A single pre-trained language model (such as mT5) still often fails to capture key information and cover sufficient content for the text summarization task. We propose a generative text summarization method (mT5-LLM) ...
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Evapotranspiration(ET)plays a crucial role in the global water and energy *** instantaneous ET(ET_(i))to daily ET(ET_(d))is vital for thermal-based ET *** methods-such as the constant evaporative fraction method(ConEF...
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Evapotranspiration(ET)plays a crucial role in the global water and energy *** instantaneous ET(ET_(i))to daily ET(ET_(d))is vital for thermal-based ET *** methods-such as the constant evaporative fraction method(ConEF),radiation-based method,and evaporative ratio method-often overlook environmental factors,leading to biased estimates of ET_(d)from ET_(i).To resolve this issue,this study aimed to assess four machine learning(ML)algorithms-XGBoost,LightGBM,AdaBoost,and Random Forest-to integrate meteorological and remote sensing data for upscaling ETi across 88 global flux *** ML model was tested with eight different variable *** indicated that XGBoost exhibited the best performance,with a root mean square error(RMSE)generally below 13 W m^(-2)in estimating ET_(d)from ET_(i).The best variable combination simultaneously considers evaporative fraction,available energy,meteorology factors,remote sensing albedo,normalized vegetation index,and leaf area *** this combination,the XGBoost model achieved an R^(2)=0.88 and an RMSE=12.33 W m^(-2),outperforming the ConEF method(R^(2)=0.71 and RMSE=18.86 Wm^(-2))and its *** findings support the application of ML models in ET upscaling,enabling ET estimation across large spatiotemporal scales.
This research discusses the advancements in developing smart skin by employing Force Sensitive Resistor (FSR). The objective of this research is to advance research on the fundamental concept of artificial skin that r...
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Computing First Network (CFN) aims to enable unified scheduling of computing nodes across the network, positioning it as a promising paradigm for managing the computationally intensive tasks of Intelligent Connected V...
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With the development of computertechnology, computer-aided diagnosis technology has become one of the important research directions in the field of medicine. Among them, considerable progress has been made in the app...
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Social media platforms help users share opinions and find new information but also spread rumors, which misinforms the public. These rumour threads often prompt users (called guardians) to respond with fact-checking a...
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Positional embedding is an effective means of injecting position information into sequential data to make the vanilla Transformer position-sensitive. Current Transformer-based models routinely use positional embedding...
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Automatic monitoring and evaluation of chronic wounds usually requires massive labeled data sets for segmentation training. Because of the high cost of time and labor, these data are usually difficult to obtain. In or...
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Traditional object detectors usually require fully annotated instances for training to achieve satisfactory detection results. However, for incompletely annotated image instances, especially in medical imaging, their ...
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The purposes of this study are to identify the aerocraft altitude. In consideration of the shortcoming of statistics and randomness of influencing factors in aerocraft flight, Fuzzy SOFM model is taken as an effective...
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