Avian incubation is characterised by the contact between the eggs and the bird's skin to transfer heat to increase egg temperature above ambient conditions. Birds can be attentive to the clutch all of the time or,...Avian incubation is characterised by the contact between the eggs and the bird's skin to transfer heat to increase egg temperature above ambient conditions. Birds can be attentive to the clutch all of the time or, particularly if only one parent incubates, attentiveness may be quite low. Attentiveness is related to egg size with large eggs having high attentiveness, whereas small eggs (<10 g) can have attentiveness ranging from 50% to 100%. Previous studies have suggested that incubation duration is a function of attentiveness albeit for small birds. This study tested the hypothesis that, after controlling for egg size and phylogeny, incubation duration would be a function of attentiveness. Data for 444 bird species representing 24 orders were analysed. Whilst egg mass had a significant relationship with incubation duration, there was no relationship with attentiveness for all of the species or a subset of the passerines. Despite egg temperature drops during an incubation recess, average day-time and night-time temperatures are similar in a range of species. Re-examination of previously reported temperature profiles recorded by dummy eggs over a 24-h period shows that after an incubation recess there seems to be an additional heat flux that raises egg temperature above that seen during night-time periods of constant incubation. The reasons why eggs under intermittent incubation are not considerably cooler than eggs during constant incubation are discussed.展开更多
Background:While nest attentiveness plays a critical role in the reproductive success of avian species,nest attentiveness data with high temporal resolution is not available for many species.However,improvements in bo...Background:While nest attentiveness plays a critical role in the reproductive success of avian species,nest attentiveness data with high temporal resolution is not available for many species.However,improvements in both video monitoring and temperature logging devices present an opportunity to increase our understanding of this aspect of avian behavior.Methods:To investigate nest attentiveness behaviors and evaluate these technologies,we monitored 13 nests across two Common Tern(Sterna hirundo)breeding colonies with a paired video camera-temperature logger approach,while monitoring 63 additional nests with temperature loggers alone.Observations occurred from May to August of 2017 on Poplar(Chesapeake Bay,Maryland,USA)and Skimmer Islands(Isle of Wight Bay,Maryland,USA).We examined data respective to four times of day:Morning(civil dawn‒11:59),Peak(12:00‒16:00),Cooling(16:01‒civil dusk),and Night(civil dusk‒civil dawn).Results:While successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day,failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods(p<0.05)with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl(Bubo virginianus).Incubation appeared to ameliorate ambient temperatures during Night,as nests were significantly warmer during Night when birds were on versus off the nest(p<0.05).Meanwhile,off-bouts during the Peak period occurred during higher ambient temperatures,perhaps due to adults leaving the nest during the hottest periods to perform belly soaking.Unfortunately,temperature logger data alone had limited ability to predict nest attentiveness status during shorter bouts,with results highly dependent on time of day and bout duration.While our methods did not affect hatching success(p>0.05),video-monitored nests did have significantly lower clutch sizes(p<0.05).Conclusions:The paired use of iButtons and video cameras enabled a detailed description of the incubation behavior of COTE.However,while promising for future research,the logistical and potential biological complications involved in the use of these methods suggest that careful planning is needed before these devices are utilized to ensure data is collected in a safe and successful manner.展开更多
Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in th...Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.展开更多
Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced tran...Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.展开更多
文摘Avian incubation is characterised by the contact between the eggs and the bird's skin to transfer heat to increase egg temperature above ambient conditions. Birds can be attentive to the clutch all of the time or, particularly if only one parent incubates, attentiveness may be quite low. Attentiveness is related to egg size with large eggs having high attentiveness, whereas small eggs (<10 g) can have attentiveness ranging from 50% to 100%. Previous studies have suggested that incubation duration is a function of attentiveness albeit for small birds. This study tested the hypothesis that, after controlling for egg size and phylogeny, incubation duration would be a function of attentiveness. Data for 444 bird species representing 24 orders were analysed. Whilst egg mass had a significant relationship with incubation duration, there was no relationship with attentiveness for all of the species or a subset of the passerines. Despite egg temperature drops during an incubation recess, average day-time and night-time temperatures are similar in a range of species. Re-examination of previously reported temperature profiles recorded by dummy eggs over a 24-h period shows that after an incubation recess there seems to be an additional heat flux that raises egg temperature above that seen during night-time periods of constant incubation. The reasons why eggs under intermittent incubation are not considerably cooler than eggs during constant incubation are discussed.
基金This work was supported by the U.S.Army Corps of Engineers(Baltimore District),U.S.Geological Survey(Patuxent Wildlife Research Center)the University of Maryland,the Maryland Department of Natural Resources(Wildlife and Heritage Program)the Maryland Environmental Service,and the Maryland Coastal Bays Program.
文摘Background:While nest attentiveness plays a critical role in the reproductive success of avian species,nest attentiveness data with high temporal resolution is not available for many species.However,improvements in both video monitoring and temperature logging devices present an opportunity to increase our understanding of this aspect of avian behavior.Methods:To investigate nest attentiveness behaviors and evaluate these technologies,we monitored 13 nests across two Common Tern(Sterna hirundo)breeding colonies with a paired video camera-temperature logger approach,while monitoring 63 additional nests with temperature loggers alone.Observations occurred from May to August of 2017 on Poplar(Chesapeake Bay,Maryland,USA)and Skimmer Islands(Isle of Wight Bay,Maryland,USA).We examined data respective to four times of day:Morning(civil dawn‒11:59),Peak(12:00‒16:00),Cooling(16:01‒civil dusk),and Night(civil dusk‒civil dawn).Results:While successful nests had mostly short duration off-bouts and maintained consistent nest attentiveness throughout the day,failed nests had dramatic reductions in nest attentiveness during the Cooling and Night periods(p<0.05)with one colony experiencing repeated nocturnal abandonment due to predation pressure from a Great Horned Owl(Bubo virginianus).Incubation appeared to ameliorate ambient temperatures during Night,as nests were significantly warmer during Night when birds were on versus off the nest(p<0.05).Meanwhile,off-bouts during the Peak period occurred during higher ambient temperatures,perhaps due to adults leaving the nest during the hottest periods to perform belly soaking.Unfortunately,temperature logger data alone had limited ability to predict nest attentiveness status during shorter bouts,with results highly dependent on time of day and bout duration.While our methods did not affect hatching success(p>0.05),video-monitored nests did have significantly lower clutch sizes(p<0.05).Conclusions:The paired use of iButtons and video cameras enabled a detailed description of the incubation behavior of COTE.However,while promising for future research,the logistical and potential biological complications involved in the use of these methods suggest that careful planning is needed before these devices are utilized to ensure data is collected in a safe and successful manner.
基金supported by the National Natural Science Foundation of China [grant numbers 42088101 and 42375048]。
文摘Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.
基金research was funded by Science and Technology Project of State Grid Corporation of China under grant number 5200-202319382A-2-3-XG.
文摘Iced transmission line galloping poses a significant threat to the safety and reliability of power systems,leading directly to line tripping,disconnections,and power outages.Existing early warning methods of iced transmission line galloping suffer from issues such as reliance on a single data source,neglect of irregular time series,and lack of attention-based closed-loop feedback,resulting in high rates of missed and false alarms.To address these challenges,we propose an Internet of Things(IoT)empowered early warning method of transmission line galloping that integrates time series data from optical fiber sensing and weather forecast.Initially,the method applies a primary adaptive weighted fusion to the IoT empowered optical fiber real-time sensing data and weather forecast data,followed by a secondary fusion based on a Back Propagation(BP)neural network,and uses the K-medoids algorithm for clustering the fused data.Furthermore,an adaptive irregular time series perception adjustment module is introduced into the traditional Gated Recurrent Unit(GRU)network,and closed-loop feedback based on attentionmechanism is employed to update network parameters through gradient feedback of the loss function,enabling closed-loop training and time series data prediction of the GRU network model.Subsequently,considering various types of prediction data and the duration of icing,an iced transmission line galloping risk coefficient is established,and warnings are categorized based on this coefficient.Finally,using an IoT-driven realistic dataset of iced transmission line galloping,the effectiveness of the proposed method is validated through multi-dimensional simulation scenarios.
