Magnetoelastic standing waves induced in UO2 by microsecond magnetic field pulses

Magnetoelastic dilatometry of the piezomagnetic antiferromagnet UO2 was performed via the fiber Bragg grating method in magnetic fields up to 150 T generated by a single-turn coil setup. We show that in microsecond timescales, pulsed-magnetic fields excite mechanical resonances at temperatures ranging from 10 to 300 K, in the paramagnetic as well as within the robust antiferromagnetic state of the material. These resonances, which are barely attenuated within the 100-µs observation window, are attributed to the strong magnetoelastic coupling in UO2 combined with the high crystalline quality of the single crystal samples. They compare well with mechanical resonances obtained by a resonant ultrasound technique and superimpose on the known nonmonotonic magnetostriction background. A clear phase shift of π in the lattice oscillations is observed in the antiferromagnetic state when the magnetic field overcomes the piezomagnetic switch field Hc=−18 T. We present a theoretical argument that explains this unexpected behavior as a result of the reversal of the antiferromagnetic order parameter at Hc.

No Increase in Short-Term Mortality Following COVID-19 Vaccination Among Nursing Home Residents

Reports of fatal adverse events following mRNA-based vaccination for COVID-19 in Norwegian nursing home (NH) residents have raised concern regarding vaccine safety in very old and frail persons. A limitation of these reports, however, is the absence of contemporaneous control groups, particularly given the high baseline mortality in this population. Using electronic health records’ data on resident deaths, hospital transfer, vaccination, and daily census from Genesis Healthcare, a large NH provider spanning 24 U.S. states, we compared 7-day mortality and hospitalization rates for vaccinated versus unvaccinated NH residents. Between December 18, 2020 and December 31, 2020, 7006 residents across 118 NHs were vaccinated with the first dose. Mortality and hospital transfer rates within 7 days of vaccination were compared to rates for: (1) unvaccinated residents in the same facility within 7 days of the vaccine clinic (n=4414), and (2) residents in 166 yet-to-be-vaccinated facilities between December 25, 2020 and January 1, 2021 (n=17,076). We excluded residents with a positive SARS-CoV-2 diagnostic test within 20 days prior to their 7-day observation window. Mortality rates per 100,000 residents were lower among vaccinated (587, 95%CI: 431, 798) versus unvaccinated residents within the same facilities (984, 95%CI: 705, 1382), and compared to residents in not-yet-vaccinated facilities (912, 95%CI: 770-1080), with overlapping 95% CIs. Hospital transfers were lower among vaccinated residents than in either comparison group, but with overlapping CIs. Our findings suggest that short term mortality rates appear unrelated to vaccination for COVID-19 in NH residents, and should dispel concerns raised by previous reports.

Determination of the source parameters of the 2011 Tohoku-Oki earthquake from three-component pre-P gravity signals recorded by dense arrays in Japan

Dynamic earthquake rupture causes mass redistribution around the fault, and the emitted propagating seismic waves are accompanied by bulk density perturbations. Both processes cause transient gravity changes prior to the arrival of P-waves. Such pre-P gravity signals have been detected in previous studies of several large earthquakes. However, the detections were limited to the vertical component of the signal owing to the high noise level in the horizontal records. In this study, we analyzed dense tiltmeter array data in Japan to search for the horizontal components of the signal from the 2011 Mw 9.1 Tohoku-Oki earthquake. Based on the synthetic waveforms computed for a realistic Earth model, we stacked the horizontal records and identified a signal that evidently exceeded the noise level. We further performed a waveform inversion analysis to estimate the source parameters. The horizontal tiltmeter data, combined with the vertical component of the broadband seismometer array data, yielded a constraint on the dip angle and magnitude of the earthquake in the ranges of 11.5°–15.3° and 8.75°–8.92°, respectively. Our results indicate that the analysis of the three components of the pre-P gravity signal avoids the intrinsic trade-off problem between the dip angle and seismic moment in determining the source mechanism of shallow earthquakes. Pre-P gravity signals open a new observation window for earthquake source studies. Graphical Abstract

Characterization of Individualized Glycemic Excursions during a Standardized Bout of Hypoglycemia-Inducing Exercise and Subsequent Hypoglycemia Treatment—A Pilot Study

The glycemic response to ingested glucose for the treatment of hypoglycemia following exercise in type 1 diabetes patients has never been studied. Therefore, we aimed to characterize glucose dynamics during a standardized bout of hypoglycemia-inducing exercise and the subsequent hypoglycemia treatment with the oral ingestion of glucose. Ten male patients with type 1 diabetes performed a standardized bout of cycling exercise using an electrically braked ergometer at a target heart rate (THR) of 50% of the individual heart rate reserve, determined using the Karvonen equation. Exercise was terminated when hypoglycemia was reached, followed by immediate hypoglycemia treatment with the oral ingestion of 20 g of glucose. Arterialized blood glucose (ABG) levels were monitored at 5 min intervals during exercise and for 60 min during recovery. During exercise, ABG decreased at a mean rate of 0.11 ± 0.03 mmol/L·min−1 (minimum: 0.07, maximum: 0.17 mmol/L·min−1). During recovery, ABG increased at a mean rate of 0.13 ± 0.05 mmol/L·min−1 (minimum: 0.06, maximum: 0.19 mmol/L·min−1). Moreover, 20 g of glucose maintained recovery from hypoglycemia throughout the 60 min postexercise observation window.

Improving the joint estimation of CO₂ and surface carbon fluxes using a Constrained Ensemble Kalman Filter in COLA (v1.0)

Atmospheric inversion of carbon dioxide (CO2) measurements to understand carbon sources and sinks has made great progress over the last two decades. However, most of the studies, including four-dimension variational (4D-Var), Ensemble Kalman filter (EnKF), and Bayesian synthesis approaches, obtains directly only fluxes while CO2 concentration is derived with the forward model as post-analysis. Kang et al. (2012) used the Local Ensemble Transform Kalman Filter (LETKF) that updates the CO2, surface carbon fluxes (SCF), and meteorology field simultaneously. Following this track, a system with a short assimilation window and a long observation window was developed (Liu et al., 2019). However, this system faces the challenge of maintaining global carbon mass. To overcome this shortcoming, here we introduce a Constrained Ensemble Kalman Filter (CEnKF) approach to ensure the conservation of global CO2 mass. After a standard LETKF procedure, an additional assimilation process is applied to adjust CO2 at each model grid point and to ensure the consistency between the analysis and the first guess of global CO2 mass. In the context of observing system simulation experiments (OSSEs), we show that the CEnKF can significantly reduce the annual global SCF bias from ~0.2 gigaton to less than 0.06 gigaton by comparing between experiments with and without it. Moreover, the annual bias over most continental regions is also reduced. At the seasonal scale, the improved system reduced the flux root-mean-square error from priori to analysis by 48–90 %, depending on the continental region. Moreover, the 2015–2016 El Nino impact is well captured with anomalies mainly in the tropics.

Evidence for V363 Cas as a First Overtone Anomalous Cepheid

V363 Cassiopeiae was observed through 51 acquisitions of each of V, B, i and z filters, during a 15 day observation window. From the observations, folded light curves were generated using a PDM technique. It was my objective to provide further evidence for this star’s reclassification as a first overtone Anomalous Cepheid, as some past papers have proposed (Fernley, 1998). Based on our light curve characteristics (shape, and period), V363 Cas appeared to favor the anomalous Cepheid class over any RR Lyrae class. My observed period of 0.545 days is higher than the typical range of periods for RRd Lyrae, reported between 0.25 and 0.49 days (Soszynski et al., 2008). The RRab type Lyrae, as some have imposed on V363 Cas (Kholopov et al., 1985), was ruled out due to the evidence for overtone pulsation by Hajdu et al. (Hajdu, Jurcsik, et al., 2009) and Fernley (Fernley, 1998). Finally, a rough distance comparison to GAIA, using Nemec’s 1994 P-L-[Fe/H] for Anomalous Cepheids (Nemec, Nemec, & Lutz, 1994), estimated the distance of V363 Cas to be closer to the distance estimated by GAIA than estimates made with RRd class equations.

CNNs for enhanced background discrimination in DSNB searches in large-scale water-Gd detectors

Gadolinium-loading of large water Cherenkov detectors is a prime method for the detection of the Diffuse Supernova Neutrino Background (DSNB). While the enhanced neutron tagging capability greatly reduces single-event backgrounds, correlated events mimicking the IBD coincidence signature remain a potentially harmful background. Neutral-Current (NC) interactions of atmospheric neutrinos potentially dominate the DSNB signal especially in the low-energy range of the observation window that reaches from about 12 to 30 MeV. The present paper investigates a novel method for the discrimination of this background. Convolutional Neural Networks (CNNs) offer the possibility for a direct analysis and classification of the PMT hit patterns of the prompt events. Based on the events generated in a simplified SuperKamiokande-like detector setup, we find that a trained CNN can maintain a signal efficiency of 96% while reducing the residual NC background to 2% of the original rate. Comparing to recent predictions of the DSNB signal and measurements of the NC background levels in Super-Kamiokande, the corresponding signal-to-background ratio is about 4:1, providing excellent conditions for a DSNB discovery.

Development of GIS Switch State Judgment System Based on Image Recognition

Aiming at the fast opening and closing speed of the GIS isolation/grounding switch, manual observation is more difficult, so it is difficult to judge the current switch status. This paper proposes an OpenCV-based image identification algorithm to identify the position of the switch movable contact during the opening and closing process of the isolating switch, thereby judging the state of the isolating switch. This system uses Raspberry Pi as the main hardware core, the server drives the CMOS camera through Raspberry Pi 4B, collects image information in the GIS optical observation window, and performs simple processing, and transmits it to the Raspberry Pi 4B based on the UDP protocol as the main core. In the upper computer and adopt the target detection algorithm based on OpenCV to track the current isolation/grounding switch contact position and determine the current opening and closing state.

The impact of recency and adequacy of historical information on sepsis predictions using machine learning

Sepsis is a major public and global health concern. Every hour of delay in detecting sepsis significantly increases the risk of death, highlighting the importance of accurately predicting sepsis in a timely manner. A growing body of literature has examined developing new or improving the existing machine learning (ML) approaches for timely and accurate predictions of sepsis. This study contributes to this literature by providing clear insights regarding the role of the recency and adequacy of historical information in predicting sepsis using ML. To this end, we implemented a deep learning model using a bidirectional long short-term memory (BiLSTM) algorithm and compared it with six other ML algorithms based on numerous combinations of the prediction horizons (to capture information recency) and observation windows (to capture information adequacy) using different measures of predictive performance. Our results indicated that the BiLSTM algorithm outperforms all other ML algorithms and provides a great separability of the predicted risk of sepsis among septic versus non-septic patients. Moreover, decreasing the prediction horizon (in favor of information recency) always boosts the predictive performance; however, the impact of expanding the observation window (in favor of information adequacy) depends on the prediction horizon and the purpose of prediction. More specifically, when the prediction is responsive to the positive label (i.e., Sepsis), increasing historical data improves the predictive performance when the prediction horizon is short-moderate.

A Hierarchical Approach to Activity Recognition and Fall Detection Using Wavelets and Adaptive Pooling

Human activity recognition has been a key study topic in the development of cyber physical systems and assisted living applications. In particular, inertial sensor based systems have become increasingly popular because they do not restrict users’ movement and are also relatively simple to implement compared to other approaches. In this paper, we present a hierarchical classification framework based on wavelets and adaptive pooling for activity recognition and fall detection predicting fall direction and severity. To accomplish this, windowed segments were extracted from each recording of inertial measurements from the SisFall dataset. A combination of wavelet based feature extraction and adaptive pooling was used before a classification framework was applied to determine the output class. Furthermore, tests were performed to determine the best observation window size and the sensor modality to use. Based on the experiments the best window size was found to be 3 s and the best sensor modality was found to be a combination of accelerometer and gyroscope measurements. These were used to perform activity recognition and fall detection with a resulting weighted F1 score of 94.67%. This framework is novel in terms of the approach to the human activity recognition and fall detection problem as it provides a scheme that is computationally less intensive while providing promising results and therefore can contribute to edge deployment of such systems.