With the rapid development of livestock and poultry breeding industries,pollution problems caused by the discharge of wastewater and manure have become increasingly severe.However,studies on the impacts of this pollut...With the rapid development of livestock and poultry breeding industries,pollution problems caused by the discharge of wastewater and manure have become increasingly severe.However,studies on the impacts of this pollution on rural residents'health are lacking.Based on data from the Peking University's China Family Panel Studies in 2010 and 2014,this paper uses a cross-sectional and panel data probit model to estimate the probability of breeding industry development in rural areas increasing the health risks of local villagers.First,the study found that the more households engaged in breeding in the region or the larger the scale of regional breeding,the higher the health risks to local villagers,particularly in areas where pigs are raised.Second,compared with individual farming,the greater the proportion of large-scale farming,the higher the health risks to villagers.Third,the development of the breeding industry seldom includes the ecological recycling of wastes and fails to reduce the use of local pesticides and fertilizers,thereby increasing the health risks to villagers.Therefore,this paper argues that providing technology to process breeding industry waste and establish an organic industrial production chain will be crucial to reducing the impact of breeding industry pollution on human health.展开更多
The Carboniferous to Permian tectono-sedimentary evolution of the southern Junggar area brings new insights into understanding the subduction-collision processes in the northern Tianshan region.Integrating geophysics,...The Carboniferous to Permian tectono-sedimentary evolution of the southern Junggar area brings new insights into understanding the subduction-collision processes in the northern Tianshan region.Integrating geophysics,geochemistry,and geochronology approaches,this study investigates the Carboniferous-Permian strata in the southern Junggar Basin.The results have revealed three distinct tectono-stratigraphic evolutionary stages,each marked by a distinctive volcano-sedimentary sequence.The Early Carboniferous strata suggest intense volcanic activities in the southern Junggar area.During the Late Carboniferous,the southern Junggar Basin was controlled by normal faulting in an extensional setting,receiving sedimentary inputs from the Junggar terrane.The Lower Permian,unconformably overlying the Upper Carboniferous,was shaped by an extensional regime and is comprised by volcano-clastic sequences that received detritus from the Yili-Central Tianshan block.These findings indicate that a Late Carboniferous forearc basin developed in the southern Junggar area,and it evolved into a post-collisional rift in the Early Permian.This period marked a dynamic shift from bidirectional subduction(rollback)to the detachment of the North Tianshan oceanic slab.We propose that the collision between the YiliCentral Tianshan block and the Junggar terrane,along with the closure of the North Tianshan Ocean,likely occurred in the Late Carboniferous(ca.306-303 Ma).展开更多
Visual tracking is a popular research area in com- puter vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rota- tion, fast motion, and occlusion. Consequent...Visual tracking is a popular research area in com- puter vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rota- tion, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate vi- sual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The co^fidence of each parti- cle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the pro- posed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.展开更多
基金the financial support from the Key Laboratory of Development and Application of Rural Renewable Energy,Ministry of Agriculture and Rural Affairs,China[Grant number.2017014]National Natural Science Foundation of China(No.71973037).
文摘With the rapid development of livestock and poultry breeding industries,pollution problems caused by the discharge of wastewater and manure have become increasingly severe.However,studies on the impacts of this pollution on rural residents'health are lacking.Based on data from the Peking University's China Family Panel Studies in 2010 and 2014,this paper uses a cross-sectional and panel data probit model to estimate the probability of breeding industry development in rural areas increasing the health risks of local villagers.First,the study found that the more households engaged in breeding in the region or the larger the scale of regional breeding,the higher the health risks to local villagers,particularly in areas where pigs are raised.Second,compared with individual farming,the greater the proportion of large-scale farming,the higher the health risks to villagers.Third,the development of the breeding industry seldom includes the ecological recycling of wastes and fails to reduce the use of local pesticides and fertilizers,thereby increasing the health risks to villagers.Therefore,this paper argues that providing technology to process breeding industry waste and establish an organic industrial production chain will be crucial to reducing the impact of breeding industry pollution on human health.
基金supported by the National Natural Science Foundation of China(Grant Nos.42172124,41702110,42330810 and U19B6003-01-01)the National Key R&D Program of China(Grant No.2023YFF0804302).
文摘The Carboniferous to Permian tectono-sedimentary evolution of the southern Junggar area brings new insights into understanding the subduction-collision processes in the northern Tianshan region.Integrating geophysics,geochemistry,and geochronology approaches,this study investigates the Carboniferous-Permian strata in the southern Junggar Basin.The results have revealed three distinct tectono-stratigraphic evolutionary stages,each marked by a distinctive volcano-sedimentary sequence.The Early Carboniferous strata suggest intense volcanic activities in the southern Junggar area.During the Late Carboniferous,the southern Junggar Basin was controlled by normal faulting in an extensional setting,receiving sedimentary inputs from the Junggar terrane.The Lower Permian,unconformably overlying the Upper Carboniferous,was shaped by an extensional regime and is comprised by volcano-clastic sequences that received detritus from the Yili-Central Tianshan block.These findings indicate that a Late Carboniferous forearc basin developed in the southern Junggar area,and it evolved into a post-collisional rift in the Early Permian.This period marked a dynamic shift from bidirectional subduction(rollback)to the detachment of the North Tianshan oceanic slab.We propose that the collision between the YiliCentral Tianshan block and the Junggar terrane,along with the closure of the North Tianshan Ocean,likely occurred in the Late Carboniferous(ca.306-303 Ma).
基金This work was supported by the National Natural Science Foundation of China （Grant Nos. 61320106006, 61532006, 61502042）.
文摘Visual tracking is a popular research area in com- puter vision, which is very difficult to actualize because of challenges such as changes in scale and illumination, rota- tion, fast motion, and occlusion. Consequently, the focus in this research area is to make tracking algorithms adapt to these changes, so as to implement stable and accurate vi- sual tracking. This paper proposes a visual tracking algorithm that integrates the scale invariance of SURF feature with deep learning to enhance the tracking robustness when the size of the object to be tracked changes significantly. Particle filter is used for motion estimation. The co^fidence of each parti- cle is computed via a deep neural network, and the result of particle filter is verified and corrected by mean shift because of its computational efficiency and insensitivity to external interference. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that the pro- posed tracking algorithm performs favorably against several state-of-the-art methods throughout the challenging factors in visual tracking, especially for scale variation.
