Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGI...Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGIS tools,multi-criteria evaluation techniques and theMaximumEntropy(MaxEnt)model to assess flood hazard zones.The key physical elements of slope,elevation,rainfall,drainage density,land use,and soil types have been integrated to identify areas vulnerable to flooding.Overlay analysis has been used to construct zones specifically designated for flood hazards.Additionally,pairwise comparison using Saaty’s scale was employed to calculate the Eigenvector weights for each physical factor.A comparison of AUC values is estimated to find the most effective method for delineating flood hazard zones.TheMaxEnt model achieved an Area Under Curve(AUC)of 0.978,outperforming the Analytical hierarchy Process(AHP)model with an AUC of 0.967.The higher AUC indicates that the MaxEnt model is better at distinguishing between positive and negative occurrences.This could lead to more reliable predictions of the flood hazard zones.Overall,the higher AUC of the MaxEnt model suggests greater reliability and robustness.展开更多
为明确影响欧洲山杨林分布的主导环境因子,量化不同时期的潜在适生区,本研究借助MaxEnt模型与ArcGIS空间分析技术,融合我国境内910个欧洲山杨林点位及生物气候数据,对其在当前及4个未来时期的潜在适生区进行预测分析。结果表面,MaxEnt...为明确影响欧洲山杨林分布的主导环境因子,量化不同时期的潜在适生区,本研究借助MaxEnt模型与ArcGIS空间分析技术,融合我国境内910个欧洲山杨林点位及生物气候数据,对其在当前及4个未来时期的潜在适生区进行预测分析。结果表面,MaxEnt模型受试者工作特征曲线下面积(Area under the curve, AUC)值为0.889,预测结果有较好可靠性。欧洲山杨林的分布主要受年降水量(bio12)、最冷季度的降水量(bio19)、最冷季度的平均温度(bio11)、年平均温度(bio1)、最干燥季度的平均温度(bio9)、最热月份的最高温度(bio5)影响。与当前适生区相比,四种气候情境下,未来四个年代的适生区均有扩张趋势。展开更多
Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLM...Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.展开更多
In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asy...In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.展开更多
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and cha...In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.展开更多
基金Lab facilities are supported by DST-FIST.The DST-FIST program,under the Department of Science and Technology,Government of India,provides financial assistance through the‘Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions’(FIST)scheme.
文摘Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGIS tools,multi-criteria evaluation techniques and theMaximumEntropy(MaxEnt)model to assess flood hazard zones.The key physical elements of slope,elevation,rainfall,drainage density,land use,and soil types have been integrated to identify areas vulnerable to flooding.Overlay analysis has been used to construct zones specifically designated for flood hazards.Additionally,pairwise comparison using Saaty’s scale was employed to calculate the Eigenvector weights for each physical factor.A comparison of AUC values is estimated to find the most effective method for delineating flood hazard zones.TheMaxEnt model achieved an Area Under Curve(AUC)of 0.978,outperforming the Analytical hierarchy Process(AHP)model with an AUC of 0.967.The higher AUC indicates that the MaxEnt model is better at distinguishing between positive and negative occurrences.This could lead to more reliable predictions of the flood hazard zones.Overall,the higher AUC of the MaxEnt model suggests greater reliability and robustness.
文摘为明确影响欧洲山杨林分布的主导环境因子,量化不同时期的潜在适生区,本研究借助MaxEnt模型与ArcGIS空间分析技术,融合我国境内910个欧洲山杨林点位及生物气候数据,对其在当前及4个未来时期的潜在适生区进行预测分析。结果表面,MaxEnt模型受试者工作特征曲线下面积(Area under the curve, AUC)值为0.889,预测结果有较好可靠性。欧洲山杨林的分布主要受年降水量(bio12)、最冷季度的降水量(bio19)、最冷季度的平均温度(bio11)、年平均温度(bio1)、最干燥季度的平均温度(bio9)、最热月份的最高温度(bio5)影响。与当前适生区相比,四种气候情境下,未来四个年代的适生区均有扩张趋势。
文摘Model evaluation using benchmark datasets is an important method to measure the capability of large language models(LLMs)in specific domains,and it is mainly used to assess the knowledge and reasoning abilities of LLMs.Therefore,in order to better assess the capability of LLMs in the agricultural domain,Agri-Eval was proposed as a benchmark for assessing the knowledge and reasoning ability of LLMs in agriculture.The assessment dataset used in Agri-Eval covered seven major disciplines in the agricultural domain:crop science,horticulture,plant protection,animal husbandry,forest science,aquaculture science,and grass science,and contained a total of 2283 questions.Among domestic general-purpose LLMs,DeepSeek R1 performed best with an accuracy rate of 75.49%.In the realm of international general-purpose LLMs,Gemini 2.0 pro exp 0205 standed out as the top performer,achieving an accuracy rate of 74.28%.As an LLMs in agriculture vertical,Shennong V2.0 outperformed all the LLMs in China,and the answer accuracy rate of agricultural knowledge exceeded that of all the existing general-purpose LLMs.The launch of Agri-Eval helped the LLM developers to comprehensively evaluate the model's capability in the field of agriculture through a variety of tasks and tests to promote the development of the LLMs in the field of agriculture.
基金Supported by the National Natural Science Foundation of China(12261018)Universities Key Laboratory of Mathematical Modeling and Data Mining in Guizhou Province(2023013)。
文摘In this paper,we establish and study a single-species logistic model with impulsive age-selective harvesting.First,we prove the ultimate boundedness of the solutions of the system.Then,we obtain conditions for the asymptotic stability of the trivial solution and the positive periodic solution.Finally,numerical simulations are presented to validate our results.Our results show that age-selective harvesting is more conducive to sustainable population survival than non-age-selective harvesting.
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金the World Climate Research Programme(WCRP),Climate Variability and Predictability(CLIVAR),and Global Energy and Water Exchanges(GEWEX)for facilitating the coordination of African monsoon researchsupport from the Center for Earth System Modeling,Analysis,and Data at the Pennsylvania State Universitythe support of the Office of Science of the U.S.Department of Energy Biological and Environmental Research as part of the Regional&Global Model Analysis(RGMA)program area。
文摘In recent years,there has been an increasing need for climate information across diverse sectors of society.This demand has arisen from the necessity to adapt to and mitigate the impacts of climate variability and change.Likewise,this period has seen a significant increase in our understanding of the physical processes and mechanisms that drive precipitation and its variability across different regions of Africa.By leveraging a large volume of climate model outputs,numerous studies have investigated the model representation of African precipitation as well as underlying physical processes.These studies have assessed whether the physical processes are well depicted and whether the models are fit for informing mitigation and adaptation strategies.This paper provides a review of the progress in precipitation simulation overAfrica in state-of-the-science climate models and discusses the major issues and challenges that remain.
