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Feature selection, widely used in data preprocessing, is a challenging problem as it involves hard combinatorial optimization. So far some meta-heuristic algorithms have shown effectiveness in solving hard combinatorial optimization problems. As the arithmetic optimization algorithm only performs well in dealing with continuous optimization problems, multiple binary arithmetic optimization algorithms (BAOAs) utilizing different strategies are proposed to perform feature selection. First, six algorithms are formed based on six different transfer functions by converting the continuous search space to the discrete search space. Second, in order to enhance the speed of searching and the ability of escaping from the local optima, six other algorithms are further developed by integrating the transfer functions and Lévy flight. Based on 20 common University of California Irvine (UCI) datasets, the performance of our proposed algorithms in feature selection is evaluated, and the results demonstrate that BAOA_S1LF is the most superior among all the proposed algorithms. Moreover, the performance of BAOA_S1LF is compared with other meta-heuristic algorithms on 26 UCI datasets, and the corresponding results show the superiority of BAOA_S1LF in feature selection. Source codes of BAOA_S1LF are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/124545-binary-arithmetic-optimization-algorithm

As one novel meta-heuristic algorithm, African Vultures Optimization Algorithm (AVOA) has been proved to be efficient in solving continuous optimization problems. However, many real-world optimization problems are in the discrete form, and the continuous characteristics of AVOA make it unsuitable for solving discrete optimization problems. Therefore, this article proposes Binary African Vultures Optimization Algorithm (BAVOA) to solve various optimization problems, especially discrete optimization problems. In BAVOA, the X-shaped transfer function is firstly adopted to convert the continuous search space into the binary search space, and then the opposition-based learning strategy and the improved multi-elite strategy are utilized to enhance the optimization ability of BAVOA. Moreover, the performance of BAVOA is evaluated by twenty-three benchmark functions with the relevant Wilcoxon rank sum tests, and the effectiveness of BAVOA is demonstrated by four engineering design problems and one combinational optimization problem. The results demonstrate that BAVOA outperforms eight well-known algorithms in addressing various optimization problems. Source codes of BAVOA are publicly available at: https://www.mathworks.com/matlabcentral/fileexchange/115350-binary-african-vultures-optimization-algorithm

Air quality prediction plays an important role in preventing air pollution and improving living environment. For this prediction, many indicators can be employed to reflect the air quality, among which air quality index (AQI) is the most commonly used. However, existing methods are relatively simple and the corresponding prediction accuracy needs to be improved. Particularly, the prediction accuracy is affected by the parameter selection of methods, and the corresponding optimization problems are usually non-convex and multi-modal. Therefore, based on long short-term memory (LSTM) neural network with improved jellyfish search optimizer (IJSO), a novel hybrid model denoted by IJSO-LSTM is proposed to predict AQI for Chengdu. In order to evaluate the optimizing ability of IJSO, other variants of jellyfish search optimizer as well as other state-of-the-art meta-heuristic algorithms are applied to optimize the hyperparameters of LSTM neural network for comparison, and the results confirm that IJSO is more suitable for optimizing LSTM neural network. In addition, compared with other well-known models, the results demonstrate IJSO-LSTM has higher prediction accuracy with root-mean-square error, mean absolute error, and mean absolute percentage error controlling below 4, 3, and 4%, respectively.

IF you had one million RMB to rent a shop front in an emergent commercial district, what sort of business would you choose？ was the questiou contestants in the International Business Contest for College Students. held by OVAL （Our Vision for Asian Leaders） China. were asked. OVAL is an international nonprofit student organization, A total of 90 college students from China. South Korea and Japan, 30 from each country, participated in its eight-day contest.

Solar and wind resources are vital for the sustainable energy transition. Although renewable potentials have been widely assessed in existing literature, few studies have examined the statistical characteristics of the inherent renewable uncertainties arising from natural randomness, which is inevitable in stochastic-aware research and applications. Here we develop a rule-of-thumb statistical learning model for wind and solar power prediction and generate a year-long dataset of hourly prediction errors of 30 provinces in China. We reveal diversified spatiotemporal distribution patterns of prediction errors, indicating that over 60% of wind prediction errors and 50% of solar prediction errors arise from scenarios with high utilization rates. The first-order difference and peak ratio of generation series are two primary indicators explaining the uncertainty distribution. Additionally, we analyze the seasonal distributions of the provincial prediction errors that reveal a consistent law in China. Finally, policies including incentive improvements and interprovincial scheduling are suggested. Renewable uncertainty analysis is vital for stochastic-aware research. This study generates a benchmark dataset of year-long hourly renewable prediction errors in China, and reveals the law of the spatiotemporal distribution of renewable uncertainty.

The goal of replenishing the cardiomyocyte (CM) population using regenerative therapies following myocardial infarction (MI) is hampered by the limited regeneration capacity of adult CMs, partially due to their withdrawal from the cell cycle. Here, we show that microRNA-128 ( miR-128 ) is upregulated in CMs during the postnatal switch from proliferation to terminal differentiation. In neonatal mice, cardiac-specific overexpression of miR-128 impairs CM proliferation and cardiac function, while miR-128 deletion extends proliferation of postnatal CMs by enhancing expression of the chromatin modifier SUZ12, which suppresses p27 (cyclin-dependent kinase inhibitor) expression and activates the positive cell cycle regulators Cyclin E and CDK2. Furthermore, deletion of miR-128 promotes cell cycle re-entry of adult CMs, thereby reducing the levels of fibrosis, and attenuating cardiac dysfunction in response to MI. These results suggest that miR-128 serves as a critical regulator of endogenous CM proliferation, and might be a novel therapeutic target for heart repair. During early postnatal development in mammals, cardiomyocytes exit the cell cycle, losing their regenerative capacity. Here the authors show that, following myocardial infarction, loss of microRNA-128 promotes cardiomyocyte proliferation and cardiac regeneration in adult mice partly via enhancing the expression of the chromatin modifier SUZ12.

Plasma hyaluronan (HA) increases systemically in type 2 diabetes (T2D) and the HA synthesis inhibitor, 4-Methylumbelliferone, has been proposed to treat the disease. However, HA is also implicated in normal physiology. Therefore, we generated a Hyaluronan Synthase 2 transgenic mouse line, driven by a tet-response element promoter to understand the role of HA in systemic metabolism. To our surprise, adipocyte-specific overproduction of HA leads to smaller adipocytes and protects mice from high-fat-high-sucrose-diet-induced obesity and glucose intolerance. Adipocytes also have more free glycerol that can be released upon beta3 adrenergic stimulation. Improvements in glucose tolerance were not linked to increased plasma HA. Instead, an HA-driven systemic substrate redistribution and adipose tissue-liver crosstalk contributes to the systemic glucose improvements. In summary, we demonstrate an unexpected improvement in glucose metabolism as a consequence of HA overproduction in adipose tissue, which argues against the use of systemic HA synthesis inhibitors to treat obesity and T2D. Hyaluronan is a naturally occurring linear polysaccharide that together with collagens, enzymes, and glycoproteins forms the extracellular matrix. Here the authors show that adipose tissue overproduction of Hyaluronan reduces fat accumulation in mice fed high-fat diet and improves systemic glucose homeostasis.

Machine learning-based generative models can generate novel molecules with desirable physiochemical and pharmacological properties from scratch. Many excellent generative models have been proposed, but multi-objective optimizations in molecular generative tasks are still quite challenging for most existing models. Here we proposed the multi-constraint molecular generation (MCMG) approach that can satisfy multiple constraints by combining conditional transformer and reinforcement learning algorithms through knowledge distillation. A conditional transformer was used to train a molecular generative model by efficiently learning and incorporating the structure–property relations into a biased generative process. A knowledge distillation model was then employed to reduce the model’s complexity so that it can be efficiently fine-tuned by reinforcement learning and enhance the structural diversity of the generated molecules. As demonstrated by a set of comprehensive benchmarks, MCMG is a highly effective approach to traverse large and complex chemical space in search of novel compounds that satisfy multiple property constraints. Combining generative models and reinforcement learning has become a promising direction for computational drug design, but it is challenging to train an efficient model that produces candidate molecules with high diversity. Jike Wang and colleagues present a method, using knowledge distillation, to condense a conditional transformer model to make it usable in reinforcement learning while still generating diverse molecules that optimize multiple molecular properties.

The basic helix-loop-helix (bHLH) transcription factors play crucial roles in various processes, such as plant development, secondary metabolism, and response to biotic/abiotic stresses. Sophora flavescens is a widely used traditional herbal medicine in clinical practice, known for its abundant flavonoids as the main active compounds. However, there has been no comprehensive analysis of S. flavescens bHLH ( SfbHLH ) gene family reported currently. In this study, we identified 167 SfbHLH genes and classified them into 23 subfamilies based on comparative genomics and phylogenetic analysis. Furthermore, widespread duplications significantly contributed to the expansion of SfbHLH family. Notably, SfbHLH042 was found to occupy a central position in the bHLH protein-protein interaction network. Transcriptome analysis of four tissues (leaf, stem, root and flower) revealed that most SfbHLH genes exhibited high expression levels exclusively in specific tissues of S. flavescens . The integrated analysis of transcriptomics and metabolomics during pod development stages revealed that SfbHLH042 may play a central role in connecting SfbHLH genes, flavonoids, and key enzymes involved in the biosynthesis pathway. Moreover, we also checked the expression of 8 SfbHLH genes using RT-qPCR analysis to realize the expression profiles of these genes among various tissues at different cultivated periods and root development. Our study would aid to understand the phylogeny and expression profile of SfbHLH family genes, and provide a promising candidate gene, SfbHLH042 , for regulating the biosynthesis of flavonoids in S. flavescens .

The high-temperature lead-bismuth eutectic (LBE) corrosion resistance and ring compression performance of the Fe15Cr11Al2Si, Fe15Cr11Al0.5Y, and Fe15Cr11Al2Si0.5Y coatings were investigated. Even if the corrosion test temperature reaches 800 °C, all these coatings can effectively protect the steel cladding tube. After the corrosion test temperature exceeded 660 °C, an obvious Al-rich oxide layer was formed on the surface of the coating, and Al element enrichment occurred at the interface between the coating and the substrate. After the corrosion test at 800 °C, holes appeared in the thick interface layer of the Fe15Cr11Al2Si0.5Y coating. The Fe15Cr11Al2Si coating cracked after the ring compression test with a deformation rate of 3%, and the coating peeled off after the deformation rate reached 5%. When the deformation rate reached 5%, there was still no cracking in the Fe15Cr11Al0.5Y coating. When the deformation rate reached 30%, the coating cracked, but the cracked coating was still tightly bonded with the substrate. The Fe15Cr11Al2Si0.5Y coating has the worst compression performance, even if the deformation rate is 1%, the coating still peels off obviously. The underlying mechanism for the evolution of corrosion resistance and compression performance was discussed.