Analysis of Factors Related to Vasovagal Response in Apheresis Blood Donors and the Establishment of Prediction Model Based on BP Neural Network Algorithm

Objective:To analyze the factors related to vessel vasovagal reaction(VVR)in apheresis donors,establish a mathematical model for predicting the correlation factors and occurrence risk,and use the prediction model to intervene in high-risk VVR blood donors,improve the blood donation experience,and retain blood donors.Methods:A total of 316 blood donors from the Xi'an Central Blood Bank from June to September 2022 were selected to statistically analyze VVR-related factors.A BP neural network prediction model is established with relevant factors as input and DRVR risk as output.Results:First-time blood donors had a high risk of VVR,female risk was high,and sex difference was significant(P value<0.05).The blood pressure before donation and intergroup differences were also significant(P value<0.05).After training,the established BP neural network model has a minimum RMS error of o.116,a correlation coefficient R=0.75,and a test model accuracy of 66.7%.Conclusion:First-time blood donors,women,and relatively low blood pressure are all high-risk groups for VVR.The BP neural network prediction model established in this paper has certain prediction accuracy and can be used as a means to evaluate the risk degree of clinical blood donors.

Prediction of the Granite-Type Gold Deposits by Grey Relational Analysis

Calculated in this paper is the relational grade between each of the 1608prediction units and the granite-type gold deposits in Jiaodong, concerning 78 geologicalvariants or factors, in terms of the relational grade analysis of the grey system. Theisopleth of the residual values is drawn through trend analysis and anomalies are outlined.Also pointed out in the present paper are the pathfinders for prospecting of gold depositsin various parts of China.

Quantitative multiparameter prediction of fault-related fractures： a case study of the second member of the Funing Formation in the Jinhu Sag, Subei Basin

In this paper, the analysis of faults with different scales and orientations reveals that the distribution of fractures always develops toward a higher degree of similarity with faults, and a method for calculating the multiscale areal fracture density is proposed using fault-fracture self-similarity theory. Based on the fracture parameters observed in cores and thin sections, the initial apertures of multiscale fractures are determined using the constraint method with a skewed distribution. Through calculations and statistical analyses of in situ stresses in combination with physical experiments on rocks, a numerical geomechanical model of the in situ stress field is established. The fracture opening ability under the in situ stress field is subsequently analyzed. Combining the fracture aperture data and areal fracture density at different scales, a calculation model is proposed for the prediction of multiscale and multiperiod fracture parameters, including the fracture porosity, the magnitude and direction of maximum permeability and the flow conductivity. Finally, based on the relationships among fracture aperture,density, and the relative values of fracture porosity and permeability, a fracture development pattern is determined.

Quantitative prediction of oil saturation of unconsolidated sandstone reservoir based on time-lapse seismic “relative difference method”: Taking Zeta oil field in West Africa as an example

In view of the disadvantage that the absolute difference of time-lapse seismic(the difference between monitoring data and base data) is not only related to the change of oil saturation, but also closely related to the thickness of reservoir, a time-lapse seismic "relative difference method"(the ratio of monitoring data to base data) not affected by the thickness of reservoir but only related to the change of fluid saturation, is proposed through seismic forward modeling after fluid displacement simulation. Given the same change of fluid saturation, the absolute difference of time-lapse seismic conforms to the law of "tuning effect" and seismic reflection of "thin bed", and the remaining oil prediction method based on absolute difference of time-lapse seismic is only applicable to the reservoirs with uniform thickness smaller than the tuning thickness or with thickness greater than the tuning thickness. The relative difference of time-lapse seismic is not affected by reservoir thickness, but only related to the change of fluid saturation. It is applicable to all the deep-sea unconsolidated sandstone reservoirs which can exclude the effect of pressure, temperature, pore type and porosity on seismic. Therefore, the relation between the relative difference of time-lapse seismic and the change of fluid saturation, which is obtained from seismic forward modeling after Gassmann fluid displacement simulation, can be used to quantitatively predict the change of reservoir water saturation and then the distribution of the remaining oil. The application of this method in deep sea Zeta oil field in west Africa shows that it is reasonable and effective.

Prediction on the relative permittivity of energy storage composite dielectrics using convolutional neural networks:A fast and accurate alternative to finite-element method

The relative permittivity is one of the essential parameters determines the physical polarization behaviors of the nanocomposite dielectrics in many applications,particularly for capacitive energy storage.Predicting the relative permittivity of particle/polymer nanocomposites from the microstructure is of great significance.However,the classical effective medium theory and physics-based numerical calculation represented by finite element method are time-consuming and cumbersome for complex structures and nonlinear problem.The work explores a novel architecture combining the convolutional neural network(ConvNet)and finite element method(FEM)to predict the relative permittivity of nanocomposite dielectrics with incorporated barium titanite(BT)particles in polyvinylidene fluoride(PVDF)matrix.The ConvNet was trained and evaluated on big datasets with 14266 training data and 3514 testing data generated form a programmatic algorithm.Through numerical experiments,we demonstrate that the trained network can efficiently provide an accurate agreement between the ConvNet model and FEM by virtue of the significant evaluation metrics R2,which reaches as high as 0.9783 and 0.9375 on training and testing data,respectively.The strong universality of the presented method allows for an extension to fast and accurately predict other properties of the nanocomposite dielectrics.

Application of coupled analysis methods for prediction of blast-induced dominant vibration frequency

Blast-induced dominant vibration frequency （DVF） involves a complex, nonlinear and small sample system considering rock properties, blasting parameters and topography. In this study, a combination of grey relational analysis and dimensional analysis procedures for prediction of dominant vibration frequency are presented. Six factors are selected from extensive effect factor sequences based on grey relational analysis, and then a novel blast-induced dominant vibration frequency prediction is obtained by dimensional analysis. In addition, the prediction is simplified by sensitivity analysis with 195 experimental blast records. Validation is carried out for the proposed formula based on the site test database of the first- period blasting excavation in the Guangdong Lufeng Nuclear Power Plant （GLNPP）. The results show the proposed approach has a higher fitting degree and smaller mean error when compared with traditional predictions.

QUICK ASSESSMENT METHODOLOGY FOR RELIABILITY OF SOLDER JOINTS IN BALL GRID ARRAY (BGA) ASSEMBLY——PART Ⅰ: CREEP CONSTITUTIVE RELATION AND FATIGUE MODEL

In this study, a new unified creep constitutive relation and a mod- ified energy-based fatigue model have been established respectively to describe the creep flow and predict the fatigue life of Sn-Pb solders. It is found that the relation successfully elucidates the creep mechanism related to current constitutive relations. The model can be used to describe the temperature and frequency dependent low cycle fatigue behavior of the solder. The relation and the model are further employed in part Ⅱ to develop the numerical simulation approach for the long-term reliability assessment of the plastic ball grid array (BGA) assembly.

Short-term travel flow prediction method based on FCM-clustering and ELM

Short-term travel flow prediction has been the core of the intelligent transport systems(ITS). An advanced method based on fuzzy C-means(FCM) and extreme learning machine(ELM) has been discussed by analyzing prediction model. First, this model takes advantages of ability to adapt to nonlinear systems and the fast speed of ELM algorithm. Second, with FCM-clustering function, this novel model can get the clusters and the membership in the same cluster, which means that the associated observation points have been chosen. Therefore, the spatial relations can be used by giving the weight to every observation points when the model trains and tests the ELM. Third, by analyzing the actual data in Haining City in 2016, the feasibility and advantages of FCM-ELM prediction model have been shown when compared with other prediction algorithms.

Mem Brain: An Easy-to-Use Online Webserver for Transmembrane Protein Structure Prediction

Membrane proteins are an important kind of proteins embedded in the membranes of cells and play crucial roles in living organisms, such as ion channels,transporters, receptors. Because it is difficult to determinate the membrane protein's structure by wet-lab experiments,accurate and fast amino acid sequence-based computational methods are highly desired. In this paper, we report an online prediction tool called Mem Brain, whose input is the amino acid sequence. Mem Brain consists of specialized modules for predicting transmembrane helices, residue–residue contacts and relative accessible surface area of a-helical membrane proteins. Mem Brain achieves aprediction accuracy of 97.9% of ATMH, 87.1% of AP,3.2 ± 3.0 of N-score, 3.1 ± 2.8 of C-score. Mem BrainContact obtains 62%/64.1% prediction accuracy on training and independent dataset on top L/5 contact prediction,respectively. And Mem Brain-Rasa achieves Pearson correlation coefficient of 0.733 and its mean absolute error of13.593. These prediction results provide valuable hints for revealing the structure and function of membrane proteins.Mem Brain web server is free for academic use and available at www.csbio.sjtu.edu.cn/bioinf/Mem Brain/.

Hysteretic energy prediction method for mainshock-aftershock sequences

Structures located in seismically active regions may be subjected to mainshock-aftershock（MSAS）sequences.present study selected two kinds of MSAS sequences,with one aftershock and two aftershocks,respectively.The aftershocksThe MSAS sequence with one aftershock exhibited a 10%to 30%hysteretic energy increase,whereas the MSAS sequence with two aftershocks presented a 20%to 40%hysteretic energy increase.Finally,a hysteretic energy prediction equation is proposed as a function of the vibration period,ductility value,and damping ratio to estimate hysteretic energy for mainshockaftershock sequences.

Prediction of Water Activity for Mixed Aqueous Solutions from the Data of Their Binary Constituent Solutions

The equation of Patwardhan and Kumar for water activities of mixed electrolyte solutions is extended to aqueous solutions containing non-electrolytes. This equation and the linear isopiestic relation are used to predict water activities of 56 ternary aqueous solutions in terms of the data of their binary subsystems. Both equation of Patwardhan and Kumar and the linear isopiestic relation can provide good predictions for water activities of the present 40 electrolyte solutions, and the linear isopiestic relation generally yields better predictions. The predictions of the extended equation of Patwardhan and Kumar and the linear isopiestic relation are in general quite reasonable for the present 8 ternary solutions of electrolytes and non-electrolytes, and the results of the linear isopiestic relation are usually better. The predictions of these two methods generally agree well with the experimental data for the 8 non-electrolyte mixtures being studied, and the linear isopiestic relation is better.

Analysis and Prediction of Regional Electricity Consumption Based on BP Neural Network

Electricity consumption forecasting is one of the most important tasks for power system workers,and plays an important role in regional power systems.Due to the difference in the trend of power load and the past in the new normal,the influencing factors are more diversified,which makes it more difficult to predict the current electricity consumption.In this paper,the grey system theory and BP neural network are combined to predict the annual electricity consumption in Jiangsu.According to the historical data of annual electricity consumption and the six factors affecting electricity consumption,the gray correlation analysis method is used to screen the important factors,and three factors with large correlation degree are selected as the input parameters of BP neural network.The power forecasting model uses nearly 18 years of data to train and validate the model.The results show that the gray correlation analysis and BP neural network method have higher accuracy in power consumption prediction,and the calculation is more convenient than traditional methods.

Matter-elements model and application for prediction of ccoal and gas outburst

The theory and method of extenics were applied to establish classical field matterelements and segment field matter elements for coal and gas outburst.A matter-element model for prediction was established based on five matter-elements,which includedgas pressure,types of coal damage,coal rigidity,initial speed of methane diffusionand in-situ stress.Each index weight was given fairly and quickly through the improvedanalytic hierarchy process,which need not carry on consistency checks,so accuracy ofassessment can be improved.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Energy Balance-related Behaviors Are Related to Cardiometabolic Parameters and Predict Adiposity in 8-14-year-old Overweight Chinese Children One Year Later

To identify target energy balance-related behaviors（ERBs）,baseline data from 141overweight or obese schoolchildren（aged 8-14years old）was used to predict adiposity[body mass index（BMI）and fat percentage]one year later.The ERBs included a modified Dietary Approach to Stop Hypertension diet score（DASH score）,leisure-time physical activity（PA,days/week）,and leisure screen time（minutes/day）.Several cardiometabolic variables were measured in the fasting state, including systolic blood pressure （SBP）, diastolic blood pressure （DBP）, blood glucose （GLU）, total cholesterol （TC）, triglycerides （TG）, low-density lipoprotein （LDL-C）, and high-density lipoprotein （HDL-C）.

Life Prediction Model of Machine Tool based on Deep Learning

In view of the shortage of traditional life prediction methods for machine tools,such as low accuracy of life prediction and few samples basis attributes,a life prediction model of machine tools combined with machine tool attributes is proposed.The life prediction model of machine tool adopts KL dispersion distribution theory,uses modal superposition method to carry out machine tool life analysis,calculates the theoretical life of machine tool,and then carries on the simulation,obtains the machine tool life prediction value.Compared with the traditional method of machine tool life prediction,the model is based on the application life fatigue damage model,which superimposes the service times and maintenance cycle of the machine tool,derives the influence factor of machine tool life,and obtains the linear relationship between the influence factor of machine tool life and the life of machine tool.The influence factor of machine tool life is introduced as the life prediction parameter of machine tool.The data transformation relationship of HT300 parts is constructed.The original part data is enhanced.The effective training set is obtained.The life prediction model of machine tool based on deep learning is completed.The quantitative analysis of machine tool life is carried out.The experiment of machine tool life prediction using training data set proves the validity of the model.Regression test was carried out on the training data set to reflect the robustness of the model.The prediction accuracy of the model is further verified by Weibull test.

Patient-Reported Outcomes in Prostate Cancer: Prospective Changes Analysis for Prognosis Prediction

Prostate cancer is affecting a higher proportion of male population. Health Related Quality of Life assessment can guide the development of an interdisciplinary and patient-centered care intervention. This study is aimed to assess Health Related Quality of Life in prostate cancer patients. Relationships between socio-demographic, clinical characteristics and patient-reported outcomes have been considered. Consecutive outpatients with prostate cancer, admitted at the Urology Clinic of the Instituto Português de Oncologia do Porto, were studied (n = 300). Health Related Quality of Life was assessed as part of the routine practice. The European Organisation for Research and Treatment of Cancer general questionnaire, QLQ-C30, and its specific module for prostate cancer patients, QLQ-PR25, were used. Evolution along time (elapsed since diagnosis, and up to 5 years) was considered in order to search for a prognosis prediction in prostate cancer patients. This study confirms the feasibility of a systematic Health Related Quality of Life assessment. Global Health Related Quality of Life was found to be higher 6 months after diagnosis, decreasing then until the second year after diagnosis and improving thereafter. A peak with better scores was identified at the fifth year after diagnosis. Social and physical dimensions revealed a similar pattern. Clinical significance was found 6 months and 5 years after diagnosis. The prospective analysis of Health Related Quality of Life changes is able to explore the patients’ outcomes in order to find patterns and relationships for prognosis prediction along the disease course. Such approach might promote patient confidence and thus a better cancer experience.

Molecular Related Indexes Assessment and Traits Correlation Analysis of Turbot(Scophthalmus maximus L.) by Using SSR Marker Correlation Coefficient in Turbot

Five molecular related indexes: MOL, SIM, d2, H0and PIC of 15 turbot parent pairs were estimated by using 10 SSR loci, which were used in correlation analysis with growth traits, DIL and DIW, of family filial from those 15 parent pairs.DIL and DIW were regressed on the MOL, SIM, d2, H0and PIC. Results showed MOL of five SSR loci(12, 17, 24, 81 and 85) and SIM of five loci(17, 21, 24, 81and 85) all shared significant positive correlation with DIL(r=0.482, P=0.035 and r=0.479, P=0.035, respectively); H0of six SSR loci(11, 17, 21, 24, 26 and 85) had significant positive correlation with DIW(r=0.551, P=0.017); PIC of two SSR loci(9and 26) had significant positive(r=0.519, P=0.024) correlation with DIL, while that and of four loci(17, 24, 27 and 85) had significant negative correlation(r=-0.519,P=0.024), with DIL. This present study suggested that filial growth expression could be predicted by using molecular related indexes in turbot breeding practice, and the accuracy of prediction depends on more SSR loci, especially associated with QTL.

Application of Question and Answering on Virtual Human Dialogue:a Review and Prediction

Nowadays,virtual human(VH) is becoming a hot research topic in virtualization.VH dialogue can be categorized as an application of natural language processing(NLP) technology,since it is relational to question and answering(QA) technologies.In order to integrate these technologies,this paper reviews some important work on VH dialogue,and predicts some research points on the view of QA technologies.