基于神经网络的粗糙表面双向反射分布函数建模

分析粗糙表面双向反射分布函数的测量方法,提出一种使用人工神经网络技术建立目标表面材料双向反射分布函数模型的方法。给出测量样品多个入射角度下的BRDF随散射角变化的曲线,从中选取部分曲线输入到神经网络,使用贝叶斯正则化方法训练网络,最终获取双向反射分布函数和入射角、散射角的映射关系模型。使用网络模型计算参与训练和未参与训练的输入角度的散射分布曲线,与实验测量曲线进行比较,结果表明：建立的模型正确,具有应用价值。     

生成模型在蛋白质序列设计中的应用

蛋白质是一切生命体的物质基础,是生命活动的主要承担者,参与各种生理功能的调节.设计具有特定功能的蛋白质在蛋白质工程、生物医药、材料科学等领域具有重要意义.蛋白质序列设计的目标是设计能够折叠成期望结构并具有相应功能的氨基酸序列,是所有理性蛋白质工程的核心问题,具有极其重要的研究和应用潜力.随着蛋白质序列数据的指数型增长和...     

RNAGCN: RNA tertiary structure assessment with a graph convolutional network

RNAs play crucial and versatile roles in cellular biochemical reactions. Since experimental approaches of determining their three-dimensional (3D) structures are costly and less efficient, it is greatly advantageous to develop computational methods to predict RNA 3D structures. For these methods, designing a model or scoring function for structure quality assessment is an essential step but this step poses challenges. In this study, we designed and trained a deep learning model to tackle this problem. The model was based on a graph convolutional network (GCN) and named RNAGCN. The model provided a natural way of representing RNA structures, avoided complex algorithms to preserve atomic rotational equivalence, and was capable of extracting features automatically out of structural patterns. Testing results on two datasets convincingly demonstrated that RNAGCN performs similarly to or better than four leading scoring functions. Our approach provides an alternative way of RNA tertiary structure assessment and may facilitate RNA structure predictions. RNAGCN can be downloaded from https://gitee.com/dcw-RNAGCN/rnagcn.     

Design consideration and modelling studies of ultrasound and ultraviolet combined approach for shelf-life enhancement of pine apple juice

Although both ultraviolet (UV) radiation and ultrasound (US) treatment have their capabilities in microbial inactivation, applying any one method alone may require a high dose for complete inactivation, which may affect the sensory and nutritional properties of pineapple juice. Hence, this study was intended to analyse and optimise the effect of combined US and UV treatments on microbial inactivation without affecting the selected quality parameters of pineapple juice. US treatment (33 kHz) was done at three different time intervals, viz. 10 min, 20 min and 30 min., after which, juice samples were subjected to UV treatment for 10 min at three UV dosage levels, viz. 1 J/cm², 1.3 J/cm², and 1.6 J/cm². The samples were evaluated for total colour difference, pH, total soluble solids (TSS), titrable acidity (TA), and ascorbic acid content; total bacterial count and total yeast count; and the standardization of process parameters was done using Response Surface Methodology and Artificial Neural Network. The results showed that the individual, as well as combined treatments, did not significantly impact the physicochemical properties while retaining the quality characteristics. It was observed that combined treatment resulted in 5 log cycle reduction in bacterial and yeast populations while the individual treatment failed. From the optimization studies, it was found that combined US and UV treatments with 22.95 min and1.577 J/cm² ensured a microbiologically safe product while retaining organoleptic quality close to that of fresh juice.     

Universality of deep convolutional neural networks

Deep learning has been widely applied and brought breakthroughs in speech recognition, computer vision, and many other domains. Deep neural network architectures and computational issues have been well studied in machine learning. But there lacks a theoretical foundation for understanding the approximation or generalization ability of deep learning methods generated by the network architectures such as deep convolutional neural networks. Here we show that a deep convolutional neural network (CNN) is universal, meaning that it can be used to approximate any continuous function to an arbitrary accuracy when the depth of the neural network is large enough. This answers an open question in learning theory. Our quantitative estimate, given tightly in terms of the number of free parameters to be computed, verifies the efficiency of deep CNNs in dealing with large dimensional data. Our study also demonstrates the role of convolutions in deep CNNs.     

基于TOPSIS模型的机场网络节点重要度评估

在机场网络中单个机场节点的失效往往会对其他的节点产生影响,特别是关键节点的失效会波及整个网络.准确客观的识别重要节点机场关乎整个机场网络的安全运营.本文分析了机场网络拓扑特性中的度、集聚系数和接近度指标,考虑了机场旅客吞吐量和所在城市人口等交通经济特性指标,使用熵权法确定权重的基础上,应用TOPSIS法构建综合评价体系模型,最后以华东地区机场网络为例进行节点重要度排序.结果表明与单一指标的评估结果相比,该方法更加全面客观的确定不同属性指标的权重,避免了不同指标取值的差异性,使评价更加全面,更符合机场网络实际运营情况.     

Explosive synchronization in a mobile network in the presence of a positive feedback mechanism

Synchronization is a process that describes the coherent dynamics of a large ensemble of interacting units.The study of explosive synchronization transition attracts considerable attention.Here,I report the explosive transition within the framework of a mobile network,while each oscillator is controlled by global-order parameters of the system.Using numerical simulation,I find that the explosive synchronization(ES)transition behavior can be controlled by simply adjusting the fraction of controlled oscillators.The influences of some parameters on explosive synchronization are studied.Moreover,due to the presence of the positive feedback mechanism,I prevent the occurrence of the synchronization of continuous-phase transition and make phase transition of the system a first-order phase transition accompanied by a hysteresis loop.     

Design and build an elastic crosslinked network to strengthen and toughen soybean-meal based bioadhesive using organo-sepiolite and greener crosslinker triglycidylamine

Poor water resistance and curing layer brittleness are significant challenges of greener soybean meal (SM) adhesive promotion and application. In this study an elastic network was built to crosslink the SM adhesive and improve the cured layer toughness of the resultant adhesive. Long-chain organo-sepiolite (OSEP) was first prepared by compound modification using KH-560 and KH-602 silane coupling agents. Triglycidylamine (TGA), a greener crosslinking agent with a large number of effective epoxy functional groups, was then synthesized. SM-based adhesives were fabricated using SM, OSEP and TGA. Results indicated that an elastic network was fabricated by the cross-linked reaction of SM, OSEP, and TGA. The elastic network effectively improved the toughness of the resultant adhesive. The wet shear strength of the plywood (PlyW) bonded by SM with 1% OSEP (SM/TGA/OSEP-1 adhesive) increased by 60% to 1.25 MPa relative to that of the SM/TGA adhesive. This study provided a greener, simple and cheap method improving the toughness and wet shear strength of SM-based adhesive, which contributes to the industrialized application of the product.     

交通事故的时空热点分析

针对目前交通事故分析中时空维度分离的不足，以H市2013―2015年的交通事故数据为研究对象，根据自组织神经网络、平行坐标系、时空颜色矩阵和时空网络核密度估计、热点分析法，分别从行政区划以及道路网络上进行交通事故的时空特征分析，从宏观和微观角度揭示交通事故的时空热点区域。结果表明，整体来说，在空间上，H市交通事故热点区域4个街道高于8个镇，呈明显的“两带一中心”分布，即硖许线与101省道形成的带状区域和市政府所在的行政中心区域；时间上，早、晚高峰时段最为严重，夜晚较为轻微，呈明显的区域特征分布，即工业办公区热点出现在早高峰时段，生活住宅区热点出现在晚高峰时段，商业消费区热点出现在夜晚时段。