110023 research outputs found
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Ubiquitous English idiom learning through mobile applications: Learning outcomes, motivation, anxiety, and behavioral patterns
No full text[[abstract]]The knowledge of English idioms is essential for advanced English learning and application, particularly for English-as-a-foreign-language (EFL) learners. While studies have shown the benefits of mobile-assisted language learning (MALL) to facilitate English learning, few studies have examined the factors that may influence MALL in light of motivation and anxiety, let alone the behavioral patterns of learners with different learning motivation and learning anxiety levels. Therefore, in this study, a mobile application named My English Idiom Learning Assistant (MEILA) rooted in the theory of Associative Fluency was developed to examine the relationships among learning outcomes, motivation, anxiety, and learning behaviors in English idiom learning. The participants were 59 students from two Freshman English classes in one private university in Central Taiwan. In this 5-week implementation, the students were engaged in 3-week self-study learning, where they were encouraged to watch 15 idiom-related animations and videos each week and make and record English sentences in MEILA. Multiple data sources were collected in this mixed-methods research, including pre- and post-test of English idiomatic understanding, two questionnaires (English learning motivation and English learning anxiety), and interviews. The results from the tests revealed the positive affordances of MEILA for facilitating English idiom learning among Taiwanese college students. The analysis of students’ behavioral patterns showed that students with different levels of motivation and anxiety (high motivation/high anxiety, high motivation/low anxiety, low motivation/high anxiety, low motivation/low anxiety) exhibited distinctive learning behaviors. Most students were positive about the use of MEILA for English idiom learning. The current findings provide recommendations to instructional designers who wish to optimize MALL for benefiting learners with different needs
The Visual Attention and Psychological Responses from Older Customers to Wellness Service Pictures of Hotels
No full text[[abstract]]Understanding the visual attention and psychological responses of consumers to marketing pictures allows hotel managers to design more attractive advertisements. Therefore, the purposes of the present study were to use an eye-tracking analysis to explore whether there were different effects from tourist hotels' wellness service pictures based on whether they had natural or built clues. The psychological responses with regard to perceived well-being and willingness to pay were also examined. Eighty-five older consumers were recruited. Their eye movement performance while observing marketing pictures with different visual clues and their subsequent psychological responses were measured. It was found that wellness service pictures with natural clues captured more visual attention and induced higher willingness-to-pay perceptions than those with built clues in these older consumers. The present results suggest that marketing pictures with natural clues may create positive visual responses in older customers and further enhance their purchase intention
A Study on Highly Effective Electromagnetic Wave Shield Textile Shell Fabrics Made of Point Polyester/Metallic Core-Spun Yarns
No full text[[abstract]]In this study, stainless steel (SS) filaments are wrapped in Ge fibers to form core-spun yarns. The yarns along with 500 D polyester (PET) fibers undergo weaving, thereby forming functional woven fabrics. The experiment is composed of two parts:yarns and fabrics. The yarns are twisted with TPI of 8, 9, 10, 11, and 12, and then tested for tensile strength and tensile elongation. The yarns possess mechanical properties that are dependent on the TPI-the higher the TPI, the better the mechanical properties. The maximal mechanical properties occur when the core-spun yarns are made of 12 TPI where the maximal tensile strength is 5.26 N and the lowest elongation is 43.2%. As for the functional woven fabrics, they are made of Ge/SS core-spun yarns as the weft yarns and 500 D PET yarns as the warp yarns. The tensile strength, tensile elongation, negative ion release, electromagnetic interference shielding effectiveness (EMI SE), and air permeability tests are conducted, determining the optimal woven fabrics. The 12 TPI core-spun yarns provide the woven fabrics with the maximal tensile strength of 153.6 N and the optimal elongation at break of 10.08%. In addition, the woven fabrics made with 8 or 9 TPI core-spun yarns exhibit an optimal EMI SE of 41 dB, an optimal air permeability of 212 cm3/cm2/s, and an optimal release amount of negative ion of 550-600 ions/cc. The proposed woven fabrics have a broad range of applications, such as functional garments and bedding
應用消費價值理論探討消費者 選擇主題旅館之因素 - 旅館類型及人格特質之調節效果
No full text[[abstract]]旅館市場競爭日益遽增，隨著旅館的普遍為追求差異化與獨特性，提高競爭優勢，開始了主題式經營。主題式經營起源於美國加州，以某一特定的主題以及特定的文化氛圍讓旅客有個性化的體驗，進而提升回客率及再訪意願。根據交通部觀光局統計指出「是否有主題活動」也是旅客考慮的重要因素之一，以此可顯示主題活動會影響旅客的選擇意願。而主題旅館在臺灣學術上尚未有正式的定義及劃分，對其相關學術研究也較少，因此本研究將結合消費者選擇行為進行研究與探討。本研究採線上問卷方式蒐集消費者資料，共回收241份有效問卷。研究分析結果顯示：1.消費價值中僅有功能價值、情感價值與附加價值對消費者選擇行為呈現顯著的正向影響關係；2.不同主題旅館的類型會調節消費價值對選擇行為間的關係；3.不同消費者的人格特質不會調節消費價值對選擇行為的關係。最後並提出相關結論及建議，作為主題旅館經營之參考依據。[[abstract]]The hotel market has become more competitive as time goes on, with common pursuit of differentiation and distinctness, increasing competitive advantages, and thus adopting theming. Originating from state of California in the U.S, theming features a specific theme and cultural atmosphere to make travelers have personalized experience, therefore increases the repeat cust omer rate and the chance of revisiting. According to the statistics showed by Tourism Bureau, MOTC, it indicates that ''whether there is a themed event or not'' is also a key factor for travelers' consideration. Hence, we know that showing themed events will influence travelers' choices making. As of now, there is no academically official definition and division in Taiwan, and the numbers of related academic research are not that much. Consequently, our research will comb in it with consumer choice behavior to conduct and investigate. The study used the convenient sampling method to hand out questionnaires in airport halls and business districts to collect 241 samples by internet questionnaires. The result showed: 1. Functional value, emotional value and conditional value have the impact on consumer choices behavior. 2. Theme hotel types have significant moderating effect between the tourist' consumption values and choice behaviors. 3. The personality traits have no moderating effect the tourist' consumption values and choice behaviors. Finally, this study puts forward relevant conclusions and suggestions as a reference for the operation of theme hotels
An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images
No full text[[abstract]]Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F 1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup
Analysis of novel variants associated with three human ovarian cancer cell lines
No full text[[abstract]]Background: Identification of mutations is of great significance in cancer research, as it can contribute to the development of therapeutic strategies and prevention of cancer formation. Ovarian cancer is one of the leading cancer-related causes of death in Taiwan. Furthermore, it has been observed that the accumulation of genetic mutations can lead to cancer.
Objective: We utilized whole-exome sequencing to explore cancer-associated missense variants in three human ovarian cancer cell lines derived from Taiwanese patients.
Methods: We utilized cell line whole-exome sequencing data, 188 patients’ whole-exome sequencing data, and in vitro experiments to verify predicted variant results. We established an effective analysis workflow for the discovery of novel ovarian cancer variants, comprising three steps: (i) use of public databases and in-house hospital data to select novel variants, (ii) investigation of protein structural stability caused by genetic mutations, and (iii) use of in vitro experiments to verify predictions.
Results: Our study enumerated 296 novel variants by imposing specific criteria and using sophisticated bioinformatics tools for further analysis. Eleven and 54 missense novel variants associated with cancerous and non-cancerous genes, respectively, were identified. A total of 13 missense mutations were found to affect the stability of protein 3D structure, while 11 disease-causing novel variants were confirmed by PCR sequencing. Among these, ten variants were predicted to be pathogenic, while the pathogenicity of one variant was uncertain.
Conclusion: It was confirmed that novel variant genes play a crucial role in ovarian cancer patients, with 11 novel variants that may promote the progression and development of ovarian cancer
AutoML-ID: Automated Machine Learning Model for Intrusion Detection Using Wireless Sensor Network
No full text[[abstract]]Momentous increase in the popularity of explainable machine learning models coupled with the dramatic increase in the use of synthetic data facilitates us to develop a cost-efficient machine learning model for fast intrusion detection and prevention at frontier areas using Wireless Sensor Networks (WSNs). The performance of any explainable machine learning model is driven by its hyperparameters. Several approaches have been developed and implemented successfully for optimising or tuning these hyperparameters for skillful predictions. However, the major drawback of these techniques, including the manual selection of the optimal hyperparameters, is that they depend highly on the problem and demand application-specific expertise. In this paper, we introduced Automated Machine Learning (AutoML) model to automatically select the machine learning model (among support vector regression, Gaussian process regression, binary decision tree, bagging ensemble learning, boosting ensemble learning, kernel regression, and linear regression model) and to automate the hyperparameters optimisation for accurate prediction of numbers of k-barriers for fast intrusion detection and prevention using Bayesian optimisation. To do so, we extracted four synthetic predictors, namely, area of the region, sensing range of the sensor, transmission range of the sensor, and the number of sensors using Monte Carlo simulation. We used 80% of the datasets to train the models and the remaining 20% for testing the performance of the trained model. We found that the Gaussian process regression performs prodigiously and outperforms all the other considered explainable machine learning models with correlation coefficient (R = 1), root mean square error (RMSE = 0.007), and bias = - 0.006. Further, we also tested the AutoML performance on a publicly available intrusion dataset, and we observed a similar performance. This study will help the researchers accurately predict the required number of k-barriers for fast intrusion detection and prevention
Association of Matrix Metallopeptidase-2 Genotypes With Risk of Gastric Cancer in Taiwan
No full text[[abstract]]Background/aim: Matrix metalloproteinase-2 (MMP-2) plays a critical role in the regulation of the extracellular matrix; however, its genotypes have seldom been examined in gastric cancer (GC). This study aimed to investigate the contribution of MMP-2 promoter -1306 (rs243865) and -735 (rs2285053) genotypes to GC risk in a cohort of Taiwanese individuals.
Materials and methods: This study included 121 GC cases and 363 age- and sex-matched controls. The genotypes of MMP-2 were determined by typical polymerase chain reaction-restriction fragment length polymorphism.
Results: The genotypic and allelic frequency analysis showed that MMP-2 rs243865 variant genotypes decreased the risk of GC. Stratification analysis showed that MMP-2 rs243865 genotypes associate with smoking, alcohol drinking, and Helicobacter pylori infection status to confer personal susceptibility to GC. There is no such association for MMP-2 rs2285053 genotype with GC risk.
Conclusion: The MMP-2 rs243865 genotypes may serve as a novel predictive marker for GC personal susceptibility among Taiwanese
Enhanced Convolutional Neural Network Model for Cassava Leaf Disease Identification and Classification
No full text[[abstract]]Cassava is a crucial food and nutrition security crop cultivated by small-scale farmers and it can survive in a brutal environment. It is a significant source of carbohydrates in African countries. Sometimes, Cassava crops can be infected by leaf diseases, affecting the overall production and reducing farmers’ income. The existing Cassava disease research encounters several challenges, such as poor detection rate, higher processing time, and poor accuracy. This research provides a comprehensive learning strategy for real-time Cassava leaf disease identification based on enhanced CNN models (ECNN). The existing Standard CNN model utilizes extensive data processing features, increasing the computational overhead. A depth-wise separable convolution layer is utilized to resolve CNN issues in the proposed ECNN model. This feature minimizes the feature count and computational overhead. The proposed ECNN model utilizes a distinct block processing feature to process the imbalanced images. To resolve the color segregation issue, the proposed ECNN model uses a Gamma correction feature. To decrease the variable selection process and increase the computational efficiency, the proposed ECNN model uses global average election polling with batch normalization. An experimental analysis is performed over an online Cassava image dataset containing 6256 images of Cassava leaves with five disease classes. The dataset classes are as follows: class 0: “Cassava Bacterial Blight (CBB)”; class 1: “Cassava Brown Streak Disease (CBSD)”; class 2: “Cassava Green Mottle (CGM)”; class 3: “Cassava Mosaic Disease (CMD)”; and class 4: “Healthy”. Various performance measuring parameters, i.e., precision, recall, measure, and accuracy, are calculated for existing Standard CNN and the proposed ECNN model. The proposed ECNN classifier significantly outperforms and achieves 99.3% accuracy for the balanced dataset. The test findings prove that applying a balanced database of images improves classification performance
Attention-based 3D CNN with residual connections for efficient ECG-based COVID-19 detection
No full text[[abstract]]Background
The world has been suffering from the COVID-19 pandemic since 2019. More than 5 million people have died. Pneumonia is caused by the COVID-19 virus, which can be diagnosed using chest X-ray and computed tomography (CT) scans. COVID-19 also causes clinical and subclinical cardiovascular injury that may be detected on electrocardiography (ECG), which is easily accessible.
Method
For ECG-based COVID-19 detection, we developed a novel attention-based 3D convolutional neural network (CNN) model with residual connections (RC). In this paper, the deep learning (DL) approach was developed using 12-lead ECG printouts obtained from 250 normal subjects, 250 patients with COVID-19 and 250 with abnormal heartbeat. For binary classification, the COVID-19 and normal classes were considered; and for multiclass classification, all classes. The ECGs were preprocessed into standard ECG lead segments that were channeled into 12-dimensional volumes as input to the network model. Our developed model comprised of 19 layers with three 3D convolutional, three batch normalization, three rectified linear unit, two dropouts, two additional (for residual connections), one attention, and one fully connected layer. The RC were used to improve gradient flow through the developed network, and attention layer, to connect the second residual connection to the fully connected layer through the batch normalization layer.
Results
A publicly available dataset was used in this work. We obtained average accuracies of 99.0% and 92.0% for binary and multiclass classifications, respectively, using ten-fold cross-validation. Our proposed model is ready to be tested with a huge ECG database