1. P'ŭllaetp'om kukka wa sanŏp hyŏngmyŏng iyagi -- 2. Yut'op'ia wa hongik in'gan mohyŏng -- 3. Saeroun pandoch'e munmyŏng ŭl yŏlda -- 4. Han'guk taehyŏngmyŏng i sijak toeŏtta.

Che 1-kwŏn. Kodae-kŭndae sasang -- che 2-kwŏn. Hyŏndae sasang.

1. Kwangja chŏngsin e kwanhan kich'ojŏk ihae -- 2. Yuga ŭi kwanggyŏn'gwan -- 3. Toga ŭi kwangŏn kwa kwangja chŏngsin -- 4. Ŭmju munhwa wa Wi-Chin ŭi imt'anjŏk kwang -- 5. Songdae ihakchadŭl ŭi kwangjagwan -- 6. Yangmyŏnghak ŭi kwanggyŏn'gwan -- 7. Chosŏnjo yuhakchadŭl ŭi kwanggyŏn'gwan -- 8. Chosŏnjo yuhakcha ŭi yokki yŏnggwi ihae -- 9. Sŏhwa tot'ongnon kwa kwanggyŏn esŏ chunghaeng ŭro -- 10. Kwanggyŏn mihak ŭi munyejŏk chŏgyong -- 11. Chungguk hoehwa e nat'anan kwanggyŏn mihak -- 12. Chungguk (...) sŏye e nat'anan kwanggyŏn mihak -- 13. Chosŏnjo hoehwa e nat'anan kwanggyŏn mihak -- 14. Chosŏnjo sŏye ŭi kwanggyŏn mihak. (shrink)

1. Yŏnŭn kŭl -- 2. '3' wŏlli ŭi ŭimi wa kŭn'gŏ -- 3. Tong, sŏyang mit chayŏn ŭi '3' wŏlli mosŭptŭl -- 4. Hanminjok ŭi '3' wŏlli mosŭptŭl -- 5. '3' wŏlli ŭi ch'angjojŏk hwakchang mit hwaryong -- 6. Naganŭn kŭl.

Uju ŭi chuin ŭn nugu in'ga -- Kwahak ch'ŏrhak iran muŏt in'ga (1993) -- Kwahak kisul ŭi ch'ukpok kwa chaeang (Kwahak, ch'ukpok in'ga chaeang in'ga, 2009) -- Munmyŏng ŭi mirae -- Saengmyŏng -- Hwan'gyŏng kwa hwan'gyŏng ch'ŏrhak -- Saengt'ae.

1. Tan'gun sinhwa ŭi wŏnhyŏngjŏk segyegwan kwa p'ungnyu chŏngsin -- 2. Wŏnhyo ŭi hwajaeng sasang kwa muaehaeng ŭi silch'ŏn -- 3. Ŭisang ŭi 'Hwaŏm ilsŭng pŏpkyedo' e nat'anan Hwaŏm sasang -- 4. Chinul ŭi Tono chŏmsu wa Chŏnghye ssangsu sasang -- 5. Sambong Chŏng To-jŏn ŭi Pulgyo paech'ŏk ŭi naeyong kwa sŏngkyŏk -- 6. T'oegye Yi Hwang ŭi ch'ŏrhakchŏk ipchang kwa 'Kyŏng' sasang -- 7. Kobong Ki Tae-sŭng ŭi hangmun chŏngsin kwa ch'ŏrhak sasang -- 8. Ugye Sŏng Hon ŭi Tohakchŏk (...) sam kwa hangmun yŏnwŏn -- 9. Yulgok Yi I ŭi Taedong sahoeron kwa ch'ŏrhakchŏk chihyang -- 10. Sŏgye Pak Se-dang ŭi Sirhakchŏk sayu ŭi kich'o -- 11. Namdang Han Wŏn-jin ŭi yŏksa insik kwa Chosŏn chunghwajuŭi -- 12. Sŏngdam Song Hwan-gi ŭi 'T'aegŭk tosŏl' haesŏk ŭi kibon ipchang -- 13. Kanjŏng Yi Nŭng-hwa 'Chosŏn Togyosa' ŭi haksulsachŏk ŭiŭi wa han'gye -- 14. Manhae Han Yong-un i pon chuch'e ŭi munje wa minjok chagyŏl ŭi wŏnch'ik -- purok. T'algyŏnggye sidae ŭi Han'guk ch'ŏrhak ŭi inmunhakchŏk ŭiŭi wa chŏnmang: Yulgok ŭi ch'ŏrhak. (shrink)

BackgroundThe COVID-19 pandemic has highlighted prosocial behavior as a professional healthcare core competency. Although medical students are expected to work in the best interests of their patients, in the pandemic context, there is a greater need for ethical attention to be paid to the way medical students deal with moral dilemmas that may conflict with their obligations.MethodsThis study was conducted in the spring semester of 2019 on 271 students majoring in health professions: medicine, dentistry, and veterinary medicine. All participants provided (...) informed consent and completed measures that assessed utilitarian moral views, cognitive reflections, cognitive reappraisal, and moral judgment.ResultsThe healthcare-affiliated students who scored higher on the instrumental harm subscale in the measurement of utilitarian moral views were more likely to endorse not only other-sacrificial actions but also self-sacrificial ones for the greater good in moral dilemma scenarios. In particular, those engaged in deliberative processes tended to make more self-sacrificial judgments. The mediation analysis also revealed that the effect of deliberative processes on self-sacrificial judgments was mediated by cognitive reappraisal.ConclusionsThese findings suggested that cognitive reappraisal through deliberative processes is involved when the students with utilitarian inclination make prosocial decisions, that it is necessary to consider both moral views and emotional regulation when admitting candidates, and that moral education programs are needed in the healthcare field. (shrink)

Sequential recommendation algorithm can predict the next action of a user by modeling the user’s interaction sequence with an item. However, most sequential recommendation models only consider the absolute positions of items in the sequence, ignoring the time interval information between items, and cannot effectively mine user preference changes. In addition, existing models perform poorly on sparse data sets, which make a poor prediction effect for short sequences. To address the above problems, an improved sequential recommendation algorithm based on short-sequence (...) enhancement and temporal self-attention mechanism is proposed in this paper. In the proposed algorithm, a backward prediction model is trained first, to predict the prior items in the user sequence. Then, the reverse prediction model is used to generate a batch of pseudo-historical items before the initial items of the short sequence, to achieve the goal of enhancing the short sequence. Finally, the absolute position information and time interval information of the user sequence are modeled, and a time-aware self-attention model is adopted to predict the user’s next action and generate a recommendation list. Various experiments are conducted on two public data sets. The experimental results show that the method proposed in this paper has excellent performance on both dense and sparse data sets, and its effect is better than that of the state of the art. (shrink)

The notions of a C-energetic subset and permeable C-value in BCK-algebras are introduced, and related properties are investigated. Conditions for an element t in [0, 1] to be an permeable C-value are provided. Also conditions for a subset to be a C-energetic subset are discussed. We decompose BCK-algebra by a partition which consists of a C-energetic subset and a commutative ideal.

Media has paid more attention recently on environmental issues caused by construction companies which imposes public opinion pressure on construction companies and could potentially impact their decision-making processes for green innovations. However, research on the relationship between public opinions pressure and construction company green innovation behavior is still limited. To understand how such public opinions pressure can impact construction companies' green transition and formulate advice accordingly, it is necessary to use empirical data to find the correlations. Therefore, this research has (...) gathered questionnaire data of the construction companies in Chengdu-Chongqing economic circle of China to study the influencing mechanism of environmental public opinion pressure on enterprise green innovation behavior, analyzes the realization path of leaders' environmental intention as a mediating variable in the impact of environmental public opinion pressure on enterprise green innovation behavior, and reveals the role boundary of environmental regulation as a moderating variable in the impact of environmental public opinion pressure on enterprise green innovation behavior. The results show that environmental public opinion pressure has a significant positive impact on enterprise green innovation behavior. More specifically, enterprise green innovation behavior is affected by leaders' environmental intention and the latter plays a partial mediating role between environmental public opinion pressure and enterprise green innovation behavior. Environmental regulation also enhances the sensitivity of companies to environmental public opinion pressure, and therefore can significantly strengthen the relationship between environmental public opinion pressure and enterprise green innovation behavior. Further research find that, compared with incentive-based environmental regulations, mandatory environmental regulations make companies more sensitive to environmental public opinion pressure and has a more significant positive moderating effect. The research conclusions could be used to provide theoretical reference with empirical data for accelerating the green innovation transformation and promoting the high-quality development in the construction industry. (shrink)