MedLens: Improve mortality prediction via medical signs selecting and regression interpolation

Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96% AUC-ROC and 0.81% AUC-PR, which exceeds the previous benchmark

Spatio-temporal prediction for distributed PV generation system based on deep learning neural network model

To obtain higher accuracy of PV prediction to enhance PV power generation technology. This paper proposes a spatio-temporal prediction method based on a deep learning neural network model. Firstly, spatio-temporal correlation analysis is performed for 17 PV sites. Secondly, we compare CNN-LSTM with a single CNN or LSTM model trained on the same dataset. From the evaluation indexes such as loss map, regression map, RMSE, and MAE, the CNN-LSTM model that considers the strong correlation of spatio-temporal correlation among the 17 sites has better performance. The results show that our method has higher prediction accuracy

A new method of lower extremity immobilization in radiotherapy

We developed a new method for immobilization of the fix lower extremities by using a thermoplastic mask, a carbon fiber base plate, a customized headrest, and an adjustable angle holder. The lower extremities of 11 patients with lower extremity tumors were immobilized by this method. CT simulation was performed for each patient. For all 11 patients, the device fit was suitable and comfortable and had good reproducibility, which was proven in daily radiotherapy

Carbon monoxide poisoning deaths in Shanghai, China: A 10-year epidemiological and comparative study with the Wuhan sample

Abstract: Carbon monoxide (CO) poisoning is a common cause of death globally. However, CO poisoning deaths in the Mainland China are rarely studied. Therefore, this study aims to explore the incidence trend of CO poisoning deaths that occurred in Pudong for a 10-year period (2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). Using official police data, a total of 139 CO poisoning events that resulted in the death of 176 victims are collected. By comparing the data from Shanghai with the previous one from Wuhan, this study presents the most up-to date information about CO poisoning deaths that happened in China. The result indicates that the CO poisoning death rate in the study area in China is in the low level around the globe. Features of fire-related CO poisoning deaths are similar between the two mega cities, but in nonfire-related CO poisoning deaths, there are some distinguishing regional features. This study also found that the CO poisoning suicides by burning coal or charcoal is increasing sharply in recent years, especially in considering about the higher rate of burning charcoal suicides in the regions around the Mainland China. Certain precautious should be taken to prevent the growing trend of coal or charcoal burning suicides in future

Physical Parameters of the Multiplanet Systems HD 106315 and GJ 9827

HD 106315 and GJ 9827 are two bright, nearby stars that host multiple super-Earths and sub-Neptunes discovered by K2 that are well suited for atmospheric characterization. We refined the planets' ephemerides through Spitzer transits, enabling accurate transit prediction required for future atmospheric characterization through transmission spectroscopy. Through a multiyear high-cadence observing campaign with Keck/High Resolution Echelle Spectrometer and Magellan/Planet Finder Spectrograph, we improved the planets' mass measurements in anticipation of Hubble Space Telescope transmission spectroscopy. For GJ 9827, we modeled activity-induced radial velocity signals with a Gaussian process informed by the Calcium II H&K lines in order to more accurately model the effect of stellar noise on our data. We measured planet masses of M_b = 4.87 ± 0.37 M_⊕, M_c = 1.92 ± 0.49 M_⊕, and M_d = 3.42 ± 0.62 M_⊕. For HD 106315, we found that such activity radial velocity decorrelation was not effective due to the reduced presence of spots and speculate that this may extend to other hot stars as well (T_(eff) > 6200 K). We measured planet masses of M_b = 10.5 ± 3.1 M_⊕ and M_c = 12.0 ± 3.8 M_⊕. We investigated all of the planets' compositions through comparison of their masses and radii to a range of interior models. GJ 9827 b and GJ 9827 c are both consistent with a 50/50 rock-iron composition, GJ 9827 d and HD 106315 b both require additional volatiles and are consistent with moderate amounts of water or hydrogen/helium, and HD 106315 c is consistent with a ~10% hydrogen/helium envelope surrounding an Earth-like rock and iron core

A second planet transiting LTT 1445A and a determination of the masses of both worlds

K.H. acknowledges support from STFC grant ST/R000824/1.LTT 1445 is a hierarchical triple M-dwarf star system located at a distance of 6.86 pc. The primary star LTT 1445A (0.257 M⊙) is known to host the transiting planet LTT 1445Ab with an orbital period of 5.36 days, making it the second-closest known transiting exoplanet system, and the closest one for which the host is an M dwarf. Using Transiting Exoplanet Survey Satellite data, we present the discovery of a second planet in the LTT 1445 system, with an orbital period of 3.12 days. We combine radial-velocity measurements obtained from the five spectrographs, Echelle Spectrograph for Rocky Exoplanets and Stable Spectroscopic Observations, High Accuracy Radial Velocity Planet Searcher, High-Resolution Echelle Spectrometer, MAROON-X, and Planet Finder Spectrograph to establish that the new world also orbits LTT 1445A. We determine the mass and radius of LTT 1445Ab to be 2.87 ± 0.25 M⊕ and 1.304-0.060+0.067 R⊕, consistent with an Earth-like composition. For the newly discovered LTT 1445Ac, we measure a mass of 1.54-0.19+0.20 M⊕ and a minimum radius of 1.15 R⊕, but we cannot determine the radius directly as the signal-to-noise ratio of our light curve permits both grazing and nongrazing configurations. Using MEarth photometry and ground-based spectroscopy, we establish that star C (0.161 M⊙) is likely the source of the 1.4 day rotation period, and star B (0.215 M⊙) has a likely rotation period of 6.7 days. We estimate a probable rotation period of 85 days for LTT 1445A. Thus, this triple M-dwarf system appears to be in a special evolutionary stage where the most massive M dwarf has spun down, the intermediate mass M dwarf is in the process of spinning down, while the least massive stellar component has not yet begun to spin down.Publisher PDFPeer reviewe

Photovoltaic power prediction based on sky images and tokens-to-token vision transformer

Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this paper, a new deep neural network structure based on vision transformer is proposed to combine sky images and Tokens-To-Token（T2T） for photovoltaic power prediction. The method uses an incremental tokenization module to aggregate neighboring image patches into tokens, which capture the local structural information of the clouds. Then, an efficient T2T-ViT backbone network is used to extract the global attentional relationships of the tokens for power prediction. In order to evaluate the performance of the proposed model, the method was compared with several deep learning architectures such as ResNet and GoogleNet on a dataset collected by the National Renewable Energy Laboratory in Colorado, USA. The results of power prediction were analysed using training loss, prediction error, and linear regression, and they show that the proposed method achieves higher prediction accuracy and lower error compared to the existing methods, especially in short- and ultra-short-term prediction. The paper demonstrates the potential of applying Transformer models to computer vision tasks for renewable energy forecasting. The results show that the proposed method achieves higher prediction accuracy and lower error than several deep learning architectures, such as ResNet and GoogleNet, especially in short- and ultra-short-term prediction

Advancements on Radar Polarization Information Acquisition and Processing

The study on radar polarization information acquisition and processing has currently been one important part of radar techniques. The development of the polarization theory is simply reviewed firstly. Subsequently, some key techniques which include polarization measurement, polarization anti-jamming, polarization recognition, imaging and parameters inversion using radar polarimetry are emphatically analyzed in this paper. The basic theories, the present states and the development trends of these key techniques are presented and some meaningful conclusions are derived

Unmanned Aerial Vehicle Recognition Based on Clustering by Fast Search and Find of Density Peaks (CFSFDP) with Polarimetric Decomposition

Unmanned aerial vehicles (UAV) have become vital targets in civilian and military fields. However, the polarization characteristics are rarely studied. This paper studies the polarization property of UAVs via the fusion of three polarimetric decomposition methods. A novel algorithm is presented to classify and recognize UAVs automatically which includes a clustering method proposed in “Science„, one of the top journals in academia. Firstly, the selection of the imaging algorithm ensures the quality of the radar images. Secondly, local geometrical structures of UAVs can be extracted based on Pauli, Krogager, and Cameron polarimetric decomposition. Finally, the proposed algorithm with clustering by fast search and find of density peaks (CFSFDP) has been demonstrated to be better than the original methods under the various noise conditions with the fusion of three polarimetric decomposition methods