The Role Analysis of Government in Intellectual Property Rights Pledge and Financing of Technological Small and Medium-sized Enterprises

Abstract Nowadays China is in the process of intellectual property rights (IPR

High-temperature high-sensitivity AlN-on-SOI Lamb wave resonant strain sensor

A piezoelectric AlN-on-SOI structured MEMS Lamb wave resonator (LWR) is presented for high-temperature strain measurement. The LWR has a composite membrane of a 1 μm thick AlN film and a 30 μm thick device silicon layer. The excited acoustic waves include Rayleigh wave and Lamb waves. A tensile strain sensor has been prepared with one LWR mounted on a uniaxial tensile plate, and its temperature characteristics from 15.4°C to 250°C and tensile strain behaviors from 0 μϵ to 400 μϵ of Rayleigh wave and S4 mode Lamb wave were tested. The temperature test verifies the adaptability of the tensile strain sensor to temperature up to 250°C, and S4 mode Lamb wave and Rayleigh wave represent almost the same temperature characteristics. The strain test demonstrates that S4 mode Lamb wave shows much higher strain sensitivity (-0.48 ppm/μϵ) than Rayleigh wave (0.05 ppm/μϵ) and confirms its advantage of strain sensitivity. Finally, for this one-LWR strain sensor, a method of beat frequency between S4 mode Lamb wave and Rayleigh wave is proposed for temperature compensation and high-sensitivity strain readout

Prickly Ash Seeds improve immunity of Hu sheep by changing the diversity and structure of gut microbiota

Prickly Ash Seeds (PAS), as a traditional Chinese medicinal herb, have pharmacological effects such as anti-asthma, anti-thrombotic, and anti-bacterial, but their impact on gut microbiota is still unclear. This study used a full-length 16 s rRNA gene sequencing technique to determine the effect of adding PAS to the diet on the structure and distribution of gut microbiota in Hu sheep. All lambs were randomly divided into two groups, the CK group was fed with a basal ration, and the LZS group was given a basal diet with 3% of PAS added to the ration. The levels of inflammatory factors (IL-10, IL-1β, and TNF-α) in intestinal tissues were measured by enzyme-linked immunosorbent assay (ELISA) for Hu sheep in the CK and LZS group. The results indicate that PAS can increase the diversity and richness of gut microbiota, and can affect the community composition of gut microbiota. LEfSe analysis revealed that Verrucomicrobiota, Kiritimatiella, WCHB 41, and uncultured_rumen_bacterium were significantly enriched in the LZS group. KEGG pathway analysis found that LZS was significantly higher than the CK group in the Excretory system, Folding, sorting and degradation, and Immune system pathways (p < 0.05). The results of ELISA assay showed that the level of IL-10 was significantly higher in the LZS group than in the CK group (p < 0.05), and the levels of TNF-α and IL-1β were significantly higher in the CK group than in the LZS group (p < 0.05). LEfSe analysis revealed that the dominant flora in the large intestine segment changed from Bacteroidota and Gammaproteobacteria to Akkermansiaceae and Verrucomicrobiae after PAS addition to Hu sheep lambs; the dominant flora in the small intestine segment changed from Lactobacillales and Aeriscardovia to Kiritimatiellae and WCHB1 41. In conclusion, the addition of PAS to sheep diets can increase the number and types of beneficial bacteria in the intestinal tract, improve lamb immunity, and reduce intestinal inflammation. It provides new insights into healthy sheep production

An Approach for Predicting Global Ionospheric TEC Using Machine Learning

Accurate corrections for ionospheric total electron content (TEC) and early warning information are crucial for global navigation satellite system (GNSS) applications under the influence of space weather. In this study, we propose to use a new machine learning model—the Prophet model, to predict the global ionospheric TEC by establishing a short-term ionospheric prediction model. We use 15th-order spherical harmonic coefficients provided by the Center for Orbit Determination in Europe (CODE) as the training data set. Historical spherical harmonic coefficient data from 7 days, 15 days, and 30 days are used as the training set to model and predict 256 spherical harmonic coefficients. We use the predicted coefficients to generate a global ionospheric TEC forecast map based on the spherical harmonic function model and select a year with low solar activity (63.4 < F10.7 < 81.8) and a year with the high solar activity (79.5 < F10.7 < 255.0) to carry out a sliding 2-day forecast experiment. Meanwhile, we verify the model performance by comparing the forecasting results with the CODE forecast product (COPG) and final product (CODG). The results show that we obtain the best predictions by using 15 days of historical data as the training set. Compared with the results of CODE’S 1-Day (C1PG) and CODE’S 2-Day (C2PG). The number of days with RMSE better than COPG on the first and second day of the low-solar-activity year is 151 and 158 days, respectively. This statistic for high-solar-activity year is 183 days and 135 days

Forecast of Ionospheric TEC Maps Using ConvGRU Deep Learning Over China

In this article, we propose a convolutional gated recurrent unit (ConvGRU) deep learning method to forecast ionospheric total electron content (TEC) over China based on the regional ionospheric maps (RIMs) from 2015 to 2018. First, we use Global Navigation Satellite System observations from the Crustal Movement Observation Network of China to generate the RIMs of China (CRIMs). Second, we use the CRIMs of 2015&#x2013;2017 as the training set to predict the ionospheric TEC over China in 2018. Finally, comparative experiments are carried out with ConvLSTM, International Reference Ionosphere (IRI), and Center for Orbit Determination in Europe&#x0027;s (CODE&#x0027;s) 1-day predicted Global Ionospheric Map (C1PG) released by CODE. In addition, we add geomagnetic indices (ap, Kp, and Dst) and solar activity index (F10.7) as the training set to analyze the prediction accuracy of the model (using -A if there are no indices, and -B if there are indices). The results illustrate that the prediction accuracy of ConvLSTM-B and ConvGRU-B models are improved on both geomagnetic storm and quiet days, and the improvement is more obvious on geomagnetic storm days. Furthermore, the root mean square error (RMSE) of the ConvGRU-B model decreases by 28&#x0025;, 22.4&#x0025;, and 5.9&#x0025; compared to that of the ConvGRU-A, IRI-2016, and ConvLSTM-B models during geomagnetic storm days, respectively. For the prediction accuracy of a certain grid point, the RMSE of the ConvGRU-B model decreases by 23&#x0025;, 32.6&#x0025;, and 19.3&#x0025; during geomagnetic quiet days and 24.4&#x0025;, 30.6&#x0025;, and 15.7&#x0025; during geomagnetic storm days compared to that of the ConvGRU-A, IRI-2016, and ConvLSTM-B models, respectively. For the forecast accuracy of TEC in different seasons, the performance of the ConvGRU-B model is also better than that of the ConvLSTM-B model in 2018. These results show that the ConvGRU-B model has competitive performance in RIMs prediction over China during the geomagnetic quiet and storm days

Local Persistent Ionospheric Positive Responses to the Geomagnetic Storm in August 2018 Using BDS-GEO Satellites over Low-Latitude Regions in Eastern Hemisphere

We present the ionospheric disturbance responses over low-latitude regions by using total electron content from Geostationary Earth Orbit (GEO) satellites of the BeiDou Navigation Satellite System (BDS), ionosonde data and Swarm satellite data, during the geomagnetic storm in August 2018. The results show that a prominent total electron content (TEC) enhancement over low-latitude regions is observed during the main phase of the storm. There is a persistent TEC increase lasting for about 1&ndash;2 days and a moderately positive disturbance response during the recovery phase on 27&ndash;28 August, which distinguishes from the general performance of ionospheric TEC in the previous storms. We also find that this phenomenon is a unique local-area disturbance of the ionosphere during the recovery phase of the storm. The enhanced foF2 and hmF2 of the ionospheric F2 layer is observed by SANYA and LEARMONTH ionosonde stations during the recovery phase. The electron density from Swarm satellites shows a strong equatorial ionization anomaly (EIA) crest over the low-latitude area during the main phase of storm, which is simultaneous with the uplift of the ionospheric F2 layer from the SANYA ionosonde. Meanwhile, the thermosphere O/N2 ratio shows a local increase on 27&ndash;28 August over low-latitude regions. From the above results, this study suggests that the uplift of F layer height and the enhanced O/N2 ratio are possibly main factors causing the local-area positive disturbance responses during the recovery phase of the storm in August 2018