Linear Fitting Function Research Articles

The Novel Method to Reconstruct Three-Dimensional Target Motion From Body Surface Motion for Dynamic Moving Phantom

We have developed the new dynamic moving phantom (DMP) with three axis drives to reproduce three-dimensional(3D) target motion plus one axis drive for body surface motion. The motion data for operating this phantom is able to be acquired by rehearsing dynamic tracking therapy (DTT) of a patient. In this study, the novel approach which reconstructs the 3D long time information from respiratory motion (RPM) data on the body surface during planning CT and the target motion measured by 4DCT is developed. First, the RPM data on body surface during planning CT in each patient was obtained by laser ranger (u(t)). Then, the average wave of u(t) when 4DCT are scanned was calculated and 3D target motion was obtained by analyzing 4DCT. Next, correlation functions between average wave and 3D target motion in LR, AP and SI directions were created. Finally, long time 3D target motion was generated by the correlation function with u(t) in each patient. Two different correlation models were tested. One is dual polynomial model which is composed of polynomial functions separated by inspiratory and expiratory phases (P(u(t))). Another one is simple linear fitting function (L(u(t))). RPM data of 8 lung cancer patients who participated in clinical trial with DTT using gimbaled linac were used in this study. The average absolute difference between actual target position that was already known from tracking log data and reconstructed position were calculated to evaluate two correlation models. The significance between the average absolute difference of L (u (t)) and that of P (u (t)) were evaluated by Wilcoxon’s signed rank one-sided test. Table summarizes the result of the average of absolute difference in each patient. The difference between actual target position and reconstructed position by L(u(t)) in LR, AP and SI were 0.12±0.08 mm, 0.53±0.29 mm, 0.31±0.18 mm, respectively. On the other hand, those by P(u(t)) in LR, AP and SI were 0.23±0.14 mm, 0.87±0.54 mm, 0.69±0.40 mm, respectively (p = 0.018, 0.005, 0.027). The novel approach which reconstructs the 3D target motion from RPM data during planning CT and 3D target motion measured by 4DCT were shown. Dual polynomial model that we investigated in this study was better to reconstruct 3D target motion.Abstract 3619Pt.No.Dual polynomial modelLinear modelLR (mm)SI (mm)AP (mm)LR (mm)SI (mm)AP (mm)AVSDAVSDAVSDAVSDAVSDAVSD10.070.040.300.150.140.100.090.070.300.240.400.2720.220.130.490.300.390.250.220.130.520.351.070.5930.020.010.440.200.100.080.050.020.680.360.420.2740.130.060.480.270.060.060.220.090.630.360.160.1250.060.050.690.430.480.290.340.191.860.941.440.7460.200.170.530.250.210.140.540.320.500.290.430.2470.100.060.420.350.270.140.130.091.040.780.300.1680.120.130.870.420.840.410.270.171.400.981.290.81AV0.120.080.530.290.310.180.230.140.870.540.690.40 Open table in a new tab

All-arranged Polygon Graphic Index Method-based Appraisal Model for Urban Smart Growth

As cities develop in a fast pace, smart growth has become the goals for the city development of each country. In this paper, we take Atlanta as an example, then we build index assessment system of smart growth, which focus on economic prospection, social equity and environment sustainability (three of “E”). We choose ten typical indexes to measure three of “E” and use all-arranged polygon graphic index method to build smart growth mode. We build linear fitting functions for ten typical indexes respectively with the origin data in Atlanta from 2006 to 2014, then we get the predictable data in 2015 through the linear fitting functions. The next step is to bring this data to the mode and compute comprehensive scores with the year ranging from 2006 to 2015; besides, we compare the score in 2015 with that calculated by actual data. Then we do a significant test for them and the result indicates that the two scores are consistent. Smart growth mode we have built is effective, so we can use it to do prediction for the urban smart growth in the future.

Abstract 095: Digital Solution Assisted Retail Pharmacists in Community Hypertension Control in China: A Real World Study of Large Samples

Objective: To identify the effects of a digital healthcare solution for hypertension management at retail pharmacies by pharmacists. Methods: We developed a digital healthcare solution that includes BP telemonitor, an App, and a cloud database with algorithms to assist: 1) CVD risk screening, 2) personalized meal planning, 3) personalized health coaching, and 4) medication guidance and compliance during the pharmacy consultation service. This solution has been implemented in 5,067 retail pharmacies in 141 cities in China as a decision support system to help pharmacists manage their hypertensive members. During Jan 1 st 2015 to Dec 31 st 2016, a total of 1,817,594 patients received in-store hypertension care and 23,024 of them were selected in this analysis following 4 inclusion criteria: 1) baseline BP was hypertensive (≥ 140/90 mm Hg), 2) the frequency of in-store hypertensive care was ≥ 1 time per month, 3) the intervention duration between the first- and last-time hypertensive care was ≥ 1 month, and 4) baseline characteristics were complete. Dynamic changes of SBP, DBP, disease staging, and population distribution over time were the major end points in this real-world, retrospective analysis. Results: SBP and DBP of the study population (n=23,024) were reduced on average by 11.2 mm Hg (baseline: 151.7 ± 14.8 mm Hg, last time: 140.5 ± 18.8 mm Hg, P< 0.001) and 6.3 mm Hg (baseline: 91.2 ± 11.9 mm Hg, last time: 84.9 ± 12.7 mm Hg, P<0.001), respectively. A total of 13,001 (56.5%) patients demonstrated improved hypertension stages by the last time BP measures with 9,625 (41.8%) of them returning to the normal range. The principal BP control effect took place after the first-time intervention, followed by gradual BP reduction over time. Simulation analysis revealed linear descending relationship of both SBP and DBP to time, and the concentration distributions were moving towards the normal BP level. The linear fitting functions were: SBP, Y = -0.103x + 140.278; DBP, Y = -0.060x + 84.799. Conclusion: Digital healthcare solution is effective in assisting retail pharmacists to deliver community hypertension care in real world.

Weekly maintenance scheduling using exact and genetic methods

The weekly maintenance schedule specifies when maintenance activities should be performed on the equipment, taking into account the availability of workers and maintenance bays, and other operational constraints. The current approach to generating this schedule is labour intensive and requires coordination between the maintenance schedulers and operations staff to minimise its impact on the operation of the mine. This paper presents methods for automatically generating this schedule from the list of maintenance tasks to be performed, the availability roster of the maintenance staff, and time windows in which each piece of equipment is available for maintenance. Both Mixed-Integer Linear Programming (MILP) and genetic algorithms are evaluated, with the genetic algorithm shown to significantly outperform the MILP. Two fitness functions for the genetic algorithm are also examined, with a linear fitness function outperforming an inverse fitness function by up to 5% for the same calculation time. The genetic algorithm approach is computationally fast, allowing the schedule to be rapidly recalculated in response to unexpected delays and breakdowns.

Re-examining the Causes and Meaning of the Risk Allocation Hypothesis.

The risk allocation hypothesis has inspired numerous studies seeking to understand how temporal variation in predation risk affects prey foraging behavior, but there has been debate about its generality and causes. I examined how imperfect information affects its predictions and sought to clarify the causes of the predicted patterns. I first confirmed that my modeling approach-given a threshold or linear fitness function-produced the risk allocation prediction that prey increase their foraging efforts during low and high risk as the proportion of high-risk periods increases. However, the causes of this result and its robustness differed for the two fitness functions. When prey that had evolved to use perfect information received imperfect information, risk allocation was reduced. However, prey that evolved to use imperfect information in some cases reversed the risk allocation prediction. The model also showed that risk allocation occurs even when prey have no knowledge that the proportions of low- and high-risk periods have changed. I conclude that risk allocation is largely not driven by prey expectations about future states of the environment but rather by the prey's current energetic state and time remaining. I discuss the consequences for experimental design and explanations for empirical results.

Facile synthesis the nitrogen and sulfur co-doped carbon dots for selective fluorescence detection of heavy metal ions

In this article, we prepare the nitrogen (N) and sulphur (S) co-doped carbon dots (NS-CDs) via a one-step hydrothermal method using the methionine as the precursor. The N and S doping content reach as high as 15.2at.% and 19.5at.%, separately. The analysis of the photoluminescence dates exhibits that the relationship between the excitation and the corresponding emission wavelength can be summarized as a linear fitting function. In addition, the as prepared NS-CDs also exhibits sensitive detection properties for heavy metal cations. With these excellent characteristics, there are extensive potential applications for NS-CDs in industry and environmental monitoring.

Linear fitting Kalman filter

Dynamic estimation in signal processing and target tracking often involves non-linear models. These non-linear models are usually linearised through the first-order Taylor approximation in estimation process. However, the error generated by the first-order Taylor approximation is not negligible when the non-linearity of a model is high or the input error is large. This study proposes a new linearisation method through minimising the error between a non-linear function and its linear approximation. A weighted least squares (WLS) algorithm is developed to estimate a linear fitting (LF) function based on the sigma points of the random variable in non-linear transformation. A linear fitting Kalman filter (LKF) is developed based on this principle. The accuracy of the LF transform is analysed using the Kullback–Leibler (KL) distance. The results show that the LF transform has less KL distance to the true distribution compared with the first-order Taylor approximation. To evaluate the estimation performance, simulations are conducted and the results are compared with those of extended Kalman filter (EKF) and unscented Kalman filter (UKF). The results demonstrate that the LKF provides better accuracy than the EKF, and has similar accuracy to the UKF with lower computational cost.

SU‐F‐T‐78: Minimum Data Set of Measurements for TG 71 Based Electron Monitor‐Unit Calculations

Purpose:Building up a TG‐71 based electron monitor‐unit (MU) calculation protocol usually involves massive measurements. This work investigates a minimum data set of measurements and its calculation accuracy and measurement time.Methods:For 6, 9, 12, 16, and 20 MeV of our Varian Clinac‐Series linear accelerators, the complete measurements were performed at different depth using 5 square applicators (6, 10, 15, 20 and 25 cm) with different cutouts (2, 3, 4, 6, 10, 15 and 20 cm up to applicator size) for 5 different SSD's. For each energy, there were 8 PDD scans and 150 point measurements for applicator factors, cutout factors and effective SSDs that were then converted to air‐gap factors for SSD 99–110cm. The dependence of each dosimetric quantity on field size and SSD was examined to determine the minimum data set of measurements as a subset of the complete measurements. The “missing” data excluded in the minimum data set were approximated by linear or polynomial fitting functions based on the included data. The total measurement time and the calculated electron MU using the minimum and the complete data sets were compared.Results:The minimum data set includes 4 or 5 PDD's and 51 to 66 point measurements for each electron energy, and more PDD's and fewer point measurements are generally needed as energy increases. Using only <50% of complete measurement time, the minimum data set generates acceptable MU calculation results compared to those with the complete data set. The PDD difference is within 1 mm and the calculated MU difference is less than 1.5%.Conclusion:Data set measurement for TG‐71 electron MU calculations can be minimized based on the knowledge of how each dosimetric quantity depends on various setup parameters. The suggested minimum data set allows acceptable MU calculation accuracy and shortens measurement time by a few hours.

Photoluminescence detection of alpha particle using DAM-ADC nuclear detector

The photoluminescence (PL) and UV–vis spectral analysis of DAM-ADC (diallyl maleate: DAM, polyallyl diglycol carbonate: ADC) nuclear detector are demonstrated for the first time. The DAM-ADC surfaces were exposed to thin 241Am disk source that emits alpha particles with activity 333kBq. It is found that the track density of the irradiated samples remarkably influences the PL characteristics of the DAM-ADC detector. The spectral peak heights and the integrated intensities under the peaks exhibit linear correlations with correlation coefficient R2=0.9636 and 0.9806, respectively for different alpha particle fluences ranging from 8.16–40.82×107particles/cm2. Additionally, a correlation coefficient R2=0.9734 was achieved for the UV–vis spectral analysis. The linear fitting functions, along with the corresponding fitting parameters were evaluated in each case. Both the PL and the UV–vis data of the irradiated DAM-ADC samples showed considerable spectral differences, and hence they would be used to offer sensitive approaches for alpha particle detection.

Low-Complexity Carrier Phase Recovery Based on Constellation Classification for M-ary Offset-QAM Signal

Low-complexity carrier phase recovery (CPR) for M-ary offset quadrature amplitude modulation (OQAM) formats is proposed and numerically verified, based on the technique of constellation classification. Although the OQAM signal has very poor tolerance to the laser linewidth due to the phase noise-induced crosstalk, we find that both even and odd samples of OQAM signal only have conventional constellation rotation with respect to phase noise. Through linear fitting of the classified constellation, carrier phase estimation can be derived from the slope of linear fitting function. Compared with our recently proposed modified blind phase search scheme, the computational complexity (CC) of proposed CPR does not increase much with the OQAM modulation level of M. Taking the CPR of 16-OQAM into account, we show that the CC can be significantly reduced by a factor of 8.47 (or 7.34) in the form of multipliers (or adders) with performance comparable to MBPS (B = 24) under the condition of Δf × T S = 5 × 10 -6 . In particular, a tolerance of linewidth and symbol duration product of 8 x 10-5, under the condition of CD = 300 ps/nm and DGD = 10 ps, can be secured for 16-OQAM, given 0.7-dB required-OSNR penalty.

Performance of Wind Pump Prototype

A wind pump prototype with 3.6 m rotor diameter, 19 m hub height above ground and 0.22 mm reciprocating pump stroke has been developed at the Department of Mechanical Engineering, Mekelle University. The prototype was designed and manufactured locally. Theoretical model based on combined efficiency of the rotor and the reciprocating pump was used to estimate the performance of the wind pump. One year wind speed data collected at 10 m height was extrapolated to the wind pump hub height using wind shear coefficient. The model assumed balanced rotor power and reciprocating pump, hence did not consider the effect of pump size. The theoretical model estimated the average daily discharge to be around 50 m 3 and 30 m 3 at 8 m and 12 m head, respectively. The prototype was tested with the same pump stroke but two different size pumps at two different heads. The pumps were with internal diameter of 55 and 70 mm and the test heads were at 8 and 12 m. Measurement of the flow rate, rotational speed and wind speed were made every 10 minutes during the test period. The data collected were analyzed to find the performance of the wind pump at the two test heads and two pump sizes. The flow rate data was plotted against binned wind speed data to determine the linear fit function. The linear fit function was then used to estimate the flow rate at any wind speed. With the 55 mm pump the measured average daily discharge was 20 and 19 m 3 at 8 m and 12 m head, respectively. With the 70 mm pump the measured average daily discharge was 41 m 3 and 30 m 3 at 8 m and 12 m head, respectively. Keywords : Wind pump, Windmill, Performance testing, Pump efficiency, Pump discharge, Ethiopia.

Simplified Modeling of Convection and Radiation Heat Transfer during Infrared Heating of PET Sheets and Preforms

Abstract Initial heating conditions and temperature effects have an important influence during the injection stretch blow molding process of PET preforms. The paper provides a simplified modeling of the infrared (IR) flux provided by the IR lamps and the convection heat transfer with air, for the finite element simulation of the heating of PET samples. This modeling enables fast thermal simulations in industrial context. First, a complete 2D simulation of the air convection around a PET sheet sample is conducted using ANSYS/FLUENT to compute the local convection heat transfer coefficient. The distribution of this coefficient along the PET wall is then interpolated by a best linear fit function of the wall position to provide the boundary condition of the convection heat transfer thereafter. This boundary condition, coupled with the calculation of the infrared flux absorbed by the PET sheet sample, allows a 3D calculation of the time evolution of the sample temperature. This calculation is validated by comparing the experimental temperature distribution of the PET sheet obtained from an IR camera with the numerical results of the simulation. Second, we focus on the modeling of the heating of a cylindrical PET preforms by IR lamps. In our approach, the IR heating flux is calculated using the spectral and surfaceto-surface radiation laws adapted to the sample geometry. The air convection effect around the preform is modeled using the heat transfer coefficient identified from the 2D plane sheet case. It is applied on the boundaries of a simpler model in Comsol where only the preform is meshed. The temperature distribution on the outer surface of the preform is compared to experimental measurements by thermal imaging. A good agreement is observed which validates the whole approach used.

Quantification of the PR-39 cathelicidin compound in porcine blood by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry.

The PR-39 porcine cathelicidin occurs naturally in animal neutrophils. Its main function is antimicrobial activity, which potentially can be used in antibiotic treatments in veterinary medicine. Investigations concerning such a use require the detection and quantification of PR-39 in a given sample. The aim of this work is to determine the concentration of PR-39 contained in porcine blood. Prior to matrix-assisted laser desorption/ionization (MALDI) analysis, the porcine blood sample was subjected to crude extraction in order to release the active form of PR-39 from the neutrophil granules. Next, gel filtration chromatography was performed to separate PR-39 from other cathelicidins present in porcine blood. Positive ion MALDI time-of-flight (TOF) mass spectra of the resulting portion of lyophilisate with unknown PR-39 content were acquired in linear mode. To quantify PR-39 in the lyophilisate sample, the standard addition method was applied. The PR-39 concentration obtained in the lyophilisate sample was then converted into the peptide concentration in porcine blood. The linear fit function of the constructed calibration curve indicates an excellent correlation between the PR-39 peak intensity and the added quantity of synthetic PR-39 (R(2) = 0.994) and a low relative standard deviation of the slope = 1.98%. From the x-intercept of the straight line, we estimated the PR-39 concentration in porcine blood to be 20.5 ± 4.6 ng/mL. The MALDI method was successfully applied for the quantitative analysis of PR-39 found in porcine blood. Compared with other available methods, it is relatively easy, inexpensive and not time-consuming. Despite the method having lower accuracy than the enzyme-linked immunosorbent assay (ELISA), the results obtained here, by a much simpler method, are in good agreement with the literature data.

Suppression of Thermal Effect on Closed Loop Fiber Optic Gyroscope

Fiber Optic Gyroscope (FOG), an inertial sensor measures the angular rotation of an object based on the principle of Sagnac effect. FOG is subjected to various environmental disturbances such as temperature, vibration etc., when it is operated in real time. Temperature variation is the major factor that affects the gyro performance. In this paper, a novel compensation approach called temperature soaking method has been presented which makes the gyro insensitive to temperature. This method has been carried out for a 3-axis (Gx, Gy and Gz) FOG and its outputs (bias values) were taken at different temperature points. In order to reduce the bias drift of FOG output, this method uses linear curve fitting function to generate the bias error coefficients. The generated bias error coefficients are subtracted from the existing bias data, which in turn reduces the bias error. The bias values obtained for 3-axis gyro at temperature points from -20°C to +60°C before compensation is then compared with the bias values obtained for after compensation at same temperature points. Experimental results demonstrate that, after compensation the bias drift greatly reduced from 15 to 0°/hr approximately. This method effectively improves the stability and performance of a gyro and makes it to withstand to different environmental conditions which in turn increases the range of applications from military, navigation to space.

Preliminary empirical model of inner boundary of ion plasma sheet

The penetration of the plasma sheet into the inner magnetosphere is important to both ring current formation and spacecraft charging at geosynchronous orbit. This paper, using hot ion data recorded by HIA of TC-1/DSP, establishes an empirical model of the inner boundary of ion plasma sheet (IBIPS) on the near equatorial plane. All IBIPS are located inside geocentric radial distance of 9 RE. We divided local times (LT) into eight local time bins and found that during quiet times (Kp⩽2−), the IBIPS is closest to the Earth on the pre-midnight side (LT=1930–2130) and farthest on the dawn side (LT=0430–0730), which differs from previous spiral models. The geocentric radius of IBIPS in each local time bin can be described by a linear fitting function: Rps=A+Bkp·Kp. The changing rate Bkp of the radius of IBIPS relative to Kp index on the midnight side (LT=2230–0130) and post-night side (LT=0130–0430) are the two largest (0.66 and 0.67), indicating that the IBIPS on the night side (LT=2230–0430) moves fastest when Kp changes. Since the IBIPSs in different local times bins have different changing rates, both the size and shape of IBIPS change when Kp varies. The correlation coefficients between the radius of IBIPS and the instantaneous Kp increase with the increase of ΔT (the time difference between IBIPS crossing time and preceding Kp interval), which suggests that with the increase of ΔT, the radius of IBIPS is more and more controlled by instantaneous Kp, and the influence of preceding Kp becomes weaker. The response time of IBIPS to Kp is between 80 and 95min. When ΔT>95min, the correlation coefficient basically keeps unchanged and only has a weak increase, suggesting that the IBIPS is mainly determined by the convection electric field represented by instantaneous Kp.

Numerical simulation study on correlation between ion current signal and NO X emissions in controlled auto-ignition engine

NOX is one of the main compositions in the modern engine emissions and the reduction requirements of NOX have turned to be more stringent. To control NOX emissions better, the technologies of NOX sensors are forced to achieve much faster response and higher accuracy. In this paper, the correlation between ion current signals and NOX emissions is studied by both experiments and simulations in a direct-injection controlled auto-ignition (CAI) engine. The investigation provides the possibility of a novel method of cycle-by-cycle NOX emissions detection. The simulation results present this positive correlation based on the chemical kinetics theory, and also directly reflect the formation order of the chemical products and the influence of temperature on the rates of main ionization and NOX generated reactions. Furthermore, the distributions of both ions and NO products are shown with the CFD results, illustrating their in-cylinder space correlation. Combined with the simulation results, the experimental results not only validate the positive correlation between two different fuel types, but also provide the evidences of linear fitting function. Based on the fitting results, the cycle-based NOX emissions could be estimated.

TU‐CD‐207‐01: Characterization of Breast Tissue Composition Using Spectral Mammography

Purpose:To investigate the feasibility of characterizing the chemical composition of breast tissue, in terms of water and lipid, by using spectral mammography in simulation and postmortem studies.Methods:Analytical simulations were performed to obtain low‐ and high‐energy signals of breast tissue based on previously reported water, lipid, and protein contents. Dual‐energy decomposition was used to characterize the simulated breast tissue into water and lipid basis materials and the measured water density was compared to the known value. In experimental studies, postmortem breasts were imaged with a spectral mammography system based on a scanning multi‐slit Si strip photon‐counting detector. Low‐ and high‐energy images were acquired simultaneously from a single exposure by sorting the recorded photons into the corresponding energy bins. Dual‐energy material decomposition of the low‐ and high‐energy images yielded individual pixel measurements of breast tissue composition in terms of water and lipid thicknesses. After imaging, each postmortem breast was chemically decomposed into water, lipid and protein. The water density calculated from chemical analysis was used as the reference gold standard. Correlation of the water density measurements between spectral mammography and chemical analysis was analyzed using linear regression.Results:Both simulation and postmortem studies showed good linear correlation between the decomposed water thickness using spectral mammography and chemical analysis. The slope of the linear fitting function in the simulation and postmortem studies were 1.15 and 1.21, respectively.Conclusion:The results indicate that breast tissue composition, in terms of water and lipid, can be accurately measured using spectral mammography. Quantitative breast tissue composition can potentially be used to stratify patients according to their breast cancer risk.

Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison

Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the best performing one was an optimized three-band combination according to (ρ560-ρ1610-ρ2190)/(ρ560+ρ1610+ρ2190) with a 10-fold cross-validation RCV2 of 0.82 (RMSECV: 0.62). This family of methods excel for their fast processing speed, e.g., 0.05s to calibrate and validate the regression function, and 3.8s to map a simulated S2 image. With regard to non-parametric methods, 11 machine learning regression algorithms (MLRAs) have been evaluated. This methodological family has the advantage of making use of the full optical spectrum as well as flexible, nonlinear fitting. Particularly kernel-based MLRAs lead to excellent results, with variational heteroscedastic (VH) Gaussian Processes regression (GPR) as the best performing method, with a RCV2 of 0.90 (RMSECV: 0.44). Additionally, the model is trained and validated relatively fast (1.70s) and the processed image (taking 73.88s) includes associated uncertainty estimates. More challenging is the inversion of a PROSAIL based radiative transfer model (RTM). After the generation of a look-up table (LUT), a multitude of cost functions and regularization options were evaluated. The best performing cost function is Pearson’s χ-square. It led to a R2 of 0.74 (RMSE: 0.80) against the validation dataset. While its validation went fast (0.33s), due to a per-pixel LUT solving using a cost function, image processing took considerably more time (01:01:47). Summarizing, when it comes to accurate and sufficiently fast processing of imagery to generate vegetation attributes, this paper concludes that the family of kernel-based MLRAs (e.g. GPR) is the most promising processing approach.

Structural and optical investigation on alpha particle irradiated CR-39 surface coated by MEH-PPV conducting polymer

Photoluminescence and UV–Vis spectral evaluation of a poly allyl diglycol carbonate (CR-39) detector coated by poly(2-methoxy-5-(2-ethyl-hexyloxy)-1,4-phenylene-vinylene) (MEH-PPV) conducting polymer are demonstrated. The CR-39 surfaces were exposed to thin 241Am disk source that emits alpha particles with activity 333kBq. Surface modification of the detector by MEH-PPV was acquired by a simple dip coating process. Our findings revealed that the spectroscopic analysis using FTIR is insensitive approach to detect the induced modifications in the irradiated samples. Additionally, the track density of the irradiated samples affects significantly the photoluminescence and UV–Vis responses of the CR-39 samples. The spectral peak heights and the integrated intensities under the peaks exhibit linear correlations (correlation coefficient R2=0.9904 to 0.9968) with the fluence of alpha particles. The linear fitting functions together with the corresponding fitting parameters were evaluated. Both techniques exhibited remarkable spectral differences for the irradiated samples, and hence they could be employed to provide sensitive methods for alpha particle detection. Results of sample fabrication and modification, along with structural and optical evaluation are addressed and thoroughly discussed.

Formulation of a fuel spray SMD model at atmospheric pressure using Design of Experiments (DoE)

Abstract Herein we describe the application of historical data design under response surface methodology to formulate the correlation of the fuel spray SMD (Sauter Mean Diameter) with factors (viscosity, fuel injection pressure, air-blast pressure). The Design Expert software was used to analyze the experimental data to obtain the equation between the factors and the response. The range of the values of variables used in this experiment were viscosity of the fuels 9.71 × 10−4 Pa–1.207 × 10−3 Pa, the fuel injection pressure 0.1–1.0 MPa, the air-blast pressure from 0 Pa to 2000 Pa and a total of 120 test points were conducted. The data were converted into logarithmic scale in order to utilize the linear fitting function of the DoE (Design of Experiments). According to the ANOVA (analysis of variance), the most influential factor to the fuel spray SMD is fuel injection pressure, followed by fuel viscosity and air-blast pressure. R2 value of 0.9339 demonstrated that the regression equation obtained by historical data design fits the experimental results consistently.