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Cognitive diagnosis models (CDMs) have been used as psychometric tools in educational assessments to estimate students' strengths and weaknesses in terms of cognitive skills learned and skills that need study. In practice, it is not uncommon that questions can often be solved using more than one strategy, which requires CDMs capable of accommodating multiple strategies. However, existing parametric multi-strategy CDMs need a large sample size to produce a reliable estimation of item parameters and examinees' proficiency class memberships, which obstructs their practical applications. This article proposes a general nonparametric multi-strategy classification method with promising classification accuracy in small samples for dichotomous response data. The method can accommodate different strategy selection approaches and different condensation rules. Simulation studies showed that the proposed method outperformed the parametric CDMs when sample sizes were small. A set of real data was analyzed as well to illustrate the application of the proposed method in practice.

Evolutionary game dynamics of two players with two strategies has been studied in great detail. These games have been used to model many biologically relevant scenarios, ranging from social dilemmas in mammals to microbial diversity. Some of these games may, in fact, take place between a number of individuals and not just between two. Here we address one-shot games with multiple players. As long as we have only two strategies, many results from two-player games can be generalized to multiple players. For games with multiple players and more than two strategies, we show that statements derived for pairwise interactions no longer hold. For two-player games with any number of strategies there can be at most one isolated internal equilibrium. For any number of players Formula with any number of strategies Formula , there can be at most Formula isolated internal equilibria. Multiplayer games show a great dynamical complexity that cannot be captured based on pairwise interactions. Our results hold for any game and can easily be applied to specific cases, such as public goods games or multiplayer stag hunts.

Meta-heuristic algorithms are among the technologies that have good performance in multilevel threshold image segmentation by obtaining optimal thresholds. However, most studies in the literature consider either a single objective function or images of a single type or low threshold levels, due to the drawbacks of poor ability to balance global and local search, premature convergence in high dimension, or low convergence efficiency of existing work in handling multi-task image segmentation. This paper aims to address these drawbacks and to develop search mechanisms and an enhanced optimizer for multilevel threshold image segmentation considering simultaneously different objective functions, both grayscale and color images, and both low and high threshold levels. More precisely, to improve the capability of balancing between global exploration and local exploitation, firstly a novel search mechanism ASSM inspired by the salp swarm optimization algorithm (SSA) is proposed, which is shown to have universality in improving a class of swarm intelligence optimization algorithms called DP-algorithms. Then, by proposing hierarchical vertical-horizontal search (HVHS) strategy and combining it with improved circle chaotic mapping initialization, lens opposition-based learning, and Lévy flight strategy, a multi-strategy collaborative ENCOA framework is constructed to prevent premature convergence in high-dimensional solution space. To evaluate the performance of the ENCOA, comparison experiments are implemented on CEC2017 benchmark suite and four engineering problems. Finally, the ENCOA is applied to multilevel threshold image segmentation on 6 grayscale images and 4 color images, by taking both Kapur’s entropy and Otsu between-class variance as the objective functions, and under threshold levels ranging from 4 to 32. It is shown that the ENCOA outperforms other recent-related algorithms in terms of both convergence accuracy and segmentation quality, especially when dealing with high threshold segmentation.

To address the limitations of the standard equilibrium optimizer (EO) in terms of insufficient optimization capability, multiple strategies are proposed to enhance its performance. These include a reverse equilibrium state pool, a non-uniform equilibrium state selection strategy, and an equilibrium state mutation strategy. The reverse equilibrium state pool is introduced to encourage candidate solutions with poorer positions to search in a wider search space, under such considerations the global search ability of the improved EO can be enhanced. The non-uniform equilibrium state selection strategy is proposed to select equilibrium state. Under the proposed selection strategy, the candidate solutions with better positions are more likely to be chosen as the equilibrium state, allowing for sufficient exploration of positions near the current optimal point. The equilibrium state mutation strategy leads to cross mutation between candidate solutions and equilibrium state, increasing the likelihood of the group exploring the global optimal solution. To verify and further analyze the performance and superiority of the improved EO, i.e., reverse equilibrium states EO (R O), 29 benchmark functions are adopted. It is verified theoretically from the experimental results that the R O is with a significant improvement in performance by comparison between the standard EO and certain frequently-used heuristic optimization algorithms. Finally, the R O is successfully applied in path planning for surface marine vehicles under the situations of both dynamic and static obstacles.

The balance between the exploration and the exploitation plays a significant role in the meta-heuristic algorithms, especially when they are used to solve large-scale optimization problems. In this paper, we propose a multiple-strategy learning particle swarm optimization algorithm, called MSL-PSO, to solve problems with large-scale variables, in which different learning strategies are utilized in different stages. At the first stage, each individual tries to probe some positions by learning from the demonstrators who have better performance on the fitness value and the mean position of the population. All the best probed positions, each of which has the best fitness among all positions probed by its corresponding individual, will compose a new temporary population. The temporary population will be sorted on the fitness values in a descending order, and will be used for each individual to find its demonstrators, which is based on the rank of the best probed solution in the temporary population and the rank of the individual in the current population, to learn using a new strategy in the second stage. The first stage is used to improve the exploration capability, and the second one is expected to balance the convergence and diversity of the population. To verify the effectiveness of MSL-PSO for solving large-scale optimization problems, some empirical experiments are conducted, which include CEC2008 problems with 100, 500, and 1000 dimensions, and CEC2010 problems with 1000 dimensions. Experimental results show that our proposed MSL-PSO is competitive or has a better performance compared with ten state-of-the-art algorithms.

Abstract This paper proposes 16 enhanced aquila optimizers with multiple strategies and applies them to the CEC2022 benchmark functions and six classic engineering application problems. The experimental comparative analysis results show that the performance of the random walk aquila optimizer (RWAO) and the crisscross aquila optimizer (CCAO) is significantly better than that of other enhanced aquila optimizers. Moreover, by comparing RWAO with over 10 existing powerful optimization techniques, it was found that RWAO has significant competitiveness. The Wilcoxon rank sum test results also proved that the RWAO and CCAO algorithms have significant differences from the basic aquila optimizer (AO), and the RWAO algorithm outperformed all the other enhanced aquila optimizers in optimizing engineering design problems. The experimental results show that the random walk and the crossover strategies can significantly enhance the optimization performance of the basic AO. The method presented in this paper has high reference value for improving the performance of other metaheuristic optimization algorithms. The detailed code publish website is https://ww2.mathworks.cn/matlabcentral/fileexchange/180254-the-sixteen-strategies-to-enhanced-ao-algorithms. Graphical Abstract Graphical Abstract

A bat algorithm (BA) is a heuristic algorithm that operates by imitating the echolocation behavior of bats to perform global optimization. The BA is widely used in various optimization problems because of its excellent performance. In the bat algorithm, the global search capability is determined by the parameter loudness and frequency. However, experiments show that each operator in the algorithm can only improve the performance of the algorithm at a certain time. In this paper, a novel bat algorithm with multiple strategies coupling (mixBA) is proposed to solve this problem. To prove the effectiveness of the algorithm, we compared it with CEC2013 benchmarks test suits. Furthermore, the Wilcoxon and Friedman tests were conducted to distinguish the differences between it and other algorithms. The results prove that the proposed algorithm is significantly superior to others on the majority of benchmark functions.

Background Acarbose, as an alpha-glucosidase inhibitor, is widely used clinically to treat type II diabetes. In its industrial production, Actinoplanes sp. SE50/110 is used as the production strain. Lack of research on its regulatory mechanisms and unexplored gene targets are major obstacles to rational strain design. Here, transcriptome sequencing was applied to uncover more gene targets and rational genetic engineering was performed to increase acarbose production. Results In this study, with the help of transcriptome information, a TetR family regulator ( TetR1 ) was identified and confirmed to have a positive effect on the synthesis of acarbose by promoting the expression of acbB and acbD . Some genes with low expression levels in the acarbose biosynthesis gene cluster were overexpressed and this resulted in a significant increase in acarbose yield. In addition, the regulation of metabolic pathways was performed to retain more glucose-1-phosphate for acarbose synthesis by weakening the glycogen synthesis pathway and strengthening the glycogen degradation pathway. Eventually, with a combination of multiple strategies and fed-batch fermentation, the yield of acarbose in the engineered strain increased 58% compared to the parent strain, reaching 8.04 g/L, which is the highest fermentation titer reported. Conclusions In our research, acarbose production had been effectively and steadily improved through genetic engineering based on transcriptome analysis and fed-batch culture strategy. Graphical Abstract

This study investigated how 22 college-level Chinese as a second language (CSL) learners used reading strategies when reading essays of various genres in a strategies-based reading instruction program, in which they were explicitly taught ten top-down reading strategies. In addition to strategy use frequency and preference, this study explored the interrelationships among multiple strategies and whether strategy use frequency correlated with reading proficiency. Qualitative and quantitative data analyses revealed that the most frequently used strategies included and . They were used in combination with other strategies in an orchestrated way. This study did not find a significant correlation between reading proficiency and total strategy use frequency or the frequency of using any single strategy. Strategy use frequency alone, without considering strategy use accuracy and appropriateness, might not be a good indicator of reading proficiency. This study provides an in-depth analysis of how CSL readers used single strategies and blended multiple strategies. Its findings shed light on second language learners’ reading process, reading difficulties, and the rationales behind their strategy use. Pedagogical implications are provided for CSL teachers regarding how to embed explicit strategy training into reading classes.

The taxonomic classification of viral sequences is frequently used for the rapid identification of pathogens, which is a key point for when a viral outbreak occurs. Both Oxford Nanopore Technologies (ONT) MinION and the Illumina (NGS) technology provide efficient methods to detect viral pathogens. Despite the availability of many strategies and software, matching them can be a very tedious and time-consuming task. As a result, we developed PIMGAVir and Vir-MinION, two metagenomics pipelines that automatically provide the user with a complete baseline analysis. The PIMGAVir and Vir-MinION pipelines work on 2nd and 3rd generation data, respectively, and provide the user with a taxonomic classification of the reads through three strategies: assembly-based, read-based, and clustering-based. The pipelines supply the scientist with comprehensive results in graphical and textual format for future analyses. Finally, the pipelines equip the user with a stand-alone platform with dedicated and various viral databases, which is a requirement for working in field conditions without internet connection.