Bipolar-CMOS-DMOS Process-Based a Robust and High-Accuracy Low Drop-Out Regulator

A 40V BCD process high-accuracy and robust Low Drop-Out Regulator was proposed and tape-out in CSMC; the LDO was integrated in a LED Control and Driver SOC of outdoor applications. The proposed LDO converted the 12V~40V input power to 5V for the low voltage circuits inside the SOC. The robustness of LDO was important because the application condition of the SOC was bad. It was simulated in all process corner, -55℃~150℃ temperature and 12V~40V power voltage conditions. Simulation result shows that the LDO works robustly in conditions mentioned above. The default precision of LDO output voltage is ±2.75% max in all conditions, moreover, by utilizing a trim circuit in the feedback network, the precision can be improved to ±0.5% max after being trimmed by 3 bit digital trim signal Trim[3:1]. The total size of the proposed LDO is 135um*450um and the maximum current consumption is 284uA

A YBCO RF-SQUID magnetometer and its applications

An applicable RF-superconducting quantum interference detector (SQUID) magnetometer was made using a bulk sintered yttrium barium copper oxide (YBCO). The temperature range of the magnetometer is 77 to 300 K and the field range 0 to 0.1T. At 77 K, the equivalent flux noise of the SQUID is 5 x 10 to minus 4 power theta sub o/square root of Hz at the frequency range of 20 to 200 Hz. The experiments show that the SQUID noise at low-frequency end is mainly from 1/f noise. A coil test shows that the magnetic moment sensitivity delta m is 10 to the minus 6th power emu. The RF-SQUID is shielded in a YBCO cylinder with a shielding ability B sub in/B sub ex of about 10 to the minus 6th power when external dc magnetic field is about a few Oe. The magnetometer is successfully used in characterizing superconducting thin films

A YBCO RF-squid variable temperature susceptometer and its applications

The Superconducting QUantum Interference Device (SQUID) susceptibility using a high-temperature radio-frequency (rf) SQUID and a normal metal pick-up coil is employed in testing weak magnetization of the sample. The magnetic moment resolution of the device is 1 x 10(exp -6) emu, and that of the susceptibility is 5 x 10(exp -6) emu/cu cm

Truth of Chinese Survivors Concealed and Rediscovered From Titanic to The Six

Conventional film Titanic fooled the Chinese audience with an untold story of Chinese survivors through following narrative features of transitivity, identification, transparency, single diegesis, closure, and pleasure. Based on its skillfully designed narration strategy, the noble image of Anglo-Saxon gentlemen was well constructed. However, documentary film The Six pieced together the fragmented records of various sources and revealed the truth of what happened to those Chinese survivors through six corresponding narrative features by Peter Wollen, namely intransitivity, estrangement, foregrounding, multiple diegesis, aperture, and unpleasure. The unfair treatment Chinese survivors received added misfortunes to their sufferings in a huge tragic disaster in a foreign land. The images and characters of Chinese survivors reconstructed in The Six clear up what has been twisted and optionally forgotten.

Combining Multi-Source Data and Machine Learning Approaches to Predict Winter Wheat Yield in the Conterminous United States

Winter wheat (Triticum aestivum L.) is one of the most important cereal crops, supplying essential food for the world population. Because the United States is a major producer and exporter of wheat to the world market, accurate and timely forecasting of wheat yield in the United States (U.S.) is fundamental to national crop management as well as global food security. Previous studies mainly have focused on developing empirical models using only satellite remote sensing images, while other yield determinants have not yet been adequately explored. In addition, these models are based on traditional statistical regression algorithms, while more advanced machine learning approaches have not been explored. This study used advanced machine learning algorithms to establish within-season yield prediction models for winter wheat using multi-source data to address these issues. Specifically, yield driving factors were extracted from four different data sources, including satellite images, climate data, soil maps, and historical yield records. Subsequently, two linear regression methods, including ordinary least square (OLS) and least absolute shrinkage and selection operator (LASSO), and four well-known machine learning methods, including support vector machine (SVM), random forest (RF), Adaptive Boosting (AdaBoost), and deep neural network (DNN), were applied and compared for estimating the county-level winter wheat yield in the Conterminous United States (CONUS) within the growing season. Our models were trained on data from 2008 to 2016 and evaluated on data from 2017 and 2018, with the results demonstrating that the machine learning approaches performed better than the linear regression models, with the best performance being achieved using the AdaBoost model (R2 = 0.86, RMSE = 0.51 t/ha, MAE = 0.39 t/ha). Additionally, the results showed that combining data from multiple sources outperformed single source satellite data, with the highest accuracy being obtained when the four data sources were all considered in the model development. Finally, the prediction accuracy was also evaluated against timeliness within the growing season, with reliable predictions (R2 > 0.84) being able to be achieved 2.5 months before the harvest when the multi-source data were combined