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https://www.selleckchem.com/products/muramyl-dipeptide.html The paper presents studies of microstructure, magnetic and corrosion properties of the Gd58Ge20Si22, Gd56Ge20Si22Co2, Gd56Ge20Si22Ti2 and Gd56Ge20Si22Cr2 (at.%) alloys after isothermal heat treatment at 1450 K for 2 h. The structure investigations of the produced materials performed by X-ray diffraction show the presence of Gd5Ge2Si2-type phase in all investigated samples. DC and AC magnetic measurements confirmed that the Curie temperature depends on the chemical composition of the produced alloys. From M(T) characteristics, it was found that the lowest Curie point (TC = 268 K) was estimated for the Gd58Ge20Si22 sample, whereas the highest value of the Curie temperature (TC = 308 K) was for the Gd56Ge20Si22Cr2 alloys. Moreover, the GdGeSi alloy without alloying additions shows the highest magnetic entropy change |ΔSM| = 15.07 J⋅kg-1⋅K-1 for the maximum magnetic field of 2 T. The maximum |ΔSM| measured for the Gd56Ge20Si22 with the addition of Co, Ti or Cr for the same magnetic field was obtained in the vicinity of the Curie point and equals to 2.92, 2.73 and 2.95 J⋅kg-1⋅K-1, respectively. Electrochemical studies of the produced materials for 60 min and 55 days exposure in 3% NaCl solution show that the highest stability and corrosion resistance were exhibited the sample with added of Ti.With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm w

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