计算机专业硬件系列课程的改革与实践

目前，高校计算机专业学生中普遍存在“重软轻硬”，甚至“只软不硬”的现象。针对这一问题，石油大学计算机系在硬件系列课程方面进行了改革尝试。通过修改教学计划，改革教学内容和模式开放实验室，加强硬件师资队伍建设等措施，激发了学生的学习兴趣，提高了硬件教学的质量，强化了学生的工程实践能力。     

石油工程师计算机使用情况的调查及其对石油工程教育的启示

在过去的２０年里，计算机的运算能力及信息处理能力有了快速的重大提高、这对石油工程师的工作已经产生了极大的影响。为了证明这一点，我们就石油工程师是如何在工作中使用计算机的总是进行了一次调查。根据调查结果，我们对大学生的工程课如何与计算机和计算法接轨提出了建议。本文还对为什么和如何把计算柚我入大学生的课程中进行了一般性的讨论。     

Automated zebrafish chorion removal and single embryo placement: optimizing throughput of zebrafish developmental toxicity screens

The potential of the developing zebrafish model for toxicology and drug discovery is limited by inefficient approaches to manipulating and chemically exposing zebrafish embryos-namely, manual placement of embryos into 96- or 384-well plates and exposure of embryos while still in the chorion, a barrier of poorly characterized permeability enclosing the developing embryo. We report the automated dechorionation of 1600 embryos at once at 4 h postfertilization (hpf) and placement of the dechorionated embryos into 96-well plates for exposure by 6 hpf. The process removed ≥95% of the embryos from their chorions with 2% embryo mortality by 24 hpf, and 2% of the embryos malformed at 120 hpf. The robotic embryo placement allocated 6-hpf embryos to 94.7% ± 4.2% of the wells in multiple 96-well trials. The rate of embryo mortality was 2.8% (43 of 1536) from robotic handling, the rate of missed wells was 1.2% (18 of 1536), and the frequency of multipicks was <0.1%. Embryo malformations observed at 24 hpf occurred nearly twice as frequently from robotic handling (16 of 864; 1.9%) as from manual pipetting (9 of 864; 1%). There was no statistical difference between the success of performing the embryo placement robotically or manually.     

A methodology for modeling the distributions of medical images and their stochastic properties

The probabilistic distribution properties of a set of medical images are studied. It is shown that the generalized Gaussian function provides a good approximation to the distribution of AP chest radiographs. Based on this result and a goodness-of-fit test, a generalized Gaussian autoregressive model (GGAR) is proposed. Its properties and limitations are also discussed. It is expected that the GGAR model will be useful in describing the stochastic characteristics of some classes of medical images and in image data compression and other applications.