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Being composed of blood cells and plasma, the blood flow has different rheological properties from common fluids. The viscoelastic properties of blood not only affect the flow characteristics of blood but also influence the shape of bleeding. In order to achieve the bleeding simulation with physical viscoelastic, we consider the effect of the aggregation behavior of red blood cells on the bleeding process. The elastic force caused by viscoelastic fluid elasticity is incorporated into the standard Navier–Stokes momentum equation, and an improved momentum equation that characterizes the rheological characteristics of viscoelastic blood is constructed. The effect of introducing elastic force on the particles motion is analyzed from the microscopic particle perspective by designing a two-dimensional particle system. The results of bleeding simulation show that the improved method eliminates the discontinuous boundary due to blood gushing out of the wound and forms irregular bleeding shapes in the bleeding process. In addition, the viscoelastic bleeding simulation exhibits a similar bleeding effect without distortion when the number of particles decreases, which further proves the reliability of proposed method. [ABSTRACT FROM AUTHOR]

The event-based finite-time H ∞ filtering problem for networked systems is concerned. A novel hybrid attack model is introduced considering denial-of-service and deception attacks in the communication channel between sampler and filter with an aperiodic form. In order to increase the effectiveness of data transmission in network communication channel and enable the filtering system against attack, an adaptive event–triggered mechanism is proposed. Sufficient condition for ensuring exponential mean-square finite-time boundedness of the filtering error system with an expected H ∞ disturbance attenuation index is obtained. The design of the filter and the adaptive event–triggered mechanism is derived. A practical example is given to demonstrate the effectiveness of the proposed filtering scheme in an active suspension system. [ABSTRACT FROM AUTHOR]

In this article, the tracking control problem is addressed for uncertain nonlinear systems with actuator saturation and unmeasurable unmodeled dynamics. Being different from the existing performance function control results, to constraint the output tracking error within a predefined boundary in finite time, an improved performance function, that is, finite‐time performance function, is introduced. The design difficulties of asymmetric input saturation and unmodeled dynamics are solved simultaneously for the first time by applying a smooth non‐affine function and a measurable dynamic signal. Furthermore, an adaptive fuzzy control scheme is established via command filtering, which not only guarantees the semi‐global boundedness of all the controlled system signals but also makes the output tracking error that can be restrained by the described performance bound within a finite‐time interval. At last, an effective example is supplied to validate the availability of the presented theoretical finding. [ABSTRACT FROM AUTHOR]

In this paper, the quantisation synchronisation control problem of bilateral teleoperation systems with communication delays is investigated. The inaccurate modelling problem including the system uncertainties and the unknown disturbances are compensated by using the radial basis function networks (RBFN). By introducing the hysteretic quantiser, the control signals are quantised to a set of discrete values to ease the accuracy requirement for actual physical systems and the requirement for communication rate. The position synchronisation of joint variables between the master and slave manipulators is achieved by using the designed control signals and adaptive parameters. Simulation results verify that the synchronisation tracking errors gradually converge to a small neighbourhood around zero. [ABSTRACT FROM AUTHOR]

Mineralocorticoid receptor antagonist (MRA) therapy may be beneficial to patients with atrial fibrillation (AF), but little is known about their use in patients with AF and subsequent outcomes. In order to better understand MRA use and subsequent outcomes, we performed a retrospective cohort study of the contemporary ORBIT-AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registry. AF progression and cardiovascular outcomes were compared using propensity-matched Cox proportional hazards modeling according to MRA use at baseline and new MRA use at follow-up versus patients with no MRA use. Among 7012 patients with nonpermanent AF, 320 patients were taking MRA at enrollment, and 416 patients initiated MRA use during follow-up. The mean patient age was 72.5 years, 56.3% were men, and 70.4% had paroxysmal AF. Among all patients taking MRAs, 434 (59.0%) had heart failure, 655 (89.0%) had hypertension, and 380 (51.6%) had both. After adjustment, new MRA use was not associated with reduced AF progression (hazard ratio, 1.18; 95% confidence interval, 0.88-1.58; P=0.27) but showed a trend towards lower risk of stroke, transient ischemic attack, or systemic embolism (hazard ratio, 0.17; 95% confidence interval, 0.02-1.23; P=0.08). Results were similar for a comparison of new MRA users and baseline MRA users compared with nonusers. In community-based outpatients with AF, the majority of MRA use was for heart failure and hypertension. MRA use also trended towards lower adjusted stroke risk. Future studies should test the hypothesis that MRA use may decrease the risk of stroke in patients with AF.

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two US academic medical centers with 216,221 adult patients hospitalized for at least 24 h. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting: in-hospital mortality (area under the receiver operator curve [AUROC] across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed traditional, clinically-used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios. In a case study of a particular prediction, we demonstrate that neural networks can be used to identify relevant information from the patient's chart.

In light of the enormous changes unfolding in the higher education landscape, we don't have to look too far to recognize evidence of the transformation and redefinition of the construct of both teaching and learning in the information age. With a growing focus on teaching and learning at all levels of post-secondary institutions, innovation is reflective in the introduction of new learning spaces, state-of-the-art technology-enhanced education, and prominence given to discussions about adapting teaching and learning to the twenty-first century. Likewise, in this article we examine the reflections, ideas, conversations and exchanges inspired by the cohort's plenary planning discussions and the current birth of innovation in reshaping Canadian higher education.

Family planning is a shared responsibility, but available male-directed contraceptive methods are either not easily reversible (vasectomy) or not sufficiently effective (condom). However, roughly 20% of couples using a contraceptive method worldwide, and up to 80% in some countries, still choose a male-directed method. Male hormonal contraception is highly effective, with perfect use failure rates of 0·6% (95% CI 0·3-1·1) if sperm concentrations of less than 1 million per mL are maintained. After cessation of male hormonal contraception, sperm output fully recovers in a predictable manner, resulting in pregnancies and livebirths. Spontaneous miscarriage and fetal malformation rates after recovery of sperm output overlap those in the general population. Short-term adverse events-acne, night sweats, increased weight, and altered mood and libido-are recognised, but are generally mild. Further optimisation of specific androgen-progestin regimens and phase 3 studies of lead combinations are still needed to successfully develop an approved male hormonal contraceptive and to identify long-term side-effects.

In this paper, an adaptive fault‐tolerant fixed‐time control problem is considered via command‐filter technique for stochastic nonlinear systems with sensor fault and actuator hysteresis. With the application of command‐filtering technique, a novel command‐filter compensate mechanism is designed, which implies that the improved control scheme not only eliminates "the explosion of complexity" but also realizes the compensate signal is bounded within fixed‐time interval. The unavailability of state variables caused by sensor fault is solved by applying parameter separation and regrouping approach. Meanwhile, an adaptive auxiliary signal is designed to cope with the backlash‐like hysteresis phenomenon, which can avoid singularity, reduce chattering, and facilitate controller design. Combining backstepping technique and Lyapunov stability theorem, an adaptive fault‐tolerant control approach is developed, which can guarantee all closed‐loop signals remain semi‐globally practical fixed‐time stable (SGPFS) in probability. The validity of the proposed strategy is illustrated by simulation examples. [ABSTRACT FROM AUTHOR]

To address model-based event-triggered fixed-time consensus of nonlinear multi-agent systems with both fixed topology and switching topologies, a novel model-based event-triggered protocol is presented. In the proposed protocol, agents in multi-agent systems update controllers by utilizing estimated state values in the triggered intervals. And for reducing the burdens of bandwidth further, the model-based event-triggered protocol is extended to self-triggered protocol which does not need continuous communication in triggered intervals. It is proven that Zeno behavior is excluded. Our main contributions are giving a novel distributed model-based event-triggered protocol for fixed-time consensus and extending it into a self-triggered protocol. Finally, a couple of simulation examples are provided to verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]