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Cell reprogramming therapy for Parkinson’s disease

Neural Regeneration Research 2024年第11期19卷 2444-2455页

作者： Wenjing dong Shuyi liu Shangang Li Zhengbo Wang State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunmingYunnan ProvinceChina Yunnan Key Laboratory of Primate Biomedical Research KunmingYunnan ProvinceChina

Parkinson’s disease is typically characterized by the progressive loss of dopaminergic neurons in the substantia nigra pars *** studies have been performed based on the supplementation of lost dopaminergic neurons to treat Parkinson’s *** initial strategy for cell replacement therapy used human fetal ventral midbrain and human embryonic stem cells to treat Parkinson’s disease,which could substantially alleviate the symptoms of Parkinson’s disease in clinical ***,ethical issues and tumor formation were limitations of its clinical *** pluripotent stem cells can be acquired without sacrificing human embryos,which eliminates the huge ethical barriers of human stem cell *** widely considered neuronal regeneration strategy is to directly reprogram fibroblasts and astrocytes into neurons,without the need for intermediate proliferation states,thus avoiding issues of immune rejection and tumor *** induced pluripotent stem cells and direct reprogramming of lineage cells have shown promising results in the treatment of Parkinson’s ***,there are also ethical concerns and the risk of tumor formation that need to be *** review highlights the current application status of cell reprogramming in the treatment of Parkinson’s disease,focusing on the use of induced pluripotent stem cells in cell replacement therapy,including preclinical animal models and progress in clinical *** review also discusses the advancements in direct reprogramming of lineage cells in the treatment of Parkinson’s disease,as well as the controversy surrounding in vivo *** findings suggest that cell reprogramming may hold great promise as a potential strategy for treating Parkinson’s disease.

关键词： animal models astrocytes autologous cell reprogramming cell therapy direct lineage reprogramming dopaminergic neurons induced pluripotent stem cells non-human primates Parkinson’s disease

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Analysis and Modeling of Time Output Characteristics for Distributed Photovoltaic and Energy Storage

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Energy Engineering 2024年第4期121卷 933-949页

作者： Kaicheng liu Chen Liang Xiaoyang dong Liping liu China Electric Power Research Institute Beijing100192China Electric Power Research Institute State Grid Gansu Electric Power CompanyLanzhou730000China State Key Laboratory of Power Grid Safety Beijing100084China

Due to the unpredictable output characteristics of distributed photovoltaics,their integration into the grid can lead to voltage fluctuations within the regional power ***,the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various *** paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic(PV)generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks(TCN)and Long Short-Term Memory(LSTM).To begin with,an analysis of the spatiotemporal distribution patterns of PV generation is conducted,and outlier data is handled using the 3σ ***,a novel approach that combines temporal convolution and LSTM networks is introduced,with TCN extracting spatial features and LSTM capturing temporal ***,a real spatiotemporal dataset from Gansu,China,is established to compare the performance of the proposed network against other *** results demonstrate that the model presented in this paper exhibits the highest predictive accuracy,with a single-step Mean Absolute Error(MAE)of 1.782 and an average Root Mean Square Error(RMSE)of 3.72 for multi-step predictions.

关键词： Photovoltaic power generation spatio-temporal prediction temporal convolutional network long short-term memory network

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Fine-grained grid computing model for Wi-Fi indoor localization in complex environments

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Journal of Electronic Science and Technology 2024年第1期22卷 42-52页

作者： Yan Liang Song Chen Xin dong Tu liu School of Computer Engineering Chengdu Technological UniversityChengdu611730China HAN Networks Corporation Limited Beijing100094China

The fingerprinting-based approach using the wireless local area network(WLAN)is widely used for indoor ***,the construction of the fingerprint database is quite *** when the position of the access point(AP)or wall changes,updating the fingerprint database in real-time is *** appropriate indoor localization approach,which has a low implementation cost,excellent real-time performance,and high localization accuracy and fully considers complex indoor environment factors,is preferred in location-based services(LBSs)*** this paper,we proposed a fine-grained grid computing(FGGC)model to achieve decimeter-level localization *** points(RPs)are generated in the grid by the FGGC ***,the received signal strength(RSS)values at each RP are calculated with the attenuation factors,such as the frequency band,three-dimensional propagation distance,and walls in complex *** a result,the fingerprint database can be established automatically without manual measurement,and the efficiency and cost that the FGGC model takes for the fingerprint database are superior to previous *** proposed indoor localization approach,which estimates the position step by step from the approximate grid location to the fine-grained location,can achieve higher real-time performance and localization accuracy *** mean error of the proposed model is 0.36 m,far lower than that of previous ***,the proposed model is feasible to improve the efficiency and accuracy of Wi-Fi indoor *** also shows high-accuracy performance with a fast running speed even under a large-size *** results indicate that the proposed method can also be suitable for precise marketing,indoor navigation,and emergency rescue.

关键词： Fine-grained grid computing (FGGC) Indoor localization Path loss Random forest Reference points(RPs)

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Improved Segmented Belief Propagation List Decoding for Polar Codes with Bit-Flipping

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China Communications 2024年第3期21卷 19-36页

作者： Mao Yinyou Yang dong liu Xingcheng Zou En School of Electronics and Information Technology(SEIT) Sun Yat-sen University(SYSU)Guangzhou 510006China School of Information Science Guangzhou Xinhua UniversityGuangzhou 510520China Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Zhuhai 519080China

Belief propagation list(BPL) decoding for polar codes has attracted more attention due to its inherent parallel nature. However, a large gap still exists with CRC-aided SCL(CA-SCL) *** this work, an improved segmented belief propagation list decoding based on bit flipping(SBPL-BF) is proposed. On the one hand, the proposed algorithm makes use of the cooperative characteristic in BPL decoding such that the codeword is decoded in different BP decoders. Based on this characteristic, the unreliable bits for flipping could be split into multiple subblocks and could be flipped in different decoders simultaneously. On the other hand, a more flexible and effective processing strategy for the priori information of the unfrozen bits that do not need to be flipped is designed to improve the decoding convergence. In addition, this is the first proposal in BPL decoding which jointly optimizes the bit flipping of the information bits and the code bits. In particular, for bit flipping of the code bits, a H-matrix aided bit-flipping algorithm is designed to enhance the accuracy in identifying erroneous code bits. The simulation results show that the proposed algorithm significantly improves the errorcorrection performance of BPL decoding for medium and long codes. It is more than 0.25 d B better than the state-of-the-art BPL decoding at a block error rate(BLER) of 10^(-5), and outperforms CA-SCL decoding in the low signal-to-noise(SNR) region for(1024, 0.5)polar codes.

关键词： belief propagation list(BPL)decoding bit-flipping polar codes segmented CRC

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Weighted Composition Operators That Preserve Frames On Weighted Hardy Spaces

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Acta Mathematica Sinica,English Series 2024年第7期40卷 1645-1654页

作者： Ya Li dong Rui liu Guo Xiang Lu School of Mathematical Sciences and LPMC Nankai UniversityTianjin300071P.R.China School of Statistics and Mathematics Zhongnan University of Economics and LawWuhan430073P.R.China

In this paper, we characterize weighted composition operators that preserve frames on the weighted Hardy spaces in the unit disk. In particular, we obtain the symbol properties of the bounded invertible weighted composition operators. Moreover, we establish the equivalence between bounded invertible operators and frame-preserving operators. Furthermore, we show that weighted composition operator preserves frames if and only if it preserves the Riesz bases property. Additionally,we investigate the weighted composition operators that preserve tight or normalized tight frames on the Dirichlet space.

关键词： Weighted composition operators Weighted Hardy spaces frames invertible

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Optimal Stochastic Scheduling Strategy of Multi-vector Energy Complex Integrated with Full-blown Power-to-biomethane Model

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 863-873页

作者： Guanwei Zeng Chengxi liu Minfang Liao Yongjian Luo Xuzhu dong Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network WuhanChina the School of Electrical Engineering and Automation Wuhan UniversityWuhanChina

We propose an optimal stochastic scheduling strategy for a multi-vector energy complex(MEC),considering a fullblown model of the power-to-biomethane(Pt M)*** conventional optimization that uses a simple efficiency coefficient to coarsely model energy conversion between electricity and biomethane,a detailed Pt M model is introduced to emphasize the reactor kinetics and chemical equilibria of *** model crystallizes the interactions between the Pt M process and MEC flexibility,allowing to adjust the operating condition of the methanation reactor for optimal MEC operation in stochastic *** optimization and flowsheet design of the Pt M process increase the average selectivity of methane(i.e.,ratio between net biomethane production and hydrogen consumption)up to 83.7%in the proposed synthesis *** results can provide information and predictions to operators about the optimal operating conditions of a Pt M unit while improving the MEC flexibility.

关键词： Multi-vector energy complex optimal stochastic scheduling power-to-biomethane unit process synthesis natural gas

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Anomaly-Resistant Decentralized State Estimation Under Minimum Error Entropy With Fiducial Points for Wide-Area Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 74-87页

作者： Bogang Qu Zidong Wang Bo Shen Hongli dong Hongjian liu the College of Automation Engineering Shanghai University of Electric PowerShanghai 200090China IEEE the Department of Computer Science Brunel University LondonUxbridgeMiddiesex UB83PHUK the College of Information Science and Technology Donghua UniversityShanghai 200051 the Engineering Research Center of Digitalized Textile and Fashion Technology Ministry of EducationShanghai 201620China the Artificial Intelligence Energy Research Institute Northeast Petroleum UniversityDaqing 163318 the Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control Northeast PetroleumDaqing 163318 the Sanya Offshore Oil&Gas Research Institute Northeast Petroleum UniversitySanya 572024China the Key Laboratory of Advanced Perception and Intelligent Control of High-End Equipment Ministry of EducationAnhui Polytechnic UniversityWuhu 2410000 the School of Mathematics and Physics Anhui Polytechnic UniversityWuhu 241000China

This paper investigates the anomaly-resistant decentralized state estimation(SE) problem for a class of wide-area power systems which are divided into several non-overlapping areas connected through transmission lines. Two classes of measurements(i.e., local measurements and edge measurements) are obtained, respectively, from the individual area and the transmission lines. A decentralized state estimator, whose performance is resistant against measurement with anomalies, is designed based on the minimum error entropy with fiducial points(MEEF) criterion. Specifically, 1) An augmented model, which incorporates the local prediction and local measurement, is developed by resorting to the unscented transformation approach and the statistical linearization approach;2) Using the augmented model, an MEEF-based cost function is designed that reflects the local prediction errors of the state and the measurement;and 3) The local estimate is first obtained by minimizing the MEEF-based cost function through a fixed-point iteration and then updated by using the edge measuring information. Finally, simulation experiments with three scenarios are carried out on the IEEE 14-bus system to illustrate the validity of the proposed anomaly-resistant decentralized SE scheme.

关键词： Decentralized state estimation(SE) measurements with anomalies minimum error entropy unscented Kalman filter wide-area power systems

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A benchmark-based method for evaluating hyperparameter optimization techniques of neural networks for surface water quality prediction

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Frontiers of Environmental Science & Engineering 2024年第5期18卷 13-27页

作者： Xuan Wang Yan dong Jing Yang Zhipeng liu Jinsuo Lu School of Environmental and Municipal Engineering Xi’an University of Architecture and TechnologyXi’an710055China Shaanxi Key Laboratory of Environmental Engineering Xi’an University of Architecture and TechnologyXi’an710055China State Key Laboratory of Green Building in West China Xi’an University of Architecture and TechnologyXi’an710055China

Neural networks(NNs)have been used extensively in surface water prediction tasks due to computing algorithm improvements and data *** essential step in developing an NN is the hyperparameter *** practice,it is common to manually determine hyperparameters in the studies of NNs in water resources *** may result in considerable randomness and require significant computation time;therefore,hyperparameter optimization(HPO)is *** study adopted five representatives of the HPO techniques in the surface water quality prediction tasks,including the grid sampling(GS),random search(RS),genetic algorithm(GA),Bayesian optimization(BO)based on the Gaussian process(GP),and the tree Parzen estimator(TPE).For the evaluation of these techniques,this study proposed a method:first,the optimal hyperparameter value sets achieved by GS were regarded as the benchmark;then,the other HPO techniques were evaluated and compared with the benchmark in convergence,optimization orientation,and consistency of the optimized *** results indicated that the TPE-based BO algorithm was recommended because it yielded stable convergence,reasonable optimization orientation,and the highest consistency rates with the benchmark *** optimization consistency rates via TPE for the hyperparameters hidden layers,hidden dimension,learning rate,and batch size were 86.7%,73.3%,73.3%,and 80.0%,*** the evaluation of HPO techniques directly based on the prediction performance of the optimized NN in a single HPO test,the proposed benchmark-based HPO evaluation approach is feasible and robust.

关键词： Neural networks Hyperparameter optimization Surface water quality prediction Bayes optimization Genetic algorithm

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Discovering Latent Variables for the Tasks With Confounders in Multi-Agent Reinforcement Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第7期11卷 1591-1604页

作者： Kun Jiang Wenzhang liu Yuanda Wang Lu dong Changyin Sun the School of Automation Southeast UniversityNanjing 210096China the School of Artificial Intelligence Anhui UniversityHefei 230601China IEEE the School of Cyber Science and Engineering Southeast UniversityNanjing 211189China the Engineering Research Center of Autonomous Unmanned System Technology Ministry of EducationHefei 230601China

Efficient exploration in complex coordination tasks has been considered a challenging problem in multi-agent reinforcement learning(MARL). It is significantly more difficult for those tasks with latent variables that agents cannot directly observe. However, most of the existing latent variable discovery methods lack a clear representation of latent variables and an effective evaluation of the influence of latent variables on the agent. In this paper, we propose a new MARL algorithm based on the soft actor-critic method for complex continuous control tasks with confounders. It is called the multi-agent soft actor-critic with latent variable(MASAC-LV) algorithm, which uses variational inference theory to infer the compact latent variables representation space from a large amount of offline ***, we derive the counterfactual policy whose input has no latent variables and quantify the difference between the actual policy and the counterfactual policy via a distance function. This quantified difference is considered an intrinsic motivation that gives additional rewards based on how much the latent variable affects each agent. The proposed algorithm is evaluated on two collaboration tasks with confounders, and the experimental results demonstrate the effectiveness of MASAC-LV compared to other baseline algorithms.

关键词： Latent variable model maximum entropy multi-agent reinforcement learning(MARL) multi-agent system

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Data and Model Driven Task Offloading Strategy in the Dynamic Mobile Edge Computing System

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Journal of Systems Science & Complexity 2024年第1期37卷 351-368页

作者： dong Hairong WU Wei SONG Haifeng liu Zhen ZHANG Zixuan School of Electronic and Information Engineering Beijing Jiaotong UniversityBeijing 100044China School of Electronic and Information Engineering BeiHang UniversityBeijing 100191China CRSC Research&Design Institute Group Co. Ltd.Beijing 100070China

Mobile Edge Computing(MEC)provides communication and computational capabilities for the industrial Internet,meeting the demands of latency-sensitive ***,traditional model-driven task offloading strategies face challenges in adapting to situations with unknown network communication status and computational *** limitation becomes notably significant in complex industrial networks of high-speed *** by these considerations,a data and model-driven task offloading problem is proposed in this paper.A redundant communication network is designed to adapt to anomalous channel states when tasks are offloaded to edge *** link switching mechanism is executed by the train according to the attributes of the completed *** task offloading optimization problem is formulated by introducing data-driven prediction of communication states into the traditional ***,the optimal strategy is achieved by employing the informer-based prediction algorithm and the quantum particle swarm optimization method,which effectively tackle real-time optimization problems due to their low time *** simulations illustrate that the data and model-driven task offloading strategy can predict the communication state in advance,thus reducing the cost of the system and improving its robustness.

关键词： Data driven model informer mobile edge computing quantum particle swarm optimization task offloading.

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