Charmonium at high temperature in two-flavor QCD

We compute charmonium spectral functions in 2-flavor QCD on anisotropic lattices using the maximum entropy method. Our results suggest that the S-waves (J/psi and eta_c) survive up to temperatures close to 2Tc, while the P-waves (chi_c0 and chi_c1) melt away below 1.2Tc.Comment: 11 pages, 19 figures. v2: expanded discussion and modified conclusions. One figure changed. To appear in PR

TO REFER OR NOT TO REFER: WHAT SHOULD WE DO?

Referral system applications are important pieces of Health System of WHOsuggesting to developed and developing countries since decades. Starts withdefining health centers as primary, secondary and tertiary, sustained with healthstaff and patients attendance. Referral system which supplies countries to use theirhuman and economical resources, are in application in very few country because ofsome obstacles. In Turkey, it is first tried in 1960's, but because of primary healthcare was not developed enough. Also in Health Reform Program, which is started toapplied in 2003, "gradual referral system" idea is accepted theorically. In 2008November, it is tried in 4 city, after couple of months in January 2009 it iscancelled.Researches are showing that, societies most of health problems are can be solved inprimary health centers. (%80-90) Thats why,referral system, is a system supportedby people who are working in these issues, because of the reasons; using moreefficient of human and financial resources, decreasing the barriers of academicdevelopment, increasing the service quality of secondary and tertiary healthcenters. However there are obstacles against referral system, still. To pass beyondthese obstacles, referral system, the advantages, obstacles and solutions must bedefined and able to be measurable.The aim of our research is to make addition to researches about family physiciansand specialists thoughts about referral system. This search is planned asqualitative. We used a detailed questinaire form for physicians, and applied with a in-depth interview method. All of the physicians were agree that referral system isneeded. But in small details they were separating

Do artificial neural networks provide improved volatility forecasts:Evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.</p

Do artificial neural networks provide improved volatility forecasts:Evidence from Asian markets

This paper enters the ongoing volatility forecasting debate by examining the ability of a wide range of Machine Learning methods (ML), and specifically Artificial Neural Network (ANN) models. The ANN models are compared against traditional econometric models for ten Asian markets using daily data for the time period from 12 September 1994 to 05 March 2018. The empirical results indicate that ML algorithms, across the range of countries, can better approximate dependencies compared to traditional benchmark models. Notably, the predictive performance of such deep learning models is superior perhaps due to its ability in capturing long-range dependencies. For example, the Neuro Fuzzy models of ANFIS and CANFIS, which outperform the EGARCH model, are more flexible in modelling both asymmetry and long memory properties. This offers new insights for Asian markets. In addition to standard statistics forecast metrics, we also consider risk management measures including the value-at-risk (VaR) average failure rate, the Kupiec LR test, the Christoffersen independence test, the expected shortfall (ES) and the dynamic quantile test. The study concludes that ML algorithms provide improving volatility forecasts in the stock markets of Asia and suggest that this may be a fruitful approach for risk management.</p

Elements of paradoxes in supply chain management literature: A systematic literature review

This study reports the results of a systematic literature review investigating paradoxes in supply chain management. This issue is important because supply chain practitioners frequently face paradoxes in industry with little direction provided in supply chain literature. Investigating the years 1997 through 2019, we identified 64 articles as the basis of our research containing a total of 68 unique paradoxes. In identifying the paradox elements (PEs), we adopted paradox theory (PT) as the base theoretical approach, which was utilized in only 7 of the articles. We employed contingency theory, institutional complexity theory, and complexity theory to support our findings. For each paradox, we also extracted and summarized managerial insights for practitioners. This study addresses the emergent needs of investigating paradoxes in the supply chain management domain to extend the use of PT and complementary theories that can aid practitioners in how to efficiently manage the paradoxes they encounter in industry