Prenatal programing of motivated behaviors:can innate immunity prime behavior?

Prenatal programming during pregnancy sets physiological outcomes in the offspring by integrating external or internal stimuli.Accordingly,pregnancy is an important stage of physiological adaptations to the environment where the fetus becomes exposed and adapted to the maternal milieu.Maternal exposure to high-energy dense diets can affect motivated behavior in the offs p ring leading to addiction and impaired sociability.A high-energy dense exposure also increases the pro-inflammatory cytokines profile in plasma and brain and favors microglia activation in the offspring.While still under investigation,prenatal exposure to high-energy dense diets promotes structural abnormalities in selective brain regions regulating motivation and social behavior in the offspring.The current review addresses the role of energy-dense foods programming central and peripheral inflammatory profiles during embryonic development and its effect on motivated behavior in the offspring.We provide preclinical and clinical evidence that supports the contribution of prenatal programming in shaping immune profiles that favor structural and brain circuit disruption leading to aberrant motivated behaviors after birth.We hope this minireview encourages future research on novel insights into the mechanisms underlying maternal programming of motivated behavior by central immune networks.

PROPORTION OF THE CEMENT RAW MATERIAL CALCULATED BY THE LINEAR PROGRAMING

This paper presents a new method .linear programming method. to calculate the proportion of the cement raw material. Its advanlages are as following : good practicability, convenience for analysis, and being easy for controlling. The mathematical model given in the paper is apt to be realized on computer.

Automatic Generation of UML Class Diagrams for Object-oriented Design and Programing Course

UML Class diagram generation from textual requirements is an important task in object-oriented design and programing course.This study proposes a method for automatically generating class diagrams from Chinese textual requirements on the basis of Natural Language Processing(NLP)and mapping rules for sentence pattern matching.First,classes are identified through entity recognition rules and candidate class pruning rules using NLP from requirements.Second,class attributes and relationships between classes are extracted using mapping rules for sentence pattern matching on the basis of NLP.Third,we developed an assistant tool integrated into a precision micro classroom system for automatic generation of class diagram,to effectively assist the teaching of object-oriented design and programing course.Results are evaluated with precision,accuracy and recall from eight requirements of object-oriented design and programing course using truth values created by teachers.Our research should benefit beginners of object-oriented design and programing course,who may be students or software developers.It helps them to create correct domain models represented in the UML class diagram.

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.

Immune signature of small bowel adenocarcinoma and the role of tumor microenvironment

In this editorial we comment on the article published“Clinical significance of programmed cell death-ligand expression in small bowel adenocarcinoma is determined by the tumor microenvironment”.Small bowel adenocarcinoma(SBA)is a rare gastrointestinal neoplasm and despite the small intestine's significant surface area,SBA accounts for less than 3%of such tumors.Early detection is challenging and the reason arises from its asymptomatic nature,often leading to late-stage discovery and poor prognosis.Treatment involves platinum-based chemotherapy with a 5-fluorouracil combination,but the lack of effective chemotherapy contributes to a generally poor prognosis.SBAs are linked to genetic disorders and risk factors,including chronic inflammatory conditions.The unique characteristics of the small bowel,such as rapid cell renewal and an active immune system,contributes to the rarity of these tumors as well as the high intratumoral infiltration of immune cells is associated with a favorable prognosis.Programmed cell death-ligand 1(PD-L1)expression varies across different cancers,with potential discrepancies in its prognostic value.Microsatellite instability(MSI)in SBA is associated with a high tumor mutational burden,affecting the prognosis and response to immunotherapy.The presence of PD-L1 and programmed cell death 1,along with tumor-infiltrating lymphocytes,plays a crucial role in the complex microenvironment of SBA and contributes to a more favorable prognosis,especially in the context of high MSI tumors.Stromal tumor-infiltrating lymphocytes are identified as independent prognostic indicators and the association between MSI status and a favorable prognosis,emphasizes the importance of evaluating the immune status of tumors for treatment decisions.

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.

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.

Multi-Timescale Distributed Approach to Generalized-Nash-Equilibrium Seeking in Noncooperative Nonconvex Games

Dear Editor,The distributed generalized-Nash-equilibrium(GNE)seeking in noncooperative games with nonconvexity is the topic of this letter.Inspired by the sequential quadratic programming(SQP)method,a multi-timescale multi-agent system(MAS)is developed,and its convergence to a critical point of the game is proven.To illustrate the qualities and efficacy of the theoretical findings,a numerical example is elaborated.

Crosstalk among mitophagy,pyroptosis,ferroptosis,and necroptosis in central nervous system injuries

Central nervous system injuries have a high rate of resulting in disability and mortality;however,at present,effective treatments are lacking.Programmed cell death,which is a genetically determined fo rm of active and ordered cell death with many types,has recently attra cted increasing attention due to its functions in determining the fate of cell survival.A growing number of studies have suggested that programmed cell death is involved in central nervous system injuries and plays an important role in the progression of brain damage.In this review,we provide an ove rview of the role of programmed cell death in central nervous system injuries,including the pathways involved in mitophagy,pyroptosis,ferroptosis,and necroptosis,and the underlying mechanisms by which mitophagy regulates pyroptosis,ferroptosis,and necro ptosis.We also discuss the new direction of therapeutic strategies to rgeting mitophagy for the treatment of central nervous system injuries,with the aim to determine the connection between programmed cell death and central nervous system injuries and to identify new therapies to modulate programmed cell death following central nervous system injury.In conclusion,based on these properties and effects,interventions targeting programmed cell death could be developed as potential therapeutic agents for central nervous system injury patients.

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.

Hepatic arterial infusion chemotherapy with anti-angiogenesis agents and immune checkpoint inhibitors for unresectable hepatocellular carcinoma and meta-analysis

BACKGROUND Hepatic arterial infusion chemotherapy(HAIC)has been proven to be an ideal choice for treating unresectable hepatocellular carcinoma(uHCC).HAIC-based treatment showed great potential for treating uHCC.However,large-scale studies on HAIC-based treatments and meta-analyses of first-line treatments for uHCC are lacking.AIM To investigate better first-line treatment options for uHCC and to assess the safety and efficacy of HAIC combined with angiogenesis inhibitors,programmed cell death of protein 1(PD-1)and its ligand(PD-L1)blockers(triple therapy)under real-world conditions.METHODS Several electronic databases were searched to identify eligible randomized controlled trials for this meta-analysis.Study-level pooled analyses of hazard ratios(HRs)and odds ratios(ORs)were performed.This was a retrospective single-center study involving 442 patients with uHCC who received triple therapy or angiogenesis inhibitors plus PD-1/PD-L1 blockades(AIPB)at Sun Yat-sen University Cancer Center from January 2018 to April 2023.Propensity score matching(PSM)was performed to balance the bias between the groups.The Kaplan-Meier method and cox regression were used to analyse the survival data,and the log-rank test was used to compare the suvival time between the groups.RESULTS A total of 13 randomized controlled trials were included.HAIC alone and in combination with sorafenib were found to be effective treatments(P values for ORs:HAIC,0.95;for HRs:HAIC+sorafenib,0.04).After PSM,176 HCC patients were included in the analysis.The triple therapy group(n=88)had a longer median overall survival than the AIPB group(n=88)(31.6 months vs 14.6 months,P<0.001)and a greater incidence of adverse events(94.3%vs 75.4%,P<0.001).CONCLUSION This meta-analysis suggests that HAIC-based treatments are likely to be the best choice for uHCC.Our findings confirm that triple therapy is more effective for uHCC patients than AIPB.

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.

Effect of eccentric and inclined loading on the bearing capacity of strip footing placed on rock mass

This paper deals with the bearing capacity determination of strip footing on a rock mass in hilly area by considering the influence of inclined and eccentric loading. Applying the generalized HoekBrown failure criterion, the failure behavior of the rock mass is modeled with the help of the power cone programming in the lower bound finite element limit analysis framework. Using bearing capacity factor(Ns), the change in bearing capacity of the strip footing due to the occurrence of eccentrically inclined loading is presented. The variations of the magnitude of Ns are obtained by examining the effects of the Hoek-Brown rock mass strength parameters(uniaxial compressive strength(sci), disturbance factor(D), rock parameter(mi), and Geological Strength Index(GSI)) in the presence of different magnitudes of eccentricity(e) and inclination angle(λ) with respect to the vertical plane, and presented as design charts. Both the inclined loading modes, i.e., inclination towards the center of strip footing(+λ) and inclination away from the center of strip footing(-λ), are adopted to perform the investigation. In addition, the correlation between the input parameters and the corresponding output is developed by utilizing the artificial neural network(ANN). Additionally, from sensitivity analysis, it is observed that inclination angle(λ) is the most sensitive parameter. For practicing engineers, the obtained design equation and design charts can be beneficial to understand the bearing capacity variation in the existence of eccentrically inclined loading in mountain areas.

Spatiotemporal transformable nano-assembly for on-demand drug delivery to enhance anti-tumor immunotherapy

Induction of tumor cell senescence has become a promising strategy for anti-tumor immunotherapy,but fibrotic matrix severely blocks senescence inducers penetration and immune cells infiltration.Herein,we designed a cancer-associated fibroblasts(CAFs)triggered structure-transformable nano-assembly(HSD-P@V),which can directionally deliver valsartan(Val,CAFs regulator)and doxorubicin(DOX,senescence inducer)to the specific targets.In detail,DOX is conjugated with hyaluronic acid(HA)via diselenide bonds(Se-Se)to form HSD micelles,while CAFs-sensitive peptide is grafted onto the HSD to form a hydrophilic polymer,which is coated on Val nanocrystals(VNs)surface for improving the stability and achieving responsive release.Once arriving at tumor microenvironment and touching CAFs,HSD-P@V disintegrates into VNs and HSD micelles due to sensitive peptide detachment.VNs can degrade the extracellularmatrix,leading to the enhanced penetration of HSD.HSD targets tumor cells,releases DOX to induce senescence,and recruits effector immune cells.Furthermore,senescent cells are cleared by the recruited immune cells to finish the integrated anti-tumor therapy.In vitro and in vivo results show that the nanoassembly remarkably inhibits tumor growth as well as lungmetastasis,and extends tumorbearing mice survival.This work provides a promising paradigm of programmed delivering multi-site nanomedicine for cancer immunotherapy.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Value Iteration-Based Cooperative Adaptive Optimal Control for Multi-Player Differential Games With Incomplete Information

This paper presents a novel cooperative value iteration(VI)-based adaptive dynamic programming method for multi-player differential game models with a convergence proof.The players are divided into two groups in the learning process and adapt their policies sequentially.Our method removes the dependence of admissible initial policies,which is one of the main drawbacks of the PI-based frameworks.Furthermore,this algorithm enables the players to adapt their control policies without full knowledge of others’ system parameters or control laws.The efficacy of our method is illustrated by three examples.

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.

Smart prediction of liquefaction-induced lateral spreading

The prediction of liquefaction-induced lateral spreading/displacement(Dh)is a challenging task for civil/geotechnical engineers.In this study,a new approach is proposed to predict Dh using gene expression programming(GEP).Based on statistical reasoning,individual models were developed for two topographies:free-face and gently sloping ground.Along with a comparison with conventional approaches for predicting the Dh,four additional regression-based soft computing models,i.e.Gaussian process regression(GPR),relevance vector machine(RVM),sequential minimal optimization regression(SMOR),and M5-tree,were developed and compared with the GEP model.The results indicate that the GEP models predict Dh with less bias,as evidenced by the root mean square error(RMSE)and mean absolute error(MAE)for training(i.e.1.092 and 0.815;and 0.643 and 0.526)and for testing(i.e.0.89 and 0.705;and 0.773 and 0.573)in free-face and gently sloping ground topographies,respectively.The overall performance for the free-face topology was ranked as follows:GEP>RVM>M5-tree>GPR>SMOR,with a total score of 40,32,24,15,and 10,respectively.For the gently sloping condition,the performance was ranked as follows:GEP>RVM>GPR>M5-tree>SMOR with a total score of 40,32,21,19,and 8,respectively.Finally,the results of the sensitivity analysis showed that for both free-face and gently sloping ground,the liquefiable layer thickness(T_(15))was the major parameter with percentage deterioration(%D)value of 99.15 and 90.72,respectively.

Leveraging diverse cell-death patterns to predict the clinical outcome of immune checkpoint therapy in lung adenocarcinoma:Based on muti-omics analysis and vitro assay

Advanced LUAD shows limited response to treatment including immune therapy.With the development of sequencing omics,it is urgent to combine high-throughput multi-omics data to identify new immune checkpoint therapeutic response markers.Using GSE72094(n=386)and GSE31210(n=226)gene expression profile data in the GEO database,we identified genes associated with lung adenocarcinoma(LUAD)death using tools such as“edgeR”and“maftools”and visualized the characteristics of these genes using the“circlize”R package.We constructed a prognostic model based on death-related genes and optimized the model using LASSO-Cox regression methods.By calculating the cell death index(CDI)of each individual,we divided LUAD patients into high and low CDI groups and examined the relationship between CDI and overall survival time by principal component analysis(PCA)and Kaplan-Meier analysis.We also used the“ConsensusClusterPlus”tool for unsupervised clustering of LUAD subtypes based on model genes.In addition,we collected data on the expression of immunomodulatory genes and model genes for each cohort and performed tumor microenvironment analyses.We also used the TIDE algorithm to predict immunotherapy responses in the CDI cohort.Finally,we studied the effect of PRKCD on the proliferation and migration of LUAD cells through cell culture experiments.The study utilized the TCGA-LUAD cohort(n=493)and identified 2,901 genes that are differentially expressed in patients with LUAD.Through KEGG and GO enrichment analysis,these genes were found to be involved in a wide range of biological pathways.The study also used univariate Cox regression models and LASSO regression analyses to identify 17 candidate genes that were best associated with mortality prognostic risk scores.By comparing the overall survival(OS)outcomes of patients with different CDI values,it was found that increased CDI levels were significantly associated with lower OS rates.In addition,the study used unsupervised cluster analysis to divide 115 LUAD patients into two distinct clusters with significant differences in OS timing.Finally,a prognostic indicator called CDI was established and its feasibility as an independent prognostic indicator was evaluated by Cox proportional risk regression analysis.The immunotherapy efficacy was more sensitive in the group with high expression of programmed cell death models.Relationship between programmed cell death(PCD)signature models and drug reactivity.After evaluating the median inhibitory concentration(IC50)of various drugs in LUAD samples,statistically significant differences in IC50 values were found in cohorts with high and low CDI status.Specifically,Gefitinib and Lapatinib had higher IC50 values in the high-CDI cohort,while Olaparib,Oxaliplatin,SB216763,and Axitinib had lower values.These results suggest that individuals with high CDI levels are sensitive to tyrosine kinase inhibitors and may be resistant to conventional chemotherapy.Therefore,this study constructed a gene model that can evaluate patient immunotherapy by using programmed cell death-related genes based on muti-omics.The CDI index composed of these programmed cell death-related genes reveals the heterogeneity of lung adenocarcinoma tumors and serves as a prognostic indicator for patients.

An Overview of Sequential Approximation in Topology Optimization of Continuum Structure

This paper offers an extensive overview of the utilization of sequential approximate optimization approaches in the context of numerically simulated large-scale continuum structures.These structures,commonly encountered in engineering applications,often involve complex objective and constraint functions that cannot be readily expressed as explicit functions of the design variables.As a result,sequential approximation techniques have emerged as the preferred strategy for addressing a wide array of topology optimization challenges.Over the past several decades,topology optimization methods have been advanced remarkably and successfully applied to solve engineering problems incorporating diverse physical backgrounds.In comparison to the large-scale equation solution,sensitivity analysis,graphics post-processing,etc.,the progress of the sequential approximation functions and their corresponding optimizersmake sluggish progress.Researchers,particularly novices,pay special attention to their difficulties with a particular problem.Thus,this paper provides an overview of sequential approximation functions,related literature on topology optimization methods,and their applications.Starting from optimality criteria and sequential linear programming,the other sequential approximate optimizations are introduced by employing Taylor expansion and intervening variables.In addition,recent advancements have led to the emergence of approaches such as Augmented Lagrange,sequential approximate integer,and non-gradient approximation are also introduced.By highlighting real-world applications and case studies,the paper not only demonstrates the practical relevance of these methods but also underscores the need for continued exploration in this area.Furthermore,to provide a comprehensive overview,this paper offers several novel developments that aim to illuminate potential directions for future research.