Precision microwave dielectric and magnetic susceptibility measurements of correlated electronic materials using superconducting cavities

We analyze microwave cavity perturbation methods, and show that the technique is an excellent, precision method to study the dynamic magnetic and dielectric response in the $GHz$ frequency range. Using superconducting cavities, we obtain exceptionally high precision and sensitivity for measurements of relative changes. A dynamic electromagnetic susceptibility $\tilde{\zeta}(T)=\zeta ^{\prime}+i\zeta ^{\prime \prime}$ is introduced, which is obtained from the measured parameters: the shift of cavity resonant frequency $\delta f$ and quality factor $Q$. We focus on the case of a spherical sample placed at the center of a cylindrical cavity resonant in the $TE_{011}$ mode. Depending on the sample characteristics, the magnetic permeability $\tilde{\mu}$, the dielectric permittivity $\tilde{\epsilon}$ and the complex conductivity $\tilde{\sigma}$ can be extracted from $\tilde{\zeta}_{H}$. A full spherical wave analysis of the cavity perturbation is given. This analysis has led to the observation of new phenomena in novel low dimensional materials.Comment: 16 pages, 5 figure

Rubidium resonant squeezed light from a diode-pumped optical-parametric oscillator

We demonstrate a diode-laser-pumped system for generation of quadrature squeezing and polarization squeezing. Due to their excess phase noise, diode lasers are challenging to use in phase-sensitive quantum optics experiments such as quadrature squeezing. The system we present overcomes the phase noise of the diode laser through a combination of active stabilization and appropriate delays in the local oscillator beam. The generated light is resonant to the rubidium D1 transition at 795nm and thus can be readily used for quantum memory experiments.Comment: 6 pages 4 figure

Drought events and their effects on vegetation productivity in China

Many parts of the world have experienced frequent and severe droughts during the last few decades. Most previous studies examined the effects of specific drought events on vegetation productivity. In this study, we characterized the drought events in China from 1982 to 2012 and assessed their effects on vegetation productivity inferred from satellite data. We first assessed the occurrence, spatial extent, frequency, and severity of drought using the Palmer Drought Severity Index (PDSI). We then examined the impacts of droughts on China\u27s terrestrial ecosystems using the Normalized Difference Vegetation Index (NDVI). During the period 1982–2012, China\u27s land area (%) experiencing drought showed an insignificant trend. However, the drought conditions had been more severe over most regions in northern parts of China since the end of the 1990s, indicating that droughts hit these regions more frequently due to the drier climate. The severe droughts substantially reduced annual and seasonal NDVI. The magnitude and direction of the detrended NDVI under drought stress varied with season and vegetation type. The inconsistency between the regional means of PDSI and detrended NDVI could be attributed to different responses of vegetation to drought and the timing, duration, severity, and lag effects of droughts. The negative effects of droughts on vegetation productivity were partly offset by the enhancement of plant growth resulting from factors such as lower cloudiness, warming climate, and human activities (e.g., afforestation, improved agricultural management practices)

Complete genomic analysis of a kingdom-crossing Klebsiella variicola isolate

© 2018 Guo, Zhai, Zhang, Li, Wang, Li, He, Hu, Kang and Gao. Bacterial isolate X39 was isolated from a community-acquired pneumonia patient in Beijing, China. A phylogenetic tree based on rpoB genes and average nucleotide identity data confirmed that isolate X39 belonged to Klebsiella variicola. The genome of K variicola X39 contained one circular chromosome and nine plasmids. Comparative genomic analyses with other K variicola isolates revealed that K variicola X39 contained the most unique genes. Of these unique genes, many were prophages and transposases. Many virulence factors were shared between K variicola X39 and Klebsiella pneumoniae F1. The pathogenicity of K. variicola X39 was compared with that of K. pneumoniae F1 in an abdominal infection model. The results indicated that K. variicola X39 was less virulent than typical clinical K. pneumoniae F1. The genome of K. variicola X39 also contained some genes involved in plant colonization, nitrogen fixation, and defense against oxidative stress. GFP-labeled K. variicola X39 could colonize maize as an endophytic bacterium. We concluded that K. variicola X39 was a kingdom-crossing strain

Performance of multiple neural networks in predicting lower limb joint moments using wearable sensors

Joint moment measurements represent an objective biomechemical parameter in joint health assessment. Inverse dynamics based on 3D motion capture data is the current 'gold standard’ to estimate joint moments. Recently, machine learning combined with data measured by wearable technologies such electromyography (EMG), inertial measurement units (IMU), and electrogoniometers (GON) has been used to enable fast, easy, and low-cost measurements of joint moments. This study investigates the ability of various deep neural networks to predict lower limb joint moments merely from IMU sensors. The performance of five different deep neural networks (InceptionTimePlus, eXplainable convolutional neural network (XCM), XCMplus, Recurrent neural network (RNNplus), and Time Series Transformer (TSTPlus)) were tested to predict hip, knee, ankle, and subtalar moments using acceleration and gyroscope measurements of four IMU sensors at the trunk, thigh, shank, and foot. Multiple locomotion modes were considered including level-ground walking, treadmill walking, stair ascent, stair descent, ramp ascent, and ramp descent. We show that XCM can accurately predict lower limb joint moments using data of only four IMUs with RMSE of 0.046 ± 0.013 Nm/kg compared to 0.064 ± 0.003 Nm/kg on average for the other architectures. We found that hip, knee, and ankle joint moments predictions had a comparable RMSE with an average of 0.069 Nm/kg, while subtalar joint moments had the lowest RMSE of 0.033 Nm/kg. The real-time feedback that can be derived from the proposed method can be highly valuable for sports scientists and physiotherapists to gain insights into biomechanics, technique, and form to develop personalized training and rehabilitation programs

Performance data of US Naval Observatory VLG-11 hydrogen masers since September, 1983

In 1983, two VLC-11 masers were delivered to the U.S. Naval Observatory by the Smithsonian Astrophysical Observatory. Last year the short-term stability of these masers was reported and the effect of this short-term stability on timekeeping performance was examined. Since the date of installation, 13 September 1983, data on the masers' long-term performance have been accumulated. The Allan variance, agma(tau), of the relative frequency between the masers reaches a minimum of about 4 parts in 10 to the 16th power at averaging times 5,000 seconds and rises at longer averaging times due, at least partly, to systematic frequency drift. The systematic frequency drifts, expressed in units of fractional frequency difference per day are discussed