Knowledge of material emissivity maps and their true temperatures is of great interest for contactless process monitoring and control with infrared cameras when strong heat transfer and temperature change are involved. This approach is always followed by emissivity or reflections issues. In this work, we describe the development of a contactless infrared imaging technique based on the pyro-reflectometry approach and a specular model of the material reflection in order to overcome emissivities and reflections problems. This approach enables in situ and real-time identification of emissivity fields and autocalibration of the radiative intensity leaving the sample by using a black body equivalent ratio. This is done to obtain the absolute temperature field of any specular material using the infrared wavelength. The presented set up works for both camera and pyrometer regardless of the spectral range. The proposed method is evaluated at room temperature with several heterogeneous samples covering a large range of emissivity values. From these emissivity fields, raw and heterogeneous measured radiative fluxes are transformed into complete absolute temperature fields.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9098899 | PMC |
http://dx.doi.org/10.1038/s41598-022-11616-8 | DOI Listing |
Membranes (Basel)
January 2025
Department of Chemical Engineering, University of Chemical Technology and Metallurgy, 1576 Sofia, Bulgaria.
This study explored the batch membrane filtration of 40% ethanol extracts from spent lavender, containing valuable compounds like rosmarinic acid, caffeic acid, and luteolin, using a polyamide-urea thin film composite X201 membrane. Conducted at room temperature and 20 bar transmembrane pressure, the process demonstrated high efficiency, with rejection rates exceeding 98% for global antioxidant activity and 93-100% for absolute concentrations of the target components. During concentration, the permeate flux declined from 2.
View Article and Find Full Text PDFSyst Rev
January 2025
Weill Cornell Medicine, Department of Medicine, 525 E 68th St, New York, NY, 10065, USA.
Background: Extreme heat events (EHEs), driven by anthropogenic climate change, exacerbate the risk of cardiovascular disease (CVD), although the underlying mechanisms are unclear. A possible mechanism leading to heat-related CVD is disturbances in sleep health, which can increase the risk of hypertension, and is associated with ideal cardiovascular health. Thus, our objective was to systematically review the peer-reviewed literature that describes the relationship between EHEs, sleep health, and cardiovascular measures and outcomes and narratively describe methodologies, evidence, and gaps in this area in order to develop a future research agenda linking sleep health, EHEs, and CVD.
View Article and Find Full Text PDFSci Rep
January 2025
Amity Institute of Environmental Sciences (AIES), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Budh Nagar, Noida, 201313, India.
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inference systems (ANFIS), and response surface methodology (RSM). To evaluate the adsorbent's potential, assessments were conducted using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM).
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).
View Article and Find Full Text PDFFront Plant Sci
January 2025
Department of Computer Science and Engineering, Indian Institute of Information Technology Design and Manufacturing (III TDM), Kurnool, Andhrapradesh, India.
Climate change poses significant challenges to global food security by altering precipitation patterns and increasing the frequency of extreme weather events such as droughts, heatwaves, and floods. These phenomena directly affect agricultural productivity, leading to lower crop yields and economic losses for farmers. This study leverages Artificial Intelligence (AI) and Explainable Artificial Intelligence (XAI) techniques to predict crop yields and assess the impacts of climate change on agriculture, providing a novel approach to understanding complex interactions between climatic and agronomic factors.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!