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.

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.

Metabolic reprogramming in skeletal cell differentiation

The human skeleton is a multifunctional organ made up of multiple cell types working in concert to maintain bone and mineral homeostasis and to perform critical mechanical and endocrine functions.From the beginning steps of chondrogenesis that prefigures most of the skeleton,to the rapid bone accrual during skeletal growth,followed by bone remodeling of the mature skeleton,cell differentiation is integral to skeletal health.

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.

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.

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.

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.

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.

Flexible Job Shop Composite Dispatching Rule Mining Approach Based on an Improved Genetic Programming Algorithm

To obtain a suitable scheduling scheme in an effective time range,the minimum completion time is taken as the objective of Flexible Job Shop scheduling Problems(FJSP)with different scales,and Composite Dispatching Rules(CDRs)are applied to generate feasible solutions.Firstly,the binary tree coding method is adopted,and the constructed function set is normalized.Secondly,a CDR mining approach based on an Improved Genetic Programming Algorithm(IGPA)is designed.Two population initialization methods are introduced to enrich the initial population,and a superior and inferior population separation strategy is designed to improve the global search ability of the algorithm.At the same time,two individual mutation methods are introduced to improve the algorithm’s local search ability,to achieve the balance between global search and local search.In addition,the effectiveness of the IGPA and the superiority of CDRs are verified through comparative analysis.Finally,Deep Reinforcement Learning(DRL)is employed to solve the FJSP by incorporating the CDRs as the action set,the selection times are counted to further verify the superiority of CDRs.

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 Computing

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.

Efficacy and predictive factors of transarterial chemoembolization combined with lenvatinib plus programmed cell death protein-1 inhibition for unresectable hepatocellular carcinoma

BACKGROUND The efficacy and safety of transarterial chemoembolization(TACE)combined with lenvatinib plus programmed cell death protein-1(PD-1)for unresectable hepato-cellular carcinoma(HCC)have rarely been evaluated and it is unknown which factors are related to efficacy.AIM To evaluate the efficacy and independent predictive factors of TACE combined with lenvatinib plus PD-1 inhibitors for unresectable HCC.METHODS This study retrospectively enrolled patients with unresectable HCC who received TACE/lenvatinib/PD-1 treatment between March 2019 and April 2022.Overall survival(OS)and progression-free survival(PFS)were determined.The objective response rate(ORR)and disease control rate(DCR)were evaluated in accordance with the modified Response Evaluation Criteria in Solid Tumors.Additionally,the prognostic factors affecting the clinical outcome were assessed.RESULTS One hundred and two patients were enrolled with a median follow-up duration of 12.63 months.The median OS was 26.43 months(95%CI:17.00-35.87),and the median PFS was 10.07 months(95%CI:8.50-11.65).The ORR and DCR were 61.76%and 81.37%,respectively.The patients with Barcelona Clinic Liver Cancer Classification(BCLC)B stage,early neutrophil-to-lymphocyte ratio(NLR)response(decrease),or early alpha-fetoprotein(AFP)response(decrease>20%)had superior OS and PFS than their counterparts.CONCLUSION This study showed that TACE/lenvatinib/PD-1 treatment was well tolerated with encouraging efficacy in patients with unresectable HCC.The patients with BCLC B-stage disease with early NLR response(decrease)and early AFP response(decrease>20%)may achieve better clinical outcomes with this triple therapy.

Cytokine release syndrome triggered by programmed death 1 blockade(sintilimab)therapy in a psoriasis patient:A case report

BACKGROUND In recent years,immune checkpoint inhibitors(ICIs)have demonstrated remarkable efficacy across diverse malignancies.Notably,in patients with advanced gastric cancer,the use of programmed death 1(PD-1)blockade has significantly prolonged overall survival,marking a pivotal advancement comparable to the impact of Herceptin over the past two decades.While the therapeutic benefits of ICIs are evident,the increasing use of immunotherapy has led to an increase in immune-related adverse events.CASE SUMMARY This article presents the case of a patient with advanced gastric cancer and chronic plaque psoriasis.Following sintilimab therapy,the patient developed severe rashes accompanied by cytokine release syndrome(CRS).Fortunately,effective management was achieved through the administration of glucocorticoid,tocilizumab,and acitretin,which resulted in favorable outcomes.CONCLUSION Glucocorticoid and tocilizumab therapy was effective in managing CRS after PD-1 blockade therapy for gastric cancer in a patient with chronic plaque psoriasis.

Key Factors to Consider When Introducing a New Vaccine in Low-Income Settings: Lessons from Malawi Expanded Program on Immunization

Introduction: As new vaccines become available, countries must assess the relevance to introduce them into their vaccination schedules. Malawi has recently introduced several new vaccines and plans to introduce more. This study was conducted to identify key factors that need to be considered when deciding to introduce a new vaccine and current challenges faced by low and middle income countries using Malawi as an example. Methodology: The study employed a desk review approach, examining published literature from various sources such as PubMed, Medline, and Google Scholar. Policy documents from organizations like the World Health Organization, GAVI the Alliance, and the Ministry of Health for Malawi were also included. A total of 99 articles and documents on new vaccine introduction, challenges of immunization, policy documents in immunization and health systems strengthening were included. The review focused on addressing five key areas critical to new vaccine introduction namely: the need for a vaccine, availability of the vaccine, safety and effectiveness of the vaccine, demand for the vaccine, and the prudent use of public or private funds. Results: Malawi considered the burden of cervical cancer and the significance of malaria in the country when introducing the HPV and malaria vaccines. The country opted for vaccines that can be handled by the cold chain capacity and available human resources. Despite that malaria vaccine and Typhoid Conjugate Vaccine trials were done in country, there are limited vaccine safety and efficacy trials conducted in Malawi, leading to a reliance on WHO-prequalified vaccines. Demand for newly introduced vaccines varied, with high demand for Oral Cholera Vaccine during a cholera outbreak, while demand for COVID-19 vaccines decreased over time. Although cost-effectiveness studies were limited in the country, 2 studies indicated that Typhoid Conjugate Vaccine and malaria vaccine would be cost effective. All these have been implemented despite having challenges like lack of accurate surveillance data, inadequate cold chain capacity, limited safety and efficacy vaccine clinical trials, political influence, and limited funding. Conclusion: Despite several challenges Malawi set a good example of the careful considerations required before introducing a new vaccine. The process involves data review, priority setting, precise planning, and consultation with stakeholders. Low-income countries should invest in vaccine safety, efficacy, and cost-effectiveness trials.

Research and Practice of Cardiopulmonary Rehabilitation Training Programs

In today’s society, the incidence of cardiopulmonary diseases is increasing annually, seriously affecting patients’ quality of life. Therefore, developing a scientific and effective rehabilitation training program is of great significance. This study first analyzes the theoretical basis of cardiopulmonary rehabilitation training, including the effects of aerobic exercise, interval training, and strength training on cardiopulmonary function. Based on this, a comprehensive rehabilitation training program is designed, which includes personalized training plans, comprehensive interventions, multidisciplinary collaboration, patient education, and regular follow-up visits. The cardiopulmonary rehabilitation training plan developed in this study has certain scientific practicability, which provides a theoretical basis for cardiopulmonary rehabilitation training, and also provides a reference for medical institutions, rehabilitation centers and communities, which is helpful for promotion and application to a wider range of patients with cardiopulmonary diseases.

Quercetin Alleviates Lipopolysaccharide-Induced Cardiac Inflammation via Inhibiting Autophagy and Programmed Cell Death

Objective The aim of this study is to explore the potential modulatory role of quercetin against Endotoxin or lipopolysaccharide(LPS)induced septic cardiac dysfunction.Methods Specific pathogen-free chicken embryos(n=120)were allocated untreated control,phosphate buffer solution(PBS)vehicle,PBS with ethanol vehicle,LPS(500 ng/egg),LPS with quercetin treatment(10,20,or 40 nmol/egg,respectively),Quercetin groups(10,20,or 40 nmol/egg).Fifteenday-old embryonated eggs were inoculated with abovementioned solutions via the allantoic cavity.At embryonic day 19,the hearts of the embryos were collected for histopathological examination,RNA extraction,real-time polymerase chain reaction,immunohistochemical investigations,and Western blotting.Results They demonstrated that the heart presented inflammatory responses after LPS induction.The LPS-induced higher mRNA expressions of inflammation-related factors(TLR4,TNFα,MYD88,NF-κB1,IFNγ,IL-1β,IL-8,IL-6,IL-10,p38,MMP3,and MMP9)were blocked by quercetin with three dosages.Quercetin significantly decreased immunopositivity to TLR4 and MMP9 in the treatment group when compared with the LPS group.Quercetin significantly decreased protein expressions of TLR4,IFNγ,MMP3,and MMP9 when compared with the LPS group.Quercetin treatment prevented LPS-induced increase in the mRNA expression of Claudin 1 and ZO-1,and significantly decreased protein expression of claudin 1 when compared with the LPS group.Quercetin significantly downregulated autophagyrelated gene expressions(PPARα,SGLT1,APOA4,AMPKα1,AMPKα2,ATG5,ATG7,Beclin-1,and LC3B)and programmed cell death(Fas,Bcl-2,CASP1,CASP12,CASP3,and RIPK1)after LPS induction.Quercetin significantly decreased immunopositivity to APOA4,AMPKα2,and LC3-II/LC3-I in the treatment group when compared with the LPS group.Quercetin significantly decreased protein expressions of AMPKα1,LC3-I,and LC3-II.Quercetin significantly decreased the protein expression to CASP1 and CASP3 by immunohistochemical investigation or Western blotting in treatment group when compared with LPS group.Conclusion Quercetin alleviates cardiac inflammation induced by LPS through modulating autophagy,programmed cell death,and myocardiocytes permeability.

An Empirical Application of User-Guided Program Analysis

Although static program analysis methods are frequently employed to enhance software quality,their efficiency in commercial settings is limited by their high false positive rate.The EUGENE tool can effectively lower the false positive rate.However,in continuous integration(CI)environments,the code is always changing,and user feedback from one version of the software cannot be applied to a subsequent version.Additionally,people find it difficult to distinguish between true positives and false positives in the analytical output.In this study,we developed the EUGENE-CI technique to address the CI problem and the EUGENE-rank lightweight heuristic algorithm to rate the reports of the analysis output in accordance with the likelihood that they are true positives.On the three projects ethereum,go-cloud,and kubernetes,we assessed our methodologies.According to the trial findings,EUGENE-CI may drastically reduce false positives while EUGENE-rank can make it much easier for users to identify the real positives among a vast number of reports.We paired our techniques with GoInsight~1 and discovered a vulnerability.We also offered a patch to the community.