Precise Reconstruction Method for Hidden Targets Based on Non-line-of-sight Radar 3D Imaging

Non-Line-Of-Sight (NLOS) 3D imaging radar is an emerging technology that utilizes multipath scattering echoes to detect hidden targets. However, this technology faces challenges such as the separation of multipath echoes, reduction of aperture occlusion, and phase errors of reflective surfaces, which hinder the high-precision imaging of hidden targets when using traditional Line-Of-Sight (LOS) radar imaging methods. To address these challenges, this paper proposes a precise imaging method for NLOS hidden targets based on Sparse Iterative Reconstruction (NSIR). In this method, we first establish a multipath signal model for NLOS millimeter-wave 3D imaging radar. By exploiting the characteristics of LOS/NLOS echoes, we extract the multipath echoes from hidden targets using a model-driven approach to realize the separation of LOS/NLOS echo signals. Second, we formulate a total variation multiconstraint optimization problem for reconstructing hidden targets, integrating multipath reflective surface phase errors. Using the split Bregman Total Variation (TV) regularization operator and the phase error estimation criterion based on the minimum mean square error, we jointly solve the multiconstraint optimization problem. This approach facilitates precise imaging and contour reconstruction of NLOS targets. Finally, we construct a planar scanning 3D imaging radar experimental platform and conduct experimental verification of targets such as knives and iron racks in a corner NLOS scenario. Results validate the capability of NLOS millimeter-wave 3D imaging radar in detecting hidden targets and the effectiveness of the method proposed in this paper

Facile Synthesis and Optical Properties of Small Selenium Nanocrystals and Nanorods

Abstract Selenium is an important element for human’s health, small size is very helpful for Se nanoparticles to be absorbed by human's body. Here, we present a facile approach to fabrication of small selenium nanoparticles (Nano-Se) as well as nanorods by dissolving sodium selenite (Na2SeO3) in glycerin and using glucose as the reduction agent. The as-prepared selenium nanoparticles have been characterized by X-ray diffraction (XRD), UV-Vis absorption spectroscopy and high resolution transmission electron microscope (HRTEM). The morphology of small Se nanoparticles and nanorods have been demonstrated in the TEM images. A small amount of 3-mercaptoproprionic acid (MPA) and glycerin play a key role on controlling the particle size and stabilize the dispersion of Nano-Se in the glycerin solution. In this way, we obtained very small and uniform Se nanoparticles; whose size ranges from 2 to 6 nm. This dimension is much smaller than the best value (>20 nm) ever reported in the literatures. Strong quantum confinement effect has been observed upon the size-dependent optical spectrum of these Se nanoparticles

Dimensionality Reduction in Complex Medical Data: Improved Self-Adaptive Niche Genetic Algorithm

With the development of medical technology, more and more parameters are produced to describe the human physiological condition, forming high-dimensional clinical datasets. In clinical analysis, data are commonly utilized to establish mathematical models and carry out classification. High-dimensional clinical data will increase the complexity of classification, which is often utilized in the models, and thus reduce efficiency. The Niche Genetic Algorithm (NGA) is an excellent algorithm for dimensionality reduction. However, in the conventional NGA, the niche distance parameter is set in advance, which prevents it from adjusting to the environment. In this paper, an Improved Niche Genetic Algorithm (INGA) is introduced. It employs a self-adaptive niche-culling operation in the construction of the niche environment to improve the population diversity and prevent local optimal solutions. The INGA was verified in a stratification model for sepsis patients. The results show that, by applying INGA, the feature dimensionality of datasets was reduced from 77 to 10 and that the model achieved an accuracy of 92% in predicting 28-day death in sepsis patients, which is significantly higher than other methods

Preprocessing Unevenly Sampled RR Interval Signals to Enhance Estimation of Heart Rate Deceleration and Acceleration Capacities in Discriminating Chronic Heart Failure Patients from Healthy Controls

Objective. The deceleration capacity (DC) and acceleration capacity (AC) of heart rate, which are recently proposed variants to the heart rate variability, are calculated from unevenly sampled RR interval signals using phase-rectified signal averaging. Although uneven sampling of these signals compromises heart rate variability analyses, its effect on DC and AC analyses remains to be addressed. Approach. We assess preprocessing (i.e., interpolation and resampling) of RR interval signals on the diagnostic effect of DC and AC from simulation and clinical data. The simulation analysis synthesizes unevenly sampled RR interval signals with known frequency components to evaluate the preprocessing performance for frequency extraction. The clinical analysis compares the conventional DC and AC calculation with the calculation using preprocessed RR interval signals on 24-hour data acquired from normal subjects and chronic heart failure patients. Main Results. The assessment of frequency components in the RR intervals using wavelet analysis becomes more robust with preprocessing. Moreover, preprocessing improves the diagnostic ability based on DC and AC for chronic heart failure patients, with area under the receiver operating characteristic curve increasing from 0.920 to 0.942 for DC and from 0.818 to 0.923 for AC. Significance. Both the simulation and clinical analyses demonstrate that interpolation and resampling of unevenly sampled RR interval signals improve the performance of DC and AC, enabling the discrimination of CHF patients from healthy controls

Chinese critical care certified course in intensive care unit: a nationwide-based analysis

Abstract Background A training program for intensive care unit (ICU) physicians entitled “Chinese Critical Care Certified Course” (5 C) started in China in 2009, intending to improve the quality of intensive care provision. This study aimed to explore the associations between the 5 C certification of physicians and the quality of intensive care provision in China. Methods This nationwide analysis collected data regarding 5 C-certified physicians between 2009 and 2019. Fifteen ICU quality control indicators (three structural, four procedural, and eight outcome-based) were collected from the Chinese National Report on the Services, Quality, and Safety in Medical Care System. Provinces were stratified into three groups based on the cumulative number of 5 C certified physicians per million population. Results A total of 20,985 (80.41%) physicians from 3,425 public hospitals in 30 Chinese provinces were 5 C certified. The deep vein thrombosis (DVT) prophylaxis rate in the high 5 C physician-number provinces was significantly higher than in the intermediate 5 C physician-number provinces (67.6% vs. 55.1%, p = 0.043), while ventilator-associated pneumonia (VAP) rate in the low 5 C physician-number provinces was significantly higher than in the high 5 C physician-number provinces (14.9% vs. 8.9%, p = 0.031). Conclusions The higher number of 5 C-certified physicians per million population seemed to be associated with higher DVT prophylaxis rates and lower VAP rates in China, suggesting that the 5 C program might have a beneficial impact on the quality of intensive care provision

Fast Parameters Estimation in Medication Efficacy Assessment Model for Heart Failure Treatment

Introduction. Heart failure (HF) is a common and potentially fatal condition. Cardiovascular research has focused on medical therapy for HF. Theoretical modelling could enable simulation and evaluation of the effectiveness of medications. Furthermore, the models could also help predict patients’ cardiac response to the treatment which will be valuable for clinical decision-making. Methods. This study presents a fast parameters estimation algorithm for constructing a cardiovascular model for medicine evaluation. The outcome of HF treatment is assessed by hemodynamic parameters and a comprehensive index furnished by the model. Angiotensin-converting enzyme inhibitors (ACEIs) were used as a model drug in this study. Results. Our simulation results showed different treatment responses to enalapril and lisinopril, which are both ACEI drugs. A dose-effect was also observed in the model simulation. Conclusions. Our results agreed well with the findings from clinical trials and previous literature, suggesting the validity of the model

Epidemiology of recurrent pulmonary tuberculosis by bacteriological features of 100 million residents in China

Abstract Background Recurrence continues to place significant burden on patients and tuberculosis programmes worldwide, and previous studies have rarely provided analysis in negative recurrence cases. We characterized the epidemiological features of recurrent pulmonary tuberculosis (PTB) patients, estimated its probability associated with different bacteriology results and risk factors. Methods Using 2005–2018 provincial surveillance data from Henan, China, where the permanent population approximately were 100 million, we described the epidemiological and bacteriological features of recurrent PTB. The Kaplan–Meier method and Cox proportional hazard models, respectively, were used to estimate probability of recurrent PTB and risk factors. Results A total of 7143 (1.5%) PTB patients had recurrence, and of 21.1% were bacteriological positive on both laboratory tests (positive–positive), and of 34.9% were negative–negative. Compared with bacteriological negative recurrent PTB at first episodes, the bacteriological positive cases were more male (81.70% vs 72.79%; P < 0.001), higher mortality risk (1.78% vs 0.92%; P = 0.003), lower proportion of cured or completed treatment (82.81% vs 84.97%; P = 0.022), and longer time from onset to end-of-treatment. The probability of recurrence was higher in bacteriological positive cases than those in bacteriological negative cases (0.5% vs 0.4% at 20 months; P < 0.05). Conclusions Based on patient’s epidemiological characteristics and bacteriological type, it was necessary to actively enact measures to control their recurrent