Search

Over the decades, testing gravitational red-shift (GRS) based on microwave links has made great process, including the GPA experiment, the planned Atomic Clock Ensemble in Space mission, and the China Space Station (CSS). Until now, the formulations of microwave links are almost all based on the time comparison. However, there are advantages of using frequency comparison instead of time comparison to test GRS. Here we develop a tri-frequency combination method based on the measurements of the frequency shifts of three independent microwave links between a space station and a ground station. Aiming at the frequency links’ accuracy of $$3\times 10^{-16}$$ 3 × 10 - 16 , we should consider various effects, including the Doppler effect, second-order Doppler effect, atmospheric frequency shift, tidal effects, refraction caused by the atmosphere, and Shapiro effect, with accuracy levels of tens of centimeters. The CSS will complete construction in 2022, and the formulation proposed in this study will enable us to test GRS at an accuracy level of at least $$2\times 10^{-6}$$ 2 × 10 - 6 , which is one order higher than the present accuracy level of $$7\times 10^{-5}$$ 7 × 10 - 5 .

Background. Anion gap (AG) has been proved to be associated with prognosis of many cardiovascular diseases. This study is aimed at exploring the association of AG with inhospital all-cause mortality and adverse clinical outcomes in coronary care unit (CCU) patients. Method. All data of this study was extracted from Medical Information Mart for Intensive Care III (MIMIC-III, version 1.4) database. All patients were divided into four groups according to AG quartiles. Primary outcome was inhospital all-cause mortality. Lowess smoothing curve was drawn to describe the overall trend of inhospital mortality. Binary logistic regression analysis was performed to determine the independent effect of AG on inhospital mortality. Result. A total of 3593 patients were enrolled in this study. In unadjusted model, as AG quartiles increased, inhospital mortality increased significantly, OR increased stepwise from quartile 2 (OR, 95% CI: 1.01, 0.74-1.38, P=0.958) to quartile 4 (OR, 95% CI: 2.72, 2.08-3.55, P<0.001). After adjusting for possible confounding variables, this association was attenuated, but still remained statistically significant (quartile 1 vs. quartile 4: OR, 95% CI: 1.02, 0.72-1.45 vs. 1.49, 1.07-2.09, P=0.019). Moreover, CCU mortality (P<0.001) and rate of acute kidney injury (P<0.001) were proved to be higher in the highest AG quartiles. Length of CCU (P<0.001) and hospital stay (P<0.001) prolonged significantly in higher AG quartiles. Maximum sequential organ failure assessment score (SOFA) (P<0.001) and simplified acute physiology score II (SAPSII) (P<0.001) increased significantly as AG quartiles increased. Moderate predictive ability of AG on inhospital (AUC: 0.6291), CCU mortality (AUC: 0.6355), and acute kidney injury (AUC: 0.6096) was confirmed. The interactions were proved to be significant in hypercholesterolemia, congestive heart failure, chronic lung disease, respiratory failure, oral anticoagulants, Beta-blocks, angiotensin-converting enzyme inhibitor (ACEI)/angiotensin receptor blocker (ARB), and vasopressin treatment subgroups. Conclusion. AG was an independent risk factor of inhospital all-cause mortality and was associated with adverse clinical outcomes in CCU patients.

General relativity theory (GRT) concludes that a precise clock ticks at different running rates if it is under the influence of different geopotentials. Therefore, by comparing the running rates of clocks at arbitrary two stations, the geopotential difference between them can be determined. In this study, with the help of two hydrogen atomic clocks (noted as H-masers), using the two-way satellite time and frequency transfer (TWSTFT) technique, we carried out experiments of the geopotential difference determination at the China Aerospace Science & Industry Corporation (CASIC), Beijing. Here the ensemble empirical mode decomposition (EEMD) method is adopted to remove periodic signals included in the original observations. Finally, the clock-comparison-determined geopotential difference in the experiments is determined. Results show that the difference between the geopotential difference determined by GRT and that determined by measuring tape is about 1316.1 ± 931.0 m2s-2, which is equivalent to 134.3 ± 95.0 m in height, and in consistence with the stability of the H-masers applied in the experiments (at the level of 10−15/day). With the rapid improvement of atomic clocks' accuracy, the geopotential determination by accurate clocks is prospective, and it is promising to realize the unification of the world vertical height system (WVHS).

According to general relativity theory (GRT), the clock at a position with lower geopotential ticks slower than an identical one at a position with higher geopotential. Here, we provide a geopotential comparison using a non-transportable hydrogen clock and a transportable hydrogen clock for altitude transmission based on the two-way satellite time and frequency transfer (TWSTFT) technique. First, we set one hydrogen clock on the fifth floor and another hydrogen clock on the ground floor, with their height difference of 22.8 m measured by tape, and compared the time difference between these two clocks by TWSTFT for 13 days. Then, we set both clocks on the ground floor and compared the time difference between the two clocks for 7 days for zero-baseline calibration (synchronization). Based on the measured time difference between the two clocks at different floors, we obtained the height difference of $28 \pm 05.4$ m, which coincides well with the tape-measured result. This experiment provides a method of height propagation using precise clocks based on the TWSTFT technique., 19 pages, 6 figures

Background: Neutrophil percentage to albumin ratio (NPAR) was proved to be correlated with the prognosis of a variety of diseases. The purpose of this study was to explore the effect of NPAR on the prognosis of coronary care unit (CCU) dpatients.Method: All data of this study was extracted from Medical Information Mart for Intensive Care III (MIMIC-III, version1.4) database. All patients were divided into four groups according to NPAR quartiles. Primary outcome was in-hospital mortality and secondary outcomes were 30-day mortality, 365-day mortality, length of CCU stay, length of hospital stay, acute kidney injury, renal replacement therapy. Multivariable binary logistic regression analysis was performed to confirm the independent effect of NPAR. Subgroup analysis was used to determine the effect of NPAR on in-hospital mortality in different subgroups. Receiver-operating characteristic (ROC) curves were applied to evaluate the ability of NPAR to predict in-hospital mortality. Kaplan–Meier curves were built to compare cumulative survival of different groups.Result: 2364 CCU patients were enrolled in this study. In-hospital mortality rate increased significantly as NPAR quartiles increased (P < 0.001). In multivariable logistic regression, NPAR was proved to be independently associated with in-hospital mortality (Quartile 4 vs Quartile 1: OR, 95% CI: 1.80, 1.19-2.72, P=0.005, P for trend = 0.001). Moderate ability of NPAR to predict in-hospital mortality was demonstrated through ROC curves, the AUC of NPAR was 0.653 (PConclusion: NPAR was an independent risk factor of in-hospital mortality in CCU patients and had a moderate ability to predict in-hospital mortality.

The present Global Navigation Satellite System (GNSS) can provide at least double-frequency observations, and especially the Galileo Navigation Satellite System (Galileo) can provide five-frequency observations for all constellation satellites. In this contribution, precision point positioning (PPP) models with Galileo E1, E5a, E5b, E5 and E6 frequency observations are established, including a dual-frequency (DF) ionospheric-free (IF) combination model, triple-frequency (TF) IF combination model, quad-frequency (QF) IF combination model, four five-frequency (FF) IF com-bination models and an FF uncombined (UC) model. The observation data of five stations for seven days are selected from the multi-GNSS experiment (MGEX) network, forming four time-frequency links ranging from 454.6 km to 5991.2 km. The positioning and time-frequency transfer performances of Galileo multi-frequency PPP are compared and evaluated using GBM (which denotes precise satellite orbit and clock bias products provided by Geo Forschung Zentrum (GFZ)), WUM (which denotes precise satellite orbit and clock bias products provided by Wuhan University (WHU)) and GRG (which denotes precise satellite orbit and clock bias products provided by the Centre National d’Etudes Spatiales (CNES)) precise products. The results show that the performances of the DF, TF, QF and FF PPP models are basically the same, the frequency stabilities of most links can reach sub10−16 level at 120,000 s, and the average three-dimensional (3D) root mean square (RMS) of position and average frequency stability (120,000 s) can reach 1.82 cm and 1.18 × 10−15, respectively. The differences of 3D RMS among all models are within 0.17 cm, and the differences in frequency stabilities (in 120,000 s) among all models are within 0.08 × 10−15. Using the GRG precise product, the solution performance is slightly better than that of the GBM or WUM precise product, the average 3D RMS values obtained using the WUM and GRG precise products are 1.85 cm and 1.77 cm, respectively, and the average frequency stabilities at 120,000 s can reach 1.13 × 10−15 and 1.06 × 10−15, respectively.

According to Einstein’s general relativity theory, a precise (atomic) clock runs faster at a position with higher gravitational potential than a clock at a position with lower potential. Here, we provide a direct clock comparison using a fixed hydrogen clock and a portable hydrogen clock for geopotential determination based on the two-way satellite time and frequency transfer (TWSTFT) technique. After synchronizing the two hydrogen clocks at positions at the same height, the clocks were compared for a height difference of 22 m over a period of 14 days using the TWSTFT technique. Interpretation of the observed time elapse implies a height difference of 175 m +/- 59 m, which matches the frequency stability of our instruments. This study and relevant experiments provide a way of determining the geopotential using ultra-high precise clocks based on the TWSTFT technique in the near future.

Background. Neutrophil percentage-to-albumin ratio (NPAR) has been proved to be associated with clinical outcome of many diseases. This study was aimed at exploring the independent effect of NPAR on all-cause mortality of critically ill patients with coronary artery disease (CAD). Method. NPAR was calculated as neutrophil percentage numerator divided by serum albumin concentration. Clinical endpoints were 30-day, 90-day, and 365-day all-cause mortality. Multivariable Cox proportional hazard models were performed to confirm the association between NPAR and all-cause mortality. Result. 3106 patients with CAD were enrolled. All-cause mortality rates of 30 days (P<0.001), 90 days (P<0.001), and 365 days (P<0.001) increased as NPAR tertiles increased. And after adjusting for possible confounding variables, NPAR was still independently associated with 30-day (third tertile group versus first tertile group: HR, 95% CI: 1.924, 1.471-2.516; P for trend < 0.001), 90-day (third tertile group versus first tertile group: HR, 95% CI: 2.053, 1.646-2.560; P for trend < 0.001), and 365-day (third tertile group versus first tertile group: HR, 95% CI: 2.063, 1.717-2.480; P for trend < 0.001) all-cause mortality in patients with CAD. Subgroup analysis did not find obvious interaction in most subgroups. Conclusion. NPAR was independently correlated with 30-day, 60-day, and 365-day all-cause mortality in critically ill patients with CAD.

This paper presents an information-driven sensor management problem, referred to as treasure hunt, which is relevant to mobile-sensor applications such as mine hunting, monitoring, and surveillance. The objective is to infer a hidden variable or treasure by selecting a sequence of measurements associated with multiple fixed targets distributed in the sensor workspace. The workspace is represented by a connectivity graph, where each node represents a possible sensor deployment, and the arcs represent possible sensor movements. An additive conditional entropy reduction function is presented to efficiently compute the expected benefit of a measurement sequence over time. Then, the optimal treasure hunt strategy is determined by a novel label-correcting algorithm operating on the connectivity graph. The methodology is illustrated through the board game of CLUEreg, which is shown to be a benchmark example of the treasure hunt problem. The game results show that a computer player implementing the strategies developed in this paper outperforms players implementing Bayesian networks, Q-learning, or constraint satisfaction, as well as human players.

This paper investigates the comparative performance of several information-driven search strategies and decision rules using a canonical target classification problem. Five sensor models are considered: one obtained from classical estimation theory and four obtained from Bernoulli, Poisson, binomial, and mixture-of-binomial distributions. A systematic approach is presented for deriving information functions that represent the expected utility of future sensor measurements from mutual information, Renyi divergence, Kullback-Leibler divergence, information potential, quadratic entropy, and the Cauchy-Schwarz distance. The resulting information-driven strategies are compared to direct-search, alert-confirm, task-driven (TS), and log-likelihood-ratio (LLR) search strategies. Extensive numerical simulations show that quadratic entropy typically leads to the most effective search strategy with respect to correct-classification rates. In the presence of prior information, the quadratic-entropy-driven strategy also displays the lowest rate of false alarms. However, when prior information is absent or very noisy, TS and LLR strategies achieve the lowest false-alarm rates for the Bernoulli, mixture-of-binomial, and classical sensor models.

A methodology is developed for planning the sensing strategy of a robotic sensor deployed for the purpose of classifying multiple fixed targets located in an obstacle-populated workspace. Existing path planning techniques are not directly applicable to robots whose primary objective is to gather sensor measurements using a bounded field of view (FOV). This paper develops a novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace. The method constructs a connectivity graph with observation cells that is pruned and transformed into a decision tree from which an optimal sensing strategy can be computed. The effectiveness of the optimal sensing strategies obtained by this methodology is demonstrated through a mine-hunting application. Numerical experiments show that these strategies outperform shortest path, complete coverage, random, and grid search strategies, and are applicable to nonoverpass capable robots that must avoid targets as well as obstacles.