Random Iteration Research Articles

Multi-objective optimization and multi-attribute decision-making support for optimal operation of multi stakeholder integrated energy systems

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approch. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity.

Weighted average algorithm: a novel meta-heuristic optimization algorithm based on the weighted average position concept

To address the ever-increasing complexity of real-world engineering challenges, meta-heuristic algorithms have been extensively studied and applied. However, balancing exploration and exploitation remains a significant challenge. In this paper, a novel meta-heuristic optimization algorithm based on the weighted average position concept, and named weighted average algorithm (WAA), is proposed and implemented. In this algorithm, the weighted average position for the whole population is first established at each iteration. Subsequently, WAA applies one of two movement strategies to balance exploration and exploitation, determined by a parameter function that depends on random constants and iteration numbers. To validate the effectiveness and reliability of WAA, it is applied to various optimization challenges, amongst which unconstrained benchmark functions and constrained engineering challenges. Based on the Friedman and Wilcoxon analyses, it can be concluded that the proposed algorithm obtained the best performance for the considered benchmark functions and engineering problems. The WAA is applied to prediction and optimization of surface waviness in Wire Arc Additive Manufacturing (WAAM) components. First, two prediction models relating WAAM process parameters and surface waviness are established based on an Artificial Neural Network (ANN) optimized by WAA, and Particle Swarm Optimization (PSO), respectively. The WAA optimized ANN (WAA/ANN) model outperforms both the standard ANN models and those optimized by PSO. Finally, leveraging the developed WAA/ANN prediction model, WAA and other optimization algorithms are applied to minimize waviness of a WAAM component, with WAA exhibiting promising performance.

A functional parcellation of the whole brain in high-functioning individuals with autism spectrum disorder reveals atypical patterns of network organization.

Researchers studying autism spectrum disorder (ASD) lack a comprehensive map of the functional network topography in the ASD brain. We used high-quality resting state functional MRI (rs-fMRI) connectivity data and a robust parcellation routine to provide a whole-brain map of functional networks in a group of seventy high-functioning individuals with ASD and a group of seventy typically developing (TD) individuals. The rs-fMRI data were collected using an imaging sequence optimized to achieve high temporal signal-to-noise ratio (tSNR) across the whole-brain. We identified functional networks using a parcellation routine that intrinsically incorporates internal consistency and repeatability of the networks by keeping only network distinctions that agree across halves of the data over multiple random iterations in each group. The groups were tightly matched on tSNR, in-scanner motion, age, and IQ. We compared the maps from each group and found that functional networks in the ASD group are atypical in three seemingly related ways: (1) whole-brain connectivity patterns are less stable across voxels within multiple functional networks, (2) the cerebellum, subcortex, and hippocampus show weaker differentiation of functional subnetworks, and (3) subcortical structures and the hippocampus are atypically integrated with the neocortex. These results were statistically robust and suggest that patterns of network connectivity between the neocortex and the cerebellum, subcortical structures, and hippocampus are atypical in ASD individuals.

FunMaps: a method for parcellating functional brain networks using resting-state functional MRI data.

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results.

Pendekatan Metode Monte Carlo untuk Simulasi Pergerakan Harga Saham

This research applies the Monte Carlo simulation method to predict the movement of Apple Inc.'s stock price over a long period of time. Using historical data of Apple's stock price from 12 December 1980 to 24 March 2022, this study aims to generate a probability distribution of the future stock price. The method involves several steps, including data collection, log return calculation, parameter estimation, and simulation of the stock price path through random iterations based on the log return distribution. The simulation results show that the closing price of Apple stock can be predicted by following the historical trend, although there are differences with the real data due to the stochastic nature of the Monte Carlo technique. This research also applies a variance reduction method to improve simulation efficiency. The findings provide a valuable perspective for investors and financial analysts in identifying investment risks and opportunities through an in-depth understanding of the dynamics of stock price movements using Monte Carlo simulation. Suggestions for future research include the use of VaR methods with historical variance and covariance approaches, as well as considering longer data periods and more stock indices for more comprehensive results.

An evolutionary theory on virus mutation in COVID-19

With the rapid evolution of SARS-CoV-2, the emergence of new strains is an intriguing question. This paper presents an evolutionary theory to analyze the mutations of the virus and identify the conditions that lead to the generation of new strains. We represent the virus variants using a 4-letter sequence based on amino acid mutations on the spike protein and employ an n-distance algorithm to derive a variant phylogenetic tree. We show that the theoretically-derived tree aligns with experimental data on virus evolution. Additionally, we propose an A-X model, utilizing the set of existing mutation sites (A) and a set of randomly generated sites (X), to calculate the emergence of new strains. Our findings demonstrate that a sufficient number of random iterations can predict the generation of new macro-lineages when the number of sites in X is large enough. These results provide a crucial theoretical basis for understanding the evolution of SARS-CoV-2.

On the stochastic bifurcations regarding random iterations of polynomials of the form

Abstract In this paper, we consider random iterations of polynomial maps $z^{2} + c_{n}$ , where $c_{n}$ are complex-valued independent random variables following the uniform distribution on the closed disk with center c and radius r. The aim of this paper is twofold. First, we study the (dis)connectedness of random Julia sets. Here, we reveal the relationships between the bifurcation radius and connectedness of random Julia sets. Second, we investigate the bifurcation of our random iterations and give quantitative estimates of bifurcation parameters. In particular, we prove that for the central parameter $c = -1$ , almost every random Julia set is totally disconnected with much smaller radial parameters r than expected. We also introduce several open questions worth discussing.

The impact of different data handling strategies on the proportion of children classified as meeting 24-h movement guidelines and associations with overweight and obesity

BackgroundDespite the widespread endorsement of 24-h movement guidelines (physical activity, sleep, screentime) for youth, no standardized processes for categorizing guideline achievement exists. The purpose of this study was to illustrate the impact of different data handling strategies on the proportion of children meeting 24-h movement guidelines (24hrG) and associations with overweight and obesity.MethodsA subset of 524 children (ages 5–12 years) with complete 24-h behavior measures on at least 10 days was used to compare the impact of data handling strategies on estimates of meeting 24hrG. Physical activity and sleep were measured via accelerometry. Screentime was measured via parent self-report. Comparison of meeting 24hrG were made using (1) average of behaviors across all days (AVG-24 h), (2) classifying each day and evaluating the percentage meeting 24hrG from 10 to 100% of their measured days (DAYS-24 h), and (3) the average of a random sample of 4 days across 10 iterations (RAND-24 h). A second subset of children (N = 475) with height and weight data was used to explore the influence of each data handling strategy on children meeting guidelines and the odds of overweight/obesity via logistic regression.ResultsClassification for AVG-24 h resulted in 14.7% of participants meeting 24hrG. Classification for DAYS-24 h resulted in 63.5% meeting 24hrG on 10% of measured days with < 1% meeting 24hrG on 100% of days. Classification for RAND-24 h resulted in 15.9% of participants meeting 24hrG. Across 10 iterations, 63.6% of participants never met 24hrG regardless of the days sampled, 3.4% always met 24hrG, with the remaining 33.0% classified as meeting 24hrG for at least one of the 10 random iterations of days. Using AVG-24 h as a strategy, meeting all three guidelines associated with lower odds of having overweight obesity (OR = 0.38, 95%CI: 0.21–0.70, p < 0.05). The RAND-24 h strategy produced a range of odds from 0.27 to 0.56. Using the criteria of needing to meet 24hrG on 100% of days, meeting all three guidelines associated with the lowest odds of having overweight and obesity as well (OR = 0.04, 95%CI: 0.01–0.18, p < 0.05).ConclusionsVarying estimates of meeting the 24hrG and the odds of overweight and obesity results from different data handling strategies and days sampled.

A New Randomized Iterative Detection Algorithm for Uplink Large-Scale MIMO Systems

In this paper, a new randomized iterative detection algorithm (NRIDA) is proposed for uplink large-scale MIMO systems, where the random iterations in it are designed to work for the detection model (denoted by y = Hx + n) directly. Different from those traditional iterations designed for the linear system (denoted by Ax = b with A = H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>H</i></sup> H and b = H <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><i>H</i></sup> y), we show that besides the complexity reduction about the matrix inversion, in the proposed NRIDA the computational complexity of matrix multiplication for the linear detection is also greatly reduced without any performance loss, thus leading to a much lower detection complexity. Meanwhile, according to convergence analysis, we demonstrate that the proposed NRIDA enjoys a globally exponential convergence performance, enabling it well suited to the various detection cases of interest. Besides, further complexity reduction and the choices of the sampling distribution in NRIDA are studied as well in full details. Moreover, in order to achieve a better detection trade-off between performance and complexity, we introduce the concept of the conditional sampling into NRIDA, which brings significant gains in both iteration convergence and efficiency. Finally, simulations with respect to the uplink large-scale MIMO detection are presented to illustrate the remarkable gains of the proposed NRIDA in both performance and complexity.

Random dynamics on real and complex projective surfaces

Abstract We initiate the study of random iteration of automorphisms of real and complex projective surfaces, as well as compact Kähler surfaces, focusing on the classification of stationary measures. We show that, in a number of cases, such stationary measures are invariant and provide criteria for uniqueness, smoothness and rigidity of invariant probability measures. This involves a variety of tools from complex and algebraic geometry, random products of matrices, non-uniform hyperbolicity, as well as recent results of Brown and Rodriguez Hertz on random iteration of surface diffeomorphisms.

Cognitive and physical age gaps in relation to mild cognitive impairment and behavioral phenotypes.

We can study how fast our biological aging clocks tick by calculating the difference (i.e., age gaps) between machine learning estimations of biological age and chronological age. While this approach has been increasingly used to study various aspects of aging, few had applied this approach to study cognitive and physical age gaps; not much is known about the behavioral and neurocognitive factors associated with these age gaps. In the present study, we examined these age gaps in relation to behavioral phenotypes and mild cognitive impairment (MCI) among community-dwelling older adults. Participants (N = 822, Agemean = 67.6) were partitioned into equally-sized training and testing samples. Cognitive and physical age-prediction models were fitted using nine cognitive and eight physical fitness test scores, respectively, within the training samples, and subsequently used to estimate cognitive and physical age gaps for each subject in the testing sample. These age gaps were then compared among those with and without MCI and correlated with 17 behavioral phenotypes in the domains of lifestyle, well-being, and attitudes. Across 5000 random train-test split iterations, we showed that older cognitive age gaps were significantly associated with MCI (versus cognitively normal) and worse outcomes across several well-being and attitude-related measures. Both age gaps were also significantly correlated with each other. These results suggest accelerated cognitive and physical aging were linked to worse well-being and more negative attitudes about the self and others and reinforce the link between cognitive and physical aging. Importantly, we have also validated the use of cognitive age gaps in the diagnosis of MCI.

Continuous Dependence in a Problem of Convergence of Random Iteration

We show that an invariant measure of a Markov operator that is contracting in the Hutchison distance, acting on a space of Borel probability measures on a Polish space, depends continuously on the given operator. In addition, we establish an estimate for a distance between invariant measures. Some applications to the weak limit of iterates of random-valued functions (in particular, the so-called random affine maps, occuring, e.g., in perpetuities analysis) are also given.

Arcsine law for random dynamics with a core

In their recent paper, Hata and Yano (2021 Stoch. Dyn. 2350006) first gave an example of random iterations of two piecewise linear interval maps without (deterministic) indifferent periodic points for which the arcsine law—a characterization of intermittent dynamics in infinite ergodic theory—holds. The key in the proof of the result is the existence of a Markov partition preserved by each interval maps. In the present paper, we give a class of random iterations of two interval maps without indifferent periodic points but satisfying the arcsine law, by introducing a concept of core random dynamics. As applications, we show that the generalized arcsine law holds for generalized Hata–Yano maps and piecewise linear versions of Gharaei–Homburg maps, both of which do not have a Markov partition in general.

Diophantine approximation and primitive prime divisors in random iterations

Diophantine approximation and primitive prime divisors in random iterations

Assessing and Predicting Green Credit Risk in the Paper Industry.

The paper industry is closely related to forestry resources, which constitute an essential part of achieving sustainable development. Green credit can provide financial support to assist the paper industry in achieving carbon neutrality. To develop a method for performing green credit risk assessments in the paper industry, first, an initial index system was established on the basis of two dimensions: financial risk and socio-environmental risk. Then, the KMV model was applied to measure credit risk. The combined results of this model, along with the environmental penalties of an enterprise, formed the basis for the classification of green credit risk. Third, the Gini index was used to filter out, one by one, the indexes with the least influence among the factors, and then random forest iterations were performed until the prediction accuracy reached the optimum, thus establishing a green credit risk prediction model for the paper industry. The results show that the accuracy of the sample classification reached 93.75%, and the accuracy of the sample classification for high-risk enterprises reached 100%. The established index system offers good guidance for the assessment of green credit risk in the paper industry, in which the interest coverage ratio, current ratio, asset-liability ratio, and green emissions are the main factors affecting green credit risk.

Can the Predictive Analytics Toolkit (PAT) handle a genomic data set?

The Predictive Analytics Toolkit (PAT) was developed to facilitate use of new approach methodologies (NAMs) to predict health hazards and risks from chemicals. PAT is a user-friendly web application that integrates many R packages to enable development and testing of prediction models without any programming. We drew from the work of Ring et al. 2021 (https://doi.org/10.1016/j.comtox.2021.100166), who used random forest models to predict in vivo transcriptomic responses in rat liver from in vitro Tox21 AC50 values for a set of 221 chemicals. Gene ontologies helped identify 735 biological pathways based on differential in vivo expression of specific gene sets. Ring et al. used 12 models that varied in use of toxicokinetics to predict in vivo activity using 5000 random forest iterations for each chemical/pathway combination (the area under the receiver-operator characteristic curve (AUC-ROC) was the measure of model performance). The highest-ranking model (Model 10) used Tox21 AC50 nominal concentrations converted to media concentrations and in vivo doses converted to circulating plasma concentrations; the lowest ranking model (Model 2) used nominal in vitro concentrations and administered in vivo dose levels. Using a subset of 10 pathways from the Ring et al. data, we used PAT to predict the AUC-ROC and to compare the best (Model 10) and worst (Model 2) performing models with only 100 random forest iterations. Using the results from PAT, Model 10 “won” in 60% of the comparisons, a value similar to that calculated for the identical set of comparisons using the supplemental data from Ring et al. (52.2%). Hence, PAT can provide a useful alternative to programming in R for prediction modeling and model performance evaluation, even for extensive genomic data sets.

The Optimization Of Failure Risk Estimation On The Uniform Corrosion Rate With A Non-Linear Function

Failures in the oil and gas pipeline system are conditions that must be avoided and anticipated because the losses due to the failures can occur at a very high level. Internal corrosion is one of the significant causes of the failures in pipeline systems. In addition, this type of corrosion is due to the high content of carbon dioxide and other corrosive substances in crude oil and natural gas. Therefore, an optimal inspection scheduling system is required to prevent the possibility of pipeline failures due to corrosion and to avoid any overspending on the budget due to excessive inspection scheduling. Risk-based testing (RBI) is one of the best methods to define a test planning system by using an optimal risk assessment. In this article, a Monte Carlo random number generator is applied by using a huge number of random iterations to approximate the actual risk value of a pipeline system with a limited sample at the scene. The nonlinear corrosion rate function is used for comparison with the commonly used linear corrosion rate function based on ASTM G-16 95. Once a risk value is estimated, the value is monitored based on an assessment of the risk matrix for each corrosion rate function by using the RBI method. The results show that the nonlinear corrosion rate function provides a more accurate approach to estimating the actual risk value and ultimately leads to an optimal inspection planning system.

Hepatic kinome atlas: An in-depth identification of kinase pathways in liver fibrosis of humans and rodents.

Resolution of pathways that converge to induce deleterious effects in hepatic diseases, such as in the later stages, have potential antifibrotic effects that may improve outcomes. We aimed to explore whether humans and rodents display similar fibrotic signaling networks. We assiduously mapped kinase pathways using 340 substrate targets, upstream bioinformatic analysis of kinase pathways, and over 2000 random sampling iterations using the PamGene PamStation kinome microarray chip technology. Using this technology, we characterized a large number of kinases with altered activity in liver fibrosis of both species. Gene expression and immunostaining analyses validated many of these kinases as bona fide signaling events. Surprisingly, the insulin receptor emerged as a considerable protein tyrosine kinase that is hyperactive in fibrotic liver disease in humans and rodents. Discoidin domain receptor tyrosine kinase, activated by collagen that increases during fibrosis, was another hyperactive protein tyrosine kinase in humans and rodents with fibrosis. The serine/threonine kinases found to be the most active in fibrosis were dystrophy type 1 protein kinase and members of the protein kinase family of kinases. We compared the fibrotic events over four models: humans with cirrhosis and three murine models with differing levels of fibrosis, including two models of fatty liver disease with emerging fibrosis. The data demonstrate a high concordance between human and rodent hepatic kinome signaling that focalizes, as shown by our network analysis of detrimental pathways. Our findings establish a comprehensive kinase atlas for liver fibrosis, which identifies analogous signaling events conserved among humans and rodents.

Risk Scoring System for Vancomycin-Associated Acute Kidney Injury.

Vancomycin-associated acute kidney injury (AKI) remains a major challenge for patients and clinicians. This study aimed to construct a risk scoring system for vancomycin-associated AKI. We retrospectively reviewed medical records of patients who underwent therapeutic drug monitoring for vancomycin from June 2018 to July 2019. We selected possible risk factors for AKI by univariate and multivariable logistic regression analyses and developed a scoring system for vancomycin-associated AKI. Machine learning methods were utilized to predict risk factors for the occurrence of AKI. The incidence of vancomycin-associated AKI was 31.7% among 104 patients included in this study. A bodyweight ≤60 kg (two points), a Charlson comorbidity index ≥3 (two points), a vancomycin trough serum level >15 μg/ml (one point), and concomitant use of ≥6 nephrotoxic agents (two points) were included to construct a risk scoring system based on the coefficient from the logistic regression model. The area under the receiver operating characteristic curve (AUROC) (mean, 95% confidence interval (CI)) across 10 random iterations using five-fold cross-validated multivariate logistic regression, elastic net, random forest, support vector machine (SVM)-linear kernel, and SVM-radial kernel models was 0.735 (0.638–0.833), 0.737 (0.638–0.835), 0.721 (0.610–0.833), 0.739 (0.648–0.829), and 0.733 (0.640–0.826), respectively. For total scores of 0–1, 2–3, 4–5, 6–7, the risk of vancomycin-associated AKI was 5, 25, 45, and 65%, respectively. Our scoring system can be applied to clinical settings in which several nephrotoxic agents are used along with vancomycin therapy.

On stochastic Kaczmarz type methods for solving large scale systems of ill-posed equations

In this article we investigate a family of stochastic gradient type methods for solving systems of linear ill-posed equations. The method under consideration is a stochastic version of the projective Landweber–Kaczmarz method in Leitão and Svaiter (2016 Inverse Problems 32 025004) (see also Leitão and Svaiter (2018 Numer. Funct. Anal. Optim. 39 1153–80)). In the case of exact data, mean square convergence to zero of the iteration error is proven. In the noisy data case, we couple our method with an a priori stopping rule and characterize it as a regularization method for solving systems of linear ill-posed operator equations. Numerical tests are presented for two linear ill-posed problems: (i) a Hilbert matrix type system with over 108 equations; (ii) a big data linear regression problem with real data. The obtained results indicate superior performance of the proposed method when compared with other well-established random iterations. Our preliminary investigation indicates that the proposed iteration is a promising alternative for computing stable approximate solutions of large scale systems of linear ill-posed equations.