We present a highly stable bow-tie power enhancement cavity for critical second harmonic generation (SHG) into the UV using a Brewster-cut β-BaBO (BBO) nonlinear crystal. The cavity geometry is suitable for all UV wavelengths reachable with BBO and can be modified to accommodate anti-reflection coated crystals, extending its applicability to the entire wavelength range accessible with non-linear frequency conversion. The cavity is length-stabilized using a fast general purpose digital PI controller based on the open source STEMlab 125-14 (formerly Red Pitaya) system acting on a mirror mounted on a fast piezo actuator. We observe 130 h uninterrupted operation without decay in output power at 313 nm. The robustness of the system has been confirmed by exposing it to accelerations of up to 1 g with less than 10% in-lock output power variations. Furthermore, the cavity can withstand 30 min of acceleration exposure at a level of 3 g without substantial change in the SHG output power, demonstrating that the design is suitable for transportable setups.
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Front Physiol
January 2025
School of Kinesiology, Auburn University, Auburn, AL, United States.
Nitric oxide (NO) is a ubiquitous signaling molecule known to modulate various physiological processes, with specific implications in skeletal muscle and broader applications in exercise performance. This review focuses on the modulation of skeletal muscle function, mitochondrial adaptation and function, redox state by NO, and the effect of nitrate supplementation on exercise performance. In skeletal muscle function, NO is believed to increase the maximal shortening velocity and peak power output of muscle fibers.
View Article and Find Full Text PDFPurpose: To examine the physiological, power-duration, nutritional intake and training characteristics of the recent lightweight (- 75 kg) 50+, 60+ and 70 + yr world champion indoor rowers.
Methods: Laboratory assessments, undertaken over 2 visits, examined body composition, pulmonary function, blood lactate/ventilatory landmarks, efficiency, fat/carbohydrate oxidation, primary component time-constant to steady-state [𝜏pc]) and peak oxygen consumption (V̇O2peak). Training, performance and nutritional intake were also reported.
ISA Trans
January 2025
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran. Electronic address:
Microgrids play an important role in stabilizing the electrical grid and they are the best route to develop green and sustainable energy. Since microgrids are expanding rapidly, it is necessary to consider the related control issues including power quality, bidirectional power flow, voltage and frequency control, and stability analysis. One of the main measurement challenges is the communication delay.
View Article and Find Full Text PDFBiosens Bioelectron
January 2025
Education Department of Guangxi Zhuang Autonomous Region, Laboratory of Optic-electric Chemo/Biosensing and Molecular Recognition, Guangxi Collaborative Innovation Center for Chemistry and Engineering of Forest Products, Guangxi Key Laboratory of Chemistry and Engineering of Forest Products, Key Laboratory of Chemistry and Engineering of Forest Products, State Ethnic Affairs Commission, School of Chemistry and Chemical Engineering, Guangxi Minzu University, Nanning 530006, China. Electronic address:
Sugarcane smut is a widespread fungal disease, which severely impairs the quality and sugar yield of sugarcane. Early detection is crucial for mitigating its impact, which makes the development of a highly sensitive and accurate detection method essential. Herein, the Mn-doped zeolite imidazolate framework (ZIF-67), synthesized via a nano-confined-reactor approach, is designed to significantly enhance electron transport and boost the enzyme loading capacity within biofuel cells, thereby potentially enhancing their overall performance.
View Article and Find Full Text PDFPLoS 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).
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