In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to...In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.展开更多
Oxygen vacancy(Vo)engineering has been recognized as one of the most effective strategies for enhancing the photocatalytic CO_(2) conversion performance of metal oxides,as it can simultaneously facilitate photogenerat...Oxygen vacancy(Vo)engineering has been recognized as one of the most effective strategies for enhancing the photocatalytic CO_(2) conversion performance of metal oxides,as it can simultaneously facilitate photogenerated charge carrier separation efficiency and provide additional surface reaction sites.However,the wide application of Vo engineering in photocatalysis are limited by its poor stability,owing to the easy recovery of these vacancy defects by atmospheric oxygen.Herein,we develop an indium(In)doping strategy to regulate the coordination environment in CeO_(2) with abundant Vo(CeO_(2-x)),thereby enhance its stability during photocatalytic CO_(2) conversion.Confirmed by positron annihilation lifetime spectroscopy(PALS),In dopants combine with Vo by substituting for part of Ce^(4+),forming In^(3+)-Vo complexes that effectively inhibit the formation of unstable va-cancy clusters.Such In^(3+)-Vo complexes can also reduce the energy required for formation of the CO products.Therefore,the optimized In-doped CeO_(2-x) exhibits excellent photocatalytic CO_(2) conversion performance,with a CO yield of 301.6μmol⋅g^(-1) after 5 h of light irradiation,and maintain high activity after four cycles of experiments.Comprehensive experimental and theoretical studies indicate that the introduction of In doping not only significantly improves the stability of Vo in CeO_(2-x),but also reconstruct the reaction kinetics of the CO_(2) conversion by forming In^(3+)-Vo complexes thus facilitating the overall reaction.展开更多
为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260...为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260全秸秆茬地洁区旋耕智能施肥播种机和3ZF-4(200)中耕追肥机,设置7种施肥模式(30 cm+15 cm宽窄行种植):以尿素4次分施(N 240 kg hm^(-2),基肥∶分蘖肥∶拔节肥∶孕穗肥=5∶1∶2∶2,窄行基施,追肥全田撒施)为对照(CK);减氮15%(N 204 kg hm^(-2))条件下设置6种处理:M_(1)(100%SRF窄行基施);M_(2)(60%SRF窄行基施+40%U拔节期窄行撒施);M_(3)(60%SRF窄行基施+40%U返青期宽行条施);M_(4)(60%SRF窄行基施+40%SRF返青期窄行撒施);M_(5)(60%SRF窄行基施+40%SRF返青期宽行条施);M_(4+5)(60%SRF窄行基施+20%SRF返青期宽行条施+20%SRF返青期窄行撒施)。研究比较不同施肥模式对小麦产量效益、根系形态生理、氮素利用效率及N_(2)O排放的影响。结果表明,与CK相比,M_(2)~M_(5)处理提高了小麦产量(4.0%~19.0%)和经济效益(13.7%~35.7%),其中M_(4)和M_(5)处理表现最优,分别增产14.1%和19.0%,经济效益提升34.5%和35.7%。这些处理明显改善了根系特性(根干重密度增加9.7%~111.8%,根系活力和氧化力分别提高6.8%~52.0%和4.2%~44.2%),降低N_(2)O累积排放量22.6%~34.5%,提高0~20 cm土层硝态氮含量11.2%~40.0%。在氮素利用方面,M_(2)~M_(5)处理均提高了籽粒氮素积累量、花后氮素积累量及其对籽粒氮素的贡献率,氮肥利用效率指标(包括偏生产力、农学效率和表观利用率)分别显著提升了22.4%~40.0%、29.7%~74.3%和9.41~18.77个百分点。值得注意的是,M_(4)和M_(5)处理表现出最优的综合效益:N_(2)O累积排放量降幅最大(分别达27.0%和34.5%),氮肥表观利用率2季均维持在43.0%以上(均值分别为43.5%和46.8%),同时在生育后期保持较高的根系活性和耕层无机氮含量。相比之下,M_(1)处理虽然实现了最大的N_(2)O减排效果(降幅35.9%),但导致减产10.4%和经济效益下降10.8%,且氮肥利用效率呈现不稳定的年际变化特征。而优化处理M_(4+5)进一步改善了根系形态生理特性,并提高氮肥表观利用率和籽粒氮素积累量。综上,减氮15%条件下(N 204 kg hm^(-2)),缓混肥2次施用处理(M_(4)和M_(5))能实现产量、经济效益、氮肥利用效率和N_(2)O减排的协同提高,并以追肥深施处理(M_(5))效应更强。本研究为稻茬小麦缓释肥减氮优化高效应用提供重要理论依据。展开更多
基金supported by Institute of Information and Communications Technology Planning and Evaluation(IITP)grant funded by the Korean government(MSIT)(No.2019-0-01842,Artificial Intelligence Graduate School Program(GIST))supported by Korea Planning&Evaluation Institute of Industrial Technology(KEIT)grant funded by the Ministry of Trade,Industry&Energy(MOTIE,Republic of Korea)(RS-2025-25448249+1 种基金Automotive Industry Technology Development(R&D)Program)supported by the Regional Innovation System&Education(RISE)programthrough the(Gwangju RISE Center),funded by the Ministry of Education(MOE)and the Gwangju Metropolitan City,Republic of Korea(2025-RISE-05-001).
文摘In real-world autonomous driving tests,unexpected events such as pedestrians or wild animals suddenly entering the driving path can occur.Conducting actual test drives under various weather conditions may also lead to dangerous situations.Furthermore,autonomous vehicles may operate abnormally in bad weather due to limitations of their sensors and GPS.Driving simulators,which replicate driving conditions nearly identical to those in the real world,can drastically reduce the time and cost required for market entry validation;consequently,they have become widely used.In this paper,we design a virtual driving test environment capable of collecting and verifying SiLS data under adverse weather conditions using multi-source images.The proposed method generates a virtual testing environment that incorporates various events,including weather,time of day,and moving objects,that cannot be easily verified in real-world autonomous driving tests.By setting up scenario-based virtual environment events,multi-source image analysis and verification using real-world DCUs(Data Concentrator Units)with V2X-Car edge cloud can effectively address risk factors that may arise in real-world situations.We tested and validated the proposed method with scenarios employing V2X communication and multi-source image analysis.
基金supported by the National Natural Science Foundation of China(No.22202152)Tianjin Municipal Science and Technology Bureau(No.24JCQNJC00990)Cangzhou Institute of Tiangong University(No.TGCYY-F-0304).
文摘Oxygen vacancy(Vo)engineering has been recognized as one of the most effective strategies for enhancing the photocatalytic CO_(2) conversion performance of metal oxides,as it can simultaneously facilitate photogenerated charge carrier separation efficiency and provide additional surface reaction sites.However,the wide application of Vo engineering in photocatalysis are limited by its poor stability,owing to the easy recovery of these vacancy defects by atmospheric oxygen.Herein,we develop an indium(In)doping strategy to regulate the coordination environment in CeO_(2) with abundant Vo(CeO_(2-x)),thereby enhance its stability during photocatalytic CO_(2) conversion.Confirmed by positron annihilation lifetime spectroscopy(PALS),In dopants combine with Vo by substituting for part of Ce^(4+),forming In^(3+)-Vo complexes that effectively inhibit the formation of unstable va-cancy clusters.Such In^(3+)-Vo complexes can also reduce the energy required for formation of the CO products.Therefore,the optimized In-doped CeO_(2-x) exhibits excellent photocatalytic CO_(2) conversion performance,with a CO yield of 301.6μmol⋅g^(-1) after 5 h of light irradiation,and maintain high activity after four cycles of experiments.Comprehensive experimental and theoretical studies indicate that the introduction of In doping not only significantly improves the stability of Vo in CeO_(2-x),but also reconstruct the reaction kinetics of the CO_(2) conversion by forming In^(3+)-Vo complexes thus facilitating the overall reaction.
基金江西省自然科学基金项目(编号:20202BAB206075)江西省教育厅科技项目(编号:GJJ201202)江西中医药大学中西医结合一级学科平台(Discipline of Chinese and Western Integrative Medicine,Jiangxi University of Chinese Medicine)。
文摘为探究稻茬小麦深施肥“一基一追”机艺融合技术的增产增效减排机制,2021—2024年在长江下游南通稻茬麦区开展大田试验。试验采用缓释掺混肥料(SRF,N∶P_(2)O_(5)∶K_(2)O=26∶12∶12)和普通尿素(U,46%N),结合自主研发的2BFGK-12(6)260全秸秆茬地洁区旋耕智能施肥播种机和3ZF-4(200)中耕追肥机,设置7种施肥模式(30 cm+15 cm宽窄行种植):以尿素4次分施(N 240 kg hm^(-2),基肥∶分蘖肥∶拔节肥∶孕穗肥=5∶1∶2∶2,窄行基施,追肥全田撒施)为对照(CK);减氮15%(N 204 kg hm^(-2))条件下设置6种处理:M_(1)(100%SRF窄行基施);M_(2)(60%SRF窄行基施+40%U拔节期窄行撒施);M_(3)(60%SRF窄行基施+40%U返青期宽行条施);M_(4)(60%SRF窄行基施+40%SRF返青期窄行撒施);M_(5)(60%SRF窄行基施+40%SRF返青期宽行条施);M_(4+5)(60%SRF窄行基施+20%SRF返青期宽行条施+20%SRF返青期窄行撒施)。研究比较不同施肥模式对小麦产量效益、根系形态生理、氮素利用效率及N_(2)O排放的影响。结果表明,与CK相比,M_(2)~M_(5)处理提高了小麦产量(4.0%~19.0%)和经济效益(13.7%~35.7%),其中M_(4)和M_(5)处理表现最优,分别增产14.1%和19.0%,经济效益提升34.5%和35.7%。这些处理明显改善了根系特性(根干重密度增加9.7%~111.8%,根系活力和氧化力分别提高6.8%~52.0%和4.2%~44.2%),降低N_(2)O累积排放量22.6%~34.5%,提高0~20 cm土层硝态氮含量11.2%~40.0%。在氮素利用方面,M_(2)~M_(5)处理均提高了籽粒氮素积累量、花后氮素积累量及其对籽粒氮素的贡献率,氮肥利用效率指标(包括偏生产力、农学效率和表观利用率)分别显著提升了22.4%~40.0%、29.7%~74.3%和9.41~18.77个百分点。值得注意的是,M_(4)和M_(5)处理表现出最优的综合效益:N_(2)O累积排放量降幅最大(分别达27.0%和34.5%),氮肥表观利用率2季均维持在43.0%以上(均值分别为43.5%和46.8%),同时在生育后期保持较高的根系活性和耕层无机氮含量。相比之下,M_(1)处理虽然实现了最大的N_(2)O减排效果(降幅35.9%),但导致减产10.4%和经济效益下降10.8%,且氮肥利用效率呈现不稳定的年际变化特征。而优化处理M_(4+5)进一步改善了根系形态生理特性,并提高氮肥表观利用率和籽粒氮素积累量。综上,减氮15%条件下(N 204 kg hm^(-2)),缓混肥2次施用处理(M_(4)和M_(5))能实现产量、经济效益、氮肥利用效率和N_(2)O减排的协同提高,并以追肥深施处理(M_(5))效应更强。本研究为稻茬小麦缓释肥减氮优化高效应用提供重要理论依据。
