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The discovery of Markov boundaries is highly effective at identifying features that are causally related to the target variable, providing strong interpretability and robustness. While there are numerous methods for discovering Markov boundaries in real-world applications, no single method is universally applicable to all datasets. Therefore, in order to balance precision and recall, we propose an ensemble framework of divide-and-conquer Markov boundary discovery algorithms based on U-I selection strategy. We put three divide-and-conquer Markov boundary methods into the framework to obtain an ensemble algorithm, focusing on judging controversial parent–child variables to further balance precision and recall. By combining multiple algorithms, the ensemble algorithm can leverage their respective strengths and more thoroughly analyze the cause-and-effect relationships of target variables through various perspectives. Furthermore, it can enhance the robustness of the algorithm and reduce dependence on a single algorithm. In the experiment, we select four advanced Markov boundary discovery algorithms as comparison algorithms and compare them on nine benchmark Bayesian networks and three real-world datasets. The results show that EDMB ranks first in the overall ranking, which illustrates the superiority of the integrated algorithm and the effectiveness of the adopted U-I selection strategy. The main contribution of this paper lies in proposing an ensemble framework for divide-and-conquer Markov boundary discovery algorithms, balancing precision and recall through the U-I selection strategy, and judging controversial parent–child variables to enhance algorithm performance and robustness. The advantage of the U-I selection strategy and its difference from existing methods is the ability to independently obtain the maximum precision and recall of multiple algorithms within the ensemble framework. By assessing controversial parent–child variables, it further balances precision and recall, leading to results that are closer to the true Markov boundary. [ABSTRACT FROM AUTHOR]

Various metaheuristic algorithms inspired by nature have been designed to deal with a variety of practical optimization problems. As an excellent metaheuristic algorithm, the improved particle swarm optimization algorithm based on grouping (IPSO) has strong global search capabilities. However, it lacks a strong local search ability and the ability to solve constrained discrete optimization problems. This paper focuses on improving these two aspects of the IPSO algorithm. Based on IPSO, we propose an improved particle swarm optimization algorithm based on variable neighborhood search (VN-IPSO) and design a 0-1 integer programming solution with constraints. In the experiment, the performance of the VN-IPSO algorithm is fully tested and analyzed using 23 classic benchmark functions (continuous optimization), 6 knapsack problems (discrete optimization), and 10 CEC2017 composite functions (complex functions). The results show that the VN-IPSO algorithm wins 18 first places in the classic benchmark function test set, including 6 first places in the solutions for seven unimodal test functions, indicating a good local search ability. In solving the six knapsack problems, it wins four first places, demonstrating the effectiveness of the 0-1 integer programming constraint solution and the excellent solution ability of VN-IPSO in discrete optimization problems. In the test of 10 composite functions, VN-IPSO wins first place four times and ranks the first in the comprehensive ranking, demonstrating its excellent solving ability for complex functions. [ABSTRACT FROM AUTHOR]

The article focuses on the limitations of current reinforcement learning-based sequential recommendation models, which fail to utilize supervision signals and auxiliary information, leading to slow convergence and limited exploration of user preferences. It mentions to overcome these challenges, the authors propose MELOD, a multiplex information-guided RL model, incorporating Teach and Explore components to accurately capture user preferences.

Selective oxidation of hydrocarbons using molecular oxygen as sole oxidant under mild conditions remains a challenging task. In this context, metal‐organic frameworks (MOFs) have been widely used in various oxidation reactions due to their porosity, high surface area and designability. However, the slow diffusion of substrates/products in micropores of three‐dimensional (3D) bulk MOFs hinders the efficient catalytic performance of such materials. Herein an ultrathin two‐dimensional (2D) porphyrin‐based Zr‐MOF nanosheet (Zr‐TCPP) is synthesized through modulator‐control strategy. Subsequently, various metal ions are anchored into the porphyrin ring by post synthesis modification to afford a series of 2D Zr‐TCPP(M) (M=Mn, Fe, Co, Ni, Cu and Zn). Various structural characterization techniques indicate Zr‐TCPP(M) is nanoflower structure with ultrathin nanoplate petals which provides fully exposed accessible active sites. Among them, Zr‐TCPP(Fe) shows excellent catalytic performance in styrene epoxidation reactions and benzylic C−H oxidation reactions using O2 as sole oxidant under ambient temperature and pressure. The remarkable activity arises from high density of exposed porphyrin‐Fe active sites, low diffusion barriers for substrates and products, as well as a similar homogeneous reaction space. Furthermore, Zr‐TCPP(Fe) nanosheet is easily recycled by centrifugation and reused at least five times without significant loss of catalytic activity. [ABSTRACT FROM AUTHOR]

The Harris Hawks Optimization algorithm (HHO) is a sophisticated metaheuristic technique that draws inspiration from the hunting process of Harris hawks, which has gained attention in recent years. However, despite its promising features, the algorithm exhibits certain limitations, including the tendency to converge to local optima and a relatively slow convergence speed. In this paper, we propose the multi-strategy improved HHO algorithm (MSI-HHO) as an enhancement to the standard HHO algorithm, which adopts three strategies to improve its performance, namely, inverted S-shaped escape energy, a stochastic learning mechanism based on Gaussian mutation, and refracted opposition-based learning. At the same time, we conduct a comprehensive comparison between our proposed MSI-HHO algorithm with the standard HHO algorithm and five other well-known metaheuristic optimization algorithms. Extensive simulation experiments are conducted on both the 23 classical benchmark functions and the IEEE CEC 2020 benchmark functions. Then, the results of the non-parametric tests indicate that the MSI-HHO algorithm outperforms six other comparative algorithms at a significance level of 0.05 or greater. Additionally, the visualization analysis demonstrates the superior convergence speed and accuracy of the MSI-HHO algorithm, providing evidence of its robust performance. [ABSTRACT FROM AUTHOR]

Sepsis-associated encephalopathy (SAE) is a severe complication of sepsis. Reduced glutathione (GSH) has antioxidant properties and is used as a neuroprotective agent in some studies. However, research on the application of exogenous GSH in the treatment of SAE is limited. This study aimed to determine the effects of exogenous GSH in pediatric SAE patients and mice. We evaluated clinical parameters, inflammatory factors, and oxidative stress before and after GSH treatment. The clinical trials demonstrated that GSH treatment improved brain damage markers (S-100 beta protein, brain fatty acid-binding protein), increased neurological status scores (Glasgow coma scale), and reduced Pediatric Risk of Mortality III scores in children with SAE. GSH treatment also significantly reduced the levels of inflammatory factors (interleukin-6, tumor necrosis factor-α) and decreased lipid peroxidation (superoxide dismutase). Additionally, GSH reduced lipid peroxidation resulting from abnormal lipid metabolism, as indicated by the levels of acyl-CoA synthetase long-chain family member 4, lysophosphatidylcholine acyltransferase 3, and glutathione peroxidase 4. In-vivo experiments showed that the neuroprotective effect of GSH was dose-dependent, with better effects observed at medium and high doses. Furthermore, GSH alleviated brain damage, suppressed the release of inflammatory factors, and inhibited lipid peroxidation in SAE mice. The animal experiments also showed that GSH reduces lipid peroxidation through the 15-lipoxygenase/phosphatidylethanolamine binding protein 1/glutathione peroxidase 4 pathway. Our study suggests that exogenous GSH has neuroprotective effects in pediatric SAE. These findings provide a basis for the potential use of GSH as a therapeutic method for SAE., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)