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More attention should be paid to the deviations between the actual diameter of the rhexis reference circle which projected by the Image-guided systems and its intended size, and assess the influence of ocular biometric parameters on the deviation. In this study, the Callisto eye image-guided system was employed to generate a digital rhexis reference circle (rhexis overlay) set at an intended diameter of 6 mm and a screenshot of the video was taken at the end of the cataract surgery, then to compare the deviation between the observed rhexis overlay diameter (ROD) and the optic diameter (6 mm). The factors influencing diameter deviation were identified with univariate and multivariate linear regression. Subgroup analyses were conducted based on factors identified in the multivariate regression. And we observed that a total of 213 eyes of 174 patients were included in the final analysis. The diameter deviation averaged − 0.24 ± 0.28 mm (P = 0.0001) and was positively correlated with phakic anterior chamber depth (ACD) (P < 0.001) and negatively correlated with white-to-white (WTW) measurement (P < 0.001). Subgroup analyses revealed significantly greater diameter deviation in cases with preoperative ACD < 2.5 mm and WTW ≥ 12.0 mm (all P < 0.05). Following optimization, the mean diameter deviation was 0.00047 ± 0.26 mm (P > 0.05). In conclusion, the Callisto eye image-guided system demonstrates good precision in CCC assistance. However, there remains a slight deviation from the intended size when generating the rhexis reference circle in eyes with shallow anterior chambers and larger corneas. Implementing an optimization process could be beneficial for these eyes. [ABSTRACT FROM AUTHOR]

The constant challenge in social interactions involves making informed decisions in the face of competitive and cooperative dilemmas. The decision-making process can be influenced by various factors present in the social context. According to the behavior-pattern-categorization framework of information acquisition, potential biases may develop at all stages of decision-making as information about social context is progressively entered and integrated. In this study, employing the Chicken Game, we investigated the influence of varying information levels within the behavior-pattern-categorization framework (i.e., competitiveness of behavior choice, uncertainty of behavior pattern, and sociality of category) on decision-making in the dilemma of competition and cooperation. Combined with reinforcement learning models, our findings from three experiments showed that participants exhibited basic complementary behavior, becoming less competitive against highly competitive opponents and vice versa. Notably, individuals exhibited varying adaptation rates to different levels of opponent competitiveness and fluctuations. Specifically, participants adapted slower to highly competitive opponents and faster to cooperative opponents. This asymmetric adaptation in social learning is related to the rate at which various levels of information are updated. The current study disentangles the different levels of information acquisition and highlights the asymmetric processing that can occur during the updating of information within each level. [ABSTRACT FROM AUTHOR]

Heteroatom doping can tune the band structure of semiconductors and enhance their carrier transfer capacity for improving the performance of photoelectrochemical water oxidation. Nevertheless, the introduction of dopants is not always beneficial. In this study, magnesium (Mg) is adopted to dope ZnIn2S4 nanosheet array photoanodes to form a type‐II band structure and reduce bulk recombination, but concurrently introduced deleterious oxygen (O) defects slow down the surface catalytic reaction kinetics. Furthermore, a facile heat treatment strategy is proposed to transform these O defects into Mg─O bonds. First‐principles calculations and electrochemical characterization indicate that the presence of Mg─O bonds provides abundant active sites and efficiently accelerates the surface oxygen evolution reaction by precisely realigning the rate‐determining step from OH* to O* (step 2) to OOH* to O2 (step 4), thereby retarding charge trapping and recombination. As a result, such a photoanode achieves a remarkable performance with a photocurrent as high as 4.91 mA cm−2 at 1.23 V versus reversible hydrogen electrode, and the onset potential shifts negatively about 340 mV. This work provides a new defect modulation idea for converting detrimental defects to favorable ones, and it can be expected to have wide applications in the fields of energy, catalysis, and optoelectronics, etc. [ABSTRACT FROM AUTHOR]

Social hierarchies can be inferred through observational learning of social relationships between individuals. Yet, little is known about the causal role of specific brain regions in learning hierarchies. Here, using transcranial direct current stimulation, we show a causal role of the medial prefrontal cortex (mPFC) in learning social versus non-social hierarchies. In a Training phase, participants acquired knowledge about social and non-social hierarchies by trial and error. During a Test phase, they were presented with two items from hierarchies that were never encountered together, requiring them to make transitive inferences. Anodal stimulation over mPFC impaired social compared with non-social hierarchy learning, and this modulation was influenced by the relative social rank of the members (higher or lower status). Anodal stimulation also impaired transitive inference making, but only during early blocks before learning was established. Together, these findings demonstrate a causal role of the mPFC in learning social ranks by observation. A brain stimulation study (tDCS) on healthy humans shows that the medial prefrontal cortex plays a causal role in learning social hierarchical relationships by observation. [ABSTRACT FROM AUTHOR]

Although defect design is an essential means of improving the performance of photocatalytic materials, defect introduction is not always beneficial. Current defect engineering focuses on avoiding or modifying introduced defects to mitigate their adverse effects on solar water splitting performance. This review comprehensively summarizes the strategies for defect introduction and discusses their crucial roles in solar water splitting. Focusing on the dual effects of defects, the most basic schemes for preparing favorable defects through pretreatment and posttreatment methods are reviewed. In addition, the review focuses on strategies for overcoming or mitigating the detrimental effects of defects to improve the performance of photocatalysts. Finally, the challenges and prospects of developing a defect‐based photocatalyst design strategy are outlined. This review provides a reference for preparing defect‐based photocatalysts for conducting more in‐depth research on defects. Additionally, it offers ideas for utilizing unfavorable defects and converting them into favorable defects for performance improvement. [ABSTRACT FROM AUTHOR]

In this article, a novel normal-skew mixture (NSM) distribution is presented to model the normal and/or heavy-tailed and/or skew nonstationary distributed noises. The NSM distribution can be formulated as a hierarchically Gaussian presentation by leveraging a Bernoulli distributed random variable. Based on this, a novel robust Kalman filtering framework can be developed utilizing the variational Bayesian method, where the one-step prediction and measurement-likelihood densities are modeled as NSM distributions. For implementation, several exemplary robust Kalman filters (KFs) are derived based on some specific cases of NSM distribution. The relationships between some existing robust KFs and the presented framework are also revealed. The superiority of the proposed robust Kalman filtering framework is validated by a target tracking simulation example. [ABSTRACT FROM AUTHOR]

Existing robust state estimation methods are generally unable to distinguish model uncertainties (state outliers) from measurement outliers as they only exploit the current measurement. In this article, the measurements in a sliding window are, therefore, utilized to better distinguish them, and an adaptive method is embedded, leading to a sliding window variational outlier-robust Kalman filter based on Student’s t-noise modeling. Target tracking simulations and experiments show that the tracking accuracy and consistency of the proposed filter are superior to those of the existing state-of-the-art outlier-robust methods thanks to the improved ability to identify the outliers but at a cost of greater computational burden. [ABSTRACT FROM AUTHOR]

The extraordinary optoelectronic properties and continued commercialization of GaN enable it a promising component for neuromorphic visual system (NVS). However, typical GaN‐based optoelectronic devices demonstrated to data only show temporary and unidirectional photoresponse in ultraviolet region, which is an insurmountable obstacle for construction of NVS in practical applications. Herein, an ultrasensitive visual sensor with phototransistor architecture consisting of AlGaN/GaN high‐electron‐mobility‐transistor (HEMT) and two‐dimensional Ruddlesden–Popper organic–inorganic halide perovskite (2D OIHP) is reported. Utilizing the significant variation in activation energy for ion transport in 2D OIHP (from 1.3 eV under dark to 0.4 eV under illumination), the sensor can efficiently perceive and storage optical information in ultraviolet–visible region. Meanwhile, the photo‐enhanced field‐effect mechanism in the depletion‐mode HEMT enables gate‐tunable negative and positive photoresponse, where some typical optoelectronic synaptic functions including inhibitory and excitatory postsynaptic current as well as paired‐pulse facilitation are demonstrated. More importantly, a NVS based on the proposed visual sensor array is constructed for achieving neuromorphic visual preprocessing with an improved color image recognition rate of 100%. [ABSTRACT FROM AUTHOR]

In order to test whether the intelligent logistics system based on the Internet of things is an important guarantee for an enterprise to have supply chain competitiveness and save a lot of costs in production and sales, this paper puts forward the innovative architecture and application of the new technology of intelligent logistics system based on the Internet of things, constructs the PLS path model and coordination degree measurement model, and measures and analyzes the coordinated development level of the "logistics regional economy" composite system and the coordinated development level of each subsystem, the coordination degree of the "logistics regional economy" composite system, and the coordination degree of each subsystem. It has effectively solved the problems of low operation efficiency and high labor cost in the procurement and inventory link; moreover, the direct effects of the development of logistics industry on the rationalization and upgrading of industrial economic structure are −0.2269 and 0.0289 respectively, which are significant at the level of 1%. The overall cointegration test shows that China's logistics industry and economic growth maintain a long-term and stable equilibrium relationship. [ABSTRACT FROM AUTHOR]

In this paper, elliptically contoured (EC) distributions are used to model outlier-contaminated measurement noises. Exploiting a heuristic approach to introduce an unknown parameter, we present an analytical update form of the joint posterior probability density function of the state vector and auxiliary random variable, from which a novel robust EC distributions-based Kalman filtering framework is first derived. To illustrate the effectiveness of the proposed framework, the convergence, robustness, optimality and computational complexity analyses of the proposed method are then given. In addition, to cope with complex noise environments, the interaction multiple model is employed to achieve the adaptive selection of EC distributions such that well-behaved estimation performance can be obtained for different noise cases. Simulation results demonstrate the validity and superiority of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper is based on the alumina ceramic manufacturing process and its quality to build a quality information management system to study alumina ceramics and its quality factors such as physical properties, chemical properties, and other pieces of related information to find a kind of alumina ceramics. The management of quality management brings efficiency and guarantee to the management system. Obviously, this research is based on the two parts of alumina ceramic tube and quality management information system for data acquisition and analysis. One part is based on the production and performance analysis of alumina ceramic tube and summarizes the quality problems that occurred in this process. This paper constructs an information management system to count these issues and conducts research on information dominance and management; among them, exponential smoothing calculation methods, linear trends, convolutional neural algorithms, and so on are applied for methodological analysis. The experimental results show that the lack of corners and uneven corners in alumina production has been greatly reduced, and the maximum probability of uneven corners has been reduced from 9.27 to 5.27. The scrap rate of ceramic tube quality decreased from the highest value of 5.82 to 3.17, and the maximum value of unqualified quality was changed from 5.76 to 3.03. [ABSTRACT FROM AUTHOR]

In this article, a new position loci-based in-motion initial alignment method is proposed for a low-cost attitude and heading reference system (AHRS). A new sliding-window-based position integration method is proposed to construct the position vector observations, which weakens the accumulated errors induced by the inertial measurement unit (IMU) bias errors in vector observations. Also, a new closed-loop method is proposed to jointly estimate the time-varying body attitude matrix and the unknown IMU biases and global position system (GPS) lever-arm based on the Kalman filter. The estimates of the time-varying body attitude matrix, IMU biases, and GPS lever-arm are all used to construct and compensate for the position vector observations, which improves the alignment accuracy. Both the simulation and car-mounted test results illustrate that the proposed alignment method can achieve better alignment accuracy than these existing methods for a position loci-aided low-cost AHRS. [ABSTRACT FROM AUTHOR]

Hardware neural networks (HNNs) can be constructed by placing synaptic devices in a crossbar array, and these systems may overcome the inherent inefficiency of conventional computing systems. In this work, non-volatile In-Ga-Zn-O transistors were designed for use in HNNs. The In-Ga-Zn-O transistors consist of a naturally oxidized nanothick Al2O3/ion gel stacking dielectric, in which the gate electric field induces positive charge trapping effect could pull the subthreshold swing down to the value of 32.9 mV/decade, which is much lower than the theoretical limit of 60 mV/decade at room temperature. A highly repeatable and robust hysteresis behavior was observed in the transfer curve of the devices. These three-terminal In-Ga-Zn-O transistors could implement important memristive synaptic functions, including non-volatile memory, synaptic plasticity, and long-term potentiation/depression. A simulated HNN built from these In-Ga-Zn-O transistors with long-term potentiation/depression and optimized amplification factor exhibits 84% recognition accurate of handwritten data. The work illustrates an important application of energy-efficient HNNs by using high-performance non-volatile neuromorphic devices. [ABSTRACT FROM AUTHOR]

In order to improve the computational efficiency and alignment accuracy of a microelectromechanical system (MEMS)-based strap-down inertial navigation system (SINS), this article proposes a high-accuracy global positioning system (GPS)-aided coarse alignment method. The attitude matrix between current and initial body frames and the unknown gyro bias, accelerometer bias, and lever arm are jointly estimated based on the proposed closed-loop approach, where the attitude error and unknown parameters are jointly inferred based on the derived linear state-space model using the Kalman filter. Simulation and experimental results illustrate that the proposed GPS-aided coarse alignment method can achieve better accuracy than existing state-of-the-art coarse alignment methods for MEMS-based SINS. [ABSTRACT FROM AUTHOR]

An artificial vision system that can simulate the visual functions of human eyes is required for biological robots. Here, In‐Ga‐Zn‐O memtransistors using a naturally oxidized Al2O3 and an ion gel as a common gate stacking dielectric is proposed. Positive charge trapping in the Al2O3 layer can be induced by modulating the gate voltage, which causes the back sweep subthreshold swing (SS) of the device to break the physical limit (≥60 mV per decade at room temperature), and the minimum SS is as low as 26.4 mV per decade. In addition, photogenerated charges in the device are captured at the In‐Ga‐Zn‐O channel/ion gel interface due to the superposition of the additional electric field generated by positive charges trapped in the Al2O3 layer and the external gate electric field. Thus, persistent photoconductivity is observed in the In‐Ga‐Zn‐O memtransistors. Finally, by employing the optoelectronic memristive functions of In‐Ga‐Zn‐O memtransistors, an artificial vision system based on artificial retinal array (ARA) and artificial neural network is proposed. An obvious improvement in the recognition rate and efficiency with the use of ARA for the image preprocessing is achieved. This study provides a new strategy for the realization of artificial vision systems. [ABSTRACT FROM AUTHOR]

This article proposes a new skewed outlier-robust localization algorithm that is based on time-difference of arrival (TDOA) measurements at an airport. A new outlier-robust filtering framework is derived based on the skew Gaussian-gamma mixture (SGGM) distribution, where the state, a mixing parameter, a shape parameter, a scale matrix, and the degrees of freedom (DOFs) are inferred simultaneously using variational Bayesian (VB) approach. An interacting multiple-model (IMM) filter with different kinematic system models is implemented to handle the multimodal dynamics of the vehicle, yielding the IMM-SGGM algorithm. In particular, a new measurement likelihood based on the SGGM distribution is derived utilizing VB inference for the combination procedure in the proposed IMM-SGGM algorithm. Car-mounted experiments using TDOA measurements at an airport were conducted to verify the effectiveness of the proposed algorithm. The performance of the proposed IMM-SGGM algorithm is evaluated through comparisons with the state-of-the-art approaches. The experimental results demonstrate that the proposed IMM-SGGM algorithm has better localization accuracy and robustness to skewed outlier measurements than the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

In this article, we focus on addressing the nonlinear filtering problem with large prior uncertainty but high measurement accuracy, which may be encountered in the application of tightly coupled global navigation satellite system (GNSS)/inertial navigation system (INS) integration. Although the existing methods, such as progressive Gaussian approximate filter (PGAF), can address this problem, it has poor estimation accuracy. To improve the estimation accuracy of PGAF, the step sizes as well as the measurement noise covariance matrix (MNCM) are jointly estimated based on the variational Bayesian approach, from which a novel PGAF with variable step size is developed. Tightly coupled GNSS/INS integration simulations illustrate that the proposed filter outperforms the existing methods both in estimation accuracy and rate of convergence. [ABSTRACT FROM AUTHOR]

A novel robust Rauch–Tung–Striebel smoothing framework is proposed based on a generalized Gaussian scale mixture (GGScM) distribution for a linear state-space model with heavy-tailed and/or skew noises. The state trajectory, mixing parameters, and unknown distribution parameters are jointly inferred using the variational Bayesian approach. As such, a major contribution of this paper is unifying results within the GGScM distribution framework. Simulation and experimental results demonstrate that the proposed smoother has better accuracy than existing smoothers. [ABSTRACT FROM AUTHOR]

This paper first presents an optimization model to flexibly control available plug-in electric vehicle (PEV) battery charging/discharging power based on three-phase power flow and sensitivity approaches. This model can achieve one of the two goals: 1) minimizing both battery charging/discharging cost and extra battery degradation cost due to vehicle-to-grid (V2G) activities (cost-reduction strategy); 2) maximizing local peak load shifting and minimizing extra battery degradation cost due to V2G activities (peak-shifting strategy). The first strategy can determine the appropriate charging/discharging rates of an available PEV battery in order to benefit both the PEV owners and the distribution utilities for the day ahead. The second strategy can reduce the peak loads of the system. Both strategies can improve the power quality at the same time. With the help of a sensitivity method, most of the nonlinear constraints are transformed into linear constraints, and the number of constraints is reduced in the model. An interior point optimization approach is utilized to solve the optimization model. The optimization model is modified further to address unexpected PEV connections and travel scenarios during operation. The effectiveness and accuracy of the proposed method are demonstrated and verified on a 75-node test feeder. [ABSTRACT FROM AUTHOR]

The charge transfer and spin coupling effects are explored at the interface of two-dimensional (2D) superconducting FeSe nanosheets and molecular photochromic potassium-7,7,8,8-tetracyanoquinodimethane (KTCNQ). Light-induced conductivity in 2D FeSe nanosheets is enhanced by the electron doping from KTCNQ by the destabilized spin-Peierls phase through their interface. Furthermore, the spin coupling at the interface of FeSe and KTCNQ shifts the dimerization transition temperature of KTCNQ. Our results suggest 2D exfoliated FeSe nanosheets as a versatile strongly correlated platform for the study of interfacial electron doping and spin coupling. [ABSTRACT FROM AUTHOR]

In this paper, in order to improve the Student's t-matching accuracy, a novel Kullback-Leibler divergence (KLD) minimization-based matching method is firstly proposed by minimizing the upper bound of the KLD between the true Student's t-density and the approximate Student's t-density. To improve the Student's t-modelling accuracy, a novel KLD minimization-based adaptive method is then proposed to estimate the scale matrices of Student's t-distributions, in which the modified evidence lower bound is maximized. A novel KLD minimization-based adaptive Student's t-filter is derived via combining the proposed Student's t-matching technique and the adaptive method. A manoeuvring target tracking example is provided to demonstrate the effectiveness and potential of the proposed filter. [ABSTRACT FROM AUTHOR]

In this paper, a new robust Kalman filtering framework for a linear system with non-Gaussian heavy-tailed and/or skewed state and measurement noises is proposed through modeling one-step prediction and likelihood probability density functions as Gaussian scale mixture (GSM) distributions. The state vector, mixing parameters, scale matrices, and shape parameters are simultaneously inferred utilizing standard variational Bayesian approach. As the implementations of the proposed method, several solutions corresponding to some special GSM distributions are derived. The proposed robust Kalman filters are tested in a manoeuvring target tracking example. Simulation results show that the proposed robust Kalman filters have a better estimation accuracy and smaller biases compared to the existing state-of-the-art Kalman filters. [ABSTRACT FROM AUTHOR]

The relationship between the personal characteristics of senior management team members and the sustainable development of the enterprise has received much attention. This study used data on A-share listed companies in China from 2009 to 2022 to examine the effect of female executives on enterprise environmental, social, and governance (ESG) performance and tested the moderating role of ownership concentration and media exposure. Empirical testing was conducted on 20,188 annual observation samples using regression analysis. The empirical results demonstrate that female executives significantly improve corporate ESG performance, especially social responsibility performance. The study also revealed that ownership concentration plays an internal moderating role on the connection between female executives and corporate ESG performance, while media exposure plays an external moderating role. The conclusions remain the same after several robustness tests, including a lag test, substitution variable estimation, and propensity score matching. An economic consequence check and heterogeneity test were also conducted. This study's findings enrich the research on ESG influencing factors and offers empirical support for the value of female participation in corporate governance. [ABSTRACT FROM AUTHOR]

Current commutation from the load current path to the commutation path is the precondition to interrupt current successfully for hybrid direct current circuit breakers (DCCB). With remarkable current commutation performance, current commutation drive circuit (CCDC), mainly consisting of air-core transformer, fast-closing switch (FCS), and precharged capacitor, is proposed by us. In this paper, to eliminate the operating losses of CCDC and to avoid unrecoverable damage of FCS, CCDC used in hybrid DCCB for current commutation has been improved. Different submodules (SM) of commutation path in hybrid DCCB are analyzed, and experiments have been carried out on a hybrid DCCB prototype. Theoretical analysis and experiment results have shown that no matter which kind of SM is used, improved CCDC can ensure reliable current commutation in hybrid DCCB. In the end, by taking total cost of CCDC into consideration, a novel optimization method is proposed to design the parameters of CCDC. With this optimization method, CCDC for hybrid DCCBs with different rated voltage and current can be designed individually and its cost could be reduced significantly. [ABSTRACT FROM AUTHOR]

The Student’s t-based nonlinear filter (STNF) can cope with the filtering problem of nonlinear systems with heavy-tailed process and measurement noises. The key problem in the design of a STNF is how to calculate the Student’s t weighted integral, and the performance of the STNF depends heavily on the used numerical integration technique. In this paper, new high-degree Student’s t spherical-radial cubature rules (STSRCRs) for the Student’s t weighted integral are proposed based on spherical-radial transformation and moment matching methods, from which new high-degree Student’s t-based cubature filters (STCFs) are developed. The proposed high-degree STSRCRs can achieve better approximation to the Student’s t weighted integral as compared with the existing third-degree Student’s t integral rules. As a result, the proposed high-degree STCFs have higher estimation accuracy than the existing STNFs. Simulation results illustrate that the proposed filters can achieve higher estimation accuracy than the existing Gaussian approximate filters, Huber-based nonlinear Kalman filters and STNFs with slightly increased computational complexities, and are computationally much more efficient than the existing Gaussian sum filter and particle filter. [ABSTRACT FROM AUTHOR]

To solve the problem of unknown noise covariance matrices inherent in the cooperative localization of autonomous underwater vehicles, a new adaptive extended Kalman filter is proposed. The predicted error covariance matrix and measurement noise covariance matrix are adaptively estimated based on an online expectation-maximization approach. Experimental results illustrate that, under the circumstances that are detailed in the paper, the proposed algorithm has better localization accuracy than existing state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

Having a large number of electric vehicles (EVs) on the road will lead to vehicle congestion, long waiting times at charging stations (CSs), and even the inability to find an available charging position. Main technical challenges in mobile EVs charging may include charging information exchanges between EVs and CSs, EV traveling route and CS selection, and EV charging planning. To solve the above problems, this study provides an optimization solution for entire-trip charging and navigation of EVs, which balances charging resources, protects the travel time of EV drivers, and prioritizes renewable energy. The communication scheme of an EV charging network is designed, including real-time communication between the CSs, EV drivers, road condition monitoring center, power grid company, and the charging control center distributed in every parking lot. CSs in each road segment sort vehicles arriving at stations, allocate charging time slots, provide charging start times, and adjust those times for EVs with reservations that have not yet arrived. EV drivers can plan vehicle charging on their smart devices. The optimization of electric navigation can reduce the travel time or cost for drivers, ensure safe electricity usage, calculate vehicle routes, select CSs, and provide charging time intervals. The effectiveness of the method is verified on a regional road network in China, and the results show that it can balance the charging resource distribution of a CS, guarantee EV state of charge level, greatly reduce travel cost or travel time, provide precise waiting time, and prioritize renewable energy. • High-speed communication enables an electric vehicle to obtain information in time. • High-speed communication provides real-time changes in a charging station. • Entire-trip navigation computation is performed on each driver' terminal device. • Each charging station sorts vehicles according to arrival time at the station. • The optimization of a driver's navigation can reduce travel time or cost. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to assess the repeatability of intraocular lens (IOL) decentration measurements obtained through Pentacam, based on corneal topographic axis (CTA) and pupillary axis (PA), and to evaluate the level of agreement between Pentacam and OPD-Scan III devices in measuring IOL decentration. Methods: In this prospective observational case series, three measurements were performed with Pentacam to evaluate the repeatability of the measurements. The analysis included the calculation of the mean and standard deviations (SD), conducting a repeated measures analysis of variance (rANOVA), and determining an intraclass correlation coefficient (ICC) to assess the repeatability of the measurements. Moreover, Bland-Altman analysis was employed to assess the agreement between Pentacam and OPD-Scan III devices in measuring IOL decentration. IOL decentration measurements were obtained with respect to both CTA and PA. Results: A total of 40 eyes from 40 patients were analyzed. The rANOVA revealed no significant difference among three consecutive measurements of IOL decentration obtained with Pentacam. The mean SD of all parameters ranged from 0.04 mm to 0.07 mm. With CTA as the reference axis, the ICC values for Pentacam measurements of IOL decentration were 0.82 mm for the X-axis, 0.76 mm for the Y-axis, and 0.82 mm for spatial distance. When using PA as the reference axis, the corresponding ICC values were 0.87, 0.89, and 0.77, respectively. The 95% limits of agreement for all IOL decentration measurements were wide when comparing Pentacam and OPD-Scan III. Conclusions: Pentacam demonstrated high repeatability in measuring IOL decentration with respect to both CTA and PA. However, due to poor agreement between Pentacam and OPD-Scan III measurements, caution should be exercised when using data interchangeably between the two devices. [ABSTRACT FROM AUTHOR]

In this paper, the in-motion coarse alignment (IMCA) for odometer-aided strap-down inertial navigation system (SINS) is investigated with the main focus on compensating for the dynamic errors of gyroscope induced by severe maneuvering. A new Kalman-filtering-based IMCA method for an odometer-aided SINS is presented. A novel closed-loop approach to estimating the attitude matrix from the current body frame to the initial body frame is proposed, in which the attitude error between the closed-loop calculation and the true attitude matrix is first estimated, and then, the estimated attitude matrix is obtained by refining the closed-loop calculation with the estimated attitude error. A linear state-space model for the attitude error is derived, and then, a Kalman filter is employed to track the attitude error. Experimental results illustrate that the proposed closed-loop approach can estimate the attitude matrix from the current body frame to the initial body frame better than the existing open-loop approach, which results in improved alignment accuracy as compared with the existing optimization-based alignment method for the odometer-aided SINS when the vehicle maneuvers severely. [ABSTRACT FROM AUTHOR]

A novel robust Student's t-based Kalman filter is proposed by using the variational Bayesian approach, which provides a Gaussian approximation to the posterior distribution. Simulation results for a manoeuvring target tracking example illustrate that the proposed filter has smaller root mean square error and bias than existing filters. [ABSTRACT FROM PUBLISHER]

In this study, the authors investigate the filtering and smoothing problems of nonlinear systems with correlated noises at one epoch apart. A pseudomeasurement equation is firstly reconstructed with a corresponding pseudomeasurement noise, which is no longer correlated with the process noise. Based on the reconstructed measurement model, new Gaussian approximate (GA) filter and smoother are derived, from which Kalman filter and smoother can be obtained for linear systems. For nonlinear systems, different GA filters and smoothers can be developed through utilizing different numerical methods for computing Gaussian-weighted integrals involved in the proposed solution. Numerical examples concerning univariate nonstationary growth model, passive ranging problem, and target tracking show the efficiency of the proposed filtering and smoothing methods for nonlinear systems with correlated noises at one epoch apart. [ABSTRACT FROM AUTHOR]

Novel Student?s t based approaches for formulating a filter and smoother, which utilize heavy tailed process and measurement noise models, are found through approximations of the associated posterior probability density functions. Simulation results for manoeuvring target tracking illustrate that the proposed methods substantially outperform existing methods in terms of the root mean square error. [ABSTRACT FROM AUTHOR]

Hybrid direct current circuit breakers (DCCBs) have great prospects for the isolation of dc faults in multi‐terminal dc grids. Current commutation from the mechanical switch branch to the static DCCB branch is the precondition to interrupt fault currents successfully for the hybrid DCCBs. A current commutation drive circuit (CCDC) is presented in this study, and it has features of low cost, low operating losses and free maintenance. Experiments have been carried out on a unidirectional 44 kV hybrid DCCB prototype with CCDC. The results show the current of 3.4 kA can be commutated by CCDC within 130 μs, and be interrupted successfully within 2 ms. For a bidirectional 80 kV hybrid DCCB cell, the effectiveness of CCDC is validated with simulations carried out in PACAD. Then, an index is defined to represent the current commutation capability of CCDC and a mathematical model for CCDC is established to calculate the index. To reduce the cost of CCDC with certain current commutation capability, an optimisation method based on genetic algorithm is proposed in this study. According to the optimisation results of CCDC suitable for bidirectional 80 kV hybrid DCCB cell, the cost of CCDC has been reduced by optimisation. [ABSTRACT FROM AUTHOR]

In this paper, a new conditional posterior Cramér-Rao lower bound (CPCRLB) is proposed for a class of nonlinear systems, in which current measurement is dependent on current state as well as one step previous state. In order to compute the proposed CPCRLB recursively, a new particle filter for such class of nonlinear systems is designed, based on which a general formulation of the proposed CPCRLB can be derived. To facilitate practical engineering applications, CPCRLBs for special cases of such class of nonlinear systems, including nonlinear systems with coloured measurement noises and nonlinear systems with correlated noises at one epoch apart, are developed, respectively. Simulation results show the efficiency and superiority of the proposed CPCRLB as compared with existing CPCRLB. [ABSTRACT FROM AUTHOR]

The hybrid direct-current circuit breaker (DCCB) consists of a mechanical switch (MS) branch and a static DCCB branch. Once a short-circuit fault is detected, the current should commutate from the MS branch to the static DCCB branch first. In this paper, two current commutation measures have been investigated. A hybrid DCCB prototype with current commutated by the arc voltage of the SF${}_{6}$ switch is developed. Based on the prototype, current commutation and interruption tests have been carried out. The results show the current of 3.4 kA can be commutated within 2.4 ms, and be interrupted within 6 ms. Then, a current commutation drive circuit (CCDC) is proposed as the other current commutation measure. Equivalent current commutation tests have been carried out. The results show with the CCDC, both positive current and negative current can be commutated within tens of microseconds. Using a CCDC in series with a mechanical vacuum switch as the MS branch, a 44 kV/3.4 kA hybrid DCCB prototype is developed. Based on the prototype, current commutation and interruption tests are carried out. According to the results, the current of 3.4 kA can be commutated within 100 $\mu $s, and be interrupted within 2 ms. [ABSTRACT FROM PUBLISHER]

In this study, the authors focus on improving measurement update of existing nonlinear Kalman approximation filter and propose a new sigma-point Kalman filter with recursive measurement update. Statistical linearization technique based on sigma transformation is utilized in the proposed filter to linearize the nonlinear measurement function, and linear measurement update is applied gradually and repeatedly based on the statistically linearized measurement equation. The total measurement update of the proposed filter is nonlinear, and the proposed filter can extract state information from nonlinear measurement better than existing nonlinear filters. Simulation results show that the proposed method has higher estimation accuracy than existing methods. [ABSTRACT FROM AUTHOR]

In this study, the authors focus on estimating the unknown constant latency probability of non‐linear systems with one‐step randomly delayed measurements using maximum likelihood (ML) criterion. A new latency probability estimation algorithm is proposed based on an expectation maximisation approach to obtain an approximate ML estimation of latency probability. The proposed algorithm consists of expectation step (E‐step) and the maximisation step (M‐step). In the E‐step, the expectation of the complete data log‐likelihood function is approximately computed based on the currently estimated latency probability, and in the M‐step, the approximate expectation is maximised using the Newton approach. The efficacy of the proposed algorithm is illustrated in a numerical example concerning univariate non‐stationary growth model. [ABSTRACT FROM AUTHOR]

Aims: Tumor‐associated macrophages (TAMs) in the immune microenvironment play an important role in the increased drug resistance and recurrence of malignant glioma, but the mechanism remains incompletely inventoried. The focus of this study was to investigate the distinctions of M2‐like TAMs in the immune microenvironment between primary and recurrent malignant glioma and its influence in the recurrence. Methods: We employed single‐cell RNA sequencing to construct a single‐cell atlas for a total of 23,010 individual cells from 6 patients with primary or recurrent malignant glioma and identified 5 cell types, including TAMs and malignant cells. Immunohistochemical techniques and proteomics analysis were performed to investigate the role of intercellular interaction between malignant cells and TAMs in the recurrence of malignant glioma. Results: Six subgroups of TAMs were annotated and M2‐like TAMs were found to increase in recurrent malignant glioma significantly. A pseudotime trajectory and a dynamic gene expression profiling during the recurrence of malignant glioma were reconstructed. Up‐regulation of several cancer pathways and intercellular interaction‐related genes are associated with the recurrence of malignant glioma. Moreover, the M2‐like TAMs can activate the PI3K/Akt/HIF‐1α/CA9 pathway in the malignant glioma cells via SPP1‐CD44‐mediated intercellular interaction. Interestingly, high expression of CA9 can trigger the immunosuppressive response in the malignant glioma, thus promoting the degree of malignancy and drug resistance. Conclusion: Our study uncovers the distinction of M2‐like TAMs between primary and recurrent glioma, which offers unparalleled insights into the immune microenvironment of primary and recurrent malignant glioma. [ABSTRACT FROM AUTHOR]

In this paper, a new particle filter is proposed to solve the nonlinear and non-Gaussian filtering problem when measurements are randomly delayed by one sampling time and the latency probability of the delay is unknown. In the proposed method, particles and their weights are updated in Bayesian filtering framework by considering the randomly delayed measurement model, and the latency probability is identified by maximum likelihood criterion. The superior performance of the proposed particle filter as compared with existing methods and the effectiveness of the proposed identification method of latency probability are both illustrated in two numerical examples concerning univariate non-stationary growth model and bearing only tracking. [ABSTRACT FROM AUTHOR]

Key message: The QGw.nau - 2D, QGw.nau - 4B and QGw.nau - 5A intervals were investigated for their effects on weight, length, width, and thickness of kernels and their differential roles in determining kernel size and shape were demonstrated. Abstract: Grain weight (GW) contributes greatly to wheat yield and is directly related to kernel size and shape. Although over 100 quantitative trait loci (QTLs) for GW have been reported in the literatures, few have been well characterized for their association with kernel traits. In this study, three GW QTLs identified in elite cultivar 'Nanda2419' ('Mentana'), including QGw.nau- 2D, QGw.nau- 4B and QGw.nau- 5A, were investigated through near isogenic line (NIL) development and evaluation. NILs for all three QTLs and one NIL with both QGw.nau- 4B and QGw.nau- 5A were developed with the help of marker-assisted selection after two to three generations of backcross using cultivar 'Wangshuibai' as the recurrent parent. One NIL with QGw.nau- 4B in the background of cultivar 'Wenmai6' was also obtained. In four different field trials, these NILs consistently produced heavier kernels than the recurrent parents. QGw.nau- 4B showed the largest effect on GW; its presence resulted in 0.4-0.5 g increase of hundred-grain weight, depending on genetic backgrounds. QGw.nau- 4B and QGw.nau- 5A functioned additively in conditioning GW. These three QTL intervals showed pleiotropic effects on, or close linkage with genes for, spike length, plant height and flag leaf width, respectively, and acted differentially in determining the kernel dimensions that are the major GW determinants. They all conditioned wider kernels with QGw.nau- 5A displaying the largest effect. QGw.nau- 4B and QGw.nau- 5A also conditioned thicker kernels but had opposite effects on kernel length. This study demonstrated that marker-assisted selection is effective for GW improvement. The availability of GW NILs could facilitate cloning of GW genes and unraveling of kernel development mechanisms. [ABSTRACT FROM AUTHOR]

The continuous development of the voltage-source-converter-based multiterminal high-voltage direct-current transmission system has led to a great need for dc circuit breakers to isolate short-circuit faults in the dc side. The operating time of dc breakers is mainly dependent on the mechanical contacts separation time. In this paper, three key units of the ultra-fast operating mechanism, including drive unit, buffer unit, and holding unit were investigated. A prototype of the 40.5-kV ultra-fast vacuum switch has been developed where the Thomson-coil actuator (TCA) is used to fulfill the function of the drive unit and the buffer unit, and a bistable spring mechanism is used as the holding unit. The state equations of the bidirectional TCA used as the drive unit and buffer unit in the operating process are completed based on the equivalent circuit method and finite-element method. The experimental and simulation results of the prototype are compared and they are in good agreement as expected. Therefore, the mathematical model is verified. The factors that influence the drive unit and the buffer unit performances are discussed. The achieved operating time of the prototype is about 2.3 ms. [ABSTRACT FROM PUBLISHER]

This paper proposes a general framework solution of Gaussian filter (GF) for both linear and nonlinear dynamic systems with correlated noises at the same epoch. Detailed discussions and simulation comparisons with existing Gaussian approximation recursive filter and existing de-correlating GF are provided, which show advantages of estimation accuracy of the proposed method in some applications. [ABSTRACT FROM AUTHOR]

The choice of free parameter in embedded cubature Kalman filter (ECKF) is important, and it is difficult to choose an optimal value in practice. To solve this problem, an adaptive method is proposed to determine the value of free parameter of ECKF based on maximum likelihood criterion. By incorporating this method in the third-degree ECKF, a new third-degree adaptive ECKF (AECKF) algorithm is obtained. To further improve the accuracy of the third-degree AECKF, a new fifth-degree AECKF based on the fifth-degree embedded cubature rule is developed. Simulation results show that the proposed algorithms have higher estimation accuracy than existing methods. [ABSTRACT FROM AUTHOR]

In this study, a novel class of interpolatory cubature Kalman filters (ICKFs) with arbitrary degrees of accuracy, including the third‐degree ICKF and the fifth‐degree ICKF, are proposed based on the interpolatory cubature rule. Existing cubature Kalman filter and unscented Kalman filter can both be deemed as special cases of the proposed ICKFs. Furthermore, the proposed fifth‐degree ICKF can achieve higher filtering accuracy than existing fifth‐degree methods by properly choosing free parameters, as illustrated in numerical simulation. [ABSTRACT FROM AUTHOR]