Artificial Intelligence in Aptamer–Target Binding Prediction

Aptamers are short single-stranded DNA, RNA, or synthetic Xeno nucleic acids (XNA) molecules that can interact with corresponding targets with high affinity. Owing to their unique features, including low cost of production, easy chemical modification, high thermal stability, reproducibility, as well as low levels of immunogenicity and toxicity, aptamers can be used as an alternative to antibodies in diagnostics and therapeutics. Systematic evolution of ligands by exponential enrichment (SELEX), an experimental approach for aptamer screening, allows the selection and identification of in vitro aptamers with high affinity and specificity. However, the SELEX process is time consuming and characterization of the representative aptamer candidates from SELEX is rather laborious. Artificial intelligence (AI) could help to rapidly identify the potential aptamer candidates from a vast number of sequences. This review discusses the advancements of AI pipelines/methods, including structure-based and machine/deep learning-based methods, for predicting the binding ability of aptamers to targets. Structure-based methods are the most used in computer-aided drug design. For this part, we review the secondary and tertiary structure prediction methods for aptamers, molecular docking, as well as molecular dynamic simulation methods for aptamer–target binding. We also performed analysis to compare the accuracy of different secondary and tertiary structure prediction methods for aptamers. On the other hand, advanced machine-/deep-learning models have witnessed successes in predicting the binding abilities between targets and ligands in drug discovery and thus potentially offer a robust and accurate approach to predict the binding between aptamers and targets. The research utilizing machine-/deep-learning techniques for prediction of aptamer–target binding is limited currently. Therefore, perspectives for models, algorithms, and implementation strategies of machine/deep learning-based methods are discussed. This review could facilitate the development and application of high-throughput and less laborious in silico methods in aptamer selection and characterization.     

面向配电网电压暂降治理的线路改造优化模型

线路改造是在电网侧进行电压暂降治理的主要方式之一。为了经济、合理地确定线路的改造位置和治理方式,建立了面向配电网电压暂降治理的线路改造优化模型。针对断路器重合闸和熔断器配合保护的配电网,考虑故障位置、故障类型等因素,讨论分析了10种典型的保护动作配合方式,评估了配电网电压暂降持续时间。结合传统电压暂降幅值的计算方法,计算线路改造治理方案的成本及效果。考虑用户电压暂降耐受能力,建立了用户生产中断次数评估模型。定义了电压暂降总支出,以总支出最小为目标,计及技术约束、经济约束、治理范围约束和风险域约束,建立了面向配电网电压暂降治理的线路改造优化模型,并基于人工蜂群算法进行模型求解。以IEEE 33节点系统为算例,考虑治理投资成本充足和不足2种场景,通过仿真验证了所提方法的正确性和实用性。     

基于改进CatBoost的电力系统暂态稳定评估方法

在实际电网的运行过程中,通过同步相量测量单元实时采集到的电网动态参数通常含有部分噪声,且有时会因通信故障造成数值的随机缺失,对基于人工智能的电力系统暂态稳定评估模型造成很大影响.为此,提出一种基于改进CatBoost的暂态稳定评估方法.通过分箱算法对输入特征数据进行离散化处理,提高模型对噪声的鲁棒性;采用加权的焦点损失函数代替交叉熵损失函数,提升模型的可信度并减少模型对失稳样本的漏判;将量测数据部分缺失的样本划分到单独的节点中继续建模,从而充分挖掘不完整样本中的暂态信息.在新英格兰10机39节点上的实验结果表明,所提方法的准确率和查全率均优于其他几类机器学习算法,而且所提方法对噪声和数值缺失表现出良好的鲁棒性且具有较快的训练速度和预测速度.     

CO2腐蚀的神经网络分析

利用神经网络的分析方法 ,对CO2 腐蚀进行分析建模 ,从系统的观点出发 ,建立了碳钢含碳量、CO2 分压、温度、流速与腐蚀速率之间的非线性对应关系 ,为准确的数学建模、预测设备的使用寿命和相关专家系统的研制开拓了新的思路     

基于PFEA、ANN和GA的U形件成形板料形状优化

将参数化网格划分系统ParaMesh和网格模型转换接口程序应用于参数化有限元分析(PFEA)和优化设计之中。通过ParaMesh建立了参数化有限元模型并进行参数化有限元分析。把人工神经网络(ANN)和遗传算法(GA)相结合进行优化设计,研究了U形件翻边成形中的板料形状优化。利用优化的结果值进行验算,获得了良好的成形件,表明基于PFEA、ANN和GA进行成形板料形状优化是可行的。     

焊接专家系统设计及开发技术

论述了焊接专家系统的基本原理和结构,着重介绍了开发焊接专家系统的技术,包括传统的开发工具和新的先进技术在焊接专家系统中的应用.     

Ti5Nb人工钉扎中心NbTi超导体的磁通钉扎机制分析

采用棒基组合的装配方法，用热挤压和冷加工相结合的工艺制备了岛状Ti5Nb人工钉扎中心NbTi超导体，其中Ti5Nb体积含量为10％。通过对此超导体进行详细的磁通钉扎机制分析，认为样品的磁通钉扎力特征主要由正常相钉扎和Δκ扎相结合的机制决定。根据分析的结果，采用多源标度方法，很好地拟合了不同线径样品的磁通钉扎力密度随磁场变化的曲线。拟合的结果表明：样品在较低场下的性能主要由正常相钉扎机制决定，而在较高场下的性能主要由Δκ钉扎机制决定。并且随着线径的减小，正常相钉扎力密度和Δκ钉扎力密度均有不同程度的提高。     

基于人工智能的钛合金热变形工艺参数优化

在深入分析热变形工艺参数对Ti-15-3合金显微组织及成形载荷的影响的基础上,以变形温度、变形程度和变形速率等热变形工艺参数作为设计变量,以显微组织和成形力的最佳综合为目标,建立了该合金热塑性成形工艺参数的多目标优化数学模型。以显微组织参数和成形力的人工神经网络预测模型作为优化算法的知识源,将人工神经网络与修正的遗传算法相结合,对Ti-15-3合金的热塑性成形工艺参数进行优化。结果表明,提出的修正的遗传算法是有效的,采用将其与人工神经网络相结合的方法对钛合金的热塑性成形工艺参数进行优化是可行的。     

ANN valuation model of material LCIA profile

Weighting model is the only valuation model of life cycle impact assessment(LCIA) profile now. It simplifies evaluation function into linear function, and makes the determination of weighting factor complicated. Therefore the valuation of LCIA profile is the most critical and controversial step in life cycle assessment(LCA). Development on valuation models, which are understood easily and accepted widely, is urgently needed in the field of LCA.The modeling approaches for the linear evaluation function were summed up. The modeling approaches for the nonlinear evaluation function were set up by function approximation theory, which include choosing preference products, forming preference data, establishing artificial neural network(ANN) and training ANN by preference data.By selecting 7 material products as preference product, experience was done with modeling approaches of the nonlinear evaluation function. The results show that the modeling approaches and valuation model of the nonlinear evaluation function are more practical than the weighting model.