Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may r...Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.展开更多
Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community asse...Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.展开更多
In this work,ofloxacin(OFL),a kind of frequently detected antibiotic in groundwater,was selected to explore its impact(at ng/L-μg/L-level)on denitrification performance in an autotrophic denitrification system driven...In this work,ofloxacin(OFL),a kind of frequently detected antibiotic in groundwater,was selected to explore its impact(at ng/L-μg/L-level)on denitrification performance in an autotrophic denitrification system driven by pyrite/sulfur(FeS2/S0).Results showed that OFL restrained nitrate removal efficiency,and the inhibition degree was positively related to the concentration of OFL.After being exposed to increased OFL(200 ng/L-100μg/L)for 69 days,higher inhibition of electron transport activity(ETSA),enzyme activities of nitrate reductase(NAR),and nitrite reductase(NIR)were acquired.Meanwhile,the extracellular protein(PN)content of sludge samples was remarkably stimulated by OFL to resist the augmented toxicity.OFL contributed to increased microbial diversity and sulfur/sulfide oxidation functional genes in ng/L-level bioreactors,whereas led to a decline inμg/L level experiments.With OFL at concentrations of 200 ng/L and 100μg/L,the whole expression of 10 key denitrification functional genes was depressed,and the higher the OFL concentration,the lower the expression level.However,no significant proliferation of antibiotic resistance genes(ARGs)either in 200 ng/L-OFL or 100μg/L-OFL groups was observed.Two-factor correlation analysis results indicated that Thiobacillus,Anaerolineae,Anaerolineales,and Nitrospirae might be the main hosts of existing ARGs in this system.展开更多
As a Burundian doctoral student at Nanjing University,my personal journey is closely intertwined with China’s development in the new era and the deepening China-Africa partnership.Recently,my experiences have given m...As a Burundian doctoral student at Nanjing University,my personal journey is closely intertwined with China’s development in the new era and the deepening China-Africa partnership.Recently,my experiences have given me a deeper appreciation of the importance of people-to-people exchanges between China and Africa.展开更多
In 2021,approximately 537 million people suffered from diabetes mellitus(DM)globally,and this figure will increase to approximately 783 million within the next quarter-century.The increasing burden of DM is a pressing...In 2021,approximately 537 million people suffered from diabetes mellitus(DM)globally,and this figure will increase to approximately 783 million within the next quarter-century.The increasing burden of DM is a pressing global public health issue.Therefore,the early identification of high-risk groups and implementation of effective intervention measures is imperative.展开更多
Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacte...Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacterales have been increasingly reported in children,with higher rates in Asian and Middle Eastern settings than in high-income countries[1,2].展开更多
The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculati...The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.展开更多
Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brou...Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brought up. The soft-measuring method and target value locked method were used in these models to confirm the actual exit gauge of passes, and thick layer division and exponential smoothing method were used to dispose the deformation resistance parameter, which could be calculated from the actual data of the rolling process. The correlative mathematical methods can also be adapted to self-learning with gaugemeter. The models were applied to the process control system of AGC （automatic gauge control） reconstruction on 2800 mm finishing mill of Anyang steel and favorable effect was obtained.展开更多
On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enha...On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enhance the precision of temperature control. By means of the division of temperature layers and the exponential smoothing disposal of the annealing experimental data, the self-learning of the heating model was carried out. Through exponentially smoothing the deviation of self-learning parameters of the heated phase in heating process, dynamic modifications of self-learning parameters and heating electric current were carried out, and the precision of temperature control was confirmed. The application indicated that the process control model for the heating system can control temperature with high precision, and the deviation can be controlled within 8 ℃.展开更多
In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples fr...In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.展开更多
A three-year study over the Bai, Jingpo and Huayaodai communities in Yunnan Province reveals that the community development is significantly influenced in various ways by such cultural factors as the concepts of devel...A three-year study over the Bai, Jingpo and Huayaodai communities in Yunnan Province reveals that the community development is significantly influenced in various ways by such cultural factors as the concepts of development; concepts and traditions of inter-community relationships, consumption, mar- riage and gender; patterns of decision-making and production, resource and income allocation; as well as the role of information dissemination systems, religion and ritual. Based on the analysis over the interactive relevance between each factor and community development, some strategies and methods for dealing with such a cultural relevance in development projects are recommended.展开更多
In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglo...In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.展开更多
Changes in the fungal and bacterial biomass and community structure in litter after the volcanic eruptions of Mount Usu, northern Japan were investigated using a chronosequence approach, which is widely used for analy...Changes in the fungal and bacterial biomass and community structure in litter after the volcanic eruptions of Mount Usu, northern Japan were investigated using a chronosequence approach, which is widely used for analyzing vegetation succession. The vegetation changed from bare ground （10 years after the eruptions） with little plant cover and poor soil to monotonic grassland dominated by Polygonum sachalinense with undeveloped soil （33 years） and then to deciduous broad-leaved forest dominated by Populus maximowiczii with diverse species composition and well-developed soil （100 years）. At three chronosequential sites, we evaluated the compositions of phospholipid fatty acids （PLFAs）, carbon （C） and nitrogen （N） contents and the isotope ratios of C （δ13C） and N （δ15N） in the litter of two dominant species, Polygonum sachalinense and Populus maximowiezii. The C/N ratio, δ13C and δ15N in the litter of these two species were higher in the forest than that in the bare ground and grassland. The PLFAs gradually increased from the bare ground to the forest, showing that microbial biomass increased with the development of the soil and/or vegetation. The fungi-to-bacteria ratio of PLFA was constant at 5.3 ± 1.4 in all three sites, suggesting that fungi were predominant. A canonical correspondence analysis suggested that the PLFA comoosition was related to the successional ages and the developing soil properties （P 〈 0.05, ANOSIM）. The chrono- sequential analysis effectively detected the successional changes in both microbial and plant communities.展开更多
To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time wen...To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.展开更多
This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the ...This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.展开更多
Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control p...Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.展开更多
A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced th...A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.展开更多
This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globall...This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.展开更多
文摘Community detection is one of the most fundamental applications in understanding the structure of complicated networks.Furthermore,it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships.Networking structures are highly sensitive in social networks,requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks.In addition,they miss out on accurately identifying clusters.Since single-objective optimization cannot always generate accurate and comprehensive results,as multi-objective optimization can.Therefore,we utilized two objective functions that enable strong connections between communities and weak connections between them.In this study,we utilized the intra function,which has proven effective in state-of-the-art research studies.We proposed a new inter-function that has demonstrated its effectiveness by making the objective of detecting external connections between communities is to make them more distinct and sparse.Furthermore,we proposed a Multi-Objective community strength enhancement algorithm(MOCSE).The proposed algorithm is based on the framework of the Multi-Objective Evolutionary Algorithm with Decomposition(MOEA/D),integrated with a new heuristic mutation strategy,community strength enhancement(CSE).The results demonstrate that the model is effective in accurately identifying community structures while also being computationally efficient.The performance measures used to evaluate the MOEA/D algorithm in our work are normalized mutual information(NMI)and modularity(Q).It was tested using five state-of-the-art algorithms on social networks,comprising real datasets(Zachary,Dolphin,Football,Krebs,SFI,Jazz,and Netscience),as well as twenty synthetic datasets.These results provide the robustness and practical value of the proposed algorithm in multi-objective community identification.
基金supported by Shanghai Municipal Natural Science Foundation,China(No.21ZR1446800)the National Natural Science Foundation of China(No.41877425)the Fundamental Research Funds for the Central Universities(No.226-2024-00052)。
文摘Microorganisms can colonize the surface of microplastics(MPs)to form a distinctive microbiome,known as a“plastisphere”which is regarded as an anthropogenic niche for microbial growth.However,bacterial community assembly in virgin and aging MP plastispheres across different habitats is poorly understood.This study aims to assess the variations in bacterial community assembly across different niches and habitats with an in situ ex-periment,in which constructed forest wetland(FW),natural lake wetland(LW),and lotus pond wetland(LP)were habitats,and plastispheres of virgin and aging low-density polyethylene(LDPE)MPs,as well as surround-ing wetland soils were niches.Significant niche-related differences in bacterial communities were observed,with lower diversity and enrichment of potential plastic-degrading bacteria in the plastisphere than in the soil bacterial communities.Furthermore,habitat-related differences exerted a more pronounced influence on the beta-diversity patterns of the bacterial communities.The linear regression analyses indicated that the local species pool con-tributed more to bacterial community assembly in the LW wetland,whereas the relative abundance of species was the primary factor in the LP wetland.The null model analysis indicated that plastisphere bacterial communi-ties were predominantly driven by the stochastic process,with a more deterministic assembly observed in the LP wetland and soil bacterial communities.Additionally,the primary ecological process shaping plastisphere com-munities shifted from drift in the virgin LDPE to homogenising dispersal in the aging LDPE.This study provides new insights into the fate and ecological impacts of MPs in wetlands,thereby facilitating the effective regulations of plastic pollution.
基金supported by the National Natural Science Foundation of China(No.42377083)the Natural Science Foundation of Sichuan Province,China(No.2025 ZNSFSC0433).
文摘In this work,ofloxacin(OFL),a kind of frequently detected antibiotic in groundwater,was selected to explore its impact(at ng/L-μg/L-level)on denitrification performance in an autotrophic denitrification system driven by pyrite/sulfur(FeS2/S0).Results showed that OFL restrained nitrate removal efficiency,and the inhibition degree was positively related to the concentration of OFL.After being exposed to increased OFL(200 ng/L-100μg/L)for 69 days,higher inhibition of electron transport activity(ETSA),enzyme activities of nitrate reductase(NAR),and nitrite reductase(NIR)were acquired.Meanwhile,the extracellular protein(PN)content of sludge samples was remarkably stimulated by OFL to resist the augmented toxicity.OFL contributed to increased microbial diversity and sulfur/sulfide oxidation functional genes in ng/L-level bioreactors,whereas led to a decline inμg/L level experiments.With OFL at concentrations of 200 ng/L and 100μg/L,the whole expression of 10 key denitrification functional genes was depressed,and the higher the OFL concentration,the lower the expression level.However,no significant proliferation of antibiotic resistance genes(ARGs)either in 200 ng/L-OFL or 100μg/L-OFL groups was observed.Two-factor correlation analysis results indicated that Thiobacillus,Anaerolineae,Anaerolineales,and Nitrospirae might be the main hosts of existing ARGs in this system.
文摘As a Burundian doctoral student at Nanjing University,my personal journey is closely intertwined with China’s development in the new era and the deepening China-Africa partnership.Recently,my experiences have given me a deeper appreciation of the importance of people-to-people exchanges between China and Africa.
基金supported by the Research Funds of the Center for Big Data and Population Health of IHM(grant number JKS2022015)the Key Scientific Research Fund of the Anhui Provincial Education Department(grant number2023AH050610)the Anhui Natural Science Foundation(grant number 1808085QH252)。
文摘In 2021,approximately 537 million people suffered from diabetes mellitus(DM)globally,and this figure will increase to approximately 783 million within the next quarter-century.The increasing burden of DM is a pressing global public health issue.Therefore,the early identification of high-risk groups and implementation of effective intervention measures is imperative.
文摘Urinary tract infections(UTIs)are among the most prevalent pediatric bacterial infections,and undertreated episodes may lead to renal scarring,hypertension,or chronic kidney disease.Multidrug-resistant(MDR)Enterobacterales have been increasingly reported in children,with higher rates in Asian and Middle Eastern settings than in high-income countries[1,2].
基金Item Sponsored by National Natural Science Foundation of China(50474016)
文摘The mathematical model for online controlling hot rolled steel cooling on run-out table （ROT for abbreviation） was analyzed, and water cooling is found to be the main cooling mode for hot rolled steel. The calculation of the drop in strip temperature by both water cooling and air cooling is summed up to obtain the change of heat transfer coefficient. It is found that the learning coefficient of heat transfer coefficient is the kernel coefficient of coiler temperature control （CTC） model tuning. To decrease the deviation between the calculated steel temperature and the measured one at coiler entrance, a laminar cooling control self-learning strategy is used. Using the data acquired in the field, the results of the self-learning model used in the field were analyzed. The analyzed results show that the self-learning function is effective.
基金Item Sponsored by National Natural Science Foundation of China (50604006)
文摘Owing to a lack of gaugemeter and the variety of steel grades and standards in some plate mills, the longand short-term self-learning models of rolling force based on gauge soft-measuring with high precision were brought up. The soft-measuring method and target value locked method were used in these models to confirm the actual exit gauge of passes, and thick layer division and exponential smoothing method were used to dispose the deformation resistance parameter, which could be calculated from the actual data of the rolling process. The correlative mathematical methods can also be adapted to self-learning with gaugemeter. The models were applied to the process control system of AGC （automatic gauge control） reconstruction on 2800 mm finishing mill of Anyang steel and favorable effect was obtained.
基金Item Sponsored by National Natural Science Foundation of China (50527402)
文摘On the basis of a simulated bright continuous annealing experimental machine, a process control model for heating system was built. The heating model was simplified and self-learning parameters were normalized to enhance the precision of temperature control. By means of the division of temperature layers and the exponential smoothing disposal of the annealing experimental data, the self-learning of the heating model was carried out. Through exponentially smoothing the deviation of self-learning parameters of the heated phase in heating process, dynamic modifications of self-learning parameters and heating electric current were carried out, and the precision of temperature control was confirmed. The application indicated that the process control model for the heating system can control temperature with high precision, and the deviation can be controlled within 8 ℃.
文摘In machine learning,positive-unlabelled(PU)learning is a special case within semi-supervised learning.In positiveunlabelled learning,the training set contains some positive examples and a set of unlabelled examples from both the positive and negative classes.Positive-unlabelled learning has gained attention in many domains,especially in time-series data,in which the obtainment of labelled data is challenging.Examples which originate from the negative class are especially difficult to acquire.Self-learning is a semi-supervised method capable of PU learning in time-series data.In the self-learning approach,observations are individually added from the unlabelled data into the positive class until a stopping criterion is reached.The model is retrained after each addition with the existent labels.The main problem in self-learning is to know when to stop the learning.There are multiple,different stopping criteria in the literature,but they tend to be inaccurate or challenging to apply.This publication proposes a novel stopping criterion,which is called Peak evaluation using perceptually important points,to address this problem for time-series data.Peak evaluation using perceptually important points is exceptional,as it does not have tunable hyperparameters,which makes it easily applicable to an unsupervised setting.Simultaneously,it is flexible as it does not make any assumptions on the balance of the dataset between the positive and the negative class.
基金the National Natural Science Foundation of China(No.61375086)the Key Project of Science and Technique Plan of Beijing Municipal Commission of Education(No.KZ201210005001)+1 种基金the National Basic Research Program(973)of China(No.2012CB720000)the China Scholarship Council Program(No.201406540017)
文摘A three-year study over the Bai, Jingpo and Huayaodai communities in Yunnan Province reveals that the community development is significantly influenced in various ways by such cultural factors as the concepts of development; concepts and traditions of inter-community relationships, consumption, mar- riage and gender; patterns of decision-making and production, resource and income allocation; as well as the role of information dissemination systems, religion and ritual. Based on the analysis over the interactive relevance between each factor and community development, some strategies and methods for dealing with such a cultural relevance in development projects are recommended.
基金Project supported by the National Natural Science Foundation of China(Grant Nos.51278101 and 51578149)the Science and Technology Program of Ministry of Transport of China(Grant No.2015318J33080)+1 种基金the Jiangsu Provincial Post-doctoral Science Foundation,China(Grant No.1501046B)the Fundamental Research Funds for the Central Universities,China(Grant No.Y0201500219)
文摘In this paper,we propose a local fuzzy method based on the idea of ＂p-strong＂ community to detect the disjoint and overlapping communities in networks.In the method,a refined agglomeration rule is designed for agglomerating nodes into local communities,and the overlapping nodes are detected based on the idea of making each community strong.We propose a contribution coefficient bvcito measure the contribution of an overlapping node to each of its belonging communities,and the fuzzy coefficients of the overlapping node can be obtained by normalizing the bvci to all its belonging communities.The running time of our method is analyzed and varies linearly with network size.We investigate our method on the computergenerated networks and real networks.The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients.Furthermore,the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity.
文摘Changes in the fungal and bacterial biomass and community structure in litter after the volcanic eruptions of Mount Usu, northern Japan were investigated using a chronosequence approach, which is widely used for analyzing vegetation succession. The vegetation changed from bare ground （10 years after the eruptions） with little plant cover and poor soil to monotonic grassland dominated by Polygonum sachalinense with undeveloped soil （33 years） and then to deciduous broad-leaved forest dominated by Populus maximowiczii with diverse species composition and well-developed soil （100 years）. At three chronosequential sites, we evaluated the compositions of phospholipid fatty acids （PLFAs）, carbon （C） and nitrogen （N） contents and the isotope ratios of C （δ13C） and N （δ15N） in the litter of two dominant species, Polygonum sachalinense and Populus maximowiezii. The C/N ratio, δ13C and δ15N in the litter of these two species were higher in the forest than that in the bare ground and grassland. The PLFAs gradually increased from the bare ground to the forest, showing that microbial biomass increased with the development of the soil and/or vegetation. The fungi-to-bacteria ratio of PLFA was constant at 5.3 ± 1.4 in all three sites, suggesting that fungi were predominant. A canonical correspondence analysis suggested that the PLFA comoosition was related to the successional ages and the developing soil properties （P 〈 0.05, ANOSIM）. The chrono- sequential analysis effectively detected the successional changes in both microbial and plant communities.
文摘To respond to the further development of college English reforms,many universities employed network-based selflearning classes to aid the traditional classroom teaching,especially in teaching listening,but as time went by,some universities gradually gave them up.The paper intends to reflect on the employment of network-based self-learning listening classes,analyz ing the learning with and without its aid,and meanwhile introduce the need to re-employ it,and discuss how we can improve the network-based self-learning classes to help with students' listening.
文摘This papcr presents a new genetic algorithms(GAs)-based method for self-learniag fuzzy control rules. An improved GA is used to learn to optimally select the fuzzy membership functions of the linguistic labels in the condition portion of each rule, and to automatically generate fuzzy control actions under each condition. The dynamics of the controlled system is unknown to the GA. The only information for evaluating performance is a failure signal indicating that the controlled system is out of control. We compare its performance with that of other learning methods for the same problem. We also examine the ability of the algorithm to adapt to changing conditions. Simulation results show that such an approach for self-learning fuzzy control rules is both effective and robust.
基金Project supported by the National Key Technology Research and Development Program (Grant No.2006BAE03A08)
文摘Control precision of coiling temperature is one of the key factors affecting the profile shape and surface quality during the cooling process of hot rolled steel strip.For this reason,the core of temperature control precision is to establish an effective cooling mathematical model with self-learning function.Starting from this point,a cooling mathematical model with nonlinear structural characteristics is established in this paper for the cooling process of hot rolled steel strip.By the analysis of self-learning ability,key parameters of the mathematical model could be constantly corrected so as to improve temperature control precision and adaptive capability of the model.The site actual application results proved the stable performance and high control precision of the proposed mathematical model,which would lay a solid foundation to improve the steel product qualities.
文摘A design idea was proposed that it was about intelligent digital welding machine with self-learning and self- regulation functions. The overall design scheme of software and hardware was provided. It was introduced that a parameter self-learning algorithm was based on large-step calibration and partial Newton interpolation. Furthermore, experimental verification was carried out with different welding technologies. The results show that weld bead is pegrect. Therefore, good welding quality and stability are obtained, and intelligent regulation is realized by parameters self-learning.
文摘This paper presents a novel method for constructing fuzzy controllers based on a real time reinforcement genetic algorithm. This methodology introduces the real-time learning capability of neural networks into globally searching process of genetic algorithm, aiming to enhance the convergence rate and real-time learning ability of genetic algorithm, which is then used to construct fuzzy controllers for complex dynamic systems without any knowledge about system dynamics and prior control experience. The cart-pole system is employed as a test bed to demonstrate the effectiveness of the proposed control scheme, and the robustness of the acquired fuzzy controller with comparable result.
