Rare Earths and Magnetic Refrigeration

Magnetic refrigeration is a revolutionary, efficient, environmentally friendly cooling technology, which is on the threshold of commercialization. The magnetic rare earth materials are utilized as the magnetic refrigerants in most cooling devices, and for many cooling application the Nd2Fe14B permanent magnets are employed as the source of the magnetic field. The status of the near room temperature magnetic cooling was reviewed.

Decoding brain responses to pixelized images in the primary visual cortex: implications for visual cortical prostheses

Visual cortical prostheses have the potential to restore partial vision. Still limited by the low-resolution visual percepts provided by visual cortical prostheses, implant wearers can currently only ＂see＂ pixelized images, and how to obtain the specific brain responses to different pixelized images in the primary visual cortex（the implant area） is still unknown. We conducted a functional magnetic resonance imaging experiment on normal human participants to investigate the brain activation patterns in response to 18 different pixelized images. There were 100 voxels in the brain activation pattern that were selected from the primary visual cortex, and voxel size was 4 mm × 4 mm × 4 mm. Multi-voxel pattern analysis was used to test if these 18 different brain activation patterns were specific. We chose a Linear Support Vector Machine（LSVM） as the classifier in this study. The results showed that the classification accuracies of different brain activation patterns were significantly above chance level, which suggests that the classifier can successfully distinguish the brain activation patterns. Our results suggest that the specific brain activation patterns to different pixelized images can be obtained in the primary visual cortex using a 4 mm × 4 mm × 4 mm voxel size and a 100-voxel pattern.

Predicting the performance of magnetocaloric systems using machine learning regressors

Since refrigeration,air-conditioning and heat pump systems account to 25–30%of all energy consumed in the world,there is a considerable potential to mitigate the Global Warming by increasing the efficiency of the related appliances.Magnetocaloric systems,i.e.refrigerators and heat pumps,are promising solutions due to their large theoretical Coefficient Of Performance(COP).However,there is still a long way to make such systems marketable.One barrier is the cost of the magnet and magnetocaloric materials,which can be overcome by decreasing the materials quantity,e.g.by optimizing the geometry with efficient dimensioning procedures.In this work,we have developed a machine learning method to predict the three most significant performance values of magnetocaloric heat pumps:temperature span,heating power and COP.We used 4 different regressors:ordinary least squares,ridge,lasso and K-Nearest Neighbors(KNN).By using a dataset generated by numerical calculations,we have arrived at minimum average relative errors of the temperature span,heating power and COP of 23%,29%and 31%,respectively.While the lasso regressor is more appropriate when using small datasets,the ordinary least squares regressor shows the best performance when using more samples.The best order of polynomials range between 3,for the heating power,to 5,for the COP.The worse performance in predicting the three performance values occurs when using the KNN regressor.Furthermore,the application of regressors to the dataset is more adequate to evaluate the temperature span rather than energetic performance values.