Ahmed Mostafa Khalil

Xylariaceae on the Fringe

The Xylariaceae is one of the best-known pyrenomycete families (Ascomycota) and is distributed throughout the world. The majority are wood inhabitants and are prevalent in tropical and subtropical regions. Halorosellinia oceanicum is the most widely distributed in mangroves and can be regarded as truly manglicolous being frequently recorded as the dominant member of the family in such environments in S.E. Asia. In Malaysian mangroves, members of the Xylariaceae have been found to be numerically important with up to 9% present in one mangrove ecosystem. A further twelve xylariaceous genera are reported as occurring as their teleomorphs in mangrove forest and their immediate surroundings including Anthostomella, Astrocystis, Biscogniauxia, Camillea, Daldinia, Fasciatispora, Hypoxylon, Kretzschmaria, Nemania, Nipicola, Rosellinia and Xylaria. Furthermore, the presence of species from a number of these taxa, especially species of Anthostomella and Xylaria, are regularly isolated as endophytes from a variety of mangrove plant species. Mangrove Xylariaceae are also well known for their ability to produce novel and often bioactive metabolites.

Commentary on Generalized intuitionistic fuzzy soft sets with applications in decision-making

HighlightsWe point out that properties (vii) and (viii) in Theorem 3.1 of Agarwal et al. (2013) for two generalized intuitionistic fuzzy soft sets are not true by using a counterexample.We show that F ? γ ? ? ? F ? γ ? c = A ? α ? for generalized intuitionistic fuzzy soft sets is not true in general.We point out that F ? γ ? ? ? F ? γ ? c = ? ? ? ? for generalized intuitionistic fuzzy soft sets is not true in general. We show that the assertions (vii) and (viii) of Theorem 3.1 proposed by Agarwal et al. Appl. Soft Comput. 13 (2013) 3552-3566 are incorrect by a counterexample.

Fuzzy Soft Expert System in Prediction of Coronary Artery Disease

Coronary artery disease affects millions of people all over the world including a major portion in Egypt every year. Although much progress has been done in medical science, early detection of this disease is still a challenge for prevention. In this paper we, will extend the concept of fuzzy soft set theory so as to develop a knowledge-based system in medicine and devise a prediction system named fuzzy soft expert system consisting of four main components. These are a fuzzification which translates inputs into fuzzy values, fuzzification of data sets to obtain fuzzy soft sets, a new fuzzy soft set by normal parameter reduction of fuzzy soft set and an algorithm to produce the resultant output. The fuzzy soft expert system developed is then used to predict for coronary artery disease using systolic blood pressure, low-density lipoprotein cholesterol, maximum heart rate, blood sugar, old peak and age of patients. A preliminary study is conducted on nine male patients undergoing treatment in the Cardiac Unit of the Faculty of Medicine, Assiut University, Egypt. It is found that the fuzzy soft expert system developed is able is to help the expert doctor to decide whether a patient needs to be given drug therapy or intervention.

A note on “possibility multi-fuzzy soft set and its application in decision making”

The aim of this paper is to correct the assertions (3) and (4) of Theorem 3.23 proposed by Zhang and Shu [Journal of Intelligent and Fuzzy Systems 27 (2014) 2115-2125]. A counterexample illustrates the flaw of the assertions. We introduce the notion of generalized possibility multi-fuzzy soft sets and use it to correct the flaw in those assertions. Finally we introduce two new definitions and five theorems to improve the work of Zhang and Shu, along with two examples.