PBL教学法在《计算机辅助药物发现》教学中的实践探索

在后基因组时代背景下,计算机辅助药物发现在现代药物研发中体现越来越重要的作用。它不仅仅是交叉理论学科,更有很强的实践性。因此,在生物信息学本科专业开展PBL教学,通过实验流程设计将教学任务从理论和实践两个层面进行模块化融合,加强理论与实践的结合,调动学生参与课堂的积极性以及独立思考的主动性,提升学生主动提出问题和解决问题能力。这一课程教学的实践探索具有十分深远的意义。

传统和机器学习策略在基于结构虚拟筛选中的应用

计算机辅助药物发现和人工智能驱动药物设计在制药行业中是减少时间和经济成本的重要策略.其中具有代表性的方法包括虚拟筛选、蛋白质-配体相互作用评估、药物药代动力学性质预测以及药物设计.通常来说,虚拟筛选是药物发现的第一步,其主要目标是识别和发现潜在的先导化合物的候选物.在过去的几十年里,已经开发了多种传统的和基于机器学习的方法来提高虚拟筛选的准确性和速度.本综述总结了传统和机器学习方法在基于结构的虚拟筛选中的应用,讨赖性较弱,且分布非常冷并在j=1处达到峰值,但(0,1,0)振动态的转论了它们的性能、优势和局限性等方面.

世纪之交的科学方法论:20世纪80年代以来科学方法论的新发展

对20世纪80年代以来科学方法论新的发展进行了系统梳理和评介,以把握世纪之交科学方法论研究的新动向.

跨课程PBL教学法在化学小分子设计和优化平台开发中的应用

在后基因组时代背景下,计算机辅助药物发现在现代药物研发中体现越来越重要的作用。它不仅仅是交叉理论学科,涉及到计算机、生物、化工等学科的内容,更具有很强的实践性。因此,本教改论文探讨如何在生物信息学本科专业开展PBL教学,以一个药物设计中具体的问题为导向,将各不同学科中关联课程的知识串联起来,包括从实验流程设计将相关课程的内容以项目内容为导向,将相关知识进行模块化融合,加强知识的融会贯通和实践转化,加深学生对不同学科知识的综合理解,调动学生参与课堂的积极性以及独立思考的主动性,提升学生主动提出问题和解决问题的能力。同时也为类似交叉学科课程的教学和课程设计提供了新的思路。

The Future of Manufacturing： A New Perspective

Many articles have been published on intelligent manufacturing, most of which focus on hardware, soft-ware, additive manufacturing, robotics, the Internet of Things, and Industry 4.0. This paper provides a dif-ferent perspective by examining relevant challenges and providing examples of some less-talked-about yet essential topics, such as hybrid systems, redefining advanced manufacturing, basic building blocks of new manufacturing, ecosystem readiness, and technology scalahility. The first major challenge is to （re-）define what the manufacturing of the future will he, if we wish to： ① raise public awareness of new manufacturing＇s economic and societal impacts, and ② garner the unequivocal support of policy- makers. The second major challenge is to recognize that manufacturing in the future will consist of sys-tems of hybrid systems of human and robotic operators; additive and suhtractive processes; metal and composite materials; and cyher and physical systems. Therefore, studying the interfaces between con- stituencies and standards becomes important and essential. The third challenge is to develop a common framework in which the technology, manufacturing business case, and ecosystem readiness can he eval- uated concurrently in order to shorten the time it takes for products to reach customers. Integral to this is having accepted measures of ＂scalahility＂ of non-information technologies. The last, hut not least, chal-lenge is to examine successful modalities of industry-academia-government collaborations through public-private partnerships. This article discusses these challenges in detail.

Investigation of Eddy Current Nondestructive Testing for Carbon Fiber-Reinforced Plastic （CFRP） Based on Electromagnetic Field Analysis

CFRP （carbon fiber reinforced plastic） is used extensively in aircraft and spacecraft structures, because of its excellent mechanical properties. Ultrasonic testing, which is used as a non-destructive testing technique for CFRP, requires a contact medium. In contrast, eddy current testing does not require a contact medium, and when used for CFRP testing it has advantages not available with other techniques. CFRP is a laminate, with each layer being anisotropically conductive, and the distribution of the induced eddy current is yet to be determined. Here, to determine the eddy current distribution in the detection of flaws in cross-ply CFRP （0°/90°） by using a cross-point probe, we performed an FEM （finite element method） analysis of electromagnetic fields. We investigated the nature of the flaw signals and the differences in eddy current distributions between materials with and without flaws.

Face recognition using both visible light image and near-infrared image and a deep network

In recent years, deep networks has achieved outstanding performance in computer vision, especially in the field of face recognition. In terms of the performance for a face recognition model based on deep network, there are two main closely related factors： 1） the structure of the deep neural network, and 2） the number and quality of training data. In real applications, illumination change is one of the most important factors that significantly affect the performance of face recognition algorithms. As for deep network models, only if there is sufficient training data that has various illumination intensity could they achieve expected performance. However, such kind of training data is hard to collect in the real world. In this paper, focusing on the illumination change challenge, we propose a deep network model which takes both visible light image and near-infrared image into account to perform face recognition. Near- infrared image, as we know, is much less sensitive to illuminations. Visible light face image contains abundant texture information which is very useful for face recognition. Thus, we design an adaptive score fusion strategy which hardly has information loss and the nearest neighbor algorithm to conduct the final classification. The experimental results demonstrate that the model is very effective in realworld scenarios and perform much better in terms of illumination change than other state-of-the-art models.

Trajectory planning for biped robot walking on uneven terrain - Taking stepping as an example

According to the features of movements of humanoid robot, a control system for humanoid robot walking on uneven terrain is present. Constraints of stepping over stairs are analyzed and the trajectories of feet are calculated by intelligent computing methods. To overcome the shortcomings resulted from directly controlling the robot by neural network （NN） and fuzzy logic controller （FLC）, a revised particle swarm optimization （PSO） algorithm is proposed to train the weights of NN and rules of FLC. Simulations and experiments on different control methods are achieved for a detailed comparison. The results show that using the proposed methods can obtain better control effect.

3D bioprinting for cell culture and tissue fabrication

Three-dimensional （3D） bioprinting is a computer-assisted technology which precisely controls spatial position of biomaterials, growth factors and living cells, offering unprecedented possibility to bridge the gap between structurally mimic tissue constructs and functional tissues or organoids. We briefly focus on diverse bioinks used in the recent progresses of biofabrication and 3D bioprinting of various tissue architectures including blood vessel, bone, cartilage, skin, heart, liver and nerve systems. This paper provides readers a guideline with the conjunction between bioinks and the targeted tissue or organ types in structuration and final functionalization of these tissue analogues. The challenges and perspectives in 3D bioprinting field are also illustrated.

Density peaks clustering based integrate framework for multi-document summarization

We present a novel unsupervised integrated score framework to generate generic extractive multi- document summaries by ranking sentences based on dynamic programming （DP） strategy. Considering that cluster-based methods proposed by other researchers tend to ignore informativeness of words when they generate summaries, our proposed framework takes relevance, diversity, informativeness and length constraint of sentences into consideration comprehensively. We apply Density Peaks Clustering （DPC） to get relevance scores and diversity scores of sentences simultaneously. Our framework produces the best performance on DUC2004, 0.396 of ROUGE-1 score, 0.094 of ROUGE-2 score and 0.143 of ROUGE-SU4 which outperforms a series of popular baselines, such as DUC Best, FGB [7], and BSTM [10].

Evolutionary computation in China： A literature survey

Evolutionary computation （EC） has received significant attention in China during the last two decades. In this paper, we present an overview of the current state of this rapidly growing field in China. Chinese research in theoretical foundations of EC, EC-based optimization, EC-based data mining, and EC-based real-world applications are summarized.

Sequential Bag-of-Words model for human action classification

Recently, approaches utilizing spatial-temporal features to form Bag-of-Words （BoWs） models have achieved great success due to their simplicity and effectiveness. But they still have difficulties when distinguishing between actions with high inter-ambiguity. The main reason is that they describe actions by orderless bag of features, and ignore the spatial and temporal structure information of visual words. In order to improve classification performance, we present a novel approach called sequential Bag-of-Words. It captures temporal sequential structure by segmenting the entire action into sub-actions. Meanwhile, we pay more attention to the distinguishing parts of an action by classifying sub- actions separately, which is then employed to vote for the final result. Extensive experiments are conducted on challenging datasets and real scenes to evaluate our method. Concretely, we compare our results to some state-of-the-art classification approaches and confirm the advantages of our approach to distinguish similar actions. Results show that our approach is robust and outperforms most existing BoWs based classification approaches, especially on complex datasets with interactive activities, cluttered backgrounds and inter-class action ambiguities.

Video-based construction vehicles detection and its application in intelligent monitoring system

While vehicle detection on highways has been reported before, to the best of our knowledge, intelligent monitoring system that aims at detecting hydraulic excavators and dump trucks on state-owned land has not been explored thoroughly yet. In this paper, we present an automatic, video-based algorithm for detecting hydraulic excavators and dump trucks. Derived from lessons learned from video processing, we proposed methods for foreground detection based on an improved frame difference algorithm, and then detected hydraulic excavators and dump trucks in the respective region of interest. From our analysis, we proposed methods based on inverse valley feature of mechanical arm and spatial-temporal reasoning for hydraulic excavator detection. In addition, we explored dump truck detection strategies that combine structured component projection with the spatial relationship. Experiments on real-monitoring sites demonstrated the promising performance of our system.

Arduino Based Planar Two DoF Robot Manipulator

In this research, two DoF five bar robot manipulator is controlled by using a human-machine interface program working in a computer. The human machine interface program is developed in Visual C#. Net environment after completing inverse kinematic analysis of the robot manipulator. Human machine interface in computer screen calculates two rotational joint variables for given positions of the robot end point. Then the computer program sends a data package containing these joint variables to Arduino microcontxoller. Arduino microcontxoller set the position of two servo motors according to calculated joint angles. Any position in workspace can be obtained by using the algorithm. The robot can follow traj ectories such as a line, a circle and a rectangle. Furthermore, a lot of patterns can be generated using function with variable radius and angle of rotation.

Tenant-centric Sub-Tenancy Architecture in Software-as-a-Service

Multi-tenancy architecture （MTA） is often used in Software-as-a-Service （SaaS） and the central idea is that multiple tenant applications can be developed using components stored in the SaaS infrastructure. Recently, MTA has been extended to allow a tenant application to have its own sub-tenants, where the tenant application acts like a SaaS infrastructure. In other words, MTA is extended to STA （Sub-Tenancy Architecture）. In STA, each tenant application needs not only to develop its own functionalities, but also to prepare an infrastructure to allow its sub-tenants to develop customized applications. This paper applies Crowdsourcing as the core to STA component in the development life cycle. In addition, to discovering adequate fit tenant developers or components to help build and compose new components, dynamic and static ranking models are proposed. Furthermore, rank computation architecture is presented to deal with the case when the number of tenants and components becomes huge. Finally, experiments are performed to demonstrate that the ranking models and the rank computation architecture work as design.

Integrating absolute distances in collaborative representation for robust image classification

Conventional sparse representation based classification （SRC） represents a test sample with the coefficient solved by each training sample in all classes. As a special version and improvement to SRC, collaborative representation based classification （CRC） obtains representation with the contribution from all training samples and produces more promising results on facial image classification. In the solutions of representation coefficients, CRC considers original value of contributions from all samples. However, one prevalent practice in such kind of distance-based methods is to consider only absolute value of the distance rather than both positive and negative values. In this paper, we propose an novel method to improve collaborative representation based classification, which integrates an absolute distance vector into the residuals solved by collaborative representation. And we named it AbsCRC. The key step in AbsCRC method is to use factors a and b as weight to combine CRC residuals rescrc with absolute distance vector disabs and generate a new dviaetion r = a·rescrc b.disabs, which is in turn used to perform classification. Because the two residuals have opposite effect in classification, the method uses a subtraction operation to perform fusion. We conducted extensive experiments to evaluate our method for image classification with different instantiations. The experimental results indicated that it produced a more promising result of classification on both facial and non-facial images than original CRC method.

Dynamic Web services discovery based on similarity computation

A new approach for dynamic Web services discovery based on similarity computation in the treatment of massive Web information resource is put forward. Firstly, three kinds of service description information, textual, semantic and structural information, were modeled to compute the similarity of services. Then a novel dynamic Web services discovery mechanism was provided and the experiment on it was carried out. Results show that the new approach achieves considerable performance on precision and efficiency metrics for dynamic Web services discovery.

Development of a System for Location Finding of Low-Level Sound Source by Using Human Acoustic System with Stochastic Resonance

The purpose of this study is to develop a system that enables location finding of a small sound. The location finding of a small sound has some difficulties such as high computational costs or disturbances from the ambient noises and reflected waves. The proposed system is composed of a biologically-inspired system which uses a hearing mechanism based on the human ear and a mechanism for perceiving weak signals that uses stochastic resonance. The location finding mechanism in the proposed system is based on the time-lag detecting architecture. On the other hand, the stochastic resonance mechanism can pick up the small sound source in the ambient noises. Using this proposed system, we implemented the location finding of small sounds through numerical simulations and hardware experiments. Good results were obtained for the small sound source location finding.

Theoretical Research on Novel Data Mining Algorithm based on Fuzzy Clustering Theory and Deep Neural Network

With the progress of computer technology, data mining has become a hot research area in the computer science community. In this paper, we undertake theoretical research on the novel data mining algorithm based on fuzzy clustering theory and deep neural network. The focus of data mining in seeking the visualization methods in the process of data mining, knowledge discovery process can be users to understand, to facilitate human-computer interaction in knowledge discovery process. Inspired by the brain structure layers, neural network researchers have been trying to multilayer neural network research. The experiment result shows that out algorithm is effective and robust.

汉语词类测试平台的设计与实现

本文讨论怎样借助计算机及相关的测试程序,来为汉语词类的划分提供一种方便和快速的途径。着重展示一种新的用计算机辅助发现词类知识的方法:把划分词类的语法功能标准组织成一套量表,再编制为软件程序,最终形成网络版词类测试平台。详细介绍了我们怎样基于《汉语词类划分手册》,设计并编程实现了网络版汉语词类测试平台。最后,分析了目前测试平台仍需改进的地方,并提出了初步的解决方案。