A Physics-informed Deep-learning Intensity Prediction Scheme for Tropical Cyclones over the Western North Pacific

Accurate prediction of tropical cyclone(TC)intensity is challenging due to the complex physical processes involved.Here,we introduce a new TC intensity prediction scheme for the western North Pacific(WNP)based on a time-dependent theory of TC intensification,termed the energetically based dynamical system(EBDS)model,together with the use of a long short-term memory(LSTM)neural network.In time-dependent theory,TC intensity change is controlled by both the internal dynamics of the TC system and various environmental factors,expressed as environmental dynamical efficiency.The LSTM neural network is used to predict the environmental dynamical efficiency in the EBDS model trained using besttrack TC data and global reanalysis data during 1982–2017.The transfer learning and ensemble methods are used to retrain the scheme using the environmental factors predicted by the Global Forecast System(GFS)of the National Centers for Environmental Prediction during 2017–21.The predicted environmental dynamical efficiency is finally iterated into the EBDS equations to predict TC intensity.The new scheme is evaluated for TC intensity prediction using both reanalysis data and the GFS prediction data.The intensity prediction by the new scheme shows better skill than the official prediction from the China Meteorological Administration(CMA)and those by other state-of-art statistical and dynamical forecast systems,except for the 72-h forecast.Particularly at the longer lead times of 96 h and 120 h,the new scheme has smaller forecast errors,with a more than 30%improvement over the official forecasts.

A Logistic-growth-equation-based Intensity Prediction Scheme for Western North Pacific Tropical Cyclones

Accurate prediction of tropical cyclone(TC)intensity remains a challenge due to the complex physical processes involved in TC intensity changes.A seven-day TC intensity prediction scheme based on the logistic growth equation(LGE)for the western North Pacific(WNP)has been developed using the observed and reanalysis data.In the LGE,TC intensity change is determined by a growth term and a decay term.These two terms are comprised of four free parameters which include a time-dependent growth rate,a maximum potential intensity(MPI),and two constants.Using 33 years of training samples,optimal predictors are selected first,and then the two constants are determined based on the least square method,forcing the regressed growth rate from the optimal predictors to be as close to the observed as possible.The estimation of the growth rate is further refined based on a step-wise regression(SWR)method and a machine learning(ML)method for the period 1982−2014.Using the LGE-based scheme,a total of 80 TCs during 2015−17 are used to make independent forecasts.Results show that the root mean square errors of the LGE-based scheme are much smaller than those of the official intensity forecasts from the China Meteorological Administration(CMA),especially for TCs in the coastal regions of East Asia.Moreover,the scheme based on ML demonstrates better forecast skill than that based on SWR.The new prediction scheme offers strong potential for both improving the forecasts for rapid intensification and weakening of TCs as well as for extending the 5-day forecasts currently issued by the CMA to 7-day forecasts.

EFFECTS OF VERTICAL WIND SHEAR ON TROPICAL CYCLONE INTENSITY CHANGE

The effects of vertical wind shear on tropical cyclone(TC) intensity change are examined based on the TC data from the China Meteorological Administration and the NCEP reanalysis daily data from 2001 to 2006.First,the influence of wind shear between different vertical levels and averages in different horizontal areas are compared.The results indicate that the effect of wind shear between 200 and 850 hPa averaged within a 200-800 km annulus on TC intensity change is larger than any other calculated vertical wind shear.High-latitude and intense TCs tend to be less sensitive to the effects of VWS than low-latitude and weak TCs.TCs experience time lags between the imposition of the shear and the weakening in TC intensity.A vertical shear of 8-9 m/s(9-10 m/s) would weaken TC intensity within 60 h(48 h).A vertical shear greater than 10 m/s would weaken TC intensity within 6 h.Finally,a statistical TC intensity prediction scheme is developed by using partial least squares regression,which produces skillful intensity forecasts when potential predictors include factors related to the vertical wind shear.Analysis of the standardized regression coefficients further confirms the obtained statistical results.

Method of expected earthquake losses estimation based on the frequency of seismic site intensity

During a given period,a site will suffer the attack from earthquake several times.But this effect is neglected in the currently used model of loss estimation from earthquake When calculating the occurrence rate of the affected intensity,the difference of the exceeding probability is used.Such treatment will underestimate the earthquake loss,especially when the exposure period is long.To overcome the shortcomings of the model currently used,a new frame of earthquake loss estimation is provided from the logic sense:during the given period,the expected earthquake loss responding to the specific affected intensity is equal to the expected number of the intensity multiplying the expected loss under the condition of such an affected intensity,and the total expected loss is equal to the effects of all the possible intensities.On the basis of the seismicity model used in compiling the 'Chinese Seismic Intensity Zoning Map(1990) ',a new formula of expected loss evaluation and the variance of the evaluation are provided.It is inferred from the example and the comparison with the currently used method that the new method is applicable and necessary.These results will lay a scientific foundation for the estimation of earthquake loss,insurance and disaster prevention.

Macroseismic intensity attenuation in Iran

Macroseismic intensity data plays an important role in the process of seismic hazard analysis as well in developing of reliable earthquake loss models. This paper presents a physical-based model to predict macroseismic intensity attenuation based on 560 intensity data obtained in Iran in the time period 1975-2013. The geometric spreading and energy absorption of seismic waves have been considered in the proposed model. The proposed easy to implement relation describes the intensity simply as a function of moment magnitude, source to site distance and focal depth. The prediction capability of the proposed model is assessed by means of residuals analysis. Prediction results have been compared with those of other intensity prediction models for Italy, Turkey, Iran and central Asia. The results indicate the higher attenuation rate for the study area in distances less than 70 km.

Predicting intensity of white-tailed deer herbivory in the Central Appalachian Mountains

In eastern North America, white-tailed deer(Odocoileus virginianus) can have profound influences on forest biodiversity and forest successional processes.Moderate to high deer populations in the central Appalachians have resulted in lower forest biodiversity.Legacy effects in some areas persist even following deer population reductions or declines. This has prompted managers to consider deer population management goals in light of policies designed to support conservation of biodiversity and forest regeneration while continuing to support ample recreational hunting opportunities. However,despite known relationships between herbivory intensity and biodiversity impact, little information exists on the predictability of herbivory intensity across the varied and spatially diverse habitat conditions of the central Appalachians. We examined the predictability of browsing rates across central Appalachian landscapes at four environmental scales: vegetative community characteristics, physical environment, habitat configuration, and local human and deer population demographics. In an information-theoretic approach, we found that a model fitting the number of stems browsed relative to local vegetation characteristics received most(62%) of the overall support of all tested models assessing herbivory impact. Our data suggest that deer herbivory responded most predictably to differences in vegetation quantity and type. No other spatial factors or demographic factors consistently affected browsing intensity. Because herbivory, vegetation communities, and productivity vary spatially, we suggest that effective broad-scale herbivory impact assessment should include spatially-balanced vegetation monitoring that accounts for regional differences in deer forage preference.Effective monitoring is necessary to avoid biodiversity impacts and deleterious changes in vegetation community composition that are difficult to reverse and/or may not be detected using traditional deer-density based management goals.

Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

The research on Hainan earthquake insurance system

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RESEARCH ON INDOOR ELECTROMAGNETIC RADIATION FIELD OF MULTIPLE ANTENNA SYSTEM

The complexity of the indoor environment brings great challenges to predict the electromagnetic radiation field of multiple antenna systems. Based on the Finite Difference Time Domain (FDTD) algorithm, using the mobile phone shielding device as the multiple antenna systems example, the mobile phone shielding device's indoor electromagnetic radiation field is researched by measurment method and simulation method. The effectivity of prediction method is verified by comparing the prediciton results with the measurment results. About 80% of the error can be controlled less than dB. The quantitative research has certain guiding significance to the prediction of the multiple antenna systems radio wave propagation.

Numerical prediction of envelope line-spectrum intensity in underwater acoustic waveguides

In order to predict the detectible range and region of passive sonar in underwater channel,the attenuation causes of envelope line-spectrum height during vessel noise propagation are analyzed and an approach of numerical prediction is proposed.In the paper a model for vessel radiated noise is established by a periodically locally stationary random process,two formulae of the envelope line-spectrum height with and without background-noise are deduced, therefore the attenuation rule of the envelope line-spectrum height is obtained.It is shown that the transmission loss of the sound level of the envelope line-spectrum is same as the sound level of the stationary spectrum,but the decrease of envelope line-spectrum height depends on a modified scale of amplitude modulation depth which is variable with the ratio of signal to noise. An approach of numerical prediction for envelope line-spectrum height is as follows：first,the transmission loss of the stationary radiated noise is derived using the numerical approaches of normal modes or wavenumber integration or PE etc.,then the ratio of signal to noise on sound field is calculated,finally the decrease of envelope line-spectrum height is obtained according to the modified scale,and the envelope line-spectrum height in sound field is predicted.The theory and the numerical prediction approach possess certain innovation,practicality,simplicity and suitability for engineering.

Brainstem folding in an influenza child with Dandy-Walker variant

BACKGROUND Influenza in children is a major cause of morbidity and mortality worldwide.Nervous system diseases are a factor relating to increased mortality rate.However, reports of how these underlying diseases contribute to the death of children with influenza are rare.CASE SUMMARY A 4-year-old-girl developed type A influenza-related encephalopathy(IAE) with seizures, acute disorder of consciousness, and intracranial hypertension(cerebrospinal fluid pressure: 250 mm H2O), and the Dandy-Walker variant was found by her first magnetic resonance imaging(MRI) when admission. Three days later, she suddenly presented anisocoria, acute pulmonary edema, and coma, and the later MRI found that she had compressed brainstem, oblongata "Zlike folding", and swelling bilateral basal ganglia. After admission, the patient were tested for routine and special biomarkers and underwent neuroimaging and neuroelectrophysiology examinations as well as Oseltamivir and intravenous immunogloblin treatments. When predicting that unstable intracranial structures detected by MRI might have disastrous consequences in the progression of IAE,she was transferred into the pediatric intensive care unit and underwent continuous assessment of clinical condition while she did not have instability of basic vital signs;at the same time, her parents were fully informed about the risk and prognosis. Although she was ultimately dead from brain stem failure, the parents expressed understanding and did not trigger a doctor-patient conflict.CONCLUSION In case of finding an unstable intracranial structure, intensive care should be given to IAE patient and their clinical condition should be monitored continuously.

Predictive risk factors for prolonged stay in intensive care unit in patients undergoing coronary artery bypass grafting surgery

Objective To describe the preoperative factors of prolonged intensive care unit length of stay after coronary artery bypass grafting. Methods From 1997 to 2009, 1318 patients underwent isolated CABG in our hospital. Retrospective analysis was performed on these cases. Univariate and multivariate analyses

Predictive risk factors associated with prolonged stay in the intensive care unit for patients undergoing coronary artery bypass grafting surgery

Objective The rate of post-operative complications has been increased with the changes in patients’age,prolonged duration,more severe and diffused lesions,and more patients with complications in recent years. We try to identify the risk factors associated with prolonged stay in the intensive care unit (ICU) after coronary artery bypass graft surgery (CABG) . Methods 1623 patients who received CABG surgery in Beijing Anzhen Hospital

Application of Equivalent Black Body Temperature in the Forecast of Tropical Cyclone Intensity

Using equivalent black body temperature （TBB） data retrieved from meteorological satellite GMS-5 during 1996-2002, the correlation between the circular symmetric/asymmetric component of TBB and the intensity of tropical cyclone （TC） at various time lags from 0 to 48 h is analyzed for the Northwest Pacific （0^-50~N, 120%155~E）, excluding landed and near-coast samples. It is found that the total TBB near southeast of the eyewall, the circular symmetric component, and the sum of the amplitudes of tangential wave numbers 1-10 （SA10） of the TBB between the radii of 0.8^o and 1.7^o are significantly and negatively correlated with the TC intensity at various time lags from 0 to 48 h. Especially, the maximum 24-h lag correlation coefficients reach -0.52, -0.58, and -0.625, respectively. A statistical prediction scheme for TC intensity is developed based on climatic persistent, synoptic, and TBB factors by stepwise regression technique. It is found that the variance contribution of the averaged TBB over the ring between 1.0^o and 1.5^o from the TC center ranks the fourth in the equation for 12-h TC intensity prediction, and those of the total TBB near southeast of the eyewall and the difference between maximum and minimum TBB between 1.1^o and 1.5^o rank the third and fifth respectively in the 24-h forecast equation. It is also shown that, with TBB factors, the following predictions are improved compared to the scheme without TBB factors： 48-h prediction for severe tropical storm （STS）, 12-h prediction for TC with a weakening rate greater than 15 m s-1/12 h, 24-h intensity prediction for TC with almost no intensity change, and 48-h prediction for TC intensifying faster than 10 m s^-1/48 h.