scholarly journals Application of WOA–SVR in Seed Vigor of High-Voltage Electric Field Treatment on Aged Cotton (Gossypium spp.) Seeds

Agronomy ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 88
Author(s):  
Zhanhua Song ◽  
Junxiang Ma ◽  
Qian Peng ◽  
Baoji Liu ◽  
Fade Li ◽  
...  
Keyword(s):  
Electric Field ◽  
Prediction Model ◽  
High Voltage ◽  
Treatment Time ◽  
Seed Vigor ◽  
Germination Index ◽  
Support Vector ◽  
Gossypium Spp ◽  
Whale Optimization ◽  
Input Variables

When seeds are treated with a high-voltage electric field (HVEF) to improve seed vigor, due to the large differences in the biological electromagnetic effects on different types of seeds, the methods of variance analysis and regression analysis based on data statistics are generally used to construct the optimal electric field dose prediction model; however, the generalization performance of the prediction model tends to be poor. To solve this problem, the electric intensity, frequency and treatment time were taken as the input variables for hybrid support vector regression (SVR) analysis to establish the prediction model of the seed comprehensive germination index. The whale optimization algorithm (WOA) was used to optimize the kernel parameters of the SVR. The optimized hybrid WOA–SVR model predicted the optimal comprehensive germination index of aged cotton (Gossypium spp.) seeds to be 329, the optimal HVEF dosage was 3.64 kV/cm × 99 s, and the frequency was 1.4 Hz. The aged cotton seeds were treated with the optimal HVEF and the germination test was carried out. Compared with the check (CK), the comprehensive germination index of seeds increased by 48%. The research results provided a new method and new idea for the optimal design of parameters for seed treatment with HVEF.

Inactivation of Listeria monocytogenes in Milk by Pulsed Electric Field

1998 ◽  
Vol 61 (9) ◽  
pp. 1203-1206 ◽  
Author(s):  
LAURA D. REINA ◽  
Z. TONY JIN ◽  
Q. HOWARD ZHANG ◽  
AHMED E. YOUSEF
Keyword(s):  
Listeria Monocytogenes ◽  
Field Strength ◽  
Electric Field ◽  
High Voltage ◽  
Foodborne Pathogens ◽  
Treatment Time ◽  
Skim Milk ◽  
Pulsed Electric Field ◽  
Lethal Effect ◽  
Treatment Temperature

Pasteurized whole, 2%, and skim milk were inoculated with Listeria monocytogenes Scott A and treated with high-voltage pulsed electric field (PEF). The effects of milk composition (fat content) and PEF parameters (electric field strength, treatment time, and treatment temperature) on the inactivation of the bacterium were studied. No significant differences were observed in the inactivation of L. monocytogenes Scott A in three types of milk by PEF treatment. With treatment at 25°C, 1- to 3-log reductions of L. monocytogenes were observed. PEF lethal effect was a function of field strength and treatment time. Higher field strength or longer treatment time resulted in a greater reduction of viable cells. A 4-log reduction of the bacterium was obtained by increasing the treatment temperature to 50°C. Results indicate that the use of a high-voltage PEF is a promising technology for inactivation of foodborne pathogens.

scholarly journals Analysis of Model Performance and Implementation of an Optimized Flood Prediction Model Using Data Mining Techniques

2021 ◽  
Vol 10 (2) ◽  
pp. 888-893
Keyword(s):  
Prediction Model ◽  
Model Performance ◽  
Warning System ◽  
Support Vector ◽  
Ann Model ◽  
Flood Prediction ◽  
River Region ◽  
Input Variables ◽  
Using Data ◽  
Artificial Neural Network Ann

Flooding is a major problem globally, and especially in SuratThani province, Thailand. Along the lower Tapeeriver in SuratThani, the population density is high. Implementing an early warning system can benefit people living along the banks here. In this study, our aim was to build a flood prediction model using artificial neural network (ANN), which would utilize water and stream levels along the lower Tapeeriver to predict floods. This model was used to predict flood using a dataset of rainfall and stream levels measured at local stations. The developed flood prediction model consisted of 4 input variables, namely, the rainfall amounts and stream levels at stations located in the PhraSeang district (X.37A), the Khian Sa district (X.217), and in the Phunphin district (X.5C). Model performance was evaluated using input data spanning a period of eight years (2011–2018). The model performance was compared with support vector machine (SVM), and ANN had better accuracy. The results showed an accuracy of 97.91% for the ANN model; however, for SVM it was 97.54%. Furthermore, the recall (42.78%) and f-measure (52.24%) were better for our model, however, the precision was lower. Therefore, the designed flood prediction model can estimate the likelihood of floods around the lower Tapee river region

Pulsed Electric Field Processing of Foods: A Review

1999 ◽  
Vol 62 (9) ◽  
pp. 1088-1096 ◽  
Author(s):  
S. JEYAMKONDAN ◽  
D. S. JAYAS ◽  
R. A. HOLLEY
Keyword(s):  
Electric Field ◽  
High Voltage ◽  
High Speed ◽  
Treatment Time ◽  
Experimental Studies ◽  
Processing System ◽  
Industrial Applications ◽  
Pulse Generation ◽  
Processing Methods ◽  
The Cost

Use of pulsed electric fields (PEFs) for inactivation of microorganisms is one of the more promising nonthermal processing methods. Inactivation of microorganisms exposed to high-voltage PEFs is related to the electromechanical instability of the cell membrane. Electric field strength and treatment time are the two most important factors involved in PEF processing. Encouraging results are reported at the laboratory level, but scaling up to the industrial level escalates the cost of the command charging power supply and of the high-speed electrical switch. In this paper, we critically review the results of earlier experimental studies on PEFs and we suggest the future work that is required in this field. Inactivation tests in viscous foods and in liquid food containing particulates must be conducted. A successful continuous PEF processing system for industrial applications has yet to be designed. The high initial cost of setting up the PEF processing system is the major obstacle confronting those who would encourage the system's industrial application. Innovative developments in high-voltage pulse technology will reduce the cost of pulse generation and will make PEF processing competitive with thermal-processing methods.

The Reconstruction and Extension of Terrestrial Water Storages Based On A Combined Prediction Model

2021 ◽  
Author(s):  
Erhao Meng ◽  
Shengzhi Huang ◽  
Qiang Huang ◽  
Linyin Cheng ◽  
Wei Fang
Keyword(s):  
Prediction Model ◽  
Yunnan Province ◽  
Water Storage ◽  
Meteorological Drought ◽  
Selection Strategy ◽  
Support Vector ◽  
Total Water ◽  
Input Selection ◽  
Soil Moisture Storage ◽  
Input Variables

Abstract The monthly changes in total water storage (△TWS) can be employed for drought and flood monitoring and early warning and can be obtained from the total water storage anomalies (TWSA) of the Gravity Recovery and Climate Experiment (GRACE). However, the relatively short GRACE time series limits its further wide application. To this end, a combined prediction (CP) model including Support Vector Machine (SVM) and Artificial Neural Network (ANN) was proposed in this study for the reconstruction and extension of monthly TWSA from 1960 to 2012. Moreover, an innovative input selection strategy is proposed to build a monthly TWSA prediction model, in which the partial correlation algorithm is used to select the best input variables from candidate input variables. These candidate input variables include streamflow, precipitation, evaporation, and soil moisture storage (SMS). The Yunnan province, a typical humid area in China, was selected as a case study. The results show that: (1) The innovative input selection strategy effectively improves the simulation ability of the model, especially when the candidate input variables influence each other; (2) The performance of the CP model using the innovative input selection strategy is best; (3) The monthly △TWS obtained from the extension of TWSA recorded five of the seven extreme meteorological drought events in Yunnan Province from 1961 to 2001, therefore, the reliability of the expanded TWSA is better than GLDAS TWSA. Generally, the findings of this study showed that the CP model using an innovative input selection strategy is a useful and powerful tool for monthly TWSA prediction.

The Effect of High-Voltage Electrostatic Field on the Water Performance

2012 ◽  
Vol 441 ◽  
pp. 695-698
Author(s):  
Qing Wang ◽  
Jie Min ◽  
Jia Tian
Keyword(s):  
Electric Field ◽  
Physical Properties ◽  
Field Intensity ◽  
High Voltage ◽  
Electrostatic Field ◽  
Electric Field Intensity ◽  
Treatment Time ◽  
Swelling Ratio ◽  
Wool Fibers ◽  
Effect Of Treatment

Water treated by external electric field with different field intensity. The absorbency of water, the changes of dyes solubility, the effect of treatment time and the changes of wool swelling were studied. The results showed that electric field treatments can alter the water physical properties to various degrees. The absorbance of water increased with increasing electric field intensity. The swelling ratio indicated the ability of solvent to penetrate the wool fibers and to swell them.

scholarly journals Nonlinear Quantitative Radiation Sensitivity Prediction Model Based on NCI-60 Cancer Cell Lines

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Chunying Zhang ◽  
Luc Girard ◽  
Amit Das ◽  
Sun Chen ◽  
Guangqiang Zheng ◽  
...  
Keyword(s):  
Prediction Model ◽  
Cell Lines ◽  
Latent Variables ◽  
Predictive Performance ◽  
Radiation Sensitivity ◽  
Gene Signature ◽  
Support Vector ◽  
Cancer Pathways ◽  
Different Types ◽  
Input Variables

We proposed a nonlinear model to perform a novel quantitative radiation sensitivity prediction. We used the NCI-60 panel, which consists of nine different cancer types, as the platform to train our model. Important radiation therapy (RT) related genes were selected by significance analysis of microarrays (SAM). Orthogonal latent variables (LVs) were then extracted by the partial least squares (PLS) method as the new compressive input variables. Finally, support vector machine (SVM) regression model was trained with these LVs to predict the SF2 (the surviving fraction of cells after a radiation dose of 2 Gyγ-ray) values of the cell lines. Comparison with the published results showed significant improvement of the new method in various ways: (a) reducing the root mean square error (RMSE) of the radiation sensitivity prediction model from 0.20 to 0.011; and (b) improving prediction accuracy from 62% to 91%. To test the predictive performance of the gene signature, three different types of cancer patient datasets were used. Survival analysis across these different types of cancer patients strongly confirmed the clinical potential utility of the signature genes as a general prognosis platform. The gene regulatory network analysis identified six hub genes that are involved in canonical cancer pathways.

scholarly journals Prediction of Electromagnetic Fields around High Voltage Transmission Lines

2017 ◽  
Vol 10 (1) ◽  
pp. 50
Author(s):  
G A Kulkarni ◽  
W Z Gandhare
Keyword(s):  
Magnetic Field ◽  
Electric Field ◽  
High Voltage ◽  
Transmission Lines ◽  
Support Vector ◽  
Hybrid Technique ◽  
External Fields ◽  
Electric And Magnetic Fields ◽  
Tower Height ◽  
Key Parameter

The electric and magnetic fields are present around the High Voltage Transmisssion Lines (HVTL) and reported to affect health of the workers working on these hotlines. The key parameter reportedly responsible for detrimental health effects, are internal induced fields in body. Induction of internal fields in different organs is heavily dependent on the external fields. Prediction of hazardous levels of external fields before measurement or in situations difficult for direct measurement will lead to identify the restrictive situations and working conditions for hotline workers. This work propose a method to model electric and magnetic field for different climbing routes using hybrid technique, formed by combining support vector machine (SVM) and neural network (NN) and also electric field and magnetic field values are predicted using NN for increase in tower height. The result shows the performance of proposed method for prediction of electric field and magnetic field for increase in tower height.

PEMANFAATAN RADIASI ENERGI TEGANGAN 150 KV UNTUK LAMPU LED PENERANGAN JALAN

Jurnal Teknik ◽  
2018 ◽  
Vol 7 (1) ◽  
Author(s):  
Mauludi Manfaluthy
Keyword(s):  
Magnetic Field ◽  
Electric Field ◽  
High Voltage ◽  
Low Frequency ◽  
Energy Utilization ◽  
World Health ◽  
Electromagnetic Signals ◽  
Health Organization ◽  
The Magnetic Field ◽  
Low Frequency Waves

WHO (World Health Organization) concludes that not much effect is caused by electric field up to 20 kV / m in humans. WHO standard also mentions that humans will not be affected by the magnetic field under  100 micro tesla and that the electric field will affect the human body with a maximum standard of 5,000 volts per meter. In this study did not discuss about the effect of high voltage radiation SUTT (High Voltage Air Channel) with human health. The research will focus on energy utilization of SUTT radiation. The combination of electric field and magnetic field on SUTT (70-150KV) can generate electromagnetic (EM) and radiation waves, which are expected to be converted to turn on street lights around the location of high voltage areas or into other forms. The design of this prototype works like an antenna in general that captures electromagnetic signals and converts them into AC waves. With a capacitor that can store the potential energy of AC and Schottky diode waves created specifically for low frequency waves, make the current into one direction (DC). From the research results obtained the current generated from the radiation is very small even though the voltage is big enough.Keywords : Radiance Energy, Joule Thief, and  LED Module.

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