Earticle

독일, 프랑스, 네덜란드는 초기에 추진했던 다문화정책을 수정하여 새로운 상황변화에 적극적으로 대응하고 있다. 외형적으로 독일은 차별배제 모델에서 동화주의로, 프랑스는 동화주의에서 통합적 정책으로, 그리고 네덜란드는 다문화주의 모델에서 동화주의 모델로 변화하였다고 평가되지만, 독일과 네덜란드의 동화주의 모델은 엄밀하게 분석해보면 전통적인 동화주의 모델과는 차이가 있다고 할 수 있다. 다문화주의 모델은 이주자의 독자적인 문화적 정체성을 인정하고 그들이 유입국에서 동등하게 생활할 수 있도록 도와주는 정책으로 평가된다. EU 3국의 사회통합 과정에서 얻을 수 있는 교훈은, 다문화사회의 사회통합이 국가의 정책적 의지만으로 또는 이주민의 노력만으로 성공할 수 없으며, 내국인과 이주민 모두 서로 존중하고 배려하는 모습에서 진정한 사회통합을 실현시킬 수 있다는 것이다. 상호 이해 없이 유입국의 경제적, 사회적 필요에 의해서만 이주민을 받아들인다면 이들의 사회적 차별과 배제로 생기는 갈등과 분열은 피할 수 없으며, 안정적인 통합을 기대할 수 없을 것이다. 오히려 이주민들은 정착이 더욱 힘들어지고, 이주민 2, 3세들은 언어소통의 어려움, 인종, 종교, 전통적 관습 등 문화적 이질감으로 사회적응이 더욱 힘들어져서, 결국에는 주류사회에 대한 반감이 커져서 범죄와 소요사태로 나가게 되는 악순환으로 이어지게 된다. 한때 이방인에게 관용을 베푸는 미덕으로 유명한 프랑스와 독일의 다문화사회가 현재 심각한 갈등과 혼란으로 심한 진통을 겪고 있는 모습에서 우리는 교훈을 얻을 수 있다. 국내 거주 외국인이 지속적으로 증가하고 있는 상황에서 글로벌 시대의 경쟁력 증진과 도약의 기회로 삼기 위해서는 일관성 있고 종합적인 정책추진과 이를 뒷받침하는 법제도를 정비해야 할 것이다. 따라서 종전처럼 동화주의적 다문화정책에 그치지 않고, 개방적인 차원에서의 사회통합적 다문화정책이 절실히 요청된다. 다문화사회에서의 진정한 사회통합은 일방적으로 자국 문화에 적응하고 동화하도록 강요하거나 정책적으로 지원하는 것만으로는 부족하고, 장기적인 차원에서 각각의 문화와 역사의 차이에 대한 이해를 촉진시키고 지역사회의 구성원으로 함께 살아가기 위한 상호이해와 교류가 선행되어야 한다.

Germany, France and the Netherlands modified their former multicultural policy and manage changing situation actively. It is regarded that Germany changed from eliminating discrimination model to assimilation, France changed from assimilation to integration policy, and the Netherlands changed from multiculturalism to assimilation. Actually assimilation models of Germany and the Netherlands are different from traditional assimilation model. The key point of multicultural model is to respect cultural identity of immigrants and to keep their own culture and life. The message from integration process of above three EU states is that social integration in multicultural society could not success only of state policy or effort of immigrants, the real social integration accomplishes when the nationals and immigrants respect each other. Permitting immigration by only economic and social needs of state without understanding each other would cause conflict derived from social discrimination and exclusion therefore stable integration could not be expected. Immigrants are getting harder to settle down at the state, the second or third generations could not adapt society more and more because of the communication, racism, religion, and traditional customs, etc. Finally it leads to crimes and demonstration caused by hostility to the mainstream. We can learn from the situation of France and Germany suffering from severe conflict and disruption, even the former is well-known for virtue of tolerance and the latter is the famous multicultural society. Resident foreigners in Korea are continually increasing. In this situation, consistent and integrated policy and legal system is needed for multicultural society to make a chance to enhance competence in world society. Beyond the assimilation policy, open minded integration policy is required to convert recognition regarding Korean society and to change attitude to make good relationship between immigrations and nationals. To make the real social integration, it is firstly needed to understand cultural and historic difference in the long term and to promote interaction to live together as community members rather than forcing cultural assimilation or simply offering subsidy.

Planthopper is a major problem pest of agricultural crops and rice, feeding mainly on the leaves and stems of crops and causing devastating damage to farmers in South Korea. Planthoppers reduce nutrients in the body of crops and cause crop diseases by destroying tissue or transmitting viruses, so accurate detection and diagnosis is essential to minimize the damage. With the development of artificial intelligence in recent years, deep learning has been widely used to diagnose the pests. Most of the pest diagnosis research and programs use approaches based on object detection and image classification. However, traditional planthopper detection models may misidentify other pests as planthoppers, which can reduce user confidence in the diagnostic model. To address this misrecognition problem, this study investigates a deep learning-based planthopper Image detection and discrimination model for detecting and classifying the planthopper images. The proposed model combines the Faster RCNN object detection model and the Resnet50 classification model to automatically detect planthoppers among other pests in aerial entomology net images. The performance measurements showed that the benchmark model using the Faster RCNN algorithm achieved a high Recall of 91.23%, but a relatively low Precision of 24.34%. On the other hand, the model proposed in this study has a high recall of 96.22% and a high precision of 96.73%, proving that it can detect planthoppers well.

개인의 정신적 피로는 인지능력 및 업무 수행능력을 감소시킬 뿐만 아니라 일상에서 발생하는 크고 작은 사고의 주요 요인이 된다. 본 논문에서는 EEG 기반의 정신적 피로 판별을 위한 CNN 모델을 제안하였다. 이를 위해 안정 상태 와 작업 상태에서의 뇌파를 수집하여 제안한 CNN 모델에 적용한 후 모델 성능을 분석하였다. 실험에 참여한 피험자들 은 모두 대학교에 재학 중인 오른손잡이 남학생들이며 평균 나이는 25.5세이다. 각 상태에서의 측정된 뇌파에 대해 스 펙트럼분석을 수행하였으며, CNN 모델의 입력데이터로써 원시 EEG 신호, 절대파워, 상대파워를 사용하여 CNN모델의 성능을 비교 분석하였다. 그 결과, 알파대역 후두엽 위치의 상대파워가 가장 좋은 성능을 나타내었다. 모델정확도는 훈 련데이터 85.6%, 검증데이터 78.5%, 시험데이터 95.7%이다. 제안한 모델은 정신적 피로 판별을 위한 자동화시스템 개발에 적용될 수 있다.

Individual mental fatigue not only reduces cognitive ability and work performance, but also becomes a major factor in large and small accidents occurring in daily life. In this paper, a CNN model for EEG-based mental fatigue discrimination was proposed. To this end, EEG in the resting state and task state were collected and applied to the proposed CNN model, and then the model performance was analyzed. All subjects who participated in the experiment were right-handed male students attending university, with and average age of 25.5 years. Spectral analysis was performed on the measured EEG in each state, and the performance of the CNN model was compared and analyzed using the raw EEG, absolute power, and relative power as input data of the CNN model. As a result, the relative power of the occipital lobe position in the alpha band showed the best performance. The model accuracy is 85.6% for training data, 78.5% for validation, and 95.7% for test data. The proposed model can be applied to the development of an automated system for mental fatigue detection.

본 연구는 북한이탈주민 여성의 우울에 기여하는 심리사회적 요인들의 통합적 영향을 규명할 목적으로 차별지각이 문화적응 스트레스를 매개로 하여 우울을 예측하는 경로에서 문화적응의 조절효과를 검증하였다. 서울 소재 국립정신건강센터 및 하나센터에서 모집한 북한이탈주민 여성 87명이 차별지각, 문화적응(남한문화지향, 북한문화지향), 문화적응 스트레스, 우울을 측정하는 자기보고식 질문지에 응답하였다. 인구통계학적 변인인 연령, 남한거주기간, 표집 기관 등은 공변인으로 분석에 포함하였다. PROCESS macro로 매개된 조절효과 모형을 분석한 결과, 차별지각이 문화적응 스트레스를 매개로 하여 우울을 예측하는 경로가 유의하게 나타났다. 그러나 이러한 매개효과가 문화적응에 따라 달라지는 양상은 확인되지 않았다. 이에 더해 문화적응 스트레스를 매개로 하지 않고 심리사회적 요인들이 우울을 예측하는 직접경로가 유의하였다. 구체적으로 차별지각과 남한문화지향의 주효과, 그리고 차별지각×북한문화지향×남한문화지향의 상호작용효과가 나타났다. 연구 결과를 바탕으로 본 연구의 이론적, 임상적, 정책적 시사점 및 후속연구에 대한 제언을 논의하였다.

The present study examined conjoint effects of perceived discrimination, acculturation, and acculturative stress to North Korean refugees’ levels of depression. Specifically, we tested a mediated moderation model in which interactive effects of perceived discrimination and acculturation were hypothesized to predict depression via their effects on acculturative stress. A total of 87 female North Korean refugees completed a set of questionnaires assessing perceived discrimination, acculturation, acculturative stress and depression. In addition, demographic factors such as age and duration of residence in the South and recruitment site were included in the analysis as covariates. Results indicated that acculturative stress partially mediated the effect of perceived discrimination on depression although this indirect effect was not moderated by acculturation. Moreover, direct effects of perceived discrimination, South Korean cultural orientation, and interactive effects of perceived discrimination x North Korean cultural orientation x South Korean cultural orientation on depression were significant. Theoretical and clinical implications and limitations of this study are discussed.

본 연구는 사회관계측정이론의 관점에서 장애인의 자아존중감에 미치는 사회적 요인에 주목하였다. 그 가운데 사회참여와 차별경험에 초점을 두고, 남성과 여성의 성별 차이를 반영하여 살펴보았다. 이를 위해 장애인고용패널조사 2차 웨이브 1차(2016년) 자료부터 8차(2023년) 자료를 활용하였으며, 연구대상은 남성 장애인 2,194명, 여성 장애인 1,202명으로 총 3,396명이다. 연구결과 남성과 여성 장애인의 사회참여와 차별경험의 수준 및 장기적인 변화 차이를 토대로 성별에 따라 4개의 잠재계층을 도출하였다. 그리고 남성과 여성 장애인의 사회참여 및 차별경험 잠재계층에 따른 자아존중감의 수준 차이를 규명하였다. 남성 장애인의 경우 “소 사회참여 감소 & 대 차별경험 감소” 잠재계층의 자아존중감이 가장 낮았고, 여성 장애인의 경우 “소 사회참여 유지 & 대 차별경험 감소” 잠재계층의 자아존중감이 가장 낮았다. 이를 통해 본 연구는 남성 장애인의 사회활동 참여 증진, 여성 장애인의 차별경험 감소를 중심으로 남성과 여성 장애인의 자아존중감을 높이기 위한 사회적 차원의 노력에 관하여 사회복지적 정책 및 실천 방안을 제안하였다.

The purpose of this study is to analyze the social factors affecting the self-esteem of people with disabilities from the perspective of Sociometer theory, focusing on participation in social participation and experiences of discrimination, considering gender differences. To this end, we used data from the second wave of the Panel Survey of Employment for the Disabled (PSED), specifically from the 1st (2016) to the 8th (2023) survey. Also, we examined 3,396 participants, including 2,194 men and 1,202 women with disabilities. This study identified 4 latent classes based on each man and woman in terms of the level and long-term changes in social participation and experiences of discrimination affecting the level of self-esteem. In the case of male disabled people, the self-esteem of the latent class of “decreased social participation & decreased experiences of discrimination” was the lowest, and in the case of female disabled people, the self-esteem of the latent class of “maintained social participation & decreased experiences of discrimination” was the lowest. Based on these findings, this study suggests social policy and practice recommendations for social-level efforts to increase the self-esteem of men and women with disabilities, focusing on improving the participation of men with disabilities in social activities and reducing the experience of discrimination among women with disabilities.

The noisy short utterance is polluted by noise and corpus is less, so the recognition rate significantly decreased. For improving recognition rate, we proposed the dual information quality discrimination algorithm to classify the speech frames: one is differences detection and discrimination algorithm (DDADA), another is the improved SNR discrimination algorithm (ISNRDA). Based on the above two algorithms, the speech frames are classified to three classes: high quality, medium quality and low quality. We proposed GMM-UBM three-stage classification model, and we combine the dual information quality discrimination algorithm with GMM-UBM three-stage classification model. Experiments show that, the dual discrimination quality algorithms can be more precise to classify speech frame, and combining it with GMM-UBM three-stage classification model can make full use of limited corpus of short utterance and can improve the speaker recognition rate of the noisy short utterance.

In the paper, first looked at the schematisation of ‘朝-往-向’ and ‘从-到’, which can be considered as ‘The move diagram’. Again, the similarities between '朝' and '从', '向' and ' 到' are summarized centering on the differences. Although it is doubtful whether we can simply compare '朝' and '从', '向' and '到' on a uniform basis, it is still generally similar in terms of the contrast between 'origin' and 'end'. First of all, if you look at the difference, The 'direction' towards the end of '朝' only suggests 'abstract mobility' such as the movement of the line of sight, but the object of '朝' is not directly related to the movement of the object. In contrast, '从' implies a strong mobility and arriving at the end of the journey at the same time as the start.

본 연구의 목적은 갈수록 증가되고 있는 노인 범죄피해에 대한 실효성 있는 예방 및 대응을 위한 학문적 기반을 마련하는 것이다. 이를 위해 「2020년도 노인실태조사」의 자료를 활용하여, 노인의 차별경험과 범죄피해의 관계에 영향을 미칠 수 있는 정신건강적 요소(우울)와 경제적 요소(소득수 준)의 구조적 관계를 검증하였다. 분석결과를 요약하면 다음과 같다. 첫째, 노인들의 차별경험은 범 죄피해 경험에 통계적으로 유의한 정(+)적 영향을 미치는 것으로 나타났다. 둘째, 노인들의 차별경험 과 범죄피해 경험의 관계에서 우울은 통계적으로 유의한 조절효과를 가졌다. 셋째, 소득수준은 노인 들의 차별경험과 범죄피해 경험의 관계에서 우울의 조절효과를 통계적으로 유의하게 조절하였다. 우 울수준이 높아질수록 소득은 차별경험이 범죄피해 경험에 미치는 영향력을 증가시키는 촉진요인으 로, 우울수준이 낮아질수록 보호요인으로 작용됨을 알 수 있었으며, 이는 소득수준이 높아질수록 더 극명하게 나타났다. 넷째, 소득수준과 관계없이 차별이 낮은 집단에서는 범죄피해 경험이 낮은 것으 로 확인되었다. 이러한 결과를 바탕으로 노인에 대한 차별적 인식을 개선하기 위한 1) 청년 공생일 자리 제공과 멘토제 도입, 2) 노인의 정신건강 상태(우울)와 소득수준을 함께 고려한 정책의 우선순 위 결정 및 개인의 맞춤형 정책지원을 제공의 필요성을 제안하고자 한다.

This study aims to establish a scholarly foundation for effective prevention and response to the increasing elderly crime victimization. To achieve this, study utilized data from the "2020 Elderly Living Conditions Survey" to examine the structural relationship between elderly discrimination experiences and crime victimization, considering factors related to mental health (depression) and economic status (monthly income). Summarizing the analysis results, this study find the following: First, elderly discrimination experiences statistically (+) significantly influence crime victimization. Second, in the relationship between elderly discrimination experiences and crime victimization, depression has a statistically significant moderating effect. Third, income level significantly moderates the effect of depression on the relationship between discrimination experiences and crime victimization. Higher levels of depression increase the impact of income on discrimination experiences as a promoting factor, while lower depression levels act as a protective factor. This effect is more pronounced with higher monthly income. Fourth, irrespective of monthly income, groups with lower discrimination experiences have lower crime victimization rates. Based on these findings, this study suggest the following policy recommendations to improve the perception of discrimination against the elderly: 1) Provide youth employment opportunities and introduce mentoring programs to promote intergenerational solidarity. 2) Prioritize policies that consider the mental health status (depression) and monthly income of the elderly, and offer personalized policy support. These measures are aimed at addressing the increasing issue of elderly crime victimization and fostering a more inclusive society.

Loose parts monitoring and detecting alarm type in real Nuclear Power Plant have challenges such as background noise, insufficient alarm data, and difficulty of distinction between alarm data that occur during start and stop. Although many signal processing methods and alarm determination algorithms have been developed, it is not easy to determine valid alarm and extract the meaning data from alarm signal including background noise. To address these issues, this paper proposes a denoising autoencoder-based majority vote classification. Training and test data are prepared by acquiring alarm data from real NPP and simulation facility for data augmentation, and noisy data is reproduced by adding Gaussian noise. Using DAEs with 3, 5, 7, and 9 layers, features are extracted for each model and classified into neural networks. Finally, the results obtained from each DAE are classified by majority voting. Also, through comparison with other methods, the accuracy and the false alarm rate are compared, and the excellence of the proposed method is confirmed.

정부는 공공데이터 품질관리 수준평가를 진행하여 공공 개방데이터의 품질관리를 진행하고 있다. 공공 개방데이터는 XML, JSON, CSV 등 여러 오픈포맷 형태로 제공되며 CSV 형식이 대다수를 차지한다. 이러한 CSV 형식의 공공 개방데이터 품질진단 시 품질진단 담당자가 공공 개방데이터 파일의 필드명과 필드 내 데이터에 의존하여 필드 별 도메인을 판단하여 진단한다. 그러나 대량의 개방 데이터 파일을 대상으로 품질진단을 수행하기 때문에 많은 시간이 소요된다. 또한 의미 파악이 어려운 필드의 경우 품질진단의 정확성이 품질진단 담당자의 데이터 이해도 역량의 영향을 받는다. 본 논문은 필드명과 데이터 분포 통계를 이용한 CSV 형식 공공 개방데이터의 도메인 판별 모델을 제안하여 품질진단 결과가 품질진단 담당자의 역량에 좌지우지 되지 않도록 일관성과 정확성을 보장하고 진단 소요 시간 단축을 지원한다. 본 논문의 모델 적용 결과 행정안전부에서 제공하는 파일형식 개방데이터 진단도구보다 2.8% 높은 약 77%의 정답률을 보였다. 이를 통해 공공데이터 품질관리 수준진단·평가에 제안 모델 적용 시 정확성을 향상시킬 수 있을 것으로 기대한다.

The government of the Republic of Korea is conducting quality management of public open data by conducting a public data quality management level evaluation. Public open data is provided in various open formats such as XML, JSON, and CSV, with CSV format accounting for the majority. When diagnosing the quality of public open data in CSV format, the quality diagnosis manager determines and diagnoses the domain for each field based on the field name and data within the field of the public open data file. However, it takes a lot of time because quality diagnosis is performed on large amounts of open data files. Additionally, in the case of fields whose meaning is difficult to understand, the accuracy of quality diagnosis is affected by the quality diagnosis person's ability to understand the data. This paper proposes a domain discrimination model for public open data in CSV format using field names and data distribution statistics to ensure consistency and accuracy so that quality diagnosis results are not influenced by the capabilities of the quality diagnosis person in charge, and to support shortening of diagnosis time. As a result of applying the model in this paper, the correct answer rate was about 77%, which is 2.8% higher than the file format open data diagnostic tool provided by the Ministry of Public Administration and Security. Through this, we expect to be able to improve accuracy when applying the proposed model to diagnosing and evaluating the quality management level of public data.

Designs for discriminating between two linear regression models are studied under $\Lambda$-type optimalities maximizing the measure for the lack of fit for the designs with fixed model inadequacy. The problem of selecting an appropriate $\Lambda$-type optimalities is shown to be closely related to the estimation method. $\Lambda$-type optimalities for the least squares and minimum bias estimation methods are considered. The minimum bias designs are suggested for the designs invariant with respect to the two estimation methods. First order minimum bias designs optimal under $\Lambda$-type optimalities are then derived. Finally for the case where the lack of fit test is significant, an approach to the construction of a second order design accommodating the optimal first order minimum bias design is illustrated.

인터넷 불법금융광고는 인터넷 카페, 블로그 등을 통해 통장매매, 신용카드·휴대폰결제현금화 및 개인신용정보매매 등 불법금융행위를 목적으로 한다. 금융감독당국의 노력에도 불구하고 불법금융행위는 줄어들지 않고 있다. 본 연구는 인터넷 불법금융광고 게시글에 파이썬 딥러닝 기반 텍스트 분류기법을 적용해 불법여부를 탐지하는 모델을 제안한다. 텍스트 분류기법으로 주로 사용되는 합성곱 신경망(CNN: Convolutional Neural Network), 순환 신경망(RNN: Recurrent Neural Network), 장단기 메모리(LSTM: Long-Short Term Memory) 및 게이트 순환 유닛(GRU: Gated Recurrent Unit)을 활용한다. 그동안 수작업으로 심사한 불법확인 결과를 기초 데이터로 이용한다. 한국어 자연어처리와 딥러닝 모델의 하이퍼파라미터 조절을 통해 최적의 성능을 보이는 모델을 완성하였다. 본 연구는 그동안 이뤄지지 않았던 인터넷 불법금융광고 판별을 위한 딥러닝 모델을 제시하였다는데 큰 의미가 있다. 또한 딥러닝 모델에서 91.3~93.4% 수준의 정확도를 보임으로써 불법금융광고 탐지에 딥러닝 모델을 실제 적용하여 불법금융광고 근절에 기여할 수 있기를 기대해 본다.

The study proposes a model that utilizes Python-based deep learning text classification techniques to detect the legality of illegal financial advertising posts on the internet. These posts aim to promote unlawful financial activities, including the trading of bank accounts, credit card fraud, cashing out through mobile payments, and the sale of personal credit information. Despite the efforts of financial regulatory authorities, the prevalence of illegal financial activities persists. By applying this proposed model, the intention is to aid in identifying and detecting illicit content in internet-based illegal financial advertisining, thus contributing to the ongoing efforts to combat such activities. The study utilizes convolutional neural networks(CNN) and recurrent neural networks(RNN, LSTM, GRU), which are commonly used text classification techniques. The raw data for the model is based on manually confirmed regulatory judgments. By adjusting the hyperparameters of the Korean natural language processing and deep learning models, the study has achieved an optimized model with the best performance. This research holds significant meaning as it presents a deep learning model for discerning internet illegal financial advertising, which has not been previously explored. Additionally, with an accuracy range of 91.3% to 93.4% in a deep learning model, there is a hopeful anticipation for the practical application of this model in the task of detecting illicit financial advertisements, ultimately contributing to the eradication of such unlawful financial advertisements.

In this study, a spiking cortical model (SCM) based n-g discrimination method is proposed. The SCM-based algorithm is compared with three other methods, namely: (i) the pulse-coupled neural network (PCNN), (ii) the charge comparison, and (iii) the zero-crossing. The objective evaluation criteria used for the comparison are the FoM-value and the time consumption of discrimination. Experimental results demonstrated that our proposed method outperforms the other methods significantly with the highest FoM-value. Specifically, the proposed method exhibits a 34.81% improvement compared with the PCNN, a 50.29% improvement compared with the charge comparison, and a 110.02% improvement compared with the zero-crossing. Additionally, the proposed method features the second-fastest discrimination time, where it is 75.67% faster than the PCNN, 70.65% faster than the charge comparison and 38.4% slower than the zero-crossing. Our study also discusses the role and change pattern of each parameter of the SCM to guide the selection process. It concludes that the SCM's outstanding ability to recognize the dynamic information in the pulse signal, improved accuracy when compared to the PCNN, and better computational complexity enables the SCM to exhibit excellent n-γ discrimination performance while consuming less time.

The dicentric chromosome assay is a gold standard method to estimate radiation exposure by calculating the ratio of dicentric chromosomes existing in cells. The objective of this study was to propose an automatic dicentric chromosome discrimination method based on deep convolutional neural networks using radiation exposure patient data. From 45 patients with radiation exposure, conventional Giemsa-stained images of 116,258 normal and 2800 dicentric chromosomes were confirmed. ImageNet was used to pre-train VGG19, which was modified and fine-tuned. The proposed modified VGG19 demonstrated dicentric chromosome discrimination performance, with a true positive rate of 0.927, a true negative rate of 0.997, a positive predictive value of 0.882, a negative predictive value of 0.998, and an area under the receiver operating characteristic curve of 0.997.

Objectives:The purpose of the present study is to examine whether emphasizing uncontrollable causal explanations of schizophrenia can reduce public stigma. Specifically, this study connects perceived amount of responsibility, anger, and discrimination toward people with schizophrenia based on attribution theory. We also explored whether there is any significant difference in the hypothesized path model leading to discrimination between Americans and Koreans. Methods:A total of 546 participants from the U.S. (n = 274) and Korea (n = 272) participated in an online experiment. Participants were randomly assigned to one of the two different scenarios describing a person with schizophrenia caused by either biogenetic or contextual cause. Results:The positive and significant associations among perceived amount of responsibility of people with schizophrenia, anger, and discrimination were found. More importantly, a multi-group analysis revealed that there were significant differences between Americans and Koreans in the hypothesized path model: (a) while American participants in the biogenetic condition perceived a smaller amount of responsibility from people with schizophrenia compared to those in the contextual condition, Korean participants did not show a significant difference in their perceived amount of responsibility from people with schizophrenia between the two causes; (b) the association between perceived amount of responsibility from people with schizophrenia and anger toward them was stronger for American participants than Korean participants. Conclusions:We found that emphasizing uncontrollable causal explanations of schizophrenia can have different impacts in reducing public stigma depending on countries. Thus, we suggest that anti-stigma programs in Korea should be designed differently from those in the U.S. beyond emphasizing just one causal explanation of schizophrenia.

본 연구에서는 닥나무 인피섬유와 이를 이용하여 제조한 한지의 FT-NIR및 FT-MIR 스펙트럼 데이터를 각각PLS-DA에 적용하여 닥나무 인피섬유 및 한지의 원산지 판별 모델을 개발하고자 하였다. 본 연구를 위하여 서로 다른 원산지의 국내산 닥나무 인피섬유 10점을 채취하여 한지로 제조하였다. 상기시료의 FT-NIR 및 FT-IR 스펙트럼 데이터는 데이터 전처리 과정을 거쳐 PLS-DA를 수행하였다. 모델링 결과, 닥나무 인피섬유와 한지의 NIR 스펙트럼 데이터가 판별모델의 교차 검증결과 및 성능평가(정확도, 민감도, 특이도)에서 모두 100 %로 MIR 스펙트럼 데이터보다 우수한 판별 성능을 나타냈다. 또한 지역별로 4 개의 그룹을 형성하는 것을 확인 할 수 있었으며, 닥나무 인피섬유와 한지의 원산지 판별 모델 간 score 형태가 유사하게 나타내는 것을 확인하였다.

The objective of this study was the development of a discrimination model for the cultivational origin of paper mulberry bast fiber and Hanji using near infrared (NIR) and mid infrared (MIR) spectroscopy combined with partial least squares discriminant analysis (PLS-DA). Paper mulberry bast fiber was purchased in 10 different regions of Korea, and used to make Hanji. PLS-DA was performed using pre-treated FT-NIR and FT-MIR spectral data for paper mulberry bast fiber and Hanji. PLS-DA of paper mulberry bast fiber and Hanji samples, using FT-NIR spectral data, showed 100 % performance in cross validation and the confusion matrix (accuracy, sensitivity, and specificity). The discrimination models showed four regional groups which demonstrated clearer separation and much superior score plots in the NIR spectral data-based model than in the MIR spectral data-based model. Furthermore, the discrimination model based on the NIR spectral data of paper mulberry bast fiber had highly similar score morphology to that of the discrimination model based on the NIR spectral data of Hanji.

Traditional Korean medicines may be managed more scientifically, through the development of logical criterion to verify their cultivation region. It contributes to advance the industry of traditional herbal medicines. Volatile compounds were obtained from 14 samples of domestic Taeksa and 30 samples of Chinese Taeksa by steam distillation. The metabolites were identified by NIST mass spectral library in the obtained gas chromatography/mass spectrometer (GC/MS) data of 35 training samples. The multivariate statistical analysis, such as Principal Component Analysis (PCA), Partial Least Squares Discriminant Analysis (PLS-DA), and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA), were performed based on the qualitative and quantitative data. Finally trans-(2,3-diphenylcyclopropyl)methyl phenyl sulfoxide (47.265 min), 1,2,3,4-tetrahydro-1-phenyl-naphthalene (47.781 min), spiro[4-oxatricyclo[5.3.0.0.(2,6)]decan-3-one-5,2'-cyclohexane] (54.62 min), 6-[7-nitrobenzofurazan-4-yl]amino-morphinan-4,5-epoxy (54.86 min), p-hydroxynorephedrine (55.14 min) were determined as marker metabolites to verify candidates for the origin of Taeksa. The statistical model was well established to determine the origin of Taeksa. The cultivation areas of test samples, each 3 domestic and 6 Chinese Taeksa were predicted by the established OPLS-DA model and it was confirmed that all 9 samples were precisely classified.

생약의 원산지를 판별하는 논리적인 일련의 기준을 개발한다면, 현재 유통되는 한약을 좀 더 과학적으로 관리 할 수 있을 것이다. 이러한 노력은 전통적인 한약 산업 발전에 기여할 것이라고 사료된다. 산수유의 원산지 판별법을 개발하기 위해, 본 연구에서는 우선 국산 산수유와 중국산 산수유를 각각 수증기 증류하고 이 때 얻은 휘발성분을 GC/MS를 이용하여 분석하였다. NIST mass spectral library의 데이터베이스로부터 정성분석한 결과를 바탕으로 데이터를 범주화(binning)하여 변수를 얻고, 이에 대하여 PCA, OPLS-DA 등 다변량 통계 분석을 수행함으로써 신속, 정확하게 국산 산수유와 중국산 산수유의 산지를 판별할 수 있는 산지 판별모델을 확립하였다. 산지 판별모델 개발을 위해서 학습집합(n=53)을 분석하여 산지 판별모델을 수립한 후, 검증집합(n=12)을 산지 판별모델에 적용함으로써 그 타당성을 확인하였다. 더불어 1-ethylbutyl-hydroperoxide, nonadecane, butylated hydroxytoluene, 5β,7βH,10α-Eudesm-11-en-1α-ol, 7,9-bis (2-methyl-2-propanyl)-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione, 그리고 2-decyldodecyl-benzene 등 6개의 마커성분을 선정할 수 있었다. 최근에 NMR을 활용한 산수유 원산지 판별에 대한 보고는 있었으나, GC/MS를 기반으로 한 대사체학 연구기법을 이용하여 산지판별 모델을 제시하는 것은 최초의 보고로서 그 의미가 크다. 본 연구결과를 활용하여 한약의 원산지 판별모델 확립과 산수유 원산지의 과학적인 관리에 적용할 수 있으리라 사료된다.

It is believed that traditional Korean medicines can be managed more scientifically through the development of logical criteria to verify their region of cultivation, and that this could contribute to the advancement of the traditional herbal medicine industry. This study attempted to determine such criteria for Sansuyu. The volatile compounds were obtained from 20 samples of domestic Corni fructus (Sansuyu) and 45 samples of Chinese Sansuyu by steam distillation. The metabolites were identified in the NIST Mass Spectral Library via the obtained gas chromatography/mass spectrometer (GC/MS) data of 53 training samples. Data binning at 0.2 min intervals was performed to normalize the number of variables used in the statistical analysis. Multivariate statistical analyses, such as principle component analysis (PCA), partial least squares-discriminant analysis (PLS-DA), and orthogonal partial least squares-discriminant analysis (OPLS-DA) were performed using the SIMCA-P software package. Significant variables with a variable importance in the projection (VIP) score higher than 1.0 were obtained from OPLS-DA, and variables that resulted in a p-value of less than 0.05 through one-way ANOVA were selected to verify the marker compounds. Finally, among the 11 variables extracted, 1-ethylbutyl-hydroperoxide (9.089 min), nonadecane (20.170 min), butylated hydroxytoluene (25.319 min), 5β,7βH,10α-eudesm-11-en-1α-ol (25.921 min), 7,9-bis(2-methyl-2-propanyl)-1-oxaspiro[4.5]deca-6,9-diene-2,8-dione (34.257 min), and 2-decyldodecyl-benzene (54.717 min) were selected as markers to indicate the origin of Sansuyu. The statistical model developed was suitable for the determination of the geographical origin of Sansuyu. The cultivation areas of four Korean and eight Chinese Sansuyu samples were predicted via the established OPLS-DA model, and it was confirmed that 11 of the 12 samples were accurately classified.

본 연구에서는 한의 변증 표준화를 위한 연구의 일환으로 수집된 임상자료를 이용하여 변증을 보다 객관적으로 변별할 수 있는 주요 지표를 도출하고 이를 이용하여 변증 판별모형을 개발하고자 하였다. 중풍 발병 1개월 이내의 환자를 대상으로 변증 임상자료를 수집하였으며, 임상 경험이 3년 이상인 전문가 2인의 변증이 일치하는 1,448개 임상자료를 분석에 사용하였다. 61개 지표 중에서 단계별선택법을 통해 4개 변증의 진단에 유의한 지표 33개를 선택하였으며, 이 지표들을 사용하여 변증 판별모형(IV)을 개발한 후, 개발된 모형의 진단 정확률과 예측 정확률을 분석하였다. 판별 모형(IV)으로 1,159개 임상자료를 사용하여 개발된 변증 판별 모형(III)과 (IV)에 의한 4개 변증의 진단 정확률은 73.68%(854/1,159)였으며, 289개로 분석한 예측 정확률은 70.59% (204/289)로 나타났다. 변증 진단 판별모형(IV)의 진단 정확률은 각각74.89%, 73.68%이고, 예측 정확률은 각각 69.90%, 70.59%로 유사하였다.

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. As a part of researches for standardization and objectification of differentiation of syndromes for stroke, in this present study, we tried to develop the statistical diagnostic tool discriminating the 4 subtypes of syndrome differentiation using the essential indices. Discriminant analysis was carried out using clinical data collected from 1,448 stroke patients who was identically diagnosed for the syndrome differentiation subtypes diagnosed by two clinical experts with more than 3 year experiences. Empirical discriminant model(IV) was constructed using 33 significant symptoms and sign indices selected by stepwise selection. The overall diagnostic accuracy and prediction rate of 4 syndrome differentiation subtypes for discriminant model (III) and (IV) were almost same as 74.89%, 73.68%, 69.90% and 70.59%, respectively.