• Journal of The Korean Dental Society of Anesthesiology
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• v.11 no.1
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• pp.16-21
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• 2011

Background: Since introduced by Gow-Gates GA in 1973, Gow-Gates mandibular nerve block (GMNB) has played an important role in the area of dental local anesthesia. However, compared to the conventional inferior alveolar nerve block (IANB), this technique seems to fail to attract the attentions of general practitioners in South Korea. The aim of this study was to prove the clinical real value, mainly the anesthetic efficacy, of GMNB in minor oral surgery. Methods: The study group comprised 40 patients (15 males and 25 females) who were randomly allocated to receive GMNB or IANB for extraction of third molars. Both techniques utilized two 1.8 ml dental cartridges of 2% lidocaine including 1:100,000 epinephrine for each patient. Pulpal and gingival tissue anesthesia of mandibular premolars and molars were recorded at 0, 15 and 40 minutes after administration of local anesthetics using both an electric pulp tester and a sharp dental explorer. Results: The success rates of pulpal and gingival tissue anesthesia in the IANB group were not significantly different from the GMNB group in overall efficacy. Patient's and operator's satisfaction ratings were also not significantly different between two groups. Interestingly, the injection pain of GMNB group was significantly lower than that of IANB group. Conclusion: This study demonstrated that the anesthetic efficacy of pulpal and gingival tissue of GMNB was not inferior to that of IANB. The GMNB could be a good alternative of the IANB in most of minor oral surgical procedures.

• Microbiology and Biotechnology Letters
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• v.49 no.2
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• pp.192-200
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• 2021

Kunun-zaki, produced by submerged fermentation of a combination of millet and sorghum, is a popular beverage in Northern Nigeria. Owing to the nature of the process involved in its production, kunun-zaki is highly susceptible to contamination by food spoilage microorganisms, leading to inconsistent quality and short shelf-life. In this study, we investigated various food spices, including cinnamon, garlic, and nutmeg, as potential preservatives that could be used to extend kunun-zaki shelf-life. Kunun-zaki varieties were fermented with each of these spices mentioned above and subjected to bacterial, nutritional, sensory, and quality maintenance assessments (using a twelve-member sensory panel to evaluate the organoleptic properties of kunun-zaki). Bacterial counts in the final products ranged between 10^5-7 CFU/ml. We identified two bacterial genera, Weissella and Enterococcus, based on partial 16S rRNA gene amplicon sequencing. Three amino acids, namely leucine, aspartate, and glutamate, were abundant in all kunun-zaki varieties, while the total essential amino acid content was above 39%, suggesting that kunun-zaki could potentially be considered as a protein-rich food source both for infants and adults. The kunun-zaki products were also rich in carbohydrates, crude proteins, ash, crude fiber, and fat, with contents estimated as 81-84, 8-11, 0.8-4.0, 2.9-3.58, and 5.1-6.3%, respectively. However, this nutritional content depreciated rapidly after 24 h of storage, except for kunun-zaki fermented with garlic, which its crude protein and fat content was maintained for up to 48 h. Our results revealed that organic spices increased the nutritional content of the kunun-zaki varieties and could be potentially be used as natural preservatives for enhancing the kunun-zaki shelf-life. However, garlic might be considered a better alternative based on our preliminary investigation. The presence of the isolated microorganisms in the analyzed kunun-zaki samples should be highlighted to raise awareness on the possible health hazards that could arise from poor handling and processing techniques.

• Journal of Microbiology and Biotechnology
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• v.29 no.9
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• pp.1470-1477
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• 2019

${\beta}_2$-adrenergic receptor (${\beta}_2-AR$) was expressed efficiently using Bac-to-Bac Baculovirus Expression System in Sf9 cells as a bio-recognition element for multianalyte screening of ${\beta}$-agonist residues in pork. Sf9 cells were selected as the expression system, and codon optimization of wild-type nucleic acid sequence and time-dependent screening of expression conditions were then carried out for enhancing expression level and biological activity. Under optimum conditions of multiplicity of infection (MOI) = 5 and 48 h post transfection, the protein yield was up to 1.23 mg/ml. After purification by chromatographic techniques, the purified recombinant protein was applied to develop a direct competitive enzyme-linked receptor assay (ELRA) and the efficiency and reliability of the assay was determined. The IC50 values of clenbuterol, salbutamol, and ractopamine were 28.36, 50.70, and $59.57{\mu}g/l$, and clenbuterol showed 47.61% and 55.94% cross-reactivities with ractopamine and salbutamol, respectively. The limit of detection (LOD) was $3.2{\mu}g/l$ and the relevant recoveries in pork samples were in the range of 73.0-91.2%, 69.4-84.6%, and 63.7-80.2%, respectively. The results showed that it had better performance compared with other present nonradioactive receptorbased assays, indicating that the genetically modified ${\beta}_2-AR$ would have great application potential in detection of ${\beta}$-agonist residues.

• IEMEK Journal of Embedded Systems and Applications
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• v.14 no.5
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• pp.277-286
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• 2019

In this paper, we propose an efficient hyperplane generation technique to classify human activity from combination of events and sequence information obtained from multiple-event sensors. By generating hyperplane efficiently, our machine learning algorithm classify with less memory and run time than the LSVM (Linear Support Vector Machine) for embedded system. Because the fact that light weight and high speed algorithm is one of the most critical issue in the IoT, the study can be applied to smart home to predict human activity and provide related services. Our approach is based on reducing numbers of hyperplanes and utilizing robust string comparing algorithm. The proposed method results in reduction of memory consumption compared to the conventional ML (Machine Learning) algorithms; 252 times to LSVM and 34,033 times to LSTM (Long Short-Term Memory), although accuracy is decreased slightly. Thus our method showed outstanding performance on accuracy per hyperplane; 240 times to LSVM and 30,520 times to LSTM. The binarized image is then divided into groups, where each groups are converted to binary number, in order to reduce the number of comparison done in runtime process. The binary numbers are then converted to string. The test data is evaluated by converting to string and measuring similarity between hyperplanes using Levenshtein algorithm, which is a robust dynamic string comparing algorithm. This technique reduces runtime and enables the proposed algorithm to become 27% faster than LSVM, and 90% faster than LSTM.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.

• International Journal of Computer Science & Network Security
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• v.22 no.4
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• pp.420-426
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• 2022

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.30 no.2
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• pp.195-200
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• 1985

This experiment was carried out to know the processes of protoplast isolation, culture and plant regeneration in aims of introducing foreign genes into plant cells through plant gene vector, and cellular selection for plant improvement. The main results indicated that 2% cellulase plus 0.5% macerozyme is proper for isolation of protoplasts from leaf mesophyll cells of N. plumbaginifolia, plating efficiency was higher in 1.4-2.0 x 10$^4$ cells/ml, complete cell wall was regenerated after 2 days culture, cell division and cell mass were observed after 4 days and 2 weeks, respectively, colony was developed after 3 weeks culture, addition of 1-2mg/l BA promoted shoot differentiation while root differentiation did not required hormone and seeds were harvested from more than 100 cell lines for further investigation and study.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.46-52
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• 2023

With billions of IoT (Internet of Things) devices populating various emerging applications across the world, detecting anomalies on these devices has become incredibly important. Advanced Intrusion Detection Systems (IDS) are trained to detect abnormal network traffic, and Machine Learning (ML) algorithms are used to create detection models. In this paper, the NSL-KDD dataset was adopted to comparatively study the performance and efficiency of IoT anomaly detection models. The dataset was developed for various research purposes and is especially useful for anomaly detection. This data was used with typical machine learning algorithms including eXtreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Deep Convolutional Neural Networks (DCNN) to identify and classify any anomalies present within the IoT applications. Our research results show that the XGBoost algorithm outperformed both the SVM and DCNN algorithms achieving the highest accuracy. In our research, each algorithm was assessed based on accuracy, precision, recall, and F1 score. Furthermore, we obtained interesting results on the execution time taken for each algorithm when running the anomaly detection. Precisely, the XGBoost algorithm was 425.53% faster when compared to the SVM algorithm and 2,075.49% faster than the DCNN algorithm. According to our experimental testing, XGBoost is the most accurate and efficient method.

• The Journal of the Convergence on Culture Technology
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• v.9 no.1
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• pp.565-570
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• 2023

There are people who are sensitive to cosmetic ingredients, such as pregnant women and skin disease patients. There are also people who experience side effects from cosmetics. To avoid this, it is cumbersome to search for harmful ingredients in cosmetics one by one when shopping. In addition, knowing and remembering functional ingredients that suit you is helpful when purchasing new cosmetics. There is a need for a system that allows you to immediately know the cosmetics ingredients in the field through photography. In this paper, we introduce an application for smartphones, <Hwa Ahn>, which allows you to immediately know the cosmetics ingredients by photographing the ingredients displayed in the cosmetics. This system is more effective and convenient than the existing system in that it automatically recognizes and automatically classifies the ingredients of the cosmetic when the camera is illuminated on the cosmetic ingredients or retrieves the photos of the cosmetic ingredients from the album. If the system is widely used, it is expected that it will prevent skin diseases caused by cosmetics in daily life and reduce purchases of cosmetics that are not suitable for you.

• Medical Lasers
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• v.8 no.1
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• pp.7-12
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• 2019

Background and Objectives Skin and soft tissue defects can be treated according to a range of strategies, such as local flap, skin graft, biological dressing, or free flap. On the other hand, free tissue transfer usually leaves a distinct scar with an inconsistency of color or hypertrophy. This problem is highlighted if the defect is located on the face, which could have devastating effects on a patient's psychosocial health. Materials and Methods The authors used an erbium : yttrium-aluminum-garnet (Er:YAG) laser to resurface the free flap skin and match the color with the surrounding facial skin. This study evaluated the effectiveness of laser skin resurfacing on the harmonious color matching of transferred flap. Patients who had undergone laser resurfacing on facial flap skin between January 2014 and December 2018 were reviewed retrospectively. An ablative 2,940-nm fractional Er:YAG laser treatment was delivered to the entire flap skin at 21 J/cm² with the treatment end-point of pinpoint bleeding. Several months later, the clinical photographs were analyzed. The L^*a^*b^* color co-ordinates of both the flap and surrounding normal skin were measured using Adobe Photoshop. The L^*a^*b^* color difference (ΔE) for the scar and normal surrounding skin were calculated using the following equation: ${\Delta}E=\sqrt{({\Delta}L)^2+({\Delta}a)^2+({\Delta}b)^2}$ Results All five patients were satisfied with the more natural appearance of the flaps. The ΔE values decreased significantly from the pre-treatment mean value of 19.64 to the post-treatment mean value of 11.39 (Wilcoxon signed-rank test, p = 0.043). Conclusion Ablative laser resurfacing can improve the aesthetic outcome of free tissue transfer on the face.