ความคาดหวังของนายจ้างต่างชาติที่มีต่อนักศึกษาไทย

Securing a good job is tough thing for new graduates as the job market is being more competitive in the present global scenario. Upcoming ASEAN Economic Community (AEC), 2015 has overwhelmed Thai graduates as it may turn out fierce competition to land a job in their own country. In this light, this study has explored the Thai students’ performance while working with international employers abroad during their paid internship through International Association for the Exchange of Students for Technical Experience (IAESTE). This paper reveals Thai students’ performance quality from the eyes of the employers with the due focus on the working efficiency in their field of study and English language skills. The study draws its result by analyzing the qualitative and quantitative data from the 110 employers outside Thailand who recruited Thai students in year 2009 and 2010

Detecting COVID-19 in chest X-ray images

One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition

Intraosseous ameloblastoma masquerading as exophytic growth: a case report

Intraosseous ameloblastoma is the most common and simple type of ameloblastoma prevalent among odontogenic tumors. Clinico-radiographically intraosseous ameloblastoma presents as slow, painless swelling or expansion of the jaws and described as multilocular expansile radiolucency that occurs most frequently in mandibular molar/ramus area. This article describes a case of follicular ameloblastoma involving 45 year old male which is different from the usual presentation, which includes-exophytic growth, different location and without expansion of the cortex

Volumetric measurement of synthetic lung nodules with multi-detector row CT: effect of various image reconstruction parameters and segmentation thresholds on measurement accuracy

PURPOSE: To evaluate the effect of various multi-detector row computed tomographic (CT) reconstruction parameters and nodule segmentation thresholds on the accuracy of volumetric measurement of synthetic lung nodules. MATERIALS AND METHODS: Synthetic lung nodules of four different diameters (3.2, 4.8, 6.4, and 12.7 mm) were scanned with multi-detector row CT. Images were reconstructed at various section thicknesses (0.75, 1.0, 2.0, 3.0, and 5.0 mm), fields of view (30, 20, and 10 cm), and reconstruction intervals (0.5, 1.0, and 2.0 mm). The nodules were segmented from the simulated background lung region by using four segmentation thresholds (-300, -400, -500, and -600 HU), and their volumes were estimated and compared with a reference standard (measurements according to fluid displacement) by computing the absolute percentage error (APE). APE was regressed against nodule size, and multivariate analysis of variance (MANOVA) was performed with APE as the dependent variable and with four within-subject factors (field of view, reconstruction interval, threshold, and section thickness). RESULTS: The MANOVA demonstrated statistically significant effects for threshold (P = .02), section thickness (P < .01), and interaction of threshold and section thickness (P = .04). The regression of mean APE values on nodule size indicates that APE progressively increases with decreasing synthetic nodule size (R2 = 0.99, P < .01). CONCLUSION: For accurate measurement of lung nodule volume, it is critical to select a section thickness and/or segmentation threshold appropriate for the size of a nodule