In this study,Bacillus mojavensis and Lactiplantibacillus herbarum were used to co-treat kitchen waste(KW)with Black soldier fly larvae(BSFL).The effects on the physicochemical properties,heavy metal content,and micro...In this study,Bacillus mojavensis and Lactiplantibacillus herbarum were used to co-treat kitchen waste(KW)with Black soldier fly larvae(BSFL).The effects on the physicochemical properties,heavy metal content,and microbial community of the BSFL sand were determined.Compared to the control group,the L.herbarum inoculation reduced 19.04%of the soluble salt(TSS),15.48%of Ni,and 13.04%of Zn in the residues;the B.mojavensis inoculation reduced 23.84%of TSS,13.61%of Pb,and 20.32%of the Ni in the residues;the L.herbarum and B.mojavensis inoculation reduced 29.53%of Cr,20.23%of Pb,18.06%of Ni,and 25.68%of the Zn in the residues.The microbial inoculants significantly enhanced the BSFL sand microbial diversity(Tukey,P<0.05).The dominant phylum and genus in the BSFL sand were Firmicutes(53.08%)and Corynebacterium(47.01%),respectively.The microbial inoculants resulted in an approximate 12%reduction in Corynebacterium.The linear discriminant analysis effective size analysis showed that the Corynebacterium abundance was significantly reduced.The microbial inoculants significantly affected the Corynebacterium relative abundance by significantly altering the substrate TSS,moisture content,and Ni.In conclusion,the effect of B.mojavensis and L.herbarum on the BSFL treatment of KW was beneficial,and their potential should be further exploited.展开更多
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
文摘In this study,Bacillus mojavensis and Lactiplantibacillus herbarum were used to co-treat kitchen waste(KW)with Black soldier fly larvae(BSFL).The effects on the physicochemical properties,heavy metal content,and microbial community of the BSFL sand were determined.Compared to the control group,the L.herbarum inoculation reduced 19.04%of the soluble salt(TSS),15.48%of Ni,and 13.04%of Zn in the residues;the B.mojavensis inoculation reduced 23.84%of TSS,13.61%of Pb,and 20.32%of the Ni in the residues;the L.herbarum and B.mojavensis inoculation reduced 29.53%of Cr,20.23%of Pb,18.06%of Ni,and 25.68%of the Zn in the residues.The microbial inoculants significantly enhanced the BSFL sand microbial diversity(Tukey,P<0.05).The dominant phylum and genus in the BSFL sand were Firmicutes(53.08%)and Corynebacterium(47.01%),respectively.The microbial inoculants resulted in an approximate 12%reduction in Corynebacterium.The linear discriminant analysis effective size analysis showed that the Corynebacterium abundance was significantly reduced.The microbial inoculants significantly affected the Corynebacterium relative abundance by significantly altering the substrate TSS,moisture content,and Ni.In conclusion,the effect of B.mojavensis and L.herbarum on the BSFL treatment of KW was beneficial,and their potential should be further exploited.
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.
