The Pharmacophore Hypothesis of Novel Inhibitors for Aurora A Kinase

A three-dimensional pharmacophore model was developed from a series of inhibitors of Aurora A kinase to discover new potent anti-cancer agents using the HypoGen module in the Catalyst software. The pharmacophore model was developed based on the structure of 20 currently available inhibitors, which were carefully selected from the literature. The best hypothesis （Hypo 1） was defined by four features： one hydrogen-bond donor and three hy- drophobic points, with the best correlation coefficient of 0.909, the lowest rms deviation of 1.563, and the highest cost difference of 99.075. The Hypo 1 was then validated by a test set consisting of 24 compounds and by a cross-validation of 95% confidence level through randomizing the data using the CatScramble program, which suggested that a predictive pharmacophore model had been successfully obtained.     

工程教育认证背景下《高分子材料成型加工》教学模式探讨

随着高等工程教育改革和社会发展,工程教育认证已经在我国高等工程专业实施。工程教育认证是高等教育工程观的重要转变,主要倡导学生中心、产出导向、持续改进三个基本理念。本文介绍了常州大学高分子材料与工程专业的专业核心课程高分子材料成型加工的建设与教学改革做法,以学生为中心,以毕业要求达成为目标,从教学目的、教学内容、教学方法和手段、持续改进等方面深程度对课程进行改革与实践,积极探索以学生为主体的教学新模式。     

校企合作下应用化工技术专业订单式人才培养模式的探索

立足于学校应用化工技术专业校企合作协议,考虑到社会及企业的要求及高职学生的特点,讨论制定了应用化工技术专业订单式人才培养方案,经过不断修改和完善,逐步取代了原来的学科课程体系,初步形成了新的人才培养方案,具体包括专业设置及培养目标定位、课程体系与教学内容的改革、人才培养方案的实施与保障及考核与评价方面的创新。     

Maneuver analysis methodology to predict vehicle impacts on training lands

Tactical mobility analysis techniques were merged with land management strategies to assess potential impacts of vehicle operations on training areas for rangeland planning and management. A vehicle mobility analysis was performed for a suite of vehicle types using the NATO Reference Mobility Model (NRMM II). Input parameters include terrain information (soil type, slope, vegetation, surface roughness, soil strength), terrain surface condition based on climate (terrain strength, freeze–thaw, moisture content, snow cover), and vehicle specifications (tire, power train, weight on each axle, ground clearance, dimensions, ride). The vehicle performance was spatially mapped over the terrain for different seasons of the year and used to calculate the maneuverable acreage, which was compared to acreage needed for training requirements. This can be related to land capability based on expected training impact (Maneuver Impact Miles, MIM) and Land Condition Curves which link training density to land condition. This methodology can be used to determine the suitability of training lands and the degree of land management or rehabilitation expected. The methodology was applied to the transformation of the Alaska training lands to support a new brigade unit called the Stryker Brigade Combat Team (SBCT3), but is equally useful for other training areas and military units. For summer use, Alaska training lands are capable of supporting four times the projected training requirements. For winter, when the ground is frozen, more than 10 times the area needed was available.     

Multilevel-in-width training for deep neural network regression

A common challenge in regression is that for many problems, the degrees of freedom required for a high-quality solution also allows for overfitting. Regularization is a class of strategies that seek to restrict the range of possible solutions so as to discourage overfitting while still enabling good solutions, and different regularization strategies impose different types of restrictions. In this paper, we present a multilevel regularization strategy that constructs and trains a hierarchy of neural networks, each of which has layers that are wider versions of the previous network's layers. We draw intuition and techniques from the field of Algebraic Multigrid (AMG), traditionally used for solving linear and nonlinear systems of equations, and specifically adapt the Full Approximation Scheme (FAS) for nonlinear systems of equations to the problem of deep learning. Training through V-cycles then encourage the neural networks to build a hierarchical understanding of the problem. We refer to this approach as multilevel-in-width to distinguish from prior multilevel works which hierarchically alter the depth of neural networks. The resulting approach is a highly flexible framework that can be applied to a variety of layer types, which we demonstrate with both fully connected and convolutional layers. We experimentally show with PDE regression problems that our multilevel training approach is an effective regularizer, improving the generalize performance of the neural networks studied.     

A real-time LPC-based vocal tract area display for voice development

This article reports the design and implementation of a graphical display that presents an approximation to vocal tract area in real time for voiced vowel articulation. The acoustic signal is digitally sampled by the system. From these data a set of reflection coefficients is derived using linear predictive coding. A matrix of area coefficients is then determined that approximates the vocal tract area of the user. From this information a graphical display is then generated. The complete cycle of analysis and display is repeated at ≈20 times/s. Synchronised audio and visual sequences can be recorded and used as dynamic targets for articulatory development. Use of the system is illustrated by diagrams of system output for spoken cardinal vowels and for vowels sung in a trained and untrained style     

Documenting laryngeal change following prolonged loud reading: A videostroboscopic study

This study investigated the effects of prolonged loud reading on trained and untrained subjects. Subjects were eight young women singers, and eight young women with limited musical experience. Each subject underwent videostroboscopic examination prior to and following 1 h of prolonged loud reading. The pretest and posttest videotaped samples were randomized and presented to three experienced judges, who evaluated various aspects of laryngeal appearance and vibratory characteristics. Analyses of group data revealed that untrained subjects showed a small but significant increase in amplitude of vocal fold excursion following the experimental task. No significant differences were noted in the trained singer group. When individual variation was analyzed, it was found that most subjects did not show many changes from pretest to posttest. It was concluded that a l-h loud-reading task was not sufficient to induce notable laryngeal alterations     

Some methods of training radial basis neural networks in solving the Navier‐Stokes equations

The purpose of this research is to analyze the application of neural networks and specific features of training radial basis functions for solving 2‐dimensional Navier‐Stokes equations. The authors developed an algorithm for solving hydrodynamic equations with representation of their solution by the method of weighted residuals upon the general neural network approximation throughout the entire computational domain. The article deals with testing of the developed algorithm through solving the 2‐dimensional Navier‐Stokes equations. Artificial neural networks are widely used for solving problems of mathematical physics; however, their use for modeling of hydrodynamic problems is very limited. At the same time, the problem of hydrodynamic modeling can be solved through neural network modeling, and our study demonstrates an example of its solution. The choice of neural networks based on radial basis functions is due to the ease of implementation and organization of the training process, the accuracy of the approximations, and smoothness of solutions. Radial basis neural networks in the solution of differential equations in partial derivatives allow obtaining a sufficiently accurate solution with a relatively small size of the neural network model. The authors propose to consider the neural network as an approximation of the unknown solution of the equation. The Gaussian distribution is used as the activation function.     

以提升创新能力为目标的基础力学实践教学初探

创新型基础力学人才的培养既要注重专业知识的学习,又要重视学生思辨、质疑与应用能力的提升。受限于不断削减的学时,传统基础力学课程仍以讲授为主,难以开展有效的实践教学环节。这种重理论轻实践的模式易导致学生学习兴趣下降、综合能力不足。为弥补实践短板,国内高校正逐步开展各类型大学生实践项目。本文以大学生创新训练项目为例,系统介绍项目实施期间教师有效的引导方式,以期为新时期力学人才培养贡献力量。

     

Macroscopic inhomogeneous deformation behavior arising in single crystal Ni–Mn–Ga foils under tensile loading

This study investigated macroscopic inhomogeneous deformation occurring in single-crystal Ni–Mn–Ga foils under uniaxial tensile loading. Two types of single-crystal Ni–Mn–Ga foil samples were examined as-received and after thermo-mechanical training. Local strain and the strain field were measured under tensile loading using laser speckle and digital image correlation. The as-received sample showed a strongly inhomogeneous strain field with intermittence under progressive deformation, but the trained sample result showed strain field homogeneity throughout the specimen surface. The as-received sample is a mainly polycrystalline-like state composed of the domain structure. The sample contains many domain boundaries and large domain structures in the body. Its structure would cause large local strain band nucleation with intermittence. However, the trained one is an ideal single-crystalline state with a transformation preferential orientation of variants after almost all domain boundary and large domain structures vanish during thermo-mechanical training. As a result, macroscopic homogeneous deformation occurs on the trained sample surface during deformation.