A Na double-edge magneto-optic filter is proposed for incorporation into the receiver of a three-frequency Na Doppler lidar to extend its wind and temperature measurements into the lower atmosphere. Two prototypes based on cold- and hot-cell designs were constructed and tested with laser scanning and quantum mechanics modeling. The hot-cell filter exhibits superior performances over the cold-cell filter containing buffer gas. Lidar simulations, metrics, and error analyses show that simultaneous wind and temperature measurements are feasible in the altitude range of 20-50 km using the hot-cell filter and reasonable Na lidar parameters.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1364/ol.34.000199 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Risk effects of environmental factors on human brucellosis in Aksu Prefecture, Xinjiang, China, 2014-2023.
Sci Rep
January 2025
School of Public Health, Xinjiang Medical University, Urumqi, China.
The context of rapid global environmental change underscores the pressing necessity to investigate the environmental factors and high-risk areas that contribute to the occurrence of brucellosis. In this study, a maximum entropy (MaxEnt) model was employed to analyze the factors influencing brucellosis in the Aksu Prefecture from 2014 to 2023. A distributed lag nonlinear model (DLNM) was employed to investigate the lagged effect of meteorological factors on the occurrence of brucellosis.
View Article and Find Full Text PDFDevelopment of a fixed-order [Formula: see text] controller for a robust P&O-MPPT strategy to control poly-crystalline solar PV energy systems.
Sci Rep
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, 21944, Saudi Arabia.
This paper presents a novel approach to modeling and controlling a solar photovoltaic conversion system(SPCS) that operates under real-time weather conditions. The primary contribution is the introduction of an uncertain model, which has not been published before, simulating the SPCS's actual functioning. The proposed robust control strategy involves two stages: first, modifying the standard Perturb and Observe (P&O) algorithm to generate an optimal reference voltage using real-time measurements of temperature, solar irradiance, and wind speed.
View Article and Find Full Text PDFEvaluating Machine Learning and Deep Learning models for predicting Wind Turbine power output from environmental factors.
PLoS One
January 2025
Renewable Energy Science and Engineering Department, Faculty of Postgraduate Studies for Advanced Sciences (PSAS), Beni-Suef University, Beni-Suef, Egypt.
This study presents a comprehensive comparative analysis of Machine Learning (ML) and Deep Learning (DL) models for predicting Wind Turbine (WT) power output based on environmental variables such as temperature, humidity, wind speed, and wind direction. Along with Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN), and Convolutional Neural Network (CNN), the following ML models were looked at: Linear Regression (LR), Support Vector Regressor (SVR), Random Forest (RF), Extra Trees (ET), Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost), Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Using a dataset of 40,000 observations, the models were assessed based on R-squared, Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).
View Article and Find Full Text PDFGlobal foot-and-mouth disease risk assessment based on multiple spatial analysis and ecological niche model.
Vet Q
December 2025
College of Veterinary Medicine, Northeast Agricultural University, Harbin, China.
Foot-and-Mouth Disease is a highly contagious transboundary animal disease. FMD has caused a significant economic impact globally due to direct losses and trade restrictions on animals and animal products. This study utilized multi-distance spatial cluster analysis, kernel density analysis, directional distribution analysis to investigate the spatial distribution patterns of historical FMD epidemics.
View Article and Find Full Text PDF[Prediction of potential geographic distribution of in Yunnan Province using random forest and maximum entropy models].
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi
December 2024
Yunnan Institute of Endemic Diseases Control and Prevention, Yunnan Provincial Key Laboratory for Zoonosis Control and Prevention, Dali, Yunnan 671000, China.
Objective: To predict the potential geographic distribution of in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into surveillance and control in Yunnan Province.
Methods: The snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population).
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!