Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.I...Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.In the HRC framework,the human is the decision maker,the robot acts as the executor,while the assembly environment provides constraints.The robot is the main executor to perform the assembly action,which has the position control,drag and drop,positive impedance control,and negative impedance control modes.To reveal the characteristics of the HRC framework,the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed.In the end,HRC assembly experiments are conducted,where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit.Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.展开更多
Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,so...Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.展开更多
The ciliate Tetrahymena is a valuable model organism in the studies of ecotoxicology. Changes in intracellular metabolism are caused by exogenous chemicals in the environment. Intracellular metabolite changes signify ...The ciliate Tetrahymena is a valuable model organism in the studies of ecotoxicology. Changes in intracellular metabolism are caused by exogenous chemicals in the environment. Intracellular metabolite changes signify toxic effects and can be monitored by metabolomics analysis. In this work, a protocol for the GC-MS-based metabolomic analysis of Tetrahymena was established. Different extraction solvents showed divergent effects on the metabolomic analysis of Tetrahymena thermophila. The peak intensity of metabolites detected in the samples of extraction solvent Formula 1(F1) was the strongest and stable, while 61 metabolites were identified. Formula 1 showed an excellent extraction performance for carbohydrates. In the samples of extraction solvent Formula 2(F2), 66 metabolites were characterized, and fatty acid metabolites were extracted. Meanwhile, 57 and 58 metabolites were characterized in the extraction with Formula 3(F3) and Formula 4(F4), respectively. However, the peak intensity of the metabolites was low, and the metabolites were unstable. These results indicated that different extraction solvents substantially affected the detected coverage and peak intensity of intracellular metabolites. A total of 74 metabolites(19 amino acids, 11 organic acids, 2 inorganic acids, 11 fatty acids, 11 carbohydrates, 3 glycosides, 4 alcohols, 6 amines, and 7 other compounds) were identified in all experimental groups. Among these metabolites, amino acids, glycerol, myoinositol, and unsaturated fatty acids may become potential biomarkers of metabolite set enrichment analysis for detecting the ability of T. thermophila against environmental stresses.展开更多
Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shap...Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.展开更多
基金supported in part by the National Natural Science Foundation of China(62293514,52275020,and 91948301)。
文摘Human–robot(HR)collaboration(HRC)is an emerging research field because of the complementary advantages of humans and robots.An HRC framework for robotic assembly based on impedance control is proposed in this paper.In the HRC framework,the human is the decision maker,the robot acts as the executor,while the assembly environment provides constraints.The robot is the main executor to perform the assembly action,which has the position control,drag and drop,positive impedance control,and negative impedance control modes.To reveal the characteristics of the HRC framework,the switch condition map of different control modes and the stability analysis of the HR coupled system are discussed.In the end,HRC assembly experiments are conducted,where the HRC assembly task can be accomplished when the assembling tolerance is 0.08 mm or with the interference fit.Experiments show that the HRC assembly has the complementary advantages of humans and robots and is efficient in finishing complex assembly tasks.
基金supported by NASA Kentucky NNX15AR69H,NSF grant nos.1940696,1903722,and 1243232Andrew Carnegie Fellowship Award no.G-F-19-56910.
文摘Soil organic carbon(SOC)in croplands is a key property of soil quality for ensuring food security and agricultural sustainability,and also plays a central role in the global carbon(C)budget.When managed sustainably,soils may play a critical role in mitigating climate change by sequestering C and decreasing greenhouse gas emissions into the atmosphere.However,the magnitude and spatio-temporal patterns of global cropland SOC are far from well constrained due to high land surface heterogeneity,complicated mechanisms,and multiple influencing factors.Here,we use a process-based agroecosystem model(DLEM-Ag)in combination with diverse spatially-explicit gridded environmental data to quantify the long-term trend of SOC storage in global cropland area during 1901-2010 and identify the relative impacts of climate change,elevated CO2,nitrogen deposition,land cover change,and land management practices such as nitrogen fertilizer use and irrigation.Model results show that the total SOC and SOC density in the 2000s increased by 125%and 48.8%,respectively,compared to the early 20th century.This SOC increase was primarily attributed to cropland expansion and nitrogen fertilizer use.Factorial analysis suggests that climate change reduced approximately 3.2%(or 2,166 Tg C)of the total SOC over the past 110 years.Our results indicate that croplands have a large potential to sequester C through implementing better land use management practices,which may partially offset SOC loss caused by climate change.
基金supported by the National Natural Science Foundation of China (Nos. 31572253, 31601857, 31702009)the Science Foundation for Youths of Shanxi Province (No. 201801D221241)the Postdoctoral Science Foundation of China (No. 2014M551961)
文摘The ciliate Tetrahymena is a valuable model organism in the studies of ecotoxicology. Changes in intracellular metabolism are caused by exogenous chemicals in the environment. Intracellular metabolite changes signify toxic effects and can be monitored by metabolomics analysis. In this work, a protocol for the GC-MS-based metabolomic analysis of Tetrahymena was established. Different extraction solvents showed divergent effects on the metabolomic analysis of Tetrahymena thermophila. The peak intensity of metabolites detected in the samples of extraction solvent Formula 1(F1) was the strongest and stable, while 61 metabolites were identified. Formula 1 showed an excellent extraction performance for carbohydrates. In the samples of extraction solvent Formula 2(F2), 66 metabolites were characterized, and fatty acid metabolites were extracted. Meanwhile, 57 and 58 metabolites were characterized in the extraction with Formula 3(F3) and Formula 4(F4), respectively. However, the peak intensity of the metabolites was low, and the metabolites were unstable. These results indicated that different extraction solvents substantially affected the detected coverage and peak intensity of intracellular metabolites. A total of 74 metabolites(19 amino acids, 11 organic acids, 2 inorganic acids, 11 fatty acids, 11 carbohydrates, 3 glycosides, 4 alcohols, 6 amines, and 7 other compounds) were identified in all experimental groups. Among these metabolites, amino acids, glycerol, myoinositol, and unsaturated fatty acids may become potential biomarkers of metabolite set enrichment analysis for detecting the ability of T. thermophila against environmental stresses.
基金supported by the National Key Research and Development Program of China under Grant No.2018YFE0206900the National Natural Science Foundation of China under Grant No.61871440 and CAAI‐Huawei Mind-Spore Open Fund.
文摘Tumour segmentation in medical images(especially 3D tumour segmentation)is highly challenging due to the possible similarity between tumours and adjacent tissues,occurrence of multiple tumours and variable tumour shapes and sizes.The popular deep learning‐based segmentation algorithms generally rely on the convolutional neural network(CNN)and Transformer.The former cannot extract the global image features effectively while the latter lacks the inductive bias and involves the complicated computation for 3D volume data.The existing hybrid CNN‐Transformer network can only provide the limited performance improvement or even poorer segmentation performance than the pure CNN.To address these issues,a short‐term and long‐term memory self‐attention network is proposed.Firstly,a distinctive self‐attention block uses the Transformer to explore the correlation among the region features at different levels extracted by the CNN.Then,the memory structure filters and combines the above information to exclude the similar regions and detect the multiple tumours.Finally,the multi‐layer reconstruction blocks will predict the tumour boundaries.Experimental results demonstrate that our method outperforms other methods in terms of subjective visual and quantitative evaluation.Compared with the most competitive method,the proposed method provides Dice(82.4%vs.76.6%)and Hausdorff distance 95%(HD95)(10.66 vs.11.54 mm)on the KiTS19 as well as Dice(80.2%vs.78.4%)and HD95(9.632 vs.12.17 mm)on the LiTS.
