Double-black (DB) nodes have no place in red-black (RB) trees. So when DB nodes are formed, they are immediately removed. The removal of DB nodes that cause rotation and recoloring of other connected nodes poses greater challenges in the teaching and learning of RB trees. To ease this difficulty, this paper extends our previous work on the symbolic arithmetic algebraic (SA) method for removing DB nodes. The SA operations that are given as, Red + Black = Black; Black - Black = Red; Black + Black = DB; and DB – Black = Black removes DB nodes and rebalances black heights in RB trees. By extension, this paper projects three SA mathematical equations, namely, general symbolic arithmetic rule, ∆_([DB,r,p]); partial symbolic arithmetic rule1, ∂_([DB,p])^'; and partial symbolic arithmetic rule2, ∂_([r])^''. The removal of a DB node ultimately affects black heights in RB trees. To balance black heights using the SA equations, all the RB tree cases, namely, LR, RL, LL, and RR, were considered in this work; and the position of the nodes connected directly or indirectly to the DB node was also tested. In this study, to balance a RB tree, the issues considered w.r.t. the different cases of the RB tree were i) whether a DB node has an inner, outer, or both inner and outer black nephews; or ii) ) whether a DB node has an inner, outer or both inner and outer red nephews. The nephews r and x in this work are the children of the sibling s to a DB, and further up the tree, the parent p of a DB is their grandparent g. Thus, r and x have indirect relationships to a DB at the point of formation of the DB node. The novelty of the SA equations is in their effectiveness in the removal of DB that involves rotation of nodes as well as the recoloring of nodes along any simple path so as to balance black heights in a tree. Our SA methods assert when, where, and how to remove a DB node and the nodes to recolor. As shown in this work, the SA algorithms have been demonstrated to be faster in performance w.r.t. to the number of steps taken to balance a RB tree when compared to the traditional RB algorithm for DB removal. The simplified and systematic approach of the SA methods has enhanced student learning and understanding of DB node removal in RB trees.

There are many complex issues with incomplete data to make decisions in the field of computer science. These issues can be resolved with the aid of mathematical instruments. When dealing with incomplete data, rough set theory is a useful technique. In the classical rough set theory the information granules are equivalence classes. However, in real life scenario tolerance relations play a major role. By employing rough sets with Maximal Compatibility Blocks (MCBs) rather than equivalence classes, we were able to handle the challenges in this research with ease. A novel approach to define matrices on MCBs and operations on them is proposed. Additionally, applied the rough matrix approach to locate a consistent block related to any set in the universal set.

Standard collocation (SCM) and perturbed collocation (PCM) are utilized as effective numerical techniques for solving fractional-order differential equations (FODEs) which focus on constructing orthogonal polynomials to serve as basis functions for approximating the solutions to these equations. The approach began by assuming an approximate solution, expressed in the constructed orthogonal polynomials. These assumed solutions were then substituted into the original FODEs. Following this, the problem was converted into a system of algebraic linear equations by collocating the equations at evenly spaced interior points. Numerical examples and the results indicated that the SCM and PCM are easy, efficient, and in good agreement compared with some existing methods and the results presented in the tables and graphs unequivocally demonstrate the efficacy of the proposed methods in solving fractional-order differential equations, yielding solutions of remarkable accuracy. However, the SCM and PCM exhibit comparable accuracy, making it difficult to identify a single superior approach, we conclude that both the proposed methods are effective and viable options for solving fractional order differential equations.

We shall introduce the notion of pseudo – complemented semigroup, which is a natural generalization of the notion of pseudo- complemented semi-lattices, and give certain properties of such semi-groups. We also introduced the notion of Baer- Stone semigroup, which is Pseudo complimented semigroup satisfy certain additional properties.

This paper aims at providing in-depth refinement to switching time-variant autoregressive processes via the mode as a stable location parameter in adopted noisy Fisher’s z-distribution that was impelled in a Bayesian setting. Explicitly, a four-parameter Fisher’s z-distribution of Bayesian Mixture Autoregressive (FZBMAR) process was proposed to congruous  k-mixture components of Fisher’s z-switching mixture autoregressive processes that was based on shifting number of modes in the marginal density of any switching time-variant series of interest. The proposed FZBMAR process was not only used to seize what is term “most likely mode value” of the present conditional modal distribution given the immediate past but was also used to capture the conditional modal distribution of the observations given the immediate past that can either be perceived as an asymmetric or symmetric distributed varieties. The proposed FZBMAR process was compared with the existing Student-t Mixture Autoregressive (StMAR) and Gaussian Mixture Autoregressive (GMAR) processes with the demonstration of monthly average share prices (stock prices) of sixteen (16) swaying European economies. Based on the findings, the FZBMAR process outperformed the existing StMAR and GMAR processes in explaining the sixteen (16) swaying European economies share prices via a minimum Pareto-Smoothed Important Sampling Leave-One-Out Cross-Validation (PSIS-LOO) error process performance in comparison with AIC, HQIC by the latters. The same singly truncated student-t prior distribution was adopted for the noisy adoption of Fisher’s z hyper-parameters and the embedded autoregressive coefficients in the proposed FZBMAR process; such that their resulting posterior distributions gave the same singly truncated student-t distribution (conjugate) with an embedded Gamma variate.