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The development of remote frequency transfer techniques, especially the appearance of optical clocks with unprecedented stability, has prompted geoscientists to study their applications in geodesy. Using remote frequency transfer technique, by frequency comparison of two optical clocks at two points P and Q connected by optical fibers, one can measure the signal's frequency shift between them, and the geopotential difference between them can be determined based on the gravity frequency shift equation. Given the orthometric height of P, the orthometric height of Q can be determined. Since the present stability of the optical clock has achieved 1 × 10−18 or better and comparing the frequency transfer via optical fiber provides stability at 10−19 level, the optical clock network enables determining the orthometric height at centimeter-level. This study provides a formulation to determine the height difference at one-centimeter level between two points on the ground based on the optical fiber frequency transfer technique.

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.

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.

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.

As the fact that most of the ground-based GPS lacks of the detection of the upper-air meteorological data, thus the application of ground-based GPS sensing of water vapor technology has been limited due to the inaccurately calculated weighted mean temperature. In that case, this paper has studied and analyzed the methods of obtaining weighted mean temperature by deriving the data from GGOS Atmosphere weighted mean temperature grid data in Xinjiang. By using the radiosonde data, this paper has evaluated the accuracy of the weighted mean temperature(GTm) derived from GGOS atmosphere weighted mean temperature grid data and considering the seasonal and geographic factors , we employed a correction model to fit the residuals of GTm. Results show that the GTm derived from mean value interpolation and corrected by correction model meet the requirements of ground-based GPS precision sensing of Water Vapor in Xinjiang ; The inner average precision RMSD is 2.33K , MAE is 1.80 K; The outer average precision RMSD is 2.36K , MAE is 1.85 K.

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.

The detective board game of CLUEreg can be viewed as a benchmark example of the treasure hunt problem, in which a sensor path is planned based on the expected value of information gathered from targets along the path. The sensor is viewed as an information gathering agent that makes imperfect measurements or observations from the targets, and uses them to infer one or more hidden variables (such as, target features or classification). The treasure hunt problem arises in many modern surveillance systems, such as demining and reconnaissance robotic sensors. Also, it arises in the board game of CLUEreg, where pawns must visit the rooms of a mansion to gather information from which the hidden cards can be inferred. In this paper, Q-learning is used to develop an automated neural computer player that plans the path of its pawn, makes suggestions about the hidden cards, and infers the answer, often winning the game. A neural network is trained to approximate the decision-value function representing the value of information, for which there exists no general closed-form representation. Bayesian inference, test (suggestions), and action (motion) decision making are unified using an MDP framework. The resulting computer player is shown to outperform other computer players implementing Bayesian networks, or constraint satisfaction.

Control and optimization of acoustic sensors can significantly impact the effectiveness of sonar deployment in variable and uncertain underwater environments. On the other hand, the design of optimal control systems requires tractable models of system dynamics, which in this case include acoustic-wave propagation phenomena. High-fidelity acoustic models that capture the influence of environmental conditions on wave propagation involve partial differential equations (PDEs), and are computationally intensive. Also, by relying on the numerical solution of PDEs for given boundary and initial conditions, they do not provide closed-form functional forms for the propagation loss or other output variables. In this paper, a simple Bayesian network (BN) model of acoustic propagation is presented for use in sonar control. The performance of the BN model compares favorably to that of a radial basis function neural network. Additionally, the sensor range dependency on spatial and temporal coordinates can be estimated and utilized to compute optimal sonar control strategies.

The detective boardgame of CLUEreg can be viewed as an example of preposterior decision problem, where decisions on how to navigate the board are based on the expected utility of the observations, and the observations are aimed at improving an inference process. The same principles arise in modern surveillance systems, such as demining sensor networks, where the sensor platforms (e.g., autonomous ground or vehicles) move about the environment in order to collect measurements or evidence from unknown targets and improve inference of unknown features. The boardgame of CLUEreg serves as a well-known and intuitive example problem, that displays the same couplings between motion planning and inference, as modern surveillance systems. In this paper, a Bayesian network (BN) approach is used to develop an automated computer player for CLUEreg, that is tested through an interactive simulation of the game. The results show that the intelligent player plans its motions according to the evidence that needs to be collected, and is capable of winning the game of CLUEreg against experienced human players

A battery is a quite complex and nonlinear system comprising interacting physical and chemical processes although it seems deceptively simple. State-of-charge (SOC), a parameter to describe how much energy battery has, is a key factor in battery management and its estimation is an important and challenging task. We develop an adaptive neuro-fuzzy inference system (ANFIS) to achieve the goal. First in this paper, nonconventional input variables of the ANFIS are selected by three different correlation analysis techniques, linear correlation analysis (LCA), nonparametric correlation analysis (NCA) and partial correlation analysis (PCA). Next, the ANFIS model of five inputs and one output is presented. Takagi and Sugeno's fuzzy if-then rules are used. Then, number determination of training data pairs is discussed. Finally, hybrid learning algorithm combining the gradient method and the least squares estimate (LSE) is adopted to train the ANFIS. Predicted results obtained by the ANFIS are compared with measured results, verifying presented ANFIS. For contrast, a three-layer feedforward back-propagation (BP) artificial neural network (ANN) is presented to estimate SOC. Compared with the BP ANN model, the ANFIS obtains better prediction performance when interpolating. Comparisons of the two approaches have highlighted the potential of ANFIS in modeling and prediction of the behavior of complex nonlinear dynamic systems.

Mobile robot often relies on a battery system as its power supply and such kind of mobile robot is called cableless mobile robot. In the past years, while there are many researches on automation and control techniques, mechanical and sensor designs of mobile robot, very little systematic and comprehensive work has been done in the design of rechargeable battery power system for cableless mobile robot. This paper discusses the above problem from three different aspects: selection of rechargeable battery type, battery management technique and energy renewal. In the section on selection of rechargeable battery, many different types of traction rechargeable battery are introduced and the factors that are crucial for cableless mobile robot applications are compared. The section on battery management technique focuses on battery state-of-charge (SOC) estimation. In the section on energy renewal, two typical charging techniques, conductive charging and inductive charging are discussed. In the final section, one implementation example, high power Ni-MH rechargeable battery system for TH-1 humanoid robot which is being developed by Tsinghua University, is introduced.