Simultaneous enhancement of coercivity and saturation magnetization in high-performance anisotropic NdFeB thick films with a Dy diffusion layer.

Generally, the addition of the Dy element leads to a decrease of the saturation magnetization and the remanent magnetization in NdFeB films due to its antiferromagnetic coupling with Fe. However, in this study, upon increasing the ratio of Dy in the Nd-Dy diffusion layers of NdFeB thick films, the saturation magnetization has an anomalously slight enhancement, while the coercivity and remanent magnetization have a large enhancement. The increase of coercivity is attributed to the decoupling between Nd2Fe14B grains and the enhanced pinning effect. Microstructural analysis revealed a layered structure composed of spherical Nd2Fe14B grains at the location of the Dy diffusion layer, which is attributed to the Dy diffusion layer reacting with the region of Nd element aggregation during the annealing process, facilitating transformation into Nd2Fe14B grains. The increase of the proportion of Nd2Fe14B grains results in a slight enhancement of saturation magnetization. By this method, we obtained a high-performance anisotropic NdFeB thick film of 28.7 µm with a coercivity of 2.46 T and a surface field of 163 Oe. This work establishes a microscale growth model for NdFeB thick films and helps to prepare high-performance NdFeB thick films applicable directly to microelectromechanical systems.

Threshold for Tonic Motor Effects from Random Waveform in a Rat Experimental Model of Frontal Cortex Stimulation.

BACKGROUND: Brain stimulation is utilized to treat a variety of neurological disorders. Clinical brain stimulation technologies currently utilize charge-balanced pulse stimulation. The brain may better respond to other stimulation waveforms. This study was designed to evaluate the motor threshold of the brain to stimulation with various waveforms. METHODS: Three stimulation waveforms were utilized on rats with surgically implanted brain electrodes: pulses, square waves, and random waveform. The peak-to-peak stimulation voltage was increased in a step-wise manner until motor signs were elicited. RESULTS: The random waveform had the highest motor threshold with brain stimulation compared to the other waveforms. Random waveform stimulation reached maximum voltage without motor side effects while stimulating through both 1 and 8 electrodes. In contrast, the stimulation thresholds for motor side effects of the other two waveforms were on average less than half of the maximum voltage and lower for stimulation through 8 electrodes than stimulation through 1 electrode (p < 0.0005). CONCLUSION: The random waveform was better tolerated than the other waveforms and may allow for the use of higher stimulation voltage without side effects.

Anomaly detection based on a dynamic Markov model.

Anomaly detection in sequence data is becoming more and more important in a wide variety of application domains such as credit card fraud detection, health care in medical field, and intrusion detection in cyber security. In the existing anomaly detection approaches, Markov chain techniques are widely accepted for their simple realization and few parameters. However, the short memory property of a classical Markov model ignores the interaction among data, and the long memory property of a higher order Markov model clouds the relationship between the previous data and current test data, and reduces the reliability of the model. Besides, both of these models cannot successfully describe the sequences changing with a tendency. In this paper, we propose an anomaly detection approach based on a dynamic Markov model. This approach segments sequence data by a sliding window. In the sliding window, we define the states of data according to the value of the data and establish a higher order Markov model with a proper order consequently, to balance the length of the memory property and keep up with the trend of sequences. In addition, an anomaly substitution strategy is proposed to prevent the detected anomalies from impacting the building of the models and keep anomaly detection continuously. The experimental results using simulated datasets and real-world datasets have demonstrated that the proposed approach improves the adaptability and stability of anomaly detection in sequence data.