Reasons for failure of genetic classifications of South Amerind populations

Previous attempts to classify South American Indian tribes according to genetic characteristics have failed to yield a hierarchical system of relationships. This can be explained by the facts that (1) tribal populations did not evolve through sequential fissions but through frequent fusions of groups with diverse histories and (2) allele frequencies have been held at nearly common values by intertribal migration or balancing selection. A valid model must allow for fusion and mixed populations as well as for fission; factor analysis or newer methods of fuzzy mathematics permit this. The effects of migration and balancing can be made more manageable by partitioning them according to the limited time periods recorded by haplotypes. An initial attempt using factor analysis and HLA haplotype data on 19 rain forest tribes revealed two overlapping clusters that are largely but not neatly separated by the lower Amazon River. Several tribes, especially in the west, were excluded from these clusters.     

治疗模式的识别及应用

本文根据模糊数学的模式识别理论,对与疾病有关的症状、体征、特殊检查结果等进行数量化处理后,並得出治疗方案的从属函数。这在治疗方案的决策方面具有重要的理论意义和实际价值。最后讨论了它在腰椎间盘突出症治疗中的具体应用。     

模糊数学在孢粉分带上的应用

笔者在研究新疆准噶尔盆地西北缘百３８井克拉玛依组粉组合时，运用模糊数学的方法进行了孢粉分带，本文介绍了进行孢粉分带所选用的数学公式，用ＢＡＳＩＣ语言所编制的计算程序以及使用方法。     

Fuzzy systems vegetation theory

Fuzzy systems vegetation theory is a comprehensive framework for the expression of vegetation theory and conceptual models, as well as the development of vegetation analyses. It is applicable to vegetation/environment relations, vegetation dynamics, and the effects of environmental dynamics on vegetation composition. Fuzzy systems vegetation theory is a fuzzy set generalization of dynamical systems theory and incorporates a formal logic and mathematics. This paper presents the elements of fuzzy systems vegetation theory and discusses the relationship of the fuzzy systems theory to the geometric concepts generally employed in vegetation theory.     

A fuzzy logic controller.

This paper describes a fuzzy sets method which is very useful for handling uncertainties and essential for knowledge acquisition of a human expert. Kinetics of a reactor is often complex and not trivial to describe by mathematical equations. Reactor control by traditional control technology is therefore difficult. A novel technology is presented. In the following a fuzzy inference (approximate reasoning) is used for decision making in analogy to human thinking, facilitating a more sophisticated control. Readers of this paper do not need any advanced mathematics beyond the four basic operations in arithmetic (+, -, x, divided by) and using the maximum and minimum values. This fuzzy inference is introduced to construct a fuzzy logic controller which is suitable for a nonlinear, multivariable and time variant system applied to a bioreactor.     

Using fuzzy numbers to propagate uncertainty in matrix-based LCI

Background, aim, and scope  Analysis of uncertainties plays a vital role in the interpretation of life cycle assessment findings. Some of these uncertainties arise from parametric data variability in life cycle inventory analysis. For instance, the efficiencies of manufacturing processes may vary among different industrial sites or geographic regions; or, in the case of new and unproven technologies, it is possible that prospective performance levels can only be estimated. Although such data variability is usually treated using a probabilistic framework, some recent work on the use of fuzzy sets or possibility theory has appeared in the literature. The latter school of thought is based on the notion that not all data variability can be properly described in terms of frequency of occurrence. In many cases, it is necessary to model the uncertainty associated with the subjective degree of plausibility of parameter values. Fuzzy set theory is appropriate for such uncertainties. However, the computations required for handling fuzzy quantities has not been fully integrated with the formal matrix-based life cycle inventory analysis (LCI) described by Heijungs and Suh (2002). Materials and methods  This paper integrates computations with fuzzy numbers into the matrix-based LCI computational model described in the literature. The approach uses fuzzy numbers to propagate the data variability in LCI calculations, and results in fuzzy distributions of the inventory results. The approach is developed based on similarities with the fuzzy economic input–output (EIO) model proposed by Buckley (Eur J Oper Res 39:54–60, 1989). Results  The matrix-based fuzzy LCI model is illustrated using three simple case studies. The first case shows how fuzzy inventory results arise in simple systems with variability in industrial efficiency and emissions data. The second case study illustrates how the model applies for life cycle systems with co-products, and thus requires the inclusion of displaced processes. The third case study demonstrates the use of the method in the context of comparing different carbon sequestration technologies. Discussion  These simple case studies illustrate the important features of the model, including possible computational issues that can arise with larger and more complex life cycle systems. Conclusions  A fuzzy matrix-based LCI model has been proposed. The model extends the conventional matrix-based LCI model to allow for computations with parametric data variability represented as fuzzy numbers. This approach is an alternative or complementary approach to interval analysis, probabilistic or Monte Carlo techniques. Recommendations and perspectives  Potential further work in this area includes extension of the fuzzy model to EIO-LCA models and to life cycle impact assessment (LCIA); development of hybrid fuzzy-probabilistic approaches; and integration with life cycle-based optimization or decision analysis. Additional theoretical work is needed for modeling correlations of the variability of parameters using interacting or correlated fuzzy numbers, which remains an unresolved computational issue. Furthermore, integration of the fuzzy model into LCA software can also be investigated.     

一种天敌控制多种害虫作用的模糊数学评价方法

从生物防治的基本思想出发,利用模糊数学和生态经济的原理,探讨天敌的作用,建立了一种天敌控制多种害虫的模糊数学评判方法,文中给出了一种天敌对害虫的影响率和影响强度、一种天敌对害虫影响的大小以及天敌对害虫的控制能力,并进行了控制分析。     

应用模糊数学评价观叶植物的观赏性

应用模糊数学方法评价52种观叶植物的观赏性,确定各种植物所处等级及又同一级别的排序,讨论模糊数学方法对于评价多指标且指标难以量化的多样品的优劣等级与名次的先进性与不足。     

公立医院公共卫生服务监管能力研究

目的 构建公立医院公共卫生服务监管的评价模型,评价公立医院公共卫生服务的监管能力。方法 采用层次分析法、综合指数法、数理统计分析、模糊数学法来构建评价模型,评价监管能力,以此来建立政府对公立医院公共卫生监管准绳。结果 给出包括5项一级指标、18项二级指标的公立医院公共卫生服务监管能力评价模型。结论 构建的公立医院公共卫生服务监管能力评价模型,为综合评价公立医院公共卫生服务监管能力提供了内容依据和量化标准,为政府有效评价公共卫生服务质量提供了科学依据。     

Fuzzy Stochastic Petri Nets for Modeling Biological Systems with Uncertain Kinetic Parameters

Stochastic Petri nets (SPNs) have been widely used to model randomness which is an inherent feature of biological systems. However, for many biological systems, some kinetic parameters may be uncertain due to incomplete, vague or missing kinetic data (often called fuzzy uncertainty), or naturally vary, e.g., between different individuals, experimental conditions, etc. (often called variability), which has prevented a wider application of SPNs that require accurate parameters. Considering the strength of fuzzy sets to deal with uncertain information, we apply a specific type of stochastic Petri nets, fuzzy stochastic Petri nets (FSPNs), to model and analyze biological systems with uncertain kinetic parameters. FSPNs combine SPNs and fuzzy sets, thereby taking into account both randomness and fuzziness of biological systems. For a biological system, SPNs model the randomness, while fuzzy sets model kinetic parameters with fuzzy uncertainty or variability by associating each parameter with a fuzzy number instead of a crisp real value. We introduce a simulation-based analysis method for FSPNs to explore the uncertainties of outputs resulting from the uncertainties associated with input parameters, which works equally well for bounded and unbounded models. We illustrate our approach using a yeast polarization model having an infinite state space, which shows the appropriateness of FSPNs in combination with simulation-based analysis for modeling and analyzing biological systems with uncertain information.     

森林群落类型分布的方位信息识别

本文通过对森林航空遥感图片经光学付里叶变换所获得的频谱分布曲线,应用模糊数学中的方位信息比较法,在不同类型森林的遥感图片的频谱之间建立了——对应的模糊关系,进行了对森林遥感图象的模式识别,并给出了与实际情况相一致的判别结果。     

模糊数学在平菇品种选育决策中的应用

应用模糊数学对 7个平菇品种进行模糊判决 ,结果表明 :在权重偏重于生物学效率的凸模糊判决中平菇 17- 2为首选。而在权重相等的模糊判决中平菇 99号为首选。对平菇 99号进行综合评判的结果为某类菇农对该品种很喜欢。     

Is the hydrophobic core a universal structural element in proteins?

The hydrophobic core, when subjected to analysis based on the fuzzy oil drop model, appears to be a universal structural component of proteins irrespective of their secondary, supersecondary, and tertiary conformations. A study has been performed on a set of nonhomologous proteins representing a variety of CATH categories. The presence of a well-ordered hydrophobic core has been confirmed in each case, regardless of the protein’s biological function, chain length or source organism. In light of fuzzy oil drop (FOD) analysis, various supersecondary forms seem to share a common structural factor in the form of a hydrophobic core, emerging either as part of the whole protein or a specific domain. The variable status of individual folds with respect to the FOD model reflects their propensity for conformational changes, frequently associated with biological function. Such flexibility is expressed as variable stability of the hydrophobic core, along with specific encoding of potential conformational changes which depend on the properties of helices and β-folds.     

APPLICATION OF FUZZY SETS AND NEURAL NETWORKS IN SENSORY ANALYSIS

This paper describes an initial but fundamental attempt to lay some groundwork for a fuzzy-set-based paradigm for sensory analysis and to demonstrate how fuzzy set and neural network techniques may lead to a natural way for sensory data interpretation. Sensory scales are described as fuzzy sets, sensory attributes as fuzzy variables, and sensory responses as sample membership grades. Multi-judge responses are formulated as a fuzzy membership vector or fuzzy histogram of response, which gives an overall panel response free of the unverifiable assumptions implied in conventional approaches. Neural networks are used to provide an effective tool for modeling and analysis of sensory responses in their naturally fuzzy and complex forms. A maximum method of defuzzification is proposed to give a crisp grade of the majority opinion. Two applications in meat quality evaluation are used to demonstrate the use of the paradigm and procedure. It is hoped that this work will bring up some new ideas and generate interest in research on application of fuzzy sets and neural networks in sensory analysis.     

一种新的中心定位算法在细胞分割中的应用

以模糊数学为基础,提出了一种新的寻找细胞核中心的方法。该方法能有效地对同一图中多个不同大小的细胞核进行定位,并且其抗噪能力较强,正确率较高。文中采用该定位算法确定细胞核的中心位置后,再应用水平集的方法对细胞显微图像进行分割。分割的结果进一步表明了这种中心定位算法是有效的和鲁棒的,能辅助其他分割算法一起完成复杂图像的分割任务。     

Operator functional state estimation based on EEG-data-driven fuzzy model

This paper proposed a max–min-entropy-based fuzzy partition method for fuzzy model based estimation of human operator functional state (OFS). The optimal number of fuzzy partitions for each I/O variable of fuzzy model is determined by using the entropy criterion. The fuzzy models were constructed by using Wang–Mendel method. The OFS estimation results showed the practical usefulness of the proposed fuzzy modeling approach.     

基于CVM的海涂湿地生态服务价值的模糊评估模型

正确评估海涂湿地生态服务价值有助于加强人们保护海涂湿地的意识,为海涂湿地围垦生态补偿标准的制定提供依据。视价格"便宜"、"适中"和"昂贵"为模糊集,基于CVM法区间型数据建立了传统模糊统计模型、赋权模糊统计模型和三相划分模型,并以此评价了杭州湾国家湿地公园单位面积年生态系统服务价值。结果显示:3种方法得到的单价分别为10.28、10.38、9.76元m~(-2)a~(-1),三者一致程度较高,与国内沿海湿地价值比较接近。分析了"生态价值"概念客观上的"模糊性"引致模糊数学模型的合理性;采用"橄榄球"式赋权建立赋权模糊统计模型以克服传统模糊统计模型中对区间数据均匀赋权的不合理性,结果在隶属度函数的光滑性和拟合度上好于后者;第三,三相划分模型拟合出3个模糊集的隶属度函数,可以比较相同价格在不同模糊集中隶属度差异。建立的模型对于基于CVM法的生态资源价值评估具有借鉴意义。     

Nonlinear modeling and adaptive monitoring with fuzzy and multivariate statistical methods in biological wastewater treatment plants

A new approach to nonlinear modeling and adaptive monitoring using fuzzy principal component regression (FPCR) is proposed and then applied to a real wastewater treatment plant (WWTP) data set. First, principal component analysis (PCA) is used to reduce the dimensionality of data and to remove collinearity. Second, the adaptive credibilistic fuzzy-c-means method is used to appropriately monitor diverse operating conditions based on the PCA score values. Then a new adaptive discrimination monitoring method is proposed to distinguish between a large process change and a simple fault. Third, a FPCR method is proposed, where the Takagi-Sugeno-Kang (TSK) fuzzy model is employed to model the relation between the PCA score values and the target output to avoid the over-fitting problem with original variables. Here, the rule bases, the centers and the widths of TSK fuzzy model are found by heuristic methods. The proposed FPCR method is applied to predict the output variable, the reduction of chemical oxygen demand in the full-scale WWTP. The result shows that it has the ability to model the nonlinear process and multiple operating conditions and is able to identify various operating regions and discriminate between a sustained fault and a simple fault (or abnormalities) occurring within the process data.     

A fuzzy logic model of fracture healing

A quantitative biomechanical model describes the tissue transformation during healing of a transverse osteotomy of a sheep metatarsal. The model predicts bridging of the bone ends through cartilage, followed by the growth of a callus cuff, and finally, the resorption of callus after ossification of the interfragmentary gap. We suggest bone density or the modulus of elasticity do not sufficiently characterize healing tissue for predictive purposes. In addition to the stimulus reflected by strain energy density we introduce a new osteogenic factor based upon stress gradients and which predicts areas of a high osteogenic capacity. Our model distinguishes three basic types of tissue, namely bone, cartilage and fibrous tissue. A fuzzy controller is proposed to model the tissue reaction. A set of fuzzy rules derived from medical knowledge has been implemented to describe tissue transformation such as intramembraneous or chondral ossification, atrophy or destruction. Fuzzy logic is able to model tissue transformation processes within the numerical simulation of remodeling processes. This approach improves the simulation tools and affords the potential to optimize planning of animal experiments and conduct parametric studies.