Evolution Strategy Research Articles

Tit-For-Tat and Win Stay-Lose Shift strategies via memory-two

Decisions in many dilemmas are based on a combination of factors, including as incentive, punishment, reputation, and memory. The impact of memory information on cooperative evolution in multi-round games is a decision-making process in group evolution. The iterated prisoner’s dilemma is an excellent model for the development of cooperation amongst the payoff-maximizing individuals. Since tit-for-tat proved successful in Axelrod’s repeated prisoner’s dilemma tournaments, there has been a great deal of interest in creating new strategies. Every iterative prisoner’s dilemma method bases its decision-making on a specific duration of past contacts with the opponent, which is referred to as the memory’s size. This study examines the impact of strategy memory size on the evolutionary stability of n-person iterated prisoner’s dilemma strategies. In this paper, we address the role that memory plays in decision-making. We interested in the model of the Iterated Prisoner’s Dilemma game for three players with memory two, and we will look at strategies with similar behavior, such as Tit-For-Tat (TFT) strategies as well as Win Stay-Lose Shift (WSLS) strategies. As a result of this paper, we have shown that the effect of memory length is almost non-existent in the competitions of strategies that we studied.

Multi-focus manipulation system based on SNES aberration self-calibration

Abstract Wavefront shaping using digital micromirror devices (DMDs) allows inertia-free focus manipulation with numerous modulation modes and high refresh rates. However, the aberration caused by the curvature of DMD affects the focusing performance. Here, we propose an aberration self-calibration method based on separable natural evolution strategies (SNES). This method searches optimal Zernike coefficients of aberration globally and completes compensation using super-pixel encoding. Compared to the genetic algorithm method, we improve the speed by 62% and achieve better-focused spots. It enables simultaneous scanning of 25 independent focal spots. This advancement supports wavefront shaping applications in optical imaging, industrial inspection, and laser processing.

Genomic analysis of Nypa fruticans elucidates its intertidal adaptations and early palm evolution.

Nypa fruticans (Wurmb), a mangrove palm species with origins dating back to the Late Cretaceous period, is a unique species for investigating long-term adaptation strategies to intertidal environments and the early evolution of palms. Here, we present a chromosome-level genome sequence and assembly for N. fruticans. We integrated the genomes of N. fruticans and other palm family members for a comparative genomic analysis, which confirmed that the common ancestor of all palms experienced a whole-genome duplication event around 89 million years ago, shaping the distinctive characteristics observed in this clade. We also inferred a low mutation rate for the N. fruticans genome, which underwent strong purifying selection and evolved slowly, thus contributing to its stability over a long evolutionary period. Moreover, ancient duplicates were preferentially retained, with critical genes having experienced positive selection, enhancing waterlogging tolerance in N. fruticans. Furthermore, we discovered that the pseudogenization of Early Methionine-labelled 1 (EM1) and EM6 in N. fruticans underly its crypto-vivipary characteristics, reflecting its intertidal adaptation. Our study provides valuable genomic insights into the evolutionary history, genome stability, and adaptive evolution of the mangrove palm. Our results also shed light on the long-term adaptation of this species and contribute to our understanding of the evolutionary dynamics in the palm family.

Research on the strategy for improving the utility of government social media information based on a multi-agent game model

Government social media (GSM) has become an important tool for government departments to open information, guide public opinion and interact with the government and the people. However, the operation and maintenance of some GSM are not standardised, and the content published is inconsistent with identity positioning, resulting in the realistic dilemma of low utility of GSM information. The purpose of this study is to explore the effective strategies to improve the effectiveness of GSM information. The research is from the perspective of information economics, this article uses evolutionary game theory to build a tripartite evolutionary game model comprising GSM operations departments, government regulators and users in order to explore the evolution process of tripartite game behaviours and the influence of subject behaviour selection on information utility. It subsequently conducts a solution and numerical simulation to demonstrate the influence of different factors on the game results. The experimental results show that there are four situations in which the utility of GSM information affects the evolution and stability strategy of the subject and that changes in different parameter values have significant effects on the results of the three-party game. The evolution trend of the subject behaviour can be changed by increasing the regulatory means of rewards and punishments and establishing an efficient operation mechanism for GSM, thus promoting system convergence to the ideal state. The results of this study can provide references and suggestions for government departments to effectively enhance the effectiveness of GSM information and promote the healthy development of GSM.

A fault diagnosis method for wireless sensor network nodes based on a belief rule base with adaptive attribute weights

Due to the harsh operating environment and ultralong operating hours of wireless sensor networks (WSNs), node failures are inevitable. Ensuring the reliability of the data collected by the WSN necessitates the utmost importance of diagnosing faults in nodes within the WSN. Typically, the initial step in the fault diagnosis of WSN nodes involves extracting numerical features from neighboring nodes. A solitary data feature is often assigned a high weight, resulting in the failure to effectively distinguish between all types of faults. Therefore, this study introduces an enhanced variant of the traditional belief rule base (BRB), called the belief rule base with adaptive attribute weights (BRB-AAW). First, the data features are extracted as input attributes for the model. Second, a fault diagnosis model for WSN nodes, incorporating BRB-AAW, is established by integrating parameters initialized by expert knowledge with the extracted data features. Third, to optimize the model's initial parameters, the projection covariance matrix adaptive evolution strategy (P-CMA-ES) algorithm is employed. Finally, a comprehensive case study is designed to verify the accuracy and effectiveness of the proposed method. The results of the case study indicate that compared with the traditional BRB method, the accuracy of the proposed model in WSN node fault diagnosis is significantly improved.

Automatic Feature Construction-Based Genetic Programming for Degraded Image Classification

Accurately classifying degraded images is a challenging task that relies on domain expertise to devise effective image processing techniques for various levels of degradation. Genetic Programming (GP) has been proven to be an excellent approach for solving image classification tasks. However, the program structures designed in current GP-based methods are not effective in classifying images with quality degradation. During the iterative process of GP algorithms, the high similarity between individuals often results in convergence to local optima, hindering the discovery of the best solutions. Moreover, the varied degrees of image quality degradation often lead to overfitting in the solutions derived by GP. Therefore, this research introduces an innovative program structure, distinct from the traditional program structure, which automates the creation of new features by transmitting information learned across multiple nodes, thus improving GP individual ability in constructing discriminative features. An accompanying evolution strategy addresses high similarity among GP individuals by retaining promising ones, thereby refining the algorithm’s development of more effective GP solutions. To counter the potential overfitting issue of the best GP individual, a multi-generational individual ensemble strategy is proposed, focusing on constructing an ensemble GP individual with an enhanced generalization capability. The new method evaluates performance in original, blurry, low contrast, noisy, and occlusion scenarios for six different types of datasets. It compares with a multitude of effective methods. The results show that the new method achieves better classification performance on degraded images compared with the comparative methods.

Pattern recognition frequency-based feature selection with multi-objective discrete evolution strategy for high-dimensional medical datasets

Feature selection has a prominent role in high-dimensional datasets to increase classification accuracy, decrease the learning algorithm computational time, and present the most informative features to decision-makers. This paper proposes a two-stage hybrid feature selection for high-dimensional medical datasets: Maximum Pattern Recognition - Multi-objective Discrete Evolution Strategy (MPR-MDES). MPR is a rapid filter ranker that significantly outperforms existing frequency-based rankers in recognizing non-linear patterns, effectively eliminating a majority of non-informative features. Then, the wrapper Multi-objective Discrete Evolution Strategy (MDES) uses the remaining features and obtains sets of solutions which are automatically presented to decision-makers. The experiments conducted on large medical datasets demonstrate that MPR-MDES achieves considerable improvements compared to state-of-the-art methods, in terms of both classification accuracy and dimensionality reduction. In this sense, the proposal successfully performs when presenting informative feature sets to decision-makers. The implementation is available on https://github.com/KhaosResearch/MPR-MDES.

How stakeholders influence MaaS implementation? An analysis based on evolutionary game theory

Mobility as a service (MaaS) is an emerging mobility service that has received high expectations from the transportation sector in recent years. MaaS integrates multiple transportation services into a single platform, enhancing the accessibility of various transportation resources. However, the complex integration involved in MaaS leads to uncertainty in stakeholder interaction and system implementation. For the successful deployment and development of MaaS, this study aims to identify stakeholder enablers and barriers as well as to solve the uncertainty in MaaS implementation. We construct a tripartite evolutionary game model among government, transportation service providers (TSPs) and travelers. By synthesizing the evolutionary stability of each participant through simulation, we explored the impact of factors on stakeholder strategy choices and further analyzed the stability of equilibrium points in the system. The results show that: 1) Government subsidies and penalties, TSPs' costs and revenues, travelers' utility and expenses all impact the strategy choices of stakeholders. 2) The costs of government policy-making and TSPs will influence the evolution of MaaS system into two different development modes: free-market model and policy-supported model. 3) Travelers’ utilities play a crucial role in promoting the stability of travelers' adoption of MaaS. This study provides new insights into identifying stakeholder demands. The findings contribute to formulating specific policy recommendations and incentive measurements to help in the strengthen and forward movement of MaaS development in the future.

Genome streamlining to improve performance of a fast-growing cyanobacterium Synechococcus elongatus UTEX 2973.

Cyanobacteria are photosynthetic organisms that have garnered significant recognition as potential hosts for sustainable bioproduction. However, their complex regulatory networks pose significant challenges to major metabolic engineering efforts, thereby limiting their feasibility as production hosts. Genome streamlining has been demonstrated to be a successful approach for improving productivity and fitness in heterotrophs but is yet to be explored to its full potential in phototrophs. Here, we present the systematic reduction of the genome of the cyanobacterium exhibiting the fastest exponential growth, Synechococcus elongatus UTEX 2973. This work, the first of its kind in a photoautotroph, involved an iterative process using state-of-the-art genome-editing technology guided by experimental analysis and computational tools. CRISPR-Cas3 enabled large, progressive deletions of predicted dispensable regions and aided in the identification of essential genes. The large deletions were combined to obtain a strain with 55-kb genome reduction. The strains with streamlined genome showed improvement in growth (up to 23%) and productivity (by 22.7%) as compared to the wild type (WT). This streamlining strategy not only has the potential to develop cyanobacterial strains with improved growth and productivity traits but can also facilitate a better understanding of their genome-to-phenome relationships.IMPORTANCEGenome streamlining is an evolutionary strategy used by natural living systems to dispense unnecessary genes from their genome as a mechanism to adapt and evolve. While this strategy has been successfully borrowed to develop synthetic heterotrophic microbial systems with desired phenotype, it has not been extensively explored in photoautotrophs. Genome streamlining strategy incorporates both computational predictions to identify the dispensable regions and experimental validation using genome-editing tool, and in this study, we have employed a modified strategy with the goal to minimize the genome size to an extent that allows optimal cellular fitness under specified conditions. Our strategy has explored a novel genome-editing tool in photoautotrophs, which, unlike other existing tools, enables large, spontaneous optimal deletions from the genome. Our findings demonstrate the effectiveness of this modified strategy in obtaining strains with streamlined genome, exhibiting improved fitness and productivity.

Trained Innate Immunity in Animal Models of Cardiovascular Diseases.

Acquisition of immunological memory is an important evolutionary strategy that evolved to protect the host from repetitive challenges from infectious agents. It was believed for a long time that memory formation exclusively occurs in the adaptive part of the immune system with the formation of highly specific memory T cells and B cells. In the past 10-15 years, it has become clear that innate immune cells, such as monocytes, natural killer cells, or neutrophil granulocytes, also have the ability to generate some kind of memory. After the exposure of innate immune cells to certain stimuli, these cells develop an enhanced secondary response with increased cytokine secretion even after an encounter with an unrelated stimulus. This phenomenon has been termed trained innate immunity (TI) and is associated with epigenetic modifications (histone methylation, acetylation) and metabolic alterations (elevated glycolysis, lactate production). TI has been observed in tissue-resident or circulating immune cells but also in bone marrow progenitors. Risk-factors for cardiovascular diseases (CVDs) which are associated with low-grade inflammation, such as hyperglycemia, obesity, or high salt, can also induce TI with a profound impact on the development and progression of CVDs. In this review, we briefly describe basic mechanisms of TI and summarize animal studies which specifically focus on TI in the context of CVDs.

Differentiated sex chromosomes, karyotype evolution, and spontaneous triploidy in carphodactylid geckos.

Geckos exhibit derived karyotypes without a clear distinction between macrochromosomes and microchromosomes and intriguing diversity in sex determination mechanisms. We conducted cytogenetic analyses in six species from the genera Nephrurus, Phyllurus, and Saltuarius of the gecko family Carphodactylidae. We confirmed the presence of a female heterogametic system with markedly differentiated and heteromorphic sex chromosomes in all examined species, typically with the W chromosome notably larger than the Z chromosome. One species, Nephrurus cinctus, possesses unusual multiple Z1Z1Z2Z2/Z1Z2W sex chromosomes. The morphology of the sex chromosomes, along with repetitive DNA content, suggests that the differentiation or emergence of sex chromosomes occurred independently in the genus Phyllurus. Furthermore, our study unveils a case of spontaneous triploidy in a fully grown individual of Saltuarius cornutus (3n = 57) and explores its implications for reproduction in carphodactylid geckos. We revealed that most carphodactylids retain the putative ancestral gekkotan karyotype of 2n = 38, characterized by predominantly acrocentric chromosomes that gradually decrease in size. If present, biarmed chromosomes emerge through pericentric inversions, maintaining the chromosome (and centromere) numbers. However, Phyllurus platurus is a notable exception, with a karyotype of 2n = 22 chromosomes. Its eight pairs of biarmed chromosomes were probably formed by Robertsonian fusions of acrocentric chromosomes. The family underscores a remarkable instance of evolutionary stability in chromosome numbers, followed by a profound transformation through parallel interchromosomal rearrangements. Our study highlights the need to continue generating cytogenetic data in order to test long-standing ideas about reproductive biology and the evolution of genome and sex determination.

Oxidative stress resistance prompts pyrroloquinoline quinone biosynthesis in Hyphomicrobium denitrificans H4-45

Pyrroloquinoline quinone (PQQ) is a natural antioxidant with diverse applications in food and pharmaceutical industries. A lot of effort has been devoted toward the discovery of PQQ high-producing microbial species and characterization of biosynthesis, but it is still challenging to achieve a high PQQ yield. In this study, a combined strategy of random mutagenesis and adaptive laboratory evolution (ALE) with fermentation optimization was applied to improve PQQ production in Hyphomicrobium denitrificans H4-45. A mutant strain AE-9 was obtained after nearly 400 generations of UV-LiCl mutagenesis, followed by an ALE process, which was conducted with a consecutive increase of oxidative stress generated by kanamycin, sodium sulfide, and potassium tellurite. In the flask culture condition, the PQQ production in mutant strain AE-9 had an 80.4% increase, and the cell density increased by 14.9% when compared with that of the initial strain H4-45. Moreover, batch and fed-batch fermentation processes were optimized to further improve PQQ production by pH control strategy, methanol and H2O2 feed flow, and segmented fermentation process. Finally, the highest PQQ production and productivity of the mutant strain AE-9 reached 307 mg/L and 4.26 mg/L/h in a 3.7-L bioreactor, respectively. Whole genome sequencing analysis showed that genetic mutations in the ftfL gene and thiC gene might contribute to improving PQQ production by enhancing methanol consumption and cell growth in the AE-9 strain. Our study provided a systematic strategy to obtain a PQQ high-producing mutant strain and achieve high production of PQQ in fermentation. These practical methods could be applicable to improve the production of other antioxidant compounds with uncleared regulation mechanisms.Key points• Improvement of PQQ production by UV-LiCl mutagenesis combined with adaptive laboratory evolution (ALE) and fermentation optimization.• A consecutive increase of oxidative stress could be used as the antagonistic factor for ALE to enhance PQQ production.• Mutations in the ftfL gene and thiC gene indicated that PQQ production might be increased by enhancing methanol consumption and cell growth.

Biocatalytic strategy for the construction of sp3-rich polycyclic compounds from directed evolution and computational modelling.

Catalysis with engineered enzymes has provided more efficient routes for the production of active pharmaceutical agents. However, the potential of biocatalysis to assist in early-stage drug discovery campaigns remains largely untapped. In this study, we have developed a biocatalytic strategy for the construction of sp3-rich polycyclic compounds via the intramolecular cyclopropanation of benzothiophenes and related heterocycles. Two carbene transferases with complementary regioisomer selectivity were evolved to catalyse the stereoselective cyclization of benzothiophene substrates bearing diazo ester groups at the C2 or C3 position of the heterocycle. The detailed mechanisms of these reactions were elucidated by a combination of crystallographic and computational analyses. Leveraging these insights, the substrate scope of one of the biocatalysts could be expanded to include previously unreactive substrates, highlighting the value of integrating evolutionary and rational strategies to develop enzymes for new-to-nature transformations. The molecular scaffolds accessed here feature a combination of three-dimensional and stereochemical complexity with 'rule-of-three' properties, which should make them highly valuable for fragment-based drug discovery campaigns.

Generating Cheap Representative Functions for Expensive Automotive Crashworthiness Optimization

Solving real-world engineering optimization problems, such as automotive crashworthiness optimization, is extremely challenging, because the problem characteristics are oftentimes not well understood. Furthermore, typical hyperparameter optimization approaches that require a large function evaluation budget are computationally hindered, if the function evaluation is expensive, e.g., requires finite element (FE) simulation runs. In this paper, we propose an approach to characterize real-world expensive black-box optimization problems using the exploratory landscape analysis (ELA). Based on these landscape characteristics, we can identify test functions that are fast-to-evaluate and representative for hyperparameter optimization purposes. Focusing on 20 problem instances from automotive crashworthiness optimization, our results reveal that these 20 crashworthiness problems exhibit landscape features different from classical optimization benchmark test suites, such as the widely-used black-box optimization benchmarking (BBOB) problem set. In fact, these 20 problem instances belong to problem classes that are distinct from the BBOB test functions based on the clustering results. Further analysis indicates that, as far as the ELA features concern, they are most similar to problem classes of tree-based test functions. By analyzing the performance of two optimization algorithms with different hyperparameters, namely the covariance matrix adaptation evolutionary strategy (CMA-ES) and Bayesian optimization (BO), we show that the tree-based test functions are indeed representative in terms of predicting the algorithm performances. Following this, such scalable and fast-to-evaluate tree-based test functions have promising potential for automated design of an optimization algorithm for specific real-world problem classes.

Asymmetric evolution of ISG15 homologs and the immune adaptation to LBUSV infection in spotted seabass (Lateolabrax maculatus)

The battle between host and viral is ubiquitous across all ecosystems. Despite this, research is scarce on the antiviral characteristics of fish, particularly in those that primarily rely on innate immune responses. This study, comprehensively explored the genetic and antiviral features of ISG15 in spotted seabass, focusing on its response to largemouth bass ulcerative syndrome virus (LBUSV). Through whole-genome BLAST and PCR cloning, two ISG15 homologs, namely LmISG15a and LmISG15b, were identified in spotted seabass, both encoding highly conserved proteins. However, a distinctive contrast emerged in their expression patterns, with LmISG15a exhibiting high expression in immune organs while LmISG15b remained largely silent across various organs. Regulatory elements analysis indicated an asymmetric evolution of the two ISG15s, with the minimal expression of LmISG15b may attribute to the loss of a necessary ISRE and an additional instability "ATTTA" motif. Association analysis demonstrated a significant correlation between LmISG15a expression and LBUSV infection. Subsequent antiviral activity detection revealed that LmISG15a interacted with LBUSV, inhibiting its replication by activating ISGylation and downstream pro-inflammatory mediators. In summary, this study unveils a distinct evolutionary strategy of fish antiviral gene ISG15 and delineates its kinetic characteristics in response to LBUSV infection.

A three-player game model for promoting enterprise green technology innovation from the perspective of media coverage.

The objective of this study was to explore the game relationship among enterprise, the government, and the public under the new media environment, so as to provide decision-making reference for improving enterprise green technology innovation and promoting economy high-quality development with new media participation. This study constructs a three-subject evolutionary game model of enterprise, government, and public based on multi-agent relationship analysis and evolutionary game theory. In addition, the derivation of an evolutionary equilibrium strategy and numerical simulation analysis is carried out to comprehensively explore the evolution trajectory of green technology innovation system under the new media environment. (1) The system may have four stable evolutionary strategies: (1,0,0), (0,0,1), (1,0,1), and (1,1,1). (2) The initial strategy probability of various actors would affect the system evolution speed but not the evolution result, and the authenticity of new media reports is an important factor determining the system evolution of green technology innovation. (3) Numerical simulation results show that a fair and just new media environment can effectively constrain the traditional production behavior of enterprise, actively guide the public to participate in supervision, and play an alternative role to government regulation to a certain extent. This study explores the evolutionary balance strategy of green technology innovation system under the new media environment, which not only enriches relevant theories of media environment governance but also has important reference value for promoting enterprises' green technology innovation and establishing an environmental governance system jointly governed by government, enterprise, public, and media.

A study on the evolution of tripartite collaborative prevention and control under public health emergencies using COVID-19 as an example

The problem of repeated epidemic fluctuations in the normalized prevention and control stage is revealed by data from January 20, 2020, to January 30, 2023. In order to improve the collaborative response of the public and government departments to public health emergencies and avoid repeated fluctuations of the epidemic, a tripartite evolutionary game model of the public, local government, and central government departments is constructed, focusing on the evolutionary paths and evolutionary stabilization strategies of the three subjects, and the influence of each element on the evolutionary results is simulated by numerical simulation in Matlab, and based on the inadequacy of the static reward and punishment mechanism, a dynamic Based on the shortcomings of static reward and punishment mechanism, dynamic reward and punishment mechanism is introduced to control the stability of the evolving system. The study shows that (1) with the increase of the initial willingness of the three parties, the rate of the public choosing the discretionary flow strategy slows down, and the collaborative prevention and control process can be accelerated. (2) The reward and punishment mechanism of central government departments has a positive incentive effect on the local government's strict prevention and control and the public's conscious isolation. Appropriately increasing rewards, formulating reasonable subsidy strategies, and increasing penalties for violations are conducive to the overall optimization of the system, and the punishment mechanism is most sensitive to the regulation of the public's discretionary mobility behavior. (3) Government departments' prevention and control costs can influence their enthusiasm for strict prevention and control and real-time supervision. Reducing the human resources cost, time cost, and financial cost of prevention and control is conducive to government departments performing their duties more responsibly. (4) The static punishment mechanism fails to make timely adjustments according to the strategy choice of each actor. It cannot control the stability of the evolving system. In contrast, the dynamic punishment mechanism considers the punishment parameters to link the casual isolation rate with the lenient prevention and control rate, which can effectively control the system's fluctuating instability and is the system's stability control strategy. Finally, combining theoretical and simulation analysis, management suggestions are made for controlling repeated fluctuations of the epidemic in practice, and the research limitations of this paper are explained.

Recent Modification Strategies of MoS2 towards Electrocatalytic Hydrogen Evolution

Hydrogen production by the electrolysis of water is a green and efficient method, which is of great significance for achieving sustainable development. Molybdenum disulfide (MoS2) is a promising electrocatalyst for hydrogen evolution reaction (HER) due to its high electrochemical activity, low cost, and abundant reserves. In comparison to the noble metal Pt, MoS2 has poorer hydrogen evolution performance in water electrolysis. Therefore, further modifications of MoS2 need to be developed aiming at improving its catalytic performance. The present work summarizes the modification strategies that have been developed in the past three years on hydrogen evolution from water electrolysis by utilizing MoS2 as the electrocatalyst and following the two aspects of internal and external modifications. The former includes the strategies of interlayer spacing, sulfur vacancy, phase transition, and element doping, while the latter includes the heterostructure and conductive substrate. If the current gap in this paper’s focus on modification strategies for electrocatalytic hydrogen evolution in water electrolysis is addressed, MoS2 will perform best in acidic or alkaline media. In addition to that, the present work also discusses the challenges and future development directions of MoS2 catalysts.

Time–jerk optimal trajectory planning for industrial robots with coupled interpolation function selection

AbstractIn the contemporary field of optimal trajectory planning for industrial robots, it is customary to construct trajectories through the manual predefinition of interpolation functions. Unfortunately, this method frequently overlooks the influence of the interpolation function itself on the optimization objectives, resulting in suboptimal outcomes. To remedy this limitation, an optimal trajectory planning method with coupled interpolation function selection is proposed, in which the total task time and the integral squared jerk are defined as optimization objectives. This method minimizes the optimization objectives while also factoring in the optimal interpolation function, and avoiding subjective interference. To address the aforementioned biobjective optimization problem better, an Improved MultiObjective Golden Eagle Optimizer is introduced. Population diversity and the ability to escape local optima are enhanced through the incorporation of Chaotic Mapping, Opposition‐Based Learning, Differential Evolution, and adaptive inertia weight strategy into the algorithm. The superiority of the algorithm is validated through a series of simulations on 17 benchmark functions. In the context of the robotic stirring operation within the automated block cast charging process, the proposed method is utilized to derive the time–jerk optimal trajectory. The results demonstrate the effectiveness of the proposed method.

Incentive policy for the comprehensive development of young medical talents: an evolutionary game study.

Currently in China, there is a lack of well-defined and viable incentive mechanisms at the governmental and hospital levels to support the development of young medical talents, thereby hindering their growth Existing studies primarily investigate the current state and trajectory of incentives, yet they inadequately address the distinctive characteristics of various stakeholders involved in medical talent incentive processes, particularly the lack of research on incentive mechanisms with Chinese attributes. This study adopts evolutionary game theory to investigate the dynamics of replication and the strategies for achieving evolutionary stability in the comprehensive development of young medical talents, considering both scenarios with and without supportive policies. In the absence of any supportive policy measures, the evolutionarily stable strategy (ESS) point is O(0,0), the unstable equilibrium point is C(1,1), and the saddle points are A(0,1), B(1,0). The initial state of the system is at the unstable equilibrium point C(1,1), which means that the young medical talents and medical institutions adopt a combination of strategies (actively seeking comprehensive development and taking incentive measures). Under the scenario with supportive policies, the ESS point is C(1,1), the unstable equilibrium point is O(0,0), and the saddle points are A(0,1), B(1,0). The initial state of the system is at the unstable equilibrium point O(0,0), which means that young medical talents and medical institutions adopt (N,N) strategy combinations (inactively seeking comprehensive development, implementing no incentive measure). (1) Government incentives play a crucial role in motivating young medical talents to seek comprehensive development. (2) The level of government incentive support for young medical talents should exceed the cost increment of individual efforts. Additionally, the policy support provided by the government to medical institutions should surpass the incentive support offered by these institutions to young medical talents. This will enhance the motivation and encouragement efforts of medical institutions in actively promoting comprehensive development among young medical talents. (3) With the backing of certain government incentive policies, medical institutions implementing incentive measures and young medical talents actively seeking comprehensive development will establish a virtuous cycle of mutual promotion.