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Zheng Lu,1,* Chunlei Wu,2,3,* Jiange Zhang,2,4,* Yu Ye,2,5 Zhifu Zhang,2,6 Ming Liao,2 Lin Huang,2 Jiarong Tian,2 Aihua Tan,2 Zengnan Mo1,2 1Department of Urology, First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 2Center for Genomic and Personalized Medicine, Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 3Urology Department, First Affiliated Hospital of Xinxiang Medical University, Weihui City, Henan Province, People’s Republic of China; 4Department of Urology, Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 5Emergency Department, Second Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 6Department of Urology, Minzu Hospital of Guangxi Zhuang Autonomous Region, Nanning, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zengnan MoDepartment of Urology, First Affiliated Hospital of Guangxi Medical University, 6 Shuangyong Road, Nanning 530021, Guangxi Zhuang Autonomous Region, People’s Republic of ChinaTel/ Fax +86 0771-5353342Email zengnanmo@hotmail.comObjective: To evaluate the effect of the drinking frequency and years on lower urinary tract symptoms (LUTS) in a large Chinese male population.Methods: The current data were obtained from a consecutive series of 3,229 men aged 18– 79 who participated in a routine physical examination in Fangchenggang First People’s Hospital, Guangxi, China. During a face-to-face interview, the detailed demographic variables about alcohol consumption, potential confounding factors were collected. LUTS were assessed by International Prostate Symptom Score (IPSS) and defined as total LUTS, irritative (IRR) and obstructive (OBS) sym

The primary purpose of the paper is to analyze the conditional correlations, conditional covariances, and co-volatility spillovers between international crude oil and associated financial markets. The paper investigates co-volatility spillovers (namely, the delayed effect of a returns shock in one physical or financial asset on the subsequent volatility or co-volatility in another physical or financial asset) between the oil and financial markets. The oil industry has four major regions, namely North Sea, USA, Middle East, and South-East Asia. Associated with these regions are two major financial centers, namely UK and USA. For these reasons, the data to be used are the returns on alternative crude oil markets, returns on crude oil derivatives, specifically futures, and stock index returns in UK and USA. The paper will also analyze the Chinese financial markets, where the data are more recent. The empirical analysis will be based on the diagonal BEKK model, from which the conditional covariances will be used for testing co-volatility spillovers, and policy recommendations. Based on these results, dynamic hedging strategies will be suggested to analyze market fluctuations in crude oil prices and associated financial markets.