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نتیجه جستجو - کره

تعداد مقالات یافته شده: 115
ردیف عنوان نوع
1 شدت و پتانسیل انتقال کرونا در کره جنوبی
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 6 - تعداد صفحات فایل doc فارسی: 14
اهداف: ازآنجایی که اولین مورد کروناویروس جدید 2019(کوید-19) در 20 ژانویۀ 2020 در کرۀ شمالی شناسایی شد، تعداد موارد به سرعت افزایش یافت به طوری که تا 6 مارس 2020، منجربه ابتلای6284 مورد و فوت42 نفر شد. اولین تحقیق درمورد گزارش تعداد تکثیر کوید-19 در کرۀ جنوبی را برای بررسی سرعت شیوع بیماری، ارائه می دهیم.
روش کار: موارد روزانۀ تأیید شدۀ کوید-19 در کرۀ جنوبی از منابع عمومی موجود استخراج شد. با استفاده از توزیع تجربی گزارشات دارای تأخیر و شبیه سازی مدل رشد کلی، تعداد تکثیر مؤثر را برمبنای توزیع احتمال گسستۀ فاصلۀ زایشی ارزیابی کردیم.
نتایج: چهار گروه اصلی را شناسایی و تعداد تکثیر را 1.5(1.6-1.4 CI: 95%) برآورد کردیم. به علاوه، نرخ رشد طبیعی 0.6 (0.7، 0.6 CI: 95%) و مقیاس بندی پارامتر رشد 0.8 (0.8،0.7 CI: 95%) برآورد شدند، که نشان-دهندۀ پویایی رشد زیر نمایی کوید-19 می باشد. نرخ مرگ و میر موارد خام در بین مردان (1.1%) در مقایسه با زنان (0.4%) بیشتر است و با افزایش سن افزایش می یابد.
نتیجه گیری: نتایج ما انتقال پایدار اولیۀ کوید-19 در کرۀ جنوبی را نشان می دهد و از اجرای اقدامات فاصله گذاری اجتماعی برای کنترل سریع شیوع بیماری حمایت می کند.
کلمات کلیدی: کروناویروس | کوید-19 | کره | تعداد تکثیر
مقاله ترجمه شده
2 Agent negotiation in an IoT-Fog based power distribution system for demand reduction
عامل مذاکره در سیستم توزیع برق مبتنی بر IoT-Fog برای کاهش تقاضا-2020
Growing energy demand is calling for an effective energy management. In smart homes all devices are connected to Internet by means of Internet of Things. There is a possible means of studying the consumer usage pattern and accordingly forecast their energy demand. Multi Agents has been used in computer science for a long time and applied for lot of applications for replicating the job of human. So towards monitoring and controlling the cyber physical systems, these multi agent system has been applied in smart transportation, smart cities, Smart Grid and so. This paper proposes a Multi-agent System (MAS) for smart energy management in an IoT based system. Inspired by the competition in human societies for accepting best proposals: this work proposes an Agent Negotiation system for demand reduction. The Agents in IoT system negotiate with the meter agent for accepting a proposal which will reduce the peak hour usage. The negotiation agent also negotiates with the meter agent for using energy when the availability of renewables are surplus. This negotiation is done with hundreds and thousands of homes thus helping Utilities to meet the supply-demand effectively. Consumers get the best pricing based on the accepted policies.
Keywords: Internet of Things | Multi-agent system | Negotiation | Distribution automation | Smart grid
مقاله انگلیسی
3 Electricity generation using biogas from organic fraction of municipal solid waste generated in provinces of China: Techno-economic and environmental impact analysis
تولید برق با استفاده از بیوگاز از کسری آلی پسماندهای جامد شهری تولید شده در استانهای چین: تحلیل تأثیر تکنو اقتصادی و زیست محیطی-2020
This study assessed the electricity generation potential of biogas from organic fraction of municipal solid waste collected for disposal from 2004 to 2018 in 31 provinces of China using landfill gas to energy (LFGTE) and anaerobic digestion (AD) technologies. Economic feasibility assessment of the technologies was carried out using Net Present Value, and Levelized Cost of Energy methods. In addition, environmental impact of waste management options based on global warming potential was assessed under three scenarios. Key findings showed that electricity generation potential of anaerobic digestion technology was higher in all the provinces. Economically, the results showed that both projects are feasible in all the 31 provinces. However, anaerobic digestion project proved to be highly feasible, with more positive net present value, and lower levelized cost of energy. Sensitivity analysis showed that both projects are infeasible with a discount rate beyond 20%. The results also showed that landfill gas without energy recovery has high global warming potential. It was realized that on the average landfill gas to energy technology could reduce global warming potential by 71.5%, while anaerobic digestion technology could reduce global warming potential by 92.7%. This study will offer scientific guidance for investment in anaerobic digestion and landfill gas to energy projects in China and other countries.
Keywords: Electricity generation potential | Organic fraction of municipal solid waste | Biogas | Landfill gas to energy technology | Anaerobic digestion technology | Global warming potential
مقاله انگلیسی
4 Negotiating team formation using deep reinforcement learning
مذاکره در مورد تشکیل تیم با استفاده از یادگیری تقویت عمیق-2020
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals. One way for agents to cooperate effectively is to form a team, make a binding agreement on a joint plan, and execute it. However, when agents are self-interested, the gains from team formation must be allocated appropriately to incentivize agreement. Various approaches for multi-agent negotiation have been proposed, but typically only work for particular negotiation protocols. More general methods usually require human input or domain-specific data, and so do not scale. To address this, we propose a framework for training agents to negotiate and form teams using deep reinforcement learning. Importantly, our method makes no assumptions about the specific negotiation protocol, and is instead completely experience driven. We evaluate our approach on both non-spatial and spatially extended team-formation negotiation environments, demonstrating that our agents beat hand-crafted bots and reach negotiation outcomes consistent with fair solutions predicted by cooperative game theory. Additionally, we investigate how the physical location of agents influences negotiation outcomes.
Keywords: Multi-agent systems | Team formation | Coalition formation | Reinforcement learning | Deep learning | Cooperative games | Shapley value
مقاله انگلیسی
5 Conformity, conflict and negotiation in criminal justice work: Understanding practice through the lens of emotional labour
انطباق ، درگیری و مذاکره در کار عدالت کیفری: درک عملکرد از طریق لنز کار عاطفی-2020
The theme of this special issue is how emotional labour can be used as a lens to consider conformity, conflict and negotiation in criminal justice work. Hochschild (1983) first developed the concept of emotional labour and defines it as ‘the management of a way of feeling to create a publicly observable facial and bodily display … which is for a wage’ (p.7, fn). Key to Hochschilds understanding of emotional labour are ‘feeling rules’. Feeling rules are ‘rules or norms according to which feelings are judged appropriate to accompanying events’ (1983: 59). It is these feeling rules or ‘display rules’ (Morris and Feldman, 1996; Ashforth and Humphrey, 1993; Rafaeli and Sutton, 1989), defined as ‘behavioural expectations about which emotions ought to be expressed and those that ought to be hidden’ (Rafaeli and Sutton, 1989: 8), that regulate the emotional labour of workers who interact directly with others. The articles within this special issue show how workers in a wide range of job roles throughout the criminal justice system traverse often complex emotional display rules. They consider both the formal and, at times, informal emotional display rules which govern criminal justice workers’ emotional lives and how these workers negotiate these expectations through conformity and adaptation. The impact of the performance of these various types of emotional labour, both within and outside the work environment, is explored alongside the support which workers require and receive.
مقاله انگلیسی
6 انتشار چهارچوب حسابداری برای پارک های صنعتی در چین
سال انتشار: 2020 - تعداد صفحات فایل pdf انگلیسی: 12 - تعداد صفحات فایل doc فارسی: 31
چین بیشترین تعداد پارک های صنعتی را در جهان دارد. این پارکها نه تنها برای تسریع بخشیدن فرایند صنعتی کشور مهم و حیاتی بلکه برای دست یافتن به اهداف تغیرات آب و هوایی خود نیزمهم و حیاتی هستند. ایجاد فهرست انتشار CO2برای پارک صنعتی اولین مرحله در تحلیل الگوهای انتشار پارک و طراحی سیاست های کم کربن می باشد. به هرحال، بیشتر انتشار قبلی برای پارک های صنعتی با اتخاذ حوزه و روش شناسی مختلف محاسبه می شود که با یکدیگر قابل مقایسه نیستند. این مطالعه روش شناسی و چارچوب خودسازگاری را برای پارک های صنعتی چین مبتنی بر داده سطح شرکت توسعه می دهد. ما هر دو حوزه انتشار 1 و2 را بررسی و فهرست ها را با 19 نوع انرژی و 39 بخش صنعتی ایجاد می کنیم که سازگار با فهرست های انتشار از سطح شهر، استانی و کشوری می باشد. چنین فهرست انتشار مبتنی بر بخش نه تنها قادرخواهد بود تا داده های حمایتی برای طراحی سیاستهای کنترل انرژی/انتشار ارائه دهد بلکه به دولت محلی/مرکزی جهت ارزیابی عملکرد کاهش انتشار پارک کمک می کند. سرانجام، مطالعه تجربی برای چهار پارک صنعتی برای تایید این روش اجرا می شود. علاوه براین، ما برنامه های پارک اکو-صنعتی را در کشورهای ژاپن، کره جنوبی و همچنین ساختار حسابداری انتشاری آنها را بررسی می کنیم. متوجه شدیم که بیشتر پارک های صنعتی ژاپن انتشارهایی با حوزه 1،2 و 3 ارائه می دهند درحالیکه برای کره جنوبی، پارک ها اکثرا در انتشار حوزه 1 تمرکز می کنند. بحث اکو-صنعتی پارک های ژاپن و کره جنوبی اهمیت قابل توجهی برای ساخت پارک های کم – کربن چین دارد.
کلمات کلیدی: انتشار CO2 | پارک های صنعتی | تغییرات آب و هوا | چین
مقاله ترجمه شده
7 Strain, negative emotion, and cyber violence among South Korean Juveniles: A mediation analysis
فشار ، احساسات منفی و خشونت سایبری در نوجوانان کره جنوبی: یک تحلیل میانجیگری-2020
While there has been moderate support for General Strain Theory (GST), the extant body of literature has been criticized for its lack of diversity (i.e., not sufficiently considering cross-cultural differences) as well as for its lack of stringency in testing mediators between strain and criminal behavior. As such, the current study investigated the intervening effect of the negative emotion of anger between school strain, as well as family strain, and cyber violence among South Korean youth. Also, it focused on their strains, which due to their traditional roots in Confucian culture, may be qualitatively and quantitatively different than those experienced by youth in Western societies. By using a manifest mediation analysis and a bias-corrected bootstrap method, this study found that the reason they committed cyber violence was not so much due to getting stressed out from the toxic relationship with their teacher as due to anger’s mediating role in the relationship between the strain and cyber violence. Results from the present study provided full support for GST’s application to a Confucian-based country. Theoretical and policy implications were discussed.
مقاله انگلیسی
8 Use of big data analysis to investigate the relationship between natural radiation dose rates and cancer incidences in Republic of Korea
استفاده از تجزیه و تحلیل داده های بزرگ برای بررسی رابطه بین میزان دوز پرتوی طبیعی و بروز سرطان در جمهوری کره-2020
In this study, we investigated whether there is a significant relationship between the natural radiation dose rate and the cancer incidents in Korea by using a big data analysis. The natural dose rate data for this analysis were the measurement data obtained from the 171 monitoring posts of the 113 administrative districts in Korea over the 10 years from 2007 and 2016. The relative cancer incidences for this analysis were the difference in the cancer patients per hundred thousand people year-on-year in the administrative districts with the five highest and the five lowest natural gamma dose rates each year over the same period. To analyze the correlation between the two variables, Spearman’s rank correlation coefficient between the two rates was derived using R, a well-known big data analysis tool. The analysis showed that Spearman’s rank correlation coefficient was more than 0.05 and that the correlation between the two variables was not statistically significant.
Keywords: Natural radiation dose rate | Cancer incident | Big data analysis | Relative cancer incidence | Spearman’s rank correlation analysis
مقاله انگلیسی
9 A hybrid approach using machine learning and genetic algorithm to inverse modeling for single sphere scattering in a Gaussian light sheet
یک روش ترکیبی با استفاده از یادگیری ماشین و الگوریتم ژنتیک برای مدل سازی معکوس برای پراکندگی تک کره در یک صفحه نور گاوسی-2019
Light scattering has been proven to be an effective tool to characterize and classify particles of different properties. However, inverse modeling to quantitatively retrieve the particle property from light scattering is still a tough task in most applications. In this paper, a hybrid approach using machine learning and genetic algorithm is developed to obtain the geometrical and optical parameters of a sphere from its angular scattering pattern in a light sheet. Scattering patterns related to different parameters are first generated by numerically solving Mie scattering based on angular spectrum theory. Multilayer perception neural network (NN) is then employed to roughly estimate the parameter, while genetic algorithm is adopted to retrieve the precise value. Influences of intensity noise on the inverse modeling are finally examined. Results suggest that the proposed hybrid approach can retrieve the parameters of the sphere from its scattering pattern with high precision in a time-effective manner, which could be widely applied in various scattering-based instruments
Keywords: Mie scattering | Inverse modeling | Machine learning | Genetic algorithm | Gaussian light sheet
مقاله انگلیسی
10 Detecting temporal changes in the temperature sensitivity of spring phenology with global warming: Application of machine learning in phenological mode
تشخیص تغییرات زمانی در حساسیت دما به فنولوژی فنر با گرم شدن کره زمین: کاربرد یادگیری ماشین در حالت فنولوژی-2019
Phenological models can effectively infer historically missing phenological data, so as to investigate the longterm relationship between plants and climate change. Large numbers of ecophysiological and statistical models have been developed in the past few decades, but these models have been unable to make accurate predictions based on external data. Machine learning (ML) methods have an advantage over traditional statistical methods for natural science studies. However, only a few phenological models have been coupled with ML methods. In this study, using long-term leaf unfolding date (LUD) observations collected in Harbin, China, we adopted three popular ML algorithms for predicting plant LUD and compared the performances of 10 phenological models. We detected the temperature sensitivity (ST) of the LUD at the species level for the periods 1962–1987 and 1988–2016 (before and after the recent, sudden warming) and temporal changes in ST with a 15-year moving window for each period. The results show that the gradient boosting decision tree (GBDT) model performs obviously better than the other models for external validation data, while avoiding model overfitting. Most species showed an increase in ST during the 1988–2016 period, and the temporal changes in ST significantly decreased during both periods. The temporal changes in ST from the phenological data predicted by the GBDT model is significantly higher than that of other models, which indicates that the traditional phenological models may underestimate the response of LUD to climate warming. We found a prevalent decline in the magnitude of ST with increasing preseason temperature variance at the species level. Our research suggests that machine learning algorithms should be more widely used in future phenological model research, and temporal changes in ST should be investigated in order to broaden our understanding of plants’ ability to adapt to future climate change.
Keywords: Leaf unfolding date | Machine learning | Phenological model | Temperature sensitivity | Temperature variance | Temporal changes
مقاله انگلیسی
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