作为一种典型的污染物,N,N-二甲基甲酰胺(N,N-dimethylformamide,DMF)具有排放量大、生态毒性高、难以降解的特性,但目前国内外尚缺乏相关基准标准研究。基于ECOTOX、Web of Science及中国知网等相关中英文文献和毒理数据库,收集并筛选...作为一种典型的污染物,N,N-二甲基甲酰胺(N,N-dimethylformamide,DMF)具有排放量大、生态毒性高、难以降解的特性,但目前国内外尚缺乏相关基准标准研究。基于ECOTOX、Web of Science及中国知网等相关中英文文献和毒理数据库,收集并筛选获得222条DMF对中国淡水生物毒性的文献数据,同时补充了DMF对麦穗鱼、黄颡鱼、河蚬、苏式尾鳃蚓、泥鳅、萼花臂尾轮虫、角突臂尾轮虫、莱茵衣藻共8种本土代表性物种的急性、慢性毒性测试数据,用于DMF的淡水生物水质基准推导。DMF水质基准共涉及26种淡水水生生物,涵盖了3个营养级,同时包含了草鱼、鳙鱼等在我国广泛分布的经济物种。其中急性数据涵盖7门15科24个物种,最敏感的物种为柱孢鱼腥藻和多变鱼腥藻;慢性毒性数据涵盖6门10科12个物种,最敏感的物种为模糊网纹溞。采用物种敏感度分布法(SSD)和毒性百分数排序法(TPR)分别计算我国淡水水生生物的DMF水质基准,其中SSD推导出短期水质基准为824.12 mg/L,长期水质基准为14.53 mg/L,TPR推导出短期水质基准为1 081.58 mg/L,长期水质基准为22.48 mg/L,建议采用SSD所推导基准作为DMF的水质标准。展开更多
Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereb...Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereby weakening the policy implications for energy conservation and emission reduction.To fill this gap,this study uses spatial-temporal logarithmic mean Divisia index models and extended Theil index inequality models to investigate the drivers of EC changes and inequality,considering the scale and structure of governmental environmental expenditure(EG)across 30 Chinese provinces from 2007 to 2021.The findings reveal that:First,EG acts as a positive driver of total EC,while industrial investment efficiency and fiscal expenditure pressure exert negative effects.Second,disparities in EG functions act as a negative driver,accounting for a 4.56%decrease in average interprovincial EC gaps.Third,China’s EC inequality demonstrated an overall upward trajectory from 0.074 to 0.093 over the period,mainly driven by positive contributions from inequalities in EG and the fiscal expenditure structure.This study highlights the importance of optimizing the government expenditure structure and scale in formulating policies for sustainable EC.展开更多
Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysi...Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.展开更多
To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spect...To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spectroscopy(RS)method.Constrained by vitrinite reflectance(Ro)data,burial and thermal evolution histories of QFm shale were reconstructed through basin numerical simulation technology.The evolution model of and critical period of organic matter was determined,and its dominant drivers were analyzed.The results show that the asphalt Raman vitrinite reflectance(_(Rmc)Ro)ranges from 3.21%to 4.15%.Thermal maturity within the trough follows a southern part>central part>northern part trend.Thermal maturity is moderate within the paleo-uplift,whereas organic matter outside the paleo-uplift has undergone graphitization.Two types of thermal evolution imprints were established:a continuous heating type and a stop heating type of Silurian–Permian.Sedimentary burial,paleogeomorphology,tectonic movement and Emeishan mantle plume are the dominant drivers of multi-stage thermal imprints of the QFm shale.The three factors are coupled with each other.The Late Caledonian and Late Indosinian are the key periods of organic matter thermal evolution.The Leshan-Longnüsi paleo-uplift weakens the thermal effect of the Permian Emeishan mantle plume.The current thermal evolution pattern of the QFm is mainly determined by the continuous subsidence of the Triassic–Cretaceous.Stop heating model of Silurian–Permian locks the maturity of organic matter in the gold window,thus controlling the enrichment of QFm shale gas.It provides new insights for shale gas migration,enrichment and effective exploration and development of shale gas in the Lower Paleozoic QFm.展开更多
Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such a...Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.展开更多
Pedestrian detection is a critical challenge in the field of general object detection,the performance of object detection has advanced with the development of deep learning.However,considerable improvement is still re...Pedestrian detection is a critical challenge in the field of general object detection,the performance of object detection has advanced with the development of deep learning.However,considerable improvement is still required for pedestrian detection,considering the differences in pedestrian wears,action,and posture.In the driver assistance system,it is necessary to further improve the intelligent pedestrian detection ability.We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection.Firstly,we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics.Secondly,we propose a novel network architecture,namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector.Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent.At last,we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle.The results establish the validity of the approach.展开更多
The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We r...The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.展开更多
基金supported by the Humanities and Social Sciences Youth Foundation,Ministry of Education of China[Grant No.24YJC630248]Sichuan Office of Philosophy and Social Science,China[Grant No.SCJJ24ND299].
文摘Energy consumption(EC)is a core factor in maintaining sustainable development;little is known about the drivers of temporal-spatial EC changes and the corresponding inequality from the perspective of government,thereby weakening the policy implications for energy conservation and emission reduction.To fill this gap,this study uses spatial-temporal logarithmic mean Divisia index models and extended Theil index inequality models to investigate the drivers of EC changes and inequality,considering the scale and structure of governmental environmental expenditure(EG)across 30 Chinese provinces from 2007 to 2021.The findings reveal that:First,EG acts as a positive driver of total EC,while industrial investment efficiency and fiscal expenditure pressure exert negative effects.Second,disparities in EG functions act as a negative driver,accounting for a 4.56%decrease in average interprovincial EC gaps.Third,China’s EC inequality demonstrated an overall upward trajectory from 0.074 to 0.093 over the period,mainly driven by positive contributions from inequalities in EG and the fiscal expenditure structure.This study highlights the importance of optimizing the government expenditure structure and scale in formulating policies for sustainable EC.
基金funded by the General Program of the National Natural Science Foundation of China[Grant No.42171300]the Strategic Research Program of the National Natural Science Foundation of China[Grant No.42542001]+1 种基金Post-funded Project of National Social Science Fund of China[Grant No.25FJYB015]Special Project of Strategic Research and Decision Support System of the Chinese Academy of Sciences[Grant No.GHJ-ZLZX-2025-48].
文摘Greenhouse gas(GHG)emissions from China’s food system are a major environmental concern;however,studies quantifying their drivers and future projections remain limited.This study uses structural decomposition analysis and growth curve models to assess food-related GHG trends from 1961 to 2020,identify key drivers and their contributions,and project emissions for 2050 under six economic and population scenarios.It also proposes reduction pathways to help China achieve its 2060 carbon neutrality goal.Animal and plant foods are categorized into 14 groups based on the similarity of their emission coefficients.China’s total food related GHG emissions rose tenfold,from 351.7 to 3719.8 million tons CO_(2)-equivalent(CO_(2)e)/year,between 1961 and 2020.Per-capita emissions increased from 532.1 to 2584.4 kg CO_(2)e/year.Emissions from plant based foods grew from 435.0 to 824.6 kg CO_(2)e/year,while animal-based emissions surged from 97.1 to 1759.8 kg CO_(2)e/year,with animal products contributing more owing to their higher emission coefficients.Key drivers include rising food intake,increasing demand for animal-based foods(especially red meat),and population growth.Scenario analyses predict that emissions will peak at 3826.2 million tons CO_(2)e/year in 2031(low economy-low population)and 3971.0 million tons CO_(2)e/year in 2039(high economy-medium population).Compared with Australian,Indian,and Japanese diets,Chinese diets exhibit lower per-capita emissions than Australia and India but have higher emissions than in Japan.Adhering to China’s national dietary guidelines could reduce Chinese per-capita food-related GHGs by 31.5%,and optimized diets could lower them by 45.3%.This study provides valuable insights for Chinese policymakers to reduce food-related GHG emissions,refine national dietary guidelines,and raise public awareness regarding the food system’s environmental impact,thus encouraging people to follow sustainable diets.
基金funded by the Innovative Research Group Project of the National Natural Science Foundation of China(Nos.U24A20592 and 42272137)Guizhou Province Science and Technology Innovation Talent Team,Construction of the Science and Technology Innovation Talent Team for the Evaluation and Development of Unconventional Natural Gas Resources in Complex Structural Areas(No.Qian Ke He Platform Talent-CXTD[2023]013)。
文摘To clarify the thermal evolution characteristics of organic matter in the ZizhongWeiyuan area in Sichuan Basin,solid bitumen reflectance of the Lower Cambrian Qiongzhusi Formation(QFm)shale was measured by Raman Spectroscopy(RS)method.Constrained by vitrinite reflectance(Ro)data,burial and thermal evolution histories of QFm shale were reconstructed through basin numerical simulation technology.The evolution model of and critical period of organic matter was determined,and its dominant drivers were analyzed.The results show that the asphalt Raman vitrinite reflectance(_(Rmc)Ro)ranges from 3.21%to 4.15%.Thermal maturity within the trough follows a southern part>central part>northern part trend.Thermal maturity is moderate within the paleo-uplift,whereas organic matter outside the paleo-uplift has undergone graphitization.Two types of thermal evolution imprints were established:a continuous heating type and a stop heating type of Silurian–Permian.Sedimentary burial,paleogeomorphology,tectonic movement and Emeishan mantle plume are the dominant drivers of multi-stage thermal imprints of the QFm shale.The three factors are coupled with each other.The Late Caledonian and Late Indosinian are the key periods of organic matter thermal evolution.The Leshan-Longnüsi paleo-uplift weakens the thermal effect of the Permian Emeishan mantle plume.The current thermal evolution pattern of the QFm is mainly determined by the continuous subsidence of the Triassic–Cretaceous.Stop heating model of Silurian–Permian locks the maturity of organic matter in the gold window,thus controlling the enrichment of QFm shale gas.It provides new insights for shale gas migration,enrichment and effective exploration and development of shale gas in the Lower Paleozoic QFm.
基金funded by the Guangzhou Development Zone Science and Technology Project(2023GH02)the University of Macao(MYRG2022-00271-FST)research grants by the Science and Technology Development Fund of Macao(0032/2022/A)and(0019/2025/RIB1).
文摘Accurately recognizing driver distraction is critical for preventing traffic accidents,yet current detection models face two persistent challenges.First,distractions are often fine-grained,involving subtle cues such as brief eye closures or partial yawns,which are easily missed by conventional detectors.Second,in real-world scenarios,drivers frequently exhibit overlapping behaviors,such as simultaneously holding a cup,closing their eyes,and yawning,leading tomultiple detection boxes and degradedmodel performance.Existing approaches fail to robustly address these complexities,resulting in limited reliability in safety critical applications.To overcome these pain points,we propose YOLO-Drive,a novel framework that enhances YOLO-based driver monitoring with EfficientViM and Polarized Spectral–Spatial Attention(PSSA)modules.Efficient ViMprovides lightweight yet powerful global–local feature extraction,enabling accurate recognition of subtle driver states.PSSA further amplifies discriminative features across spatial and spectral domains,ensuring robust separation of concurrent distraction cues.By explicitly modeling fine-grained and overlapping behaviors,our approach delivers significant improvements in both precision and robustness.Extensive experiments on benchmark driver distraction datasets demonstrate that YOLO-Drive consistently out-performs stateof-the-art models,achieving higher detection accuracy while maintaining real-time efficiency.These results validate YOLO-Drive as a practical and reliable solution for advanced driver monitoring systems,addressing long-standing challenges of subtle cue recognition and multi-cue distraction detection.
文摘Pedestrian detection is a critical challenge in the field of general object detection,the performance of object detection has advanced with the development of deep learning.However,considerable improvement is still required for pedestrian detection,considering the differences in pedestrian wears,action,and posture.In the driver assistance system,it is necessary to further improve the intelligent pedestrian detection ability.We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection.Firstly,we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics.Secondly,we propose a novel network architecture,namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector.Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent.At last,we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle.The results establish the validity of the approach.
基金National Key Research and Development Program of China,No.2023YFE0208100,No.2021YFC3000201Natural Science Foundation of Henan Province,No.232300420165。
文摘The Qinba Mountains are climatically and ecologically recognized as the north-south transitional zone of China.Analysis of its phenology is critical for comprehending the response of vegetation to climatic change.We retrieved the start of spring phenology(SOS)of eight forest communities from the MODIS products and adopted it as an indicator for spring phenology.Trend analysis,partial correlation analysis,and GeoDetector were employed to reveal the spatio-temporal patterns and climatic drivers of SOS.The results indicated that the SOS presented an advance trend from 2001 to 2020,with a mean rate of−0.473 d yr^(−1).The SOS of most forests correlated negatively with air temperature(TEMP)and positively with precipitation(PRE),suggesting that rising TEMP and increasing PRE in spring would forward and delay SOS,respectively.The dominant factors influencing the sensitivity of SOS to climatic variables were altitude,forest type,and latitude,while the effects of slope and aspect were relatively minor.The response of SOS to climatic factors varied significantly in space and among forest communities,partly due to the influence of altitude,slope,and aspect.
