Recursive Algorithm Research Articles

Prognostic value of FDX1, the cuprotosis key gene, and its prediction models across imaging modalities and histology.

Cuprotosis has been identified as a novel way of cell death. The key regulator ferredoxin 1 (FDX1) was explored via pan-cancer analysis, and its prediction models were proposed across seven malignancies and two imaging modalities. The prognostic value of FDX1 was explored via 1654 cases of 33 types of cancer in the Cancer Genome Atlas database. The MRI cohort of hepatocellular carcinoma in the First Affiliated Hospital of Fujian Medical University, and CT and MRI images from the Cancer Imaging Archive, REMBRANDT and Duke databases were exploited to formulate radiomic models to predict FDX1 expression. After segmentation of volumes of interest and feature extraction, the recursive feature elimination algorithm was used to screen features, logistic regression was used to model features, immunohistochemistry staining with FDX1 antibody was performed to test the radiomic model. FDX1 was found to be prognostic in various types of cancer. The area under the receiver operating characteristic curve of radiomic models to predict FDX1 expression reached 0.825 (95% CI = 0.739-0.911). Cross-tissue compatibility was confirmed in pan-cancer validation and test cohorts. Mechanistically, the radiomic score was significantly correlated with various immunosuppressive genes and gene mutations. The radiomic score was also found to be an independent prognostic factor, making it a potentially actionable biomarker in the clinical setting. The expression of FDX1 could be non-invasively predicted via radiomics. The radiomic patterns with biological and clinical relevance across histology and modalities could have a broad impact on a larger population of patients.

A U-shaped association between composite dietary antioxidant index and migraine in US adults: a nationwide cross-sectional study.

The field of dietary therapies for migraine has grown in popularity. Less research has been conducted to establish the relationship between migraine and the composite dietary antioxidant index (CDAI), a crucial indicator for evaluating the overall combined effects of multiple dietary antioxidants. Therefore, this study addressed this gap based on the National Health and Nutrition Examination Survey (NHANES) database. Multivariate logistic regression equations were used to investigate the relationship between CDAI and migraine, and smoothed-fitted curves were plotted. After a nonlinear relationship was discovered, the recursive algorithm and a two-stage linear regression model were employed to calculate the turning point. Additional stratified analyses were performed to explore differences between populations. This study included a total of 9,190 participants aged 20 years old or older. A U-shaped association was observed between the CDAI and migraine, with an inflection point of 0.2. They were negatively correlated before the inflection point with OR of 0.93 (95% CI = 0.88-0.97) and positively correlated after the inflection point with OR of 1.04 (95% CI = 1.01-1.07). This U-shaped relationship persisted among people aged <60 and ≥60 years, women, and people with BMI <30 and ≥30. We identified a U-shaped association between CDAI and migraine in the U.S. adult population. Further case-control studies and experimental research are needed to explore the underlying mechanisms of action.

Research on assessment method of maximum distributed generation hosting capacity in distribution system with high shares of renewables and power electronics.

With the rapid development of distributed generation (DG) within the framework of modern power systems, accurately assessing the maximum DG hosting capacity in distribution networks is crucial for ensuring the safe and stable operation of the power grid. This paper first introduces an assessment model of maximum DG hosting capacity in distribution network based on optimal power flow (OPF). Then, a two-step method that combines the linearization method and the recursive method is proposed, which consists of two parts: firstly, using linearization method to quickly calculate the preliminary assessment value of maximum DG hosting capacity, and then using a recursive method to accurately correct the preliminary assessment value. Additionally, the proposed improved comprehensive sensitivity index and safety constraint verification method can enhance the computational efficiency and accuracy of the recursive algorithm. Finally, the proposed methods were simulated and validated on the IEEE 33-bus system.

A novel temporal finite element method to solve static viscoelastic problems

A novel temporal finite element method is presented to solve static viscoelastic problems, which is more flexible and appropriate to describe temporal variation of displacement than conventional finite difference or numerical integral methods. By using virtual work principle, a spatial finite element based governing equation is derived in terms of displacement and its derivatives. Then two temporal finite element models are developed using Gurtin variational principle and weighted residual technique. A kind of hybrid shape functions with polynomial and trigonometric basis is stressed to give more flexible descriptions of time varying variables. A criterion of stability analysis is derived, which can numerically be conducted when the constitution of shape functions and step size are prescribed. A recursive algorithm toward end is developed by which the temporal FE analysis can be conducted via a matrix power product with the initial condition, instead of step marching. The proposed approach is available for the viscoelastic model described by linear differential equations with order h ≤ 2, and can conveniently be combined with well-developed numerical algorithms to tackle with boundary value problems, such as FEM, SBFEM etc. Various numerical examples, including those with static/harmonic loads, creep, stress singularity and heterogeneous structures, etc. are provided to illustrate the efficiency of the proposed approaches, and impacts of the temporal FE model, constitution of shape functions, and step size, etc. are taken into account. Satisfactory results are achieved in comparison with analytical or ABAQUS-based solutions.

A Recursive Optimization Approach for Buffer Allocation in Large Production Lines

Buffer allocation is a critical issue in the design stage of manufacturing systems, as buffer capacities may have a great impact on system performance. In this paper, we consider the problem of minimizing the total buffer capacity of a flow line to achieve a desired production rate. A recursive optimization approach is proposed to solve the problem in large production lines. Instead of optimizing a long line directly, the proposed approach decomposes it into two sub-lines, optimizes them recursively, and combines their solutions to find the optimal buffer distribution of the original line. Two different recursive algorithms are developed and their performance is demonstrated by comparing them with a gradient search algorithm. The numerical results show that the recursive algorithms are almost as accurate as the gradient algorithm, but much more efficient, especially for large production lines.

Self-organization of square nitrogen islands on copper (100) surfaces: Island pair distributions based on an ab-initio DFT and mesoscopic interaction model.

Earlier scanning tunneling microscopy (STM) studies have shown that nitrogen forms square c(2x2) islands on Cu(100) surfaces, the assembly of which depends on coverage. Recent calculations have revealed that adatom-adatom interactions are compatible with the formation of square c(2x2) islands. The pair distribution of these islands is a topic of the current investigation. In addition, the domain structure of islands found recently in STM experiments is discussed and explained using two types of island interactions. The potential used for the interaction between N square islands on copper is a combination of previous ab initio calculations and an updated mesoscopic interaction scheme. The 2D-BGY integral equation is extended to handle binary objects like two different types of islands. It is solved for up to a quarter island coverage using a numerical recursion algorithm. Coverage beyond half is accessible with island vacancy symmetry. The calculation predicts the formation of ordered island assemblies with increasing coverage. These results reasonably explain the previous experimental observations. The handling of binary objects also seems to be applicable to assemblies of two adatom types as found in catalysis. The assumptions and limitations of the model are discussed and open questions are formulated.

Electric vehicles charging infrastructure framework using internet of things

Electric vehicles (EVs) sales have grown rapidly recently, and more growth is expected over the coming years. A challenging problem arises when managing different battery requirements of moving EVs through reliable Charging Stations (CSs). Current concerns for EV users are long waiting lines at CSs and dropping below a predefined battery capacity limit. For this reason, this paper proposes an Internet of Things (IoT)-based EV charging scheduling system, which with the use of IoT technologies, decides the optimal assignment between EVs and Charging Points (CPs) located at different CSs at given time t. By using cloud computing and real time data such as number of EVs, number of CSs, number of CPs at different CSs … etc; the scheduling controller uses a recursive algorithm to generate all possible scenarios, and then shares the optimal assignment (that minimizes the average waiting time and fulfill battery constraints and charging needs) with all EVs. To test the validity of the IOT based scheduling system, sensitivity analysis by running different scenarios (pertaining to different parameters) was conducted. The different scenarios were compared to a base scenario where the system was not used and real-life random assignment is considered. The different run scenarios show superiority over the base scenario in terms of average waiting time (WT) and battery capacity threshold. For example, in the base scenario, violation of battery capacity threshold occurred 9.1% of the time, making random selection an unreliable choice versus no violations when the IOT scheduling system is used. Also, all tested scenarios under the IOT scheduling system show shorter average WT compared to the base scenario. For instance, scenarios 2 and 3 show more than 35% and 55% decrease in WT compared to the base scenario.

Serum uric acid and prostate cancer: findings from the NHANES (2007-2020).

The relationship between serum uric acid (SUA) levels and prostate cancer (PCa) remains controversial. This cross-sectional study investigated the association between SUA levels and PCa incidence. A total of 9,776 participants aged ≥40 from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2020 were included, 503 of whom had PCa. Weighted univariate logistic regression, multivariate logistic regression, and smooth-fitting curve analyses were used to analyze the association between SUA and PCa incidence. Concurrently, the fitted smoothing curves were used to explore the potential non-linear relationships. If non-linearity was observed, a recursive algorithm further calculated the inflection point. Three models were used to analyze the correlation between SUA levels and PCa incidence. All regression models demonstrated a negative correlation between SUA levels and PCa incidence (model 1: OR = 0.88, 95% CI=0.80-0.97; model 2: OR = 0.87, 95% CI=0.80-0.96; model 3: OR = 0.87, 95% CI=0.78-0.96). According to the trend test, with increasing SUA, the risk of PCa showed a downward trend (three models P for trend = 0.037, 0.015, 0.016). According to the subgroup analysis, a significant negative correlation between SUA and PCa was detected in individuals aged >60 years, non-Hispanic whites, those of other races, and those with hypertension. Moreover, the association between SUA and PCa followed a U-shaped curve among participants without hypertension, and the inflection point of SUA was 5.1 mg/dl. This cross-sectional study revealed a negative relationship between SUA levels and the risk of PCa, particularly in specific demographic groups. These findings offer a fresh perspective on the role of SUA in PCa patients, potentially paving the way for new approaches for the prevention and treatment of PCa. However, further studies are necessary to validate these findings.

Motion State Estimation with Bandwidth Constraints and Mixed Cyber-Attacks for Unmanned Surface Vehicles: A Resilient Set-Membership Filtering Framework.

This paper investigates the motion state estimation problem of the unmanned surface vehicle (USV) steering system in wireless sensor networks based on the binary coding scheme (BCS). In response to the presence of bandwidth constraints and mixed cyber-attacks in USV communication networks, this paper proposes an improved set-membership state estimation algorithm based on BCS. This algorithm partially addresses the problem of degraded performance in USV steering motion state estimation caused by mixed cyber-attacks and bandwidth constraints. Furthermore, this paper proposes a robust resilient filtering framework considering the possible occurrence of unknown but bounded (UBB) noises, model parameter uncertainties, and estimator gain perturbations in practical scenarios. The proposed framework can accurately estimate the sway velocity, yaw velocity, and roll velocity of the USV under the concurrent presence situation of mixed cyber-attacks, communication capacity constraints, UBB noises, model parameter uncertainties, and estimator gain perturbations. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. Finally, the effectiveness of the proposed estimation algorithm is verified through a simulation experiment.

Energy Efficiency of Plasma Jets: Electrical Modeling Based on Experimental Results

This paper focuses on the determination of and improvement in the energy efficiency of plasma jets. To achieve this goal, an equivalent electrical model of a discharge reactor was developed, incorporating variable electrical parameters. The evolution of these parameters was determined by a mathematical identification method based on the recursive least squares algorithm (RLSA). The good agreement between the measured currents and those calculated using our electrical circuit, as well as the significant shapes of the estimated parameters, confirmed the accuracy of the parameter estimation method. This allowed us to use these parameters to determine the energy delivered to the reactor and that used during the discharge. This made our reactor controllable at the energy level. Thus, the ratio between these two energies allowed us to calculate the energy efficiency of plasma jets at each discharge instant. We also studied the effect of the applied voltage on efficiency. We found that efficiency was increased from 75% to 90% by increasing the voltage from 6 kV to 8 kV. All the results found in this work were interpreted and compared with the discharge behavior. This proposed model will help us to choose the right operating conditions to reach the maximum efficiency.

Saddle-to-saddle dynamics in diagonal linear networks*

Abstract In this paper we fully describe the trajectory of gradient flow over two-layer diagonal linear networks for the regression setting in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minimum ℓ 1 -norm solution. We explicitly characterise the visited saddles as well as the jump times through a recursive algorithm reminiscent of the LARS algorithm used for computing the Lasso path. Starting from the zero vector, coordinates are successively activated until the minimum ℓ 1 -norm solution is recovered, revealing an incremental learning. Our proof leverages a convenient arc-length time-reparametrisation which enables to keep track of the transitions between the jumps. Our analysis requires negligible assumptions on the data, applies to both under and overparametrised settings and covers complex cases where there is no monotonicity of the number of active coordinates. We provide numerical experiments to support our findings.

The Miskolc Method: Modelling the Evolution of a Natural City with Recursive Algorithms Using Simulated Morphogenesis

This article explores the application of procedural design methods in urban morphology, drawing inspiration from the innovative work of the Architectural Workshop of Miskolc in Hungary during the late 20th century. This study presents a generative approach termed “Simulated Morphogenesis” (or the “Miskolc Method”), which models organic city growth by analysing historical urban tissues and applying recursive algorithms to simulate natural urban development. The method leverages advanced generative tools, such as Rhinoceros 3D and Grasshopper, to model the step-by-step growth of Central European cities, with a particular focus on Miskolc. By incorporating controlled randomness into the algorithmic processes, the method captures the complexity of organic urban growth while maintaining structured development. The Miskolc Method emphasizes the importance of continuity and context, allowing for the “healing” of urban fabric discontinuities or the generation of new urban structures. This article demonstrates how this approach, while rooted in geometrical analysis, offers a valuable foundation for preliminary urban planning. The findings are relevant for understanding the morphogenesis of cities and provide a flexible framework applicable to various urban contexts globally.

Recursive algorithm for generating high-temperature expansions for spin systems and the chiral nonlinear susceptibility

Recursive algorithm for generating high-temperature expansions for spin systems and the chiral nonlinear susceptibility

Estimation of gross calorific value of coal based on the cubist regression model.

The gross calorific value (GCV) of coal is an important parameter for evaluating coal quality, and regression analysis methods can be used to predict GCV. In this study, we proposed a GCV prediction model based on cubist regression. To develop a good regression model, feature selection of input variables was performed using a correlation analysis and a recursive feature elimination algorithm. Thus, in this study, we determined three sets of variables as the optimal combination for regression models: proximate analysis variables (Set 1: moisture, standard ash, and volatile matter), element analysis variables (Set 2: carbon, sulfur, and oxygen), and comprehensive index variables (Set 3: carbon, volatile matter, standard ash, sulfur, moisture, and hydrogen). Results for comparison with multiple linear regression, random forest regression, and numerous previous prediction models, such as gradient boosting regression tree, support vector regression (SVR), backpropagation neural networks, and particle swarm optimization-artificial neural network (PSO-ANN), indicate that these seven regression models have the best fitting effect on the comprehensive index variables among the three sets of input variables. The cubist model showed higher prediction accuracy and lower error than most other models (R2, mean absolute error, root mean square error, and average absolute relative deviation percentage values are 0.990, 0.476, 0.668, and 0.086% for the proximate analysis variables; 0.992, 0.381, 0.596, and 0.140% for element analysis variables; and 0.999, 0.161, 0.219, and 0.087% for comprehensive index variables, respectively). The cubist model combines the advantages of decision tree and linear regression, which not only enables it to perform well in terms of accuracy but also makes the model highly interpretable because it is based on multiple sublinear equations. In addition, the cubist model shows obvious advantages in terms of running speed, especially compared with SVR and PSO-ANN, which require complex parameter optimization. In summary, the cubist model considers the prediction accuracy, model interpretability, and computational efficiency as well as provides a new and effective method for GCV prediction.

State Estimation for Measurement-Saturated Memristive Neural Networks with Missing Measurements and Mixed Time Delays Subject to Cyber-Attacks: A Non-Fragile Set-Membership Filtering Framework

This paper is concerned with the state estimation problem based on non-fragile set-membership filtering for a class of measurement-saturated memristive neural networks (MNNs) with unknown but bounded (UBB) noises, mixed time delays and missing measurements (MMs), subject to cyber-attacks under the framework of weighted try-once-discard protocol (WTOD protocol). Considering bandwidth-limited open networks, this paper proposes an improved set-membership filtering based on WTOD protocol to partially solve the problem that multiple sensor-related problems and multiple network-induced phenomena influence the state estimation performance of MNNs. Moreover, this paper also discusses the gain perturbations of the estimator and proposes an improved non-fragile estimation framework based on set-membership filtering, which enhances the robustness of the estimation approach. The proposed estimation framework can effectively estimate the state of MNNs with UBB noises, estimator gain perturbations, mixed time-delays, cyber-attacks, measurement saturations and MMs. This paper first utilizes mathematical induction to provide the sufficient conditions for the existence of the desired estimator, and obtains the estimator gain by solving a set of linear matrix inequalities. Then, a recursive optimization algorithm is utilized to achieve optimal estimation performance. The effectiveness of the theoretical results is verified by comparative numerical simulation examples.

A recursive algorithm for dynamics of planar multibody systems with frictional unilateral constraints

The contact impact problem in planar multibody systems can be efficiently solved by formulating it as the linear complementarity problem, which requires a complex modeling process. To simplify the process, a recursive algorithm for the dynamics of planar multibody systems with frictional unilateral constraints is proposed based on the reduced multibody system transfer matrix method. Firstly, the contact forces of frictional unilateral constraints are integrated into the recurrence relations of system components using Lagrange multipliers. Subsequently, the relative motion equations of the contact positions are discretized in time, which are then utilized to describe the linear complementarity problem for systems. The dynamics of the system is solved by the Moreau time-stepping method with the recursive method. Finally, the proposed algorithm was validated using the woodpecker toy and used to model a slider-crank mechanism with clearance, which shows its characteristics of facilitating modeling, universal, and highly programmable. This recursive algorithm provides an effective tool for solving non-smooth planar multibody systems while extending the application of the multibody system transfer matrix method.

Low-complexity recursive constrained maximum Versoria criterion adaptive filtering algorithm

Linearly-constrained adaptive filtering algorithms have emerged as promising candidates for system estimation. The existing methods such as the constrained least mean square algorithm rely on mean square error based learning, which delivers suboptimal performance under non-Gaussian noise environments. Therefore, the recursive constrained maximum Versoria criterion (RCMVC) algorithm has been derived and is robust against impulsive distortions. Nonetheless, RCMVC suffers from a notable computational overhead stemming from matrix inversion operations. To circumvent this issue, utilizing the weighting method and the dichotomous coordinate descent (DCD) iteration method, this paper derives a low-complexity version of the RCMVC algorithm called DCD-RCMVC, which alleviates the requirement of matrix inversion and enhances the estimation accuracy and robustness against non-Gaussian interference. Furthermore, we also present a comprehensive theoretical analysis of the DCD-RCMVC algorithm, encompassing discussions on its equivalence, convergence properties, and computational complexity. Simulations performed for system identification problems indicate that the DCD-RCMVC algorithm outperforms the existing state-of-art approaches.

Auxiliary model-based maximum likelihood multi-innovation recursive least squares identification for multiple-input multiple-output systems

The aim of this paper is to propose novel identification methods for multiple-input multiple-output systems. Through decomposing a system into subsystems, the system identification model is derived. Based on the obtained sub-model, an auxiliary model-based maximum likelihood recursive least squares algorithm is derived for parameter estimation. For further enhancing the estimation accuracy, the auxiliary model-based maximum likelihood multi-innovation recursive least squares algorithm (AM-ML-MIRLS) is proposed based on the proposed algorithm. Simulation results test the proposed algorithms are all effective, and proves that the proposed AM-ML-MIRLS algorithm has the superior performances in capturing the dynamic properties of the system.

The novel methods of insulation detection based on Adaptive Levenberg–Marquardt algorithm and Third-Order Variable Forgetting Factor Recursive Least Squares-Decouple algorithm

Electric vehicles (EVs) are central to the future of automotive development, with high-voltage insulation performance critical for operational safety. Existing insulation detection methods face challenges such as limited scope, low accuracy, poor interference resistance, and slow response. This study introduces two insulation detection models based on the unbalanced bridge method and low-frequency signal injection, analyzing their theoretical effectiveness and confirming superior detection capability in the unbalanced bridge method. Furthermore, to address feedback voltage waveform issues in this method, an adaptive Levenberg–Marquardt (ALM) algorithm is proposed to prevent the divergence typically seen in traditional approaches. Additionally, a decoupling algorithm utilizing a Third-order Variable Forgetting Factor Recursive Least Squares (TVFF-Decouple) simplifies algorithm complexity significantly while enabling anomaly detection. Finally, AEKF and SRCKF algorithms were used for observation, identifying an optimal combination that reduces noise interference effectively. Simulations and bench tests demonstrate that the proposed methods swiftly and accurately detect positive and negative insulation resistances and equivalent Y capacitance under various conditions.

Collaborative train timetabling and passenger flow control in oversaturated metro lines considering state-dependence

Congestion downgrades the moving speed of passengers in trains and platforms. This in turn affects congestion. That is, the moving speed of passengers interacts with congestion. It is state-dependent and obeys the fundamental diagram. This interaction makes the system unstable and can result in substantial passenger delays and heightened security risks in oversaturated metro lines. This study aims to rectify this by developing a more realistic collaborative optimization approach. Firstly, we formulate a deterministic fluid queuing network model to accurately capture the dynamic propagation of congestion in oversaturated metro lines. This model incorporates the fundamental diagram, thereby effectively considering the interaction between passenger movement speeds and congestion. A recursive algorithm is devised to solve the fluid queuing network model. The complexity of the algorithm is independent of the capacities of the stations and trains. This provides the possibility of analyzing large-scale metro networks. Based on the queuing network model, a mixed-integer nonlinear programming model aimed at minimizing the total passenger waiting time is built. To solve the model effectively, we employ an alternating direction method of multipliers framework to decompose the model into nonlinear programming and integer nonlinear programming problems, which are respectively solved by improved simultaneous perturbation stochastic approximation and adaptive large neighborhood search algorithms. Finally, a small-scale case and a real-world case with data from Chengdu metro lines are implemented to demonstrate the performance and effectiveness of the proposed approach. The experimental results provide some interesting insights. (1) Without consideration of state-dependence, the queuing model underestimates the passenger waiting time and overestimates the service levels. (2) As demand increases, excluding extreme oversaturation scenarios, the optimized results of the state-independent model progressively deteriorate compared to our state-dependent model, with the largest percentage decline being 50.23%. This is especially noticeable when the train capacity is limited. (3) The contribution ratio of passenger flow control in mitigating congestion gradually surpasses that of train timetabling with increasing demand. In the small-scale case, the former contribution ratio to optimization rises from negligible to 98.19%.