在线社交网络控制实验的现状与展望

在线社交网络已发展成为一个独特的电子生态系统，其应用深刻影响着人们生活的方方面面。由于在线社交网络特性复杂，分析在线社交网络形成和变化中的规律成为当前计算机科学、社会学和物理学的一项挑战。传统上，在线社交网络实证研究主要采用计算机辅助的被动数据获取和分析方式。近年来，在真实大规模在线社交网络上直接进行控制实验从而主动获取数据并开展分析研究的方式广受关注。评述了这一领域的研究进展，包括：社交网络控制实验的主要研究模式；控制实验方法在社交网络结构、信息传播、行为和心理学等领域取得的主要成果以及主要实验工具的适用条件和局限性。最后，展望了人工智能技术在社交网络控制实验中的应用潜力，分析了智能算法对降低实验成本和提高实验效率的作用。     

Review on the Use of Artificial Intelligence to Predict Fire Performance of Construction Materials and Their Flame Retardancy

The evaluation and interpretation of the behavior of construction materials under fire conditions have been complicated. Over the last few years, artificial intelligence (AI) has emerged as a reliable method to tackle this engineering problem. This review summarizes existing studies that applied AI to predict the fire performance of different construction materials (e.g., concrete, steel, timber, and composites). The prediction of the flame retardancy of some structural components such as beams, columns, slabs, and connections by utilizing AI-based models is also discussed. The end of this review offers insights on the advantages, existing challenges, and recommendations for the development of AI techniques used to evaluate the fire performance of construction materials and their flame retardancy. This review offers a comprehensive overview to researchers in the fields of fire engineering and material science, and it encourages them to explore and consider the use of AI in future research projects.     

人工智能在锂离子电池研发中的应用

锂离子电池已成为解决现代社会储能问题的最佳解决方案之一。然而,电池材料和器件开发都是复杂的多变量问题,传统的依赖研究人员进行实验的试错法在电池性能提升方面遇到了瓶颈。人工智能（AI）具有强大的高速、海量数据处理能力,是上述突破研究瓶颈的最具潜力的技术。其中,机器学习 (ML) 算法在评估多维数据变量和集合之间的组合关联方面的独特优势有望帮助研究人员发现不同因素之间的相互作用规律并阐明材料合成和设备制造的机制。本综述总结了锂离子电池传统研究方法遇到的各种挑战,并详细介绍了人工智能在电池材料研究、电池器件设计与制造、材料与器件表征、电池循环寿命与安全性评估等方面的应用。最重要的是,我们介绍了AI和ML在电池研究中面临的挑战,并讨论了它们应用的缺点和前景。我们相信,未来实验科学家、数学建模专家和AI专家之间更紧密的合作将极大地促进AI和ML方法用以解决传统方法难以克服的电池和材料问题。     

Computational modeling of Hg/Ni ions separation via MOF/LDH nanocomposite: Machine learning based modeling

Nowadays, sustainable supplement of water has recently been identified as a vital necessity due to the existence of limited drinkable water sources. To do this, various techniques are being developed to remove various types of pollutants from water/wastewater sources. Adsorption of common water pollutants using nanocomposite materials has been of great popularity in recent years due to its high efficiency. This paper aims to develop various models based on machine learning approach to study their efficiency on predicting the experimentally measured results of Hg/Ni ions removal from water sources. To do this, this study attempts regression on a small data set using two parameters as inputs and two parameters as outputs. In this dataset, the inputs are Ion and C0, and the outputs are Ce and Qe. AdaBoost (Adaptive Boosting), a well-known ensemble method, was applied on top of three different models, including Decision Tree Regression (DT), Gaussian Process Regression (GPR), and Linear Regression (LR). After fine-tuning their hyper-parameters, the optimized model was evaluated through various metrics. For example, the R² for ADA + GPR model has a score of 0.998 for Ce and 0.999 for Qe as the best model among these three models. This model in RMSE is the best and illustrates 0.1512 and 1.490 for Ce and Qe as error. Eventually, ADA + GPR has been selected as the optimized model with optimized dataset: (Ion = Ni, C0 = 250, Ce = 206.0). But for Qe, different amounts are illustrated: (Ion = Hg, C0 = 106.7, Ce = 577.35)     

Artificial intelligence-based microfluidic platforms for the sensitive detection of environmental pollutants: Recent advances and prospects

Environmental pollution and its drastic effects on human and animal health have urged governments to implement strict policies to minimize damage. The first step in applying such policies is to find reliable methods to detect pollution in various media, including water, food, soil, and air. In this regard, various approaches such as spectrophotometric, chromatographic, and electrochemical techniques have been proposed. To overcome the limitations associated with conventional analytical methods, microfluidic devices have emerged as sensitive technologies capable of generating high content information during the past few years. The passage of contaminant samples through the microfluidic channels provides essential details about the whole environment after detection by the detector. In the meantime, artificial intelligence is an ideal means to identify, classify, characterize, and even predict the data obtained from microfluidic systems. The development of microfluidic devices with integrated machine learning and artificial intelligence is promising for the development of next-generation monitoring systems. Combination of the two systems ensures time efficient setups with easy operation. This review article is dedicated to the recent developments in microfluidic chips coupled with artificial intelligence technology for the evolution of more convenient pollution monitoring systems.     

面向信息化战争的广义兰切斯特战争模型

21世纪是一个信息化时代,为了更好的面向信息化战争.通过对传统的兰切斯特微分方程进行改进,考虑了信息化的诸多因素,引入了战场情报感知系数和信息优势系数.构建了数学模型,并对其进行差分处理求解,比较分析了交战双方对战场感知系数变化和信息优势系数变化情况下,对战争的影响.以海湾战争为例,检验了模型的可行性.     

A self‐adaptive intelligence gray prediction model with the optimal fractional order accumulating operator and its application

The self‐adaptive intelligence gray predictive model (SAIGM) has an alterable‐flexible model structure, and it can build a dynamic structure to fit different external environments by adjusting the parameter values of SAIGM. However, the order number of the raw SAIGM model is not optimal, which is an integer. For this, a new SAIGM model with the fractional order accumulating operator (SAIGM_FO) was proposed in this paper. Specifically, the final restored expression of SAIGM_FO was deduced in detail, and the parameter estimation method of SAIGM_FO was studied. After that, the Particle Swarm Optimization algorithm was used to optimize the order number of SAIGM_FO, and some steps were provided. Finally, the SAIGM_FO model was applied to simulate China's electricity consumption from 2001 to 2008 and forecast it during 2009 to 2015, and the mean relative simulation and prediction percentage errors of the new model were only 0.860% and 2.661%, in comparison with the ones obtained from the raw SAIGM model, the GM(1, 1) model with the optimal fractional order accumulating operator and the GM(1, 1) model, which were (1.201%, 5.321%), (1.356%, 3.324%), and (2.013%, 23.944%), respectively. The findings showed both the simulation and the prediction performance of the proposed SAIGM_FO model were the best among the 4 models.     

结构形状优化与智能模型化

阻碍形状优化软件广泛应用的障碍之一是依据自然设计度量描述和建立设计模型、分析模型、优化模型以及实现三个模型之间的转换.本文称这一困难为结构形状优优设计软件的适用性.本文提出了一种方法用来动态地确定平面连续体结构形状优化过程中的边界,应用基于人工智能方法的启发式规则与技术,自动生成由设计单元法表示的几何模型,也就是将一个结构自动剖分成若干个大的四边形映射单元.这些大单元对于进一步的网络生成是必要的,同时也是向全自动计算机辅助形状优化系统前进的重要一步.     

Condition Monitoring of Ground Anchorages by Dynamic Impulses: GRANIT System

The GRANIT system operates by applying an impulse of known force by means of an impact device that is attached to the tendon of the anchorage. The vibration response signals resulting from this impulse are complex in nature and require analysis to be undertaken in order to extract information from the vibrational response signatures that is relevant to the condition of the anchorage. In the system, the complicated relationship that exists between characteristics of an anchorage and its response to an impulse is identified and learned by a novel artificial intelligence network based on artificial intelligence techniques.The results presented in this paper demonstrate the potential of the GRANIT system to diagnose the integrity of ground anchorages at a site near Stone, England, by using a trained neural network capable of diagnosing the post-tension level of the anchorage. This neural network was used for the diagnosis of load in a second ground anchorage adjacent to the original anchorage used for the training of the neural network. Further tests were taken with a different anchor head configuration of the anchorage and a different relationship between the signature response of the anchorage to an applied impulse and its post-tension level was found.Problems encountered during the diagnosis of this second set of test signatures by the trained neural network are investigated with the use of a lumped parameter dynamic model. This model is able to identify the parameters in the anchorage system that affect this change in response signature. The results from the investigation lead to a new form of classification for the installed anchorages, based on their anchor head configuration.Laboratory strand anchorage tests were undertaken in order to compare with and validate the results obtained from the field tests and the lumped parameter dynamic model.