This experiment aims to isolate and inhibit three bacteria strains to provide candidate strains for the development and application of probiotics.Using bacterial morphological identification,16S rDNA sequence alignmen...This experiment aims to isolate and inhibit three bacteria strains to provide candidate strains for the development and application of probiotics.Using bacterial morphological identification,16S rDNA sequence alignment,and genetic evolution analysis,three strains were identified as Bacillus haynesii,named HP01,HD02,and HK03.Through biosurfactant activity tests,C-TAB tests,hemolysis tests,and antibacterial activity analyses,the results showed that all three strains of B.haynesii exhibited significant biosurfactant activity.Additionally,the solutions of the three strains demonstrated a pronounced antibacterial effect on Staphylococcus aureus.The resistance and safety of commonly used drugs were evaluated using the tablet diffusion method and a mouse feeding test.The results indicated that the three strains were not resistant to commonly used antibacterial drugs,and the oral bacterial solution was not pathogenic and had high safety in mice.The study concluded that all three B.haynesii strains met the basic conditions for use,with B.haynesii HP01 being the most promising candidate.展开更多
Accurately filling in missing heating data is essential for ensuring data quality in applications such as energy management optimization and building efficiency analysis.Traditional machine learning methods use histor...Accurately filling in missing heating data is essential for ensuring data quality in applications such as energy management optimization and building efficiency analysis.Traditional machine learning methods use historical heating data as an input feature to predict the following missing data.However,when the duration of missing data is long,previous estimated values are inevitably used for further imputation,leading to error accumulation and a growing deviation from true values.To overcome this problem,this paper proposes a generative network that can fill missing data solely based on weather and temporal data,without using previous imputed values for further imputation.Our method outperformed the state of the art such as Seq2seq and Transformer,achieving relative normalized root mean square error(NRMSE)reductions of 1.65%to 41.38%,0.30%to 66.43%,and 14.84%to 50.22%across three different data sources.In addition,with our proposed method,the effect of selecting different weather variables on model performance,and the benefits of transfer learning under limited data were also demonstrated.The relative NRMSE reduction is between 3.88%to 15.85%in cold months and from 7.49%to 12.29%in warm months when applying transfer learning.展开更多
基金Natural Science Foundation of Guangdong Province in 2023(No.2023A1515012181)Self-funded Science and Technology Innovation Project of Foshan City in 2022(No.220001005797)+1 种基金Basic and Applied Basic Research Foundation of Guangdong Province in 2022(No.2022A1515140052)Innovation Project of Guangdong Graduate Education in 2022(No.2022JGXM129,No.2022JGXM128)and 2023(No.2023ANLK-080)。
文摘This experiment aims to isolate and inhibit three bacteria strains to provide candidate strains for the development and application of probiotics.Using bacterial morphological identification,16S rDNA sequence alignment,and genetic evolution analysis,three strains were identified as Bacillus haynesii,named HP01,HD02,and HK03.Through biosurfactant activity tests,C-TAB tests,hemolysis tests,and antibacterial activity analyses,the results showed that all three strains of B.haynesii exhibited significant biosurfactant activity.Additionally,the solutions of the three strains demonstrated a pronounced antibacterial effect on Staphylococcus aureus.The resistance and safety of commonly used drugs were evaluated using the tablet diffusion method and a mouse feeding test.The results indicated that the three strains were not resistant to commonly used antibacterial drugs,and the oral bacterial solution was not pathogenic and had high safety in mice.The study concluded that all three B.haynesii strains met the basic conditions for use,with B.haynesii HP01 being the most promising candidate.
文摘Accurately filling in missing heating data is essential for ensuring data quality in applications such as energy management optimization and building efficiency analysis.Traditional machine learning methods use historical heating data as an input feature to predict the following missing data.However,when the duration of missing data is long,previous estimated values are inevitably used for further imputation,leading to error accumulation and a growing deviation from true values.To overcome this problem,this paper proposes a generative network that can fill missing data solely based on weather and temporal data,without using previous imputed values for further imputation.Our method outperformed the state of the art such as Seq2seq and Transformer,achieving relative normalized root mean square error(NRMSE)reductions of 1.65%to 41.38%,0.30%to 66.43%,and 14.84%to 50.22%across three different data sources.In addition,with our proposed method,the effect of selecting different weather variables on model performance,and the benefits of transfer learning under limited data were also demonstrated.The relative NRMSE reduction is between 3.88%to 15.85%in cold months and from 7.49%to 12.29%in warm months when applying transfer learning.
