Macroalgae dominate nutrient dynamics and function as high-value foods for microbial,meio-and macrofaunal communities in coastal ecosystems.Because of this vital role,it is important to clarify the physiological infor...Macroalgae dominate nutrient dynamics and function as high-value foods for microbial,meio-and macrofaunal communities in coastal ecosystems.Because of this vital role,it is important to clarify the physiological information associated with environmental changes as it reflects their growth potential.To evaluate the effects of the changes in salinity and nutrients,the photosynthetic efficiency of a green macroalga Ulva fasciata from the Daya Bay was tested at a range of salinity(i.e.,31 to 10 psu)and nitrogen content(i.e.,5 to 60μmol L^(-1)).The results showed that cellular chlorophyll a(Chl a),carbohydrate and protein contents of U.fasciata were increased due to reduced salinity,and were decreased by interactive nitrogen enrichment.Within a short culture period(i.e.,18 h),the reduced salinity decreased the maximum photosynthetic efficiency(rETRmax and Pmax)derived from the rapid light response curve and photosynthetic oxygen evolution rate versus irradiance curve,respectively,as well as the saturation irradiance(E_(K)).This reducing effect diminished with enlonged cultivation time and reversed to a stimulating effect after 24 h of cultivation.The nitrogen enrichment stimulated the rETRmax and Pmax,as well as the E_(K),regardless of salinity,especially within short-term cultivation period(i.e.,<24 h).In addition,our results indicate that seawater freshening lowers the photosynthetic efficiency of U.fasciata in the short term,which is mitigated by nitrogen enrichment,but stimulates it in the long term,providing insight into how macroalgae thrive in coastal or estuarine waters where salinity and nutrients normally covary strongly.展开更多
To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-l...To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.展开更多
基金funded by the National Key Research and Development Program of China(No.20022YFC3102405)the National Natural Science Foundation of China(Nos.42425004,32371665)the Natural Science Foundation of Guangdong Province(Nos.2022A1515011461,2022A1515011831)。
文摘Macroalgae dominate nutrient dynamics and function as high-value foods for microbial,meio-and macrofaunal communities in coastal ecosystems.Because of this vital role,it is important to clarify the physiological information associated with environmental changes as it reflects their growth potential.To evaluate the effects of the changes in salinity and nutrients,the photosynthetic efficiency of a green macroalga Ulva fasciata from the Daya Bay was tested at a range of salinity(i.e.,31 to 10 psu)and nitrogen content(i.e.,5 to 60μmol L^(-1)).The results showed that cellular chlorophyll a(Chl a),carbohydrate and protein contents of U.fasciata were increased due to reduced salinity,and were decreased by interactive nitrogen enrichment.Within a short culture period(i.e.,18 h),the reduced salinity decreased the maximum photosynthetic efficiency(rETRmax and Pmax)derived from the rapid light response curve and photosynthetic oxygen evolution rate versus irradiance curve,respectively,as well as the saturation irradiance(E_(K)).This reducing effect diminished with enlonged cultivation time and reversed to a stimulating effect after 24 h of cultivation.The nitrogen enrichment stimulated the rETRmax and Pmax,as well as the E_(K),regardless of salinity,especially within short-term cultivation period(i.e.,<24 h).In addition,our results indicate that seawater freshening lowers the photosynthetic efficiency of U.fasciata in the short term,which is mitigated by nitrogen enrichment,but stimulates it in the long term,providing insight into how macroalgae thrive in coastal or estuarine waters where salinity and nutrients normally covary strongly.
基金supported by Lanzhou Science and Technology Plan Project(No.2023-3-104)Gansu Province Higher Education Industry Support Plan Project(No.2023CYZC-40)Gansu Province Excellent Graduate“Innovation Star”Program(No.2023CXZX-546)。
文摘To improve the accuracy of indoor localization methods with channel state information(CSI)images,a localization method that used CSI images from selected multiple access points(APs)was proposed.The method had an off-line phase and an on-line phase.In the off-line phase,three APs were selected from the four APs in the localization area based on the received signal strength indication(RSSI).Next,CSI data was collected from the three selected APs using a commercial Intel 5300 network interface card.A single-channel subimage was constructed for each selected AP by combining the amplitude information from different antennas and the phase difference information between neighboring antennas.These sub-images were then merged to form a three-channel RGB image,which was subsequently fed into the convolutional neural network(CNN)for training.The CNN model was saved upon completion of training.In the on-line phase,the CSI data from the target device was collected,converted into images using the same process as in the off-line phase,and fed into the well-trained CNN model.Finally,the real position of the target device was estimated using a weighted centroid algorithm based on the model’s output probabilities.The proposed method was validated in indoor environments using two datasets,achieving good localization accuracy.
