The COMPASS system (IBADosimetry) is a quality assurance (QA) tool whichreconstructs 3D doses inside a phantom or a patient CT. The dose is predictedaccording to the RT plan with a correction derived from 2D measurementsof a matrix detector. This correction method is necessary since a directreconstruction of the fluence with a high resolution is not possible becauseof the limited resolution of the matrix used, but it comes with a blurring of thedosewhich creates inaccuracies in the dose reconstruction. This paper describesthe method and verifies its capability to detect errors in the positioning of aMLC with 10 mm leaf width in a phantom geometry. Dose reconstruction wasperformed forMLC position errors of various sizes at various locations for bothrectangular and intensity-modulated radiotherapy (IMRT) fields and comparedto a reference dose. It was found that the accuracy with which an error inMLCposition is detected depends on the location of the error relative to the detectorsin the matrix. The reconstructed dose in an individual rectangular field for leafpositioning errors up to 5 mm was correct within 5% in 50% of the locations.At the remaining locations, the reconstruction of leaf position errors larger than3 mm can show inaccuracies, even though these errors were detectable in thedose reconstruction. Errors larger than 9 mm created inaccuracies up to 17% ina small area close to the penumbra. The QA capability of the system was testedthrough gamma evaluation. Our results indicate that themean gamma providedby the system is slightly increased and that the number of points above gamma 1ensures error detection for QA purposes. Overall, the correction kernel methodused by the COMPASS system is adequate to perform QA of IMRT treatmentplans with a regular MLC, despite local inaccuracies in the dose reconstruction.
In this work, a feasible and low-cost approach is proposed for level measurement in multiphase systems inside tanks used for petroleum-derived oil production. The developed level sensor system consisted of light-emitting diodes (LEDs), light-dependent resistor (LDR), and a low-cost microprocessor. Two different types of oil were tested: AW460 and AW68. Linear regression (LR) was applied for 11 scenarios and showed a direct correlation between the level of oil and the sensor’s output. The measurement with AW460 oil presented a perfect linear behavior, while for AW68, a higher standard deviation was obtained justifying the occurrence of the nonlinearity in several scenarios. In order to overcome the nonlinear effect, two machine learning (ML) techniques were tested: K-nearest neighbors regression (KNNR) and multilayer perceptron (MLP) neural network regression. The highest correlation coefficient ( R2 ) and the lowest root mean squared error (RMSE) were obtained for AW68 with MLP. Therefore, MLP was used for regression (level prediction for water, oil, and emulsion) as well as classification (identify the type of oil in the reservoir) simultaneously. The suggested network exhibited a high accuracy for oil identification (99.801%) and improved linear performance in regression ( R2 = 0.9989 and RMSE = 0.065).
This study explored the dimensionality and measurement invariance of a multidimensional measure for evaluating teachers’ perceptions of the quality of their relationships with principals at the dyadic level. Participants were 630 teachers (85.9% female) from 220 primary and 204 secondary schools across the Netherlands. Teachers completed the 10-item Principal–Teacher Relationship Scale (PTRS) for their principals. Confirmatory factor analyses (CFA) provided evidence for a two-factor model, including a relational Closeness and Conflict dimension. Additionally, multigroup CFA results indicated strong invariance of the PTRS across school type, teacher gender, and teaching experience. Last, secondary school teachers and highly experienced teachers reported lower levels of Closeness and higher levels of Conflict in the relationship with their principal compared to primary school teachers and colleagues with less experience. Accordingly, the PTRS can be considered a valid and reliable measure that adds to the methodological repertoire of educational leadership research by focusing on both positive and negative aspects of dyadic principal–teacher relationships.
