• Anatomy and Cell Biology
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• v.56 no.1
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• pp.86-93
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• 2023

Age at death estimation has always been a crucial yet challenging part of identification process in forensic field. The use of human skeletons have long been explored using the principle of macro and micro-architecture change in correlation with increasing age. The clavicle is recommended as the best candidate for accurate age estimation because of its accessibility, time to maturation and minimal effect from weight. Our study applies pre-trained convolutional neural network in order to achieve the most accurate and cost effective age estimation model using clavicular bone. The total of 988 clavicles of Thai population with known age and sex were radiographed using Kodak 9000 Extra-oral Imaging System. The radiographs then went through preprocessing protocol which include region of interest selection and quality assessment. Additional samples were generated using generative adversarial network. The total clavicular images used in this study were 3,999 which were then separated into training and test set, and the test set were subsequently categorized into 7 age groups. GoogLeNet was modified at two layers and fine tuned the parameters. The highest validation accuracy was 89.02% but the test set achieved only 30% accuracy. Our results show that the use of medial clavicular radiographs has a potential in the field of age at death estimation, thus, further study is recommended.

• International Journal of Computer Science & Network Security
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• v.24 no.1
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• pp.226-234
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• 2024

The frequent handover problem and playing ping-pong effects in 5G (5th Generation) ultra-dense networking cannot be effectively resolved by the conventional handover decision methods, which rely on the handover thresholds and measurement reports. For instance, millimetre-wave LANs, broadband remote association techniques, and 5G/6G organizations are instances of group of people yet to come frameworks that request greater security, lower idleness, and dependable principles and correspondence limit. One of the critical parts of 5G and 6G innovation is believed to be successful blockage the board. With further developed help quality, it empowers administrator to run many systems administration recreations on a solitary association. To guarantee load adjusting, forestall network cut disappointment, and give substitute cuts in case of blockage or cut frustration, a modern pursuing choices framework to deal with showing up network information is require. Our goal is to balance the strain on BSs while optimizing the value of the information that is transferred from satellites to BSs. Nevertheless, due to their irregular flight characteristic, some satellites frequently cannot establish a connection with Base Stations (BSs), which further complicates the joint satellite-BS connection and channel allocation. SF redistribution techniques based on Deep Reinforcement Learning (DRL) have been devised, taking into account the randomness of the data received by the terminal. In order to predict the best capacity improvements in the wireless instruments of 5G and 6G IoT networks, a hybrid algorithm for deep learning is being used in this study. To control the level of congestion within a 5G/6G network, the suggested approach is put into effect to a training set. With 0.933 accuracy and 0.067 miss rate, the suggested method produced encouraging results.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.101-110
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• 2024

Purpose : The purpose of this study provided an overview of the general status and recent intervention approaches in overseas research related to feeding problems in children with autism spectrum disorder (ASD). This review aims to explore interventions for feeding problems in order to provide higher quality follow-up research directions and implications, particularly focusing on providing recommendations for future research in the context of domestic studies. Methods : Analyzing studies published in international journals from 2017 to 2023. This review involved six selected articles, through Embase, Pubmed, RISS, KISS database search engine. A literature analysis that includes inclusion and exclusion criteria, six selected articles were examined. The literature analysis categorized the general status of the research and intervention approaches and treatment components into intervention, treatment settings and therapists, and dependent variables, respectively. Results : Among feeding intervention approaches, parent education interventions based on behavioral therapy had the highest proportion, followed by multidisciplinary interventions. To maintain the effectiveness of interventions over the long term and to generalize them to the home environment, parent education that utilizes parents as mediators is considered a crucial factor. The most commonly observed effects as dependent variables were changes in the consumption of disliked foods, health foods and alterations in feeding behavior. Conclusion : This study introduces various intervention approaches for addressing feeding problems in children with autism spectrum disorder (ASD), focusing on the positive effects demonstrated by active intervention research in abroad. Furthermore, it underscores the need for additional research in Korea to validate the efficacy of these feeding intervention methods. Lastly, the study outlines future research directions aimed at developing feeding programs to support children with ASD and their families coping with feeding issues.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.715-727
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• 2024

During fast neutron imaging, besides the dark current noise and readout noise of the CCD camera, the main noise in fast neutron imaging comes from high-energy gamma rays generated by neutron nuclear reactions in and around the experimental setup. These high-energy gamma rays result in the presence of high-density gamma white spots (GWS) in the fast neutron image. Due to the microscopic quantum characteristics of the neutron beam itself and environmental scattering effects, fast neutron images typically exhibit a mixture of Gaussian noise. Existing denoising methods in neutron images are difficult to handle when dealing with a mixture of GWS and Gaussian noise. Herein we put forward a deep learning approach based on the Swin Transformer UNet (SUNet) model to remove high-density GWS-Gaussian mixture noise from fast neutron images. The improved denoising model utilizes a customized loss function for training, which combines perceptual loss and mean squared error loss to avoid grid-like artifacts caused by using a single perceptual loss. To address the high cost of acquiring real fast neutron images, this study introduces Monte Carlo method to simulate noise data with GWS characteristics by computing the interaction between gamma rays and sensors based on the principle of GWS generation. Ultimately, the experimental scenarios involving simulated neutron noise images and real fast neutron images demonstrate that the proposed method not only improves the quality and signal-to-noise ratio of fast neutron images but also preserves the details of the original images during denoising.

• The Journal of the Convergence on Culture Technology
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• v.10 no.2
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• pp.257-266
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• 2024

This study analyzed the characteristics of each type of subjective perception of cabin crew by applying Q-methodology to understand the competencies required of airline cabin crew. As a result of analyzing 33 Q-samples and 33 P-samples using the Ken-Q Analysis program, four types were identified: "Physical strength and appearance quality-oriented", "job performance-oriented", "communication ability-oriented", and "job consciousness-oriented". Most types showed high agreement on physical factors, ability to cope with emergency situations and work responsibility. The results can be used as basic data to develop effective curriculum for airline training course and airline service majors, and it can be a reference material to help job seekers understand the job and cultivate necessary competencies.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.2
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• pp.119-125
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• 2024

This paper investigates the effect of an artificial intelligence attention redirection compensation strategy system on the data labeling work attention concentration by individuals with developmental disabilities. Task accuracy and task performance for each session were used as measures of attention concentration. As a result of the study, after the intervention was applied, a significant improvement in attention concentration was observed in all study subjects compared to self-serving task. These results mean that artificial intelligence technology can have a positive effect on improving the attention span of people with developmental disabilities during data labeling tasks. This study shows that the application of artificial intelligence technology can improve the quality of learning data by improving the accuracy of data labeling tasks for people with developmental disabilities, and is expected to provide important implications for vocational training programs related to data labeling for people with developmental disabilities.

• Journal of Hospice and Palliative Care
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• v.27 no.1
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• pp.1-10
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• 2024

This article underscores the importance of integrating comprehensive palliative care for noncancer patients who are undergoing hemodialysis, with an emphasis on the aging populations in Asian nations such as Taiwan, Japan, the Republic of Korea, and China. As the global demographic landscape shifts towards an aging society and healthcare continues to advance, a marked increase has been observed in patients undergoing hemodialysis who require palliative care. This necessitates an immediate paradigm shift to incorporate this care, addressing the intricate physical, psychosocial, and spiritual challenges faced by these individuals and their families. Numerous challenges impede the provision of effective palliative care, including difficulties in prognosis, delayed referrals, cultural misconceptions, lack of clinician confidence, and insufficient collaboration among healthcare professionals. The article proposes potential solutions, such as targeted training for clinicians, the use of telemedicine to facilitate shared decision-making, and the introduction of time-limited trials for dialysis to overcome these obstacles. The integration of palliative care into routine renal treatment and the promotion of transparent communication among healthcare professionals represent key strategies to enhance the quality of life and end-of-life care for people on hemodialysis. By embracing innovative strategies and fostering collaboration, healthcare providers can deliver more patient-centered, holistic care that meets the complex needs of seriously ill patients within an aging population undergoing hemodialysis.

• Korean Journal of Clinical Laboratory Science
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• v.56 no.1
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• pp.89-98
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• 2024

Gross examination techniques (GETs) of specimens collected from cancer surgery or endoscopy comprise the act of recording visual information about cancer for accurate histopathological diagnosis and collecting sections of the lesion to create microscopic specimens. GETs must include concise and accurate expressions, appropriate structuring, sufficient resections, error-free standardization of important information, and photo-diagramming of complex specimens. To increase the satisfaction of pathological interpretation, it is a task that must be performed accurately and carefully to gain confidence on a theoretical and practical basis with a sufficient understanding of gross examination. Based on the experience of clinical pathologists in the field of GETs, additional specimen types should be identified as viable candidates. Also, their needs and concerns regarding treatment should be carefully considered. In addition, departments at each institution should review the national focus on clinical partnerships, continuous professional training, diagnostic errors, and value-based healthcare provision.

• Journal of Preventive Medicine and Public Health
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• v.57 no.3
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• pp.212-222
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• 2024

Objectives: Epilepsy is a chronic disease that requires long-term treatment and intervention from health workers. Medication adherence is a factor that influences the success of therapy for patients with epilepsy. Therefore, this study aimed to analyze the role of pharmacists in improving the clinical outcomes of epilepsy patients, focusing on medication adherence. Methods: A scoping literature search was conducted through the ScienceDirect, PubMed, and Google Scholar databases. The literature search included all original articles published in English until August 2023 for which the full text was available. This scoping review was carried out by a team consisting of pharmacists and neurologists following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) Extension for Scoping Reviews and the Joanna Briggs Institute guidelines, including 5 steps: identifying research questions, finding relevant articles, selecting articles, presenting data, and compiling the results. Results: The literature search yielded 10 studies that discussed pharmacist interventions for patients with epilepsy. Five articles described educational interventions involving drug-related counseling with pharmacists. Two articles focused on similar pharmacist interventions through patient education, both verbal and written. Three articles discussed an epilepsy review service, a multidisciplinary intervention program involving pharmacists and other health workers, and a mixed intervention combining education and training with therapy-based behavioral interventions. Conclusions: Pharmacist interventions have been shown to be effective in improving medication adherence in patients with epilepsy. Furthermore, these interventions play a crucial role in improving other therapeutic outcomes, including patients' knowledge of self-management, perceptions of illness, the efficacy of antiepileptic drugs in controlling seizures, and overall quality of life.

• Journal of Information Processing Systems
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• v.20 no.3
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• pp.375-390
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• 2024

Edge computing architecture has effectively alleviated the computing pressure on cloud platforms, reduced network bandwidth consumption, and improved the quality of service for user experience; however, it has also introduced new security issues. Existing anomaly detection methods in big data scenarios with cloud-edge computing collaboration face several challenges, such as sample imbalance, difficulty in dealing with complex network traffic attacks, and difficulty in effectively training large-scale data or overly complex deep-learning network models. A lightweight deep-learning model was proposed to address these challenges. First, normalization on the user side was used to preprocess the traffic data. On the edge side, a trained Wasserstein generative adversarial network (WGAN) was used to supplement the data samples, which effectively alleviates the imbalance issue of a few types of samples while occupying a small amount of edge-computing resources. Finally, a trained lightweight deep learning network model is deployed on the edge side, and the preprocessed and expanded local data are used to fine-tune the trained model. This ensures that the data of each edge node are more consistent with the local characteristics, effectively improving the system's detection ability. In the designed lightweight deep learning network model, two sets of convolutional pooling layers of convolutional neural networks (CNN) were used to extract spatial features. The bidirectional long short-term memory network (BiLSTM) was used to collect time sequence features, and the weight of traffic features was adjusted through the attention mechanism, improving the model's ability to identify abnormal traffic features. The proposed model was experimentally demonstrated using the NSL-KDD, UNSW-NB15, and CIC-ISD2018 datasets. The accuracies of the proposed model on the three datasets were as high as 0.974, 0.925, and 0.953, respectively, showing superior accuracy to other comparative models. The proposed lightweight deep learning network model has good application prospects for anomaly traffic detection in cloud-edge collaborative computing architectures.