A Zeroing Neural Network Approach for Calculating Time-Varying G-Outer Inverse of Arbitrary Matrix.

Calculation of the time-varying (TV) matrix generalized inverse has grown into an essential tool in many fields, such as computer science, physics, engineering, and mathematics, in order to tackle TV challenges. This work investigates the challenge of finding a TV extension of a subclass of inner inverses on real matrices, known as generalized-outer (G-outer) inverses. More precisely, our goal is to construct TV G-outer inverses (TV-GOIs) by utilizing the zeroing neural network (ZNN) process, which is presently thought to be a state-of-the-art solution to tackling TV matrix challenges. Using known advantages of ZNN dynamic systems, a novel ZNN model, called ZNNGOI, is presented in the literature for the first time in order to compute TV-GOIs. The ZNNGOI performs excellently in performed numerical simulations and an application on addressing localization problems. In terms of solving linear TV matrix equations, its performance is comparable to that of the standard ZNN model for computing the pseudoinverse.

Various solitons induced by relative phase in the nonlinear Schrödinger Maxwell-Bloch system.

We study the effect of relative phase on the characteristics of rogue waves and solitons described by rational solutions in the nonlinear Schrödinger Maxwell-Bloch system. We derived the rational rogue wave and soliton solutions with adjustable relative phase and present the parameter range of different types of rogue waves and solitons. Our findings show that the relative phase can alter the distribution of rational solitons and even change the type of rational solitons, leading to a rich array of rational soliton types by adjusting the relative phase. However, the relative phase does not affect the structure of the rogue wave, because the relative phase of the rogue wave changes during evolution. In particular, we confirm that the rational solitons with varying relative phases and the rogue waves at corresponding different evolution positions share the same distribution mode. This relationship holds true for rogue waves or breathers and their stable counterparts solitons or periodic waves in different nonlinear systems. The implications of our study are significant for exploring fundamental excitation elements in nonlinear systems.

The Protective Effect of Chronic Intermittent Hypobaric Hypoxia on Preventing the Destruction of CD34+ Haematopoietic Stem Cells in Aplastic Anaemia by Modulating the Th1/Th2 Balance.

Aplastic anaemia (AA) is a haematopoietic disorder caused by immune-mediated attack on haematopoietic stem cells (HSCs). Stem cell transplantation and immunosuppressive therapy remain the major treatment choice for AA patients but have limited benefits and undesired side effects. The aim of our study was to clarify the protective role of immunity of chronic intermittent hypobaric hypoxia (CIHH) and the underlying mechanism in AA. Our integrative analysis demonstrated that CIHH pre-treatment significantly improved haematopoiesis and survival in an AA rat model. We further confirmed that CIHH pre-treatment was closely associated with the Th1/Th2 balance and a large number of negative regulatory haematopoietic factors, such as TNF-α and IFN-γ, produced by hyperactive Th1 lymphocytes released in AA rats, which induced the death program in a large number of CD34+ HSCs by activating the Fas/FasL apoptosis pathway, while CIHH pre-treatment effectively downregulated the expression of TNF-α and IFN-γ, resulting in a reduction in Fas antigen expression in CD34+ HSCs. In summary, this study provides evidence that CIHH has good protective effect against AA by modulating immune balance in Th1/Th2 cells and may provide a new therapeutic strategy.

Neural Networks for Portfolio Analysis With Cardinality Constraints.

Portfolio analysis is a crucial subject within modern finance. However, the classical Markowitz model, which was awarded the Nobel Prize in Economics in 1991, faces new challenges in contemporary financial environments. Specifically, it fails to consider transaction costs and cardinality constraints, which have become increasingly critical factors, particularly in the era of high-frequency trading. To address these limitations, this research is motivated by the successful application of machine learning tools in various engineering disciplines. In this work, three novel dynamic neural networks are proposed to tackle nonconvex portfolio optimization under the presence of transaction costs and cardinality constraints. The neural dynamics are intentionally designed to exploit the structural characteristics of the problem, and the proposed models are rigorously proven to achieve global convergence. To validate their effectiveness, experimental analysis is conducted using real stock market data of companies listed in the Dow Jones Index (DJI), covering the period from November 8, 2021 to November 8, 2022, encompassing an entire year. The results demonstrate the efficacy of the proposed methods. Notably, the proposed model achieves a substantial reduction in costs (which combines investment risk and reward) by as much as 56.71% compared with portfolios that are averagely selected.

Neural Networks for Portfolio Analysis in High-Frequency Trading.

High-frequency trading proposes new challenges to classical portfolio selection problems. Especially, the timely and accurate solution of portfolios is highly demanded in financial market nowadays. This article makes progress along this direction by proposing novel neural networks with softmax equalization to address the problem. To the best of our knowledge, this is the first time that softmax technique is used to deal with equation constraints in portfolio selections. Theoretical analysis shows that the proposed method is globally convergent to the optimum of the optimization formulation of portfolio selection. Experiments based on real stock data verify the effectiveness of the proposed solution. It is worth mentioning that the two proposed models achieve 5.50 % and 5.47 % less cost, respectively, than the solution obtained by using MATLAB dedicated solvers, which demonstrates the superiority of the proposed strategies.

Phosphodiesterase type 10A inhibitor attenuates lung fibrosis by targeting myofibroblast activation.

Pulmonary fibrosis (PF) is a fatal and irreversible respiratory disease accompanied by excessive fibroblast activation. Previous studies have suggested that cAMP signaling pathway and cGMP-PKG signaling pathway are continuously down-regulated in lung fibrosis, whereas PDE10A has a specifically expression in fibroblasts/myofibroblasts in lung fibrosis. In this study, we demonstrated that overexpression of PDE10A induces myofibroblast differentiation, and papaverine, as a PDE10A inhibitor used for vasodilation, inhibits myofibroblast differentiation in human fibroblasts, Meanwhile, papaverine alleviated bleomycin-induced pulmonary fibrosis and amiodarone-induced oxidative stress, papaverine downregulated VASP/ß-catenin pathway to reduce the myofibroblast differentiation. Our results first demonstrated that papaverine inhibits TGFß1-induced myofibroblast differentiation and lung fibrosis by VASP/ß-catenin pathway.

Advances on intelligent algorithms for scientific computing: an overview.

The field of computer science has undergone rapid expansion due to the increasing interest in improving system performance. This has resulted in the emergence of advanced techniques, such as neural networks, intelligent systems, optimization algorithms, and optimization strategies. These innovations have created novel opportunities and challenges in various domains. This paper presents a thorough examination of three intelligent methods: neural networks, intelligent systems, and optimization algorithms and strategies. It discusses the fundamental principles and techniques employed in these fields, as well as the recent advancements and future prospects. Additionally, this paper analyzes the advantages and limitations of these intelligent approaches. Ultimately, it serves as a comprehensive summary and overview of these critical and rapidly evolving fields, offering an informative guide for novices and researchers interested in these areas.

Time­dependent changes in NLRP3 and Nrf2 levels in lipopolysaccharide­induced acute lung injury.

Acute lung injury (ALI) and acute respiratory distress syndrome (ARDS) are severe clinical conditions with a high mortality rate. Nucleotide­binding oligomerization domain (NOD)­like receptor containing pyrin domain 3 (NLRP3) and nuclear factor E2­related factor 2 (Nrf2) have been reported to be associated with ALI. However, the dynamic changes in the levels of these factors in lipopolysaccharide (LPS)­induced lung injury remain unclear. Thus, the present study aimed to determine the LPS­induced activation of immunological cascades, as well as the NLRP3/Nrf2 signaling pathway at different stages of lung injury. For this purpose, mice were divided into six groups as follows: The control, LPS­4 h, LPS­24 h, LPS­48 h, LPS­96 h and LPS­144 h groups. LPS (4 mg/kg) was administered intratracheally to induce lung injury. Flow cytometry was used to determine the changes in macrophages, neutrophils and T­cell subsets in lung tissue, hematoxylin and eosin staining were used to measure the histopathological changes in lung tissues, ELISA was performed to evaluate the levels of cytokines, western blot analysis was used to measure the levels of inflammatory proteins, and reverse transcription­quantitative PCR used to determine the mRNA level of a target gene. Following LPS administration, evident histopathological damage with neutrophil infiltration was observed which peaked at 48 h. The levels of interleukin­1ß, keratinocyte­derived chemokine, macrophage inflammatory protein 2 and tumor necrosis factor a were markedly increased in bronchoalveolar lavage fluid and serum from the mice, and these levels peaked at 4 h. Moreover, LPS promoted Toll like receptor­4 expression and reactive oxygen species production, thus activating NLRP3/Nrf2 signaling and pyroptosis. Collectively, the present study demonstrates that LPS triggers multiple inflammatory molecules and immune cells during ALI, which may be closely involved in the irregular redox status, NLRP3/Nrf2 pathway and pyroptosis.

Egret Swarm Optimization Algorithm: An Evolutionary Computation Approach for Model Free Optimization.

A novel meta-heuristic algorithm named Egret Swarm Optimization Algorithm (ESOA) is proposed in this paper, which is inspired by two egret species' hunting behavior (Great Egret and Snowy Egret). ESOA consists of three primary components: a sit-and-wait strategy, aggressive strategy as well as discriminant conditions. The learnable sit-and-wait strategy guides the egret to the most probable solution by applying a pseudo gradient estimator. The aggressive strategy uses random wandering and encirclement mechanisms to allow for optimal solution exploration. The discriminant model is utilized to balance the two strategies. The proposed approach provides a parallel framework and a strategy for parameter learning through historical information that can be adapted to most scenarios and has well stability. The performance of ESOA on 36 benchmark functions as well as 3 engineering problems are compared with Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Grey Wolf Optimizer (GWO), and Harris Hawks Optimization (HHO). The result proves the superior effectiveness and robustness of ESOA. ESOA acquires the winner in all unimodal functions and reaches statistic scores all above 9.9, while the scores are better in complex functions as 10.96 and 11.92.

Bio-inspired Machine Learning for Distributed Confidential Multi-Portfolio Selection Problem.

The recently emerging multi-portfolio selection problem lacks a proper framework to ensure that client privacy and database secrecy remain intact. Since privacy is of major concern these days, in this paper, we propose a variant of Beetle Antennae Search (BAS) known as Distributed Beetle Antennae Search (DBAS) to optimize multi-portfolio selection problems without violating the privacy of individual portfolios. DBAS is a swarm-based optimization algorithm that solely shares the gradients of portfolios among the swarm without sharing private data or portfolio stock information. DBAS is a hybrid framework, and it inherits the swarm-like nature of the Particle Swarm Optimization (PSO) algorithm with the BAS updating criteria. It ensures a robust and fast optimization of the multi-portfolio selection problem whilst keeping the privacy and secrecy of each portfolio intact. Since multi-portfolio selection problems are a recent direction for the field, no work has been done concerning the privacy of the database nor the privacy of stock information of individual portfolios. To test the robustness of DBAS, simulations were conducted consisting of four categories of multi-portfolio problems, where in each category, three portfolios were selected. To achieve this, 200 days worth of real-world stock data were utilized from 25 NASDAQ stock companies. The simulation results prove that DBAS not only ensures portfolio privacy but is also efficient and robust in selecting optimal portfolios.