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The 224 nm sample with the minimum values oft,s, and Cr-O1-Cr bond angle exhibits the maximum value of MEC (-ΔS) = 37.8 J kg-1 K-1at 5 K under a field variation (ΔH) of 7 T and its large estimated RCP of 623.6 J Kg-1is comparable with those of typical MC materials. Both (-ΔS) and RCP are shown to scale with the saturation magnetizationMS, suggesting thatMSis the crucial factor controlling their magnitudes. Assuming (-ΔS) ∼ (ΔH)n, the temperature dependence ofnfor the six samples are determined,nvarying between 1.3 at 5 K ton= 2.2 at 130 K in line with its expected magnitudes based on mean-field theory. These results on structure-property correlations and scaling in GdCrO3suggest that its MC properties are tunable for potential low-temperature magnetic refrigeration applications.Strategic electron beam (e-beam) irradiation on the surface of an ultrathin ( less then 100 nm) film of polystyrene-poly(methyl methacrylate) (PS-PMMA) random copolymer followed by solvent annealing stimulates a special variety of dewetting, leading to large-area hierarchical nanoscale patterns. For this purpose, initially, a negative (positive) tone of resist PS (PMMA) under weak e-beam exposure is exploited to produce an array of sites composed of cross-linked PS (chain-scissioned PMMA). Subsequently, annealing with the help of a developer solvent engenders dewetted patterns in the exposed zones where PMMA blocks are confined by the blocks of cross-linked PS. The e-beam dosage was systematically varied from 180μC cm-2to 10 000μC cm-2to explore the tone reversal behavior of PMMA on the dewetted patterns. Remarkably, at relatively higher e-beam dosing, both PMMA and PS blocks act as negative tones in the exposed zone. In contrast, the chain scission of PMMA in the periphery of the exposed regions due to scattered secondary electrons caused confined dewetting upon solvent annealing. Such occurrences eventually lead to pattern miniaturization an order of magnitude greater than with conventional thermal or solvent vapor annealed dewetting. Selleck Ganetespib Selective removal of PMMA blocks of RCP using a suitable solvent provided an additional 50% reduction in the size of the dewetted features.Objective. Development of a brain-computer interface (BCI) requires classification of brain neural activities to different states. Functional near-infrared spectroscopy (fNIRS) can measure the brain activities and has great potential for BCI. In recent years, a large number of classification algorithms have been proposed, in which deep learning methods, especially convolutional neural network (CNN) methods are successful. fNIRS signal has typical time series properties, we combined fNIRS data and kinds of CNN-based time series classification (TSC) methods to classify BCI task.Approach. In this study, participants were recruited for a left and right hand motor imagery experiment and the cerebral neural activities were recorded by fNIRS equipment (FOIRE-3000). TSC methods are used to distinguish the brain activities when imagining the left or right hand. We have tested the overall person, single person and overall person with single-channel classification results, and these methods achieved excellent classification results. We also compared the CNN-based TSC methods with traditional classification methods such as support vector machine.Main results. Experiments showed that the CNN-based methods have significant advantages in classification accuracy the CNN-based methods have achieved remarkable results in the classification of left-handed and right-handed imagination tasks, reaching 98.6% accuracy on overall person, 100% accuracy on single person, and in the single-channel classification an accuracy of 80.1% has been achieved with the best-performing channel.Significance. These results suggest that using the CNN-based TSC methods can significantly improve the BCI performance and also lay the foundation for the miniaturization and portability of training rehabilitation equipment.Purpose.Respiration-induced motion introduces significant positioning uncertainties in radiotherapy treatments for thoracic sites. Accounting for this motion is a non-trivial task commonly addressed with surrogate-based strategies and latency compensating techniques. This study investigates the potential of a new unified probabilistic framework to predict both future target motion in real-time from a surrogate signal and associated uncertainty.Method.A Bayesian approach is developed, based on a Kalman filter theory adapted specifically for surrogate measurements. Breathing motions are collected simultaneously from a lung target, two external surrogates (abdominal and thoracic markers) and an internal surrogate (liver structure) for 9 volunteers during 4 min, in which severe breathing changes occur to assess the robustness of the method. A comparison with an artificial non-linear neural network (NN) is performed, although no confidence interval prediction is provided. A static worst-case scenario and a simple the proposed framework.With the emergence of online MRI radiotherapy treatments, MR-based workflows have increased in importance in the clinical workflow. However proper dose planning still requires CT images to calculate dose attenuation due to bony structures. In this paper, we present a novel deep image synthesis model that generates in an unsupervised manner CT images from diagnostic MRI for radiotherapy planning. The proposed model based on a generative adversarial network (GAN) consists of learning a new invariant representation to generate synthetic CT (sCT) images based on high frequency and appearance patterns. This new representation encodes each convolutional feature map of the convolutional GAN discriminator, leading the training of the proposed model to be particularly robust in terms of image synthesis quality. Our model includes an analysis of common histogram features in the training process, thus reinforcing the generator such that the output sCT image exhibits a histogram matching that of the ground-truth CT. This CT-matched histogram is embedded then in a multi-resolution framework by assessing the evaluation over all layers of the discriminator network, which then allows the model to robustly classify the output synthetic image. Experiments were conducted on head and neck images of 56 cancer patients with a wide range of shape sizes and spatial image resolutions. The obtained results confirm the efficiency of the proposed model compared to other generative models, where the mean absolute error yielded by our model was 26.44(0.62), with a Hounsfield unit error of 45.3(1.87), and an overall Dice coefficient of 0.74(0.05), demonstrating the potential of the synthesis model for radiotherapy planning applications.