Fluorescence color diversity of great barrier reef corals

A group of variously colored proteins belonging to the green fAuorescent protein(GFP)family are responsible for coloring coral tissues.Corals of the Great Barrier Reef were studied with the custom-built fiber laser fluorescence spectrometers.Spectral analysis showed that most of the excarmined corals contained multiple fuorescent peaks ranging from 470 to 620nm.This obser-vation was attributed to the presence of multiple genes of GFP-like proteins in a single coral,as well as by the photo-induced post-translational modifcations of certain GFP-like proteins.We isolated a novel photo-convertible fuorescent protein(FP)from one of the tested corals.We:propose that two processes may explain the observed diversity of the fuorescent spectra in corals:(1)dark post-translational modifcation(maturation),and(2)color photo-conversion of certain maturated proteins in response to sunlight.

Small ORC （Organic Rankine Cycle） Power Units for F^emote Applications

Over the last 45 years, the development of backbone telecommunications systems and construction of strategic oil and gas pipelines has spanned tens of thousands ofkilometres in harsh environments （arctic to tropical, unmanned or developing areas）. As the vast majority of these areas are without an electrical power lines infrastructure, the need arose for a highly reliable and maintenance-free power supply to allow the continuous operations of these projects. ORC （Organic Rankine Cycle） based on CCVT （closed cycle vapor turbogenerator） was specially developed for the special requirements. The paper presents the CCVT and its mode of operation, design criteria and case studies in projects implemented in the last 45 years.

Improved forecasting via physics-guided machine learning as exemplified using“21·7”extreme rainfall event in Henan

As a natural disaster,extreme precipitation is among the most destructive and influential,but predicting its occurrence and evolution accurately is very challenging because of its rarity and uniqueness.Taking the example of the“21·7”extreme precipitation event(17–21 July 2021)in Henan Province,this study explores the potential of using physics-guided machine learning to improve the accuracy of forecasting the intensity and location of extreme precipitation.Three physics-guided ways of embedding physical features,fusing physical model forecasts and revised loss function are used,i.e.,(1)analyzing the anomalous circulation and thermodynamical factors,(2)analyzing the multi-model forecast bias and the associated underlying reasons for it,and(3)using professional forecasting knowledge to design the loss function,and the corresponding results are used as input for machine learning to improve the forecasting accuracy.The results indicate that by learning the relationship between anomalous physical features and heavy precipitation,the forecasting of precipitation intensity is improved significantly,but the location is rarely adjusted and more false alarms appear.Possible reasons for this are as follows.The anomalous features used here mainly contain information about large-scale systems and factors which are consistent with the model precipitation deviation;moreover,the samples of extreme precipitation are sparse and so the algorithm used here is simple.However,by combining“good and different”multi models with machine learning,the advantages of each model are extracted and then the location of the precipitation center in the forecast is improved significantly.Therefore,by combining the appropriate anomalous features with multi-model fusion,an integrated improvement of the forecast of the rainfall intensity and location is achieved.Overall,this study is a novel exploration to improve the refined forecasting of heavy precipitation with extreme intensity and high variability,and provides a reference for the deep fusion of physics and artificial intelligence methods to improve intense rain forecast.