Stopping Criterion Research Articles

Distributed Average Consensus Algorithms in d-Regular Bipartite Graphs: Comparative Study

Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.

Estimation of small failure probabilities by partially Bayesian active learning line sampling: Theory and algorithm

Line sampling (LS) has proved to be a highly promising advanced simulation technique for assessing small failure probabilities. Despite the great interest in practical engineering applications, many efforts from the research community have been devoted to improving the standard LS. This paper aims at offering some new insights into the LS method, leading to an innovative method, termed ‘partially Bayesian active learning line sampling’ (PBAL-LS). The problem of evaluating the failure probability integral in the LS method is treated as a Bayesian, rather than frequentist, inference problem, which allows to incorporate our prior knowledge and model the discretization error. The Gaussian process model is used as the prior distribution for the distance function, and the posterior mean, and an upper bound of the posterior variance of the failure probability are derived. Based on the posterior statistics of the failure probability, we also put forward a learning function and a stopping criterion, which enable us to use active learning. Besides, an efficient algorithm is also designed to implement the PBAL-LS method, with the ability to automatically adjust the important direction and efficiently process the lines. Five numerical examples are studied to demonstrate the performance of the proposed PBAL-LS method against several existing methods.

Forecasting and optimization stock predictions: Varying asset profile, time window, and hyperparameter factors

Machine learning has made significant progress in various fields, including financial markets. Numerous studies have applied different machine learning algorithms to predict stock market behavior, but these studies often face challenges in terms of data acquisition and preparation, algorithm design, hyperparameter optimization, and feature selection, as well as the inherent volatility of stocks. In this work, our aim is to review the literature for comprehensive studies that address these challenges and enhance the state-of-the-art by introducing novel factors, such as multi-time windows, training batch size, stopping criteria, training data ratio, and financial technical indicators. We observe statistical significance when varying the training period window, with a p-value lower than 0.0001. However, genetic-based hyperparameter optimization brings about a significant 40% improvement compared to random-grid search. Concerning the inclusion of technical indicators, we see little improvement in terms of prediction accuracy, but there is some improvement in directional prediction accuracy across several stocks. Overall, the results show high variation with respect to the time window chosen for conducting a study. Additionally, we discover that the characteristics of the stock and the time period, including the length of the time period and the specific start and end dates, significantly impact prediction accuracy.

Package AdvEMDpy: Algorithmic variations of empirical mode decomposition in Python

AbstractThis work presents a $\textsf{Python}$ EMD package named AdvEMDpy that is both more flexible and generalises existing empirical mode decomposition (EMD) packages in $\textsf{Python}$ , $\textsf{R}$ , and $\textsf{MATLAB}$ . It is aimed specifically for use by the insurance and financial risk communities, for applications such as return modelling, claims modelling, and life insurance applications with a particular focus on mortality modelling. AdvEMDpy both expands upon the EMD options and methods available, and improves their statistical robustness and efficiency, providing a robust, usable, and reliable toolbox. Unlike many EMD packages, AdvEMDpy allows customisation by the user, to ensure that a broader class of linear, non-linear, and non-stationary time series analyses can be performed. The intrinsic mode functions (IMFs) extracted using EMD contain complex multi-frequency structures which warrant maximum algorithmic customisation for effective analysis. A major contribution of this package is the intensive treatment of the EMD edge effect which is the most ubiquitous problem in EMD and time series analysis. Various EMD techniques, of varying intricacy from numerous works, have been developed, refined, and, for the first time, compiled in AdvEMDpy. In addition to the EMD edge effect, numerous pre-processing, post-processing, detrended fluctuation analysis (localised trend estimation) techniques, stopping criteria, spline methods, discrete-time Hilbert transforms (DTHT), knot point optimisations, and other algorithmic variations have been incorporated and presented to the users of AdvEMDpy. This paper and the supplementary materials provide several real-world actuarial applications of this package for the user’s benefit.

Proof Load Testing Method by the American Association of State Highway and Transportation Officials and Suggestions for Improvement

Because of the aging of infrastructure, methods are explored by which the reliability of existing bridges and viaducts can be assessed. In cases in which limited information of the structure is available or its condition is of concern, proof load testing may be used to demonstrate sufficient live load carrying capacity. Proof load tests in the U.S.A. are typically performed using the Manual for Bridge Evaluation (MBE) published by the American Association of State Highway and Transportation Officials (AASHTO). The proof load is expressed by the regular live load model magnified by the target proof load factor. The level of reliability obtained using the target proof load factor is not explicitly stated in the MBE, but is of particular interest. In this article, relevant background documents are investigated to uncover the underlying calculations, assumptions, and input data. Current challenges in proof load testing are described in which the considerations of time dependence, stop criteria, available information, and system-level assessment are highlighted. Subsequently, improvements to the MBE proof load testing background are suggested. An example calculation using traffic data from the Netherlands shows that the HL93 load model and Eurocode LM1 provide a reasonably constant proof load factor with span length for bending and shear. However, the HS20 load model does not scale well with increasing span length. It is found that the magnitude of the target load as specified through the proof load factor is directly related to the desired level of reliability. Although the MBE proof load testing method is practical, several challenges remain.

An interface preserving and residual-based adaptivity for phase-field modeling of fully Eulerian fluid-structure interaction

In this paper, we present a novel interface preserving and residual-based adaptivity procedure for the phase-field modeling of a fully Eulerian fluid-structure interaction. The proposed adaptive fully Eulerian procedure involves a fixed background unstructured finite element mesh on which the fluid-structure boundary is treated implicitly via a diffused interface description. We employ the stabilized finite element formulation and a partitioned iterative procedure to solve the coupled system of the Allen-Cahn phase-field equation with the unified momentum equation comprising solid and fluid dynamics. To evaluate the solid stresses, the left Cauchy-Green deformation tensor is convected at each time step to trace the evolution of the solid strain in the Eulerian reference frame. We utilize a residual based error indicator and the newest vertex bisection algorithm for the adaptive refinement/coarsening of the unstructured mesh. Through the adaptive finite element method, we aim to preserve the profile of the diffused interface in the presence of convective distortion. We propose an early stopping criterion for the adaptivity of the interface dynamics by combining the effects of the phase-field interface thickness parameter and the mesh resolution. In addition, we control the size of the refined/coarsened elements by introducing a characteristic length scale governed by a weighted harmonic mean of the desired bulk and the interface mesh resolutions. The proposed stopping criterion and the characteristic length scale provide significant reductions in the computational cost while simultaneously ensuring convergence properties of the coupled fluid-structure system. We perform a detailed convergence and accuracy analysis via two benchmark problems namely, the pure solid system and a coupled fluid-solid system with an interface in a rectangular domain. We further provide a comparative study between feature- and residual based adaptive strategies. We systematically assess the performance of the adaptive procedure in terms of conservation properties and computational efficiency. Finally, we demonstrate our fully Eulerian adaptive solver to simulate the contact and bouncing phenomenon between an elastic solid and a rigid wall.

Observability-Aware Online Multi-Lidar Extrinsic Calibration

Accurate and robust extrinsic calibration is necessary for deploying autonomous systems which need multiple sensors for perception. In this paper, we present a robust system for real-time extrinsic calibration of multiple lidars in vehicle base frame without the need for any fiducial markers or features. We base our approach on matching absolute GNSS (Global Navigation Satellite System) and estimated lidar poses in real-time. Comparing rotation components allows us to improve the robustness of the solution than traditional least-square approach comparing translation components only. Additionally, instead of comparing all corresponding poses, we select poses comprising maximum mutual information based on our novel observability criteria. This allows us to identify a subset of the poses helpful for real-time calibration. We also provide stopping criteria for ensuring calibration completion. To validate our approach extensive tests were carried out on data collected using Scania test vehicles (7 sequences for a total of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\approx$</tex-math></inline-formula> 6.5 Km). The results presented in this paper show that our approach is able to accurately determine the extrinsic calibration for various combinations of sensor setups.

Enhanced Tunicate Swarm Algorithm for Big Data Optimization

Today, with the increasing use of technology tools in daily life, big data has gained even more importance. In recent years, many methods have been used to interpret big data. One of them is metaheuristic algorithms. Meta-heuristic methods, which have been used by very few researchers yet, have become increasingly common. In this study, Tunicate Swarm Algorithm (TSA), which has been newly developed in recent years, was chosen to solve big data optimization problems. The Enhanced TSA (ETSA) was obtained by first developing the swarm action of the TSA. In order to show the achievements of TSA and ETSA, various classical benchmark functions were determined from the literature. The success of ETSA has been proven on these benchmark functions. Then, the successes of TSA and ETSA are shown in detail on big datasets containing six different EEG signal data, with five different population sizes (10, 20, 30, 50, 100) and three different stopping criteria (300, 500, 1000). The results were compared with the Jaya, SOA, and SMA algorithms selected from the literature, and the success of ETSA was determined. The results show that ETSA has sufficient success in solving big data optimization problems and continuous optimization problems.

A New Stopping Criterion for Krylov Solvers Applied in Interior Point Methods

When an iterative method is applied to solve the linear equation system in interior point methods (IPMs), the attention is usually placed on accelerating their convergence by designing appropriate preconditioners, but the linear solver is applied as a black box with a standard termination criterion which asks for a sufficient reduction of the residual in the linear system. Such an approach often leads to an unnecessary 'oversolving' of linear equations. In this paper, an IPM that relies on an inner termination criterion not based on the residual of the linear system is introduced and analyzed. Moreover, new indicators for the early termination of the inner iterations are derived from a deep understanding of IPM needs. The new technique has been adapted to the Conjugate Gradient (CG) and to the Minimum Residual method (MINRES) applied in the IPM context. The new criterion has been tested on a set of quadratic optimization problems including compressed sensing, image processing and instances with partial differential equation constraints, and it has been compared to standard residual tests with variable tolerance. Evidence gathered from these computational experiments shows that the new technique delivers significant improvements in terms of inner (linear) iterations and those translate into significant savings of the IPM solution time.

A Triple Deep Image Prior Model for Image Denoising Based on Mixed Priors and Noise Learning

Image denoising poses a significant challenge in computer vision due to the high-level visual task’s dependency on image quality. Several advanced denoising models have been proposed in recent decades. Recently, deep image prior (DIP), using a particular network structure and a noisy image to achieve denoising, has provided a novel image denoising method. However, the denoising performance of the DIP model still lags behind that of mainstream denoising models. To improve the performance of the DIP denoising model, we propose a TripleDIP model with internal and external mixed images priors for image denoising. The TripleDIP comprises of three branches: one for content learning and two for independent noise learning. We firstly use a Transformer-based supervised model (i.e., Restormer) to obtain a pre-denoised image (used as external prior) from a given noisy image, and then take the noisy image and the pre-denoised image as the first and second target image, respectively, to perform the denoising process under the designed loss function. We add constraints between two-branch noise learning and content learning, allowing the TripleDIP to employ external prior while enhancing independent noise learning stability. Moreover, the automatic stop criterion we proposed prevents the model from overfitting the noisy image and improves the execution efficiency. The experimental results demonstrate that TripleDIP outperforms the original DIP by an average of 2.79 dB and outperforms classical unsupervised methods such as N2V by an average of 2.68 dB and the latest supervised models such as SwinIR and Restormer by an average of 0.63 dB and 0.59 dB on the Set12 dataset. This can mainly be attributed to the fact that two-branch noise learning can obtain more stable noise while constraining the content learning branch’s optimization process. Our proposed TripleDIP significantly enhances DIP denoising performance and has broad application potential in scenarios with insufficient training datasets.

Reliability-Based Proof Load Factors for Assessment of Bridges

Advanced reliability assessment can be required when evaluating existing bridge structures through proof load testing. The target load of a proof load test typically requires input from resistance models; however, bridges with a low documentation level are often the most relevant for proof load testing, and resistance models thus involve significant uncertainties. This paper presents a different approach to proof loading and target proof load, in which the resistance model is not included in the determination of this load. Instead, the target proof load is determined based on load models coupled to the desired target failure probability, thus ensuring sufficient safety of the existing bridge. The method is in line with the proposal for a new Eurocode on assessment of existing structures and is deemed to be superior to existing approaches. An essential assumption of the method is that monitoring systems can identify stop criteria before irreversible damage occurs. The approach was applied in a case study, where an OT-slab bridge with a span of 6.5 m was proof-load-tested and reclassified for capacity upgrading of a road stretch in Denmark.

Renin-Angiotensin System Modulation With Synthetic Angiotensin (1-7) and Angiotensin II Type 1 Receptor–Biased Ligand in Adults With COVID-19

Preclinical models suggest dysregulation of the renin-angiotensin system (RAS) caused by SARS-CoV-2 infection may increase the relative activity of angiotensin II compared with angiotensin (1-7) and may be an important contributor to COVID-19 pathophysiology. To evaluate the efficacy and safety of RAS modulation using 2 investigational RAS agents, TXA-127 (synthetic angiotensin [1-7]) and TRV-027 (an angiotensin II type 1 receptor-biased ligand), that are hypothesized to potentiate the action of angiotensin (1-7) and mitigate the action of the angiotensin II. Two randomized clinical trials including adults hospitalized with acute COVID-19 and new-onset hypoxemia were conducted at 35 sites in the US between July 22, 2021, and April 20, 2022; last follow-up visit: July 26, 2022. A 0.5-mg/kg intravenous infusion of TXA-127 once daily for 5 days or placebo. A 12-mg/h continuous intravenous infusion of TRV-027 for 5 days or placebo. The primary outcome was oxygen-free days, an ordinal outcome that classifies a patient's status at day 28 based on mortality and duration of supplemental oxygen use; an adjusted odds ratio (OR) greater than 1.0 indicated superiority of the RAS agent vs placebo. A key secondary outcome was 28-day all-cause mortality. Safety outcomes included allergic reaction, new kidney replacement therapy, and hypotension. Both trials met prespecified early stopping criteria for a low probability of efficacy. Of 343 patients in the TXA-127 trial (226 [65.9%] aged 31-64 years, 200 [58.3%] men, 225 [65.6%] White, and 274 [79.9%] not Hispanic), 170 received TXA-127 and 173 received placebo. Of 290 patients in the TRV-027 trial (199 [68.6%] aged 31-64 years, 168 [57.9%] men, 195 [67.2%] White, and 225 [77.6%] not Hispanic), 145 received TRV-027 and 145 received placebo. Compared with placebo, both TXA-127 (unadjusted mean difference, -2.3 [95% CrI, -4.8 to 0.2]; adjusted OR, 0.88 [95% CrI, 0.59 to 1.30]) and TRV-027 (unadjusted mean difference, -2.4 [95% CrI, -5.1 to 0.3]; adjusted OR, 0.74 [95% CrI, 0.48 to 1.13]) resulted in no difference in oxygen-free days. In the TXA-127 trial, 28-day all-cause mortality occurred in 22 of 163 patients (13.5%) in the TXA-127 group vs 22 of 166 patients (13.3%) in the placebo group (adjusted OR, 0.83 [95% CrI, 0.41 to 1.66]). In the TRV-027 trial, 28-day all-cause mortality occurred in 29 of 141 patients (20.6%) in the TRV-027 group vs 18 of 140 patients (12.9%) in the placebo group (adjusted OR, 1.52 [95% CrI, 0.75 to 3.08]). The frequency of the safety outcomes was similar with either TXA-127 or TRV-027 vs placebo. In adults with severe COVID-19, RAS modulation (TXA-127 or TRV-027) did not improve oxygen-free days vs placebo. These results do not support the hypotheses that pharmacological interventions that selectively block the angiotensin II type 1 receptor or increase angiotensin (1-7) improve outcomes for patients with severe COVID-19. ClinicalTrials.gov Identifier: NCT04924660.

Simulation-based multi-objective optimization framework for sustainable management of coastal aquifers in semi-arid regions

Groundwater is a strategic source of water supply, especially in arid and semi-arid coastal regions. Growing demand, along with scarce water sources, may impose intense pressure on this precious resource. This pressure will degrade water quality for future use and cause social inequality, despite supplying current needs. A novel sustainable management model for water allocation is developed to address these interconnected concerns in coastal aquifers. Three aspects of sustainable development are considered: groundwater quality with total dissolved solids (TDS) indicator for the environmental part, gross value added from water for the economic efficiency, and the Gini coefficient for social inclusion and equity. The problem is solved with a simulation-based multi-objective optimization framework using a numerical variable-density simulation code and three approved evolutionary algorithms, NSGA-II, NRGA, and MOPSO. The obtained solutions are integrated to enhance the solutions' quality by using each algorithm's strengths and dominated members' elimination. In addition, the optimization algorithms are compared. The results showed that NSGA-II is the best in terms of solutions quality, with the least number of total dominated members (20.43%) and a 95% success rate of obtained Pareto front. NRGA was supreme in finding extreme solutions, the least computational time, and diversity, with an 11.6% higher diversity value than the second competitive NSGA-II. MOPSO was the best in spacing quality indicator, followed by NSGA-II, showing their great arrangement and evenness in obtained solution space. MOPSO has the propensity for premature convergence and needs more stringent stopping criteria. The method is applied to a hypothetical aquifer. Still, the obtained Pareto fronts are determined to assist decision-makers in real-world coastal sustainable management problems by illustrating existing patterns among different objectives.

A new active-learning estimation method for the failure probability of structural reliability based on Kriging model and simple penalty function

Since the Kriging model can provide the mean value of the performance function at a sample point and the corresponding variance among the various surrogate models, many Kriging-based reliability analysis methods have been developed to estimate accurate failure probabilities of engineering structure problems. Various active learning functions have been proposed to reduce the computation burden of Kriging-based reliability analysis for engineering problems. This paper proposes a new active learning Kriging-based method for estimating the failure probability. A penalty learning function is proposed to improve the efficiency and accuracy of the failure probability estimate based on a simple penalty function. Also, a distance constraint term is considered to avoid a cluster of the selected sample points. Additionally, by an error-based stopping criterion, convergence conditions of the proposed new active learning reliability analysis method are investigated. Finally, five numerical examples are provided to verify the accuracy and effectiveness of the proposed method and convergence strategy. Numerical results demonstrated that the novel proposed method significantly improves the computational efficiency of reliability analysis with high estimating accuracy of the failure probability.

Quantifying Schumann resonances’ variation over time through statistical differences

Schumann resonances’ statistical parameters vary over time, which gives way to analyze them as a stochastic process. By splitting the Schumann resonance’s records into segments and obtaining their empirical distribution function, the differences can be evaluated by calculating the Kolmogorov–Smirnov distance between them. The analysis’ results allow for the characterization of the Schumann resonance’s variations, along with the typical values it reaches under the chosen metric. It is shown how divergence is mitigated through sample averaging, and also how divergent samples impact on the Fast Fourier Transform algorithm.Divergence quantification adds a layer of information for data processing. Knowing the changes experienced over time in Schumann resonances gives a way to know what kind of mathematical procedures can be applied to the signal. Quantification of signal variations over time can identify error sources in specific procedures, filter out samples unfit under certain analyses, or serve as a stop criteria for cumulative analyses.

How to Limit Label Dissipation in Neural-network Validation: Exploring Label-free Early-stopping Heuristics

In recent years, deep learning (DL) has achieved impressive successes in many application domains, including Handwritten-Text Recognition. However, DL methods demand a long training process and a huge amount of human-based labeled data. To address these issues, we explore several label-free heuristics for detecting the early-stopping point in training convolutional-neural networks: (1) Cumulative Distribution of the standard deviation of kernel weights (SKW) ; (2) the moving standard deviation of SKW, and (3) the standard deviation of the sum of weights over a window in the epoch series. We applied the proposed methods to the common RIMES and Bentham data sets as well as another highly challenging historical data set. In comparison with the usual stopping criterion which uses labels for validation, the label-free heuristics are at least 10 times faster per epoch when the same training set is used. The use of alternative stopping heuristics may require additional epochs, however, they never require the original computing time. The character error rate (%) on the test set of the label-free heuristics is about a percentage point less in comparison to the usual stopping criterion. The label-free early-stopping methods have two benefits: They do not require a computationally intensive evaluation of a validation set per epoch and all labels can be used for training, specifically benefitting the underrepresented word or letter classes.

Refining the time–frequency characteristic of non-stationary signal for improving time–frequency representation under variable speeds

Time–frequency ridge not only exhibits the variable process of non-stationary signal with time changing but also provides the information of signal synchronous or non-synchronous components for subsequent detection research. Consequently, the key is to decrease the error between real and estimated ridge in the time–frequency domain for accurate detection. In this article, an adaptive weighted smooth model is presented as a post-processing tool to refine the time–frequency ridge which is based on the coarse estimated time–frequency ridge using newly emerging time–frequency methods. Firstly, the coarse ridge is estimated by using multi-synchrosqueezing transform for vibration signal under variable speed conditions. Secondly, an adaptive weighted method is applied to enhance the large time–frequency energy value location of the estimated ridge. Then, the reasonable smooth regularization parameter associated with the vibration signal is constructed. Thirdly, the majorization–minimization method is developed for solving the adaptive weighted smooth model. Finally, the refined time–frequency characteristic is obtained by utilizing the stop criterion of the optimization model. Simulation and experimental signals are given to validate the performance of the proposed method by average absolute errors. Compared with other methods, the proposed method has the highest performance in refinement accuracy.

A Novel MPPT Controller Based on Mud Ring Optimization Algorithm for Centralized Thermoelectric Generator under Dynamic Thermal Gradients

Most industrial processes generate raw heat. To enhance the efficiency of industrial operations, this raw heat is recovered. Thermoelectric generators (TEG), as solid state devices, provide an excellent application of heat recovery in the form of most manageable electrical power. This work presents a novel MPPT controller based on the Mud Ring Optimization algorithm for a centralized Thermoelectric Generator (TEG) under dynamic thermal gradients. The existing stochastic optimization algorithm for Maximum Power Point Tracking (MPPT) control in renewable energy systems exhibits several limitations that affect its performance in MPPT control. The convergence speed, local minima trap, hyper parameters’ sensitivity toward the population size, acceleration coefficients, and the stopping criterion all impact the convergence stability. In addition to these limitations, sensor noise sensitivity in measurement fluctuates the control system leading to reduced performance. Therefore, the careful design and implementation of the MRO algorithm is crucial to overcome these limitations and achieve a satisfactory performance in MPPT control. The results of this study contribute to developing more efficient MPPT control of TEG systems and implementing renewable energy technologies. The algorithm effectively tracks the maximum power point in dynamic thermal environments and increases the power output compared to conventional MPPT methods. The findings illustrate the efficacy of the proposed controller providing a higher power output (Avg. 99.95%) and faster response time (220 ms) under dynamic thermal conditions achieving 38–70% faster tracking of the GM in dynamic operating conditions.

DeepVID: deep-learning accelerated variational image decomposition model tailored to fringe pattern filtration

The algorithms defined as variational image decomposition (VID) constitute the state-of-the-art in solving the image prefiltration problem. However, the discussion about the advantages and disadvantages of different VID models in the context of fringe pattern prefiltration is yet to be addressed and this work is the first one leaning into this issue. The unsupervised variational image decomposition (uVID) algorithm allows for automatic, accurate and robust preprocessing of diverse fringe patterns and introduces the parameters and stopping criterion for Chambolle’s iterative projection algorithm to separate the fringes and background. However, determining the stopping criterion in each iteration is a severely time-consuming process, which is particularly important given the fact that in many cases thousands of iterations must be calculated to obtain a satisfactory fringe pattern decomposition result. Therefore, the idea of using convolutional neural network to map the relationship between the fringe pattern spatial intensity distribution and the required number of Chambolle projection iterations has emerged. That way, it is no longer required to determine the value of the stopping criterion in every iteration, but the appropriate number of iterations is known in advance via machine learning process. We showed that the calculation time is reduced on average by 3–4 times by employing the deep learning-based acceleration (convolutional neural network called DeepVID) without jeopardizing the overall accuracy of the prefiltration. This way an important progress in developing uVID algorithm features towards real-time studies of dynamic phenomena is reported in this contribution. For the sake of metrological figure of merit, we employ deep learning based solution, for the first time to the best of our knowledge, to accelerate powerful and well-established VID approach, not to bypass it completely.

An expert consensus to define how higher standards of equitable care for von Willebrand disease can be achieved in the UK and Republic of Ireland

Von Willebrand Disease (VWD) is the most common inherited bleeding disorder. However, recognition of the disease by both the public and healthcare professionals lags behind that of other bleeding disorders, leading to delays in diagnosis and treatment for patients. Updated national guidelines are needed to highlight an appropriate pathway for managing VWD patients in a timelier manner. To identify ways in which care for VWD can be achieved on a more equitable basis. Using a modified Delphi approach, a panel of VWD experts developed 29 statements across five key themes. These were used to form an online survey that was distributed to healthcare professionals involved in VWD care across the UK and Republic of Ireland (ROI). Stopping criteria comprised 50 responses received, a 3-month window for response (February-April 2022) and 90% of statements passing consensus threshold. Threshold for consensus for each statement was agreed at 75%. A total of 66 responses were analysed with 29/29 statements achieving consensus of which 27 attained ≥90% agreement. From the high degree of consensus, eight recommendations were derived regarding how detection and management of VWD can be improved to provide equity of care between men and women. Implementation of these eight recommendations across the VWD pathway has the potential to raise the standard of care for patients in the UK and ROI by reducing delays to diagnosis and treatment initiation.