The Smith-Waterman algorithm for local sequence alignment is one of the most important techniques in computational molecular biology. This ingenious dynamic programming approach was designed to reveal the highly conserved fragments by discarding poorly conserved initial and terminal segments. However, the existing notion of local similarity has a serious flaw: it does not discard poorly conserved intermediate segments. The Smith-Waterman algorithm finds the local alignment with maximal score but it is unable to find local alignment with maximum degree of similarity (e.g. maximal percent of matches). Moreover, there is still no efficient algorithm that answers the following natural question: do two sequences share a (sufficiently long) fragment with more than 70% of similarity? As a result, the local alignment sometimes produces a mosaic of well-conserved fragments artificially connected by poorly-conserved or even unrelated fragments. This may lead to problems in comparison of long genomic sequences and comparative gene prediction as recently pointed out by Zhang et al. (Bioinformatics, 15, 1012-1019, 1999). In this paper we propose a new sequence comparison algorithm (normalized local alignment ) that reports the regions with maximum degree of similarity. The algorithm is based on fractional programming and its running time is O(n2log n). In practice, normalized local alignment is only 3-5 times slower than the standard Smith-Waterman algorithm.

Given strings S1, S2, and P, the constrained longest common subsequence problem for S1 and S2 with respect to P is to find a longest common subsequence lcs of S1 and S2 which contains P as a subsequence. We present an algorithm which improves the time complexity of the problem from the previously known O(rn2m2) to O(rnm) where r, n, and m are the lengths of P, S1, and S2, respectively. As a generalization of this, we extend the definition of the problem so that the lcs sought contains a subsequence whose edit distance from P is less than a given parameter d. For the latter problem, we propose an algorithm whose time complexity is O(drnm).

Given strings S1, S2, and a regular expression R, we introduce regular expression constrained sequence alignment as the problem of finding the maximum alignment score between S1 and S2 over all alignments such that in these alignments there exists a segment where some substring s1 of S1 is aligned with some substring s2 of S2, and both s1 and s2 match R, i.e. s1, s2 ∈ L(R) where L(R) is the regular language described by R. A motivation for the problem is that protein sequences can be aligned in a way that known motifs guide the alignments. We present an O(nmr) time algorithm for the regular expression constrained sequence alignment problem where n, and m are the lengths of S1, and S2, respectively, and r is in the order of the size of the transition function of a finite automaton M that we create from a nondeterministic finite automaton N accepting L(R). M contains O(t 2 ) states if N has t states.

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The aim of this study was to discuss benefits and challenges of flipped learning in teaching English as a foreign or second language through a systematic review. Prior to conducting this systematic review, 78 studies published in journals that are indexed in Web of Science (WOS), ERIC, ScienceDirect, SCOPUS, IGI Global, and Wiley Online Library databases were selected in accordance with a set of inclusion and exclusion criteria. As a result of the coding process, themes and sub-themes emerged based on the content analysis. The findings reveal that there is an increase in the publications on the implementation of flipped learning in teaching English as a foreign or second language. It is also seen that the majority of the studies about the implementation of flipped language learning includes university students as participants. One of the most reported benefits of the use of flipped learning in this field is that it has positive effects on enhancing students' English language skills such as writing and speaking. In some of the reviewed studies, however, some issues like access to the Internet and workload for both students and teachers are among the most expressed challenges.

A common model for computing the similarity of two strings X and Y of lengths m, and n respectively with m n, is to transform X into Y through a sequence of three types of edit operations: insertion, deletion, and substitution. The model assumes a given cost function which assigns a non-negative real weight to each edit operation. The amortized weight for a given edit sequence is the ratio of its weight to its length, and the minimum of this ratio over all edit sequences is the normalized edit distance. Existing algorithms for normalized edit distance computation with proven complexity bounds require Omn 2 time in the worst-case. We give an Omn log n-time algorithm for the problem when the cost function is uniform, i.e, the weight of each edit operation is constant within the same type, except substitutions can have different weights depending on whether they are matching or non-matching.

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