The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especial...The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especially for the formation of acetic acid and propionic acid.The average value of total volatile fatty acid at pH 8 was 8814 mg COD/L,1.5 times of that under blank condition.The average yield of acetic acid and propionic acid was 3302 mg COD/L and 2891 mg COD/L,respectively.The activities of key functional enzymes such as phosphotransacetylase,acetokinase,oxaloacetate transcarboxylase and succinylcoA transferase were all enhanced.To further explore the regulatory mechanisms within the system,the distribution of microorganisms at different levels in the fermentation system was obtained by microbial sequencing,results indicating that the relative abundances of Clostridiales,Bacteroidales,Chloroflexi,Clostridium,Bacteroidetes and Propionibacteriales,which were great contributors for the hydrolysis and acidification,increased rapidly at pH 8 compared with the blank group.Besides,the proportion of genes encoding key enzymes was generally increased,which further verified the mechanism of hydrolytic acidification and acetic acid production of organic matter under pH regulation.展开更多
Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective...Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective way of sorting.Owing to significant domain gaps between natural images and kitchen waste images,it is difficult to reflect the characteristics of diverse scales and dense distribution in kitchen waste based on an ImageNet pre-trained model,leading to poor generalisation.In this article,the authors propose the first pre-trained model for kitchen waste sorting called KitWaSor,which combines both contrastive learning(CL)and masked image modelling(MIM)through self-supervised learning(SSL).First,to address the issue of diverse scales,the authors propose a mixed masking strategy by introducing an incomplete masking branch based on the original random masking branch.It prevents the complete loss of small-scale objects while avoiding excessive leakage of large-scale object pixels.Second,to address the issue of dense distribution,the authors introduce semantic consistency constraints on the basis of the mixed masking strategy.That is,object semantic reasoning is performed through semantic consistency constraints to compensate for the lack of contextual information.To train KitWaSor,the authors construct the first million-level kitchen waste dataset across seasonal and regional distributions,named KWD-Million.Extensive experiments show that KitWaSor achieves state-of-the-art(SOTA)performance on the two most relevant downstream tasks for kitchen waste sorting(i.e.image classification and object detection),demonstrating the effectiveness of the proposed KitWaSor.展开更多
This study evaluated the hydrocarbon-bearing potential of Upper Jurassic marine source rocks in the Qiangtang （羌塘） Basin through a comprehensive organic geochemical analysis of the samples from a large number of o...This study evaluated the hydrocarbon-bearing potential of Upper Jurassic marine source rocks in the Qiangtang （羌塘） Basin through a comprehensive organic geochemical analysis of the samples from a large number of outcrops in different structural units to predict the location of favorable hydro- carbon kitchens, based on the evaluation standards of Mesozoic marine source rocks in the Qiangtang Ba- sin. Rocks＇ depositional environment, thickness and organic geochemistry feature were analyzed in this study. The principal controlling factors of the occurrences of favorable source rocks were analyzed. Upper Jurassic Suowa （索瓦） Formation source rocks are mainly platform limestone in the Dongcuo （洞错）-Hulu （葫芦） Lake deep sag and Tupocuo （吐坡错）-Baitan （白滩） Lake deep sag. Lithologically, the Suowa Fro- mation is made up of a suite of marls in intra-platform sags, micrites and black shales, which were all de- posited in the closed, deep and static water depositional environment. Marl could form hydrocarbon-rich source rocks and its organic matter type is mainly II type in mature to highly-mature stage, the limestone forms a medium-level source rock. In addition, the favorable source kitchen of limestone is larger than that of mudstone. This study provides an important reference for the evaluation of Jurassic marine source rocks and for prediction of petroleum resources in the Qiangtang Basin.展开更多
基金supported by the National Key Research and Development Program of China(No.2019YFC1906304).
文摘The anaerobic acid production experiments were conducted with the pretreated kitchen waste under pH adjustment.The results showed that pH 8 was considered to be the most suitable condition for acid production,especially for the formation of acetic acid and propionic acid.The average value of total volatile fatty acid at pH 8 was 8814 mg COD/L,1.5 times of that under blank condition.The average yield of acetic acid and propionic acid was 3302 mg COD/L and 2891 mg COD/L,respectively.The activities of key functional enzymes such as phosphotransacetylase,acetokinase,oxaloacetate transcarboxylase and succinylcoA transferase were all enhanced.To further explore the regulatory mechanisms within the system,the distribution of microorganisms at different levels in the fermentation system was obtained by microbial sequencing,results indicating that the relative abundances of Clostridiales,Bacteroidales,Chloroflexi,Clostridium,Bacteroidetes and Propionibacteriales,which were great contributors for the hydrolysis and acidification,increased rapidly at pH 8 compared with the blank group.Besides,the proportion of genes encoding key enzymes was generally increased,which further verified the mechanism of hydrolytic acidification and acetic acid production of organic matter under pH regulation.
基金National Key Research and Development Program of China,Grant/Award Number:2021YFC1910402。
文摘Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective way of sorting.Owing to significant domain gaps between natural images and kitchen waste images,it is difficult to reflect the characteristics of diverse scales and dense distribution in kitchen waste based on an ImageNet pre-trained model,leading to poor generalisation.In this article,the authors propose the first pre-trained model for kitchen waste sorting called KitWaSor,which combines both contrastive learning(CL)and masked image modelling(MIM)through self-supervised learning(SSL).First,to address the issue of diverse scales,the authors propose a mixed masking strategy by introducing an incomplete masking branch based on the original random masking branch.It prevents the complete loss of small-scale objects while avoiding excessive leakage of large-scale object pixels.Second,to address the issue of dense distribution,the authors introduce semantic consistency constraints on the basis of the mixed masking strategy.That is,object semantic reasoning is performed through semantic consistency constraints to compensate for the lack of contextual information.To train KitWaSor,the authors construct the first million-level kitchen waste dataset across seasonal and regional distributions,named KWD-Million.Extensive experiments show that KitWaSor achieves state-of-the-art(SOTA)performance on the two most relevant downstream tasks for kitchen waste sorting(i.e.image classification and object detection),demonstrating the effectiveness of the proposed KitWaSor.
基金supported by the National Natural Science Foundation of China(Nos.41372139,41072098,41002027)the National Major Projects of China(Nos.2011ZX05018-001-002,2011ZX05009-002-205)
文摘This study evaluated the hydrocarbon-bearing potential of Upper Jurassic marine source rocks in the Qiangtang （羌塘） Basin through a comprehensive organic geochemical analysis of the samples from a large number of outcrops in different structural units to predict the location of favorable hydro- carbon kitchens, based on the evaluation standards of Mesozoic marine source rocks in the Qiangtang Ba- sin. Rocks＇ depositional environment, thickness and organic geochemistry feature were analyzed in this study. The principal controlling factors of the occurrences of favorable source rocks were analyzed. Upper Jurassic Suowa （索瓦） Formation source rocks are mainly platform limestone in the Dongcuo （洞错）-Hulu （葫芦） Lake deep sag and Tupocuo （吐坡错）-Baitan （白滩） Lake deep sag. Lithologically, the Suowa Fro- mation is made up of a suite of marls in intra-platform sags, micrites and black shales, which were all de- posited in the closed, deep and static water depositional environment. Marl could form hydrocarbon-rich source rocks and its organic matter type is mainly II type in mature to highly-mature stage, the limestone forms a medium-level source rock. In addition, the favorable source kitchen of limestone is larger than that of mudstone. This study provides an important reference for the evaluation of Jurassic marine source rocks and for prediction of petroleum resources in the Qiangtang Basin.
