In this paper we consider techniques to identify and remove redundant predicates during predicate abstraction. We give three criteria for identifying redundancy. A predicate is redundant if any of the following three holds (i) the predicate is equivalent to a propositional function of other predicates. (ii) removing the predicate preserves safety properties satisfied by the abstract model (iii)...

Profit growth is a measure of the company's ability to increase net profit from year year. The purpose this study examine and analyze effect Debt Equity Ratio, Current Total Assets Turnover company size on in banking sub-sector companies listed Indonesia Stock Exchange during 2017-2020. population were 43 sample contained 24 that met criteria. sampling method was carried out by purposive method...

The purpose of this study is to determine the effect company’s growth, fixed asset intensity, company size, operating cash flow, and leverage revaluation in manufacturing companies listed on Indonesia Stock Exchange (IDX) for period 2018-2020. This tested classical assumption, namely multicollinearity test before testing hypothesis using logistic regression analysis techniques. used 444 data ob...

A thinning algorithm is used to reduce unnecessary information by peeling objects layer by layer so that the result is sufficient to allow topological analysis. It has several applications, but is particularly useful for skeletonization. A new sequential thinning algorithm is introduced to preserve both the topology and geometry of the object. In sequential thinning, only a single point may be ...

An important question in designing cryptographic functions including substitution boxes (S-boxes) is the relationships among the various nonlinearity criteria each of which indicates the strength or weakness of a cryptographic function against a particular type of cryptanalytic attacks. In this paper we reveal, for the rst time, interesting connections among the strict avalanche characteristics...

Multicollinearity in factor analysis has negative effects, including unreliable structure, inconsistent loadings, inflated standard errors, reduced discriminant validity, and difficulties interpreting factors. It also leads to stability, hindered replication, misinterpretation of importance, increased parameter estimation instability, power detect the true compromised model fit indices, biased ...