Direct Pointwise Comparison of FE Predictions to StereoDIC Measurements:Developments and Validation Using Double Edge-Notched Tensile Specimen

To compare finite element analysis(FEA)predictions and stereovision digital image correlation(StereoDIC)strain measurements at the same spatial positions throughout a region of interest,a field comparison procedure is developed.The procedure includes(a)conversion of the finite element data into a triangular mesh,(b)selection of a common coordinate system,(c)determination of the rigid body transformation to place both measurements and FEA data in the same system and(d)interpolation of the FEA nodal information to the same spatial locations as the StereoDIC measurements using barycentric coordinates.For an aluminum Al-6061 double edge notched tensile specimen,FEA results are obtained using both the von Mises isotropic yield criterion and Hill’s quadratic anisotropic yield criterion,with the unknown Hill model parameters determined using full-field specimen strain measurements for the nominally plane stress specimen.Using Hill’s quadratic anisotropic yield criterion,the point-by-point comparison of experimentally based full-field strains and stresses to finite element predictions are shown to be in excellent agreement,confirming the effectiveness of the field comparison process.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Multi-Perspective Data Fusion Framework Based on Hierarchical BERT: Provide Visual Predictions of Business Processes

Predictive Business Process Monitoring(PBPM)is a significant research area in Business Process Management(BPM)aimed at accurately forecasting future behavioral events.At present,deep learning methods are widely cited in PBPM research,but no method has been effective in fusing data information into the control flow for multi-perspective process prediction.Therefore,this paper proposes a process prediction method based on the hierarchical BERT and multi-perspective data fusion.Firstly,the first layer BERT network learns the correlations between different category attribute data.Then,the attribute data is integrated into a weighted event-level feature vector and input into the second layer BERT network to learn the impact and priority relationship of each event on future predicted events.Next,the multi-head attention mechanism within the framework is visualized for analysis,helping to understand the decision-making logic of the framework and providing visual predictions.Finally,experimental results show that the predictive accuracy of the framework surpasses the current state-of-the-art research methods and significantly enhances the predictive performance of BPM.

Screening and immune infiltration analysis of ferroptosis-related genes in pancreatic cancer with predictions for traditional Chinese medicine treatments

Background:This study aims to explore the involvement of ferroptosis-related genes and pathogenesis in pancreatic cancer and predict potential therapeutic interventions using Traditional Chinese Medicine(TCM).Methods:We utilized gene expression datasets,ferroptosis upregulated genes and applied machine learning algorithms,including LASSO and SVM-RFE,to identify key ferroptosis-related genes in pancreatic cancer.Perform Gene Ontology,Kyoto Encyclopedia of Genes and Genomes,and Disease Ontology enrichment analysis,immune infiltration analysis and correlation analysis between immune infiltrating cells and characteristic genes on differentially expressed genes using the R software package.Retrieve potential traditional Chinese medicine for targeted ferroptosis gene therapy for pancreatic cancer through Coremine and Herb databases.Results:Seventeen feature genes were identified,with significant implications for immune cell infiltration in pancreatic cancer.The results of immune cell infiltration analysis showed that B cells naive,B cells memory,T cells regulatory,and M0 macrophages were significantly upregulated in pancreatic cancer patients;Mast cells resting were significantly downregulated.Chinese herbal medicines such as ginkgo,turmeric,ginseng,Codonopsis pilosula,Zedoary turmeric,deer tendons,senna leaves,Guanmu Tong,Huangqi,and Banzhilian are potential drugs for targeted ferroptosis gene therapy for pancreatic cancer.Conclusion:TIMP1 emerged as a key gene,with several TCM herbs predicted to modulate its expression,offering new avenues for treatment.

From Social Media to Ballot Box:Leveraging Location-Aware Sentiment Analysis for Election Predictions

Predicting election outcomes is a crucial undertaking,and various methods are employed for this purpose,such as traditional opinion polling,and social media analysis.However,traditional polling approaches often struggle to capture the intricate nuances of voter sentiment at local levels,resulting in a limited depth of analysis and understanding.In light of this challenge,this study focuses on predicting elections at the state/regional level along with the country level,intending to offer a comprehensive analysis and deeper insights into the electoral process.To achieve this,the study introduces the Location-Based Election Prediction Model(LEPM),which utilizes social media data,specifically Twitter,and integrates location-aware sentiment analysis techniques at both the state/region and country levels.LEPM predicts the support and opposing strength of each political party/candidate.To determine the location of users/voters who have not disclosed their location information in tweets,the model utilizes a Voter Location Detection(VotLocaDetect)approach,which leverages recent tweets/posts.The sentiment analysis techniques employed in this study include rule-based sentiment analysis,Valence Aware Dictionary and Sentiment Reasoner(VADER)as well as transformers-based sentiment analysis such as Bidirectional Encoder Representations from Transformers(BERT),BERTweet,and Election based BERT(ElecBERT).This study uses the 2020 United States(US)Presidential Election as a case study.By applying the LEPM model to the election,the study demonstrates its ability to accurately predict outcomes in forty-one states,achieving an 0.84 accuracy rate at the state level.Moreover,at the country level,the LEPM model outperforms traditional polling results.With a low Mean Absolute Error(MAE)of 0.87,the model exhibits more precise predictions and serves as a successful alternative to conventional polls and other methodologies.Leveraging the extensive social media data,the LEPM model provides nuanced insights into voter behavior,enabling policymakers to make informed decisions and facilitating in-depth analyses of elections.The study emphasizes the importance of using social media data for reliable election prediction and offers implications for enhancing prediction accuracy and understanding voter sentiment and behavior.

Not Relying on the Newton Gravitational Constant Gives More Accurate Gravitational Predictions

The Newton gravitational constant is considered a cornerstone of modern gravity theory. Newton did not invent or use the gravity constant;it was invented in 1873, about the same time as it became standard to use the kilogram mass definition. We will claim that G is just a term needed to correct the incomplete kilogram definition so to be able to make gravity predictions. But there is another way;namely, to directly use a more complete mass definition, something that in recent years has been introduced as collision-time and a corresponding energy called collision-length. The collision-length is quantum gravitational energy. We will clearly demonstrate that by working with mass and energy based on these new concepts, rather than kilogram and the gravitational constant, one can significantly reduce the uncertainty in most gravity predictions.

Time trends and gender disparities of Chinese cataract burden and their predictions

AIM:To evaluate the trends and changes in the number and rates of disability-adjusted life years(DALYs)and prevalence of cataract in China between 1990 and 2019,and to predict the trends of cataract burden from 2020 to 2030.METHODS:The Global Burden of Diseases(GBD)database was employed to collect the data on DALYs and the prevalence of cataract in China,which was distinguished by age and sex during the past three decades from 1990 to 2019,and then changes in the number and rates of cataract from 2020 to 2030 were predicted.All data were analyzed by the R program(version 4.2.2)and GraphPad Prism 9.0 statistics software.RESULTS:The number of DALYs of cataract increased from 449322.84 in 1990 to 1087987.61 in 2019,number of cataract cases increased from 5607600.94 in 1990 to 18142568.96 in 2019.The age-standardized DALY rates(ASDR)generally increased slightly[estimated annual percentage change(EAPC=0.1;95%CI:-0.24 to 0.45)],age-standardized prevalence rates(ASPR)also increased(EAPC=0.88;95%CI:0.6 to 1.15).Cataract burden increased with age and female gender.Among the causes of cataract,air pollution was the most important,followed by smoking,high fasting plasma glucose,and high body mass index(BMI).The burden of cataract is predicted to grow persistently from 2020 to 2030,the number of DALYs and prevalence for cataract will rise to 2336431 and 43698620 respectively by 2030,the ASDR is predicted to be 85/100000 and ASPR will be 1586/100000 in 2030,females will still be at greater risk of suffering from cataract than males.CONCLUSION:The burden of cataract in China kept rising from 1990 to 2019.Increasing age and female gender are risk factors for cataract.Air pollution,smoking,high fasting plasma glucose,and high BMI are associated with cataract.The burden of cataract in China will gradually increase from 2020 to 2030,the elderly women in particular need attention.Our results may be of help for providing reference strategies to reduce cataract burden in the near future.

Strategies to improve genomic predictions for 35 duck carcass traits in an F2 population

Background Carcass traits are crucial for broiler ducks,but carcass traits can only be measured postmortem.Genomic selection(GS)is an effective approach in animal breeding to improve selection and reduce costs.However,the performance of genomic prediction in duck carcass traits remains largely unknown.Results In this study,we estimated the genetic parameters,performed GS using different models and marker densi-ties,and compared the estimation performance between GS and conventional BLUP on 35 carcass traits in an F2 population of ducks.Most of the cut weight traits and intestine length traits were estimated to be high and moder-ate heritabilities,respectively,while the heritabilities of percentage slaughter traits were dynamic.The reliability of genome prediction using GBLUP increased by an average of 0.06 compared to the conventional BLUP method.The Permutation studies revealed that 50K markers had achieved ideal prediction reliability,while 3K markers still achieved 90.7%predictive capability would further reduce the cost for duck carcass traits.The genomic relationship matrix nor-malized by our true variance method instead of the widely used 2pi(1-pi)could achieve an increase in prediction reliability in most traits.We detected most of the bayesian models had a better performance,especially for BayesN.Compared to GBLUP,BayesN can further improve the predictive reliability with an average of 0.06 for duck carcass traits.Conclusion This study demonstrates genomic selection for duck carcass traits is promising.The genomic prediction can be further improved by modifying the genomic relationship matrix using our proposed true variance method and several Bayesian models.Permutation study provides a theoretical basis for the fact that low-density arrays can be used to reduce genotype costs in duck genome selection.

The Influence of Arctic Sea Ice Concentration Perturbations on Subseasonal Predictions of North Atlantic Oscillation Events

The influence of Arctic sea ice concentration (SIC) on the subseasonal prediction of the North Atlantic Oscillation (NAO) event is investigated by utilizing the Community Atmospheric Model version 4. The optimal Arctic SIC perturbations which exert the greatest influence on the onset of an NAO event from a lead of three pentads (15 days) are obtained with a conditional nonlinear optimal perturbation approach. Numerical results show that there are two types of optimal Arctic SIC perturbations for each NAO event, with one weakening event (marked as type-1) and another strengthening event (marked as type-2). For positive NAO events, type-1 optimal SIC perturbations mainly show positive SIC anomalies in the Greenland, Barents, and Okhotsk Seas, while type-2 perturbations mainly feature negative SIC anomalies in these regions. For negative NAO events, the optimal SIC perturbations have almost opposite patterns to those in positive events, although there are some differences among these SIC perturbations due to different atmospheric initial conditions. Further diagnosis reveals that the optimal Arctic SIC perturbations first modify the surface turbulent heat flux and the temperature in the lower troposphere via diabatic processes. Afterward, the temperature in the low troposphere is mainly affected by dynamic advection. Finally, potential vorticity advection plays a crucial role in the 500-hPa geopotential height prediction in the northern North Atlantic sector during pentad 4, which influences NAO event prediction. These results highlight the importance of Arctic SIC on NAO event prediction and the spatial characteristics of the SIC perturbations may provide scientific support for target observations of SIC in improving NAO subseasonal predictions.

Accurate Machine Learning Predictions of Sci-Fi Film Performance

A groundbreaking method is introduced to leverage machine learn-ing algorithms to revolutionize the prediction of success rates for science fiction films.In the captivating world of the film industry,extensive research and accurate forecasting are vital to anticipating a movie’s triumph prior to its debut.Our study aims to harness the power of available data to estimate a film’s early success rate.With the vast resources offered by the internet,we can access a plethora of movie-related information,including actors,directors,critic reviews,user reviews,ratings,writers,budgets,genres,Facebook likes,YouTube views for movie trailers,and Twitter followers.The first few weeks of a film’s release are crucial in determining its fate,and online reviews and film evaluations profoundly impact its opening-week earnings.Hence,our research employs advanced supervised machine learning techniques to predict a film’s triumph.The Internet Movie Database(IMDb)is a comprehensive data repository for nearly all movies.A robust predictive classification approach is developed by employing various machine learning algorithms,such as fine,medium,coarse,cosine,cubic,and weighted KNN.To determine the best model,the performance of each feature was evaluated based on composite metrics.Moreover,the significant influences of social media platforms were recognized including Twitter,Instagram,and Facebook on shaping individuals’opinions.A hybrid success rating prediction model is obtained by integrating the proposed prediction models with sentiment analysis from available platforms.The findings of this study demonstrate that the chosen algorithms offer more precise estimations,faster execution times,and higher accuracy rates when compared to previous research.By integrating the features of existing prediction models and social media sentiment analysis models,our proposed approach provides a remarkably accurate prediction of a movie’s success.This breakthrough can help movie producers and marketers anticipate a film’s triumph before its release,allowing them to tailor their promotional activities accordingly.Furthermore,the adopted research lays the foundation for developing even more accurate prediction models,considering the ever-increasing significance of social media platforms in shaping individ-uals’opinions.In conclusion,this study showcases the immense potential of machine learning algorithms in predicting the success rate of science fiction films,opening new avenues for the film industry.

Seasonal and Extraseasonal Predictions of Summer Monsoon Precipitation by Gcms

A semi-operational real time short-term climate prediction system has been developed in the Center of Climate and Environment Prediction Research (CCEPRE), Institute of Atmospheric Physics/Chinese Academy of Sciences. The system consists of the following components: the AGCM and OGCM and their coupling, initial conditions and initialization, practical schemes of anomaly prediction, ensemble prediction and its standard deviation, correction of GCM output, and verification of prediction. The experiences of semi-operational real-time prediction by using this system for six years (1989-1994) and of hindcasting for 1980-1989 are reported. It is shown that in most cases large positive and negative anomalies of summer precipitation resulting in disastrous climate events such as severe flood or drought over East Asia can be well predicted for two seasons in advance, although the quantitatively statistical skill scores are only satisfactory due to the difficulty in correctly predicting the signs of small anomalies. Some methods for removing the systematic errors and introducing corrections to the GCM output are suggested. The sensitivity of prediction to the initial conditions and the problem of ensemble prediction are also discussed in the paper.

PREDICTIONS OF 3-D STRONGLY SWIRLING GAS-SOLID TWO-PHASE FLOW WITH GAS COMBUSTION

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Stage Predictions of Landslide and Subsidence from an Once-Through Cycle

In this paper both processes of landslide and subsidence are considered to be limited systems. Each of these systems in nature might be regarded as an organism. Generally their lifespan must develop with common ecological characteristics, including several evolutional stages, such as initiation, growth, maturation, decline and death. Among these stages, maturation is emphasized so as to find the occurring or thriving date of both systems. An once-through cycle of both landslide and subsidence is established and is accurately predicted by a developed, mathematic model of the Poisson cycle. The Weibull distribution is cited for a landslide example. Both fundamentals are discussed. Stage predictions of landslide and subsidence are performed for several examples. Back analysis of landslides that have already happened are studied with the same model. And when compared with results from the biological mathematic model and with practical results, it is found that they correspond. Stage prediction of subsidences is also researched by the principle of the Poisson cycle.

A Comparison of Breast Surgeon and Nomogram-Generated Risk Predictions of Sentinel and Non-Sentinel Node Metastases

Memorial Sloan-Kettering Cancer Center (MSKCC) has developed 2 nomograms: the Sentinel Lymph Node Nomogram (SLNN), which is used to predict the likelihood of sentinel lymph node (SLN) metastases in patients with invasive breast cancer, and the Non-Sentinel Lymph Node Nomogram (NSLNN), which is used to predict the likelihood of residual axillary disease after a positive SLN biopsy. Our purpose was to compare the accuracy of MSKCC nomogram predictions with those made by breast surgeons. Two questionnaires were built with characteristics of two sets of 33 randomly selected patients from the MSKCC Sentinel Node Database. The first included only patients with invasive breast cancer, and the second included only patients with invasive breast cancer and positive SLN biopsy. 26 randomly selected Brazilian breast surgeons were asked about the probability of each patient in the first set having SLN metastases and each patient in the second set having additional non-SLN metastases. The predictions of the nomograms and breast surgeons were compared. There was no correlation between nomogram risk predictions and breast surgeon risk prediction estimates for either the SLNN or the NSLNN. The area under the receiver operating characteristics curves (AUCs) were 0.871 and 0.657 for SLNN and breast surgeons, respectively (p 0.0001), and 0.889 and 0.575 for the NSLNN and breast surgeons, respectively (p 0.0001). The nomograms were significantly more accurate as prediction tools than the risk predictions of breast surgeons in Brazil. This study demonstrates the potential utility of both nomograms in the decision-making process for patients with invasive breast cancer.

Case Study: ENSO Events, Rainfall Variability and the Potential of SOI for the Seasonal Precipitation Predictions in Iran

The studies in recent decades show that many natural disasters such as tropical severe storms, hurricanes development, torrential rain, river flooding, and landslides in some regions of the world and severe droughts and wildfires in other areas are due to El Nino-Southern Oscillation (ENSO). This research aims to contribute to an improved definition of the relation between ENSO and seasonal (autumn and winter) variability of rainfall over Iran. The results show that during autumn, the positive phase of SOI is associated with decrease in the rainfall amount in most part of the country;negative phase of SOI is associated with a significant increase in the rainfall amount. It is also found that, during the winter time when positive phase of SOI is dominant, winter precipitation increases in most areas of the eastern part of the country while at the same time the decreases in the amount of rainfall in other parts is not significant. Moreover, with negative phase of SOI in winter season the amount of rainfall in most areas except south shores of Caspian Sea in the north decreases, so that the decrease of rainfall amount in the eastern part is statistically significant.

Adaptive Optics System and Its Application Predictions at 1.2m Telescope of Yunnan Observatory

A 61 element adaptive optical system has been preliminary tested in the Coudé path of the 1 2m telescope at the Yunnan observatory this year. The whole system will be fully operated next year. This paper describes the AO system performances and its first experiment results, and the possible astronomical research topics.

Predictions in Quantile Regressions

Two different tools to evaluate quantile regression forecasts are proposed: MAD, to summarize forecast errors, and a fluctuation test to evaluate in-sample predictions. The scores of the PISA test to evaluate students’ proficiency are considered. Growth analysis relates school attainment to economic growth. The analysis is complemented by investigating the estimated regression and predictions not only at the centre but also in the tails. For out-of-sample forecasts, the estimates in one wave are employed to forecast the following waves. The reliability of in-sample forecasts is controlled by excluding the part of the sample selected by a specific rule: boys to predict girls, public schools to forecast private ones, vocational schools to predict non-vocational, etc. The gradient computed in the subset is compared to its analogue computed in the full sample in order to verify the validity of the estimated equation and thus of the in-sample predictions.

The Impact of Pre-listening Activities on Predictions in English Listening Comprehension

The study intends to find out the impact of pre-listening activities on predictions in English listening comprehension.Based on the analysis of the verbal reports of the 8 subjects, listeners facilitated with the pre-listening activities were able to have a more detailed and complete predictions of the text content and their evaluation of their own listening comprehension showed more consistency with the predictions. Both vocabulary and background knowledge provided in the pre-listening activities were significant in the process. Whereas, listeners lacking the pre-listening activities could have some misleading predictions of the text content which were beyond the original listening texts.

Multi-Year Simulations and Experimental Seasonal Predictions for Rainy Seasons in China by Using a Nested Regional Climate Model (RegCM_NCC) Part Ⅱ:The Experimental Seasonal Prediction

A nested regional climate model has been experimentally used in the seasonal prediction at the China National Climate Center （NCC） since 2001. The NCC/IAP （Institute of Atmospheric Physics） T63 coupled GCM （CGCM） provides the boundary and initial conditions for driving the regional climate model （RegCM_NCC）. The latter has a 60-km horizontal resolution and improved physical parameterization schemes including the mass flux cumulus parameterization scheme, the turbulent kinetic energy closure scheme （TKE） and an improved land process model （LPM）. The large-scale terrain features such as the Tibetan Plateau are included in the larger domain to produce the topographic forcing on the rain-producing systems. A sensitivity study of the East Asian climate with regard to the above physical processes has been presented in the first part of the present paper. This is the second part, as a continuation of Part Ⅰ. In order to verify the performance of the nested regional climate model, a ten-year simulation driven by NCEP reanalysis datasets has been made to explore the performance of the East Asian climate simulation and to identify the model＇s systematic errors. At the same time, comparative simulation experiments for 5 years between the RegCM2 and RegCM_NCC have been done to further understand their differences in simulation performance. Also, a ten-year hindcast （1991-2000） for summer （June-August）, the rainy season in China, has been undertaken. The preliminary results have shown that the RegCM_NCC is capable of predicting the major seasonal rain belts. The best predicted regions with high anomaly correlation coefficient （ACC） are located in the eastern part of West China, in Northeast China and in North China, where the CGCM has maximum prediction skill as well. This fact may reflect the importance of the largescale forcing. One significant improvement of the prediction derived from RegCM_NCC is the increase of ACC in the Yangtze River valley where the CGCM has a very low, even a negative, ACC. The reason behind this improvement is likely to be related to the more realistic representation of the large-scale terrain features of the Tibetan Plateau. Presumably, many rain-producing systems may be generated over or near the Tibetan Plateau and may then move eastward along the Yangtze River basin steered by upper-level westerly airflow, thus leading to enhancement of rainfalls in the mid and lower basins of the Yangtze River. The real-time experimental predictions for summer in 2001, 2002, 2003 and 2004 by using this nested RegCM-NCC were made. The results are basically reasonable compared with the observations.

Changing trends of disease burden of gastric cancer in China from 1990 to 2019 and its predictions:Findings from Global Burden of Disease Study

Objective:China is one of the countries with the heaviest burden of gastric cancer(GC)in the world.Understanding the epidemiological trends and patterns of GC in China can contribute to formulating effective prevention strategies.Methods:The data on incidence,mortality,and disability-adjusted life-years(DALYs)of GC in China from1990 to 2019 were obtained from the Global Burden of Disease Study(2019).The estimated annual percentage change(EAPC)was calculated to evaluate the temporal trends of disease burden of GC,and the package Nordpred in the R program was used to perform an age-period-cohort analysis to predict the numbers and rates of incidence and mortality in the next 25 years.Results:The number of incident cases of GC increased from 317.34 thousand in 1990 to 612.82 thousand in2019,while the age-standardized incidence rate(ASIR)of GC decreased from 37.56 per 100,000 in 1990 to 30.64 per 100,000 in 2019,with an EAPC of-0.41[95%confidence interval(95%CI):-0.77,-0.06].Pronounced temporal trends in mortality and DALYs of GC were observed.In the next 25 years,the numbers of new GC cases and deaths are expected to increase to 738.79 thousand and 454.80 thousand,respectively,while the rates of incidence and deaths should steadily decrease.The deaths and DALYs attributable to smoking were different for males and females.Conclusions:In China,despite the fact that the rates of GC have decreased during the past three decades,the numbers of new GC cases and deaths increased,and will continue to increase in the next 25 years.Additional strategies are needed to reduce the burden of GC,such as screening and early detection,novel treatments,and the prevention of risk factors.