A Unified Approach to Similarity Measures Between Intuitionistic Fuzzy Sets

Two general classes of similarity measures between intuitionistic fuzzy sets (hereafter IFSs) are introduced. Some properties of these measures are investigated with respect to some well‐known reasonable axioms. It is shown that several similarity measures between IFSs introduced elsewhere can be expressed by these definitions as special cases. Some illustrative and practical examples from the areas of pattern recognition, fuzzy clustering analysis, and decision making will be used to investigate the performance of the proposed measures with respect to some common measures. The results indicate that these measures can provide a useful way for measuring the degree of similarity between IFSs and that the proposed approach performs well in pattern recognition, fuzzy clustering, and decision making.     

A novel adaptive fuzzy predictive control for hybrid systems with mixed inputs

This paper proposes a new adaptive nonlinear model predictive control (NMPC) methodology for a class of hybrid systems with mixed inputs. For this purpose, an online fuzzy identification approach is presented to recursively estimate an evolving Takagi–Sugeno (eTS) model for the hybrid systems based on a potential clustering scheme. A receding horizon adaptive NMPC is then devised on the basis of the online identified eTS fuzzy model. The nonlinear MPC optimization problem is solved by a genetic algorithm (GA). Diverse sets of test scenarios have been conducted to comparatively demonstrate the robust performance of the proposed adaptive NMPC methodology on the challenging start-up operation of a hybrid continuous stirred tank reactor (CSTR) benchmark problem.     

Modified self-organising maps with a new topology and initialisation algorithm

Mapping quality of the self-organising maps (SOMs) is sensitive to the map topology and initialisation of neurons. In this article, in order to improve the convergence of the SOM, an algorithm based on split and merge of clusters to initialise neurons is introduced. The initialisation algorithm speeds up the learning process in large high-dimensional data sets. We also develop a topology based on this initialisation to optimise the vector quantisation error and topology preservation of the SOMs. Such an approach allows to find more accurate data visualisation and consequently clustering problem. The numerical results on eight small-to-large real-world data sets are reported to demonstrate the performance of the proposed algorithm in the sense of vector quantisation, topology preservation and CPU time requirement.     

Landsat ETM+ imaging for mineral potential mapping: application to Avaj area,Qazvin, Iran

The Avaj area is located in the northwestern part of Iran, on the boundary between the Orumieh-Dokhtar and Sanandaj-Sirjan geological zones. It corresponds to two different geological sub-zones:Abe-Garm, to the north, which is related to the volcanic belt of Orumieh-Dokhtar; and the Razan sub-zone, to the south, which is related to the Sanandaj-Sirjan zone. Lithological aspects of different rock units indicate the presence of four distinct volcanic groups of andesite, basalt, tuff, and dacite. The present research utilizes various Landsat Enhanced Thematic Mapper Plus (ETM+) image-processing techniques, including false colour composite images, colour composite ratio images, and standard principal component (PC) image analysis on six bands and colour composite selective PC images, filtering and supervised classification. Nonetheless, colour composite selective images may be the most reliable method for enhancement of areas with hydrothermal alteration. All the techniques used clearly show the pervasive alteration of kaolinite-argillite in the area. These alterations are mainly related to the andesite, and tuffs have the same trend as fault directions.     

A low-overhead scheme for testing a bit-level finite ring systolic array

Testing large VLSI circuits is a difficult and challenging problem for designers. Large unstructured circuits are often impossible to test. The number of test vectors also tend to be large and difficult to generate using automated tools for testing. In this paper, we have investigated the testing of systolic arrays built from a finite ring cell that has been proposed recently for digital signal processing functions. The cell has been shown to allow encoding, decoding and general inner product type computations for residue number system applications, with considerable advantages over equivalent binary implementation. As a further feature, we show, in this paper, that an array of such cells is remarkably easy to test for stuck-at faults. Generating test vectors for these arrays is also straightforward.     

Estimating botnet virulence within mathematical models of botnet propagation dynamics

Mathematical models of botnet propagation dynamics are increasingly deemed to have potential for significant contribution to botnet mitigation. Botnet virulence, which comprises network vulnerability rate and network infection rate, is a key factor in those models. In this paper we discuss a practical approach that draws on epidemiological models in biology to estimate the botnet virulence in a network. Our research provides mathematical models of botnet propagation dynamics with concrete measures of botnet virulence, which make those models practical and hence employable in mitigation of real world botnets in a timely fashion. The approach is based on random sampling and follows a novel application of statistical learning and inference in a botnet-versus-network setting. We have implemented this research in the Matlab programming language. In this paper, we discuss an experimental evaluation of the effectiveness of this research with respect to botnet propagation dynamics realistically simulated in a GTNetS network simulation platform.     

Putative mechanism for anticancer properties of Ag–PP (NPs) extract

One of the most important challenges in treating cancer is the invasion and the angiogenesis of cancer cells. The synthesis of green nanoparticles (NPs) and their use in therapeutic fields is one of the most effective methods with minimal side effects in cancer treatment. In this study, cytotoxic and anti‐angiogenic effects of silver NPs (AgNPs) coated with palm pollen extract [Ag–PP(NPs)] were evaluated. For this purpose, the cells were treated with NPs and then were subjected to trypan blue testing (48 h). Then, the cancer invasion was evaluated by the scratch procedure and the expressions of the vascular endothelial growth factor (VEGF) and its receptor (VEGF‐R) genes were estimated using real‐time PCR assay. Also, the angiogenesis effect of the NPs was investigated with chick chorioallantoic membrane (CAM) assay. The Ag–PP(NPs) induced cytotoxicity on MCF7 cells. The findings also showed that Ag–PP(NPs) inhibit invasive cancer cells and reduce the expression of VEGF and VEGF‐R and significantly reduced the number and vessels lengths and the lengths and weights of the embryos in CAM assay. Ag–PP(NPs) with the induction of cytotoxic effects, metastatic inhibition and anti‐angiogenesis properties should be considered as an appropriate option for treatment of cancerInspec keywords: nanomedicine, genetics, cellular biophysics, toxicology, patient treatment, silver, cancer, biochemistry, biomedical materials, nanoparticles, nanofabrication, membranesOther keywords: minimal side effects, cancer treatment, silver NPs, cancer invasion, vascular endothelial growth factor, receptor genes, VEGF‐R, real‐time polymerase chain reaction assay, angiogenesis effect, chick chorioallantoic membrane assay, MCF7 cells, invasive cancer cells, cytotoxic effects, putative mechanism, anticancer properties, antiangiogenic effects, antiangiogenesis properties, Ag–PP‐induced cytotoxicity, metastatic inhibition, palm pollen extraction, trypan blue testing, time 48.0 hour, Ag     

Rapid determination of soil unconfined compressive strength using reflectance spectroscopy

Bulletin of Engineering Geology and the Environment - Understanding the physical and especially mechanical properties of forest soils is very important in forest engineering operations including...     

Deoxycholate‐Based Glycosides (DCGs) for Membrane Protein Stabilisation

Detergents are an absolute requirement for studying the structure of membrane proteins. However, many conventional detergents fail to stabilise denaturation‐sensitive membrane proteins, such as eukaryotic proteins and membrane protein complexes. New amphipathic agents with enhanced efficacy in stabilising membrane proteins will be helpful in overcoming the barriers to studying membrane protein structures. We have prepared a number of deoxycholate‐based amphiphiles with carbohydrate head groups, designated deoxycholate‐based glycosides (DCGs). These DCGs are the hydrophilic variants of previously reported deoxycholate‐based N‐oxides (DCAOs). Membrane proteins in these agents, particularly the branched diglucoside‐bearing amphiphiles DCG‐1 and DCG‐2, displayed favourable behaviour compared to previously reported parent compounds (DCAOs) and conventional detergents (LDAO and DDM). Given their excellent properties, these agents should have significant potential for membrane protein studies.