The utilization of nuclear power will persist as a prominent energy source in the foreseeable future.However,it presents substantial challenges concerning waste disposal and the potential emission of untreated radioac...The utilization of nuclear power will persist as a prominent energy source in the foreseeable future.However,it presents substantial challenges concerning waste disposal and the potential emission of untreated radioactive substances,such as radioactive 129I and 131I.The transportation of radioactive iodine poses a significant threat to both the environment and human health.Nevertheless,effectively,rapidly removing iodine ion from water using porous adsorbents remains a crucial challenge.In this work,three kinds of multiple sites porous organic polymers(POPs,POP-1,POP-2,and POP-3)have been developed using a monomer pre-modification strategy for highly efficient and fast I_(3) absorption from water.It is found that the POPs exhibited exceptional performance in terms of I3 adsorption,achieving a top-performing adsorption capacity of 5.25 g g^(-1) and the fastest average adsorption rate(K_(80%)=4.25 g g^(-1) h^(-1))with POP-1.Moreover,POP-1 exhibited exceptional capacity for the removal of I3 fromflowing aqueous solutions,with 95%removal efficiency observed even at 0.0005 mol L^(-1).Such results indicate that this material has the potential to be utilized for the emergency preparation of potable water in areas contaminated with radioactive iodine.The adsorption process can be effectively characterized by the Freundlich model and the pseudo-second-order model.The exceptional I_(3) absorption capacity is primarily attributed to the incorporation of a substantial number of active adsorption sites,including bromine,carbonyl,and amide groups.展开更多
Using ground-based telescopes,the multi-color photometric observations of the contact binary EF Boo were obtained in 2020,2023,and 2024.Combining these with 7-sectors of light curves from TESS data,the variations of t...Using ground-based telescopes,the multi-color photometric observations of the contact binary EF Boo were obtained in 2020,2023,and 2024.Combining these with 7-sectors of light curves from TESS data,the variations of the O'Connell effect in continuous time and shapes of light curves over several years were identified.Three sets of typical light curves were analyzed to determine the photometric solutions via the Wilson-Devinney program.Considering the spectroscopic mass ratio of q=0.53,these photometric solutions suggest that EF Boo is a W-type W UMa contact binary with the averaged filling factor of f=22.26%,a small temperature difference,and a cool spot on the primary component.If the variations of the O'Connell effect are due to the magnetic activity of this cool spot,the longitudinal location varied from 50.4 to 302.7 over the time interval of 1434 days.Based on all CCD minimum times from ground-based telescope and TESS data,the O-C curve was also analyzed.A cyclic oscillation(A3=0.00575 days,T3=27.8 yr)superimposed on a secular increase(dP/dt=6.74×10^(-8)day yr^(-1))was discovered for the first time.The successive increase is possibly a result of mass transfer from the less massive star to the more massive one.The cyclic oscillations were possibly explained by the light-travel time effect via a third body or the magnetic activities.From the short cadence observations from TESS,we also calculated the value of the O'Connell effect and O-C value for each cycle and found no correlation between the O'Connell effect and O-C over nearly 30 days across different sectors.展开更多
Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also i...Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.展开更多
基金support from the National Natural Science Foundation of China(No.22273016,22273017,22233006)Plan for Henan Province University Science and Technology Innovation Team(No.25IRTSTHN002)+1 种基金Young Backbone Teacher Training Program of Henan Province(2023GGJS036)the 111 project(No.D17007).
文摘The utilization of nuclear power will persist as a prominent energy source in the foreseeable future.However,it presents substantial challenges concerning waste disposal and the potential emission of untreated radioactive substances,such as radioactive 129I and 131I.The transportation of radioactive iodine poses a significant threat to both the environment and human health.Nevertheless,effectively,rapidly removing iodine ion from water using porous adsorbents remains a crucial challenge.In this work,three kinds of multiple sites porous organic polymers(POPs,POP-1,POP-2,and POP-3)have been developed using a monomer pre-modification strategy for highly efficient and fast I_(3) absorption from water.It is found that the POPs exhibited exceptional performance in terms of I3 adsorption,achieving a top-performing adsorption capacity of 5.25 g g^(-1) and the fastest average adsorption rate(K_(80%)=4.25 g g^(-1) h^(-1))with POP-1.Moreover,POP-1 exhibited exceptional capacity for the removal of I3 fromflowing aqueous solutions,with 95%removal efficiency observed even at 0.0005 mol L^(-1).Such results indicate that this material has the potential to be utilized for the emergency preparation of potable water in areas contaminated with radioactive iodine.The adsorption process can be effectively characterized by the Freundlich model and the pseudo-second-order model.The exceptional I_(3) absorption capacity is primarily attributed to the incorporation of a substantial number of active adsorption sites,including bromine,carbonyl,and amide groups.
基金support of the staff of the 85 cm,60 cm telescopes at the Xinglong observational station of the National Astronomical Observatories,Chinese Academy of Sciences,and TESS team works funding by the NASA Science Mission directorate.This work is sponsored by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(No.2022D01A164)the National Natural Science Foundation of China(Nos.U1831109 and 12103030)。
文摘Using ground-based telescopes,the multi-color photometric observations of the contact binary EF Boo were obtained in 2020,2023,and 2024.Combining these with 7-sectors of light curves from TESS data,the variations of the O'Connell effect in continuous time and shapes of light curves over several years were identified.Three sets of typical light curves were analyzed to determine the photometric solutions via the Wilson-Devinney program.Considering the spectroscopic mass ratio of q=0.53,these photometric solutions suggest that EF Boo is a W-type W UMa contact binary with the averaged filling factor of f=22.26%,a small temperature difference,and a cool spot on the primary component.If the variations of the O'Connell effect are due to the magnetic activity of this cool spot,the longitudinal location varied from 50.4 to 302.7 over the time interval of 1434 days.Based on all CCD minimum times from ground-based telescope and TESS data,the O-C curve was also analyzed.A cyclic oscillation(A3=0.00575 days,T3=27.8 yr)superimposed on a secular increase(dP/dt=6.74×10^(-8)day yr^(-1))was discovered for the first time.The successive increase is possibly a result of mass transfer from the less massive star to the more massive one.The cyclic oscillations were possibly explained by the light-travel time effect via a third body or the magnetic activities.From the short cadence observations from TESS,we also calculated the value of the O'Connell effect and O-C value for each cycle and found no correlation between the O'Connell effect and O-C over nearly 30 days across different sectors.
文摘Agriculture plays a crucial role in the economy,and there is an increasing global emphasis on automating agri-cultural processes.With the tremendous increase in population,the demand for food and employment has also increased significantly.Agricultural methods traditionally used to meet these requirements are no longer ade-quate,requiring solutions to issues such as excessive herbicide use and the use of chemical fertilizers.Integration of technologies such as the Internet of Things,wireless communication,machine learning,artificial intelligence(AI),and deep learning shows promise in addressing these challenges.However,there is a lack of comprehensive documentation on the application and potential of AI in improving agricultural input efficiency.To address this gap,a desk research approach was used by utilizing peer-reviewed electronic databases like PubMed,Scopus,Goo-gle Scholar,Web of Science,and Science Direct for relevant articles.Out of 327 initially identified articles,180 were deemed pertinent,focusing primarily on AI’s potential in enhancing yield through better management of nutrients,water,and weeds.Taking into account researchfindings worldwide,we found that AI technologies could assist farmers by providing recommendations on the optimal nutrients to enhance soil quality and deter-mine the best time for irrigation or herbicide application.The present status of AI-driven automation in agricul-ture holds significant promise for optimizing agricultural input utilization and reducing resource waste,particularly in the context of three pillars of crop management,i.e.,nutrient,irrigation,and weed management.
