Study on Pharmacological Effects of Peikun Pills

[Objectives]To explore the effects of Peikun Pill on blood pressure of healthy adult male rats.[Methods]The duodenum of anesthetized rats was given different doses of Peikun Pills,normal saline was used as a blank control group,and captopril was used as a positive western medicine control group.The systolic blood pressure,diastolic blood pressure,mean blood pressure and pulse pressure difference(systolic blood pressure-diastolic blood pressure),and heart rate were measured,myocardial tension time index was calculated,and statistical processing was performed.[Results]Peikun Pills have a significant pressure boosting effect,but have no effect on the myocardial tension time index.Captopril has the effects of lowering blood pressure,slowing down the heart rate and reducing the myocardial tension time index,and significantly increasing the pulse pressure difference.[Conclusions]Peikun Pills can increase blood pressure of rats.Peikun Pills are suitable for women with hypotension accompanied by anemia.However,patients with hypertension are prohibited to use.

Analysis of characteristics and evaluation of forecast accuracy for Super Typhoon Doksuri(2023)

Super Typhoon Doksuri is a significant meteorological challenge for China this year due to its strong intensity and wide influence range,as well as significant and prolonged hazards.In this work,we studied Doksuri's main characteristics and assessed its forecast accuracy meticulously based on official forecasts,global models and regional models with lead times varying from 1 to 5 days.The results indicate that Typhoon Doksuri underwent rapid intensification and made landfall at 09:55 BJT on July 28 with a powerful intensity of 50 m s−1 confirmed by the real-time operational warnings issued by China Meteorological Administration(CMA).The typhoon also caused significant wind and rainfall impacts,with precipitation at several stations reaching historical extremes,ranking eighth in terms of total rainfall impact during the event.The evaluation of forecast accuracy for Doksuri suggests that Shanghai Multi-model Ensemble Method(SSTC)and Fengwu Model are the most effective for short-term track forecasts.Meanwhile,the forecasts from the European Centre for Medium-Range Weather Forecasts(ECMWF)and United Kingdom Meteorological Office(UKMO)are optimal for long-term predictions.It is worth noting that objective forecasts systematically underestimate the typhoon maximum intensity.The objective forecast is terribly poor when there is a sudden change in intensity.CMA-National Digital Forecast System(CMA-NDFS)provides a better reference value for typhoon accumulated rainfall forecasts,and regional models perform well in forecasting extreme rainfall.The analyses above assist forecasters in pinpointing challenges within typhoon predictions and gaining a comprehensive insight into the performance of each model.This improves the effective application of model products.

Performance of tropical cyclone forecasts in the western North Pacific in 2017

The forecasts of tropical cyclones(TC)in 2017 from five official guides,six global models,six regional models and six ensemble systems were assessed to study the current capability of track and intensity forecasts for the western North Pacific.The results show that the position errors for official agencies were under 100,165,265,335 and 425 km at the lead times of 24,48,72,96 and 120 h,respectively.As the forecast lead times increased,the forecasted TCs propagated,on average,too slow for most official guides.It is encouraging to note that all the models had positive skill scores,there is an overall upward trend in the skill scores of the models during from 2010 to 2017.Furthermore,both global and regional models’intensity forecast skill was increasing year by year from 2010 to 2017.For the ensemble prediction systems(EPSs),ECMWF-EPS was the best forecast system for the lead time less than 72 h,beyond the 72 h,the best EPS belong to NCEP-GEFS.

A method to automatically ascertain the identities of tropical cyclones in tropical cyclone warning messages

In the operational forecasting of tropical cyclones(TCs),decoding TC warning messages from global centers,along with extracting,organizing,and storing useful track observations and forecasts,are fundamental tasks.The technical core lies in accurately identifying distinct TC individuals through automated programming methods.Based on the statistical characteristics of historical distances between TC individuals,this study designs a novel method for automatic identification of TC individuals and establishes a database of TC track observations and forecasts by integrating the persistent features from various elements in TC warning messages.This method accurately identifies each TC individual and assigns it a unique database number through a two-step process:initially,through the'Same Center same Number Comparison(SCNC)'identi-fication method,followed by the'Spatio-Temeporal Distance Comparison(STDC)'identification method.On this basis,we obtain a well-organized and comprehensive dataset that covers entire TC life time.Over the past decade,the operational practice has demonstrated that this method is accurate and efficient,providing solid data support for the TC forecasting operation,the assessment of TC forecasting accuracy,the compilation of TC yearbook,and TC-related research.