Experimental validation with field circuit by changing start voltage of salient pole synchronous motor

This paper reports on the experimental validation characteristics of a salient pole synchronous motor with the starting field circuit in contact. The starting characteristics were experimentally evaluated at voltages of 100%, 75%, and 50% using a 1.5 kVA salient pole synchronous motor. The signal for pulling into step was applied to the field circuit by using the control module for all test conditions. The results confirmed the proper pulling into step at 100% of voltage, and the results of the tests at 50% and 75% of voltage revealed that the pulling into step under these conditions becomes stable at the suitable slip condition.     

Real-time 3D microtubule gliding simulation accelerated by GPU computing

A microtubule gliding assay is a biological experiment observing the dynamics of microtubules driven by motor proteins fixed on a glass surface. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics on computers, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation parameters in a real-time fashion. One of the technical challenges when creating a real-time 3D simulation is balancing the 3D rendering and the computing performance. Graphics processor unit (GPU) programming plays an essential role in balancing the millions of tasks, and makes this real-time 3D simulation possible. By the use of general-purpose computing on graphics processing units (GPGPU) programming we are able to run the simulation in a massively parallel fashion, even when dealing with more complex interactions between microtubules such as overriding and snuggling. Due to performance being an important factor, a performance model has also been constructed from the analysis of the microtubule simulation and it is consistent with the performance measurements on different GPGPU architectures with regards to the number of cores and clock cycles.     

Enhanced crystallization of poly(lactide) stereocomplex by xylan propionate

The effect of xylan propionate (XylPr) as a novel biomass‐derived nucleating agent on the poly(lactide) sterecomplex was investigated. Addition of XylPr to an enantiomeric blend of poly(l ‐lactide) (PLLA) and poly(d ‐lactide) (PDLA) was performed in either the solution state or molten state. The solution blend of PLLA/PDLA with XylPr was prepared by mixing equal volumes of 1 wt% XylPr/PLLA and 1 wt% XylPr/PDLA solutions in chloroform and precipitating in methanol. The solution blend with XylPr showed shorter half‐time crystallization than the solution blend without XylPr in isothermal crystallization between 80 and 140 °C, although homocrystallization occurred. Enhanced stereocomplex crystallization in the solution blend with XylPr was observed at 180 °C, where no crystallization occurred in the solution blend without XylPr. Addition of XylPr to PLLA/PDLA blend in the molten state was performed at 240 °C. Thereafter, the melt blend of PLLA/PDLA with or without XylPr was either quenched in iced water or isothermally crystallized directly from the melt. Isothermal crystallization of the melt‐quenched blend with XylPr gave a similar result to the solution blend with XylPr. In contrast, the melt‐crystallized blend with XylPr formed only stereocomplex crystals after crystallization above 140 °C. Furthermore, the melt‐crystallized blend with XylPr showed a higher crystallinity index and melting temperature than the melt‐crystallized blend without XylPr. This shows that XylPr promotes stereocomplex crystallization only when the blend of PLLA/PDLA with XylPr is directly crystallized from the molten state just after blending. © 2016 Society of Chemical Industry     

Evaluating the performance of machine learning methods and variable selection methods for predicting difficult-to-measure traits in Holstein dairy cattle using milk infrared spectral data

Fourier-transform infrared (FTIR) spectroscopy is a powerful high-throughput phenotyping tool for predicting traits that are expensive and difficult to measure in dairy cattle. Calibration equations are often developed using standard methods, such as partial least squares (PLS) regression. Methods that employ penalization, rank-reduction, and variable selection, as well as being able to model the nonlinear relations between phenotype and FTIR, might offer improvements in predictive ability and model robustness. This study aimed to compare the predictive ability of 2 machine learning methods, namely random forest (RF) and gradient boosting machine (GBM), and penalized regression against PLS regression for predicting 3 phenotypes differing in terms of biological meaning and relationships with milk composition (i.e., phenotypes measurable directly and not directly in milk, reflecting different biological processes which can be captured using milk spectra) in Holstein-Friesian cattle under 2 cross-validation scenarios. The data set comprised phenotypic information from 471 Holstein-Friesian cows, and 3 target phenotypes were evaluated: (1) body condition score (BCS), (2) blood β-hydroxybutyrate (BHB, mmol/L), and (3) κ-casein expressed as a percentage of nitrogen (κ-CN, % N). The data set was split considering 2 cross-validation scenarios: samples-out random in which the population was randomly split into 10-folds (8-folds for training and 1-fold for validation and testing); and herd/date-out in which the population was randomly assigned to training (70% herd), validation (10%), and testing (20% herd) based on the herd and date in which the samples were collected. The random grid search was performed using the training subset for the hyperparameter optimization and the validation set was used for the generalization of prediction error. The trained model was then used to assess the final prediction in the testing subset. The grid search for penalized regression evidenced that the elastic net (EN) was the best regularization with increase in predictive ability of 5%. The performance of PLS (standard model) was compared against 2 machine learning techniques and penalized regression using 2 cross-validation scenarios. Machine learning methods showed a greater predictive ability for BCS (0.63 for GBM and 0.61 for RF), BHB (0.80 for GBM and 0.79 for RF), and κ-CN (0.81 for GBM and 0.80 for RF) in samples-out cross-validation. Considering a herd/date-out cross-validation these values were 0.58 (GBM and RF) for BCS, 0.73 (GBM and RF) for BHB, and 0.77 (GBM and RF) for κ-CN. The GBM model tended to outperform other methods in predictive ability around 4%, 1%, and 7% for EN, RF, and PLS, respectively. The prediction accuracies of the GBM and RF models were similar, and differed statistically from the PLS model in samples-out random cross-validation. Although, machine learning techniques outperformed PLS in herd/date-out cross-validation, no significant differences were observed in terms of predictive ability due to the large standard deviation observed for predictions. Overall, GBM achieved the highest accuracy of FTIR-based prediction of the different phenotypic traits across the cross-validation scenarios. These results indicate that GBM is a promising method for obtaining more accurate FTIR-based predictions for different phenotypes in dairy cattle.     

Possibility of Japanese Cedar Pollen Causing False Positives in the Deep Mycosis Test

Because Japanese cedar pollen (JCP) contains beta-1,3-d-glucan (BG), there is concern that its lingering presence in the atmosphere, especially during its scattering period, may cause false positives in the factor-G-based Limulus amebocyte lysate (LAL) assay used to test for deep mycosis (i.e., G-test). Hence, we examined whether the LAL assay would react positively with substances contained in JCP by using the G-test to measure JCP particles and extracts. BG was purified from the JCP extract on a BG-specific affinity column, and the percentage extractability was measured using three different BG-specific quantitative methods. The G-test detected 0.4 pg BG in a single JCP particle and 10 fg from a single particle in the extract. The percentage extractability of JCP-derived BG was not significantly different among the three quantitative methods. As the JCP particles should technically have been removed during serum separation, they should be less likely to be a direct false-positive factor. However, given that the LAL-assay-positive substances in the JCP extract were not distinguishable by the three BG-specific quantitative methods, we conclude that they may cause the background to rise. Therefore, in Japan false positives arising from JCP contamination should be considered when testing patients for deep mycosis.     

Application results of a prototype ultrasonic liquid film sensor to a 7 MPa steam–water two-phase flow experiment

A prototype ultrasonic liquid film sensor was applied to a high-temperature steam–water two-phase flow experiment. The liquid film sensor was vertically installed in a loop which was connected to HUSTLE, a multi-purpose steam source test facility. The hydraulic diameter of the measurement section was 9.4 mm. The output waveforms of the sensor were acquired with a digital oscilloscope. The fluid temperature and system pressure were kept at 288 °C and 7.2 MPa, respectively, during the experiment. The pulse-echo method was used to calculate the liquid film thickness. The cross-correlation calculation was utilized to determine the time difference between the pulse reflected at the sensor surface and the pulse reflected at the liquid film surface. The time-averaged liquid film thicknesses were less than 0.055 mm in the annular flow condition. The increase of the time-averaged thickness was small with the change of the gas momentum flux. The film thicknesses measured with the sensor were compared with the past experimental results; the former were smaller than one-fourth of the thickness estimated as the mean film thickness. The comparison results suggested that the continuous liquid sublayer thickness was measured with the liquid film sensor.