Abstract:To study the effects of pulse modulated radio frequency electromagnetic field on locomotor activity and neurotransmitter concentration in different brain regions of mice. Wild type C57BL/6J mice were randomly divided into intervention group (n＝9) and control group (n＝6). The intervention group was given pulse modulated RF electromagnetic field stimulation, while the control group was not given stimulation; 30 minutes a day for 5 days. The behavior of mice was recorded by camera, and the concentration of various neurotransmitters in mouse brain was measured by HPLC-MS. After the intervention, the locomotor activity of mice in the intervention group decreased every day compared with that before the intervention (P＜0.05); Intracortical concentrations of γ-aminobutyric acid, acetylcholine and other neurotransmitters, tryptophan and phenylalanine changed significantly (P＜0.05), the level of serotonin exhibited a tendency of decrease, but the concentration of glutamate did not change significantly. There were no significant changes in the locomotor activity, concentration of neurotransmitters, tryptophan and phenylalanine in the brain of control group animals. The 5-day continuous intervention had no negative effect on the anxiety level and autonomous behavior of mice. Pulse modulated RF electromagnetic field could exhibit rapid impact on the locomotor activity of mice, as well as the concentration of various neurotransmitters in different brain regions. The increased level of introcortical γ-aminobutyric acid may be involved in the reduced locomotor activity of mice following such RF electromagnetic field treatment.

Abstract:Photothrombotic ischemia is a common experimental ischemic stroke model. In response to light stimulation, activated photosensitive dyes produce reactive oxygen species, which in turn induces damage to the vascular endothelial cells, causing platelet adhesion, aggregation and thrombosis. Since the conventional photothrombotic ischemia model produces only a tiny ischemic penumbra which can’t properly represent the clinical pathology, a modified proximal middle cerebral artery occlusion model was established in this study. The mouse proximal middle artery was irradiated by laser for 3 minutes to induce thrombosis following injection with the light-sensitive dye Rose Bengal and subsequently evaluated by 2,3,5-triphenyltetrazolium chloride staining, immunofluorescence, and flow cytometry. The results showed that this model produced a stable infarct area of 9% to 15% in the striatal and cortical regions, which is larger than the conventional photothrombotic ischemia. Resident microglia, infiltrating myeloid cells, and lymphocytes in the infarcted tissue were identified by flow cytometry. It is suggested that the modified proximal middle artery occlusion model can be applied to study the pathological and immune mechanisms after ischemic stroke injury.

Abstract:Electrical nerve stimulation is an effective method for certain treatments by affecting the central or peripheral nervous system. The instability of electrical nerve stimulation is a critical problem in clinical practice. It is a widely held view that the mechanism of the instability is that the electrical stimulation disturbs the membrane potential of nerve axons. However, due to the lack of a computable macro model for electrical nerve stimulation, it is difficult to effectively study the specific impact of its membrane potential disturbance on electrical stimulation for a long time. Based on the previously proposed circuit-probability theory, this study qualitatively analyzes the instability of electrical nerve stimulation to effectively research the influence of membrane potential disturbance on electrical stimulation. The results show that the current-instability curve of animal experimental data and qualitative simulation is highly consistent, which further indicates that the circuit-probability theory might explain the membrane potential disturbance caused by electrical stimulation and has instructive significance for the practical application of electrical nerve stimulation.

Abstract:Blood pressure is a physiological indicator of human body. Continuous measurement of arterial blood pressure in each cardiac cycle is an important basis for real time diagnoses. Most of the cuffless continuous blood pressure measurements are performed according to the predictive models based on the pulse wave and electrocardiogram signals. However, they may produce errors due to the limited measurements. In this paper, multiple physical signs, such as impedance cardiogram, are explored to improve the measured accuracy of blood pressure. Experiments were conducted upon 55 volunteers, and results show that the random forest model based on multi-parameter feature fusion outperformed the linear model based on a single feature, with mean absolute errors of 2.56 mmHg and 1.91 mmHg for the prediction of systolic and diastolic blood pressure, respectively. It proves that the proposed cuffless blood pressure prediction model based on the multi-feature fusion could improve the accuracy of blood pressure prediction.

Abstract:Underwater optical imaging has the problem of “not seeing far” and “not seeing clearly” due to the absorption and scattering of the water body. Underwater laser range-gated imaging technology can improve the underwater optical imaging distance and image contrast. The paper presents the research of underwater long-range target intelligent identification system based on underwater laser range-gated imaging technology. Laboratory test results show imaging distances in excess of 7 times the attenuation length. The study combines deep learning algorithms to achieve quasi-real-time detection of targets in power-constrained hardware conditions. The combination of underwater laser range-gated imaging technology and deep learning algorithms is expected to enable underwater optical imaging to “seeing far” and “seeing clearly”, while “seeing fast” and “seeing accurately”.

Abstract:The sentence embeddings using Siamese BERT-Networks pre-trained language model has two shortcomings in its presentation layer for text matching, that is, (1) two queried texts are directly computed after they are represented in vectors by the BERT Encoder, (2) such computation does not consider the needs to refine the granular representation of the two queried texts. As such presented semantics could be deviated and it is also difficult to assess the importance of single words in text matching. This paper proposes an improved text similarity matching model SBMAA based on SBERT pre-trained language model. Firstly, the hidden layer vectors of the two queries passing through the SBERT model are obtained, and then the similarity matrix between the two is calculated. The attention mechanism is used to encode the tokens in the two sentences again to obtain interactive features and pool them. Finally, the fully connected layer is connected for prediction. This method introduces the multi-head attention alignment mechanism, which is a common way of interactive text matching algorithm, and strengthens the correlation degree between similar texts, so that the model can achieve more accurate matching effect. The experimental results on ATEC 2018 NLP data set and CCKS 2018 Webank Customer Question Matching dataset show that compared with the five popular text similarity matching models ESIM, ConSERT, BERT-whitening, SimCSE and Baseline model SBERT, The proposed SBMAA model achieves 84.7% and 90.4% in F1 evaluation index, 18.6% and 8.7% higher than Baseline, respectively. It also shows good effect in accuracy and recall rate, and has certain robustness.

Abstract:Nowadays, triboelectric nanogenerators have shown their potential in energy harvesting research with far-reaching impacts, since they have simple structure and wide applicability. However, several drawbacks have yet to be overcome for further extension of its application and commercialization. One major issue is friction, the origin of energy generation and a major factor in limiting energy conversion efficiency. The friction induces energy loss by heat dissipation and also causes the loss of friction layers, lowering the device’s durability. Meanwhile, the friction also increases the threshold force required to drive the device. A multi-layer stacked device with increased friction area will be difficult to be powered by slight shaking generated by wind or human walking. This study proposes a shaking pulse generator based on the principle of programmable Triboelectric Nanogenerator to solve the above-mentioned issues. Even if the material of the friction layers is the same, such as the PTFE film, hundreds of volts can still be achieved. Unlike the traditional theory, there is no real contact friction which minimizes energy loss, reduces driving energy, and improves energy conversion efficiency.