The timing of river entry in the Atlantic salmon is known to depend on genetic, demographic and environmental factors, but little is known about the relative magnitude of among population and among year variation and ...The timing of river entry in the Atlantic salmon is known to depend on genetic, demographic and environmental factors, but little is known about the relative magnitude of among population and among year variation and covariation in this respect in natural state Atlantic salmon rives. To investigate this, variability in the timing of river entry in three historical Finnish Atlantic salmon populations were analyzed using salmon trap data collected during 1870- 1902. The analyses reveled that 1 ） the timing of river entry differed substantially and consistently among the rivers, and that 2） variation among the rivers was much larger than variation among years. Annual variations were not explained by regional environmental conditions, whereas in one river the timing of the local flood peak was a significant predictor of the timing of river entry. Differences in the timing of salmon entry to geographically closely situated rivers suggests that a regionally fixed opening date for coastal fisheries might not be the best management strategy as it may lead to uneven exploitation of salmon populations from different rivers [ Current Zoology 55 （5） ： 342 - 349, 2009] .展开更多
The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inferenc...The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.展开更多
Road condition is an important variable to measure in order to decrease road and vehicle operating/maintenance costs, but also to increase ride comfort and traffic safety. By using the built-in vibration sensor in sma...Road condition is an important variable to measure in order to decrease road and vehicle operating/maintenance costs, but also to increase ride comfort and traffic safety. By using the built-in vibration sensor in smart phones, it is possible to collect road roughness data which can be an indicator of road condition up to a level of Class 2 or 3 in a simple and cost efficient way. Since data collection therefore is possible to be done more frequently, one can better monitor roughness changes over time. The continuous data collection can also give early warnings of changes and damage, enable new ways to work in the operational road maintenance management, and can serve as a guide for more accurate surveys for strategic asset management and pavement planning. Collected measurement data are wirelessly transferred by the operator when needed via a web service to an internet mapping server with spatial filtering functions. The measured data can be aggregated in preferred sections, as well as exported to other GlS （geographical information systems） or road management systems. Our conclusion is that measuring roads with smart phones can provide an efficient, scalable, and cost-effective way for road organizations to deliver road condition data.展开更多
基金supported by Academy of Finland and the University of Helsinki
文摘The timing of river entry in the Atlantic salmon is known to depend on genetic, demographic and environmental factors, but little is known about the relative magnitude of among population and among year variation and covariation in this respect in natural state Atlantic salmon rives. To investigate this, variability in the timing of river entry in three historical Finnish Atlantic salmon populations were analyzed using salmon trap data collected during 1870- 1902. The analyses reveled that 1 ） the timing of river entry differed substantially and consistently among the rivers, and that 2） variation among the rivers was much larger than variation among years. Annual variations were not explained by regional environmental conditions, whereas in one river the timing of the local flood peak was a significant predictor of the timing of river entry. Differences in the timing of salmon entry to geographically closely situated rivers suggests that a regionally fixed opening date for coastal fisheries might not be the best management strategy as it may lead to uneven exploitation of salmon populations from different rivers [ Current Zoology 55 （5） ： 342 - 349, 2009] .
基金Supported by the National Science Foundation of China under Grant Nos. 10774150,10834014the China 973-Program under Grant Nos. 2007CB935903 and HKUST605010
文摘The fully connected Hopfield network is inferred based on observed magnetizations and pairwise correlations.We present the system in the glassy phase with low temperature and high memory load.We find that the inference error is very sensitive to the form of state sampling.When a single state is sampled to compute magnetizations and correlations,the inference error is almost indistinguishable irrespective of the sampled state.However,the error can be greatly reduced if the data is collected with state transitions.Our result holds for different disorder samples and accounts for the previously observed large fluctuations of inference error at low temperatures.
文摘Road condition is an important variable to measure in order to decrease road and vehicle operating/maintenance costs, but also to increase ride comfort and traffic safety. By using the built-in vibration sensor in smart phones, it is possible to collect road roughness data which can be an indicator of road condition up to a level of Class 2 or 3 in a simple and cost efficient way. Since data collection therefore is possible to be done more frequently, one can better monitor roughness changes over time. The continuous data collection can also give early warnings of changes and damage, enable new ways to work in the operational road maintenance management, and can serve as a guide for more accurate surveys for strategic asset management and pavement planning. Collected measurement data are wirelessly transferred by the operator when needed via a web service to an internet mapping server with spatial filtering functions. The measured data can be aggregated in preferred sections, as well as exported to other GlS （geographical information systems） or road management systems. Our conclusion is that measuring roads with smart phones can provide an efficient, scalable, and cost-effective way for road organizations to deliver road condition data.
