受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长...受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长、影响范围最广的2009-2010年极端干旱事件,利用CLM5.0陆面过程模式(Community Land Model version 5.0)对2009-2010年极端干旱事件下植被生长进行数值模拟,并将模拟结果与三套遥感数据[Global Inventory Modeling and Mapping Studies(GIMMS),Global Land Surface Satellite(GLASS),Global Mapping(GLOBMAP)]进行对比验证CLM5.0对西南地区植被对干旱响应的模拟适用性。结果表明,2001-2016年,中国西南地区发生3例持续时间超过6个月的极端干旱事件,其中持续时间最长、最严重的干旱发生在2009-2010年。模拟发现在2009-2010年极端干旱期间,CLM5.0对植被与干旱的相关性、滞后响应、累积效应以及抵抗力和恢复力的模拟效果较好,植被对干旱的响应强度呈从东南向西北递减的特征,68.66%的区域植被对干旱表现出滞后响应,且滞后响应(78.02%)、累积效应(89.17%)与干旱均呈现较大面积的正相关,与多源遥感的描述有较高的一致性。在对不同植被类型的干旱抵抗力和恢复力的模拟方面,CLM5.0的模拟表现也较为出色,森林比灌木和草甸有更强的干旱抵抗力,且森林的干旱抵抗力和恢复力呈现明显的相反趋势。本研究使用CLM5.0模型模拟与多源遥感验证的方法,为理解西南地区植被对干旱的多方面响应提供了一个补充视角,有助于较全面地评估和预测西南干旱对植被活动的影响。展开更多
Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosupp...Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.展开更多
文摘受全球变化影响,原本湿润的西南地区自21世纪以来干旱事件频发,已对区域内植被生长造成了不同程度的抑制,威胁西南生态屏障安全。本研究采用标准化降水蒸散指数分析了西南地区2001-2016年极端干旱事件的频率和特征,选择了持续时间最长、影响范围最广的2009-2010年极端干旱事件,利用CLM5.0陆面过程模式(Community Land Model version 5.0)对2009-2010年极端干旱事件下植被生长进行数值模拟,并将模拟结果与三套遥感数据[Global Inventory Modeling and Mapping Studies(GIMMS),Global Land Surface Satellite(GLASS),Global Mapping(GLOBMAP)]进行对比验证CLM5.0对西南地区植被对干旱响应的模拟适用性。结果表明,2001-2016年,中国西南地区发生3例持续时间超过6个月的极端干旱事件,其中持续时间最长、最严重的干旱发生在2009-2010年。模拟发现在2009-2010年极端干旱期间,CLM5.0对植被与干旱的相关性、滞后响应、累积效应以及抵抗力和恢复力的模拟效果较好,植被对干旱的响应强度呈从东南向西北递减的特征,68.66%的区域植被对干旱表现出滞后响应,且滞后响应(78.02%)、累积效应(89.17%)与干旱均呈现较大面积的正相关,与多源遥感的描述有较高的一致性。在对不同植被类型的干旱抵抗力和恢复力的模拟方面,CLM5.0的模拟表现也较为出色,森林比灌木和草甸有更强的干旱抵抗力,且森林的干旱抵抗力和恢复力呈现明显的相反趋势。本研究使用CLM5.0模型模拟与多源遥感验证的方法,为理解西南地区植被对干旱的多方面响应提供了一个补充视角,有助于较全面地评估和预测西南干旱对植被活动的影响。
文摘Artificial intelligence(AI)is increasingly recognized as a transformative force in the field of solid organ transplantation.From enhancing donor-recipient matching to predicting clinical risks and tailoring immunosuppressive therapy,AI has the potential to improve both operational efficiency and patient outcomes.Despite these advancements,the perspectives of transplant professionals-those at the forefront of critical decision-making-remain insufficiently explored.To address this gap,this study utilizes a multi-round electronic Delphi approach to gather and analyses insights from global experts involved in organ transplantation.Participants are invited to complete structured surveys capturing demographic data,professional roles,institutional practices,and prior exposure to AI technologies.The survey also explores perceptions of AI’s potential benefits.Quantitative responses are analyzed using descriptive statistics,while open-ended qualitative responses undergo thematic analysis.Preliminary findings indicate a generally positive outlook on AI’s role in enhancing transplantation processes,particularly in areas such as donor matching and post-operative care.These mixed views reflect both optimism and caution among professionals tasked with integrating new technologies into high-stakes clinical workflows.By capturing a wide range of expert opinions,the findings will inform future policy development,regulatory considerations,and institutional readiness frameworks for the integration of AI into organ transplantation.
