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The relationship between duration perception and the feeling of time passing (passage of time) is not yet understood. In the present study, we assessed introspective reaction times (RT) and passage of time judgments in a speeded RT task. Task difficulty was manipulated in a numerical comparison task by numerical distance (distance from the number 45) and notation (digit vs. word). The results showed that both effects were reflected in introspective RTs, replicating previous results. Moreover, passage of time judgments showed a very similar pattern, with slower passage of time for more difficult comparisons. These results suggest that in the millisecond range judgments of duration and passage of time largely mirror each other when participants introspect about their own RT performance.

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In order to improve the real-time performance of the trajectory tracking of autonomous vehicles, this paper applies the alternating direction multiplier method (ADMM) to the receding optimization of model predictive control (MPC), which improves the computational speed of the algorithm. Based on the vehicle dynamics model, the output equation of the autonomous vehicle trajectory tracking control system is constructed, and the auxiliary variable and the dual variable are introduced. The quadratic programming problem transformed from the MPC and the vehicle dynamics constraints are rewritten into the solution of the ADMM form, and a decreasing penalty factor is used during the solution process. The simulation verification is carried out through the joint simulation platform of Simulink and Carsim. The results show that, compared with the active set method (ASM) and the interior point method (IPM), the algorithm proposed in this paper can not only improve the accuracy of trajectory tracking, but also exhibits good real-time performance in different prediction time domains and control time domains. When the prediction time domain increases, the calculation time shows no significant difference. This verifies the effectiveness of the ADMM in improving the real-time performance of MPC.

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Medical ultrasound technology has garnered significant attention in recent years, with Ultrasound-guided regional anesthesia (UGRA) and carpal tunnel diagnosis (CTS) being two notable examples. Instance segmentation, based on deep learning approaches, is a promising choice to support the analysis of ultrasound data. However, many instance segmentation models cannot achieve the requirement of ultrasound technology e.g. real-time. Moreover, fully supervised instance segmentation models require large numbers of images and corresponding mask annotations for training, which can be time-consuming and labor-intensive in the case of medical ultrasound data. This paper proposes a novel weakly supervised framework, CoarseInst, to achieve real-time instance segmentation of ultrasound images with only box annotations. CoarseInst not only improves the network structure, but also proposes a two-stage "coarse-to-fine" training strategy. Specifically, median nerves are used as the target application for UGRA and CTS. CoarseInst consists of two stages, with pseudo mask labels generated in the coarse mask generation stage for self-training. An object enhancement block is incorporated to mitigate the performance loss caused by parameter reduction in this stage. Additionally, we introduce a pair of loss functions, the amplification loss, and the deflation loss, that work together to generate the masks. A center area mask searching algorithm is also proposed to generate labels for the deflation loss. In the self-training stage, a novel self-feature similarity loss is designed to generate more precise masks. Experimental results on a practical ultrasound dataset demonstrate that CoarseInst could achieve better performance than some state-of-the-art fully supervised works.

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This research investigates the number of on-time flights (OTFs) at European airports and how this number is influenced by an airport's flight connectivity. We conduct a spatial statistical analysis of the spatial context relationship using econometric models, and the interaction between the number of airport's on-time flights (OTFs) and flight connectivity. Using 2017 and 2018 data, we characterize the relationship between a European airport's air connectivity index (ACI) and the number of flights that depart or arrive at a gate within 15 min of schedule (OTFs). We also analyze the relationship between OTFs at a given airport and those of neighboring airports. As the distances between airports increase, autocorrelation shifts from a positive to a negative sign meaning that at greater distances, airports' on-time performance is less dissimilar. We find that before the pandemic and the ensuing global travel shutdown, a spatially lagged term of ACI improves the model's ability to account for variations in OTFs across airports. Flight delay propagation in the air transport system caused delays to occur due to the shared resources underlying an initially delayed flight and subsequent flights. This analysis offers a rational for increasing airport connectivity as a way of improving the share of on-time flights of European airports.

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In this study, a distributed output-feedback design approach for ensuring fault-tolerant initial network connectivity and preselected-time consensus tracking performance is proposed for a class of uncertain time-delay nonlinear multiagent systems (TDNMSs) with unexpected actuator and communication faults. It is assumed that time-varying state delays and system nonlinearities in TDNMSs are unknown. The main contribution of this study is to provide a delay-independent output-feedback control strategy to address a fault-tolerant initial connectivity preservation problem in the consensus tracking field. A local delay-independent adaptive state observer using neural networks is designed for each follower, and the boundedness of local observation errors is proved by constructing a Lyapunov-Krasovskii functional and adaptive tuning laws. Then, the local nonlinear relative output errors using a time-varying function with a preselected convergence time are derived to design simple local delay-independent trackers. The stability of the proposed consensus tracking system is analyzed, and simulation comparison results demonstrate the validity of the proposed strategy.

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Different from the finite/fixed-time control methodologies on longitudinal/attitude synchronization or 2-D motion of UAVs, this article attempts to propose a distributed adaptive specified-time control scheme for synchronization tracking of networked 6-degree-of-freedom (DOF) UAVs. To be specific, the novel specified-time performance functions (STPFs) are designed in such a way that the desired performance bounds can be imposed on velocity and attitude tracking errors. Based on the transformed errors, by utilizing the barrier Lyapunov functions (BLFs), a distributed specified-time control scheme is constructed with adaptive robustifying terms to enhance the fault-tolerant ability and compensate the modeling uncertainties. By means of Lyapunov stability theory, it is proved that the resulting control scheme can guarantee the boundedness of all closed-loop state variables, and preserve the guaranteed performance bounds for synchronization tracking errors of velocity and attitude at the same time. Theoretical results are confirmed by experiment and simulation validations.

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This paper examines the effects of the COVID-19 pandemic on flight delays in the U.S. airline industry. Using daily data on COVID-19 cases and flight on-time performance, and controlling for product, carrier and market characteristics, we find that increases in reported COVID-19 cases are associated with reductions in both departure and arrival delays. Specifically, a standard deviation increase in COVID-19 cases reduces arrival delay by 1 min 42 s and departure delay by 2 min, on average. Our results suggest that despite the economic fallout from the pandemic, a silver lining emerges-flights are departing and arriving with less delay amid the pandemic.

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Real-time soft tissue characterization is significant to robotic assisted minimally invasive surgery for achieving precise haptic control of robotic surgical tasks and providing realistic force feedback to the operator. This paper presents a nonlinear methodology for online soft tissue characterization. An extended Kalman filter (EKF) is developed based on dynamic linearization of the nonlinear H-C contact model in terms of system state for online characterization of soft tissue parameters. To handle the resultant linearization modelling error, an innovation orthogonal EKF is further developed by incorporating an adaptive factor in the EKF filtering to adaptively adjust the innovation covariance according to the principle of innovation orthogonality. Simulation and experimental results as well as comparison analysis demonstrate that the proposed methodology can effectively characterize soft tissue parameters, leading to dramatically improved accuracy comparing to recursive least square estimation. Further, the proposed methodology also requires a smaller computational load and can achieve the real-time performance for soft tissue characterization.

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In the construction process of smart cities, more and more video surveillance systems have been deployed for traffic, office buildings, shopping malls, and families. Thus, the security of video surveillance systems has attracted more attention. At present, many researchers focus on how to select the region of interest (RoI) accurately and then realize privacy protection in videos by selective encryption. However, relatively few researchers focus on building a security framework by analyzing the security of a video surveillance system from the system and data life cycle. By analyzing the surveillance video protection and the attack surface of a video surveillance system in a smart city, we constructed a secure surveillance framework in this manuscript. In the secure framework, a secure video surveillance model is proposed, and a secure authentication protocol that can resist man-in-the-middle attacks (MITM) and replay attacks is implemented. For the management of the video encryption key, we introduced the Chinese remainder theorem (CRT) on the basis of group key management to provide an efficient and secure key update. In addition, we built a decryption suite based on transparent encryption to ensure the security of the decryption environment. The security analysis proved that our system can guarantee the forward and backward security of the key update. In the experiment environment, the average decryption speed of our system can reach 91.47 Mb/s, which can meet the real-time requirement of practical applications.

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OBJECTIVE: With most paediatric emergency research in Australia conducted at tertiary EDs, it is important to understand how presentations differ between those at tertiary paediatric EDs and all other EDs. METHODS: Retrospective epidemiological study assessing paediatric case-mix and time-based performance metrics (aged 0-14 years) obtained from a national health service minimum dataset for the 2017-2018 financial year, comparing tertiary paediatric EDs and all other EDs. We defined a 'major tertiary paediatric hospital' as one which was accredited for training in both paediatric emergency medicine and paediatric intensive care. RESULTS: Of the 1 695 854 paediatric ED presentations, 23.8% were seen in nine major metropolitan tertiary paediatric hospitals. Reasons for presentations were more distinctive between cohorts among children aged 10-14 years, where psychiatric illness (5.2% vs 2.5%) and neurological illness (4.5% vs 2.5%) were more commonly seen in major tertiary paediatric EDs. Australian Indigenous children were significantly less likely to present to tertiary paediatric EDs (3.0%), compared with other EDs (9.7%) (odds ratio 0.27, 95% confidence interval 0.26-0.27). While median waiting times were longer in major tertiary paediatric EDs (28 min [interquartile range 11-65]) than in other EDs (20 min [interquartile range 8-48], P < 0.001), patients were also less likely to leave without being seen (5.5% in tertiary paediatric EDs vs 6.9% in other EDs; odds ratio 0.80, 95% confidence interval 0.78-0.81). CONCLUSIONS: The present study identified key areas of difference in paediatric presentations between tertiary paediatric EDs and other EDs. It is vital to broaden paediatric ED research beyond tertiary paediatric centres, to ensure relevance and generalisability.

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BACKGROUND AND OBJECTIVE: Soft tissue modelling is crucial to surgery simulation. This paper introduces an innovative approach to realistic simulation of nonlinear deformation behaviours of biological soft tissues in real time. METHODS: This approach combines the traditional nonlinear finite-element method (NFEM) and nonlinear Kalman filtering to address both physical fidelity and real-time performance for soft tissue modelling. It defines tissue mechanical deformation as a nonlinear filtering process for dynamic estimation of nonlinear deformation behaviours of biological tissues. Tissue mechanical deformation is discretized in space using NFEM in accordance with nonlinear elastic theory and in time using the central difference scheme to establish the nonlinear state-space models for dynamic filtering. RESULTS: An extended Kalman filter is established to dynamically estimate nonlinear mechanical deformation of biological tissues. Interactive deformation of biological soft tissues with haptic feedback is accomplished as well for surgery simulation. CONCLUSIONS: The proposed approach conquers the NFEM limitation of step computation but without trading off the modelling accuracy. It not only has a similar level of accuracy as NFEM, but also meets the real-time requirement for soft tissue modelling.

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In the current era of big data, huge quantities of valuable data, which may be of different levels of veracity, are being generated at a rapid rate. Embedded into these big data are implicit, previously unknown and potentially useful information and valuable knowledge that can be discovered by data science solutions, which apply techniques like data mining. There has been a trend that more and more collections of these big data have been made openly available in science, government and non-profit organizations so that people could collaboratively study and analysis these open big data. In this article, we focus on open big data for public transit because public transit (e.g., bus) as a means of transportation is a vital part of many people's lives. As time is a precious resource, bus delays could negatively affect commuters' plans. Unfortunately, they are inevitable. Hence, many existing works focused on predicting bus delays. However, predicting on-time or early buses is also important. For instance, commuters who come to a bus stop on time may still miss their buses if the buses leave early. So, in this article, we examine open big data about bus performance (e.g., early, on-time, and late stops). We analyze the data with frequent pattern mining and make predictions with decision-tree based classification. For illustration, we perform predictive analytics on real-life open big data available on Winnipeg Open Data Portal, about bus performance from Winnipeg Transit. It shows the benefits of predictive analytics on open big data for supporting smart transportation services.

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Embedded and mobile smart devices face problems related to limited computing power and excessive power consumption. To address these problems, we propose Mixed YOLOv3-LITE, a lightweight real-time object detection network that can be used with non-graphics processing unit (GPU) and mobile devices. Based on YOLO-LITE as the backbone network, Mixed YOLOv3-LITE supplements residual block (ResBlocks) and parallel high-to-low resolution subnetworks, fully utilizes shallow network characteristics while increasing network depth, and uses a "shallow and narrow" convolution layer to build a detector, thereby achieving an optimal balance between detection precision and speed when used with non-GPU based computers and portable terminal devices. The experimental results obtained in this study reveal that the size of the proposed Mixed YOLOv3-LITE network model is 20.5 MB, which is 91.70%, 38.07%, and 74.25% smaller than YOLOv3, tiny-YOLOv3, and SlimYOLOv3-spp3-50, respectively. The mean average precision (mAP) achieved using the PASCAL VOC 2007 dataset is 48.25%, which is 14.48% higher than that of YOLO-LITE. When the VisDrone 2018-Det dataset is used, the mAP achieved with the Mixed YOLOv3-LITE network model is 28.50%, which is 18.50% and 2.70% higher than tiny-YOLOv3 and SlimYOLOv3-spp3-50, respectively. The results prove that Mixed YOLOv3-LITE can achieve higher efficiency and better performance on mobile terminals and other devices.

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The purpose of the present study was to compare time results from a roller-skiing double poling (DP) time trial with different physiological variables, muscular strength variables, and DP characteristics in both male and female young competitive skiers with the same relative training background. In order to do this, 28 (16 women and 12 men) well-trained 16-25-year-old cross-country skiers from three Norwegian high schools for skiers, as well as local high performance competitive skiers from the South-East of Norway were recruited to participate in the study. All participants were tested for; maximal oxygen uptake in running, Peak oxygen uptake in DP, lactate threshold in DP, DP economy, time to voluntary exhaustion in DP, force analyses in DP, one repetition maximum and power output in pulldown, and leg press and a time trial during DP roller skiing. The results expressed strong correlations between roller skiing time trial performance and maximal strength in pull-down, both independent (r xy = -0.83, p < 0.01) and dependent (r xy-z = -0.50, p < 0.02) of sex. Higher maximal upper body strength was related to higher DP peak forces (PF) (r xy = 0.78, p < 0.02), lower DP frequency (r xy = -0.71, p < 0.01), and shorter DP contact time (CT) (r xy = -0.48, p < 0.02). The practical implications of the present study is to acknowledge maximal upper body strength as a performance determining factor in DP. This point at the importance of including maximal strength training in cross-country skiers training programs.

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BACKGROUND AND OBJECTIVES: Cutting procedures are the most common operations in surgical simulation. In order to provide realistic visual feedback with the details of the internal structures of soft tissue to the operator, a novel volumetric geometric model is presented for cutting procedures in surgical simulation. METHODS: A novel volumetric geometric model, which is based on volume rendering and the Bézier curve, is presented for cutting procedures. The Bézier curve is used to optimize the physical model of cutting simulation, making the edge of incision smooth without increasing the computational load of the physical model. Volume rendering is used to render the cutting process, which improves significantly the realism of simulation since both surface textures and the details of the internal structures of soft tissues are rendered. RESULTS AND CONCLUSIONS: The simulation results show that the edges of the incision optimized by using the proposed geometric model are smooth and the details of internal structures of soft tissue can be rendered. In comparison with other volumetric models, the computational efficiency is much improved. Compared with conventional cutting simulation methods, the proposed volumetric geometric model improves the effects of visual feedback since both surface and internal structures are rendered according to the optimized physical model.

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This paper presents a new neural network methodology for modelling of soft tissue deformation for surgical simulation. The proposed methodology formulates soft tissue deformation and its dynamics as the neural propagation and dynamics of cellular neural networks for real-time, realistic, and stable simulation of soft tissue deformation. It develops two cellular neural network models; based on the bioelectric propagation of biological tissues and principles of continuum mechanics, one cellular neural network model is developed for propagation and distribution of mechanical load in soft tissues; based on non-rigid mechanics of motion in continuum mechanics, the other cellular neural network model is developed for governing model dynamics of soft tissue deformation. The proposed methodology not only has computational advantage due to the collective and simultaneous activities of neural cells to satisfy the real-time computational requirement of surgical simulation, but also it achieves physical realism of soft tissue deformation according to the bioelectric propagation manner of mechanical load via dynamic neural activities. Furthermore, the proposed methodology also provides stable model dynamics for soft tissue deformation via the nonlinear property of the cellular neural network. Interactive soft tissue deformation with haptic feedback is achieved via a haptic device. Simulations and experimental results show the proposed methodology exhibits the nonlinear force-displacement relationship and associated nonlinear deformation of soft tissues. Furthermore, not only isotropic and homogeneous but also anisotropic and heterogeneous materials can be modelled via a simple modification of electrical conductivity values of mass points.

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OBJECTIVE: User safety and the quality of diagnostics on the epilepsy monitoring unit (EMU) depend on reaction to seizures. Online seizure detection might improve this. While good sensitivity and specificity is reported, the added value above staff response is unclear. We ascertained the added value of two electroencephalograph (EEG) seizure detection algorithms in terms of additional detected seizures or faster detection time. METHODS: EEG-video seizure recordings of people admitted to an EMU over one year were included, with a maximum of two seizures per subject. All recordings were retrospectively analyzed using Encevis EpiScan and BESA Epilepsy. Detection sensitivity and latency of the algorithms were compared to staff responses. False positive rates were estimated on 30 uninterrupted recordings (roughly 24 h per subject) of consecutive subjects admitted to the EMU. RESULTS: EEG-video recordings used included 188 seizures. The response rate of staff was 67%, of Encevis 67%, and of BESA Epilepsy 65%. Of the 62 seizures missed by staff, 66% were recognized by Encevis and 39% by BESA Epilepsy. The median latency was 31 s (staff), 10 s (Encevis), and 14 s (BESA Epilepsy). After correcting for walking time from the observation room to the subject, both algorithms detected faster than staff in 65% of detected seizures. The full recordings included 617 h of EEG. Encevis had a median false positive rate of 4.9 per 24 h and BESA Epilepsy of 2.1 per 24 h. CONCLUSIONS: EEG-video seizure detection algorithms may improve reaction to seizures by improving the total number of seizures detected and the speed of detection. The false positive rate is feasible for use in a clinical situation. Implementation of these algorithms might result in faster diagnostic testing and better observation during seizures.

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Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

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BACKGROUND: Realistic and real-time modeling and simulation of soft tissue deformation is a fundamental research issue in the field of surgical simulation. OBJECTIVE: In this paper, a novel cellular neural network approach is presented for modeling and simulation of soft tissue deformation by combining neural dynamics of cellular neural network with ChainMail mechanism. METHOD: The proposed method formulates the problem of elastic deformation into cellular neural network activities to avoid the complex computation of elasticity. The local position adjustments of ChainMail are incorporated into the cellular neural network as the local connectivity of cells, through which the dynamic behaviors of soft tissue deformation are transformed into the neural dynamics of cellular neural network. RESULTS: Experiments demonstrate that the proposed neural network approach is capable of modeling the soft tissues' nonlinear deformation and typical mechanical behaviors. CONCLUSIONS: The proposed method not only improves ChainMail's linear deformation with the nonlinear characteristics of neural dynamics but also enables the cellular neural network to follow the principle of continuum mechanics to simulate soft tissue deformation.

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Objective To develop a high-definition video display system for medical endoscope based on system on chip (SoC),and meet the requirement of high-resolution and real-time video display.Method A CMOS camera was used for video data capture.A SoC chip,integrated with a dual-core ARM Crotex-A9 processor and a field programmable gate array (FPGA),was employed as the kernel of the system.The HPS part of the SoC was used to build an embedded system to realize human-computer interaction,and the FPGA part was used to store and cache video data.The HPS and FPGA part were connected through a high-performance ARM AMBA AXI bus bridge broadband system,so as to achieve encoding of the cached video data and real-time display on screen.Results A high-definition video display system was built based on SoC.This system can achieve capture,processing and realtime display of high-definition video,as well as video freeze function.Conclusions The experimental results indicate that this system is feasible and effective,and possesses the advantages of customizability,multiple-exploitation and high performance of real-time video display.