Study on Antibiotic Prophylactic Use Behavior and Influencing Factors of Livestock and Poultry Breeding Farms and Households from Risk Cognitive Perspective

Based on the theory of planned behavior( TPB),Taking 399 livestock and poultry farms and households in Shandong Province as samples,risk cognitive variables were introduced. Besides,with the aid of the structural equation model( SEM) and partial least squares method( PLS),through an empirical analysis on antibiotic prophylactic behavior of livestock and poultry breeding farms and households in the context of " antibiotic free" production,the response mechanism of " antibiotic free" production was explored. Results indicated that the use of antibiotic prophylaxis by livestock and poultry farms and households is still very common. In the observation samples,61. 4% clearly expressed they would use antibiotic prophylaxis; the understanding of hazard of improper antibiotic prophylaxis was inadequate,only 32. 3% breeding households believed that the overuse of antibiotic prophylaxis is the main reason leading to excessive drug residue in animal products. This study was in line with the process of deduction of TPB. The breeding households' reduction of antibiotic prophylaxis is influenced by their intentions,while the intention is influenced by the attitude,subjective norms and perceptual behavior control; different risks have different influences,but most risks have greater influence on perceptual behavior control,because the perceptual behavior control determines whether breeding households have the ability of implementing the corresponding behavior.

VPLS Based Quality and Cost Control for Tennessee Eastman Process

Product quality and operation cost control obtain increasing emphases in modern chemical system engineering. To improve the fault detection power of the partial least square (PLS) method for quality control, a new QRPV statistic is proposed in terms of the VP (variable importance in projection) indices of monitored process variables, which is significantly advanced over and different from the conventional Q statistic. QRPV is calculated only by the residuals of the remarkable process variables (RPVs). Therefore, it is the dominant relation between quality and RPV not all process variables (as in the case of the conventional PLS) that is monitored by this new VP-PLS (VPLS) method. The combination of QRPV and T2 statistics is applied to the quality and cost control of the Tennessee Eastman (TE) process, and weak faults can be detected as quickly as possible. Consequently, the product quality of TE process is guaranteed and operation costs are reduced.

Method for UAV spraying pattern measurement with PLS model based spectrum analysis

Unmanned aerial vehicle(UAV)chemical application is widely used for crop protection,and spraying pattern is one of the most important factors that influence the chemical control efficacy.A method for UAV spraying pattern measurement with partial least squares(PLS)model based spectrum analysis was proposed in this study to measure the UAV spraying pattern more accurately.The method involved the steps of fluorescent tracer solution spray and its droplets collection,the spectrum on paper strip acquiring,spectrum processing and analysis,PLS modeling.In order to verify the applicability of the method and obtain the parameters of the PLS model,UAV spraying experiments were performed in the field.Then Fluorescent tracer solution was sprayed and its droplets are collected by paper strip,and the original spectrum on paper strip obtained by a spectrometer was processed by the Savitzky-Golay and standard normalized variable(SNV)method.The prediction model of coverage rate selected as the droplet deposition parameter to measure the UAV spraying pattern,was established by using PLS method.To verify the superiority of the PLS model,a traditional linear regression(LR)model of coverage rate was established simultaneously.The results demonstrate that the method with PLS model based spectrum analysis can measure the UAV spraying pattern effectively,and PLS model has a better performance of RV2=0.94 and RMSEP=0.9446 than that of the LR model.

Measurements and analysis of water content in winter wheat leaf based on terahertz spectroscopy

Wheat is a major grain crop in China.Water is one of the most important factors which influence the lifecycle and yield of wheat.It is of great significance to study the water content at the key stages of wheat growth in order to make irrigation decision to raise its yield.As Terahertz(THz)spectroscopy is a brand new sensing technology and sensitive to water absorption,the relationship between terahertz spectra and water content in winter wheat leaf was investigated and a preliminary result was presented in this paper.Forty winter wheat leaves samples with diverse range of water content(42.8%-72.5%)were collected.The Terahertz time domain spectra(THz-TDS)were first obtained and then transformed into Frequency-domain amplitude with the Fast Fourier Transformation(FFT)method.The absorption and refractive index spectra were then calculated.The spectra were linearly fitted to obtain the slope and intercept used for building a calibration model.The partial least squares(PLS)method and linear regression were employed to establish models to determine leaf water content in the winter wheat.The predicted correlation coefficient and the root mean square error of the optimal model established with the Frequency-domain amplitude parameter at 0.3 THz by linear regression were 0.812%and 4.4%,respectively.The results showed that terahertz spectroscopy performed well in water content prediction and could be an effective and potential method for leaf water content measurement in winter wheat.