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With the development of multi-constellation multi-frequency Global Navigation Satellite Systems (GNSS), more and more observations are available for tightly coupled GNSS/Inertial Navigation System (INS) integration. Concerning the accuracy, robustness, and computational burden issues in the integration, we proposed a robust and computationally efficient implementation. The new tight integration model uses pseudorange, Doppler and carrier phase simultaneously, to achieve the maximum possible navigation accuracy for a single receiver. The resultant high-dimensional observation vector is then processed by a sequential Kalman Filter (KF) to improve the computational efficiency in the measurement update step. Based on the innovation of the sequential KF, a robust estimation method with Gaussian test is further devised to detect and adapt the faults in individual GNSS channels. Two field vehicular tests are conducted to evaluate the performance improvements of the proposed method, compared with loose coupling and conventional tight coupling. Test results in favorable environments indicate that the proposed method can significantly improve the velocity and attitude accuracy by 69.42% and 47.16% over loose coupling and by 64.75% and 30.88% over conventional tight coupling, respectively. Moreover, the computational efficiency is also improved by about 53.09% for the proposed method, compared with batch KF processing. In GNSS challenging environments, the proposed method also shows superiority in terms of velocity and attitude accuracy, and better bridging capability during the GNSS partial or complete outages. These results demonstrate that the proposed method is able to provide a more robust and accurate solution in real-time vehicular navigation. [ABSTRACT FROM AUTHOR]

The advance of MEMS-based inertial sensors successfully expands their applications to small unmanned aerial vehicles (UAV), thus resulting in the challenge of reliable and accurate in-flight alignment for airborne MEMS-based inertial navigation system (INS). In order to strengthen the rapid response capability for UAVs, this paper proposes a robust in-flight alignment scheme for airborne MEMS-INS aided by global navigation satellite system (GNSS). Aggravated by noisy MEMS sensors and complicated flight dynamics, a rotation-vector-based attitude determination method is devised to tackle the in-flight coarse alignment problem, and the technique of innovation-based robust Kalman filtering is used to handle the adverse impacts of measurement outliers in GNSS solutions. The results of flight test have indicated that the proposed alignment approach can accomplish accurate and reliable in-flight alignment in cases of measurement outliers, which has a significant performance improvement compared with its traditional counterparts. [ABSTRACT FROM AUTHOR]

The control of nonlinear networked systems is one of the challenging problems in the network science and engineering fields. Based on physical and realistic considerations, it is a critical goal to control the systems towards certain desired behaviors that are essential for the system's mission regarding a selected task in various fields. To explore a practical framework of controllability, in this study, we theoretically prove the existence of control functions that can drive the nonlinear networked system from one initial trajectory to another desired trajectory under the condition that the considered system is Lipschitz continuous and dissipative. In particular, we propose a strategy for identifying the driver nodes that need to be controlled and design a general form of control functions for realizing such a transition. Moreover, a graph theory--based algorithm is used to identify the driver nodes. Furthermore, we demonstrate the effectiveness of our theoretical results using a chaotic system and two biological systems, including a system of mammalian circadian rhythms and a complex disease-related system of hepatocellular carcinoma (HCC) by steering the systems towards their attractors, including point, periodic, and chaotic attractors. Numerical experiments reveal that controlling a single driver node is sufficient to drive a system to certain desired behaviors. More importantly, we also identify some critical biochemical molecules for controlling circadian rhythms and potential therapeutic strategies for curing or relieving HCC. These results suggest that our approach will provide a powerful tool for revealing molecular mechanisms of biological systems and identifying drug targets from the viewpoint of control theory. [ABSTRACT FROM AUTHOR]

The major challenge of current Strapdown Inertial Naviagtion System/Celestial Navigation System (SINS/CNS) integrated systems is navigation accuracy degradation due to the failure to accurately estimate the accelerometer bias even when using stellar refraction information (e.g. the number of refraction stars is less than three). To solve this problem, this paper presents a new method for improving the accuracy of the traditional inertial-based integrated systems installed on ballistic vehicles. In an analogy with nonholonomic constraints in land navigation, this algorithm exploits the constraints that govern the motion of a ballistic vehicle in the free flight phase to obtain accelerometer bias observations. Improvements in dynamic equations are used to reduce the propagation of navigation errors, and high-rate virtual constraints are used to reduce the impact of bias errors. An information filter is devised to fuse the multi-rate observations from multiple sources, i.e. SINS, CNS and model constraints. The proposed method is also evaluated by long-range ballistic vehicle navigation simulations. The results indicate that the proposed constrained algorithm can address the degradation problem with remarkable accuracy improvements without adding extra sensors, enhancing the SINS-based navigation performance for ballistic applications. [ABSTRACT FROM AUTHOR]

The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers. [ABSTRACT FROM AUTHOR]

Several liquid sample injection methods have been developed to satisfy the requirements for serial femtosecond X-ray nanocrystallography, which enables radiation-damage-free determination of molecular structure at room temperature. Time-resolved nanocrystallography would combine structure analysis with chemical kinetics by determining the structures of the transient states and chemical kinetic mechanisms simultaneously. A windowless liquid mixing jet device has been designed for this purpose. It achieves fast uniform mixing of substrates and enzymes in the jet within 250 ms, with an adjustable delay between mixing and probing by the X-ray free-electron laser beam of up to 1 s for each frame of a 'movie'. The principle of the liquid mixing jet device is illustrated using numerical simulation, and experimental results are presented using a fluorescent dye. [ABSTRACT FROM AUTHOR]

Low-latency high-rate ( $$>$$ 1 Hz) precise real-time kinematic (RTK) can be applied in high-speed scenarios such as aircraft automatic landing, precise agriculture and intelligent vehicle. The classic synchronous RTK (SRTK) precise differential GNSS (DGNSS) positioning technology, however, is not able to obtain a low-latency high-rate output for the rover receiver because of long data link transmission time delays (DLTTD) from the reference receiver. To overcome the long DLTTD, this paper proposes an asynchronous real-time kinematic (ARTK) method using asynchronous observations from two receivers. The asynchronous observation model (AOM) is developed based on undifferenced carrier phase observation equations of the two receivers at different epochs with short baseline. The ephemeris error and atmosphere delay are the possible main error sources on positioning accuracy in this model, and they are analyzed theoretically. In a short DLTTD and during a period of quiet ionosphere activity, the main error sources decreasing positioning accuracy are satellite orbital errors: the 'inverted ephemeris error' and the integration of satellite velocity error which increase linearly along with DLTTD. The cycle slip of asynchronous double-differencing carrier phase is detected by TurboEdit method and repaired by the additional ambiguity parameter method. The AOM can deal with synchronous observation model (SOM) and achieve precise positioning solution with synchronous observations as well, since the SOM is only a specific case of AOM. The proposed method not only can reduce the cost of data collection and transmission, but can also support the mobile phone network data link transfer mode for the data of the reference receiver. This method can avoid data synchronizing process besides ambiguity initialization step, which is very convenient for real-time navigation of vehicles. The static and kinematic experiment results show that this method achieves 20 Hz or even higher rate output in real time. The ARTK positioning accuracy is better and more robust than the combination of phase difference over time (PDOT) and SRTK method at a high rate. The ARTK positioning accuracy is equivalent to SRTK solution when the DLTTD is 0.5 s, and centimeter level accuracy can be achieved even when DLTTD is 15 s. [ABSTRACT FROM AUTHOR]

X-ray crystallography of G protein--coupled receptors and other membrane proteins is hampered by difficulties associated with growing sufficiently large crystals that withstand radiation damage and yield high-resolution data at synchrotron sources. We used an x-ray free-electron laser (XFEL) with individual 50-femtosecond-duration x-ray pulses to minimize radiation damage and obtained a high-resolution room-temperature structure of a human serotonin receptor using sub-10-micrometer microcrystals grown in a membrane mimetic matrix known as lipidic cubic phase. Compared with the structure solved by using traditional microcrystallography from cryo-cooled crystals of about two orders of magnitude larger volume, the room-temperature XFEL structure displays a distinct distribution of thermal motions and conformations of residues that likely more accurately represent the receptor structure and dynamics in a cellular environment. [ABSTRACT FROM AUTHOR]