In this paper, bondage and non-bondage set of a fuzzy graph are discussed. The bondage number b(G) and non-bondage number bn(G) of a fuzzy graph G are defined. The upper bound for both b(G) and bn(G) are given. Also some results on b(G) and bn(G) are discussed. The exact values of b(G) and bn(G) are determined for several classes of fuzzy graphs. AMS Subject Classification: 03E72, 05C40, 05C72

Boolean networks (BNs) are known as a mathematical model of genetic networks. In this paper, we overview algorithmic aspects of inference, analysis and control of BNs while focusing on the authors’ works. For inference of BN, we review results on the sample complexity required to uniquely identify a BN. For analysis of BN, we review efficient algorithms for identifying singleton attractors. For...

Designing a large Bayesian network (BN) has been regarded as a diicult process. It has been suggested that BN libraries can be used to facilitate the construction of a large BN. That is, a large BN can be deened in terms of smaller BNs stored in a library. In this paper, we point out that it may be possible to combine the conditional independen-cies deened by the smaller BNs, but not the smalle...

A sequence (bn) of positive numbers is said to be δ -quasi-monotone, if bn → 0, bn > 0 ultimately and ∆bn ≥ −δn, where (δn) is a sequence of positive numbers (see [3]). Let (φn) be a sequence of complex numbers and let ∑an be a given infinite series with partial sums (sn). We denote by σα n and tα n the nth Cesàro means of order α , with α > −1, of the sequences (sn) and (nan), respectively, i.e.,

The usual proofs of these three classical theorems are based on the sequences bn, An and Bn, where Zn = An/Bn, and the three-term recurrence relations for An and Bn. Our aim is to unify and generalise these results by giving new, geometric, proofs (apart from the elementary monotonicity statement, and subsequent convergence, in Theorem 1.1). First, however, we restate these three results (again...

The presence of noise or errors in the stated feature values of biomedical data can lead to incorrect prediction. We introduce a Bayesian Network-based Noise Correction framework named BN-NC. After data preprocessing, a Bayesian Network (BN) is learned to capture the feature dependencies. Using the BN to predict each feature in turn, BN-NC estimates a feature’s error rate as the deviation betwe...

An urn contains black and red balls. Let Zn be the proportion of black balls at time n and 0 ≤ L < U ≤ 1 random barriers. At each time n, a ball bn is drawn. If bn is black and Zn−1 < U , then bn is replaced together with a random number Bn of black balls. If bn is red and Zn−1 > L, then bn is replaced together with a random number Rn of red balls. Otherwise, no additional balls are added, and ...

OBJECTIVE Research shows a significant association between eating disorders (ED) and substance use disorders (SUD). The objective of this study is to examine the prevalence, chronology, and possibility of shared familial risk between SUD and ED symptomatology. METHOD Subjects included 1,206 monozygotic and 877 dizygotic adult female twins. ED symptomatology included anorexia (AN) and bulimia ...