Generalized Parametric Prediction Model of the Mean Radiative Temperature for Microwave Slant Paths in All-Weather Condition

When machining the complex parts of aircraft engines, the milling force for the circular contour must be accurately predicted to reduce machining vibration. In this paper, the prediction model of the mean milling force per tooth during machining circular contour is developed. Firstly, the formulas of the entry angle, the exit angle and the equivalent feed per tooth are established through the analysis of circular contour milling process. Then, the equation of the mean milling force per tooth is deduced based on mechanistic force model during the circular contour machining process. Finally, the prediction model of mean milling force per tooth during machining circular contour is developed using MATLAB programming. The relationship between the milling force per tooth and surface curvature radius of the machined workpiece is also analyzed in this paper.

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Development of Macroscopic Cell-Based Logistic Lane Change Prediction Model

Journal of Advanced Transportation ◽

10.1155/2021/7905609 ◽

2021 ◽

Vol 2021 ◽

pp. 1-17

Author(s):

Christina Ng ◽

Susilawati Susilawati ◽

Md Abdus Samad Kamal ◽

Irene Mei Leng Chew

Keyword(s):

Prediction Model ◽

Transmission Model ◽

Lane Change ◽

Traffic Data ◽

Cell Transmission Model ◽

Traffic State ◽

Proposed Model ◽

Significant Difference ◽

The Mean ◽

Change Prediction

This paper aims at developing a macroscopic cell-based lane change prediction model in a complex urban environment and integrating it into cell transmission model (CTM) to improve the accuracy of macroscopic traffic state estimation. To achieve these objectives, first, based on the observed traffic data, the binary logistic lane change model is developed to formulate the lane change occurrence. Second, the binary logistic lane change is integrated into CTM by refining CTM formulations on how the vehicles in the cell are moving from one cell to another in a longitudinal manner and how cell occupancy is updated after lane change occurrences. The performance of the proposed model is evaluated by comparing the simulated cell occupancy of the proposed model with cell occupancy of US-101 next generation simulation (NGSIM) data. The results indicated no significant difference between the mean of the cell occupancies of the proposed model and the mean of cell occupancies of actual data with a root-mean-square-error (RMSE) of 0.04. Similar results are found when the proposed model was further tested with I80 highway data. It is suggested that the mean of cell occupancies of I80 highway data was not different from the mean of cell occupancies of the proposed model with 0.074 RMSE (0.3 on average).

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Functional Network Alterations as Markers for Predicting the Treatment Outcome of Cathodal Transcranial Direct Current Stimulation in Focal Epilepsy

Frontiers in Human Neuroscience ◽

10.3389/fnhum.2021.637071 ◽

2021 ◽

Vol 15 ◽

Author(s):

Jiaxin Hao ◽

Wenyi Luo ◽

Yuhai Xie ◽

Yu Feng ◽

Wei Sun ◽

...

Keyword(s):

Treatment Outcome ◽

Prediction Model ◽

Direct Current ◽

Transcranial Direct Current Stimulation ◽

Path Length ◽

Focal Epilepsy ◽

Functional Network ◽

Functional Networks ◽

Direct Current Stimulation ◽

The Mean

Background and PurposeTranscranial direct current stimulation (tDCS) is an emerging non-invasive neuromodulation technique for focal epilepsy. Because epilepsy is a disease affecting the brain network, our study was aimed to evaluate and predict the treatment outcome of cathodal tDCS (ctDCS) by analyzing the ctDCS-induced functional network alterations.MethodsEither the active 5-day, −1.0 mA, 20-min ctDCS or sham ctDCS targeting at the most active interictal epileptiform discharge regions was applied to 27 subjects suffering from focal epilepsy. The functional networks before and after ctDCS were compared employing graph theoretical analysis based on the functional magnetic resonance imaging (fMRI) data. A support vector machine (SVM) prediction model was built to predict the treatment outcome of ctDCS using the graph theoretical measures as markers.ResultsOur results revealed that the mean clustering coefficient and the global efficiency decreased significantly, as well as the characteristic path length and the mean shortest path length at the stimulation sites in the fMRI functional networks increased significantly after ctDCS only for the patients with response to the active ctDCS (at least 20% reduction rate of seizure frequency). Our prediction model achieved the mean prediction accuracy of 68.3% (mean sensitivity: 70.0%; mean specificity: 67.5%) after the nested cross validation. The mean area under the receiver operating curve was 0.75, which showed good prediction performance.ConclusionThe study demonstrated that the response to ctDCS was related to the topological alterations in the functional networks of epilepsy patients detected by fMRI. The graph theoretical measures were promising for clinical prediction of ctDCS treatment outcome.

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SBS Content Detection for Modified Asphalt Using Deep Neural Network

Advances in Materials Science and Engineering ◽

10.1155/2020/2513147 ◽

2020 ◽

Vol 2020 ◽

pp. 1-13

Author(s):

Zhixiang Wang ◽

Jiange Li ◽

Zhengqi Zhang ◽

Youxiang Zuo

Keyword(s):

Neural Network ◽

Prediction Model ◽

Deep Neural Network ◽

Modified Asphalt ◽

Standard Curve ◽

Predicting Performance ◽

Curve Method ◽

The Mean ◽

The Fourier Transform ◽

Styrene Butadiene

This study proposes a prediction model for accurately detecting styrene-butadiene-styrene (SBS) content in modified asphalt using the deep neural network (DNN). Traditional methods used for evaluating the SBS content are inaccurate and complicated because they are prone to produce errors by manual computation. Feature data of SBS content are derived from the spectra, which are obtained by the Fourier-transform infrared spectroscopy test. After designing DNN, preprocessed feature data are utilized as training and testing data and are fed into the DNN via a feature matrix. Furthermore, comparative studies are conducted to verify the accuracy of the proposed model. Results show that the mean square error value decreased by 68% for DNN with noise and dimension reduction. The DNN-based prediction model showed that the correlation coefficient between the target value and the mean predicted value is 0.9978 and 0.9992 for training and testing samples, respectively, indicating its remarkable accuracy and applicability after training. In comparison with the standard curve method and the random forest method, the precision of DNN is greater than 98% for the same test conditions, achieving the best predicting performance.

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A Prediction Model Based on Deep Belief Network and Least Squares SVR Applied to Cross-Section Water Quality

Water ◽

10.3390/w12071929 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1929

Author(s):

Jianzhuo Yan ◽

Ya Gao ◽

Yongchuan Yu ◽

Hongxia Xu ◽

Zongbao Xu

Keyword(s):

Neural Network ◽

Water Quality ◽

Prediction Model ◽

Least Squares ◽

Quality Parameters ◽

Deep Belief Network ◽

Water Quality Parameters ◽

Series Data ◽

Belief Network ◽

The Mean

Recently, the quality of fresh water resources is threatened by numerous pollutants. Prediction of water quality is an important tool for controlling and reducing water pollution. By employing superior big data processing ability of deep learning it is possible to improve the accuracy of prediction. This paper proposes a method for predicting water quality based on the deep belief network (DBN) model. First, the particle swarm optimization (PSO) algorithm is used to optimize the network parameters of the deep belief network, which is to extract feature vectors of water quality time series data at multiple scales. Then, combined with the least squares support vector regression (LSSVR) machine which is taken as the top prediction layer of the model, a new water quality prediction model referred to as PSO-DBN-LSSVR is put forward. The developed model is valued in terms of the mean absolute error (MAE), the mean absolute percentage error (MAPE), the root mean square error (RMSE), and the coefficient of determination ( R 2 ). Results illustrate that the model proposed in this paper can accurately predict water quality parameters and better robustness of water quality parameters compared with the traditional back propagation (BP) neural network, LSSVR, the DBN neural network, and the DBN-LSSVR combined model.

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External Validation of the Dutch SOURCE Survival Prediction Model in Belgian Metastatic Oesophageal and Gastric Cancer Patients

Cancers ◽

10.3390/cancers12040834 ◽

2020 ◽

Vol 12 (4) ◽

pp. 834

Author(s):

J.J. van Kleef ◽

H.G. van den Boorn ◽

R.H.A. Verhoeven ◽

K. Vanschoenbeek ◽

A. Abu-Hanna ◽

...

Keyword(s):

Gastric Cancer ◽

Prediction Model ◽

Oesophageal Cancer ◽

Prediction Error ◽

Prediction Models ◽

Mean Difference ◽

Survival Prediction ◽

Cancer Model ◽

The Mean ◽

Mean Prediction Error

The SOURCE prediction model predicts individualised survival conditional on various treatments for patients with metastatic oesophageal or gastric cancer. The aim of this study was to validate SOURCE in an external cohort from the Belgian Cancer Registry. Data of Belgian patients diagnosed with metastatic disease between 2004 and 2014 were extracted (n = 4097). Model calibration and discrimination (c-indices) were determined. A total of 2514 patients with oesophageal cancer and 1583 patients with gastric cancer with a median survival of 7.7 and 5.4 months, respectively, were included. The oesophageal cancer model showed poor calibration (intercept: 0.30, slope: 0.42) with an absolute mean prediction error of 14.6%. The mean difference between predicted and observed survival was −2.6%. The concordance index (c-index) of the oesophageal model was 0.64. The gastric cancer model showed good calibration (intercept: 0.02, slope: 0.91) with an absolute mean prediction error of 2.5%. The mean difference between predicted and observed survival was 2.0%. The c-index of the gastric cancer model was 0.66. The SOURCE gastric cancer model was well calibrated and had a similar performance in the Belgian cohort compared with the Dutch internal validation. However, the oesophageal cancer model had not. Our findings underscore the importance of evaluating the performance of prediction models in other populations.

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Prediction Model of Serum Lithium Concentrations

Pharmacopsychiatry ◽

10.1055/s-0043-116855 ◽

2017 ◽

Vol 51 (03) ◽

pp. 82-88 ◽

Cited By ~ 1

Author(s):

Kazunari Yoshida ◽

Hiroyuki Uchida ◽

Takefumi Suzuki ◽

Masahiro Watanabe ◽

Nariyasu Yoshino ◽

...

Keyword(s):

Standard Deviation ◽

Prediction Model ◽

Predictive Accuracy ◽

Compartment Model ◽

Absolute Error ◽

Oral Dosage ◽

Therapeutic Drug ◽

Suitable Model ◽

Blood Draw ◽

The Mean

Abstract Introduction Therapeutic drug monitoring is necessary for lithium, but clinical application of several prediction strategies is still limited because of insufficient predictive accuracy. We herein proposed a suitable model, using creatinine clearance (CLcr)-based lithium clearance (Li-CL). Methods Patients receiving lithium provided the following information: serum lithium and creatinine concentrations, time of blood draw, dosing regimen, concomitant medications, and demographics. Li-CL was calculated as a daily dose per trough concentration for each subject, and the mean of Li-CL/CLcr was used to estimate Li-CL for another 30 subjects. Serum lithium concentrations at the time of sampling were estimated by 1-compartment model with Li-CL, fixed distribution volume (0.79 L/kg), and absorption rate (1.5/hour) in the 30 subjects. Results One hundred thirty-one samples from 82 subjects (44 men; mean±standard deviation age: 51.4±16.0 years; body weight: 64.6±13.8 kg; serum creatinine: 0.78±0.20 mg/dL; dose of lithium: 680.2±289.1 mg/day) were used to develop the pharmacokinetic model. The mean±standard deviation (95% confidence interval) of absolute error was 0.13±0.09 (0.10–0.16) mEq/L. Discussion Serum concentrations of lithium can be predicted from oral dosage with high precision, using our prediction model.

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ECONOMIC ANALYSIS OF YAM PROCESSING INTO YAM FLOUR IN SAKI AGRO ECOLOGICAL ZONE OF OYO STATE, NIGERIA

Journal of Humanities, Social Science and Creative Arts ◽

10.51406/jhssca.v15i1.2124 ◽

2021 ◽

Vol 15 (1) ◽

Author(s):

M. O. OGIERIAKHI ◽

I. Y. UDEZI ◽

C. P. OSAYI

Keyword(s):

Economic Analysis ◽

Technical Efficiency ◽

Stochastic Frontier ◽

Weather Condition ◽

Ecological Zone ◽

Yam Flour ◽

Efficiency Factors ◽

The Mean ◽

Major Factors ◽

Structured Questionnaire

The study examined the economic analysis of yam processing into yam flour in Saki Agro ecological Zone of Oyo State. It specifically described the profitability of yam processing; examined the technical efficiency of yam processors and determined the effect of the socio-economic characteristics on technical efficiency. Data were collected with the aid of structured questionnaire and analyzed using descriptive statistics, profitability and budgetary analysis as well as stochastic frontier model and Garrett scale. The study reveals that majority of the respondents were female (88%) with a mean age of 47. The result shows that the rate of return on investment was 12 percent. The mean technical efficiency of the processors was 85% indicating that the yam processors were relatively efficient in allocating their limited resources. Some observable variables relating to socioeconomic characteristics such as processing experience and sex of the respondents significantly explains the variation in technical efficiency. Factors such as high cost of yam tubers, poor weather condition and inadequate processing facilities are the major factors that hinder the processing activities in the study area. The study therefore recommends that government policies should be made to improve the provision of inputs such as yam tubers and capital equipment at affordable price.

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Research on a Multi-Parameter Fusion Prediction Model of Pressure Relief Gas Concentration Based on RNN

Energies ◽

10.3390/en14051384 ◽

2021 ◽

Vol 14 (5) ◽

pp. 1384

Author(s):

Shuang Song ◽

Shugang Li ◽

Tianjun Zhang ◽

Li Ma ◽

Shaobo Pan ◽

...

Keyword(s):

Prediction Model ◽

Search Method ◽

Evaluation Index ◽

Batch Size ◽

Grid Search ◽

Gas Concentration ◽

Grid Search Method ◽

Performance Evaluation Index ◽

The Mean ◽

Concentration Prediction

The effective prediction of gas concentration and the reasonable formulation of corresponding safety measures have important significance for improving the level of coal mine safety. To improve the accuracy of gas concentration prediction and enhance the applicability of the models, this paper starts with actual coal mine production monitoring data, improves the accuracy of gas concentration prediction through multi-parameter fusion prediction, and constructs a recurrent neural network (RNN)-based multi-parameter fusion prediction of coal face gas concentration. We determined the performance evaluation index of the model’s prediction method; used the grid search method to optimize the hyperparameters of the batch size; and used the number of neurons, the learning rate, the discard ratio, the network depth, and the early stopping method to prevent overfitting. The gas concentration prediction models—based on RNN and PSO-SVR and PSO-Adam-BP neural networks—were compared and analyzed experimentally with the mean absolute percentage error (MAPE) as the performance evaluation index. The result show that using the grid search method to adjust the batch size, the number of neurons, the learning rate, the discard ratio, and the network depth can effectively find the optimal hyperparameter combination. The training error can be reduced to 0.0195. Therefore, Adam’s optimized RNN gas concentration prediction model had higher accuracy and stability than the BP neural network and SVR. During training, the mean absolute error (MAE) could be reduced to 0.0573, and the root mean squared error (RMSE) could be reduced to 0.0167; however, the MAPE could be reduced to 0.3384% during prediction. The RNN gas concentration prediction model and parameter optimization method based on Adam optimization can effectively predict gas concentration. This method shows high accuracy in the prediction of gas concentration time series and can be used as a reference model for predicting mine gas concentration.

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Prediction Model for Cigarette Yields Derived from Data Obtained under Two Different Machine Smoking Regimes

Beiträge zur Tabakforschung International/Contributions to Tobacco Research ◽

10.1515/cttr-2015-0022 ◽

2015 ◽

Vol 26 (7) ◽

Author(s):

Stéphane Colard

Keyword(s):

Carbon Monoxide ◽

Prediction Model ◽

Smoking Intensity ◽

Burning Process ◽

Testing Scheme ◽

The Mean ◽

The Difference ◽

Burning Model ◽

Ignition Propensity

Summary“Tar”, nicotine and carbon monoxide (TNCO) cigarette yields determined under different smoking regimes, with and without ventilation blocking, are linearly related to the difference Δt between the smouldering time (cigarette combustion with no puffing) and the smoking time (cigarette combustion with puffing). Δt forms then the basis of yield predictions. The smoulder rate determination used in the calculation of Δt can be difficult for low ignition propensity cigarettes which present some tendency for selfextinguishment. This issue was overcome in a novel testing scheme involving the determination of number of puffs and smoking times under two different smoking regimes and inputting this data into a cigarette burning model. This enabled us to characterise the burning process and provided an extensive set of information such as the mean smoulder rate between puffs or the mass of tobacco burnt during puffs regardless of the smoking regime applied.Good correlations were observed between the mass of tobacco burnt during puffs and TNCO or B[a]P yields. Correlations provide a way to link yields from one smoking regime to another and confirm that yields determined from one regime are sufficient to establish the relationships between yields and smoking intensity. It was concluded that smoke yields for arbitrary smoking regimes can potentially be predicted by determining the puff numbers and smoking times from two different smoking regimes and the smoke yields from only one regime. This testing scheme allows a comprehensive characterisation of a cigarette at reduced cost. [Beitr. Tabakforsch. Int. 26 (2015) 320-333]

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