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农产品质量安全监管检测的重要性及提升对策

食品安全导刊 2024年第13期 12-14页

作者：朱江桐梓县农业农村局贵州桐梓563200

农产品质量安全监管监测工作过程中,检测人员需要根据农产品具体情况进行针对性检测。农产品质量安全监管的主要目的是保证农产品质量安全,进而确保其不会危害人们的身体健康。因此,如何有效开展农产品质量安全监管检测至关重要。本文... 详细信息

农产品质量安全监管监测工作过程中,检测人员需要根据农产品具体情况进行针对性检测。农产品质量安全监管的主要目的是保证农产品质量安全,进而确保其不会危害人们的身体健康。因此,如何有效开展农产品质量安全监管检测至关重要。本文深入探讨农产品质量安全测现状,分析农产品质量安全监管检测工作的重要性,并提出一系列加强监管检监管检测的策略,以保证农产品质量安全。

关键词：农产品质量安全监管检测

维普期刊数据库博看期刊

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仔猪黄白痢的诊断与防控

广东饲料 2024年第1期33卷 50-52页

作者：朱疆江苏省镇江市丹阳市动物疫病预防控制中心

仔猪黄痢和仔猪白痢是生猪养殖业中常见的急性传染性疾病,发病速度快,潜伏周期短,病程短,死亡率高,经常出现整窝发病死亡,严重影响生猪养殖业的健康发展。因此对仔猪黄白痢的病原、发病因素及临床症状进行了研究,为该病的预防和治疗方... 详细信息

仔猪黄痢和仔猪白痢是生猪养殖业中常见的急性传染性疾病,发病速度快,潜伏周期短,病程短,死亡率高,经常出现整窝发病死亡,严重影响生猪养殖业的健康发展。因此对仔猪黄白痢的病原、发病因素及临床症状进行了研究,为该病的预防和治疗方法提供参考,减少养殖户的经济损失。

关键词：仔猪黄白痢病原流行病学诊断防治

SST data assimilation experiments using an adaptive variational method

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Chinese Science Bulletin 2002年第23期47卷 2010-2013,2029页

作者： zhu jiang WANG Hui ZHOU Guangqing ICCES Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China National Natural Science Foundation of China Beijing 100083China

An adaptive variational data assimilation method is proposed by zhu and Kamachi[1]. This method can adaptively adjust the model state without knowing explicitly the model error covariance matrix. The method enables very flexible ways to form some reduced order problems. A proper reduced order problem not only reduces computational burden but also leads to corrections that are more consistent with the model dynamics that trends to produce better forecast. These features make the adaptive variational method a good candidate for SST data assimilation because the model error of an ocean model is usually difficult to estimate. We applied this method to an SST data assimilation problem using the LOTUS data sets and an ocean mixed layer model (Mellor-Yamada level 2.5). Results of assimilation experiments showed good skill of improvement subsurface temperatures by assimilating surface observation alone.

关键词： data assimilation adaptive variational method sea surface temperature oceanic mixed layer.

Global Upper Ocean Heat Content Estimation: Recent Progress and the Remaining Challenges

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Atmospheric and Oceanic Science Letters 2015年第6期8卷 333-338页

作者： CHENG Li-Jing zhu jiang John ABRAHAM International Center for Climate and Environment Sciences (ICCES) Institute of Atmospheric Physics Chinese Academy of Sciences University of St. Thomas

Ocean heat content(OHC)change contributes substantially to global sea level rise,so it is a vital task for the climate research community to estimate historical OHC.While there are large uncertainties regarding its value,in this study,the authors discuss recent progress to reduce the errors in OHC estimates,including corrections to the systematic biases in expendable bathythermograph(XBT)data,filling gaps in the data,and choosing a proper climatology.These improvements lead to a better reconstruction of historical upper(0–700 m)OHC change,which is presented in this study as the Institute of Atmospheric Physics(IAP)version of historical upper OHC assessment.Challenges still remain;for example,there is still no general consensus on mapping methods.Furthermore,we show that Coupled Model Intercomparison Project,Phase 5(CMIP5)simulations have limited ability in capturing the interannual and decadal variability of historical upper OHC changes during the past 45 years.

关键词： ocean heat content temperature observation XBT bia

Preliminary Studies on Predicting the Tropical Indian Ocean Sea Surface Temperature through Combined Statistical Methods and Dynamic ENSO Prediction

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Atmospheric and Oceanic Science Letters 2013年第1期6卷 52-59页

作者： WANG Li-Wei ZHENG Fei zhu jiang International Center for Climate and Environment Science(ICCES) Institute of Atmospheric PhysicsChinese Academy of Sciences Graduate University of Chinese Academy of Sciences Beijing Institute of Applied Meteorology

The sea surface temperature(SST) in the Indian Ocean affects the regional climate over the Asian continent mostly through a modulation of the monsoon system.It is still difficult to provide an a priori indication of the seasonal variability over the Indian Ocean.It is widely recognized that the warm and cold events of SST over the tropical Indian Ocean are strongly linked to those of the equatorial eastern Pacific.In this study,a statistical prediction model has been developed to predict the monthly SST over the tropical Indian Ocean.This model is a linear regression model based on the lag relationship between the SST over the tropical Indian Ocean and the Ni o3.4(5°S-5°N,170°W-120°W) SST Index.The predictor(i.e.,Ni o3.4 SST Index) has been operationally predicted by a large size ensemble El Ni o and the Southern Oscillation(ENSO) forecast system with coupled data assimilation(Leefs_CDA),which achieves a high predictive skill of up to a 24-month lead time for the equatorial eastern Pacific SST.As a result,the prediction skill of the present statistical model over the tropical Indian Ocean is better than that of persistence prediction for January 1982 through December 2009.

关键词： ENSO预测热带印度洋统计方法海表面温度印度洋海温线性回归模型预测模型太平洋海温

The Impact of "Bad" Argo Profiles on Ocean Data Assimilation

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Atmospheric and Oceanic Science Letters 2010年第2期3卷 59-63页

作者： YAN Chang-Xiang zhu jiang International Center for Climate and Environment Science Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry （LAPC） Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

Recent studies have found cold biases in a fraction of Argo profiles (hereinafter referred to as bad Array for Real-time Geostrophic Oceanography (Argo) profiles) due to the pressure drifts during 2003 and 2006. These bad Argo profiles have had an important impact on in situ observation-based global ocean heat content esti- mates. This study investigated the impact of bad Argo profiles on ocean data assimilation results that were based on observations from diverse ocean observation systems, such as in situ profiles (e.g., Argo, expendable bathy- thermograph (XBT), and Tropical Atmosphere Ocean (TAO), remote-sensing sea surface temperature products and satellite altimetry between 2004 and 2006. Results from this work show that the upper ocean heat content analysis is vulnerable to bad Argo profiles and demon- strate a cooling trend in the studied period despite the multiple independent data types that were assimilated. When the bad Argo profiles were excluded from the as- similation, the decreased heat content disappeared and a warming occurred. Combination of satellite altimetry and mass variation data from gravity satellite demonstrated an increase, which agrees well with the increased heat con- tent. Additionally, when an additional Argo profile quality control procedure was utilized that simply removed the profiles that presented static unstable water columns, the results were very similar to those obtained when the bad Argo profiles were excluded from the assimilation. This indicates that an ocean data assimilation that uses multiple data sources with improved quality control could be less vulnerable to a major observation system failure, such as a bad Argo event.

关键词：数据同化海洋学海洋观测系统质量控制程序原位观测卫星测高热含量全球海洋

A Reconstructed Wind Stress Dataset for Climate Research over the Tropical Pacific during a 153-Year Period

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Atmospheric and Oceanic Science Letters 2009年第5期2卷 277-283页

作者： Zheng Fei Du Juan zhu jiang International Center for Climate and Environment Science (ICCES) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China Graduate School of the Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC) Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China

There are close relationships between the sea surface temperature (SST) and the surface wind over the tropical Pacific.To study the past climate variability over the tropical Pacific,the long-term monthly wind stress anomalies over the tropical Pacific for the period of 1856–2008 are reconstructed with an SVD (singular value decomposition)-based statistical atmospheric model,where the wind stress anomalies are slave and directly correspond to the SST anomalies.The verification results show that the reconstructed wind stress data have high correlations and a small root mean square (RMS) error with the three reanalysis/simulated surface wind datasets from the last 50 years.In addition,the simulated SST anomalies from an intermediate oceanic model (IOM),which is forced by the reconstructed wind stress,can simulate the realistic interannual and decadal variability of the ENSO (El Nio-Southern Oscillation);this indicates that this new long-term wind stress dataset is useful for various climate studies,especially for the large-scale interannual and decadal variability.

关键词： reconstructed wind stress statistical atmospheric model ENSO

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运动调控尿酸的作用机制

中国组织工程研究 2024年第34期28卷 5552-5557页

作者：吴昱苇朱江郑兵吴宗辉西南大学体育学院重庆市400715 西南大学运动康复研究所重庆市400715 西南大学医院重庆市400715

背景:体内尿酸积累过多会导致高尿酸血症和痛风等疾病,是心血管和肾功能障碍的危险因素。国内外多个运动医学组织机构都倡导制定运动处方来防治慢性疾病,然而在预防和治疗高尿酸血症的实践中此方法尚未得到有效开展。目的:以综述高尿酸... 详细信息

背景:体内尿酸积累过多会导致高尿酸血症和痛风等疾病,是心血管和肾功能障碍的危险因素。国内外多个运动医学组织机构都倡导制定运动处方来防治慢性疾病,然而在预防和治疗高尿酸血症的实践中此方法尚未得到有效开展。目的:以综述高尿酸血症形成和运动对尿酸的调控及相关机制为基础,凝练出适合大众化运动预防和辅助治疗高尿酸血症的方案,提出不同高尿酸血症阶段患者运动时的注意事项。方法:以“尿酸,痛风,有氧运动,力量训练,高强度间歇训练,肥胖”为中文检索词,以“uric acid,gout,aerobic exercise,strength training,high-intensity interval training,obesity”为英文检索词,分别检索PubMed数据库和中国知网数据库,搜寻各数据库建库至2023年10月发表的所有研究。通过阅读文题和摘要进行初步筛选,排除重复性研究及内容不相关的文献,最后纳入64篇文献进行综述。结果与结论:高尿酸血症的形成由肝脏合成尿酸过多、肾脏肾外排泄不足或二者共同导致。运动可通过调节抗氧化酶活性、尿酸排泄蛋白的表达、脂代谢途径来改善体内的尿酸水平,有氧运动、力量训练、高强度间歇等运动可有效调节尿酸水平、减轻炎症反应、促进尿酸排泄蛋白的表达和脂代谢,达到预防和治疗高尿酸血症的效果。目前,关于运动对尿酸水平直接调节机制的研究尚显不足,未来的研究需要更加深入地探索运动对尿酸水平调节的可能机制,并且细化不同运动方式对不同特征高尿酸血症患者的影响。

关键词：高尿酸血症痛风运动尿酸炎症反应尿酸排泄蛋白脂代谢

Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast

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Atmospheric and Oceanic Science Letters 2013年第6期6卷 405-409页

作者： WANG Li-Wei ZHENG Fei zhu jiang International Center for Climate and Environment Science (ICCES) Institute of Atmospheric Physics Chinese Academy of Sci-ences Graduate University of Chinese Academy of Sciences Beijing Institute of Applied Meteorology

Weather and climate in East China are closely related to the variability of the western Pacific subtropical high(WPSH), which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature(SST) over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial.

关键词：西太平洋副热带高压热带印度洋海温预测西太平洋副高春夏季节中国东部组成部分统计模型