Globally,grape cultivation spans vast areas and achieves substantial yields,making grapes and related industries vital economic pillars for many nations.In grape production,efficient and precise management during key ...Globally,grape cultivation spans vast areas and achieves substantial yields,making grapes and related industries vital economic pillars for many nations.In grape production,efficient and precise management during key growth stages is essential for enhancing both yield and quality.In view of the problems that during the grape inflorescences and young fruits stage,the targets are small in size,easily obscured by branches and leaves,and highly similar in color to the background,resulting in poor recognition performance of existing detection methods in complex natural environments,which in turn restricts the application of precision spraying technology.This paper establishes a dedicated dataset for grape inflorescences and young fruits in Xinjiang and proposes an improved lightweight detection model,YOLOv8-FCD.The model incorporates a PConv-based C2f_Faster module to reduce parameter count and computational complexity,replaces the original upsampling method with the CARAFE module to enhance feature extraction capability,and introduces the Detect_SEAM detection head to improve recognition accuracy under occlusion and small-target conditions.Experimental results show that the YOLOv8-FCD model achieves a detection precision(P)of 93.7%and a recall(R)of 87.3%,with a mean average precision(mAP)of 94.6%.Compared to the original YOLOv8n model,P improved by 8.2%,mAP increased by 2.6%,and the model size is reduced to 85.71%of the original.This model provides effective technical support for the identification of grape inflorescences and young fruits in intelligent spraying for plant protection.展开更多
面向GW级大规模RePtA系统,建立基于Copula理论的风光概率预测模型,并综合考虑安装地点、储能配置策略与运行策略对系统容量的优化配置的影响,提出可再生能源制氨(renewable power to ammonia,RePtA)系统双层优化配置模型。该模型以极小...面向GW级大规模RePtA系统,建立基于Copula理论的风光概率预测模型,并综合考虑安装地点、储能配置策略与运行策略对系统容量的优化配置的影响,提出可再生能源制氨(renewable power to ammonia,RePtA)系统双层优化配置模型。该模型以极小化单位氨成本为目标函数,优化配置风电系统、光伏系统、电解制氢系统、化学储能系统和储氢系统的容量,并采用遗传算法对优化模型进行求解。以GW级RePtA系统为例,定量分析和比较了5类典型安装地点配置不同储能的情况下对系统最优配置以及经济性的影响。展开更多
目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根...目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根据每次入院时BMI和代谢情况分为BMI正常、超重和代谢综合征3种状态,采用多状态Markov模型分析转移规律。结果纳入398例研究对象的892条观测记录,中位年龄56岁,中位随访时间40个月。结果显示3种状态间共发生494次转移,其中5.1%由BMI正常转移为超重,5.5%由超重转移为MetS。超重发展为MetS的转移强度最高,是超重变为BMI正常的9.52倍。48.53个月后,BMI正常的抑郁症患者开始转移为MetS。对于超重的患者,8.77个月后开始转移为MetS。36个月后,BMI正常或超重者转移为MetS的概率为31.4%和50.4%;对于合并MetS者,36个月后仍为MetS的概率为51.2%。多因素分析显示未婚是体质量正常的抑郁症患者转移为超重的危险因素,而具有较高的受教育程度是超重的抑郁症患者转移为MetS的保护因素。结论抑郁症患者发展为MetS的强度和风险较高,发生MetS后不易好转,提示加强抑郁症患者的BMI管理和MetS的干预。展开更多
基金The Major Science and Technology Special Project of Xinjiang Uygur Autonomous Region of China“Research,development,and integrated promotion of technologies for enhancing quality and efficiency across the entire industrial chain of Xinjiang honeydew melons”(2024A02007).
文摘Globally,grape cultivation spans vast areas and achieves substantial yields,making grapes and related industries vital economic pillars for many nations.In grape production,efficient and precise management during key growth stages is essential for enhancing both yield and quality.In view of the problems that during the grape inflorescences and young fruits stage,the targets are small in size,easily obscured by branches and leaves,and highly similar in color to the background,resulting in poor recognition performance of existing detection methods in complex natural environments,which in turn restricts the application of precision spraying technology.This paper establishes a dedicated dataset for grape inflorescences and young fruits in Xinjiang and proposes an improved lightweight detection model,YOLOv8-FCD.The model incorporates a PConv-based C2f_Faster module to reduce parameter count and computational complexity,replaces the original upsampling method with the CARAFE module to enhance feature extraction capability,and introduces the Detect_SEAM detection head to improve recognition accuracy under occlusion and small-target conditions.Experimental results show that the YOLOv8-FCD model achieves a detection precision(P)of 93.7%and a recall(R)of 87.3%,with a mean average precision(mAP)of 94.6%.Compared to the original YOLOv8n model,P improved by 8.2%,mAP increased by 2.6%,and the model size is reduced to 85.71%of the original.This model provides effective technical support for the identification of grape inflorescences and young fruits in intelligent spraying for plant protection.
文摘面向GW级大规模RePtA系统,建立基于Copula理论的风光概率预测模型,并综合考虑安装地点、储能配置策略与运行策略对系统容量的优化配置的影响,提出可再生能源制氨(renewable power to ammonia,RePtA)系统双层优化配置模型。该模型以极小化单位氨成本为目标函数,优化配置风电系统、光伏系统、电解制氢系统、化学储能系统和储氢系统的容量,并采用遗传算法对优化模型进行求解。以GW级RePtA系统为例,定量分析和比较了5类典型安装地点配置不同储能的情况下对系统最优配置以及经济性的影响。
文摘目的评估抑郁症患者中体质量指数(body mass index,BMI)正常、超重和代谢综合征(metabolic syndrome,MetS)的转移规律。方法以2016年1月至2021年11月期间于首都医科大学附属北京安定医院治疗,有多次入院记录的抑郁症患者为研究对象,根据每次入院时BMI和代谢情况分为BMI正常、超重和代谢综合征3种状态,采用多状态Markov模型分析转移规律。结果纳入398例研究对象的892条观测记录,中位年龄56岁,中位随访时间40个月。结果显示3种状态间共发生494次转移,其中5.1%由BMI正常转移为超重,5.5%由超重转移为MetS。超重发展为MetS的转移强度最高,是超重变为BMI正常的9.52倍。48.53个月后,BMI正常的抑郁症患者开始转移为MetS。对于超重的患者,8.77个月后开始转移为MetS。36个月后,BMI正常或超重者转移为MetS的概率为31.4%和50.4%;对于合并MetS者,36个月后仍为MetS的概率为51.2%。多因素分析显示未婚是体质量正常的抑郁症患者转移为超重的危险因素,而具有较高的受教育程度是超重的抑郁症患者转移为MetS的保护因素。结论抑郁症患者发展为MetS的强度和风险较高,发生MetS后不易好转,提示加强抑郁症患者的BMI管理和MetS的干预。
