In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feas...In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.展开更多
【目的】研究香料烟在云南的气候适生区,为其合理种植提供理论依据。【方法】使用ArcGIS将气候数据结合地形校正进行协同克里金插值,利用最大熵(maximum entropy,MaxEnt)模型筛选影响香料烟分布的气象因子,最后使用ArcGIS对云南省香料...【目的】研究香料烟在云南的气候适生区,为其合理种植提供理论依据。【方法】使用ArcGIS将气候数据结合地形校正进行协同克里金插值,利用最大熵(maximum entropy,MaxEnt)模型筛选影响香料烟分布的气象因子,最后使用ArcGIS对云南省香料烟的气候适生区进行评价。【结果】MaxEnt模型的曲线下面积(the area under curve,AUC)值为0.993,可精准预测云南省香料烟的气候适生区。影响香料烟在云南省分布的气象因子为2月降雨量、1月日照时间、3月日照时间、3月平均气温、3月降雨量、4月降雨量、1月降雨量、2月日照时间和4月最高气温。香料烟在云南省的最适宜种植区(四级适生区)主要分布在保山、德宏和临沧;适宜种植区(三级适生区)主要分布在保山、德宏、临沧、玉溪、楚雄和大理。MaxEnt模型预测结果与香料烟种植区拟合度较高,其种植区主要分布在四级和三级适生区,极少数分布在二级和一级适生区。【结论】云南省适合种植香料烟的地区主要在西南部,适宜种植区主要为沿怒江、澜沧江、黑惠江及其支流的干热河谷地区。2月降雨量、1月日照时间、3月日照时间和3月平均气温是影响香料烟在云南种植的主要气象因子。展开更多
Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of...Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.展开更多
基金supported by the Advanced Functional Composites Technology Key Laboratory Fund under Grant No.6142906220404Sichuan Province Centralized Guided Local Science and Technology Development Special Project under Grant No.2022ZYD0121。
文摘In this paper,an effective algorithm for optimizing the subarray of conformal arrays is proposed.The method first divides theconformal array into several first-level subarrays.It uses the X algorithm to solve the feasible solution of first-level subarray tiling and employs the particle swarm algorithm to optimize the conformal array subarray tiling scheme with the maximum entropy of the planar mapping as the fitness function.Subsequently,convex optimization is applied to optimize the subarray amplitude phase.Data results verify that the method can effectively find the optimal conformal array tiling scheme.
文摘【目的】研究香料烟在云南的气候适生区,为其合理种植提供理论依据。【方法】使用ArcGIS将气候数据结合地形校正进行协同克里金插值,利用最大熵(maximum entropy,MaxEnt)模型筛选影响香料烟分布的气象因子,最后使用ArcGIS对云南省香料烟的气候适生区进行评价。【结果】MaxEnt模型的曲线下面积(the area under curve,AUC)值为0.993,可精准预测云南省香料烟的气候适生区。影响香料烟在云南省分布的气象因子为2月降雨量、1月日照时间、3月日照时间、3月平均气温、3月降雨量、4月降雨量、1月降雨量、2月日照时间和4月最高气温。香料烟在云南省的最适宜种植区(四级适生区)主要分布在保山、德宏和临沧;适宜种植区(三级适生区)主要分布在保山、德宏、临沧、玉溪、楚雄和大理。MaxEnt模型预测结果与香料烟种植区拟合度较高,其种植区主要分布在四级和三级适生区,极少数分布在二级和一级适生区。【结论】云南省适合种植香料烟的地区主要在西南部,适宜种植区主要为沿怒江、澜沧江、黑惠江及其支流的干热河谷地区。2月降雨量、1月日照时间、3月日照时间和3月平均气温是影响香料烟在云南种植的主要气象因子。
基金funded by National Key R&D Program of China(No. 2021YFC2600400)National Natural Science Foundation of China(No. 32171794)Forestry Science and Technology Innovation Special of Jiangxi Forestry Department (No. 201912)
文摘Background: Hylurgus ligniperda(Fabricius) is native to Europe but has established populations in many countries and regions. H. ligniperda mainly infests Pinus species, and can cause severe weakness and even death of the host through its boring activity;it can also be a vector of various pathogenic fungi. This study was conducted to investigate the environmental variables limiting the distribution of H. ligniperda and the change trend of its suitable areas under climate change.Results: We used a maximum entropy model to predict the potential geographical distribution of H. ligniperda on a global scale under near current and future climatic scenarios using its occurrence data and environmental variables. The result shows that the areas surrounding the Mediterranean region, the eastern coastal areas of Asia, and the southeastern part of Oceania are highly suitable for H. ligniperda. The environmental variables with the greatest effect on the distribution of H. ligniperda were determined using the jackknife method and Pearson’s correlation analysis and included the monthly average maximum temperature in April, precipitation of driest quarter, the monthly average minimum temperature in December, precipitation of coldest quarter, mean temperature of driest quarter and mean diurnal range.Conclusions: Excessive precipitation in winter and low temperatures in spring had a great effect on the distribution of H. ligniperda. The potential geographical distribution of H. ligniperda was predicted to change under future climatic conditions compared with near current climate conditions. Highly suitable areas, moderately suitable areas and low suitable areas were predicted to increase by 59.99%, 44.43% and 22.92%, respectively, under the2081–2100 ssp245 scenario.
