Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, respectively.Then, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation significantly.Finally, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

Uncertainty quantification of inverse analysis for geomaterials using probabilistic programming

Uncertainty is an essentially challenging for safe construction and long-term stability of geotechnical engineering.The inverse analysis is commonly utilized to determine the physico-mechanical parameters.However,conventional inverse analysis cannot deal with uncertainty in geotechnical and geological systems.In this study,a framework was developed to evaluate and quantify uncertainty in inverse analysis based on the reduced-order model(ROM)and probabilistic programming.The ROM was utilized to capture the mechanical and deformation properties of surrounding rock mass in geomechanical problems.Probabilistic programming was employed to evaluate uncertainty during construction in geotechnical engineering.A circular tunnel was then used to illustrate the proposed framework using analytical and numerical solution.The results show that the geomechanical parameters and associated uncertainty can be properly obtained and the proposed framework can capture the mechanical behaviors under uncertainty.Then,a slope case was employed to demonstrate the performance of the developed framework.The results prove that the proposed framework provides a scientific,feasible,and effective tool to characterize the properties and physical mechanism of geomaterials under uncertainty in geotechnical engineering problems.

Combining reinforcement learning with mathematical programming:An approach for optimal design of heat exchanger networks

Heat integration is important for energy-saving in the process industry.It is linked to the persistently challenging task of optimal design of heat exchanger networks(HEN).Due to the inherent highly nonconvex nonlinear and combinatorial nature of the HEN problem,it is not easy to find solutions of high quality for large-scale problems.The reinforcement learning(RL)method,which learns strategies through ongoing exploration and exploitation,reveals advantages in such area.However,due to the complexity of the HEN design problem,the RL method for HEN should be dedicated and designed.A hybrid strategy combining RL with mathematical programming is proposed to take better advantage of both methods.An insightful state representation of the HEN structure as well as a customized reward function is introduced.A Q-learning algorithm is applied to update the HEN structure using theε-greedy strategy.Better results are obtained from three literature cases of different scales.

Adaptive Optimal Discrete-Time Output-Feedback Using an Internal Model Principle and Adaptive Dynamic Programming

In order to address the output feedback issue for linear discrete-time systems, this work suggests a brand-new adaptive dynamic programming(ADP) technique based on the internal model principle(IMP). The proposed method, termed as IMP-ADP, does not require complete state feedback-merely the measurement of input and output data. More specifically, based on the IMP, the output control problem can first be converted into a stabilization problem. We then design an observer to reproduce the full state of the system by measuring the inputs and outputs. Moreover, this technique includes both a policy iteration algorithm and a value iteration algorithm to determine the optimal feedback gain without using a dynamic system model. It is important that with this concept one does not need to solve the regulator equation. Finally, this control method was tested on an inverter system of grid-connected LCLs to demonstrate that the proposed method provides the desired performance in terms of both tracking and disturbance rejection.

In vivo astrocyte reprogramming following spinal cord injury

Harmful and helpful roles of astrocytes in spinal cord injury(SCI):SCI induce gradable sensory,motor and autonomic impairments that correlate with the lesion severity and the rostro-caudal location of the injury site.The absence of spontaneous axonal regeneration after injury results from neuron-intrinsic and neuron-extrinsic parameters.Indeed,not only adult neurons display limited capability to regrow axons but also the injury environment contains inhibitors to axonal regeneration and a lack of growth-promoting factors.Amongst other cell populations that respond to the lesion,reactive astrocytes were first considered as only detrimental to spontaneous axonal regeneration.Indeed,astrocytes.

Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity

Enhanced osteoclastogenesis and osteoclast activity contribute to the development of osteoporosis,which is characterized by increased bone resorption and inadequate bone formation.As novel antiosteoporotic therapeutics are needed,understanding the genetic regulation of human osteoclastogenesis could help identify potential treatment targets.This study aimed to provide an overview of transcriptional reprogramming during human osteoclast differentiation.Osteoclasts were differentiated from CD14+monocytes from eight female donors.RNA sequencing during differentiation revealed 8980 differentially expressed genes grouped into eight temporal patterns conserved across donors.These patterns revealed distinct molecular functions associated with postmenopausal osteoporosis susceptibility genes based on RNA from iliac crest biopsies and bone mineral density SNPs.Network analyses revealed mutual dependencies between temporal expression patterns and provided insight into subtype-specific transcriptional networks.The donor-specific expression patterns revealed genes at the monocyte stage,such as filamin B(FLNB)and oxidized low-density lipoprotein receptor 1(OLR1,encoding LOX-1),that are predictive of the resorptive activity of mature osteoclasts.The expression of differentially expressed G-protein coupled receptors was strong during osteoclast differentiation,and these receptors are associated with bone mineral density SNPs,suggesting that they play a pivotal role in osteoclast differentiation and activity.The regulatory effects of three differentially expressed G-protein coupled receptors were exemplified by in vitro pharmacological modulation of complement 5 A receptor 1(C5AR1),somatostatin receptor 2(SSTR2),and free fatty acid receptor 4(FFAR4/GPR120).Activating C5AR1 enhanced osteoclast formation,while activating SSTR2 decreased the resorptive activity of mature osteoclasts,and activating FFAR4 decreased both the number and resorptive activity of mature osteoclasts.In conclusion,we report the occurrence of transcriptional reprogramming during human osteoclast differentiation and identified SSTR2 and FFAR4 as antiresorptive G-protein coupled receptors and FLNB and LOX-1 as potential molecular markers of osteoclast activity.These data can help future investigations identify molecular regulators of osteoclast differentiation and activity and provide the basis for novel antiosteoporotic targets.

Glia-to-neuron reprogramming to the rescue?

Over the last two decades,the dogma that cell fate is immutable has been increasingly challenged,with important implications for regenerative medicine.The brea kth rough discovery that induced pluripotent stem cells could be generated from adult mouse fibroblasts is powerful proof that cell fate can be changed.An exciting extension of the discovery of cell fate impermanence is the direct cellular reprogram ming hypothesis-that terminally differentiated cells can be reprogrammed into other adult cell fates without first passing through a stem cell state.

Improved Unit Commitment with Accurate Dynamic Scenarios Clustering Based on Multi-Parametric Programming and Benders Decomposition

Stochastic unit commitment is one of the most powerful methods to address uncertainty. However, the existingscenario clustering technique for stochastic unit commitment cannot accurately select representative scenarios,which threatens the robustness of stochastic unit commitment and hinders its application. This paper providesa stochastic unit commitment with dynamic scenario clustering based on multi-parametric programming andBenders decomposition. The stochastic unit commitment is solved via the Benders decomposition, which decouplesthe primal problem into the master problem and two types of subproblems. In the master problem, the committedgenerator is determined, while the feasibility and optimality of generator output are checked in these twosubproblems. Scenarios are dynamically clustered during the subproblem solution process through the multiparametric programming with respect to the solution of the master problem. In other words, multiple scenariosare clustered into several representative scenarios after the subproblem is solved, and the Benders cut obtainedby the representative scenario is generated for the master problem. Different from the conventional stochasticunit commitment, the proposed approach integrates scenario clustering into the Benders decomposition solutionprocess. Such a clustering approach could accurately cluster representative scenarios that have impacts on theunit commitment. The proposed method is tested on a 6-bus system and the modified IEEE 118-bus system.Numerical results illustrate the effectiveness of the proposed method in clustering scenarios. Compared withthe conventional clustering method, the proposed method can accurately select representative scenarios whilemitigating computational burden, thus guaranteeing the robustness of unit commitment.

Neurocircuit regeneration by extracellular matrix reprogramming

The brain's extracellular matrix(ECM),which is comprised of protein and glycosaminoglycan(GAG)scaffolds,constitutes 20%-40% of the human brain and is considered one of the largest influencers on brain cell functioning(Soles et al.,2023).Synthesized by neural and glial cells,the brain's ECM regulates a myriad of homeostatic cellular processes,including neuronal plasticity and firing(Miyata et al.,2012),cation buffering(Moraws ki et al.,2015),and glia-neuron interactions(Anderson et al.,2016).Considering the diversity of functions,dynamic remodeling of the brain's ECM indicates that this understudied medium is an active participant in both normal physiology and neurological diseases.

Rapid Prototype Development Approach for Genetic Programming

Genetic Programming (GP) is an important approach to deal with complex problem analysis and modeling, and has been applied in a wide range of areas. The development of GP involves various aspects, including design of genetic operators, evolutionary controls and implementations of heuristic strategy, evaluations and other mechanisms. When designing genetic operators, it is necessary to consider the possible limitations of encoding methods of individuals. And when selecting evolutionary control strategies, it is also necessary to balance search efficiency and diversity based on representation characteristics as well as the problem itself. More importantly, all of these matters, among others, have to be implemented through tedious coding work. Therefore, GP development is both complex and time-consuming. To overcome some of these difficulties that hinder the enhancement of GP development efficiency, we explore the feasibility of mutual assistance among GP variants, and then propose a rapid GP prototyping development method based on πGrammatical Evolution (πGE). It is demonstrated through regression analysis experiments that not only is this method beneficial for the GP developers to get rid of some tedious implementations, but also enables them to concentrate on the essence of the referred problem, such as individual representation, decoding means and evaluation. Additionally, it provides new insights into the roles of individual delineations in phenotypes and semantic research of individuals.

A Dimensional Reduction Approach Based on Essential Constraints in Linear Programming

This paper presents a new dimension reduction strategy for medium and large-scale linear programming problems. The proposed method uses a subset of the original constraints and combines two algorithms: the weighted average and the cosine simplex algorithm. The first approach identifies binding constraints by using the weighted average of each constraint, whereas the second algorithm is based on the cosine similarity between the vector of the objective function and the constraints. These two approaches are complementary, and when used together, they locate the essential subset of initial constraints required for solving medium and large-scale linear programming problems. After reducing the dimension of the linear programming problem using the subset of the essential constraints, the solution method can be chosen from any suitable method for linear programming. The proposed approach was applied to a set of well-known benchmarks as well as more than 2000 random medium and large-scale linear programming problems. The results are promising, indicating that the new approach contributes to the reduction of both the size of the problems and the total number of iterations required. A tree-based classification model also confirmed the need for combining the two approaches. A detailed numerical example, the general numerical results, and the statistical analysis for the decision tree procedure are presented.

Optimization Techniques for GPU-Based Parallel Programming Models in High-Performance

This study embarks on a comprehensive examination of optimization techniques within GPU-based parallel programming models,pivotal for advancing high-performance computing(HPC).Emphasizing the transition of GPUs from graphic-centric processors to versatile computing units,it delves into the nuanced optimization of memory access,thread management,algorithmic design,and data structures.These optimizations are critical for exploiting the parallel processing capabilities of GPUs,addressingboth the theoretical frameworks and practical implementations.By integrating advanced strategies such as memory coalescing,dynamic scheduling,and parallel algorithmic transformations,this research aims to significantly elevate computational efficiency and throughput.The findings underscore the potential of optimized GPU programming to revolutionize computational tasks across various domains,highlighting a pathway towards achieving unparalleled processing power and efficiency in HPC environments.The paper not only contributes to the academic discourse on GPU optimization but also provides actionable insights for developers,fostering advancements in computational sciences and technology.

下行RIS-NOMA的用户集群方法

为提高用户分布非均匀的场景下可重构智能表面(RIS)辅助下行非正交多址(NOMA)通信系统的性能,提出了一种分布式RIS辅助下行NOMA的用户聚类方案。首先,采用自适应几何分布(AGD)聚类算法划分用户集群(UCs),从而为各UC匹配RIS。然后,利用分式规划(FP)方法将非凸最大化频谱效率问题转换为凸优化问题。最后,为UC内用户逐级进行功率分配(PA)与被动波束形成(PB)。仿真结果表明,与谱聚类(SPC)、K均值(K-means)、高斯混合模型(GMM)聚类方案和正交多址(OMA)方案相比,在AGD方案下,功率对频谱效率提升了7%、14%、19%和42%的增益,反射面对频谱效率提升了16%、19%、26%和40%的增益。

Sequential quadratic programming-based non-cooperative target distributed hybrid processing optimization method

The distributed hybrid processing optimization problem of non-cooperative targets is an important research direction for future networked air-defense and anti-missile firepower systems. In this paper, the air-defense anti-missile targets defense problem is abstracted as a nonconvex constrained combinatorial optimization problem with the optimization objective of maximizing the degree of contribution of the processing scheme to non-cooperative targets, and the constraints mainly consider geographical conditions and anti-missile equipment resources. The grid discretization concept is used to partition the defense area into network nodes, and the overall defense strategy scheme is described as a nonlinear programming problem to solve the minimum defense cost within the maximum defense capability of the defense system network. In the solution of the minimum defense cost problem, the processing scheme, equipment coverage capability, constraints and node cost requirements are characterized, then a nonlinear mathematical model of the non-cooperative target distributed hybrid processing optimization problem is established, and a local optimal solution based on the sequential quadratic programming algorithm is constructed, and the optimal firepower processing scheme is given by using the sequential quadratic programming method containing non-convex quadratic equations and inequality constraints. Finally, the effectiveness of the proposed method is verified by simulation examples.

A true triaxial strength criterion for rocks by gene expression programming

Rock strength is a crucial factor to consider when designing and constructing underground projects.This study utilizes a gene expression programming(GEP)algorithm-based model to predict the true triaxial strength of rocks,taking into account the influence of rock genesis on their mechanical behavior during the model building process.A true triaxial strength criterion based on the GEP model for igneous,metamorphic and magmatic rocks was obtained by training the model using collected data.Compared to the modified Weibols-Cook criterion,the modified Mohr-Coulomb criterion,and the modified Lade criterion,the strength criterion based on the GEP model exhibits superior prediction accuracy performance.The strength criterion based on the GEP model has better performance in R2,RMSE and MAPE for the data set used in this study.Furthermore,the strength criterion based on the GEP model shows greater stability in predicting the true triaxial strength of rocks across different types.Compared to the existing strength criterion based on the genetic programming(GP)model,the proposed criterion based on GEP model achieves more accurate predictions of the variation of true triaxial strength(s1)with intermediate principal stress(s2).Finally,based on the Sobol sensitivity analysis technique,the effects of the parameters of the three obtained strength criteria on the true triaxial strength of the rock are analysed.In general,the proposed strength criterion exhibits superior performance in terms of both accuracy and stability of prediction results.

Micra AV无导线起搏器植入术后随访优化对房室同步率的影响

目的分析Micra AV无导线起搏器植入术后的房室同步率,探讨术后程控优化对房室同步率的影响。方法采用前瞻性队列研究,选取2022年8月至2023年6月于首都医科大学附属北京安贞医院行Micra AV无导线起搏器植入的持续三度房室传导阻滞患者,于术后1周、1个月和3个月分别进行程控和优化,记录房室同步率、电学参数和超声心动图等。结果共入选68例植入Micra AV的三度房室传导阻滞患者,平均年龄(68.2±9.7)岁,其中男47例(69.1%)。所有患者均成功植入Micra AV,术后无严重并发症发生;术中和术后1周、1个月、3个月的平均阈值、感知、阻抗参数稳定;术后超声心动图左心室射血分数变化差异无统计学意义(P=0.162);术后1周、1个月、3个月的平均房室同步率分别为(75.2%比83.8%比91.6%,P=0.001),差异有统计学意义。结论Micra AV作为心房机械感知,需要个性化来调整相关参数。术后随访程控和优化对于三度房室传导阻滞患者植入Micra AV后的房室同步起搏和舒适度体验具有重要作用。

ITP患者PD-1/PD-L1表达特点及其在Treg与Breg细胞之间的相互作用机制分析

目的探讨原发免疫性血小板减少症(ITP)患者细胞程序性死亡受体-1(PD-1)/细胞程序性死亡配体1(PD-L1)表达特点及其在调节性T细胞(Treg)、调节性B细胞(Breg)间的相互作用。方法选取2018年12月—2022年1月在我院治疗的ITP患者106例作为观察组,其中轻度患者32例,中度患者44例,重度患者30例。同时选取同期健康志愿者100例作为对照组。检测两组Treg细胞百分比、Breg细胞百分比、Treg细胞表面PD-1阳性率、Breg细胞表面PD-L1阳性率等,同时分析观察组不同病情程度患者各指标差异。结果观察组Breg细胞百分比、Treg细胞百分比、TGF-β、IL-10和IL-4水平均明显低于对照组(P<0.05);观察组Treg细胞表面PD-1阳性率、Breg细胞表面PD-L1阳性率、可溶性程序性细胞死亡蛋白-1(sPD-1)和IL-17水平均明显高于对照组(均P<0.05);两组可溶性程序性细胞死亡蛋白配体-1(sPD-L1)水平比较差异无统计学意义(P>0.05)。观察组重度患者Breg细胞百分比、Treg细胞百分比均明显低于轻度和中度患者(均P<0.05),而Treg细胞表面PD-1阳性率、Breg细胞表面PD-L1阳性率均明显高于轻度和中度患者(均P<0.05)。Treg细胞表面PD-1阳性率与Breg细胞表面PD-L1阳性率呈正相关(r=0.446,P<0.05)。观察组治疗后Breg细胞百分比、Treg细胞百分比、TGF-β、IL-10和IL-4水平有所升高(P<0.05),而Treg细胞表面PD-1阳性率、Breg细胞表面PD-L1阳性率、sPD-1和IL-17水平有所降低(P<0.05),治疗前后sPD-L1水平比较差异无统计学意义(P>0.05)。结论ITP患者Treg细胞表面PD-1和Breg细胞表面PD-L1阳性率明显升高,与患者病情严重程度呈正相关,同时Treg细胞表面PD-1和Breg细胞表面PD-L1表达之间存在相关性。

集成AI大语言模型的在线编程实验平台设计与实现

传统在线编程实验平台受限于反馈信息的局限性、忽视代码风格及规范性、代码的可读性、代码的效率等多种因素,不利于学生的学习和发展。该文设计并实现了一个集成AI大语言模型的在线编程实验平台方案,该方案在传统平台基础上,引入了大语言模型的实时代码建议、程序安全扫描和判题结果分析优化功能。通过AI大语言模型的实时分析、诊断和优化建议,大大提高了教学效果,增强了学生与系统的互动,能够使学生更好的理解编程概念并提升解决问题的能力。该研究证实了AI模型在编程教育中的价值,并为人工智能在高等教育技术的应用提供了新的视角和策略。

PDCD4和miR-107在阴茎癌组织中的表达及与患者预后的关系

目的:探究程序性细胞死亡因子4(PDCD4)及微小RNA-107(miR-107)在阴茎癌组织中的表达水平及与患者预后的关系。方法:选取2018年01月至2020年06月经本院确诊阴茎癌并行手术治疗患者32例,术中分别取患者阴茎癌组织(研究组)及癌旁正常阴茎组织(对照组)各32对,应用实时定量PCR(quantitative reverse transcription-PCR)检测两组PDCD4和miR-107的表达水平并分析其与临床病理参数及预后的关系,并采用Kaplan-Meier进行生存曲线分析及Logistic回归分析影响阴茎癌患者预后的因素。结果:研究组PDCD4表达量明显低于对照组,miR-107表达量明显高于对照组(P均<0.05);PDCD4和miR-107的表达与阴茎癌患者淋巴结转移、组织分级相关(P均<0.05),与年龄、吸烟史、饮酒史、肿瘤部位无关(P均>0.05);应用Kaplan-Meier法对生存率进行分析,miR-107低表达组生存率显著高于miR-107高表达组,PDCD4低表达组生存率低于PDCD4高表达组(P均<0.05)。多因素分析结果显示,TNM分期高、淋巴结转移、PDCD4低表达和miR-107高表达均是影响阴茎癌患者预后的独立危险因素(均P<0.05)。结论:阴茎癌患者miR-107呈异常高表达,PDCD4呈低表达,二者均是影响阴茎癌的独立危险因素且有望作为预测阴茎癌患者预后的重要指标。

基于MRI影像组学构建PD-1/PD-L1抑制剂治疗dMMR/MSI-H直肠癌疗效的预测模型

目的:探讨MRI影像组学模型在程序性细胞死亡蛋白-1(PD-1)/程序性细胞死亡-配体1(PD-L1)抑制剂联合全程新辅助治疗(TNT)局部进展期直肠癌(LARC)的疗效预测价值。方法:收集河南中医药大学第一附属医院PD-1/PD-L1抑制剂联合TNT治疗的80例错配修复基因缺陷(dMMR)/微卫星高度不稳定(MSI-H)基因型中低位LARC患者的临床和影像资料。将入组患者按7∶3比例分为训练集和测试集,提取影像组学特征,从中筛选并构建影像组学模型。描绘影像组学模型的Rad-score与病理金标准之间的受试者工作特征(ROC)曲线,计算曲线下面积(AUC),并评价模型的诊断效能。采用决策曲线分析(DCA)计算风险阈值的范围,并评估临床获益情况。收集湖南省人民医院25例dMMR/MSI-H基因型LARC患者的影像资料作为外部验证集。结果:训练集、测试集及外部验证集三者之间的临床特征无统计学差异(P>0.05)。经过降维处理、t检验及一致性检验以及LASSO交叉验证后,筛选出一阶偏度特征和体积2个特征构建影像组学模型。训练集、测试集和外部验证集的影像组学预测模型ROC曲线的AUC、灵敏度、特异度、阳性预测值和阴性预测值分别为0.920、97.1%、85.7%、91.9%、94.7%;0.885、80.0%、88.9%、92.3%、72.7%;0.875、87.5%、88.9%、93.3%、80.0%。DCA曲线显示,当风险阈值范围为0%~82%时,采用影像组学模型预测LARC患者为病理完全缓解(pCR)的获益大于将所有患者都视为pCR或者无病理完全缓解(npCR)。结论:基于MRI影像组学构建的dMMR/MSI-H型局部进展期直肠癌PD-1/PD-L1抑制剂联合全程新辅助放化疗疗效预测模型,有较大潜力为不同基因分型的直肠癌患者制定个体化治疗策略提供量化依据。