the question of whether to cover emerging genetic tests for cancer and other diseases. This paper outlines issues in the economic evaluation of new genetic tests, illustrating key methodological issues and policy implications with findings from a comprehensive and systematic review of the 14 full economic evaluations published over the past 5 years that have addressed both the costs and consequences of molecular genetic tests. Key questions for framing an evaluation include: whose viewpoint matters, which costs and consequences are relevant, and to which clinical alternatives should new genetic tests be compared? While economic evaluation research can inform coverage decisions about genetic tests, the coverage decision-making process must also inform economic researchers about the aims, context, and value systems within which genetic tests will be covered and practised.     

Hopkinson拉杆平板挂钩试件结构智能协同优化

与霍普金森拉杆装置中常用的螺纹、胶粘等固定连接方式相比,平板挂钩试件具有连接形式简单、可实现快速组装等优势。针对平板挂钩试件在拉伸过程中因结构几何效应引起的数据测量误差问题,基于影响拉伸试件测量精度的指标:应力平衡达到时间、变形均匀程度、过渡段相对变形以及非轴向力水平,采用正交试验设计、反向传播（back propagation,BP）神经网络与遗传算法相结合的多目标智能协同优化算法对平板挂钩试件的结构参数进行优化,得到了平板挂钩试件最优的结构参数组合,有限元模拟和实验验证了最优结构参数的有效性。该研究结果可为基于平板挂钩试件的霍普金森拉伸实验的数据可靠性分析提供参考。     

Common autosomal recessive diseases in Oman derived from a hospital-based registry

OBJECTIVES: The local frequencies of genetic disorders in Oman apart from hemoglobinopathies are largely unknown. The aim of the present study was to evaluate birth prevalence of commonly diagnosed autosomal recessive diseases and to estimate needs and priorities of genetic services. METHODS: Analysis of the years 1993-2002 using a hospital-based registry of genetic diseases was attempted. More than 3,000 records were reviewed. Only patients with definite diagnosis were included in the analysis. Genetically determined diseases occurring less frequently than 1 in 50,000 births are not included. RESULTS: A number of rare autosomal recessive diseases are found to have a prevalence at least 1 in 50,000 livebirths. CONCLUSIONS: The data suggest that genetic diseases are important as major contributors to perinatal and childhood mortality and morbidity. The need for preventive genetic service is essential for the health of the community in Oman. Autosomal recessive diseases were frequently concentrated in specific geographical areas, which can be explained by founder effect and genetic drift. However, the hospital-based registry may present incomplete information. Further prospective studies are needed to provide more detailed data.     

A novel technique for the construction of strong S-boxes based on chaotic Lorenz systems

In cryptographic systems, the encryption process relies on the nonlinear mapping of original data or plaintext to the secure data. The mapping of data is facilitated by the application of the substitution process embedded in the cipher. It is desirable to have resistance against differential cryptanalysis, which assists in providing clues about the composition of keys, and linear secret system, where a simple approximation is created to emulate the original cipher characteristics. In this work, we propose the use of nonlinear functional chaos-based substitution process which employs a continuous time Lorenz system. The proposed substitution system eliminates the need of independent round keys in a substitution-permutation network. The performance of the new substitution box is evaluated by nonlinearity analysis, strict avalanche criterion, bit independence criterion, linear approximation probability, and differential approximation probability.     

路面结构模量反算的遗传-模拟退火混合算法

路面结构层模量反算是路面结构无损检测与评价的基础。本文针对遗传算法和模拟退火算法各自的特点，在传统遗传算法的搜索过程中引入模拟退火算法的Metropolis接受准则，以增强算法的全局搜索能力。在此基础上，将遗传-模拟退火混合算法应用于路面结构层材料模量的反算分析。文中分别对理论数据和实测数据进行了计算，并与目前国内外已有的路面反算软件的分析结果进行了分析比较。数值结果表明，该方法具有较强的全局收敛性，数值结果稳定可靠。     

Optimization of heat transfer coefficient correlation at supercritical pressure using genetic algorithms

The paper presents a new heat transfer correlation of water at supercritical pressure after review on existing heat transfer correlations. The new correlation is optimized by genetic algorithms based on existing test data. Based on current results, we conclude that genetic algorithms are effective to search a global optimized correlation but it is important to carefully select representative and authentic test data to reach an optimized solution and special attention needs to be paid on the deteriorated heat transfer region in the design of supercritical water reactor because it can not be predicted well by any correlations reviewed. Supported by the National Basic Research Program of China (973 Program No. 2007CB209800) and Atomic Energy of Canada Limited.     

IDENTIFICATION AND PREDICTION OF UNSTEADY TRANSONIC AERODYNAMIC LOADS BY MULTI-LAYER FUNCTIONALS

Nonlinear unsteady aerodynamic effects present major modelling difficulties in the analysis and control of aeroelastic response. A rigorous mathematical framework, that can account for the complex nonlinearities and time-history effects of the unsteady aerodynamic response, is provided by the use of functional representations. A recent development, based on functional approximation theory, has achieved a new functional form; namely, multi-layer functionals. The development of a multi-layer functional for discrete-time, finite memory, causal systems has been shown to be realizable via finite impulse response neural networks. Identification of an appropriate temporal neural network model of the nonlinear transonic aerodynamic response is facilitated via a supervised training process using multiple input–output sets, with data obtained by an Euler CFD code. The training process is based on a genetic algorithm to optimize the network architecture, combined with a random search algorithm to update weight and bias values. The approach is examined for two different multiple aerodynamic input–output data sets, and in both cases, the prediction properties of the network model establish the multi-layer functional as a suitable representation of unsteady aerodynamic response.     

Identification of vibration loads on hydro generator by using hybrid genetic algorithm

Vibration dynamic characteristics have been a major issue in the modeling and mechanical analysis of large hydro generators. An algorithm is developed for identifying vibration dynamic characteristics by means of hybrid genetic algorithm. From the measured dynamic responses of a hydro generator, an appropriate estimation algorithm is needed to identify the loading parameters, including the main frequencies and amplitudes of vibrating forces. In order to identify parameters in an efficient and robust manner, an optimization method is proposed that combines genetic algorithm with simulated annealing and elitist strategy. The hybrid genetic algorithm is then used to tackle an ill-posed problem of parameter identification, in which the effectiveness of the proposed optimization method is confirmed by its comparison with actual observation data.The project supported by the National Natural Science Foundation of China (10472025). The English text was polished by Yunming Chen.     

Parameter identification for viscoplastic models based on analytical derivatives of a least-squares functional and stability investigations

In this work a unified strategy for identification of material parameters of viscoplastic constitutive equations from uniaxial test data is presented. Gradient-based descent methods (e.g. Gauss-Newton method, Quasi-Newton method) are used for minimization of a least-squares functional, thus requiring the associative gradient. The corresponding sensitivity analysis is explained in detail, where as a main result a recursion formula is obtained. Furthermore, the stability of the numerical results for the material parameters is investigated by use of the eigenvalues for the Hessian of the least-squares functional. Numerical examples are presented in the context of monotonic and cyclic loading. In particular, comparative results with a genetic algorithm reflect the efficiency of our strategy with respect to execution time, and we study the effect of perturbations of the experimental data on the stability of the parameters. In one example we demonstrate how possible instabilities can be circumvented by a regularization of the basic least-squares functional.     

On the Distribution and Scaling of Convective Wavespeeds in the Shear Layers of Heated Supersonic Jets

The noise generated by supersonic plumes is of growing concern given the enormous peak noise intensity radiated by tactical aircraft engines. A key component of this noise is the enhanced radiation of mixing noise caused by large scale eddies convecting supersonically relative to the surrounding quiescent medium. As very little data exist for eddy convection in high Reynolds number, supersonic plumes, our current ability to develop concepts that alter compressible eddy convection is limited. Herein we present new experimental data of eddy convective wavespeeds in the developing shear layer of supersonic heated jets. A new scaling of the wavespeed in radial similarity coordinates is proposed which takes into account the influence of the ratio of static densities between the jet and ambient streams. In particular, we observe a structural change in wavespeed spectra at the end of the potential core—in addition to high turbulence levels, the potential core breakdown region can have enhanced eddy wavespeeds, increasing noise radiation efficiency. The results provide a first examination of the interplay of density ratio effects and the dynamic breakdown process of the potential core in supersonic jets—physics integral to the noise generation process.     

Dependence of combustion dynamics in a gasoline engine upon the in-cylinder flow field, determined by high-speed PIV

We apply time-resolved high-speed particle image velocimetry (PIV) in an optically accessible gasoline engine to determine the effect of the in-cylinder flow field upon combustion dynamics. Our PIV setup involves solid particles as tracer, which enables also measurements at firing top dead center and during the combustion process itself. We analyze the flow field for the entire intake and compression phase, as well as the decay of a prominent large-scale tumble structure in the flow field. The data indicate significant cycle-to-cycle flow field variations, characterized by detection of kinetic energy and tumble center. Measurements in fired engine operation demonstrate the influence of the flow field on combustion dynamics. At stoichiometric operation, we find that variations in the kinetic energy of the flow field are a major cause of cycle-to-cycle variations. From simultaneous imaging of the combustion flame and PIV at lean operation, we find that the velocity distribution in the flow field induces a macroscopic motion of the flame kernel??which significantly effects the combustion process.     

Three-Dimensional Characterization of Permeability at the Core Scale

In this article, we introduce an integrated method for characterizing permeability heterogeneity at the core scale. It combines the results of laboratory core flooding with already-developed field scale history matching techniques such as gradual deformation and pilot points. Prior to any experiment, X-ray computed tomography (CT) imaging techniques are used to obtain three-dimensional porosity distribution in cores. The samples are submitted to viscous, miscible displacement of water by water–glycerin mixture. The dynamic data collected during injection are the time variations in inlet–outlet pressure drop and three-dimensional CT-scan concentration maps of invading fluid collected at successive times. We develop an inversion or matching process which takes advantage of the available data to characterize the spatial distribution of permeability heterogeneities within core samples. Permeability is assumed to be related to porosity. This matching process involves two successive optimizations. First, an initial permeability guess derived from porosity is modified by varying deterministic parameters until the corresponding simulated pressure answer fits the measured pressure drop. Second, an extended optimization process with both deterministic and stochastic parameters is run to match pressure drop and concentration data. This methodology is applied to a synthetic example for which the permeability–porosity relation is known. It yields a three-dimensional permeability model reproducing the reference pressure and concentration maps. The methodology is also applied to experimental data. In this case, it provides three-dimensional permeability models leading to an improved, but perfectible data match. A major difficulty is the unknown relationship between permeability and porosity.