Selecting an appropriate planting density is an effective way to improve crop water productivity(WPC).However, there is a lack of research on the balance between evapotranspiration(ET) partitioning, water consumption,...Selecting an appropriate planting density is an effective way to improve crop water productivity(WPC).However, there is a lack of research on the balance between evapotranspiration(ET) partitioning, water consumption, and grain production under different summer maize planting densities. To close this knowledge gap, a two-year field experiment was conducted in the North China Plain(NCP) to reveal the effects of different planting densities(HD: 100,000 plants ha^(-1);MD: 78,000 plants ha^(-1);LD:56,000 plants ha^(-1)) on ET partitioning, grain yield, and water productivity of summer maize. The water-heat-carbon-nitrogen simulator(WHCNS) model was employed to calculate ET partitioning and perform scenario simulation after calibration and validation. The results showed that compared to the LD treatment, ET of the summer maize and grain yield in the MD and HD treatments significantly increased. Model simulations showed that the ratio of evaporation to ET ranged from 25.6% to 30.7%and reduced as increasing planting densities. Increasing planting density enhanced total transpiration of summer maize more than 20 mm, comparing to LD treatment, and the most significant differences between various planting densities appeared at the mid-growth stage(August 1 to 31). Scenario simulations indicated that grain yield and WPCof summer maize were consistently higher in wet and normal years compared to drought years, exhibiting a trend of initially increasing and then decreasing with increasing planting density. The highest grain yield and WPCof summer maize were observed at a planting density of approximately 80,000 plants ha^(-1). The results provide theoretical support for selecting a summer maize planting density and effectively utilizing agricultural water in the NCP.展开更多
Purpose:The advent of chatbots based on large language models(LLMs),such as ChatGPT,has significantly transformed knowledge acquisition.However,the application of LLMs in glaucoma patient education remains elusive.In ...Purpose:The advent of chatbots based on large language models(LLMs),such as ChatGPT,has significantly transformed knowledge acquisition.However,the application of LLMs in glaucoma patient education remains elusive.In this study,we comprehensively compared the performance of four common LLMs–Qwen,Baichuan 2,ChatGPT-4o,and PaLM 2–in the context of glaucoma patient education.Methods:Initially,senior ophthalmologists were asked with scoring responses generated by the LLMs,which were answers to the most frequent glaucoma-related questions posed by patients.The Chinese Readability Platform was employed to assess the recommended reading age and reading difficulty score of the four LLMs.Subsequently,optimized models were filtered,and 29 glaucoma patients participated in posing questions to the chatbots and scoring the answers within a real-world clinical setting.Attending ophthalmologists were also required to score the answers across five dimensions:correctness,completeness,readability,helpfulness,and safety.Patients,on the other hand,scored the answers based on three dimensions:satisfaction,readability,and helpfulness.Results:In the first stage,Baichuan 2 and ChatGPT-4o outperformed the other two models,though ChatGPT-4o had higher recommended reading age and reading difficulty scores.In the second stage,both Baichuan 2 and ChatGPT-4o demonstrated exceptional performance among patients and ophthalmologists,with no statistically significant differences observed.Conclusions:Our research identifies Baichuan 2 and ChatGPT-4o as prominent LLMs,offering viable options for glaucoma education.展开更多
This research interprets the background of Jinzhou section of the Peking-Mukden Railway,and puts forward 65 heritages as cases based on the scope definition and investigation.After the data collection,processing,and v...This research interprets the background of Jinzhou section of the Peking-Mukden Railway,and puts forward 65 heritages as cases based on the scope definition and investigation.After the data collection,processing,and visualization,the database composed of 9 sub-databases,with B/S architecture mode,is constructed based on SQL server platform.The ArcGIS tool is used to analyze the distribution of the heritages,including spatial distribution characteristics,spatial agglomeration,and spatial equilibrium.“Image and model information database”and“text attribute information database”is used to analyze the architectural ontology features.The conclusions are drawn as follows:1)The integral distribution has the characteristics of“cohesion”,while the 5 medium types of heritages show obvious and different directions.2)The overall pattern of spatial agglomeration is characterized by high cohesion with a single high agglomeration point as the core.The low agglomeration area shows a point-line-point pattern.3)The integral heritages and three main types of buildings differ in distribution,and the equilibrium is low.The architectural ontology analysis shows that the image information can be used as the basis for ontology characteristics analysis,architectural form and style judgment,and architectural functional space analysis.展开更多
基金supported in part by the Key R&D Program of Shandong Province,China (2023CXGC010703)the National Key Research and Development Program of China (2022YFD2300905-01)the Natural Science Foundation of Shandong Province,China (ZR2021MC123)。
文摘Selecting an appropriate planting density is an effective way to improve crop water productivity(WPC).However, there is a lack of research on the balance between evapotranspiration(ET) partitioning, water consumption, and grain production under different summer maize planting densities. To close this knowledge gap, a two-year field experiment was conducted in the North China Plain(NCP) to reveal the effects of different planting densities(HD: 100,000 plants ha^(-1);MD: 78,000 plants ha^(-1);LD:56,000 plants ha^(-1)) on ET partitioning, grain yield, and water productivity of summer maize. The water-heat-carbon-nitrogen simulator(WHCNS) model was employed to calculate ET partitioning and perform scenario simulation after calibration and validation. The results showed that compared to the LD treatment, ET of the summer maize and grain yield in the MD and HD treatments significantly increased. Model simulations showed that the ratio of evaporation to ET ranged from 25.6% to 30.7%and reduced as increasing planting densities. Increasing planting density enhanced total transpiration of summer maize more than 20 mm, comparing to LD treatment, and the most significant differences between various planting densities appeared at the mid-growth stage(August 1 to 31). Scenario simulations indicated that grain yield and WPCof summer maize were consistently higher in wet and normal years compared to drought years, exhibiting a trend of initially increasing and then decreasing with increasing planting density. The highest grain yield and WPCof summer maize were observed at a planting density of approximately 80,000 plants ha^(-1). The results provide theoretical support for selecting a summer maize planting density and effectively utilizing agricultural water in the NCP.
基金supported by National Science Fund for Distinguished Young Scholars(82425015)National Natural Science Foundation of China(82171102)+3 种基金National youth talent support program(QWF158001)National Key Research and Development Program of China(2023YFA0915000)Shanghai Medical Innovation Research Program(22Y21900900)of J.HNational Natural Science Foundation of China(82271044)of X.Z.
文摘Purpose:The advent of chatbots based on large language models(LLMs),such as ChatGPT,has significantly transformed knowledge acquisition.However,the application of LLMs in glaucoma patient education remains elusive.In this study,we comprehensively compared the performance of four common LLMs–Qwen,Baichuan 2,ChatGPT-4o,and PaLM 2–in the context of glaucoma patient education.Methods:Initially,senior ophthalmologists were asked with scoring responses generated by the LLMs,which were answers to the most frequent glaucoma-related questions posed by patients.The Chinese Readability Platform was employed to assess the recommended reading age and reading difficulty score of the four LLMs.Subsequently,optimized models were filtered,and 29 glaucoma patients participated in posing questions to the chatbots and scoring the answers within a real-world clinical setting.Attending ophthalmologists were also required to score the answers across five dimensions:correctness,completeness,readability,helpfulness,and safety.Patients,on the other hand,scored the answers based on three dimensions:satisfaction,readability,and helpfulness.Results:In the first stage,Baichuan 2 and ChatGPT-4o outperformed the other two models,though ChatGPT-4o had higher recommended reading age and reading difficulty scores.In the second stage,both Baichuan 2 and ChatGPT-4o demonstrated exceptional performance among patients and ophthalmologists,with no statistically significant differences observed.Conclusions:Our research identifies Baichuan 2 and ChatGPT-4o as prominent LLMs,offering viable options for glaucoma education.
基金National Natural Science Foundation of China(Grant No.52078107).
文摘This research interprets the background of Jinzhou section of the Peking-Mukden Railway,and puts forward 65 heritages as cases based on the scope definition and investigation.After the data collection,processing,and visualization,the database composed of 9 sub-databases,with B/S architecture mode,is constructed based on SQL server platform.The ArcGIS tool is used to analyze the distribution of the heritages,including spatial distribution characteristics,spatial agglomeration,and spatial equilibrium.“Image and model information database”and“text attribute information database”is used to analyze the architectural ontology features.The conclusions are drawn as follows:1)The integral distribution has the characteristics of“cohesion”,while the 5 medium types of heritages show obvious and different directions.2)The overall pattern of spatial agglomeration is characterized by high cohesion with a single high agglomeration point as the core.The low agglomeration area shows a point-line-point pattern.3)The integral heritages and three main types of buildings differ in distribution,and the equilibrium is low.The architectural ontology analysis shows that the image information can be used as the basis for ontology characteristics analysis,architectural form and style judgment,and architectural functional space analysis.
