Recursive Scheme Research Articles

Bridging the gap: mapping co-creation methods for public health impact and innovation

Abstract Background Co-creation offers a promising approach to public health innovation, particularly for tackling complex issues. However, the lack of a standardized methodology limits its reliability and reproducibility, especially across diverse contexts. This study explores co-creation methods used by researchers and practitioners, aiming to shed light on their implications for public health. Understanding this methodological landscape can enhance the effective implementation of co-creation processes to address complex challenges. Methods Utilizing the Systematic Methods Overview approach, we examined co-creation methods. Data were gathered from the Health CASCADE Co-Creation Database and grey literature using an artificial intelligence-assisted recursive search strategy and a two-step screening process. We conducted textual, comparative, and bibliometric analyses to evaluate methodological underpinnings and relationships between methods. Results Analysis of 2,627 academic articles and grey literature sources identified over 900 co-creation methods. While surveys, focus groups, and group discussions were prevalent, participatory methods were underutilized. Notably, 91.3% of methods co-occurred, with a predominant combination of qualitative approaches. Furthermore, a significant discrepancy between research and practice was evident, with only 10.2% convergence. Conclusions This study revealed a critical gap between co-creation in research and practice, emphasizing the need to bridge this methodological divide for advancing co-creation as a reliable approach in public health. The over-reliance on qualitative methods raises concerns about tokenistic engagement and undermines meaningful public participation in co-creation. Addressing this trend is vital to preserve the essence of true co-creation and maximize its potential for addressing complex public health challenges. Key messages • Mapping the diverse methods of co-creation in public health reveals both richness and disparities in approach, from surveys to participatory methods. • Addressing the discrepancy between co-creation in research and practice is vital for establishing a reliable and effective approach to tackling public health challenges.

Joint hybrid recursive feature elimination based channel selection and ResGCN for cross session MI recognition

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

Free vibration analysis of bio-inspired helicoidal laminated composite square and annular plates having circular openings using isogeometric analysis

Present work aims to carry out free vibration analysis of bio-inspired helicoidal laminated composite square and annular-shaped plates with circular openings. Refined plate theory, created within the context of isogeometric analysis (IGA) is used during the present study. The theory is coded in MATLAB to study the behavior of the plates and the results are validated with the frequencies obtained using ABAQUS. Recursive, semi-circular, exponential, and Fibonacci helicoidal schemes are used during the present study for studying the free vibration behavior and the frequency is compared with the conventional cross-ply and quasi-isotropic laminates. Influence of position of opening, dimensions of opening, number of plies, and end condition, on the free vibration behavior of square and annular shaped bio-inspired helicoidal plates is carried out. The plates with CCCC and CCCF ends having openings at the quarter length are found to have higher frequency by 3.518 % and 0.3897 % compared to the values of the frequency of the plate with opening at the centre with the same perimeter. Opposite trend is observed for plate with PPPP, CFCF, and CFFF end conditions, where the plate with opening at centre exhibits higher value of the frequency by 2.427 %, 0.821 %, and 1.232 %, respectively. When the opening is near to the corners, the decrease in frequency by 3.913 %, 1.576 %, 1.193 %, and 1.437 % is observed for CCCC, CCFF, CCCF, and CFCF end conditions while increase by 1.490 % and 2.717 % is observed for the CFFF and PPPP end conditions. The mode shape of the plate is found to be greatly influenced by the position and dimension of the hole and end conditions of the plate.

A Telescopic Independent Component Analysis on Functional Magnetic Resonance Imaging Data Set.

Our study introduces "telescopic independent component analysis (TICA)", a novel approach using a recursive ICA strategy for exploring brain function across multiple spatial scales using fMRI data. TICA constructs spatial hierarchies and identifies functional sources such as default mode network (DMN), visual network (VN), and right frontoparietal network (RFPN). By applying TICA, we uncovered hierarchical structures within these networks and revealed differences in the DMN between healthy individuals and those with schizophrenia. This approach offers a promising new tool for studying function dynamics comprehensively.

Enhanced Hybrid Algorithms for Segmentation and Reconstruction of Granular Grains From X‐Ray Micro Computed‐Tomography Images

ABSTRACTAccurate three‐dimensional (3D) reconstruction of granular grains from x‐ray micro‐computed tomography (µCT) images is a long‐standing challenge, particularly for dense soil samples. This study develops a machine learning (ML) enhanced approach to automatically reconstruct granular grains from µCT images. The novel academic contributions of this paper include (a) a hierarchical strategy based on parameter‐independent polygonal approximation, area, and concavity analysis, for the first time, to identify and eliminate both intergranular and intragranular voids; (b) incorporation of a recursive segmentation scheme and ML‐based grain classifier to avoid over‐segmentation; (c) novel modifications on the determination of splitting paths to enhance segmentation accuracy; and (d) an effective approach of assigning initial level set functions for reconstructing granular grains automatically. The hybrid ML algorithm is applied to µCT images of dense Mojave Mars Simulant. The results indicate that the proposed method can accurately segment grain clumps with unclear boundaries. The new automatic reconstruction algorithm eliminates ineffective operations and achieves a three‐fold increase in computational speed than previous methods documented in the literature. Ninety‐one percent of grains with distinct boundaries can be reconstructed and the reconstruction ratio reaches 81% even for grains without distinct boundaries. The overall reconstruction ratio of grains increases by 20% compared with previous methods, achieving a step‐change improvement for one‐to‐one mapping of real soil samples.

Robust recursive sigma point Kalman filtering for Huber-based generalized M-estimation

For nonlinear state estimation driven by non-Gaussian noise, the estimator is required to be updated iteratively. Since the iterative update approximates a linear process, it fails to capture the nonlinearity of observation models, and this further degrades filtering accuracy and consistency. Given the flaws of nonlinear iteration, this work incorporates a recursive strategy into generalized M-estimation rather than the iterative strategy. The proposed algorithm extends nonlinear recursion to nonlinear systems using the statistical linear regression method. The recursion allows for the gradual release of observation information and consequently enables the update to proceed along the nonlinear direction. Considering the correlated state and observation noise induced by recursions, a separately reweighting strategy is adopted to build a robust nonlinear system. Analogous to the nonlinear recursion, a robust nonlinear recursive update strategy is proposed, where the associated covariances and the observation noise statistics are updated recursively to ensure the consistency of observation noise statistics, thereby completing the nonlinear solution of the robust system. Compared with the iterative update strategies under non-Gaussian observation noise, the recursive update strategy can facilitate the estimator to achieve higher filtering accuracy, stronger robustness, and better consistency. Therefore, the proposed strategy is more suitable for the robust nonlinear filtering framework.

A Novel Multiobjective Genetic Programming Approach to High-Dimensional Data Classification.

The development of data sensing technology has generated a vast amount of high-dimensional data, posing great challenges for machine learning models. Over the past decades, despite demonstrating its effectiveness in data classification, genetic programming (GP) has still encountered three major challenges when dealing with high-dimensional data: 1) solution diversity; 2) multiclass imbalance; and 3) large feature space. In this article, we have developed a problem-specific multiobjective GP framework (PS-MOGP) for handling classification tasks with high-dimensional data. To reduce the large solution space caused by high dimensionality, we incorporate the recursive feature elimination strategy based on mining the archive of evolved GP solutions. A progressive domination Pareto archive evolution strategy (PD-PAES), which optimizes the objectives in a specific order according to their objectives, is proposed to evaluate the GP individuals and maintain a better diversity of solutions. Besides, to address the seriously imbalanced class issue caused by traditional binary decomposition (BD) one versus rest (OVR) for multiclass classification problems, we design a method named BD with a similar positive and negative class size (BD-SPNCS) to generate a set of auxiliary classifiers. Experimental results on benchmark and real-world datasets demonstrate that our proposed PS-MOGP outperforms state-of-the-art traditional and evolutionary classification methods in the context of high-dimensional data classification.

Distributed Output-Feedback Asymptotic Consensus Tracking for High-Order Multiagent Systems With Quantized Input.

This article is devoted to distributed adaptive asymptotic consensus tracking control based on output feedback for the uncertain high-order multiagent systems with input quantization. Compared with the output-feedback canonical form, the system takes unmeasured states-dependent nonlinearities into account and also includes unknown parameters and quantized input. The improved K -filters with one dynamic gain are constructed to dispose the unmeasured states-dependent nonlinearities and estimate the unknown states. Then, the novel recursive control strategy with the aid of new first-order dynamic parameter filters is proposed, which is able to effectively counteract the filter errors and steer the consensus tracking errors to zero asymptotically with low design complexity. Moreover, the new funnel variable combined with prespecified time performance function is first introduced, which can predefine practical transition time and maximum overshoot of consensus error. Finally, simulation results are presented to illustrate the validity and superiority of the proposed scheme.

Transfer condition assessment of gas turbines via double multi-task Gaussian process

Predictive maintenance and health management are crucial means to enhance the operational reliability and safety of gas turbines, with time series condition assessment being a pivotal step in this process. However, the complexity of gas turbine and the harsh operating conditions raise the high cost and significant difficulty in acquiring data via sensors, which pose challenges for the conventional data-driven time series modeling methods. These result in the scarcity of available historical data and the deteriorate of condition assessment accuracy. To this end, this paper proposes an end-to-end, probabilistic transfer learning approach based on multi-task Gaussian process (MTGP) for the condition assessment of gas turbines with limited sensor data. For achieving effective transfer condition assessment, the proposed method, named double MTGP (dMTGP), employs (i) a recurrent neural networks (RNN) projector to extract the explicit evolution patterns, (ii) adopts the MTGP module to transfer knowledge from the source domain to the target domain, and (iii) presents a recursive MTGP strategy to have shared knowledge transferred cross outputs. Finally, the superiority and robustness of the proposed method are comprehensively validated against state-of-the-art competitors on four practical gas turbine units.

Existence and Numerical Investigation of Monkey-Pox Mathematical Model by Natural Adomain Decomposition Method

In this paper, studied the mathematical model concerning the transmission of Monkey-Pox disease. A class viral disease that mostly occurs in west and central Africa, transmitted from animals into human is belonging to the Small-pox family known is Monkey-pox infections disease. According to the scientist the primary best of the proposed disease is still in doubt. The proposed model will be investigate for the purpose of both qualitative and numerical solutions. At the early stage of this study, investigate the existence of proposed model. In this connection, the authors developed the desired condition of existence and stability for consider model by using the tools of analysis. At the second phase of this research work,the author investigated the numerical solutions for the consider Monkey-pox transmission diseases model. For numerical investigation, the authors use the tool of well know semi-analytical techniques known as Natural Transform coupled with Adomain Decomposition Method. The consider techniques are powerful tools for of obtaining approximate solutions of differential equation or system of differential equations. The proposed techniques base on recursive scheme for solutions of system of differential equations. For the authenticity and accuracy of obtain solutions, the obtain solutions are visualized graphically to desired the dynamical behavior of desired results with the help of Mathematica. That show the proposed method is best tools for solution of differential equations.

Distributed resilient adaptive consensus tracking control of nonlinear multi-agent systems dealing with deception attacks via K-filters approach

It poses technical difficulty to achieve distributed consensus tracking control with input quantizations and deception attacks for nonlinear multi-agent systems subject to mismatched parametric uncertainties via backstepping design. The underlying problem becomes even more complicated because i) the system contains mismatched uncertainties; ii) the output becomes unavailable after attacks, making the conventional recursive control design strategy using traditional error surfaces unsuitable; iii) since only the compromised output signal is available, the impacts of nonlinear items related to unknown attack weights become difficult to be explicitly addressed in the existing available output feedback control results. In this paper, we present a solution to circumvent these obstacles by constructing a set of new K-filters using compromised output only, which, together with a coordinate transformation involving the attacked output and filter variables, allow a new form of backstepping based distributed resilient adaptive control protocol to be developed. Meanwhile, an extra compensation term is incorporated into the real controller to handle the effect of quantization on the system stability. It is shown that, with proper choices of Lyapunov functions, the global uniform boundedness of all the closed-loop signals and the asymptotically output consensus tracking can be achieved. A practice example is provided to verify the benefits of our scheme.

Deep self-supervised spatial-variant image deblurring

Most existing model-based and learning-based image deblurring methods usually use synthetic blur-sharp training pairs to remove blur. However, these approaches do not perform well in real-world applications as the blur-sharp training pairs are difficult to be obtained and the blur in real-world scenarios is spatial-variant. In this paper, we propose a self-supervised learning-based image deblurring method that can deal with both uniform and spatial-variant blur distributions. Moreover, our method does not need for blur-sharp pairs for training. In our proposed method, we design the Deblurring Network (D-Net) and the Spatial Degradation Network (SD-Net). Specifically, the D-Net is designed for image deblurring while the SD-Net is used to simulate the spatial-variant degradation. Furthermore, the off-the-shelf pre-trained model is employed as the prior of our model, which facilitates image deblurring. Meanwhile, we design a recursive optimization strategy to accelerate the convergence of the model. Extensive experiments demonstrate that our proposed model achieves favorable performance against existing image deblurring methods.

Patterning strategies in grade 1 students with low and high number sense proficiency

AbstractRepeating patterning proficiency predicts students’ later mathematical proficiency. A comparative multi-case design enabled the present study to compare patterning success and strategy use for repeating patterns of 75 Norwegian 6-year-old grade 1 students. We provided the students with duplicate, extend, transfer, and unit isolation activities in a semi-structured, task-based interview that was video-recorded. The students’ number sense was also measured using a digital assessment. In-depth analysis of 52 students (divided evenly into two groups based on their number sense scores, lowest and highest) was performed. A statistically significant group difference was found for extend activities. Multimodal video analysis of strategies employed revealed that both student groups used recursive and different strategies and only students with high number sense used relational strategies. The students with low number sense used more variants of recursive patterning strategies. Our findings also demonstrated that attention to pattern features may both hinder identification of a pattern’s underlying structure and reflect advanced strategy use rather than not showing pattern awareness. We discuss group differences in terms of decomposition and awareness of mathematical pattern and structure and emphasise the need for scaffolding awareness of mathematical pattern and structure through communication.

Momentum recursive DARTS

DARTS has emerged as a popular method for neural architecture search (NAS) owing to its efficiency and simplicity. It employs gradient-based bi-level optimization to iteratively optimize the upper-level architecture parameters and lower-level super-network weights. The key challenge in DARTS is the accurate estimation of gradients for two-level object functions, leading to significant errors in gradient approximation. To address this issue, we propose a new approach, MR-DARTS, that incorporates a momentum term and a recursive scheme to improve gradient estimation. Specifically, we leverage historical information by using a running average of past observed gradients to enhance the quality of current gradient estimation in both upper-level and lower-level functions. Our theoretical analysis shows that the variance of our estimated gradient decreases with each iteration. By utilizing momentum and a recursive scheme, MR-DARTS effectively controls the error in stochastic gradient updates that result from inaccurate gradient estimation. Furthermore, we utilize the Neumann series approximation and Hessian Vector Product scheme to reduce computational requirements and memory usage. We evaluate our proposed method on several benchmarks and demonstrate its effectiveness through comprehensive experiments.

Temporal feature decomposition fusion network for building energy multi-step prediction

Accurate building energy prediction methods have become a key factor in achieving energy-saving goals. Traditional methods for building energy multi-step prediction often use recursive or direct strategies to address time series prediction problems, which may neglect the data sequence correlation and result in the cumulative error. To solve the above problem, this paper proposed a Temporal Feature Decomposition Fusion Network (TFDFNet) model for building energy consumption multi-step prediction, with an encoder-decoder architecture. In the encoder, feature fusion layers is employed to consider the influence of different feature sequences on the predicted sequence. Through the decomposition of historical load data sequences, different hierarchical sequence structures are constructed to enhance the interpretability and predictability of the data. In the decoder, a simple and efficient network is constructed using MLP to decode the encoded information and obtain the prediction results. Experimental results show that, the proposed model achieves higher prediction accuracy and more stable convergence than the other five comparable methods, which also indicates the potential of achieving excellent building energy consumption multi-step prediction results with a simple model design.

ALGORITHM FOR ESTIMATING THE PARAMETRIC ROLL MOTIONS

For a ship, both synchronous and parametric roll motions can be modelled by nonlinear second-order differential equations with the roll angle as dependent variable and the nonlinearities coming from the damping and restoring moments. In the absence of exact solution techniques, approximate solutions for such equations can be obtained, in principle, analytically or numerically. In this paper, we focused on a fast and accurate recursive scheme based on the segmentation of the time domain into a fine grid of intervals of equal lengths. Due to the smallness of each time subinterval, a reset of the terms of the initial nonlinear equation of motion allows replacing it with a linear one in which the constant coefficients represent approximate values of the former variable coefficients in the middle of each subinterval. The linear equation is solved via the Laplace transform and the resulting solution together with its first derivatives is used to generate the iterative algorithm. This technique was applied to the case of two typical roll equations. The first describes the synchronous roll of a vehicle ferry model equipped or not with bilge keels while the second estimates the parametric roll of a container ship model. In both cases, the analyzed recursive algorithm not only generated results in full agreement with those provided by the ode45 solver in Matlab but also lead to a gain in computation time and offered more flexibility in imposing some restrictive conditions associated, for example, with exceeding some oscillation amplitudes or to the ship capsizing. Key words: synchronous and parametric ship rolling, iterative scheme

Enhanced and generalized one–step Neville algorithm: Fractional powers and access to the convergence rate

The recursive Neville algorithm allows one to calculate interpolating functions recursively. Upon a judicious choice of the abscissas used for the interpolation (and extrapolation), this algorithm leads to a method for convergence acceleration. For example, one can use the Neville algorithm in order to successively eliminate inverse powers of the upper limit of the summation from the partial sums of a given, slowly convergent input series. Here, we show that, for a particular choice of the abscissas used for the extrapolation, one can replace the recursive Neville scheme by a simple one-step transformation, while also obtaining access to subleading terms for the transformed series after convergence acceleration. The matrix-based, unified formulas allow one to estimate the rate of convergence of the partial sums of the input series to their limit. In particular, Bethe logarithms for hydrogen are calculated to 100 decimal digits. Generalizations of the method to series whose remainder terms can be expanded in terms of inverse factorial series, or series with half-integer powers, are also discussed.

Efficient Guided Wave Modelling for Tomographic Corrosion Mapping via One-Way Wavefield Extrapolation

Mapping corrosion depths along pipeline sections using guided-wave-based tomographic methods is a challenging task. Accurate defect sizing depends heavily on the precision of the forward model in guided wave tomography. This model is fitted to measured data using inversion techniques. This study evaluates the effectiveness of a recursive extrapolation scheme for tomography applications and full waveform inversion. It employs a table-driven approach, with precomputed extrapolation operators stored across a spectrum of wavenumbers. This enables fast modelling for extensive pipe sections, approaching the speed of ray tracing while accurately handling complex velocity models within the full frequency band. This ensures an accurate representation of diffraction phenomena. The study examines the assumptions underlying the extrapolation approach, namely, the negligible reflection and conversion of modes at defects. In our tomography approach, we intend to use multiple wave modes—A0, S0, and SH1—and helical paths. The acoustic extrapolation method is validated through numerical studies for different wave modes, solving the 3D elastodynamic wave equation. Comparison with an experimentally measured single-mode wavefield from an aluminium plate with an artificial defect reveals good agreement.

Recursive Deformable Pyramid Network for Unsupervised Medical Image Registration.

Complicated deformation problems are frequently encountered in medical image registration tasks. Although various advanced registration models have been proposed, accurate and efficient deformable registration remains challenging, especially for handling the large volumetric deformations. To this end, we propose a novel recursive deformable pyramid (RDP) network for unsupervised non-rigid registration. Our network is a pure convolutional pyramid, which fully utilizes the advantages of the pyramid structure itself, but does not rely on any high-weight attentions or transformers. In particular, our network leverages a step-by-step recursion strategy with the integration of high-level semantics to predict the deformation field from coarse to fine, while ensuring the rationality of the deformation field. Meanwhile, due to the recursive pyramid strategy, our network can effectively attain deformable registration without separate affine pre-alignment. We compare the RDP network with several existing registration methods on three public brain magnetic resonance imaging (MRI) datasets, including LPBA, Mindboggle and IXI. Experimental results demonstrate our network consistently outcompetes state of the art with respect to the metrics of Dice score, average symmetric surface distance, Hausdorff distance, and Jacobian. Even for the data without the affine pre-alignment, our network maintains satisfactory performance on compensating for the large deformation. The code is publicly available at https://github.com/ZAX130/RDP.

Multiple resonance in coupled Duffing oscillators and nonlinear normal modes.

The resonance of N linearly coupled damped Duffing oscillators with a constant frequency sinusoidal driving force acting on the first oscillator is studied analytically by calculating the fixed points of the corresponding dynamical system and numerically using a fourth-order multivariate Runge-Kutta method. For a chain with N oscillators, we establish a general recursion scheme in the form of a system of equationsthat relates the amplitudes of the oscillators and the driving frequency, capable of describing resonance curves. We consider in detail the case of an oscillator chain with N=2 for high values of the driving amplitude and stiffness, and find hysteretical unstable regions in the resonance curves. In this unstable driving frequency regime, analysis of the time series reveals the presence of nonlinear normal modes visible as beating quasiperiodic oscillations.