A new systematic firefly algorithm for forecasting the durability of reinforced recycled aggregate concrete

This study presents a new systematic algorithm to optimize the durability of reinforced recycled aggregate concrete. The proposed algorithm integrates machine learning with a new version of the firefly algorithm called chaotic based firefly algorithm (CFA) to evolve a rational and efficient predictive model. The CFA optimizer is augmented with chaotic maps and Lévy flight to improve the firefly performance in forecasting the chloride penetrability of strengthened recycled aggregate concrete (RAC). A comprehensive and credible database of distinctive chloride migration coefficient results is used to establish the developed algorithm. A dataset composite of nine effective parameters, including concrete components and fundamental characteristics of recycled aggregate (RA), is used as input to predict the migration coefficient of strengthened RAC as output. k-fold cross validation algorithm is utilized to validate the hybrid algorithm. Three numerical benchmark analyses are applied to prove the superiority and applicability of the CFA algorithm in predicting chloride penetrability. Results show that the developed CFA approach significantly outperforms the firefly algorithm on almost tested functions and demonstrates powerful prediction. In addition, the proposed strategy can be an active tool to recognize the contradictions in the experimental results and can be especially beneficial for assessing the chloride resistance of RAC.     

Application of robust machine learning methods to modeling hydrogen solubility in hydrocarbon fuels

Having accurate information about the hydrogen solubility in hydrocarbon fuels and feedstocks is very important in petroleum refineries and coal processing plants. In the present work, extreme gradient boosting (XGBoost), multi-layer perceptron (MLP) trained with Levenberg–Marquardt (LM) algorithm, adaptive boosting support vector regression (AdaBoost?SVR), and a memory-efficient gradient boosting tree system on adaptive compact distributions (LiteMORT) as four novel machine learning methods were used for estimating the hydrogen solubility in hydrocarbon fuels. To achieve this goal, a database containing 445 experimental data of hydrogen solubilities in 17 various hydrocarbon fuels/feedstocks was collected in wide-spread ranges of operating pressures and temperatures. These hydrocarbon fuels include petroleum fractions, refinery products, coal liquids, bitumen, and shale oil. Input parameters of the models are temperature and pressure along with density at 20 °C, molecular weight, and weight percentage of carbon (C) and hydrogen (H) of hydrocarbon fuels. XGBoost showed the highest accuracy compared to the other models with an overall mean absolute percent relative error of 1.41% and coefficient of determination (R²) of 0.9998. Also, seven equations of state (EOSs) were used to predict hydrogen solubilities in hydrocarbon fuels. The 2- and 3-parameter Soave-Redlich-Kwong EOS rendered the best estimates for hydrogen solubilities among the EOSs. Moreover, sensitivity analysis indicated that pressure owns the highest influence on hydrogen solubilities in hydrocarbon fuels and then temperature and hydrogen weight percent of the hydrocarbon fuels are ranked, respectively. Finally, Leverage approach results exhibited that the XGBoost model could be well trusted to estimate the hydrogen solubility in hydrocarbon fuels.     

Hydrangea like composite catalysts of ultrathin Mo2S3 nanosheets assembled on N,S-dual-doped graphitic biocarbon spheres with highly electrocatalytic activity for HER

Water electrolysis is the most clean and high-efficiency technology for production of hydrogen, an ultimate clean energy in future. Highly efficient non-noble electrocatalysts for hydrogen evolution reaction (HER) are desirable for large scale production of hydrogen by water electrolysis. Especially, exposing as many active sites as possible is a vital way to improve activities of the catalysts. Herein, a series of new hydrangea like composite catalysts of ultrathin Mo₂S₃ nanosheets assembled uprightly and interlacedly on N, S-dual-doped graphitic biocarbon spheres were facilely prepared. The unique structure endowed the catalysts highly exposed edge active sites and prominently high activities for HER. Especially, the optimized catalyst Mo₂S₃/NSCS-50 exhibited as low as 106 mV of overpotential at 10 mA/cm² (denoted as ?₁₀). The catalyst also showed low Tafel slope of 53 mV/dec, low electron transfer resistance of 34 Ω and high stability evidenced by the result that the current density only attenuated 11.7% after 10 h i-t test. The catalyst has shown broad prospect for commercial application in water electrolysis.     

机器翻译译文质量估计综述

机器翻译译文质量估计(Quality Estimation,QE)是指在不需要人工参考译文的条件下,估计机器翻译系统产生的译文的质量,对机器翻译研究和应用具有很重要的价值。机器翻译译文质量估计经过最近几年的发展,取得了丰富的研究成果。该文首先介绍了机器翻译译文质量估计的背景与意义;然后详细介绍了句子级QE、单词级QE、文档级QE的具体任务目标、评价指标等内容,进一步概括了QE方法发展的三个阶段: 基于特征工程和机器学习的QE方法阶段,基于深度学习的QE方法阶段,融入预训练模型的QE方法阶段,并介绍了每一阶段中的代表性研究工作;最后分析了目前的研究现状及不足,并对未来QE方法的研究及发展方向进行了展望。     

Neural network-based flight control systems: Present and future

As the first review in this field, this paper presents an in-depth mathematical view of Intelligent Flight Control Systems (IFCSs), particularly those based on artificial neural networks. The rapid evolution of IFCSs in the last two decades in both the methodological and technical aspects necessitates a comprehensive view of them to better demonstrate the current stage and the crucial remaining steps towards developing a truly intelligent flight management unit. To this end, in this paper, we will provide a detailed mathematical view of Neural Network (NN)-based flight control systems and the challenging problems that still remain. The paper will cover both the model-based and model-free IFCSs. The model-based methods consist of the basic feedback error learning scheme, the pseudocontrol strategy, and the neural backstepping method. Besides, different approaches to analyze the closed-loop stability in IFCSs, their requirements, and their limitations will be discussed in detail. Various supplementary features, which can be integrated with a basic IFCS such as the fault-tolerance capability, the consideration of system constraints, and the combination of NNs with other robust and adaptive elements like disturbance observers, would be covered, as well. On the other hand, concerning model-free flight controllers, both the indirect and direct adaptive control systems including indirect adaptive control using NN-based system identification, the approximate dynamic programming using NN, and the reinforcement learning-based adaptive optimal control will be carefully addressed. Finally, by demonstrating a well-organized view of the current stage in the development of IFCSs, the challenging issues, which are critical to be addressed in the future, are thoroughly identified. As a result, this paper can be considered as a comprehensive road map for all researchers interested in the design and development of intelligent control systems, particularly in the field of aerospace applications.     

Prediction of fish quality level with machine learning

In this study, sea bream, sea bass, anchovy and trout were captured and recorded using a digital camera during refrigerated storage for 7 days. In addition, their total viable counts (TVC) were determined on a daily basis. Based on the TVC, each fish was classified as ‘fresh’ when it was <5 log cfu per g, and as ‘not fresh’ when it was >7 log cfu per g. They were uploaded on a web-based machine learning software called Teachable Machine (TM), which was trained about the pupils and heads of the fish. In addition, images of each species from different angles were uploaded to the software in order to ensure the recognition of fish species by TM. The data of the study indicated that the TM was able to distinguish fish species with high accuracy rates and achieved over 86% success in estimating the freshness of the fish species tested.     

雪菊精油对希氏肠球菌产酪胺及相关基因表达的影响

探讨不同质量浓度雪菊精油对希氏肠球菌（Enterococcus hirae）N47产酪胺的影响机制。利用反转录实时定量聚合酶链式反应分析E. hirae在雪菊精油作用下酪氨酸脱羧途径相关基因的表达情况；利用高效液相色谱法检测不同质量浓度雪菊精油对E. hirae产酪胺的影响。并将E. hirae接入到含不同质量浓度雪菊精油的熏马肠中发酵，评估香肠pH值、菌落总数和酪胺积累量。结果表明：在E. hirae纯培养体系和熏马肠体系中，雪菊精油通过抑制微生物的生长和酪氨酸脱羧途径中tyr DC、tyr P基因的表达，降低酪胺的积累量（P＜0.05）。当雪菊精油添加量为1/2最小抑菌浓度（minimal inhibitory concentration，MIC）和MIC时，熏马肠中酪胺的含量分别为78.52 mg/kg和45.83 mg/kg，较对照组分别减少了64.72%和79.41%。     

基于乙酰胆碱酯酶和氧化应激研究海胆酮对阿尔茨海默症的作用机制

海胆酮是一种酮式类胡萝卜素，主要从海胆及藻类等海洋生物中提取。本文研究海胆酮对乙酰胆碱酯酶（acetylcholinesterase，AChE）的抑制作用，应用酶动力学、荧光光谱、圆二色光谱和分子对接技术研究海胆酮对AChE的抑制机理，并用淀粉样β蛋白片段25～35（amyloid beta-peptide 25-35，Aβ25-35）诱导大鼠肾上腺嗜铬细胞瘤细胞（PC12细胞）建立阿尔茨海默症（Alzheimer’s disease，AD）模型，研究海胆酮对AD细胞模型氧化应激损伤的作用。结果表明，海胆酮有很强的AChE抑制活性，其半抑制质量浓度为（16.29±0.97）μg/mL，抑制常数Ki为3.82 μg/mL，表现为竞争性抑制；海胆酮可诱导AChE二级结构改变，更容易与AChE活性中心氨基酸Ser200、His440、Trp84和Tyr121结合，阻碍底物碘代硫代乙酰胆碱（acetylthiocholine iodide，ATCI）与酶结合，从而引起酶活力降低。海胆酮能有效抑制Aβ25-35诱导PC12细胞的AChE活力，降低丙二醛含量，增加超氧化物歧化酶、过氧化氢酶和谷胱甘肽过氧化物酶活力，减轻Aβ25-35诱导的PC12细胞氧化应激损伤。本研究基于AChE和氧化应激阐明了海胆酮对AD的潜在作用机制，为海胆酮在功能食品、生物医药等领域的应用提供了数据支持和理论根据。     

Deterministic and probabilistic ship pitch prediction using a multi-predictor integration model based on hybrid data preprocessing,reinforcement learning and improved QRNN

The deterministic and probabilistic prediction of ship motion is important for safe navigation and stable real-time operational control of ships at sea. However, the volatility and randomness of ship motion, the non-adaptive nature of single predictors and the poor coverage of quantile regression pose serious challenges to uncertainty prediction, making research in this field limited. In this paper, a multi-predictor integration model based on hybrid data preprocessing, reinforcement learning and improved quantile regression neural network (QRNN) is proposed to explore the deterministic and probabilistic prediction of ship pitch motion. To validate the performance of the proposed multi-predictor integrated prediction model, an experimental study is conducted with three sets of actual ship longitudinal motions during sea trials in the South China Sea. The experimental results indicate that the root mean square errors (RMSEs) of the proposed model of deterministic prediction are 0.0254°, 0.0359°, and 0.0188°, respectively. Taking series #2 as an example, the prediction interval coverage probabilities (PICPs) of the proposed model of probability predictions at 90%, 95%, and 99% confidence levels (CLs) are 0.9400, 0.9800, and 1.0000, respectively. This study signifies that the proposed model can provide trusted deterministic predictions and can effectively quantify the uncertainty of ship pitch motion, which has the potential to provide practical support for ship early warning systems.