Athletic Training for Korean EFL Learners’ Anxiety Resistance, Pragmatic Awareness, and Motivation for English Learning

"Salim Bullen", "Young Woo Cho" 培材大學校 人文科學硏究所 2015 人文論叢 Vol.32 No.-

This study investigated whether athletic training, in the form of soccer, may improve Korean college-level EFL learners’ anxiety resistance, pragmatic awareness, and motivation. Fifty-six Korean college students divided into two groups (control and treatment) completed a questionnaire on their self-evaluations of anxiety, pragmatic ability, and motivation. The treatment group (n=28) participated in a soccer training program for two months led by the researcher, who was a native speaker of English, and a bilingual Korean/English speaking assistant. The control group (n=28) received no athletic training during the same period, but practiced English in their regular English courses. At the completion of the program, both groups completed the same questionnaire. Results from ANCOVA analyses showed significant improvements in the two areas: anxiety resistance and pragmatic awareness for the treatment group. However, no significant improvement was found in motivation. These results suggest that athletic training may be used as part of regular EFL instruction to help L2 learners lower anxiety and raise pragmatic awareness. However, motivation is a more complex issue that needs to be addressed from a broader perspective. 운동훈련은 제2언어학습과 커뮤니케이션을 보조할 수 있는 독특한 언어학습 도구이다. 운동훈련을 통하여 제2언어학습자들은 교실 밖 환경에서 실시간으로 의사소통을 하는데 필요한 의사소통 기술을 발달시킬 수 있으며 이를 통하여 영어를 통한 의사소통이 더 의미 있게 된다. 그러나 운동과 제2언어학습 및 의사소통을 통합했을 때 얻을 수 있는 유익을 반영하는 기존연구는 없었다. 따라서 본 연구는 운동의 한 형태로서 축구를 사용하여 제2언어학습자들의 불안에 대한 저항력과, 학습동기, 화용적 인식을 강화할 수 있는 가능성을 탐색하는 것을 목적으로 하였다. 대학 영어강좌를 수강하는 56명의 한국 성인 영어학습자들이 본 연구에 참여하였다. 학습자들은 처치집단과 통제집단 두 집단으로 나뉘어 연구가 시작되기 전 학습불안과, 화용능력, 학습동기에 관한 스스로의 인식에 관한 설문지를 작성하였다. 처치집단(n=28)은 원어민인 연구자와 한국어/영어 이중언어를 구사하는 연구보조원이 이끄는 축구훈련 프로그램에 두 달 동안 참여하였다. 통제집단(n=28)은 같은 기간 어떤 종류의 운동훈련도 받지 않았으며 정규영어강좌에서 학습하였다. 프로그램 종료 후 두 집단은 처음과 동일한 설문지를 작성하였다. 두 집단의 응답을 비교했을 때 처치집단이 학습불안에 대한 저항력과 화용적 인식에서 통제집단보다 더 높은 수준의 유의미한 향상이 있음이 나타났다. 그러나 학습동기에서는 유의미한 향상에 관한 차이점이 발견되지 않았다. 이 결과들은 운동을 통하여 제2언어학습자들이 영어사용자들과 의사소통할 때 느끼는 불안감을 완화시킬 수 있고 화용적 인식을 증대시킬 수 있지만 운동과 제2언어학습동기와의 관계를 더 잘 이해하기 위해서는 추가적인 연구가 필요함을 시사한다.

Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Salim Miloudi,Sid Ahmed Rahal,Salim Khiat 한국정보처리학회 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact,when a multi-branch company needs to explore its distributed data for decision making, it is imperative toclassify these multiple databases into similar clusters before analyzing the data. To search for the bestclassification of a set of n databases, existing algorithms generate from 1 to (n2–n)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification aresubsets of clusters in the next classification), existing algorithms generate each classification independently,that is, without taking into account the use of clusters from the previous classification. Consequently, existingalgorithms are time consuming, especially when the number of candidate classifications increases. Toovercome the latter problem, we propose in this paper an efficient approach that represents the problem ofclassifying the multiple databases as a problem of identifying the connected components of an undirectedweighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of ouralgorithm against existing works and that it overcomes the problem of increase in the execution time.

A review of vibration-based MEMS hybrid energy harvesters

Mohammed Salim,Hakim S. Sultan Aljibori,Dhia Salim,M. Haris M. Khir,A. Sh. Kherbeet 대한기계학회 2015 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.29 No.11

Energy harvesters based on Micro-electromechanical systems (MEMS) are well known nowadays due to their small features, abilityfor monolithic integration with the integrated circuit in a single platform, robust, and easily fabricated in bulk. The piezoelectric (PZT)and the electromagnetic (EM) generators are examples of such energy harvesters. To further increase their effectiveness in harvestingambient energy, researchers started to venture into hybrid energy harvesters. Few literature reviews may be found on MEMS harvestingdevices, but not on hybrid harvesters in specific. This paper intended to further elaborate on the hybrid energy harvesters, reported Literatureon such harvesters for recent years with different architectures, models, and results are presented. Table of power comparison betweenthe reported harvesters is presented and discussed.

Contribution to Improve Database Classification Algorithms for Multi-Database Mining

Miloudi, Salim,Rahal, Sid Ahmed,Khiat, Salim Korea Information Processing Society 2018 Journal of information processing systems Vol.14 No.3

Database classification is an important preprocessing step for the multi-database mining (MDM). In fact, when a multi-branch company needs to explore its distributed data for decision making, it is imperative to classify these multiple databases into similar clusters before analyzing the data. To search for the best classification of a set of n databases, existing algorithms generate from 1 to ($n^2-n$)/2 candidate classifications. Although each candidate classification is included in the next one (i.e., clusters in the current classification are subsets of clusters in the next classification), existing algorithms generate each classification independently, that is, without taking into account the use of clusters from the previous classification. Consequently, existing algorithms are time consuming, especially when the number of candidate classifications increases. To overcome the latter problem, we propose in this paper an efficient approach that represents the problem of classifying the multiple databases as a problem of identifying the connected components of an undirected weighted graph. Theoretical analysis and experiments on public databases confirm the efficiency of our algorithm against existing works and that it overcomes the problem of increase in the execution time.

The Psychological Impact in the Repetition Style in the Poetry of Nazik Al-Malaika

( Salim Al Tie ) 한국외국어대학교 중동연구소 2011 중동연구 Vol.30 No.2

When you read Nazik Al-Malaika s`poetry, you will find psychological effects of the poet, especially in the way of repetition. Therefore, I think that is the phenomenon of psychological style of angelic poetry for Nazik, and form of the various levels is an important tributary tagged at the level of poetic texts, such as stress, attention, pleasure and emotion. It stimulated the poetic imagination to fly through the passages of graphic correlation depends on the other collected by repeating asymmetric units. The repetition in the texts of the poet is a technology that psychological constructions of each word contained in the style of repetition it was charged with the dimensions of semantic psychological and clear, and to highlight aspects of the dominant idea and the situation on the emotional content of poetic texts. As in the repetition in its methods of different nuclei of key experiences of poetry to express the intended rhetorical as the big energy rhythmic, as an expression taking into account the emotions and feelings with appropriate music poetry even counting integral part of the technical architecture of the text, which attracts the senses of hearing and sight to the receiver of sympathy with the poet and the impact of her mental condition; And work to raise the level of feeling in the poem, in addition to the rhythmic role, which was a rewind or resonance to the voice of the poet internal emotion generated from the depths of the same before the explosion.

Recent advances in the metamaterial-inspired biosensors

Salim, Ahmed,Lim, Sungjoon Elsevier 2018 Biosensors & bioelectronics Vol.117 No.-

<P><B>Abstract</B></P> <P>Metamaterials (MM)-inspired microwave biosensors are a valuable addition to the field of diagnostic approaches and prognostic tools. The fundamental principle behind these biosensors is unique dielectric signatures corresponding to healthy/diseased tissues. Relying on nonionizing radiation and offering an increased resolution with accuracy comparable to that of ultrasound devices, they are an attractive solution for noninvasive and label-free biosensing applications. High-quality-factor MM-inspired resonators are integrated with microfluidics to accelerate the lab-on-chip and point-of-care diagnostic approaches owing to the small detection volume and overall compact size of these devices. A variety of biomolecular detection, glucose detection and hyperthermia treatment using state-of-the-art MM-inspired biosensors have been discussed. Optical transduction techniques (e.g., surface plasmon resonance) which enhance the sensitivity in terms of limit-of-detection and resolution, have also been outlined. Utilization of microwave biosensors as therapeutic agents is at its initial stages owing to lack of required sensitivity and reliability in recently proposed MM-inspired biosensors.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Commonly used materials and fabrication techniques for RF biosensors have been studied. </LI> <LI> Design strategies and simulation setup of RF biosensors have been studied. </LI> <LI> The most recent metamaterial-inspired biosensors have been reviewed by dividing GHz and THz domains. </LI> <LI> Finally, challenges and resolving strategies for metamaterial-inspired biosensors are discussed. </LI> </UL> </P>

Microfluidic Biosensor Based on Microwave Substrate-Integrated Waveguide Cavity Resonator

Salim, Ahmed,Kim, Sung-Hwan,Park, Joong Yull,Lim, Sungjoon Hindawi Limited 2018 Journal of sensors Vol.2018 No.-

<P>A microfluidic biosensor is proposed using a microwave substrate-integrated waveguide (SIW) cavity resonator. The main objectives of this noninvasive biosensor are to detect and analyze biomaterial using tiny liquid volumes (3 <I>μ</I>L). The sensing mechanism of our proposed biosensor relies on the dielectric perturbation phenomenon of biomaterial under test, which causes a change in resonance frequency and return loss (amplitude). First, an SIW cavity is realized on a Rogers RT/Duroid 5870 substrate. Then, a microwell made from polydimethylsiloxane (PDMS) material is loaded on the SIW cavity to observe the perturbation phenomenon. The microwell is filled with phosphate-buffered saline (PBS) solution (reference biological medium). To demonstrate the sensing behavior, the fibroblast (FB) cells from the lungs of a human male subject are analyzed and one-port S-parameters are measured. The resonance frequency of the structure with FB cells is observed to be 13.48 GHz. The reproducibility and repeatability of our proposed biosensor are successfully demonstrated through full-wave simulations and measurements. The resonance frequency of the FB-loaded microwell showed a shift of 170 MHz and 20 MHz, when compared to those of empty and PBS-loaded microwells. Its analytical limit of detection is 213 cells/<I>μ</I>L. Our proposed biosensor is noncontact and reliable. Furthermore, it is miniaturized, inexpensive, and fabricated using simple- and easy-design processes.</P>

Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures

Salim Lahmiri,Debra Ann Dawson,Amir Shmuel 대한의용생체공학회 2018 Biomedical Engineering Letters (BMEL) Vol.8 No.1

Parkinson’s disease (PD) is a widespreaddegenerative syndrome that affects the nervous system. Itsearly appearing symptoms include tremor, rigidity, andvocal impairment (dysphonia). Consequently, speechindicators are important in the identification of PD basedon dysphonic signs. In this regard, computer-aided-diagnosissystems based on machine learning can be useful inassisting clinicians in identifying PD patients. In this work,we evaluate the performance of machine learning basedtechniques for PD diagnosis based on dysphonia symptoms. Several machine learning techniques were consideredand trained with a set of twenty-two voice disordermeasurements to classify healthy and PD patients. Thesemachine learning methods included linear discriminantanalysis (LDA), k nearest-neighbors (k-NN), naı¨ve Bayes(NB), regression trees (RT), radial basis function neuralnetworks (RBFNN), support vector machine (SVM), andMahalanobis distance classifier. We evaluated the performanceof these methods by means of a tenfold cross validationprotocol. Experimental results show that the SVMclassifier achieved higher average performance than allother classifiers in terms of overall accuracy, G-mean, andarea under the curve of the receiver operating characteristicplot. The SVM classifier achieved higher performancemeasures than the majority of the other classifiers also interms of sensitivity, specificity, and F-measure statistics. The LDA, k-NN and RT achieved the highest averageprecision. The RBFNN method yielded the highestF-measure.; however, it performed poorly in terms of otherperformance metrics. Finally, t tests were performed toevaluate statistical significance of the results, confirmingthat the SVM outperformed most of the other classifiers onthe majority of performance measures. SVM is a promisingmethod for identifying PD patients based on classificationof dysphonia measurements.

Probabilistic Models for Local Patterns Analysis

Salim, Khiat,Hafida, Belbachir,Ahmed, Rahal Sid Korea Information Processing Society 2014 Journal of information processing systems Vol.10 No.1

Recently, many large organizations have multiple data sources (MDS') distributed over different branches of an interstate company. Local patterns analysis has become an effective strategy for MDS mining in national and international organizations. It consists of mining different datasets in order to obtain frequent patterns, which are forwarded to a centralized place for global pattern analysis. Various synthesizing models [2,3,4,5,6,7,8,26] have been proposed to build global patterns from the forwarded patterns. It is desired that the synthesized rules from such forwarded patterns must closely match with the mono-mining results (i.e., the results that would be obtained if all of the databases are put together and mining has been done). When the pattern is present in the site, but fails to satisfy the minimum support threshold value, it is not allowed to take part in the pattern synthesizing process. Therefore, this process can lose some interesting patterns, which can help the decider to make the right decision. In such situations we propose the application of a probabilistic model in the synthesizing process. An adequate choice for a probabilistic model can improve the quality of patterns that have been discovered. In this paper, we perform a comprehensive study on various probabilistic models that can be applied in the synthesizing process and we choose and improve one of them that works to ameliorate the synthesizing results. Finally, some experiments are presented in public database in order to improve the efficiency of our proposed synthesizing method.

Stacked insecticidal genes in potatoes exhibit enhanced toxicity against Colorado potato beetle, Leptinotarsa decemlineata (Coleoptera: Chrysomelidae)

Salim Muhammad,Bakhsh Allah,Gökçe Ayhan 한국식물생명공학회 2021 Plant biotechnology reports Vol.15 No.2

The present study was performed to express stacked insecticidal genes in potato cv. Lady Olympia and Agria to encode resistance against Colorado potato beetle (CPB), Leptinotarsa decemlineata (Say). Bacillus thuringiensis (Bt) gene (cry3A), synthetic hybrid (SN-19) and plant proteinase inhibitor Oryza cystatin II (OCII) cloned in pCAMBIA1301 binary vector in two diferent combinations as of DS-1 (cry3A+SN-19 genes) and DS-2 (OCII+SN-19 genes) constructs and further transformed to two potato cultivars using Agrobacterium-mediated transformation. All molecular analyses confrmed gene integration and expression in a total of 27 primary transformants in both Agria and Lady Olympia. Insecticidal efects of T0 progeny transgenic potato plants were tested against CPB under laboratory conditions. Transgenic plants of Agria and Lady Olympia transformed with DS-1 and DS-2 constructs caused 100% mortality to all larval stages and adults of CPB. However, 100% mortality of tested insects took a longer time in the adult stage (10–14 days) compared to larval stages (2–6 days). Foliage consumption by L2-L4 larval stages and adults of CPB was signifcantly reduced in Agria and Lady Olympia plants transformed with DS-1 and DS-2 constructs, as compared to their control plants. Lower foliage consumption of transgenic plants by L1 larval stages was also observed, but the reduction was only statistically signifcant for some of the tested plants. These promising results indicate that the transgenic potato plants exhibit a high potential in controlling CPB population and are a useful tool in the management of imidacloprid-resistant CPB.