Search results for: maximal data sets

Authors: Chollawat Pookpienlert, Jintana Sanwong

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An element a of a semigroup S is called left (right) regular if there exists x in S such that a=xa² (a=a²x) and said to be intra-regular if there exist u,v in such that a=ua²v. Let T(X) be the semigroup of all full transformations on a set X under the composition of maps. For a fixed nonempty subset Y of X, let Fix(X,Y)={α ™ T(X) : yα=y for all y ™ Y}, where yα is the image of y under α. Then Fix(X,Y) is a semigroup of full transformations on X which fix all elements in Y. Here, we characterize left regular, right regular and intra-regular elements of Fix(X,Y) which characterizations are shown as follows: For α ™ Fix(X,Y), (i) α is left regular if and only if Xα\Y = Xα²\Y, (ii) α is right regular if and only if πα = πα², (iii) α is intra-regular if and only if | Xα\Y | = | Xα²\Y | such that Xα = {xα : x ™ X} and πα = {xα⁻¹ : x ™ Xα} in which xα⁻¹ = {a ™ X : aα=x}. Moreover, those regularities are equivalent if Xα\Y is a finite set. In addition, we count the number of those elements of Fix(X,Y) when X is a finite set. Finally, we determine the maximal congruence ρ on Fix(X,Y) when X is finite and Y is a nonempty proper subset of X. If we let | X \Y | = n, then we obtain that ρ = (Fixn x Fixn) ∪ (H ε x H ε) where Fixn = {α ™ Fix(X,Y) : | Xα\Y | < n} and H ε is the group of units of Fix(X,Y). Furthermore, we show that the maximal congruence is unique.

Keywords: intra-regular, left regular, maximal congruence, right regular, transformation semigroup

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Authors: Sinead Impey, Damon Berry, Selma Furtado, Miriam Galvin, Loretto Grogan, Orla Hardiman, Lucy Hederman, Mark Heverin, Vincent Wade, Linda Douris, Declan O'Sullivan, Gaye Stephens

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Healthcare is a knowledge-rich environment. This knowledge, while valuable, is not always accessible outside the borders of individual clinics. This research aims to address part of this problem (at a study site) by constructing a maximal data set (knowledge artefact) for motor neurone disease (MND). This data set is proposed as an initial knowledge base for a concurrent project to develop an MND patient data platform. It represents the domain knowledge at the study site for the duration of the research (12 months). A knowledge elicitation method was also developed from the lessons learned during this process - the WICKED method. WICKED is an anagram of the words: eliciting and confirming data, information, knowledge, wisdom. But it is also a reference to the concept of wicked problems, which are complex and challenging, as is eliciting expert knowledge. The method was evaluated at a second site, and benefits and limitations were noted. Benefits include that the method provided a systematic way to manage data, information, knowledge and wisdom (DIKW) from various sources, including healthcare specialists and existing data sets. Limitations surrounded the time required and how the data set produced only represents DIKW known during the research period. Future work is underway to address these limitations.

Keywords: healthcare, knowledge acquisition, maximal data sets, action design science

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Authors: Nikolai Krainiukov, Boris Melnikov

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We study the properties of maximal prefix codes. The codes have many applications in computer science, theory of formal languages, data processing and data classification. Practical application is based on the representation of the maximal prefix codes as a sequence of words in a specific order. Our approach to study uses finite state automata (so-called flower automata) for the representation of prefix codes. An important task is the decomposition of prefix codes into prime prefix codes (factors). We discuss the properties of such prefix code decompositions. A linear time algorithm is designed which find the prime decomposition. To verify the correctness of the proposed algorithms, we implemented a system computer algebra GAP.

Keywords: maximal prefix code, regular languages, flower automata, prefix code decomposing

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Authors: Severino Gervacio, Velimor Almonte, Emmanuel Natalio

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A simple graph is planar if it there is a way of drawing it in the plane without edge crossings. A planar graph which is not a proper spanning subgraph of another planar graph is a maximal planar graph. We prove that for complete tripartite graphs of order at most 9, the only ones that contain a spanning maximal planar subgraph are K1,1,1, K2,2,2, K2,3,3, and K3,3,3. The main result gives a necessary and sufficient condition for the complete tripartite graph Kx,y,z to contain a spanning maximal planar subgraph.

Keywords: complete tripartite graph, graph, maximal planar graph, planar graph, subgraph

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Authors: Tanvi P. Patel, Warish D. Patel

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Association rule mining forms the core of data mining and it is termed as one of the well-known methodologies of data mining. Objectives of mining is to find interesting correlations, frequent patterns, associations or casual structures among sets of items in the transaction databases or other data repositories. Hence, association rule mining is imperative to mine patterns and then generate rules from these obtained patterns. For efficient targeted query processing, finding frequent patterns and itemset mining, there is an efficient way to generate an itemset tree structure named Memory Efficient Itemset Tree. Memory efficient IT is efficient for storing itemsets, but takes more time as compare to traditional IT. The proposed strategy generates maximal frequent itemsets from memory efficient itemset tree by using levelwise pruning. For that firstly pre-pruning of items based on minimum support count is carried out followed by itemset tree reconstruction. By having maximal frequent itemsets, less number of patterns are generated as well as tree size is also reduced as compared to MEIT. Therefore, an enhanced approach of memory efficient IT proposed here, helps to optimize main memory overhead as well as reduce processing time.

Keywords: association rule mining, itemset mining, itemset tree, meit, maximal frequent pattern

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Authors: Irfana M., Priyandi Kabasi

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Children with specific language impairment (SLI) have difficulty acquiring and using language despite having all the requirements of cognitive skills to support language acquisition. These children have normal non-verbal intelligence, hearing, and oral-motor skills, with no history of social/emotional problems or significant neurological impairment. Nevertheless, their language acquisition lags behind their peers. Phonological complexity can be considered to be the major factor that causes the inaccurate production of speech in this population. However, the implementation of various ranges of complex phonological stimuli in the treatment session of SLI should be followed for a better prognosis of speech accuracy. Hence there is a need to study the levels of phonological complexity. The present study consisted of 7 individuals who were diagnosed with SLI and 10 developmentally normal children. All of them were Hindi speakers with both genders and their age ranged from 4 to 5 years. There were 4 sets of stimuli; among them were minimal contrast vs maximal contrast nonwords, minimal coarticulation vs maximal coarticulation nonwords, minimal contrast vs maximal contrast words and minimal coarticulation vs maximal coarticulation words. Each set contained 10 stimuli and participants were asked to repeat each stimulus. Results showed that production of maximal contrast was significantly accurate, followed by minimal coarticulation, minimal contrast and maximal coarticulation. A similar trend was shown for both word and non-word categories of stimuli. The phonological complexity effect was evident in the study for each participant group. Moreover, present study findings can be implemented for the management of SLI, specifically for the selection of stimuli.

Keywords: coarticulation, minimal contrast, phonological complexity, specific language impairment

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Authors: Younies Saeed Hassan Mahmoud, Mai Mabrouk, Elsayed Sallam

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Analysing DNA microarray data sets is a great challenge, which faces the bioinformaticians due to the complication of using statistical and machine learning techniques. The challenge will be doubled if the microarray data sets contain missing data, which happens regularly because these techniques cannot deal with missing data. One of the most important data analysis process on the microarray data set is feature selection. This process finds the most important genes that affect certain disease. In this paper, we introduce a technique for imputing the missing data in microarray data sets while performing feature selection.

Keywords: DNA microarray, feature selection, missing data, bioinformatics

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Authors: Krishnendra Shekhawat

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Given a graph G = (V, E), a rectangular dual of G represents the vertices of G by a set of interior-disjoint rectangles such that two rectangles touch if and only if there is an edge between the two corresponding vertices in G. Rectangular duals do not exist for every graph, so we can define maximal rectangular duals. A maximal rectangular dual is a rectangular dual of a graph G such that there exists no graph G ′ with a rectangular dual where G is a subgraph of G ′. In this paper, we enumerate all maximal rectangular duals (or, to be precise, the corresponding planar graphs) up to six nodes and presents a necessary condition for the existence of a rectangular dual. This work allegedly has applications in integrated circuit design and architectural floor plans.

Keywords: adjacency, degree sequence, dual graph, rectangular dual

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Authors: Vadim E. Levit, Eugen Mandrescu

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A graph is well-covered if all its maximal independent sets are of the same size. A well-covered graph is 1-well-covered if deletion of every vertex of the graph leaves it well-covered. It is known that a graph without isolated vertices is 1-well-covered if and only if every two disjoint independent sets are included in two disjoint maximum independent sets. Well-covered graphs are related to combinatorial commutative algebra (e.g., every Cohen-Macaulay graph is well-covered, while each Gorenstein graph without isolated vertices is 1-well-covered). Our intent is to construct several infinite families of 1-well-covered graphs using the following known graph operations: corona, join, and rooted product of graphs. Adopting some known techniques used to advantage for well-covered graphs, one can prove that: if the graph G has no isolated vertices, then the corona of G and H is 1-well-covered if and only if H is a complete graph of order two at least; the join of the graphs G and H is 1-well-covered if and only if G and H have the same independence number and both are 1-well-covered; if H satisfies the property that every three pairwise disjoint independent sets are included in three pairwise disjoint maximum independent sets, then the rooted product of G and H is 1-well-covered, for every graph G. These findings show not only how to generate some more families of 1-well-covered graphs, but also that, to this aim, sometimes, one may use graphs that are not necessarily 1-well-covered.

Keywords: maximum independent set, corona, concatenation, join, well-covered graph

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Authors: Zsolt Lipcsey, Sampson Marshal Imeh

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We investigate a certain characterisation for rank of a semigroup by Howie and Ribeiro (1999), to ascertain the relevance of the concept of independence. There are cases where the concept of independence fails to be useful for this purpose. One would expect the basic element to be the maximal independent subset of a given semigroup. However, we construct examples for semigroups where finite basis exist and the basis is larger than the number of independent elements.

Keywords: generating sets, independent set, rank, cyclic semigroup, basis, commutative

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Authors: Tamanna Jethava, Rahul Joshi

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Frequent Item sets play an essential role in many data Mining tasks that try to find interesting patterns from the database. Typically it refers to a set of items that frequently appear together in transaction dataset. There are several mining algorithm being used for frequent item set mining, yet most do not scale to the type of data we presented with today, so called “BIG DATA”. Big Data is a collection of large data sets. Our approach is to work on the frequent item set mining over the large dataset with scalable and speedy way. Big Data basically works with Map Reduce along with HDFS is used to find out frequent item sets from Big Data on large cluster. This paper focuses on using pre-processing & mining algorithm as hybrid approach for big data over Hadoop platform.

Keywords: frequent item set mining, big data, Hadoop, MapReduce

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Authors: G. S. Thakur

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In this paper, four new operators (O1, O2, O3, O4) are proposed, defined and considered to study the new properties and identities on hesitant fuzzy sets. These operators are useful for different operation on hesitant fuzzy sets. The various theorems are proved using the new operators. The study of the proposed new operators has opened a new area of research and applications.

Keywords: vague sets, hesitant fuzzy sets, intuitionistic fuzzy set, fuzzy sets, fuzzy multisets

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Authors: Shai Sarussi

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Let S be an integral domain with field of fractions F and let A be an F-algebra. An S-subalgebra R of A is called S-nice if R∩F = S and the localization of R with respect to S \{0} is A. Denoting by W the set of all S-nice subalgebras of A, and defining a notion of open sets on W, one can view W as a T0-Alexandroff space. Thus, the algebraic structure of W can be viewed from the point of view of topology. It is shown that every nonempty open subset of W has a maximal element in it, which is also a maximal element of W. Moreover, a supremum of an irreducible subset of W always exists. As a notable connection with valuation theory, one considers the case in which S is a valuation domain and A is an algebraic field extension of F; if S is indecomposed in A, then W is an irreducible topological space, and W contains a greatest element.

Keywords: integral domains, Alexandroff topology, prime spectrum of a ring, valuation domains

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Authors: Apkar Salatian

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To relieve the burden of reasoning on a point to point basis, in many domains there is a need to reduce large and noisy data sets into trends for qualitative reasoning. In this paper we propose and describe a new architectural design pattern called REDUCER for reducing large and noisy data sets that can be tailored for particular situations. REDUCER consists of 2 consecutive processes: Filter which takes the original data and removes outliers, inconsistencies or noise; and Compression which takes the filtered data and derives trends in the data. In this seminal article, we also show how REDUCER has successfully been applied to 3 different case studies.

Keywords: design pattern, filtering, compression, architectural design

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Authors: N. D. Iswarya, M. A. Maluk Mohamed, N. Vijaya

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Grid is infrastructure that allows the deployment of distributed data in large size from multiple locations to reach a common goal. Scheduling data intensive applications becomes challenging as the size of data sets are very huge in size. Only two solutions exist in order to tackle this challenging issue. First, computation which requires huge data sets to be processed can be transferred to the data site. Second, the required data sets can be transferred to the computation site. In the former scenario, the computation cannot be transferred since the servers are storage/data servers with little or no computational capability. Hence, the second scenario can be considered for further exploration. During scheduling, transferring huge data sets from one site to another site requires more network bandwidth. In order to mitigate this issue, this work focuses on incorporating cognitive science in scheduling. Cognitive Science is the study of human brain and its related activities. Current researches are mainly focused on to incorporate cognitive science in various computational modeling techniques. In this work, the problem solving approach of human brain is studied and incorporated during the data intensive scheduling in grid environments. Here, a cognitive engine is designed and deployed in various grid sites. The intelligent agents present in CE will help in analyzing the request and creating the knowledge base. Depending upon the link capacity, decision will be taken whether to transfer data sets or to partition the data sets. Prediction of next request is made by the agents to serve the requesting site with data sets in advance. This will reduce the data availability time and data transfer time. Replica catalog and Meta data catalog created by the agents assist in decision making process.

Keywords: data grid, grid workflow scheduling, cognitive artificial intelligence

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Authors: Surabhi Maiti, Prajakta Tamhankar, Prachi Uttam Mehta

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Since the accomplishment of the Human Genome Project, there has been an unparalled escalation in the sequencing of genomic data. This project has been the first major vault in the field of medical research, especially in genomics. This project won accolades by using a concept called Bigdata which was earlier, extensively used to gain value for business. Bigdata makes use of data sets which are generally in the form of files of size terabytes, petabytes, or exabytes and these data sets were traditionally used and managed using excel sheets and RDBMS. The voluminous data made the process tedious and time consuming and hence a stronger framework called Hadoop was introduced in the field of genetic sciences to make data processing faster and efficient. This paper focuses on using SPARK which is gaining momentum with the advancement of BigData technologies. Cloud Storage is an effective medium for storage of large data sets which is generated from the genetic research and the resultant sets produced from SPARK analysis.

Keywords: human genome project, Bigdata, genomic data, SPARK, cloud storage, Hadoop

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Authors: Asha Gupta, Ramandeep Kaur

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The purpose of this paper is to study a class of closed sets, called generalized pre-closed sets with respect to an ideal (briefly Igp-closed sets), which is an extension of generalized pre-closed sets in general topology. Then, by using these sets, the concepts of Igp- compact spaces along with some classes of maps like continuous and closed maps via ideals have been introduced and analogues of some known results for compact spaces, continuous maps and closed maps in general topology have been obtained.

Keywords: ideal, gp-closed sets, gp-closed maps, gp-continuous maps

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Authors: Samia Alblwi, Amani Ayad

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Mutation testing is the art of generating syntactic variations of a base program and checking whether a candidate test suite can identify all the mutants that are not semantically equivalent to the base: this technique is widely used by researchers to select quality test suites. One of the main obstacles to the widespread use of mutation testing is cost: even small pro-grams (a few dozen lines of code) can give rise to a large number of mutants (up to hundreds): this has created an incentive to seek to reduce the number of mutants while preserving their collective effectiveness. Two criteria have been used to reduce the size of mutant sets: equiva-lence, which aims to partition the set of mutants into equivalence classes modulo semantic equivalence, and selecting one representative per class; subsumption, which aims to define a partial ordering among mutants that ranks mutants by effectiveness and seeks to select maximal elements in this ordering. In this paper we analyze these two policies using analytical and em-pirical criteria.

Keywords: mutation testing, mutant sets, mutant equivalence, mutant subsumption, mutant set minimization

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Authors: J. K. Kavitha, D. Manjula, U. Kanimozhi

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Mining frequent and high utility item sets have gained much significance in the recent years. When the data arrives sporadically, incremental and interactive rule mining and utility mining approaches can be adopted to handle user’s dynamic environmental needs and avoid redundancies, using previous data structures, and mining results. The dependence on recommendation systems has exponentially risen since the advent of search engines. This paper proposes a model for building a recommendation system that suggests frequent and high utility item sets over dynamic datasets for a cluster based location prediction strategy to predict user’s trajectories using the Efficient Incremental Rule Mining (EIRM) algorithm and the Fast Update Utility Pattern Tree (FUUP) algorithm. Through comprehensive evaluations by experiments, this scheme has shown to deliver excellent performance.

Keywords: data sets, recommendation system, utility item sets, frequent item sets mining

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Authors: Inayatur Rehman

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Molodtstove developed the theory of soft sets which can be seen as an effective tool to deal with uncertainties. Since the introduction of this concept, the application of soft sets has been restricted to associative algebraic structures (groups, semi groups, associative rings, semi-rings etc.). Acceptably, though the study of soft sets, where the base set of parameters is a commutative structure, has attracted the attention of many researchers for more than one decade. But on the other hand there are many sets which are naturally endowed by two compatible binary operations forming a non-associative ring and we may dig out examples which investigate a non-associative structure in the context of soft sets. Thus it seems natural to apply the concept of soft sets to non-commutative and non-associative structures. In present paper, we make a new approach to apply Molodtsoves notion of soft sets to LA-ring (a class of non-associative ring). We extend the study of soft commutative rings from theoretical aspect.

Keywords: soft sets, LA-rings, soft LA-rings, soft ideals, soft prime ideals, idealistic soft LA-rings, LA-ring homomorphism

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Authors: Usman Qamar, Younus Javed

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Frequent pattern mining is the process of finding a pattern (a set of items, subsequences, substructures, etc.) that occurs frequently in a data set. It was proposed in the context of frequent itemsets and association rule mining. Frequent pattern mining is used to find inherent regularities in data. What products were often purchased together? Its applications include basket data analysis, cross-marketing, catalog design, sale campaign analysis, Web log (click stream) analysis, and DNA sequence analysis. However, one of the bottlenecks of frequent itemset mining is that as the data increase the amount of time and resources required to mining the data increases at an exponential rate. In this investigation a new algorithm is proposed which can be uses as a pre-processor for frequent itemset mining. FASTER (FeAture SelecTion using Entropy and Rough sets) is a hybrid pre-processor algorithm which utilizes entropy and rough-sets to carry out record reduction and feature (attribute) selection respectively. FASTER for frequent itemset mining can produce a speed up of 3.1 times when compared to original algorithm while maintaining an accuracy of 71%.

Keywords: rough-sets, classification, feature selection, entropy, outliers, frequent itemset mining

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Authors: T. Poppendieker, H. Maurer, C. Segieth

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Over the past ten years, foam rolling has become a new trend in the fitness and health market. It is also a frequently used technique for self-massage. However, the scope of effects from foam rolling has only recently started to be researched and understood. The focus of this study is to examine the effects of prolonged foam rolling on muscle performance. Isometric muscle force was used as a parameter to determine an improving impact of the myofascial roller in two different application volumes. Besides the maximal muscle force, data were also collected on muscle activation during all tests. Twenty-four (17 females, 7 males) healthy students with an average age of 23.4 ± 2.8 years were recruited. The study followed a cross-over pre-/post design in which the order of conditions was counterbalanced. The subjects performed a one-minute and three-minute foam rolling application set on two separate days. Isometric maximal muscle force of the dominant calf was tested before and after the self-myofascial release application. The statistic software program SPSS 22 was used to analyze the data of the maximal isometric force of the calf muscle by a 2 x 2 (time of measurement x intervention) analysis of variance with repeated measures. The statistic significance level was set at p ≤ 0.05. Neither for the main effect of time of measurement (F(1,23) = .93, p = .36, f = .20) nor for the interaction of time of measurement x intervention (F(1,23) = 1.99, p = .17, f = 0.29) significant p-values were found. However, the effect size indicates a mean interaction effect with a tendency of greater pre-post improvements under the three-minute foam rolling condition. Changes in maximal force did not correlate with changes in EMG-activity (r = .02, p = .95 in the short and r = -.11, p = .65 in the long rolling condition). Results support findings of previous studies and suggest a positive potential for use of the foam roll as a means for keeping muscle force at least at the same performance level while leading to an increase in flexibility.

Keywords: application volume differences, foam rolling, isometric maximal force, self-myofascial release

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Authors: Stacey Herzog, Mitchell McCleary, Istvan Kovacs

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Purpose: Maximal strength and maximal power are key athletic abilities in many sports disciplines. In recent years, velocity-based training (VBT) with a relatively high 75-85% 1RM resistance has been popularized in preparation for powerlifting and various other sports. The purpose of this study was to discover differences between beginner/intermediate and advanced lifters’ push/press performances after a heavy resistance-based BP training program. Methods: A six-week, three-workouts per week program was administered to 52 young, physically active adults (age: 22.4±5.1; 12 female). The majority of the participants (84.6%) had prior experience in bench pressing. Typical workouts began with BP using 75-95% 1RM in the 1-5 repetition range. The sets in the lower part of the range (75-80% 1RM) were performed with velocity-focus as well. The BP sets were followed by seated dumbbell presses and six additional upper-body assistance exercises. Pre- and post-tests were conducted on five test exercises: one-repetition maximum BP (1RM), calculated relative strength index: BP/BW (RSI), four-repetition maximal-effort dynamic BP for peak concentric velocity with 80% 1RM (4RV), 4-repetition ballistic pushups (BPU) for height (4PU), and seated medicine ball toss for distance (MBT). For analytic purposes, the participant group was divided into two subgroups: self-indicated beginner or intermediate initial resistance training status (BITS) [n=21, age: 21.9±3.6; 10 female] and advanced initial resistance training status (ATS) [n=31, age: 22.7±5.9; 2 female]. Pre- and post-test results were compared within subgroups. Results: Paired-sample t-tests indicated significant within-group improvements in all five test exercises in both groups (p < 0.05). BITS improved 18.1 lbs. (13.0%) in 1RM, 0.099 (12.8%) in RSI, 0.133 m/s (23.3%) in 4RV, 1.55 in. (27.1%) in BPU, and 1.00 ft. (5.8%) in MBT, while the ATS group improved 13.2 lbs. (5.7%) in 1RM, 0.071 (5.8%) in RSI, 0.051 m/s (9.1%) in 4RV, 1.20 in. (13.7%) in BPU, and 1.15 ft. (5.5%) in MBT. Conclusion: While the two training groups had different initial resistance training backgrounds, both showed significant improvements in all test exercises. As expected, the beginner/intermediate group displayed better relative improvements in four of the five test exercises. However, the medicine ball toss, which had the lightest resistance among the tests, showed similar relative improvements between the two groups. These findings relate to two important training principles: specificity and transfer. The ATS group had more specific experiences with heavy-resistance BP. Therefore, fewer improvements were detected in their test performances with heavy resistances. On the other hand, while the heavy resistance-based training transferred to increased power outcomes in light-resistance power exercises, the difference in the rate of improvement between the two groups disappeared. Practical applications: Based on initial training status, S&C coaches should expect different performance gains in maximal strength training-specific test exercises. However, the transfer from maximal strength to a non-training-specific performance category along the F-v curve continuum (i.e., light resistance and high velocity) might not depend on initial training status.

Keywords: exercise, power, resistance training, strength

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Authors: Samet Aktaş, Nurtekin Erkmen, Faruk Guven, Halil Taskin

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This study aimed to determine the effects of 3 versus 3 small-sided games (SSG) with different recovery times on soma physiological parameters in soccer players. Twelve soccer players from Regional Amateur League volunteered for this study (mean±SD age, 20.50±2.43 years; height, 177.73±4.13 cm; weight, 70.83±8.38 kg). Subjects were performing soccer training for five days per week. The protocol of the study was approved by the local ethic committee in School of Physical Education and Sport, Selcuk University. The subjects were divided into teams with 3 players according to Yo-Yo Intermittent Recovery Test. The field dimension was 26 m wide and 34 m in length. Subjects performed two times in a random order a series of 3 bouts of 3-a-side SSGs with 3 min and 5 min recovery durations. In SSGs, each set were performed with 6 min duration. The percent of maximal heart rate (% HRmax), blood lactate concentration (LA) and Rated Perceived Exertion (RPE) scale points were collected before the SSGs and at the end of each set. Data were analyzed by analysis of variance (ANOVA) with repeated measures. Significant differences were found between %HRmax in before SSG and 1st set, 2nd set, and 3rd set in both SSG with 3 min recovery duration and SSG with 5 min recovery duration (p<0.05). Means of %HRmax in SSG with 3 min recovery duration at both 1st and 2nd sets were significantly higher than SSG with 5 min recovery duration (p<0.05). No significant difference was found between sets of either SSGs in terms of LA (p>0.05). LA in SSG with 3 min recovery duration was higher than SSG with 5 min recovery duration at 2nd sets (p<0.05). RPE in soccer players was not different between SSGs (p>0.05).In conclusion, this study demonstrates that exercise intensity in SSG with 3 min recovery durations is higher than SSG with 5 min recovery durations.

Keywords: small-sided games, soccer, heart rate, lactate

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Authors: Kristian Bautista, Ruben A. Idoy

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A bio-inspired metaheuristic algorithm inspired by the theory of collective animal behavior (CAB) was integrated to density-based clustering modeled as multimodal optimization problem. The algorithm was tested on synthetic, Iris, Glass, Pima and Thyroid data sets in order to measure its effectiveness relative to CDE-based Clustering algorithm. Upon preliminary testing, it was found out that one of the parameter settings used was ineffective in performing clustering when applied to the algorithm prompting the researcher to do an investigation. It was revealed that fine tuning distance δ3 that determines the extent to which a given data point will be clustered helped improve the quality of cluster output. Even though the modification of distance δ3 significantly improved the solution quality and cluster output of the algorithm, results suggest that there is no difference between the population mean of the solutions obtained using the original and modified parameter setting for all data sets. This implies that using either the original or modified parameter setting will not have any effect towards obtaining the best global and local animal positions. Results also suggest that CDE-based clustering algorithm is better than CAB-density clustering algorithm for all data sets. Nevertheless, CAB-density clustering algorithm is still a good clustering algorithm because it has correctly identified the number of classes of some data sets more frequently in a thirty trial run with a much smaller standard deviation, a potential in clustering high dimensional data sets. Thus, the researcher recommends further investigation in the post-processing stage of the algorithm.

Keywords: clustering, metaheuristics, collective animal behavior algorithm, density-based clustering, multimodal optimization

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Authors: Yaeji Lim, Hee-Seok Oh

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The coherence analysis measures the linear time-invariant relationship between two data sets and has been studied various fields such as signal processing, engineering, and medical science. However classical coherence analysis tends to be sensitive to outliers and focuses only on mean relationship. In this paper, we generalized cross periodogram to quantile cross periodogram and provide richer inter-relationship between two data sets. This is a general version of Laplace cross periodogram. We prove its asymptotic distribution under the long range process and compare them with ordinary coherence through numerical examples. We also present real data example to confirm the usefulness of quantile coherence analysis.

Keywords: coherence, cross periodogram, spectrum, quantile

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Authors: Dongkwon Han, Sangho Kim, Sunil Kwon

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Unconventional gas resources have dramatically changed the future energy landscape. Unlike conventional gas resources, the key challenges in unconventional gas have been the requirement that applies to advanced approaches for production forecasting due to uncertainty and complexity of fluid flow. In this study, artificial neural network (ANN) model which integrates machine learning and data-driven approach was developed to predict productivity in shale gas. The database of 129 wells of Eagle Ford shale basin used for testing and training of the ANN model. The Input data related to hydraulic fracturing, well completion and productivity of shale gas were selected and the output data is a cumulative production. The performance of the ANN using all data sets, clustering and variables importance (VI) models were compared in the mean absolute percentage error (MAPE). ANN model using all data sets, clustering, and VI were obtained as 44.22%, 10.08% (cluster 1), 5.26% (cluster 2), 6.35%(cluster 3), and 32.23% (ANN VI), 23.19% (SVM VI), respectively. The results showed that the pre-trained ANN model provides more accurate results than the ANN model using all data sets.

Keywords: unconventional gas, artificial neural network, machine learning, clustering, variables importance

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Authors: M. Y. Bakeir

Abstract:

Rough set theory is a recent approach for reasoning about data. It has achieved a large amount of applications in various real-life fields. The main idea of rough sets corresponds to the lower and upper set approximations. These two approximations are exactly the interior and the closure of the set with respect to a certain topology on a collection U of imprecise data acquired from any real-life field. The base of the topology is formed by equivalence classes of an equivalence relation E defined on U using the available information about data. The theory of generalized topology was studied by Cs´asz´ar. It is well known that generalized topology in the sense of Cs´asz´ar is a generalization of the topology on a set. On the other hand, many important collections of sets related with the topology on a set form a generalized topology. The notion of Nano topology was introduced by Lellis Thivagar, which was defined in terms of approximations and boundary region of a subset of an universe using an equivalence relation on it. The purpose of this paper is to introduce a new generalized topology in terms of rough set called nano generalized topology

Keywords: rough sets, topological space, generalized topology, nano topology

Procedia PDF Downloads 402

Authors: Mohamed M. Ragab, Neveen A. Abdel Raoof, Reham H. Diab

Abstract:

Background: Delayed onset muscle soreness (DOMS) is the most common symptom when ordinary individuals and athletes are exposed to unaccustomed physical activity, especially eccentric contraction which impairs athletic performance, ordinary people work ability and physical functioning. A multitude of methods have been investigated to reduce DOMS. One of the valuable method to control DOMS is repeated bout effect (RBE) as a prophylactic method. Purpose: To compare the repeated bout effect of submaximal eccentric contraction versus maximal isometric contraction on induced DOMS. Methods: Sixty normal male volunteers were assigned randomly into three groups of equal number: Group (A) “first study group”: 20 subjects received submaximal eccentric contraction on non-dominant elbow flexors as prophylactic exercise. Group (B) “second study group”: 20 subjects received maximal isometric contraction on non-dominant elbow flexors as prophylactic exercise. Group (C) “control group”: 20 subjects did not receive any prophylactic exercise. Maximal isometric contraction peak torque of elbow flexors and patient related elbow evaluation (PREE) scale were measured for each subject 3 times before, immediately after and 48 hours after induction of DOMS. Results: Post-hoc test for maximal isometric peak torque and PREE scale immediately and 48 hours after induction of DOMS revealed that group (A) and group (B) resulted in significant decrease in maximal isometric strength loss and elbow pain and disability rather than control group (C), but submaximal eccentric group (A) was more effective than maximal isometric group (B) as it showed more rapid recovery of functional strength and less degrees of elbow pain and disability. Conclusion: Both submaximal eccentric contraction and maximal isometric contraction were effective in prevention of DOMS but submaximal eccentric contraction had the greatest protective effect.

Keywords: delayed onset muscle soreness, maximal isometric peak torque, patient related elbow evaluation scale, repeated bout effect

Procedia PDF Downloads 326

Authors: Yunus Doğan, Ahmet Durap

Abstract:

Coastal regions are the one of the most commonly used places by the natural balance and the growing population. In coastal engineering, the most valuable data is wave behaviors. The amount of this data becomes very big because of observations that take place for periods of hours, days and months. In this study, some statistical methods such as the wave spectrum analysis methods and the standard statistical methods have been used. The goal of this study is the discovery profiles of the different coast areas by using these statistical methods, and thus, obtaining an instance based data set from the big data to analysis by using data mining algorithms. In the experimental studies, the six sample data sets about the wave behaviors obtained by 20 minutes of observations from Mersin Bay in Turkey and converted to an instance based form, while different clustering techniques in data mining algorithms were used to discover similar coastal places. Moreover, this study discusses that this summarization approach can be used in other branches collecting big data such as medicine.

Keywords: clustering algorithms, coastal engineering, data mining, data summarization, statistical methods

Procedia PDF Downloads 334