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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.
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.
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.
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.
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.
Purpose The purpose of this study is to show the effectiveness of a physiological signal denoising approach called EMDDWT- CLS. Methods This paper presents a new approach for signal denoising based on empirical mode decomposition (EMD), discrete wavelet transform (DWT) thresholding, and constrained least squares (CLS). In particular, the noisy signal is decomposed by empirical mode decomposition (EMD) to obtain intrinsic mode functions (IMFs) plus a residue. Then, each IMF is denoised by using the discrete wavelet transform (DWT) thresholding technique. Finally, the denoised signal is recovered by performing a weighted summation of the denoised IMFs except the residue. The weights are determined by estimating a constrained least squares coefficients; where, the sum of the coefficients is constrained to unity. We used human ECG and EEG signals, and also two EEG signals from left and right cortex of two healthy adult rats. Results The 36 experimental results show that the proposed EMD-DWT-CLS provides higher signal-to-noise ratio (SNR) and lower mean of squared errors (MSE) than the classical EMD-DWT model. Conclusions Based on comparison with classical EMDDWT model used in the literature, the proposed approach was found to be effective in human and animal physiological signals denoising.
The influence of supplementing diets with various levels of organic zinc (OZ) on the performance, meat quality attributes, and sensory properties of broiler chickens was investigated. A total of 3,200 1-d-old female broiler chicks were randomly allotted to 16 floor pens (replicates) with 200 birds per pen. A corn-wheat-soybean meal basal diet (control) was formulated and 20 ppm OZ (20 OZ), 40 ppm OZ (40 OZ), or 80 ppm OZ (80 OZ) was added to the basal diet to form four dietary treatments with four replicates per treatment. Live performance of broiler chickens, meat quality, and sensory properties were evaluated. The results showed no significant difference among the treatments for live performance of broiler chickens. Significant increases (p<0.05) in thigh skin epidermis and dermis thickness were shown in the OZ supplementation groups; however, no effect of OZ on the thickness of back skin epidermis or dermis was found. Dietary OZ levels did not affect the pH of breast and thigh meat or the water holding capacity (WHC) of thigh meat, but the WHC of breast meat increased significantly (p<0.05) when birds were fed 40 OZ and 80 OZ. Results of a sensory analysis showed no differences among the dietary treatments. In conclusion, dietary OZ did not affect live performance or sensory properties of broiler chickens but did increase the WHC of breast meat and thickness of skin layers; thus, improving carcass quality in broiler chickens.
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.
Carbon dioxide injection is a widely known method of enhanced oil recovery (EOR). It is critical for the $CO_2$ EOR that the injected $CO_2$ to reach a condition fully miscible with oil. To reach the miscible point, a certain level of pressure is required, which is known as minimum miscibility pressure (MMP). In this study, a MMP prediction method using a process simulator is proposed. To validate the results of the simulation, those are compared to a slim tube experiment and several empirical correlations of previous literatures. Aspen HYSYS is utilized as the process simulator to create a model of $CO_2$/crude oil encounter. The results of the study show that the process simulator model is capable of predicting MMP and comparable to other published methods.