Some Notes on Monotone TSK Fuzzy Inference Systems

This article presents our recent research on monotone Takagi-Sugeno-Kang (TSK) Fuzzy inference systems (FISs). We outline a few remarks on the necessary and sufficient conditions for TSK-FIS to be monotone, building upon the Ordered Weighted Averaging (OWA) principle and the orness concept. Some remarks for constructing monotone TSK-FIS from monotone data, extended from our previous findings, are further elucidated

Monotone Interval Fuzzy Inference Systems

—In this paper, we introduce the notion of a monotone fuzzy partition, which is useful for constructing a monotone zeroorder Takagi–Sugeno–Kang Fuzzy Inference System (ZOTSKFIS). It is known that a monotone ZOTSK-FIS model can always be produced when a consistent, complete, and monotone fuzzy rule base is used. However, such an ideal situation is not always available in practice, because a fuzzy rule base is susceptible to uncertainties, e.g., inconsistency, incompleteness, and nonmonotonicity. As a result, we devise an interval method to model these uncertainties by considering the minimum interval of acceptability of a fuzzy rule, resulting in a set of monotone interval-valued fuzzy rules. This further leads to the formulation of a Monotone Interval Fuzzy Inference System (MIFIS) with a minimized uncertainty measure. The proposed MIFIS model is analyzed mathematically and evaluated empirically for the Failure Mode and Effect Analysis (FMEA) application. The results indicate that MIFIS outperforms ZOTSK-FIS, and allows effective decision making using uncertain fuzzy rules solicited from human experts in tackling real-world FMEA problems

Monotone Fuzzy Rule Interpolation for TSK-FIS-Like n-Ary Aggregation Functions

Fuzzy Rule Interpolation (FRI) is important for fuzzy inference systems modeling pertaining to a sparse fuzzy rule base system. The focus of this paper is on a specific class of FRI, i.e., monotone FRI (MFRI), for modeling monotone Takagi-Sugeno-Kang Fuzzy Inference System (TSK-FIS) in the presence of a monotone sparse fuzzy rule base. On the other hand, a function is denoted as an n-ary aggregation function for a given n-dimensional input space and an output space when both the monotone and boundary properties are satisfied. In this paper, a set of sufficient conditions derived from the principles of Ordered Weighted Averaging (OWA) and the concept of orness for TSK-FIS to obey the monotone property is firstly formulated. We show that it is necessary to have a dense fuzzy rule base, which can be obtained by interpolation of fuzzy rules in a sparse fuzzy rule base, for constructing a monotone TSK-FIS. We then devise a two-stage MFRI for establishing monotone TSK-FIS. The first stage comprises a sufficient condition, inspired from the orness concept, to generate intermediate fuzzy membership functions (FMFs). The second stage deduces the monotone consequent of each intermediate rule from the available sparse fuzzy rules. We further extend our MFRI formulation to form TSK-FIS-like n-ary aggregation functions

Multi-expert decision-making with incomplete and noisy fuzzy rules and the monotone test

The use of Fuzzy Inference System (FIS) in decision making problems has received little attention so far. This may be due to the difficulty in gathering a complete set of fuzzy rules, which is free from noise, and the complexity in constructing an FIS model that is able to satisfy a number of important properties, including the monotonicity property. Previously, we have proposed a single-input Monotone-Interval FIS (MI-FIS) model, which can handle incomplete and non-monotone fuzzy rules. Besides that, we have proposed the idea of a monotone test (MT) for a set of fuzzy rules, which give an indication pertaining to the degree of monotonicity of a fuzzy rules set. In this paper, a multi-input MI-FIS model is firstly presented. The focus of this paper is on the use of MI-FIS and MT for undertaking multi expert decision-making (MEDM) problems. A three-phase MEDM framework consists of modelling, aggregation, and exploitation phases is proposed. In the modelling phase, an MT index for each fuzzy rule base from each expert, which is potentially non-monotone and incomplete, is obtained. The provided fuzzy rule bases are also modelled as MI-FISs. In the aggregation phase, an overall collective rating score of an alternative from a number of experts is obtained through the fuzzy weighted averaging operator. We suggest including MT as part of the aggregation phase. In exploitation phase, a rank ordering procedure among the alternatives is established using a possibility method. The developed framework is evaluated with simulated information. The results show that including the MT index in the aggregation phase is able to increase the robustness of the proposed FIS-MEDM model in the presence of noisy fuzzy rule sets

Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an n-Ary Aggregation Function

Despite the popularity and practical importance of the fuzzy inference system (FIS), the use of an FIS model as an n-ary aggregation function, which is characterized by both the monotonicity and boundary properties, is yet to be established. This is because research on ensuring that FIS models satisfy the monotonicity property, i.e., monotone FIS, is relatively new, not to mention the additional requirement of satisfying the boundary property. The aim of this article, therefore, is to establish the parametric conditions for the Takagi–Sugeno–Kang (TSK) FIS model to operate as an n-ary aggregation function (hereafter denoted as n-TSK-FIS) via the specifications of fuzzy membership functions and fuzzy rules. An absorption property with fuzzy rules interpretation is outlined, and the use of n-TSK-FIS as a uninorm is explained. Exploiting the established parametric conditions, a framework for which an n-TSK-FIS model can be constructed from data samples is formulated and analyzed, along with a number of remarks. Synthetic data sets and a benchmark example on education assessment are presented and discussed. To be best of the authors’ knowledge, this article serves as the first use of the TSK-FIS model as an n-ary aggregation function

Parametric Conditions for a Monotone TSK Fuzzy Inference System to be an n-Ary Aggregation Function

Despite the popularity and practical importance of the Fuzzy Inference System (FIS), the use of an FIS model as an n -ary aggregation function, which is characterized by both the monotonicity and boundary properties, is yet to be established. This is because research on ensuring that FIS models satisfy the monotonicity property, i.e., monotone FIS, is relatively new, not to mention the additional requirement of satisfying the boundary property. The aim of this paper, therefore, is to establish the parametric conditions for the Takagi-Sugeno-Kang (TSK) FIS model to operate as an n -ary aggregation function (hereafter denoted as n -TSK-FIS) via the specifications of fuzzy membership functions (FMFs) and fuzzy rules. An absorption property with fuzzy rules interpretation is outlined, and the use of n -TSK-FIS as a uni-norm is explained. Exploiting the established parametric conditions, a framework for which an n -TSK-FIS model can be constructed from data samples is formulated and analyzed, along with a number of remarks. Synthetic data sets and a benchmark example on education assessment are presented and discussed. To be best of the authors' knowledge, this study serves as the first use of the TSK-FIS model as an n -ary aggregation function

Gene expression profiling of breast cancer survivability by pooled cDNA microarray analysis using logistic regression, artificial neural networks and decision trees

BACKGROUND: Microarray technology can acquire information about thousands of genes simultaneously. We analyzed published breast cancer microarray databases to predict five-year recurrence and compared the performance of three data mining algorithms of artificial neural networks (ANN), decision trees (DT) and logistic regression (LR) and two composite models of DT-ANN and DT-LR. The collection of microarray datasets from the Gene Expression Omnibus, four breast cancer datasets were pooled for predicting five-year breast cancer relapse. After data compilation, 757 subjects, 5 clinical variables and 13,452 genetic variables were aggregated. The bootstrap method, Mann–Whitney U test and 20-fold cross-validation were performed to investigate candidate genes with 100 most-significant p-values. The predictive powers of DT, LR and ANN models were assessed using accuracy and the area under ROC curve. The associated genes were evaluated using Cox regression. RESULTS: The DT models exhibited the lowest predictive power and the poorest extrapolation when applied to the test samples. The ANN models displayed the best predictive power and showed the best extrapolation. The 21 most-associated genes, as determined by integration of each model, were analyzed using Cox regression with a 3.53-fold (95% CI: 2.24-5.58) increased risk of breast cancer five-year recurrence… CONCLUSIONS: The 21 selected genes can predict breast cancer recurrence. Among these genes, CCNB1, PLK1 and TOP2A are in the cell cycle G2/M DNA damage checkpoint pathway. Oncologists can offer the genetic information for patients when understanding the gene expression profiles on breast cancer recurrence

Pandrug-Resistant Acinetobacter baumannii Causing Nosocomial Infections in a University Hospital, Taiwan

The rapid emergence (from 0% before 1998 to 6.5% in 2000) of pandrug-resistant Acinetobacter baumannii (PDRAB) was noted in a university hospital in Taiwan. To understand the epidemiology of these isolates, we studied 203 PDRAB isolates, taken from January 1999 to April 2000: 199 from 73 hospitalized patients treated at different clinical settings in the hospital and 4 from environmental sites in an intensive-care unit. Pulsed-field gel electrophoresis analysis and random amplified polymorphic DNA (RAPD) generated by arbitrarily primed polymerase chain reaction of these 203 isolates showed 10 closely related genotypes (10 clones). One (clone 5), belonging to pulsotype E and RAPD pattern 5, predominated (64 isolates, mostly from patients in intensive care). Increasing use of carbapenems and ciprofloxacin (selective pressure) as well as clonal dissemination might have contributed to the wide spread of PDRAB in this hospital

Transposon-induced epigenetic silencing in the X chromosome as a novel form of dmrt1 expression regulation during sex determination in the fighting fish

16 pages, 6 figures, supplementary Information https://doi.org/10.1186/s12915-021-01205-y.-- Availability of data and materials: Sequences used for RAD, RNA, and genome sequencing are achieved in the DDBJ Sequencing Read Archive (SRA) database under BioProject ID: PRJDB7253- PRJDB7255 [23, 88]Background. Fishes are the one of the most diverse groups of animals with respect to their modes of sex determination, providing unique models for uncovering the evolutionary and molecular mechanisms underlying sex determination and reversal. Here, we have investigated how sex is determined in a species of both commercial and ecological importance, the Siamese fighting fish Betta splendens. Results. We conducted association mapping on four commercial and two wild populations of B. splendens. In three of the four commercial populations, the master sex determining (MSD) locus was found to be located in a region of ~ 80 kb on LG2 which harbours five protein coding genes, including dmrt1, a gene involved in male sex determination in different animal taxa. In these fish, dmrt1 shows a male-biased gonadal expression from undifferentiated stages to adult organs and the knockout of this gene resulted in ovarian development in XY genotypes. Genome sequencing of XX and YY genotypes identified a transposon, drbx1, inserted into the fourth intron of the X-linked dmrt1 allele. Methylation assays revealed that epigenetic changes induced by drbx1 spread out to the promoter region of dmrt1. In addition, drbx1 being inserted between two closely linked cis-regulatory elements reduced their enhancer activities. Thus, epigenetic changes, induced by drbx1, contribute to the reduced expression of the X-linked dmrt1 allele, leading to female development. This represents a previously undescribed solution in animals relying on dmrt1 function for sex determination. Differentiation between the X and Y chromosomes is limited to a small region of ~ 200 kb surrounding the MSD gene. Recombination suppression spread slightly out of the SD locus. However, this mechanism was not found in the fourth commercial stock we studied, or in the two wild populations analysed, suggesting that it originated recently during domestication. Conclusions. Taken together, our data provide novel insights into the role of epigenetic regulation of dmrt1 in sex determination and turnover of SD systems and suggest that fighting fish are a suitable model to study the initial stages of sex chromosome evolutionThis study was supported by the internal funding of the Temasek Life Sciences Laboratory, SingaporeWith the institutional support of the ‘Severo Ochoa Centre of Excellence’ accreditation (CEX2019-000928-S)Peer reviewe

An advanced biomass gasification technology with integrated catalytic hot gas cleaning. Part III: Effects of inorganic species in char on the reforming of tars from wood and agricultural wastes

Char is used directly as a catalyst for the catalytic reforming of tar during gasification. Experiments have been carried out to examine the effects of inorganics in char as a catalyst for the catalytic reforming of tar during the gasification of mallee wood, corn stalk and wheat straw in a pilot plant. The char catalyst was prepared from the pyrolysis of mallee wood at a fast heating rate. The catalytic activities of char and acid-washed char for tar reforming were compared under otherwise identical gasification conditions. For all biomass feedstocks tested for gasification, the tar contents in product gas could be drastically reduced by the catalyst, reaching a tar concentration level well below 100 mg/N m3. The acid-washed char also showed profound activity for tar reforming although its catalytic activity was definitely lower than the raw char. Both catalysts could effectively reform the aromatic ring systems (especially large aromatic ring systems with three or more fused benzene rings) in tars as is revealed using UV-fluorescence spectroscopy. The char itself was also partially gasified. After being used as a catalyst, the condensation of the aromatic rings and the accumulation of inorganic species led to drastic changes in char reactivity with O2 at 400 °C. The inorganic species in char tended to enhance the formation of H2 and CO during the reforming reactions in the catalytic reactor