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Natural polyphenols as novel interventions for aging and age-related diseases:Exploring efficacy,mechanisms of action and implications for future research

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Chinese Herbal Medicines 2025年第2期17卷 279-291页

作者： Wenze Wu Yan Mi Qingqi Meng Ning Li Wei Li Pu Wang Yue Hou Liaoning Key Laboratory of Bioresource Research and Development National Frontiers Science Center for Industrial Intelligence and Systems OptimizationKey Laboratory of Data Analytics and Optimization for Smart IndustryMinistry of EducationCollege of Life and Health SciencesNortheastern UniversityShenyang 110169China Key Laboratory for TCM Material Basis Study and Innovative Drug Development of Shenyang City School of Traditional Chinese Materia MedicaShenyang Pharmaceutical UniversityShenyang 117004China Faculty of Pharmaceutical Sciences Toho UniversityMiyamaFunabashiChiba 274-8510Japan College of Pharmacy Shenzhen Technology UniversityShenzhen 518118China

Natural polyphenols are a group of components widely found in traditional Chinese medicines and have been demonstrated to delay or prevent the development of aging and age-related diseases in recent *** far as we know,the studies of natural polyphenols in aging and aging-related diseases have never been extensively *** the present paper,we reviewed recent advances of natural polyphenols in aging and common age-related diseases and the current technological methods to improve the bioavailability of natural *** results showed that natural polyphenols have the potential to prevent or treat aging and common age-related diseases through multiple ***,structural modifications,and matrix processing could provide strong technical support for the development of natural polyphenols to prevent or treat aging and age-related *** conclusion,natural polyphenols have important potential in the prevention and treatment of aging and age-related diseases.

关键词： aging cancer cardiovascular disease natural polyphenols neurodegenerative diseases

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AcArm: A Novel Semiconductor Wafer Handling Robot

AcArm: A Novel Semiconductor Wafer Handling Robot

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China Semiconductor Technology International Conference (CSTIC)

作者： Donglin Chen Lixin Tang Dehong Cong Jingchao Qiao National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Ministry of Education Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China

In the field of semiconductor manufacturing, highly automated production methods have become an inevitable trend of development thanks to their precise, stable and reliable characteristics. This paper focuses on the handling link in the semiconductor production process, designed a novel handling robot. Minimum Snap method is used to plan the end-effector trajectory of the robot, the control method of the robot is based on the position control, and the experiment environment is the co-simulation environment of Simulink and Adams. All the simulation testing of the planning and the control method has been done on the virtual prototype.

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Evolution and detection of vector superradiant instabilities

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Physical Review D 2024年第8期110卷 083029页

作者： Yin-Da Guo Nayun Jia Shou-Shan Bao Hong Zhang Xin Zhang Key Laboratory of Particle Physics and Particle Irradiation (Ministry of Education) Institute of Frontier and Interdisciplinary Science Department of Physics Key Laboratory of Cosmology and Astrophysics (Liaoning) and College of Sciences Key Laboratory of Data Analytics and Optimization for Smart Industry (Ministry of Education) National Frontiers Science Center for Industrial Intelligence and Systems Optimization

Ultralight vectors can extract energy and angular momentum from a Kerr black hole (BH) due to superradiant instability, resulting in the formation of a BH-condensate system. In this work, we carefully investigate the evolution of this system numerically with multiple superradiant modes. Simple formulas are obtained to estimate important timescales, maximum masses of different modes, as well as the BH mass and spin at various times. Due to the coexistence of modes with small frequency differences, the BH-condensate system emits gravitational waves with a unique beat signature, which could be directly observed by current and projected interferometers. Besides, the current BH spin-mass data from the binary BH merger events already exclude the vector mass in the range 5×10−15 eV<μ<9×10−12 eV.

关键词： Gravitational wave sources Phenomenology Vector mesons

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Dynamic Locomotion of Quadruped Robot in Sloping Terrain

Dynamic Locomotion of Quadruped Robot in Sloping Terrain

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International Symposium on Autonomous Systems (ISAS)

作者： Donglin Chen Dehong Cong Xiangman Song National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China

In order to improve the locomotion capability of quadruped robot in the sloping terrain and enhance whose adaptability to the environment, this paper proposes a method to realize dynamic locomotion of quadruped robot in sloping terrain completely based on model predictive control (MPC) and proprioception. According to the foot contact signal and the robot body posture information measured by inertial measurement unit (IMU), quadruped robot actively changes whose body posture to adapt to different sloping terrains. This paper builds quadruped robot virtual prototype and test terrain in Webots simulation environment. The experimental results show that with this method, the roll angle fluctuation range of the quadruped robot is less than 0.05rad and the yaw angle fluctuation range is not more than 0.003rad. At the same time, when the angle of slope changes, the terrain adaptation time of the quadruped robot is less than 1.8s. The above results show that quadruped robot has excellent movement ability in sloping terrain and environmental adaptability under the condition of ensuring stability with the method proposed in this paper.

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Accelerating the Decomposition-Based Multiobjective Evolutionary Algorithm via Evolutionary Direction Vector

Accelerating the Decomposition-Based Multiobjective Evolutio...

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data-driven optimization of Complex Systems (DOCS), International Conference on

作者： Jingchuan Zhang Yaxue Liu Zhiming Dong Xianpeng Wang National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education Shenyang China

ISBN: (数字)9798350377842

ISBN: (纸本)9798350377859

In the process of multi-objective optimization, a large amount of data is generated, which contains the problem-related information. Learning knowledge from this data and applying it to the evolutionary process can improve the quality of the offspring solutions. Most existing knowledge learning methods suffer from high algorithm complexity, and there are still few methods designed specifically for the decomposition-based MOEAs. This paper proposes an evolutionary direction vector (EDV)-guided multiobjective evolutionary algorithm based on decomposition (MOEA/D-EDV). It collects the high-quality evolutionary process data based on the solution's performance in the objective space and their movement directions in the decision space. It then extracts the problem-related knowledge using an efficient learning method to accelerate the evolutionary process. Experimental results on 30 benchmark problems demonstrate that the EDV-guided evolution can significantly accelerate the evolutionary process, and the overall performance of the proposed MOEA/D-EDV is significantly better than the comparative algorithms in the literature.

关键词： Learning systems Evolutionary computation Benchmark testing Vectors Complexity theory Complex systems optimization

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Reverse Distribution Based Video Moment Retrieval for Effective Bias Elimination 39

Reverse Distribution Based Video Moment Retrieval for Effect...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Kong, Lingdu Yang, Xiaochun Li, Tieying Wang, Bin Zhou, Xiangmin Northeastern University China National Frontiers Science Center for Industrial Intelligence and Systems Optimization China Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China RMIT University Australia

ISBN: (纸本)157735897X

Video Moment Retrieval (VMR) aims to identify a temporal segment in an untrimmed video that best matches a given textual query. Bias in VMR is a critical issue, where the model achieves favorable results even if disregarding the video input. Existing evaluation methods, such as Resplitting, have attempted to address bias by creating out-of-distribution (OOD) datasets. However, these methods provide an incomplete definition of bias and do not quantify bias. To this end, we provide a comprehensive definition of bias in VMR, encompassing both data bias and model bias. Besides, our evaluation metrics can analyze the magnitude of these biases better. To address both data and model biases comprehensively, we introduce Reverse Distribution based VMR (ReDis-VMR). This novel approach dynamically generates datasets with inverse distributions tailored to different models based on Gaussian kernel estimation. As a result, it enables a more accurate evaluation of model performance. Building on ReDis-VMR, we further propose the Dynamic Expandable Adjustment (DEA) pipeline. DEA incrementally expands the model structure to enhance its focus on video and text features, and it incorporates a fair loss to minimize the influence of concentrated data distributions. The experiments on bias ratio demonstrate that our ReDis method achieves state-of-the-art performance in bias elimination, while the results on moment retrieval confirm the effectiveness of our DEA framework across three evaluation methods, two datasets, and three baselines. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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Robust inference of gravitational wave source parameters in the presence of noise transients using normalizing flows

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Physical Review D 2025年第2期111卷 024019-024019页

作者： Chun-Yu Xiong Tian-Yang Sun Jing-Fei Zhang Xin Zhang Liaoning Key Laboratory of Cosmology and Astrophysics College of Sciences Northeastern University Shenyang 110819 China MOE Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Shenyang 110819 China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang 110819 China

Gravitational wave (GW) detection is of paramount importance in fundamental physics and GW astronomy, yet it presents formidable challenges. One significant challenge is the removal of noise transient artifacts known as “glitches,” which greatly impact the search and identification of GWs. Recent research has achieved remarkable results in data denoising, often using effective modeling methods to remove glitches. However, for glitches from uncertain or unknown sources, current methods cannot completely eliminate them from the GW signal. In this work, we leverage the inherent robustness of machine learning to obtain reliable posterior parameter distributions directly from GW data contaminated by glitches. Our network model provides reasonable and rapid parameter inference even in the presence of glitches, without needing to remove them. We also investigate various factors affecting the rationality of parameter inference in our normalizing flow network, including glitch and GW parameters. The results demonstrate that the normalizing flow can reasonably infer the source parameters of GWs even with unknown contamination. We find that the nature of the glitch itself is the only factor that can affect the rationality of the inferred results. With improvements to our model, we anticipate accelerating the localization of electromagnetic counterparts and providing priors for more accurate deglitching, thereby speeding up subsequent data processing procedures.

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Reinforcement Learning for Dynamic Mutation Process Control in Multi-Objective Differential Evolution

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IFAC-PapersOnLine 2022年第15期55卷 117-122页

作者： Lue Tao Gongshu Wang Yang Yang Yun Dong Lijie Su Key Laboratory of Data Analytics and Optimization for Smart Industry (Northeastern University) Ministry of Education China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University China Liaoning Key Laboratory of Manufacturing System and Logistics Optimization Northeastern University China

To improve the search performance of the multi-objective differential evolution algorithm, we use a reinforcement learning agent to control the dynamic mutation process. First, a novel multi-objective optimization framework with new mutation operators is developed. Then, a Q-learning algorithm is introduced to select mutation operators with different characteristics adaptively. Specifically, we design three feature functions to describe the state of the population observed by the agent. Based on the observed state, the agent dynamically selects the mutation operator in each generation, which strongly improved the searching ability. We compare the performance of the proposed method with two state-of-art algorithms on benchmark functions and find that the proposed algorithm has a better performance in multiple indicators. At the same time, the component validity analysis also shows the effectiveness of the Q-learning and the framework introduced in this paper. Finally, the learning process shows that the agent can achieve asymptotic convergence.

关键词： Reinforcement Learning Evolutionary Algorithm Multi-Objective Deferential Evolution Mutation Process Control Operator Selection

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Distributed Proximal-Correction Algorithm for The Sum of Maximal Monotone Operators

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IEEE Transactions on Automatic Control 2024年

作者： Gong, Kai Zhang, Liwei The School of Mathematical Sciences Dalian University of Technology Liaoning 116024 China The National Frontiers Science Center for Industrial Intelligence and System Optimization the Key Laboratory of Data Analysis and Optimization for Smart Industry Ministry of Education Northeastern University Shenyang110819 China

This paper focuses on a class of inclusion problems of maximal monotone operators over directed multi-agent networks, where each agent is characterized by a splittable local operator. By integrating the ideas from correction, inexact proximal methods, and push-sum protocol, we proposed a distributed proximal-correction algorithm (referred to as DPCA-ϵik) to solve this inclusion problem of operators, where ϵik is an agent predetermined accuracy for approximately computing proximal mappings. Under mild assumptions on problem setup and agents specified precision ϵik, we proved the convergence and sublinear rate o(1/k) of DPCA-ϵik in terms of the first-order optimality residual. Furthermore, under the metric regularity of local operators of agents, we established its linear convergence rate. Numerical simulations and comparisons with other distributed algorithms illustrate the effectiveness of the proposed algorithm. ©1963-2012 IEEE.

关键词： Consensus algorithm

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A Method for Predicting Mechanical Properties of Steel Based on Graph Convolutional Networks

SSRN

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SSRN 2024年

作者： Ban, Yunqi Yang, Yang Wang, Xianpeng Wu, Zhiyuan National Frontiers Science Center for Industrial Intelligence and Systems Optimization Northeastern University Shenyang110819 China Key Laboratory of Data Analytics and Optimization for Smart Industry Northeastern University Ministry of Education Shenyang110819 China Liaoning Engineering Laboratory of Data Analytics and Optimization for Smart Industry Shenyang China College of Electronic and Information Engineering Tongji University Shanghai201804 China Northeastern University China

In steel materials, several mechanical properties are interrelated and there is an inherent topology structure among these mechanical properties. Based on this motivation, a multitask neural network model based graph convolutional network is proposed to predict mechanical properties. First, a directed graph is built over the mechanical properties, with each mechanical property representing a node on the graph. Then this graph convolutional network, which represents the information propagation among mechanical properties, is combined with a multitask neural network and trained together. By comparing with K-NearestNeighbor (KNN), support vector regression (SVR), random forest (RF), Extreme Gradient Boosting (XGBoost), Cross-Stitch Network, Multi-gate Mixture-of-Experts (MMOE) and multitask neural (MTN) algorithms, the proposed algorithm is optimal in all of the metrics root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2) that indicate the predictive performance on the prediction of tensile strength (TS), yield strength (YS) and elongation (EL). Specifically, R2 is 0.9797 in tensile strength, R2 is 0.9747 in yield strength and R2 is 0.8316 in elongation. In addition, a deep representation of the various influences on mechanical properties is obtained through the parameters of the model with validity and interpretability. Material insights are extracted from this deep representation, including the comprehensive interaction of processing and chemical composition on the mechanical properties as well as the mechanical properties with each other. Further, the prediction accuracy can be improved by the combination of different structures of neural networks with graph convolutional network to prove the effectiveness of graph convolutional network. © 2024, The Authors. All rights reserved.

关键词： Tensile strength

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