取代酚对日本长腿蛙蝌蚪急性毒性的QSAR研究

用Hartree-Fock(HF)和DFT-B3LYP方法,分别在较高基组6-31G**和6.311G**水平下,全优化计算了21种取代酚化合物.从中获得分予最高占用和最低空轨道能(EHOMO和ELUMO)、前线轨道能级差(△E＝EHOMO-ELUMO)、分子总能量(ET)、氧原了所带的最高正电荷(QH+)、最负原子的静电荷(Q-)、分子偶极矩(μ)和分子体积(V)等描述符.结合文献中标题化合物对日本长腿蛙蝌蚪的毒性值(-logLC50)和辛醇/水分配系数(logKow),由线性同归方法成功建立包含10gKow、ELUMO和Q-的三参数QSAR模型.其中,模型(5)的复相关系数R2=0.944 8,交叉验证系数Q2=0.920 5,标准偏差SE=0.187,Fisher检验值F=98.390,故其预测能力较好.由此推断,标题物对日本长腿蛙蝌蚪的毒性作用分为2步,首先穿过细胞壁在细胞内富集,以logKow和Q-描述;其次与亲核试剂发生亲电反应,以风ELUMO表示.     

Real-time foveation filtering using nonlinear Mipmap interpolation

In recent years, several techniques have been proposed to simulate the gaze effect of the Human Visual System (HVS). It is believed that this effect is due to the foveation filtering. Current techniques to simulate the foveation filtering in computer graphics are either slow or suffer from artifacts and limitations. In this paper, we present a new approach of foveation filtering based on the Mipmap Pyramid of the current view by considering the relationship between the Gaussian kernel and Mipmap level. Due to the nonlinear Mipmap interpolation under the Bilateral Filtering scheme, we are able to simulate the foveation filtering more naturally and efficiently than in previous work. Moreover, a detail enhancement method based on the Cornsweet illusion is proposed to augment the gazing effect. We demonstrate our new approach with a variety of examples and provide comparisons with recent approaches.     

An axiom system for sequence-based specification

This paper establishes an axiomatic foundation and a representation theorem for the rigorous, constructive process, called sequence-based specification, of deriving precise specifications from ordinary (informal) statements of functional requirements. The representation theorem targets a special class of Mealy state machines, and algorithms are presented for converting from the set of sequences that define the specification to the equivalent Mealy machine, and vice versa. Since its inception, sequence-based specification has been effectively used in a variety of real applications, with gains reported in quality and productivity. This paper establishes the mathematical foundation independently of the process itself.     

Dynamically modulated intensity interrogation scheme using waveguide coupled surface plasmon resonance sensors

An electro-optically modulated intensity interrogation method based on tunable waveguide coupled surface plasmon resonance sensors has been proposed. It has been theoretically and experimentally demonstrated that the proposed scheme can enable sensitive measurement of measurand variations. By modulating the refractive index in the waveguide layer, this interrogation method yields modulated signal whose amplitude is related to measurand's refractive index. This amplitude modulated signal offers a higher signal to noise ratio and eliminates additive noise in the sensor system. A preliminary investigation using saline buffers with different NaCl concentrations shows a resolution of 2.3 × 10^?6 refractive index unit by our approach. Resolution can be controlled by the amplitude of the applied modulation voltage and can be further enhanced by optimizing the device structure or improving the electro-optical (E-O) coefficient of the E-O material. This approach is simple, stable, and promising for low-cost or multi-channel SPR biosensor applications.     

Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital

“Emergency medicine” is the front line of medical service a hospital provides; also it is the department people seek medical care from immediately after an emergency happens. The statistics by the Department of Health, Executive Yuan, indicate that over years, the number of people at the emergency department has been increasing. The US has introduced and practiced the triage system in the emergency medicine in 1960, whereby to aid the emergency department in allocating the patients, to give them appropriate medical care by the fast decision of the nurses and doctors in case of the patients’ seriousness through their judgment.This study takes on the knowledge contained in the massive data of unknown characteristics in the triage database at a Taiwanese regional hospital, using the cluster analysis and the rough set theory as tools for data mining to extract, with the analysis software ROSE2 (Rough Sets Data Explorer) and through rule induction technique, the imprecise, uncertain and vague information of rules from the massive database, and builds the model that is capable of simplifying massive data while maintaining the accuracy in classifying rules. After analyzing and evaluating the knowledge obtained from relevant mining in the hospitals past medical data for the consumption of emergency medical resources, this thesis proposes suggestions as reference for the hospitals in subsequent elevation of medical quality and decrease in operative costs.     

Development of a diagnostic system using a testing-based approach for strengthening student prior knowledge

Students learn new instructions well by building on relevant prior knowledge, as it affects how instructors and students interact with the learning materials. Moreover, studies have found that good prior knowledge can enable students to attain better learning motivation, comprehension, and performance. This suggests it is important to assist students in obtaining the relevant prior knowledge, as this can enable them to engage meaningfully with the learning materials. Tests are often used to help instructors assess students’ prior knowledge. Nevertheless, conventional testing approaches usually assign only a score to each student, and this may mean that students are unable to realize their own individual weaknesses. To address this problem, instructors can diagnose the test results to provide more detailed information to each student, but this is obviously a time-consuming process. Therefore, this study proposes a testing-based diagnosis system to assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken. Furthermore, an experiment was conducted to evaluate the effectiveness of the proposed approach in an interdisciplinary course, since several studies have indicated that students learn more and better in such courses when applying relevant prior knowledge to what they are learning. The experimental results show that the developed system is able to effectively diagnose students’ prior knowledge and enhance their learning motivation and performance on an interdisciplinary course. In addition, two diagnostic evaluations were also conducted to assess whether the diagnoses given by the system were consistent with the decisions of experts. The results demonstrate that the proposed system can effectively assist instructors and students in diagnosing and strengthening prior knowledge before new instruction is undertaken, since the diagnoses produced by the system were broadly consistent with those of experts.     

Piecewise Pseudo-Maximum Likelihood Estimation for Risk Aversion Case in First-Price Sealed-Bid Auction

In the literature, more and more clues show that the bidders indeed tend to risk averse. But as a good structure estimation procedure in first-price sealed-bid auctions, the traditional PPMLE (Piecewise Pseudo-Maximum Likelihood Estimation) approach is only applicable to the risk neutrality case. This study generalizes this approach to the risk aversion case, and obtains a simple estimation procedure for the Pareto family of private costs. In the procedure, the estimation order of distribution parameters of private costs is not important, which is different from the traditional PPMLE method. Though there is no unique estimator for θ ₁ and γ, some guidance is given for real-world applications on the basis of our Monte Carlo simulation experiments. What’s more, this procedure can be reduced further, if one is only interested in estimating the private costs. Extensive simulation experimental results indicate that our approach outperforms or matches at least the traditional one in the case of risk neutrality, and is applicable to the risk aversion case.     

Self-adaptive interval type-2 neural fuzzy network control for PMLSM drives

This paper proposes a self-adaptive interval type-2 neural fuzzy network (SAIT2NFN) control system for the high-precision motion control of permanent magnet linear synchronous motor (PMLSM) drives. The antecedent parts in the SAIT2NFN use interval type-2 fuzzy sets to handle uncertainties in PMLSM drives, including payload variation, external disturbance, and sense noise. The SAIT2NFN is firstly trained to model the inverse dynamics of PMLSM through concurrent structure and parameter learning. The fuzzy rules in the SAIT2NFN can be generated automatically by using online clustering algorithm to obtain a suitable-sized network structure, and a back propagation is proposed to adjust all network parameters. Then, a robust SAIT2NFN inverse control system that consists of the SAIT2NFN and an error-feedback controller is proposed to control the PMLSM drive in a changing environment. Moreover, the Kalman filtering algorithm with a dead zone is derived using Lyapunov stability theorem for online fine-tuning all network parameters to guarantee the convergence of the SAIT2NFN. Experimental results show that the proposed SAIT2NFN control system achieves the best tracking performance in comparison with type-1 NFN control systems.     

飞思卡尔推出一站式32位工业连接解决方案简化嵌入式开发

<正>嵌入式开发人员面临着用更少资源做更多事情的压力,他们不但要增强性能应用和连通性,而且要降低成本,加快产品上市。为了帮助开发人员成功应对设计