• PNF and Movement
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• v.12 no.2
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• pp.115-121
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• 2014

Purpose: The study aimed to examine the influence of PNF direct and indirect breathing treatments for patients with cervical spinal cord injuries who had breathing problems. Methods: For each cervical spinal cord patient, force vital capacity (FVC), peak expiratory flow, maximum phonation time (MPT), rib cage width, and VAS were measured pre-intervention and four weeks after post-intervention. The indirect method and the direct method were used for interventions. We treated patients with the indirect method using scapular anterior depression pattern, bilateral extensor pattern with rhythmic initiation, and a combination of isotonic. We treated patients with the direct method, applying pressure on the sternum and using rhythmic initiation (hold relax and stretch reflex) for the rib cage. Training occurred for 50 minutes a day and three days per week for four weeks. Results: FVC, MPT, peak expiratory flow, and rib cage width were increased and decreased at the VAS point for rolling after treatment. Conclusion: Patients with cervical spinal cord injuries who had breathing problems felt uncomfortable when they had conversations on a couch. We found that PNF direct and indirect treatments improved rib cage width and breathing functions of patients with cervical spinal cord injuries.

• International Journal of Computer Science & Network Security
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• v.21 no.1
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• pp.40-48
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• 2021

Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naive Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.1
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• pp.419-430
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• 2021

The study aims to analyze the factors that shape patient loyalty, namely, by involving the service quality factor (SERVQUAL), hospital image, patient value, and patient satisfaction in private hospitals. This study was conducted in Makassar City, Indonesia, with a sample of 296 eligible samples from private hospitals. The sample criteria were patients with outpatient and hospitalization status. Then, this study developed 23 hypotheses to test the statistical relationship between direct, intervening and multiple-effect models. Problem-solving and research focus are carried out using a quantitative method approach with a PLS-SEM-based testing tool. The bootstrapping method is being used with the constant bootstrapping step to demonstrate the results of hypothesis testing; we find that the overall hypothesis has a positive and significant effect. The combination of testing models involving several variables shows that a patient's loyalty can be formed if a patient's satisfaction has been realized. Satisfaction can be realized if the value-customer has been felt by the patients. Therefore, the hospital image must be directly proportional to service quality. Service quality is the essence of service that directly affects customers; service quality is also the reason that shapes consumer perceptions in increasing rationalization and solid customer (patient's) decision-making.

• Geomechanics and Engineering
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• v.24 no.4
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• pp.359-370
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• 2021

Joint roughness is combination of primary and secondary roughness. Ordinarily primary roughness is a geostatistical part of a joint surface that has a periodic nature but secondary roughness or unevenness is a statistical part of that which have a random nature. Using roughness generating algorithms is a useful method for evaluation of joint roughness. In this paper after determining geostatistical parameters of the joint profile, were presented two roughness generating algorithms using Mount-Carlo method for evaluation of primary (GJRGAP) and secondary (GJRGAS) roughness. These based on geostatistical parameters (range and sill) and statistical parameters (standard deviation of asperities height, SDH, and standard deviation of asperities angle, SDA) for generation two-dimensional joint roughness profiles. In this study different geostatistical regions were defined depending on the range and SDH. As SDH increases, the height of the generated asperities increases and asperities become sharper and at a specific range (a specific curve) relation between SDH and SDA is linear. As the range in GJRGAP becomes larger (the base of the asperities) the shape of asperities becomes flatter. The results illustrate that joint profiles have larger SDA with increase of SDH and decrease of range. Consequencely increase of SDA leads to joint roughness parameters such Z2, Z3 and RP increases. The results showed that secondary roughness or unevenness has a great influence on roughness values. In general, it can be concluded that the shape and size of asperities are appropriate parameters to approach the field scale from the laboratory scale.

• Ocean Systems Engineering
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• v.11 no.1
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• pp.43-58
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• 2021

In the present work, a 3D numerical model is proposed to study local scouring around single vertical piers with different cross-section shapes under steady-current flow. The model solves the flow field and sediment transport processes using a coupled approach. The flow field is obtained by solving the Unsteady Reynolds Averaged Navier-Stokes (URANS) equations in combination with the k-ω SST turbulence closure model and the sediment transport is considered using both bedload and suspended load models. The proposed model is validated against the empirical measurements of local scour around single vertical piers with circular, square, and diamond cross-section shapes obtained from the literature. The measurement of scour depth in equilibrium condition for the simulations reveal the differences of 4.6%, 6.7% and 13.1% from the experimental measurements for the circular, square, and diamond pier cases, respectively. The model displayed a remarkable performance in the prediction of scour around circular and square piers where horseshoe vortices (HSVs) have a leading impact on scour progression. On the other hand, the maximum deviation was found in the case of the diamond pier where HSVs are weak and have minimum impact on the formation of local scour. Overall, the results confirm that the prediction capability of the present model is almost independent of the strength of the formed HSVs and pier cross-section shapes.

• Asia Pacific Journal of Corpus Research
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• v.2 no.1
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• pp.35-45
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• 2021

With the wide spread of the Common European Framework of Reference (CEFR) scales, many studies attempt to apply them in routine teaching and rater training, while more evidence regarding criterial features at different CEFR levels are still urgently needed. The current study aims to explore complexity features that distinguish and predict CEFR proficiency levels in oral performance. Using a quantitative/corpus-based approach, this research analyzed lexical and syntactic complexity features over 80 transcriptions (includes A1, A2, B1 CEFR levels, and native speakers), based on an interview test, Standard Speaking Test (SST). ANOVA and correlation analysis were conducted to exclude insignificant complexity indices before the discriminant analysis. In the result, distinctive differences in complexity between CEFR speaking levels were observed, and with a combination of six major complexity features as predictors, 78.8% of the oral transcriptions were classified into the appropriate CEFR proficiency levels. It further confirms the possibility of predicting CEFR level of L2 learners based on their objective linguistic features. This study can be helpful as an empirical reference in language pedagogy, especially for L2 learners' self-assessment and teachers' prediction of students' proficiency levels. Also, it offers implications for the validation of the rating criteria, and improvement of rating system.

• Korean Journal of Head & Neck Oncology
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• v.37 no.2
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• pp.11-17
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• 2021

Head and neck cancer is the 6^th most frequently diagnosed solid tumor in the world. Alcohol consumption, smoking, and HPV infection are associated with the incidence of head and neck squamous cell carcinoma (HNSCC). Although a multidisciplinary approach is a key strategy for the treatment of locally advanced HNSCC, systemic therapy is the mainstream of recurrent or metastatic HNSCC treatment. Stage IV HNSCC has a relatively poor prognosis with median overall survival of around one year. There have been many clinical trials to investigate the efficacy of target agents in the treatment of HNSCC. In the HPV-negative HNSCC, TP53 and CDKN2A are the most commonly mutated genes. In the HPV-positive HNSCC, the PI3K pathway is frequently altered. EGFR, PI3K, cell cycle pathway, MET, HRAS, and IL6/JAK/STAT pathway are explored targets in HNSCC. In this study, we review the target pathways and agents under research. We also introduce here umbrella trials of recurrent or metastatic HNSCC conducted by the Korea Cancer Study Group. The combination of target agents with immune checkpoint inhibitors or cytotoxic chemotherapies would be a future step in the precision medicine of HNSCC treatment.

• East Asian Journal of Business Economics (EAJBE)
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• v.10 no.2
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• pp.19-30
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• 2022

Purpose - The research aims to explore the ability of the combination of the Technology Acceptance Model (TAM) and Theory of Reasoned Action (TRA) to predict and explain the online shopping intention of Vietnamese consumers. The findings of this study provide empirical results to assess the factors influencing behavioural intention in the E-commerce field. Research design, data, and methodology - The research approach of this study is quantitative. The data was collected from 214 respondents on e-commerce platforms. The collected data will then be analyzed to test the proposed hypothesis in this study. Multiple Regression Analysis and Simple Linear Regression are employed to test the hypothesis. Result - Perceived benefits, Perceived risk reduction, and trust positively influence Attitude toward using Ecommerce. There is a positive relationship between Subjective norms and Behavioural intention to shop online. There is a positive relationship between Attitude toward using E-commerce and Behavioural intention to shop online. Conclusion - This study is based on the Theory of Reasoned Action (TRA) model and the Technology Acceptance Model (TAM) to explore the factors influencing the online shopping intention of Vietnamese consumers. Besides, this paper contributes to the managerial implications for E-commerce managers and policymakers to promote E-commerce among Vietnamese consumers.

• Journal of Korea Multimedia Society
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• v.24 no.12
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• pp.1624-1631
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• 2021

With the continued development of image processing technology, we live in a time when it is difficult to visually discriminate processed (or tampered) images from real images. However, as the risk of fake images being misused for crime increases, the importance of image forensic science for identifying fake images is emerging. Currently, various deep learning-based identifiers have been studied, but there are still many problems to be used in real situations. Due to the inherent characteristics of deep learning that strongly relies on given training data, it is very vulnerable to evaluating data that has never been viewed. Therefore, we try to find a way to improve generalization ability of deep learning-based fake image identifiers. First, images with various contents were added to the training dataset to resolve the over-fitting problem that the identifier can only classify real and fake images with specific contents but fails for those with other contents. Next, color spaces other than RGB were exploited. That is, fake image identification was attempted on color spaces not considered when creating fake images, such as HSV and YCbCr. Finally, dropout, which is commonly used for generalization of neural networks, was used. Through experimental results, it has been confirmed that the color space conversion to HSV is the best solution and its combination with the approach of increasing the training dataset significantly can greatly improve the accuracy and generalization ability of deep learning-based identifiers in identifying fake images that have never been seen before.

• Journal of Korea Trade
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• v.23 no.2
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• pp.59-75
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• 2019

Purpose - The International Maritime Organization (IMO) has promulgated strict regulations on emissions in the maritime shipping industry. LNG (Liquefied Natural Gas) is, therefore, recognized as the optimal fuel alternative solution. The aim of this study is to select the most suitable location for an LNG bunkering port. This is formulated as a multiple-criteria ranking problem regarding four candidate ports in South Korea: the ports of Busan, Gwangyang, Incheon, and Ulsan. Design/Methodology/approach - An analysis employing the Consistent Fuzzy Preference Relation (CFPR) methodology is carried out, and the multiple-criteria evaluation of various factors influencing the location selection, such as the average loading speed of LNG, the number of total ships, the distance of the bunkering shuttle, and the degree of safety is performed. Then, based on the combination of both the collected real data and experts' preferences, the final ranking of the four ports is formulated. Findings - The port of Busan ranks first, followed by the ports of Gwangyang and Ulsan, with the port of Incheon last on the list. Originality/value - The Korean government could proceed with a clear vision of the candidate ports' ranking in terms of the LNG bunkering terminal selection problem.