We introduce an extended SEIR infectious disease model with data assimilation for the study of the spread of COVID-19. In this framework, undetected asymptomatic and pre-symptomatic cases are taken ...into account, and the impact of their uncertain proportion is fully investigated. The standard SEIR model does not consider these populations, while their role in the propagation of the disease is acknowledged. An ensemble Kalman filter is implemented to assimilate reliable observations of three compartments in the model. The system tracks the evolution of the effective reproduction number and estimates the unobservable subpopulations. The analysis is carried out for three main prefectures of Japan and for the entire country of Japan. For these four communities, our estimated effective reproduction numbers are more stable than the corresponding ones estimated by a different method (Toyokeizai). We also perform sensitivity tests for different values of some uncertain medical parameters, like the relative infectivity of symptomatic/asymptomatic cases. The regional analysis results suggest the decreasing efficiency of the states of emergency.
To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion ...conspicuity among the reconstruction strength levels.
This prospective study included 59 patients with 373 hepatic lesions who underwent dynamic contrast-enhanced CT of the abdomen. All images were reconstructed using four reconstruction algorithms, including 40% adaptive statistical iterative reconstruction–Veo (ASiR-V) and DLIR at low, medium, and high-strength levels (DLIR-L, DLIR-M, and DLIR-H, respectively). The signal-to-noise ratio (SNR) of the abdominal aorta, portal vein, liver, pancreas, and spleen and the lesion-to-liver contrast-to-noise ratio (CNR) were calculated and compared among the four reconstruction algorithms. The diagnostic acceptability was qualitatively assessed and compared among the four reconstruction algorithms and the conspicuity of hepatic lesions was compared between <5 and ≥5 mm lesions.
The SNR of each anatomical structure (p<0.0001) and CNR (p<0.0001) were significantly higher in DLIR-H than the other reconstruction algorithms. Diagnostic acceptability was significantly better in DLIR-M than the other reconstruction algorithms (p<0.0001). The conspicuity of hepatic lesions was highest when using 40% ASiR-V and tended to lessen as the reconstruction strength level was getting higher in DLIR, especially in <5 mm lesions; however, all hepatic lesions could be detected.
DLIR improved the SNR, CNR, and image quality compared with 40% ASiR-V, while making it possible to decrease lesion conspicuity using higher reconstruction strength.
•The DLIR demonstrated a significant noise reduction and improved image quality.•The DLIR could be used as a surrogate for the IR method.•Higher strength of the DLIR was possible to decrease lesion conspicuity.
In this paper, we describe the development of monolithic pixel detectors using silicon-on-insulator (SOI) technology for a rapid X-ray residual stress measurement system. Conventional two-dimensional ...X-ray detectors are not suitable for rapid X-ray residual stress measurement because of their large pixel size and slow readout. For this reason, we developed highly sensitive SOI monolithic pixel detectors that are made up of smaller pixels and can provide a more rapid X-ray residual stress measurement readout. The detectors are fabricated using a 0.2μm CMOS fully-depleted SOI process (Lapis Semiconductor Co., Ltd). The SOI wafer is made by directly bonding a thick, high-resistivity Si wafer and a low-resistivity Si CMOS wafer. The process does not make use of mechanical bump bonding. We developed an integration-type SOI pixel detector, INTPIX4, for a rapid X-ray residual stress measurement system; it uses a float zone (FZ) or Czochralski (Cz) silicon wafer. Cz SOI detectors have been in use since 2005. After 2011, FZ SOI detectors were successfully fabricated. In this paper, we state recent progresses and test results of the SOI monolithic pixel detector using a FZ silicon and compare them with the results obtained using the Cz detector.
Background
The effectiveness of moisturizers in preventing infant atopic dermatitis (AD) remains unclear. We previously showed that using 2e moisturizer of commercial moisturizer (Shiseido Japan Co., ...Ltd.) at least once a day significantly prevented AD in infants as compared with as‐needed petroleum jelly. This trial aimed to determine the effectiveness of twice‐ or once‐daily application of Fam's Baby moisturizer (Fam's Inc.) in preventing AD compared with once‐daily 2e moisturizer.
Methods
This trial was a single‐centre, three‐parallel‐group, assessor‐blinded, superiority, individually randomized, controlled, phase II trial that was conducted from 25 August 2020 to 28 September 2021. We randomly assigned 60 newborns with at least one parent or sibling who has AD to receive Fam's Baby moisturizer twice daily (Group A) or once daily (Group B), or 2e once daily (Group C) in a 1:1:1 ratio until they were 32 weeks old. The primary outcome was the time of AD onset.
Results
Atopic dermatitis was observed in 11/20 (55%), 5/20 (25%) and 10/20 (50%), infants in Groups A, B and C, respectively. Cumulative incidence values for AD according to the Kaplan–Meier method showed that infants in Group B tended to maintain an intact skin for a longer period than those in Group C (median time, not reached NR vs. 212 days, log‐rank test, p = 0.064). Cox regression analysis showed that the risk of AD tended to be lower in Group B (hazard ratio with group C as control, 0.36; 95% confidential intervals: 0.12–1.06). No serious adverse events occurred in any of the enrolled infants.
Conclusion
Fam's Baby moisturizer may better prevent AD than 2e. Further large‐scale trials should be performed to confirm the efficacy of Fam's Baby moisturizer in preventing AD in infants.
Systematic biases in numerical weather prediction models cause forecast deviation from reality. While model biases also affect data assimilation and degrade the analysis accuracy, observation ...information incorporated through data assimilation can provide information for detecting and alleviating such biases. In this study, the application of machine learning to model bias correction is demonstrated, emphasizing the effectiveness of recurrent neural networks. Idealized experiments are performed using the two‐scale coupled Lorenz‐96 model as the true system and single Lorenz‐96 model as the imperfect forecast model, to compare the effectiveness of bias correction methods based on various architectures of neural networks and simple linear regression. The neural networks generally outperformed linear regression, and recurrent neural networks showed the best ability in finding the systematic bias component from the analysis increment data. Bias correction using the recurrent neural networks also gives the most significant improvement in reducing the error growth rate in extended range forecasts. The results suggest that including past time series of the forecast variables improve model bias correction when limited information of the observation is incorporated through data assimilation.
Plain Language Summary
The application of neural networks to model bias correction through data assimilation is demonstrated using idealized experiments, in which observation is noisy and sparse. Among various neural network architectures, recurrent neural networks showed the best performance in correcting the systematic model bias and improving the extended forecast accuracy. The results suggest that including past time series of the forecast variables improve model bias correction when limited information of the observation is incorporated through data assimilation.
Key Points
Application of neural networks to model bias correction through data assimilation is tested using Lorenz‐96 system experiments
Recurrent neural networks worked better in correcting systematic model errors among other network architectures
Including past time series data to the input variables can be advantageous when observation is partial and noisy
We have developed an advanced data assimilation system for a global aerosol model with a four-dimensional ensemble Kalman filter in which the Level 1B data from the Cloud-Aerosol Lidar and Infrared ...Pathfinder Satellite Observations (CALIPSO) were successfully assimilated for the first time, to the best of the authors' knowledge. A one-month data assimilation cycle experiment for dust, sulfate, and sea-salt aerosols was performed in May 2007. The results were validated via two independent observations: 1) the ground-based lidar network in East Asia, managed by the National Institute for Environmental Studies of Japan, and 2) weather reports of aeolian dust events in Japan. Detailed four-dimensional structures of aerosol outflows from source regions over oceans and continents for various particle types and sizes were well reproduced. The intensity of dust emission at each grid point was also corrected by this data assimilation system. These results are valuable for the comprehensive analysis of aerosol behavior as well as aerosol forecasting.
Abstract
Materials with strongly correlated electrons often exhibit interesting physical properties. An example of these materials is the layered oxide perovskite Sr
2
RuO
4
, which has been ...intensively investigated due to its unusual properties. Whilst the debate on the symmetry of the superconducting state in Sr
2
RuO
4
is still ongoing, a deeper understanding of the Sr
2
RuO
4
normal state appears crucial as this is the background in which electron pairing occurs. Here, by using low-energy muon spin spectroscopy we discover the existence of surface magnetism in Sr
2
RuO
4
in its normal state. We detect static weak dipolar fields yet manifesting at an onset temperature higher than 50 K. We ascribe this unconventional magnetism to orbital loop currents forming at the reconstructed Sr
2
RuO
4
surface. Our observations set a reference for the discovery of the same magnetic phase in other materials and unveil an electronic ordering mechanism that can influence electron pairing with broken time reversal symmetry.
Convective precipitation systems in the summer often cause sudden heavy precipitation and largely affect various human activities, but the rapid evolution limits our predicting capability. ...Phased‐array weather radars (PAWRs) with a high spatiotemporal resolution are useful for observing such precipitation system. A recently developed numerical weather prediction (NWP) system assimilates PAWR observations with a 500‐m mesh NWP model. It initiates 30‐min extended forecasts every 30 s, much more frequently than the operational NWP and nowcasting systems. This study investigates the benefits of the 30‐s‐updating NWP system in a single but representative convective precipitation event in which a convective cloud developed within 10 min, and its evolution was not well predicted by operational precipitation nowcasting. The rapidly updating NWP system successfully predicts the evolution of the convective cloud. Assimilating the PAWR observations every 30 s continuously modifies the moisture and dynamical fields and improves the forecast accuracy consistently.
Plain Language Summary
Convective precipitation systems in the summer have a large impact on human activities, but predicting their rapid evolution is a challenge. Next‐generation weather radars with a high spatiotemporal resolution observe the rapid development of such systems in detail and have recently been used for numerical weather prediction (NWP) using a physics‐based numerical model. A new NWP system aims to take full advantage of frequent observations from next‐generation weather radar and updates 30‐min forecasts every 30 s. This study investigates the performance of the new frequently updating NWP system in a typical convective precipitation event in which a convective cloud developed within 10 min. The new NWP system successfully predicts the convective cloud's rapid development, although a short‐range operational precipitation nowcast fails to predict it. The use of frequent observations from the new weather radar continuously improves the forecast accuracy and enables us to know the evolution of the convective cloud at an earlier stage. These results indicate the benefits of a frequently updating NWP system with a next‐generation weather radar for the prediction of convective precipitation systems.
Key Points
A numerical weather prediction (NWP) system updated every 30 s with a phased‐array weather radar is evaluated in a representative event
The rapid development of a convective cloud is well predicted by the NWP system, whereas operational nowcast has limited capability
The forecast accuracy improves continuously every 30 s by data assimilation, indicating the benefits of the rapidly updating NWP
We investigated the operating conditions of a baffled membrane bioreactor (B-MBR) under which long-term stable operation can be achieved through the continuous operation of a pilot-scale B-MBR. Under ...appropriate operating conditions, the B-MBR was capable of achieving excellent treated water quality in terms of biochemical oxygen demand and concentration of total nitrogen. Excellent removal of total phosphorus was also achieved. In addition, the degree of membrane fouling was acceptable, indicating that stable continuous operation of a B-MBR is possible under the operating conditions adopted in the present study. Estimation of the specific energy consumption in hypothetical full-scale B-MBRs operated under the conditions recommended by the findings was also performed in this study. The results suggest that energy consumption in full-scale B-MBRs would be in the range of 0.20-0.22 kWh/m
. These results strongly suggest that energy consumption in MBR operation can be significantly reduced by applying the concept of a B-MBR.
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