• Safety and Health at Work
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• v.2 no.1
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• pp.1-8
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• 2011

The knowledge on the etiology of breast cancer has advanced substantially in recent years, and several etiological factors are now firmly established. However, very few new discoveries have been made in relation to occupational risk factors. The International Agency for Research on Cancer has evaluated over 900 different exposures or agents to-date to determine whether they are carcinogenic to humans. These evaluations are published as a series of Monographs (www.iarc.fr). For breast cancer the following substances have been classified as "carcinogenic to humans" (Group 1): alcoholic beverages, exposure to diethylstilbestrol, estrogen-progestogen contraceptives, estrogen-progestogen hormone replacement therapy and exposure to X-radiation and gamma-radiation (in special populations such as atomic bomb survivors, medical patients, and in-utero exposure). Ethylene oxide is also classified as a Group 1 carcinogen, although the evidence for carcinogenicity in epidemiologic studies, and specifically for the human breast, is limited. The classification "probably carcinogenic to humans" (Group 2A) includes estrogen hormone replacement therapy, tobacco smoking, and shift work involving circadian disruption, including work as a flight attendant. If the association between shift work and breast cancer, the most common female cancer, is confirmed, shift work could become the leading cause of occupational cancer in women.

• Korean Journal of Head & Neck Oncology
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• v.40 no.1
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• pp.1-5
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• 2024

The Bethesda System for Reporting Thyroid Cytopathology (TBSRCT) is crucial for cytopathologists to use a standardized, category-based reporting system for thyroid fine needle aspirations and is effective for clear communication with the referring physicians. The new Bethesda System for Reporting Thyroid Cytopathology, the third edition in 2023, provides several key updates. The most important update is the assignment of only single name for each of the six diagnostic categories: (I) nondiagnostic; (II) benign; (III) atypia of undetermined significance; (IV) follicular neoplasm; (V) suspicious for malignancy; and (VI) malignant. An implied risk of malignancy (ROM) for each of six categories has been updated based on extensively published data since the second edition of TBSRTC in 2017 and offers both an average ROM for each category and the expected range of cancer risk. Estimated final ROM after excluding "Noninvasive Follicular Thyroid Neoplasm with Papillary Like Nuclear Features (NIFTP)" for each of six categories has been updated based on the reported mean decreases in the ROM if excluding NIFTP. For atypia of undetermined significance (AUS) category, the subcategorization is simplified and more formalized into 2 subgroups, AUS-nuclear atypia or AUS-other, based on the implied ROM and molecular profiling. For the pediatric thyroid disease, pediatric ROMs and management algorithms are newly added for the same six reporting categories for this age group. New or revised disease nomenclatures including high-grade follicular-derived carcinoma has been updated according to the recently published 2022 World Health Organization Classification of Thyroid Neoplasms. Brand new two chapters are added including clinical perspectives and imaging studies (Chap. 13) and the use of molecular and other ancillary tests (Chap. 14). The atlas is updated with new images to illustrate more effectively for new disease entity and diagnostic criteria.

• Korean Journal of Child Studies
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• v.27 no.2
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• pp.195-213
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• 2006

Development of the 3 scales comprising the present research was based on review of literature, existing screening scales, and empirical research pertaining to (Scale I) the development of social and emotional problems of children, (Scale II) parent-child relations, and (Scale III) assessment of children's environment. Professionals in each area approved a draft of the new screening scale. The clinical group was classified into normal and at-risk groups based on the Denver II scale and the Child Behavior Check List(ages 1.5-5). The clinical groups were administered the newly developed screening scale to see whether the same classification pertained. Results proved the cross-validity of the new scale.

• Osteoporosis and Sarcopenia
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• v.3 no.3
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• pp.117-122
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• 2017

Sarcopenia is the degenerative loss of muscle mass and function with aging. Recently sarcopenia was recognized as a clinical disease by the International Classification of Disease, 10th revision, Clinical Modification. An imbalance between protein synthesis and degradation causes a gradual loss of muscle mass, resulting in a decline of muscle function as a progress of sarcopenia. Many mechanisms involved in the onset of sarcopenia include age-related factors as well as activity-, disease-, and nutrition-related factors. The stage of sarcopenia reflecting the severity of conditions assists clinical management of sarcopenia. It is important that systemic descriptions of the disease conditions include age, sex, and other environmental risk factors as well as levels of physical function. To develop a new therapeutic intervention needed is the detailed understanding of molecular and cellular mechanisms by which apoptosis, autophagy, atrophy, and hypertrophy occur in the muscle stem cells, myotubes, and/or neuromuscular junction. The new strategy to managing sarcopenia will be signal-modulating small molecules, natural compounds, repurposing of old drugs, and muscle-specific microRNAs.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.148-162
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• 2023

Classification systems can significantly assist the medical sector by allowing for the precise and quick diagnosis of diseases. As a result, both doctors and patients will save time. A possible way for identifying risk variables is to use machine learning algorithms. Non-surgical technologies, such as machine learning, are trustworthy and effective in categorizing healthy and heart-disease patients, and they save time and effort. The goal of this study is to create a medical intelligent decision support system based on machine learning for the diagnosis of heart disease. We have used a mixed feature creation (MFC) technique to generate new features from the UCI Cleveland Cardiology dataset. We select the most suitable features by using Least Absolute Shrinkage and Selection Operator (LASSO), Recursive Feature Elimination with Random Forest feature selection (RFE-RF) and the best features of both LASSO RFE-RF (BLR) techniques. Cross-validated and grid-search methods are used to optimize the parameters of the estimator used in applying these algorithms. and classifier performance assessment metrics including classification accuracy, specificity, sensitivity, precision, and F1-Score, of each classification model, along with execution time and RMSE the results are presented independently for comparison. Our proposed work finds the best potential outcome across all available prediction models and improves the system's performance, allowing physicians to diagnose heart patients more accurately.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.528-535
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• 2022

Although the domestic aviation industry has made rapid progress with the development of aircraft manufacturing and transportation technologies, aviation safety accidents continue to occur. The supervisory agency classifies hazards and risks based on risk-based aviation safety data, identifies safety trends for each air transportation operator, and conducts pre-inspections to prevent event and accidents. However, the human classification of data described in natural language format results in different results depending on knowledge, experience, and propensity, and it takes a considerable amount of time to understand and classify the meaning of the content. Therefore, in this journal, the fine-tuned KoBERT model was machine-learned over 5,000 data to predict the classification value of new data, showing 79.2% accuracy. In addition, some of the same result prediction and failed data for similar events were errors caused by human.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.82-95
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• 2024

Analyzing the aftermath of events at domestic nuclear power plants brings in the question: "Why do workers not comply with the prescribed procedures?" The current investigation of nuclear power plant events identifies their reasons considering the factors affecting the workers' behaviors. However, there are some complications to it: in addition to confirming the action such as an error or a violation, there is a limit to identifying the intention of the actor. To overcome this limitation, the study analyzed and examined the reasons for non-compliance identified in nuclear power plant events by Reason's rule-related behavior classification. For behavior analysis, I selected unit behaviors for events that are related to human and organizational factors and occurred at domestic nuclear power plants since 2017, and then I applied the rule-related behavior classification introduced by Reason (2008). This allowed me to identify the intentions by classifying unit behaviors according to quality and compliance with the rules. I also identified the factors that influenced unit behaviors. The analysis showed that most often, non-compliance only pursued personal goals and was based on inadequate risk appraisal. On the other hand, the analysis identified cases where it was caused by such factors as poorly written procedures or human system interfaces. Therefore, the probability of non-compliance can be reduced if these factors are properly addressed. Unlike event investigation techniques that struggle to identify the reasons for employee behavior, this study provides a new interpretation of non-compliance in nuclear power plant events by examining workers' intentions based on the concept of rule-related behavior classification.

• Journal of the Korean Society of Safety
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• v.19 no.4 s.68
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• pp.123-128
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• 2004

This study investigated effect of arm posture, repetition of wrist motion and external load on perceived discomfort. The arm postures were controlled by shoulder flexion, elbow flexion, and ist motions such as flexion, extension, radial deviation and ulnar deviation. An experiment was conducted to measure discomfort scores for experimental treatments using the magnitude estimation, in which the L16 orthogonal array was adopted for reducing the size of experiment. The results showed that while the effect of the shoulder flexion, repetition of wrist motion and external load was statistically significant at $\alpha=0.05$or 0.10, that of the elbow and wrist motions was not. Discomfor ratings increased linearly as levels of wrist repetition and external load increased. This implies that the existing posture classification schemes such as OWAS, RULA, which do not properly consider effect of motion repetition and external load, may underestimate postural load. Based on the regression equation for wrist repetition and external load, isocomfort region indicating the region within which discomfort scores were expected to be the same was proposed. It is recommended that when assessing risk of postures or developing new posture classification schemes, motion repetition and external load as well as posture itself be fully taken into consideration for precisely evaluating postural stress.

• Journal of Integrative Natural Science
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• v.4 no.3
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• pp.238-242
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• 2011

For any pattern matching based algorithm in WLAN environment, the characteristics of signal to noise ratio(SNR) to multiple access points(APs) are utilized to establish database in the training phase, and in the estimation phase, the actual two dimensional coordinates of mobile unit(MU) are estimated based on the comparison between the new recorded SNR and fingerprints stored in database. The system that uses the artificial neural network(ANN) falls in a local minima when it learns many nonlinear data, and its classification accuracy ratio becomes low. To make up for this risk, the SVM/ANN hybrid algorithm is proposed in this paper. The proposed algorithm is the method that ANN learns selectively after clustering the SNR data by SVM, then more improved performance estimation can be obtained than using ANN only and The proposed algorithm can make the higher classification accuracy by decreasing the nonlinearity of the massive data during the training procedure. Experimental results indicate that the proposed SVM/ANN hybrid algorithm generally outperforms ANN algorithm.

• Safety and Health at Work
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• v.12 no.3
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• pp.416-420
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• 2021

Hazardous area classification design is required to reduce the explosion risk in process plants. Among the international design guidelines, only IEC 60079-10-1 proposes a new type of zone, namely zone 2 NE, to prevent explosion hazards. We studied how to meet the zone 2 NE grade for a facility handling hydrogen gas, which is considered as most dangerous among explosive gases. Zone 2 NE can be achieved considering the grade of release, as well as the availability and effectiveness of ventilation, which are factors indicative of the facility condition and its surroundings. In the present study, we demonstrate that zone 2 NE can be achieved when the degree of ventilation is high by accessing temperature, pressure, and size of leak hole. The release characteristic can be derived by substituting the process condition of the hydrogen gas facility. The equations are summarized considering relation of the operating temperature, operating pressure, and size of leak hole. Through this relationship, the non-hazardous condition can be realized from the perspective of inherent safety by the combination of each parameter before the initial design of the hydrogen gas facility.