The management of vegetation classifications with fuzzy clustering

Questions: Does fuzzy clustering provide an appropriate numerical framework to manage vegetation classifications? What is the best fuzzy clustering method to achieve this? Material: We used 531 relevés from Catalonia (Spain), belonging to two syntaxonomic alliances of mesophytic and xerophytic montane pastures, and originally classified by experts into nine and 13 associations, respectively. Methods: We compared the performance of fuzzy C‐means (FCM), noise clustering (NC) and possibilistic C‐means (PCM) on four different management tasks: (1) assigning new relevé data to existing types; (2) updating types incorporating new data; (3) defining new types with unclassified relevés; and (4) reviewing traditional vegetation classifications. Results: As fuzzy classifiers, FCM fails to indicate when a given relevé does not belong to any of the existing types; NC might leave too many relevés unclassified; and PCM membership values cannot be compared. As unsupervised clustering methods, FCM is more sensitive than NC to transitional relevés and therefore produces fuzzier classifications. PCM looks for dense regions in the space of species composition, but these are scarce when vegetation data contain many transitional relevés. Conclusions: All three models have advantages and disadvantages, although the NC model may be a good compromise between the restricted FCM model and the robust but impractical PCM model. In our opinion, fuzzy clustering might provide a suitable framework to manage vegetation classifications using a consistent operational definition of vegetation type. Regardless of the framework chosen, national/regional vegetation classification panels should promote methodological standards for classification practices with numerical tools.     

Fuzzy logic for phytosociology

The present paper aims to give an analysis of properties of the phytosociological language, which relates to the vagueness of some concepts in vegetation science.A translation of the simplest synsystematic propositions into possibility distributions has been proposed. Inferential relationships between sentences mentioned using the concept of semantic entailment have been reported.Sentences describing habitat requirements of syntaxa are in fact disguised conditionals; their paraphrases have been given the form of implication. Since these sentences include fuzzy predicates their meaning is a fuzzy relation. The latter may constitute the basis of prediction by means of the compositional rule of inference.     

A biogeographical analysis of the European Atlantic lowland heathlands

Questions: What is the climatic envelope of European Atlantic heathlands and the relationship between their floristic geographical variability and climatic parameters? Are the biogeographic patterns extracted from genuine heath plants comparable to those extracted from the accompanying flora? To what extent does the species composition extracted from phytosociological data support the current theory of refuge areas of heath vegetation in southern Atlantic Europe during the Pleistocene ice ages? Location: Atlantic Europe and NW Morocco. Methods: The geographical territory in which Atlantic heathlands occur was divided into 23 sectors following geographic and chorological criteria. A presence–absence table with 333 taxa was then constructed with the available phytosociological data. The taxa were classified into different groups according to their phytosociological affinity. Several types of numerical analysis were performed with this matrix and the climatic data obtained from meteorological sources. Results: Heathlands require a humid and oceanic climate and are limited by cold temperatures in the north and by summer droughts in the south. The highest floristic richness of this vegetation type is found in NW Iberia. Ordinations indicate a strong correlation between floristic composition of biogeographic sector and summer drought (Ios) and thermicity (It). Conclusions: The main climatic factors determining lowland heathland floristic distribution are thermicity and summer drought. The current optimal conditions for heath flora in NW Iberia suggest that there were southern refuges during the Pleistocene ice ages from which northward expansion has taken place.     

Segmentation of electron tomographic data sets using fuzzy set theory principles

In electron tomography the reconstructed density function is typically corrupted by noise and artifacts. Under those conditions, separating the meaningful regions of the reconstructed density function is not trivial. Despite development efforts that specifically target electron tomography manual segmentation continues to be the preferred method. Based on previous good experiences using a segmentation based on fuzzy logic principles (fuzzy segmentation) where the reconstructed density functions also have low signal-to-noise ratio, we applied it to electron tomographic reconstructions. We demonstrate the usefulness of the fuzzy segmentation algorithm evaluating it within the limits of segmenting electron tomograms of selectively stained, plastic embedded spiny dendrites. The results produced by the fuzzy segmentation algorithm within the framework presented are encouraging.     

模糊ＩＳＯＤＡＴＡ在草地植物群落分类上的应用

 模糊集合理论在元素与集合的关系中引入了连续变化的隶属度,因而能适应植物群落作连续和明显间断分布同时存在的情况。本文运用以模糊集合理论为基础的ISODATA聚类分析方法对松嫩平原草地植物群落进行了数量分类,结果说明模糊ISODATA聚类比传统的分类方法有更为客观、灵活的特点,是一种较为理想的植被数量分类方法。     

A Logic Programming and Expert Statistical Systems Approach for Tissue Characterization in Magnetic Resonance Imaging

The main research goal has been to evaluate significant factors affecting the in vivo magnetic resonance imaging (MRI) parameters T, T, T₂, and ¹H density. This approach differs significantly from other such projects in that the experimental data analysis is being performed while concurrently developing automated, computer-aided analysis software for such MRI tissue parameters. In the experimental portion of the project, statistical analyses, and a heuristic minimum/maximum discriminant analysis algorithm have been explored. Both methods have been used to classify tissue types from 1.5 Tesla transaxial MR images of the human brain. The developing program, written in the logic programming language Prolog, is similar in a number of ways to many existing expert systems now in use for other medical applications; inclusion of the underlying statistical data base and advanced statistical analyses is the main differentiating feature of the current approach. First results indicate promising classification accuracy of various brain tissues such as gray and white matter, as well as differentiation of different types of gray matter and white matter (e.g.: caudate-nucleus vs. thalamus, both representatives of gray matter; and, cortical white matter vs. internal capsule as representative of white matter). Taking all four tissue types together, the percentage of correct classifications ranges from 73 to 87%.     

Fuzzy polynucleotide spaces and metrics

The study of genetic sequences is of great importance in biology and medicine. Mathematics is playing an important role in the study of genetic sequences and, generally, in bioinformatics. In this paper, we extend the work concerning the Fuzzy Polynuclotide Space (FPS) introduced in Torres, A., Nieto, J.J., 2003. The fuzzy polynuclotide Space: Basic properties. Bioinformatics 19(5); 587–592 and Nieto, J.J., Torres, A., Vazquez-Trasande, M.M. 2003. A metric space to study differences between polynucleotides. Appl. Math. Lett. 27:1289–1294: by studying distances between nucleotides and some complete genomes using several metrics. We also present new results concerning the notions of similarity, difference and equality between polynucleotides. The results are encouraging since they demonstrate how the notions of distance and similarity between polynucleotides in the FPS can be employed in the analysis of genetic material.     

Using palaeolimnological and limnological data to reconstruct the recent history of European lake ecosystems: introduction

1. As future climate change is expected to have a major impact on freshwater lake ecosystems, it is important to assess the extent to which changes taking place in freshwater lakes can be attributed to the degree of climate change that has already taken place. 2. To address this issue, it is necessary to examine evidence spanning many decades by combining long‐term observational data sets and palaeolimnological records. 3. Here, we introduce a series of case studies of seven European lakes for which both long‐term data sets and sediment records are available. Most of the sites have been affected by eutrophication and are now in recovery. 4. The studies attempt to disentangle the effects of climate change from those of nutrient pollution and conclude that nutrient pollution is still the dominant factor controlling the trophic state of lakes. 5. At most sites, however, there is also evidence of climate influence related in some cases to natural variability in the climate system, and in others to the trend to higher temperatures over recent decades attributed to anthropogenic warming. 6. More generally and despite some problems, the studies indicate the value of combining limnological and palaeolimnological records in reconstructing lake history and in disentangling the changing role of different pressures on lake ecosystems.     

Analysing treatment means in plant tissue culture research

Many researchers set up an experiment, make measurements, do an analysis of variance, calculate the mean response for each treatment, and then try to decide if the treatment means are significantly different and why. Much too frequently, Duncan's multiple range test is used to test differences among means. It is only one of a number of techniques that can be used to examine treatment means. Some researchers are unaware of the different techniques and that the interpretation of the results of an experiment is strongly influenced by the technique used i.e. using two different techniques might produce two different interpretations of the results. Selection of the appropriate technique to use for a particular experiment depends upon the nature of the treatments and the objectives of the research. This paper discusses four techniques (ranking treatment means, multiple comparison procedures, fitting response models, and using contrasts to make planned comparisons) that can be used to examine treatment means and presents examples of the use of each one for plant tissue culture research.Abbreviations (IAA) indole-3-acetic acid - (IBA) indole-3-butyric acid - (NAA) naphthalene-acetic acid - (2,4-D) 2, 4-dichlorophenoxyacetic acid - (BA) benzyladenine - (PBA) tetrahydropyranylbenzyladenine - (2iP) 2 isopentenyl adenine - (2iPA) 2 isopentenyl adenosineJournal Paper J-12661 of the Iowa Agriculture and Home Economics Experiment Station, Ames. Project 2210.     

Fuzzy methods for assessing criteria and indicators of sustainable forest management

This paper describes the general concepts, meaning, and definitions of sustainability and proposes the use of soft methodologies, particularly fuzzy set theory, for its assessment. Criteria and indicators (C&I) are described as instruments to assess forest sustainability. Basic elements and concepts of fuzzy sets are described, including membership functions and their interpretations in the context of sustainable forest management. Moreover, fuzzy operators that can combine the operational concepts of sustainability, namely criteria and indicators are described. A simple illustrative example is described to demonstrate the application of these methodologies.     

Automated identification of diseases related to lymph system from lymphography data using artificial immune recognition system with fuzzy resource allocation mechanism (fuzzy-AIRS)

Artificial immune recognition system (AIRS) classification algorithm, which has an important place among classification algorithms in the field of artificial immune systems, has showed an effective and intriguing performance on the problems it was applied. AIRS was previously applied to some medical classification problems including breast cancer, Cleveland heart disease, diabetes and it obtained very satisfactory results. So, AIRS proved to be an efficient artificial intelligence technique in medical field. In this study, the resource allocation mechanism of AIRS was changed with a new one determined by fuzzy-logic. This system, named as fuzzy-AIRS was used as a classifier in the diagnosis of lymph diseases, which is of great importance in medicine. The classifications of lymph diseases dataset taken from University of California at Irvine (UCI) Machine Learning Repository were done using 10-fold cross-validation method. Reached classification accuracies were evaluated by comparing them with reported classifiers in UCI web site in addition to other systems that are applied to the related problems. Also, the obtained classification performances were compared with AIRS with regard to the classification accuracy, number of resources and classification time. While only AIRS algorithm obtained 83.138% classification accuracy, fuzzy-AIRS classified the lymph diseases dataset with 90.00% accuracy. For lymph diseases dataset, fuzzy-AIRS obtained the highest classification accuracy according to the UCI web site. Beside of this success, fuzzy-AIRS gained an important advantage over the AIRS by means of classification time. By reducing classification time as well as obtaining high classification accuracies in the applied datasets, fuzzy-AIRS classifier proved that it could be used as an effective classifier for medical problems.     

The concept and application of expert systems in the field of microbiological safety

Summary Recent developments in microbiology have led to the construction of mathematical models that can be used reliably to predict the growth and death responses of microorganisms under a wide range of situations relevant to food safety and spoilage. During the same period developments in available software and hardware platforms for information technology (IT) have meant that the construction of expert systems in disciplines like microbiology have become a reality. The concept and stages of development of an expert system are described using an example of a prototype system that assesses the microbiological safety of chilled ready-to-eat meals.