Band 40/41 Surface Acoustic Wave Filters on 42°YX-Lithium Tantalate Substrate with Suppression of Transverse Leakage.

The transverse leakage of leaky surface acoustic waves (LSAWs) occurs on 42°YX-lithium tantalate substrates (42LT), which increases the insertion loss, narrows the bandwidth and flattens the roll-off of band 40/41 SAW filters and duplexers. In this work, LSAW characteristics with different metal materials and thicknesses are calculated by the finite element method (FEM), which determines the IDT material and thickness used for band 40/41 SAW filter design. To deeply understand transverse leakage and suppress it, the effects of different gap and dummy lengths on transverse leakage are simulated and discussed. Then, a new technique of using a wider dummy without any additional lithographic or depositing processes is proposed to suppress the leakage. Its effectiveness is validated by both simulations and experiments. Then, the technique is extended to applications of band 40 and 41 SAW filters. The experimental results show that with the wider dummy structure, the band 40 and 41 SAW filters achieve a more than 0.2 dB improvement in the insertion loss, a wider bandwidth and a steeper roll-off characteristic. This technique may also be extended to the other band SAW filter applications.

The role of amygdala-ventral pallidum pathway in depression-like behaviors in male mice.

The basolateral amygdala (BLA) appears to serve an important function in the pathophysiology of depression. Depressive symptoms, such as anhedonia are largely caused by dysfunction in the brain's reward system, in which the ventral pallidum (VP) participates in by controlling dopamine release. However, the role of the BLA-VP pathway in the development of depression remains poorly understood. To investigate this pathway, we employed the Chronic Unpredictable Mild Stress (CUMS) mouse model, in which we injected retroAAV expressing GFP-Cre into the VP and AAV expressing hM4Di-mCherry into the BLA. We then used CNO to activate the Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) for all behavioral tests. The CUMS procedure resulted in significant depression symptoms such as decreased sucrose preference, limited weight gain, decreased immobile latency, and increased immobile time in the forced swim and tail suspension tests. Inhibition of the BLA-VP glutamatergic projections reversed these depression-like behaviors. We found that suppressing the BLA-VP circuitry had beneficial effects on CUMS-induced depression-like behaviors such as anorexia, anhedonia, and despair. Specifically, upon suppression of glutamatergic projections in the BLA-VP circuitry, these depression-like behaviors were significantly alleviated, which highlights the vital role of this circuitry in the development of depression. Furthermore, the beneficial effects of suppressing this circuitry seem to be associated with the brain's reward system, warranting further investigation.

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat.

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.

5G Traffic Prediction Based on Deep Learning.

The demand of wireless access users is increasing explosively. The 5G network traffic is increasing exponentially and showing a trend of diversity and heterogeneity, which makes network traffic forecasting face many challenges. By studying the actual performance of the 5G network, this paper makes an accurate prediction of the 5G network and builds a smoothed long short-term memory (SLSTM) traffic prediction model. The model updates the number of layers and hidden units according to the prediction accuracy adaptive mechanism. At the same time, in order to reduce the randomness of the 5G traffic sequence, the output feature sequence of the original time series is stabilized by the seasonal time difference method. In the experiments, the prediction results of the proposed algorithm are compared with those of the traditional algorithms. The results show that the SLSTM algorithm can effectively improve the accuracy of 5G traffic prediction. The model can be used for 5G traffic prediction for decision-making.