Let (G, E) be a quasi-ordered group such that E∩E -1 is infinite, (G, G +) an ordered group with G +EG, and (G, G 1) the partially ordered group induced by (G, E).Let γ E, G + ∶T G + →T E and γ E, G 1 ∶T G 1 →T...Let (G, E) be a quasi-ordered group such that E∩E -1 is infinite, (G, G +) an ordered group with G +EG, and (G, G 1) the partially ordered group induced by (G, E).Let γ E, G + ∶T G + →T E and γ E, G 1 ∶T G 1 →T E be the corresponding natural morphisms between Toeplitz algebras. We prove that the kernel Ker γ E, G + is rigid,while Ker γ E, G 1 is equal to the compact-operator ideal on 2(G 1), and all Fredholm operators in the Toeplitz algebra T G 1 are of index zero.展开更多
The main aim of the paper is to explain the reason and mechanism of technological diffusion in industry cluster. Following the idea showed in Zhou Qin’s model, we further develop the theoretical analysis that how the...The main aim of the paper is to explain the reason and mechanism of technological diffusion in industry cluster. Following the idea showed in Zhou Qin’s model, we further develop the theoretical analysis that how the technological gap between strong enterprises and weak enterprises determines the level and speed of technological diffusion. The bigger the technological disparity between strong enterprise and weak enterprise is, the quicker technology spreads and knowledge overflows; on the contrary, the smaller the technological disparity between strong enterprise and weak enterprise is, the slower technology spread and knowledge overflows. Therefore, this kind of mechanism is helpful for enterprises in an industry cluster to learn from each other and to enable each enterprise close to the “the average level” of technology or knowledge. As a result, we think that, there exists a close relation between technological gap and technological diffusion. The paper puts forward the way of knowledge overflows of the strong enterprises: imitation- demonstration effect, the longitudinal connection in the industrial chain, the labor force flow and transfer, informal exchange.展开更多
Field work was conducted in the southern half of the Molopo Nature Reserve(MNR) near Vostershoop in the North West Province of South Africa to(1) describe the past and present distribution of Red-billed Spurfowl(Ptern...Field work was conducted in the southern half of the Molopo Nature Reserve(MNR) near Vostershoop in the North West Province of South Africa to(1) describe the past and present distribution of Red-billed Spurfowl(Pternistis adspersus) in South Africa and(2) to describe the dispersion of Red-billed Spurfowl at waterholes in the MNR.The Red-billed Spurfowl did not colonize this area from Bo-tswana in the 1990s as reported in Hockey et al.(2005) and their distribution status and population sizes are not determined by long distance(30-100 km) and/or seasonal movements between the two countries.Red-billed Spurfowl are sparsely distributed and mainly occur in clusters near man-made waterholes.Waterholes provide water and food found in and around antelope droppings.The movement of the Red-billed Spurfowl between waterholes over short distances of 2-5 km was probably encouraged by the sinking of more boreholes since the 1980s(and the creation of 'veeposte'(game/livestock camps) around them).Low rainfall that results in limited insects is probably the single most important factor limiting populations of the Red-billed Spurfowl in South Africa.展开更多
Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete par...Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.展开更多
基金the National Natural Science Foundation of China (No. 10371051 and 10201007)
文摘Let (G, E) be a quasi-ordered group such that E∩E -1 is infinite, (G, G +) an ordered group with G +EG, and (G, G 1) the partially ordered group induced by (G, E).Let γ E, G + ∶T G + →T E and γ E, G 1 ∶T G 1 →T E be the corresponding natural morphisms between Toeplitz algebras. We prove that the kernel Ker γ E, G + is rigid,while Ker γ E, G 1 is equal to the compact-operator ideal on 2(G 1), and all Fredholm operators in the Toeplitz algebra T G 1 are of index zero.
文摘The main aim of the paper is to explain the reason and mechanism of technological diffusion in industry cluster. Following the idea showed in Zhou Qin’s model, we further develop the theoretical analysis that how the technological gap between strong enterprises and weak enterprises determines the level and speed of technological diffusion. The bigger the technological disparity between strong enterprise and weak enterprise is, the quicker technology spreads and knowledge overflows; on the contrary, the smaller the technological disparity between strong enterprise and weak enterprise is, the slower technology spread and knowledge overflows. Therefore, this kind of mechanism is helpful for enterprises in an industry cluster to learn from each other and to enable each enterprise close to the “the average level” of technology or knowledge. As a result, we think that, there exists a close relation between technological gap and technological diffusion. The paper puts forward the way of knowledge overflows of the strong enterprises: imitation- demonstration effect, the longitudinal connection in the industrial chain, the labor force flow and transfer, informal exchange.
文摘Field work was conducted in the southern half of the Molopo Nature Reserve(MNR) near Vostershoop in the North West Province of South Africa to(1) describe the past and present distribution of Red-billed Spurfowl(Pternistis adspersus) in South Africa and(2) to describe the dispersion of Red-billed Spurfowl at waterholes in the MNR.The Red-billed Spurfowl did not colonize this area from Bo-tswana in the 1990s as reported in Hockey et al.(2005) and their distribution status and population sizes are not determined by long distance(30-100 km) and/or seasonal movements between the two countries.Red-billed Spurfowl are sparsely distributed and mainly occur in clusters near man-made waterholes.Waterholes provide water and food found in and around antelope droppings.The movement of the Red-billed Spurfowl between waterholes over short distances of 2-5 km was probably encouraged by the sinking of more boreholes since the 1980s(and the creation of 'veeposte'(game/livestock camps) around them).Low rainfall that results in limited insects is probably the single most important factor limiting populations of the Red-billed Spurfowl in South Africa.
基金partly supported by the Natural Science Foundation of China under Grant Nos.71101100 and 70731160635New Teachers’Fund for Doctor Stations,Ministry of Education under Grant No.20110181120047+5 种基金Excellent Youth Fund of Sichuan University under Grant No.2013SCU04A08China Postdoctoral Science Foundation under Grant Nos.2011M500418,2012T50148 and 2013M530753Frontier and Cross-innovation Foundation of Sichuan University under Grant No.skqy201352Soft Science Foundation of Sichuan Province under Grant No.2013ZR0016Humanities and Social Sciences Youth Foundation of the Ministry of Education of China under Grant No.11YJC870028Selfdetermined Research Funds of CCNU from the Colleges’ Basic Research and Operation of MOE under Grant No.CCNU13F030
文摘Abstract Accurate forecast of future container throughput of a port is very important for its con struction, upgrading, and operation management. This study proposes a transfer forecasting model guided by discrete particle swarm optimization algorithm （TF-DPSO）. It firstly transfers some related time series in source domain to assist in modeling the target time series by transfer learning technique, and then constructs the forecasting model by a pattern matching method called analog complexing. Finally, the discrete particle swarm optimization algorithm is introduced to find the optimal match between the two important parameters in TF-DPSO. The container throughput time series of two im portant ports in China, Shanghai Port and Ningbo Port are used for empirical analysis, and the results show the effectiveness of the proposed model.
