The issues of surface stress-induced deflection of a microcantilever with various widths and overall microcantilever sensitivity enhancement of microcantilever-based biosensors are investigated in this paper. A remarkably precise and simple analytical formula for calculating surface stress-induced deflection of a microcantilever with various widths is deduced. Particularly, the effect of surface stress on the location of the microcantilever's neutral axis is considered. This explicit analytical formula is validated by the finite element method simulation. An analytical equation for computing the fundamental resonant frequency of a microcantilever with various widths is also derived. This paper explores the deflections and resonant frequencies of the microcantilevers having basic and modified shapes. It is found that minimizing the effective mass near the microcantilever's free end and the clamping width at the fixed end significantly enhances the overall microcantilever sensitivity. A novel microcantilever, which is expected to have much more excellent performance and overall sensitivity than the simple rectangular-shaped microcantilever, is proposed as sensor element in biological detection.
Persistent and stable drug memories lead to a high rate of relapse among addicts. A number of studies have found that intervention in addiction-related memories can effectively prevent relapse. Deep brain stimulation (DBS) exhibits distinct therapeutic effects and advantages in the treatment of neurological and psychiatric disorders. In addition, recent studies have also found that the substantia nigra pars reticulata (SNr) could serve as a promising target in the treatment of addiction. Therefore, the present study aimed to investigate the effect of DBS of the SNr on the reinstatement of drug-seeking behaviors. Electrodes were bilaterally implanted into the SNr of rats before training of methamphetamine-induced conditioned place preference (CPP). High-frequency (HF) or low-frequency (LF) DBS was then applied to the SNr during the drug-free extinction sessions. We found that HF DBS, during the extinction sessions, facilitated extinction of methamphetamine-induced CPP and prevented drug-primed reinstatement, while LF DBS impaired the extinction. Both HF and LF DBS did not affect locomotor activity or induce anxiety-like behaviors of rats. Finally, HF DBS had no effect on the formation of methamphetamine-induced CPP. In conclusion, our results suggest that HF DBS of the SNr could promote extinction and prevent reinstatement of methamphetamine-induced CPP, and the SNr may serve as a potential therapeutic target in the treatment of drug addiction.
There are inevitable multiphase flow problems in the process of subsea oil-gas acquisition and transportation, of which the two-phase flow involving gas and liquid is given much attention. The performance of pipelines and equipment in subsea systems is greatly affected by various flow patterns. As a result, correctly and efficiently identifying the flow pattern in a pipeline is critical for the oil and gas industry. In this study, two attention modules, the convolutional block attention module (CBAM) and efficient channel attention (ECA), are introduced into a convolutional neural network (ResNet50) to develop a gas–liquid two-phase flow pattern identification model, which is named CBAM-ECA-ResNet50. To verify the accuracy and efficiency of the proposed model, a collection of gas–liquid two-phase flow pattern images in a vertical pipeline is selected as the dataset, and data augmentation is employed on the training set data to enhance the generalization capability and comprehensive performance of the model. Then, comparison models similar to the proposed model are obtained by adjusting the order and number of the two attention modules in the two positions and by inserting other different attention modules. Afterward, ResNet50 and all proposed models are applied to classify and identify gas–liquid two-phase flow pattern images. As a result, the identification accuracy of the proposed CBAM-ECA-ResNet50 is observed to be the highest (99.62%). In addition, the robustness and complexity of the proposed CBAM-ECA-ResNet50 are satisfactory.
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