Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

Dynamic plugging regulating strategy of pipeline robot based on reinforcement learning

Pipeline isolation plugging robot (PIPR) is an important tool in pipeline maintenance operation. During the plugging process, the violent vibration will occur by the flow field, which can cause serious damage to the pipeline and PIPR. In this paper, we propose a dynamic regulating strategy to reduce the plugging-induced vibration by regulating the spoiler angle and plugging velocity. Firstly, the dynamic plugging simulation and experiment are performed to study the flow field changes during dynamic plugging. And the pressure difference is proposed to evaluate the degree of flow field vibration. Secondly, the mathematical models of pressure difference with plugging states and spoiler angles are established based on the extreme learning machine (ELM) optimized by improved sparrow search algorithm (ISSA). Finally, a modified Q-learning algorithm based on simulated annealing is applied to determine the optimal strategy for the spoiler angle and plugging velocity in real time. The results show that the proposed method can reduce the plugging-induced vibration by 19.9% and 32.7% on average, compared with single-regulating methods. This study can effectively ensure the stability of the plugging process.

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

Advanced Policy Learning Near-Optimal Regulation

Designing advanced design techniques for feedback stabilization and optimization of complex systems is important to the modern control field. In this paper, a near-optimal regulation method for general nonaffine dynamics is developed with the help of policy learning. For addressing the nonaffine nonlinearity, a pre-compensator is constructed, so that the augmented system can be formulated as affine-like form. Different cost functions are defined for original and transformed controlled plants and then their relationship is analyzed in detail. Additionally, an adaptive critic algorithm involving stability guarantee is employed to solve the augmented optimal control problem. At last, several case studies are conducted for verifying the stability, robustness, and optimality of a torsional pendulum plant with suitable cost.

Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation theory.For the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost function.For the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement data.By combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop system.The proposed decomposition design not only bypasses the numerical stiffness but also alleviates the high-dimensionality.The efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

Exploration and Practice of “Construction Engineering Regulations” Course Reform

Based on the analysis of the existing teaching situation of the“Construction Engineering Regulations”course,this paper divides the course content into three parts according to the course characteristics and content,and explores three corresponding teaching modes.The proportion of student-led relationships in the three teaching modes is 80%,60%,and 90%,respectively,realizing a teaching mechanism centered on students and stimulating students’interest in independent learning.Teaching methods such as problem-oriented learning,group discussion,student reporting,MOOC(massive open online course),case analysis,etc.,have been used to establish a variety of comprehensive examination mechanisms such as quiz games,follow-up tests,and work displays.Practice has shown that after adopting these three teaching modes,classroom teaching efficiency has significantly improved,and students’abilities in exploration,expression,innovation,and team cooperation have also been enhanced.

基于M-learning的高职生自主学习平台构建与推广——高职院校图书馆在微时代的服务创新

基于M-learning的高职生自主学习平台构建与推广是高职院校图书馆在微时代为适应高职教育国际化、信息化发展趋势而推出的创新服务举措。平台的构建应在成熟的E-learning自主学习平台(如moo-dle、blackboard或者其他的专业平台)的基础上融入移动信息技术,使之能兼容E-learning自主学习平台上的资源,同时支持安卓等系统的使用以及多样化的应用软件(APP)进入,从而使平台实用、易用。平台的推广则应注重与课堂教学的有机融合并用多种形式进行立体宣传。.

Data-Driven Global Robust Optimal Output Regulation of Uncertain Partially Linear Systems

In this paper, a data-driven control approach is developed by reinforcement learning (RL) to solve the global robust optimal output regulation problem (GROORP) of partially linear systems with both static uncertainties and nonlinear dynamic uncertainties. By developing a proper feedforward controller, the GROORP is converted into a global robust optimal stabilization problem. A robust optimal feedback controller is designed which is able to stabilize the system in the presence of dynamic uncertainties. The closed-loop system is ensured to be input-to-output stable regarding the static uncertainty as the external input. This robust optimal controller is numerically approximated via RL. Nonlinear small-gain theory is applied to show the input-to-output stability for the closed-loop system and thus solves the original GROORP. Simulation results validates the efficacy of the proposed methodology.

Research on College Studentst'Autonomous EFL Learning Affer Course Exemption

By analyzing the English learning logs of 12 students in a provincial university in south-west China after they had been exempted from taking college English courses,this study investigated college students’autonomous EFL(English as a foreign language)learning after course exemption,including the use of mediational means in EFL learning,EFL learning hours,and other factors affecting EFL learning,in the hope of giving new perspectives on college ELF curriculum design,teaching,and education management.

Multi-Agent Hierarchical Graph Attention Reinforcement Learning for Grid-Aware Energy Management

The increasing adoption of renewable energy has posed challenges for voltage regulation in power distribution networks.Gridaware energy management,which includes the control of smart inverters and energy management systems,is a trending way to mitigate this problem.However,existing multi-agent reinforcement learning methods for grid-aware energy management have not sufficiently considered the importance of agent cooperation and the unique characteristics of the grid,which leads to limited performance.In this study,we propose a new approach named multi-agent hierarchical graph attention reinforcement learning framework(MAHGA)to stabilize the voltage.Specifically,under the paradigm of centralized training and decentralized execution,we model the power distribution network as a novel hierarchical graph containing the agent-level topology and the bus-level topology.Then a hierarchical graph attention model is devised to capture the complex correlation between agents.Moreover,we incorporate graph contrastive learning as an auxiliary task in the reinforcement learning process to improve representation learning from graphs.Experiments on several real-world scenarios reveal that our approach achieves the best performance and can reduce the number of voltage violations remarkably.

Research Needs and Applications of Machine Learning｡ｪPredicting Logistics Stress by Machine Learning

Machine learning is the use of computers to learn the intrinsic laws and information contained in data through algorithms to gain new experience and knowledge,in order to improve the intelligence of computers,so that they can make decisions similar to those made by humans when faced with problems.With the development of various industries,the amount of data has increased and the efficiency of data processing and analysis has become more demanding,a series of machine learning algorithms have emerged.Machine learning algorithms are essentially steps and processes that apply a large number of statistical principles to solve optimisation problems.Appropriate machine learning algorithms can be used to solve practical problems more efficiently for a wide range of model requirements.This paper presents the interim state of a dynamic disruption management software solution for logistics,using machine learning methods to study the extent to which stress is predicted based on physiological and subjective parameters,to prevent physical and mental stress on workers in the logistics industry,to maintain their health,to make them more optimistic and better able to adapt to their work,and to facilitate more accurate deployment of human resources by companies according to the real-time requirements of the logistics industry.

The Relationship of Effortful Control to Academic Achievement via Children’s Learning-Related Behaviors

Effortful control (EC) is a temperamental self-regulatory capacity, defined as the efficiency of executive attention [1], which is related to individual differences in self-regulation. Although effortful control covers some dispositional self-regulatory abilities important to cope with social demands of successful adaptation to school, such as attention regulation, individual differences in EC have recently been associated with school functioning through academic achievement including the efficient use of learning-related behaviors, which have been found to be a necessary precursor of learning and they refer to a set of children’s behaviors that involve organizational skills and appropriate habits of study. Therefore, the aim of this study is to review the literature on EC’s relationship to academic achievement via learning-related behaviors, which reflect the use of metacognitive control processes in kindergarten and elementary school students. The findings indicate that EC affects academic achievement through the facilitation of the efficient use of metacognitive control processes.

Theories of Self-regulated Learning

The literature about self-regulated learning is elaborated on in this paper with the definition coming first and the main theories in the field following after.

Self-adaptive large neighborhood search algorithm for parallel machine scheduling problems

A self-adaptive large neighborhood search method for scheduling n jobs on m non-identical parallel machines with mul- tiple time windows is presented. The problems＇ another feature lies in oversubscription, namely not all jobs can be scheduled within specified scheduling horizons due to the limited machine capacity. The objective is thus to maximize the overall profits of processed jobs while respecting machine constraints. A first-in- first-out heuristic is applied to find an initial solution, and then a large neighborhood search procedure is employed to relax and re- optimize cumbersome solutions. A machine learning mechanism is also introduced to converge on the most efficient neighborhoods for the problem. Extensive computational results are presented based on data from an application involving the daily observation scheduling of a fleet of earth observing satellites. The method rapidly solves most problem instances to optimal or near optimal and shows a robust performance in sensitive analysis.

Residential HVAC Aggregation Based on Risk-averse Multi-armed Bandit Learning for Secondary Frequency Regulation

As the penetration of renewable energy continues to increase,stochastic and intermittent generation resources gradually replace the conventional generators,bringing significant challenges in stabilizing power system frequency.Thus,aggregating demand-side resources for frequency regulation attracts attentions from both academia and industry.However,in practice,conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control signals.The risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating,ventilation,and air conditioning(HVAC)to provide reliable secondary frequency regulation.Compared with the conventional approach,the simulation results show that the risk-averse multiarmed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the control.Besides,the proposed approach is more robust to random and changing behaviors of the users.

Optimal Frequency Regulation Based on Characterizing the Air Conditioning Cluster by Online Deep Learning

The air conditioning cluster(ACC)is a potential candidate to provide frequency regulation reserves.However,the effective assessment of the ACC willing reserve capacity is often an obstacle for existing demand response(DR)programs,influenced by incentive prices,temperatures,etc.In this paper,the complex relationship between the ACC willing reserve capacity and its key influence factors is defined as a demand response characteristic(DRC).To learn about DRC along with real-time frequency regulation,an online deep learning-based DRC(ODLDRC)modeling methodology is designed to continuously retrain the deep neural network-based model.The ODL-DRC model trained by incoming new data does not require massive historical training data,which makes it more time-efficient.Then,the coordinate operation between ODL-DRC modeling and optimal frequency regulation(OFR)is presented.A robust decentralized sliding mode controller(DSMC)is designed to manage the ACC response power in primary frequency regulation against any ACC response uncertainty.An ODL-DRC model-based OFR scheme is formulated by taking the learning error into consideration.Thereby,the ODL-DRC model can be applied to minimize the total operational cost while maintaining frequency stability,without waiting for a well-trained model.The simulation cases validate the superiority of the OFR based on characterizing the ACC by online learning,which can capture the real DRC and simultaneously optimize the regulation performance with strong robustness against any ACC response uncertainty and learning error.

Learning-based adaptive optimal output regulation of linear and nonlinear systems:an overview

This paper reviews recent developments in learning-based adaptive optimal output regulation that aims to solve the problem of adaptive and optimal asymptotic tracking with disturbance rejection.The proposed framework aims to bring together two separate topics—output regulation and adaptive dynamic programming—that have been under extensive investigation due to their broad applications in modern control engineering.Under this framework,one can solve optimal output regulation problems of linear,partially linear,nonlinear,and multi-agent systems in a data-driven manner.We will also review some practical applications based on this framework,such as semi-autonomous vehicles,connected and autonomous vehicles,and nonlinear oscillators.

Neurocomputing van der Pauw function for the measurement of a semiconductor's resistivity without use of the learning rate of weight vector regulation

Van der Pauw＇s function is often used in the measurement of a semiconductor＇s resistivity. However, it is difficult to obtain its value from voltage measurements because it has an implicit form. If it can be expressed as a polynomial, a semiconductor＇s resistivity can be obtained from such measurements. Normally, five orders of the abscissa can provide sufficient precision during the expression of any non-linear function. Therefore, the key is to determine the coefficients of the polynomial. By taking five coefficients as weights to construct a neuronetwork, neurocomputing has been used to solve this problem. Finally, the polynomial expression for van der Pauw＇s function is obtained.

Effects of English Learning Motivational Beliefs on Non-English Majors’ Use of Motivational Regulation Strategies

This study aims to explore the effects of motivational beliefs on non-English majors’ use of motivational regulation strategies by establishing a structural equation model. The survey of 301 sophomores indicated that(1)three motivational beliefs,self-efficacy,mastery goal orientation and task value correlated positively and significantly with each other;(2)all of them had direct and positive effects on the use of motivational regulation strategies and could predict 53% variance of the use of motivational regulation strategies;(3)self-efficacy had the largest direct and positive effects,mastery goal orientation’s effects were the second,and task value’s effects were the third.

N6-甲基腺苷相关调节因子与骨关节炎:生物信息学和实验验证分析

背景:越来越多证据表明N6-甲基腺苷(N6-methyladenosine,m6A)调节因子与骨关节炎密切相关,被认为是防治骨关节炎新方向,但具体作用机制不明。目的:通过对骨关节炎基因芯片数据集进行生物信息学分析,探讨m6A对骨关节炎的作用,解析骨关节炎发病机制。方法:首先利用R软件提取GEO数据库中GSE1919数据集中骨关节炎相关m6A调节因子及其表达量,进而对提取结果行基因差异分析及GO、KEGG富集分析;接着对PPI网络拓扑学分析结果和机器学习结果取交集得到m6A关键调节因子,并通过体外细胞实验验证。结果与结论:①提取得到16个骨关节炎相关m6A调节因子表达量,通过差异分析获得ZC3H13、YTHDC1、YTHDF3、HNRNPC等11个m6A差异调节因子;②GO富集分析显示,骨关节炎相关m6A差异调节因子在生物过程中主要于mRNA转运、RNA分解代谢、胰岛素样生长因子受体信号通路调控等发挥作用;③KEGG富集分析显示,差异调节因子主要参与p53、白细胞介素17和AMPK信号通路;④综合PPI网络拓扑学分析和机器学习结果获得m6A关键调节因子——YTHDC1;⑤体外细胞实验结果表明,m6A关键调节因子——YTHDC1在对照组与骨关节炎组中表达存在显著差异(P<0.05);⑥结果显示,YTHDC1与骨关节炎发生发展密切相关,有望成为m6A治疗骨关节炎的分子靶点。