Enhancing Service Capabilities by Adding Electron-Beam Irradiator to Gamma Irradiation Facility

2021 ◽  
Vol 55 (s3) ◽  
pp. 27-34
Author(s):  
Gilmara C. de Luca ◽  
John Schlecht ◽  
Bart Croonenborghs
Keyword(s):  
Electron Beam ◽  
Success Rate ◽  
Bulk Density ◽  
Laboratory Testing ◽  
Cross Validation ◽  
Polymeric Materials ◽  
Main Challenge ◽  
The Cross ◽  
Transition Approach ◽  
Made In

Abstract In 2013, Sterigenics undertook the addition of a 10-MeV electron beam (e-beam) accelerator at its facility in Jarinu, Brazil. A gamma irradiator was already located at this facility, which processed materials and provided irradiation services in Brazil. The decision to implement an e-beam accelerator at the same facility was made in order to diversify the technology that could be offered and to rapidly increase the overall capacity of the facility. In addition, the e-beam technology was complementary to the existing gamma pallet irradiator and thus provided an internal backup for some processes. The main challenge for staff at the Brazil facility was cross-validating processes carried out by the existing gamma irradiator with processes performed with the new e-beam accelerator. The overall success rate in the cross-validation of processes between the two modalities was positive. Products for healthcare, laboratory testing, and other low-bulk-density products that basically consisted of commonly used polymeric materials were most suitable for cross-validation. Products of higher bulk density, greater heterogeneity, or variability between packaging systems and products with dose specifications for a tote rather than a pallet gamma irradiator presented limitations in the cross-validation success rate. This article focuses on the transition approach, discusses the types of products that were successfully cross-validated in e-beam from gamma, and presents examples where such cross-validation was not pursued.

Energy Distribution in Electron Beams

1972 ◽  
Vol 30 ◽  
pp. 568-569
Author(s):  
Tamotsu Ohno
Keyword(s):  
Electron Beam ◽  
Energy Distribution ◽  
Cross Section ◽  
Integral Curve ◽  
Electron Beams ◽  
Electron Gun ◽  
Angular Divergence ◽  
Apparent Increase ◽  
Integral Width ◽  
The Cross

The energy distribution in an electron; beam from an electron gun provided with a biased Wehnelt cylinder was measured by a retarding potential analyser. All the measurements were carried out with a beam of small angular divergence (<3xl0-4 rad) to eliminate the apparent increase of energy width as pointed out by Ichinokawa.The cross section of the beam from a gun with a tungsten hairpin cathode varies as shown in Fig.1a with the bias voltage Vg. The central part of the beam was analysed. An example of the integral curve as well as the energy spectrum is shown in Fig.2. The integral width of the spectrum ΔEi varies with Vg as shown in Fig.1b The width ΔEi is smaller than the Maxwellian width near the cut-off. As |Vg| is decreased, ΔEi increases beyond the Maxwellian width, reaches a maximum and then decreases. Note that the cross section of the beam enlarges with decreasing |Vg|.

Searching for the causes why some of the paintings by Mihaly Munkacsy are darkening

1990 ◽  
Vol 48 (2) ◽  
pp. 204-205
Author(s):  
Imre Pozsgai ◽  
Klara Erdöhalmi-Torok
Keyword(s):  
Electron Beam ◽  
Cross Section ◽  
Polyester Resin ◽  
National Gallery ◽  
X Ray ◽  
Art Historians ◽  
Made In ◽  
Grinding And Polishing

The paintings by the great Hungarian master Mihaly Munkacsy (1844-1900) made in an 8-9 years period of his activity are deteriorating. The most conspicuous sign of the deterioration is an intensive darkening. We have made an attempt by electron beam microanalysis to clarify the causes of the darkening. The importance of a study like this is increased by the fact that a similar darkening can be observed on the paintings by Munkacsy’s contemporaries e.g Courbet and Makart. A thick brown mass the so called bitumen used by Munkacsy for grounding and also as a paint is believed by the art historians to cause the darkening.For this study, paint specimens were taken from the following paintings: “Studio”, “Farewell” and the “Portrait of the Master’s Wife”, all of them are the property of the Hungarian National Gallery. The paint samples were embedded in a polyester resin “Poly-Pol PS-230” and after grinding and polishing their cross section was used for x-ray mapping.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

Prairie Thunder flax

2014 ◽  
Vol 94 (2) ◽  
pp. 445-449 ◽  
Author(s):  
S. D. Duguid ◽  
K. Y. Rashid ◽  
E. O. Kenaschuk
Keyword(s):  
Fusarium Oxysporum ◽  
Linum Usitatissimum ◽  
Seed Quality ◽  
Agronomic Traits ◽  
Research Station ◽  
Linum Usitatissimum L ◽  
The Cross ◽  
Made In

Duguid, S. D., Rashid, K. Y. and Kenaschuk, E. O. 2014. Prairie Thunder flax. Can. J. Plant Sci. 94: 445–449. Prairie Thunder, medium-maturing oilseed flax (Linum usitatissimum L.), was released in 2006 by Agriculture and Agri-Food Canada, Morden Research Station, Morden, Manitoba. Developed from the cross AC Watson/FP1043 made in 1995, Prairie Thunder was evaluated in the Flax Cooperative Trials (2002–2004) before being registered in 2006. Prairie Thunder's desirable combination of improved agronomic traits, seed quality and superior wilt (Fusarium oxysporum Schlecht. f. sp. lini (Bolley) Snyder & Hansen) resistance should make this cultivar useful for producers and the flax industry.

scholarly journals Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

Artificial Intelligence–Assisted Prediction of Late-Onset Cardiomyopathy Among Childhood Cancer Survivors

2021 ◽  
pp. 459-468
Author(s):  
Fatma Güntürkün ◽  
Oguz Akbilgic ◽  
Robert L. Davis ◽  
Gregory T. Armstrong ◽  
Rebecca M. Howell ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Cancer Survivors ◽  
Childhood Cancer ◽  
Clinical Features ◽  
Cross Validation ◽  
Childhood Cancer Survivors ◽  
Model Based ◽  
Extreme Gradient Boosting ◽  
The Cross

PURPOSE Early identification of childhood cancer survivors at high risk for treatment-related cardiomyopathy may improve outcomes by enabling intervention before development of heart failure. We implemented artificial intelligence (AI) methods using the Children's Oncology Group guideline–recommended baseline ECG to predict cardiomyopathy. MATERIAL AND METHODS Seven AI and signal processing methods were applied to 10-second 12-lead ECGs obtained on 1,217 adult survivors of childhood cancer prospectively followed in the St Jude Lifetime Cohort (SJLIFE) study. Clinical and echocardiographic assessment of cardiac function was performed at initial and follow-up SJLIFE visits. Cardiomyopathy was defined as an ejection fraction < 50% or an absolute drop from baseline ≥ 10%. Genetic algorithm was used for feature selection, and extreme gradient boosting was applied to predict cardiomyopathy during the follow-up period. Model performance was evaluated by five-fold stratified cross-validation. RESULTS The median age at baseline SJLIFE evaluation was 31.7 years (range 18.4-66.4), and the time between baseline and follow-up evaluations was 5.2 years (0.5-9.5). Two thirds (67.1%) of patients were exposed to chest radiation, and 76.6% to anthracycline chemotherapy. One hundred seventeen (9.6%) patients developed cardiomyopathy during follow-up. In the model based solely on ECG features, the cross-validation area under the curve (AUC) was 0.87 (95% CI, 0.83 to 0.90), whereas the model based on clinical features had an AUC of 0.69 (95% CI, 0.64 to 0.74). In the model based on ECG and clinical features, the cross-validation AUC was 0.89 (95% CI, 0.86 to 0.91), with a sensitivity of 78% and a specificity of 81%. CONCLUSION AI using ECG data may assist in the identification of childhood cancer survivors at increased risk for developing future cardiomyopathy.

Some Remarks on the Density of Interplanetary Dust Grains

1985 ◽  
Vol 85 ◽  
pp. 137-140
Author(s):  
P.L. Lamy
Keyword(s):  
Kinetic Energy ◽  
Bulk Density ◽  
Cross Section ◽  
Optical Sensors ◽  
Interplanetary Dust ◽  
Relevant Parameter ◽  
Dust Grains ◽  
Gravitational Forces ◽  
The Cross

AbstractThe relevance of the bulk density as a physical parameter characterizing interplanetary dust grains is discussed. The various measurements which lead to a determination of this parameter are reviewed. The specific case of the collected interplanetary dust grains is considered.The bulk density of interplanetary dust grains has been and is still a matter of controversy. This quantity cannot, in general, be directly measured; it is used to relate the mass and the size of a grain. This duality stems from physics itself as there are interactions sensitive to the mass (e.g., gravitational forces) while others are sensitive to the size or the cross-section (e.g., light scattering, radiation pressure, gas and plasma interactions). The measuring technics of the grains reflect this duality as, for instance, impact sensors are generally sensitive to the kinetic energy and thus to the mass, while optical sensors are sensitive to the cross-section. One sees that the density is not strictly speaking the relevant parameter, but what is needed is a relationship between mass and average cross-section.

scholarly journals Factors Influencing Matching of Ride-Hailing Service Using Machine Learning Method

2019 ◽  
Vol 11 (20) ◽  
pp. 5615 ◽  
Author(s):  
Myungsik Do ◽  
Wanhee Byun ◽  
Doh Kyoum Shin ◽  
Hyeryun Jin
Keyword(s):  
Machine Learning ◽  
Success Rate ◽  
Cross Validation ◽  
Average Distance ◽  
Machine Learning Method ◽  
Land Uses ◽  
Taxi Drivers ◽  
Factors Influencing ◽  
Taxi Service ◽  
The City

It is common to call a taxi by taxi-apps in Korea and it was believed that an app-taxi service would provide customers with more convenience. However, customers’ requests can often be denied, as taxi drivers can decide whether to take calls from customers or not. Therefore, studies on factors that determine whether taxi drivers refuse or accept calls from customers are needed. This study investigated why taxi drivers might refuse calls from customers and factors that influence the success of matching within the service. This study used origin-destination data in Seoul and Daejeon obtained from T-map Taxis, which was analyzed via a decision tree using machine learning. Cross-validation was also performed. Results showed that distance, socio-economic features, and land uses affected matching success rate. Furthermore, distance was the most important factor in both Seoul and Daejeon. The matching success rate in Seoul was lowest for trips shorter than the average at midnight. In Daejeon, the rate was lowest when the calls were made for trips either shorter or longer than the average distance. This study showed that the matching success for ride-hailing services can be differentiated particularly by the distance of the requested trip depending on the size of the city.

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