论毛泽东关于社会主义矛盾学说的创立及其意义

在《关于正确处理人民内部矛盾的问题》一文中,毛泽东从哲学高度指出了社会主义社会存在两类性质不同的矛盾,首次创立了关于社会主义社会矛盾的学说,本文将以全新的视角,解读其重大理论意义和实践意义,深入研究其理论价值,从而为当代中国构建社会主义和谐社会和全面建设小康社会提供理论支撑．     

Influence of sugar filter mud on formation of portland cement clinker

To explore the mechanisms of filter mud (FM) in the portland cement clinker formation, and to make effective use of FM in the cement production as lime-based raw materials, the influences of FM on the apparent activation energy of calcium carbonate decomposition, liquid phase amount, crystalline phase and tricalcium silicate polymorphism were investigated by TA, SEM and XRD. The experimental results show that FM can reduce apparent activation energy of calcium carbonate decomposition, and increase liquid phase amount. Appropriate FM replacement ratios are useful to promoting the C₃S formation and heightening the C₃S content. New phases of α-C₂S and C₂S·0.5Ca₃ (PO₄)₂ can be found in the clinkers while the ratios are above 15%. In addition, impurities in FM can induce the C₃S polymorph to transform.     

Heterogeneous acid catalytic esterification by porous polyoxometalate-tantalum pentoxide nanocomposites

Polyoxometalates (POMs) are useful catalysts in both redox and acid catalysis reactions since its catalytic features can be controlled at a molecular level. We investigated POM (POM = H₃PW₁₂O₄₀) on tantalum pentoxide (Ta₂O₅) via hydrolysis of tantalum pentachloride (TaCl₅) in the presence of POM using the sol-gel method for acid catalytic reaction. All obtained amorphous materials have been characterized by inductively coupled plasma atomic emission spectroscopy (ICP-AES), Fourier transform infrared (FT-IR) spectra, Raman scattering spectroscopy, ³¹P magic-angle spinning (MAS) NMR, field emission scanning electron microscopy (FESEM), and nitrogen adsorption/desorption analysis, in order to confirm the structure integrity of the Keggin unit in as-prepared composites and investigate the morphology and surface textural property of the composites. Catalytic activity of the catalysts has been evaluated for esterification of acetic acid with ethanol as a model reaction with different catalytic reaction parameters such as POM loading, reaction time, catalyst dose, molar ratio of the reactants, etc. The conversion of ethanol reached 87.4 % and the turnover frequency (TOF) was 2.9×10³.Meanwhile, it is easy to separate and recover POM/Ta₂O₅ from the reaction system.     

跨国建设投资项目协同管理的多元策略

随着上海经济的迅猛发展,建设工程项目市场全球化的趋势愈演愈烈,在沪投资的国际工程项目越来越多。这些外资投资项目在为我国建设企业引入新的管理理念和技术的同时,也与我国传统的建设项目管理模式发生着碰撞。为此,亟待根据我国建设企业项目管理现状,构建适应跨国投资项目良性运作的协同管理体系。     

基于动机模型的自主性虚拟人行为选择研究

自主性虚拟人的研究是人工生命和计算机动画交叉融合的新领域.但是由于人的心理活动是一个整体的过程,动机、感知等这些参数都是互有联系和影响的,目前的研究仍然只是局部和有限的.借鉴马斯洛理论,基于动机模型框架提出一种简化的抑制和疲劳模型控制的虚拟人自主行为选择机制,实验结果表明,该方法较好地解决了在资源有限的动态虚拟环境中,虚拟人如何在多个相互抑制的行为之间对行为进行仲裁和选择.经实验证明,该研究可以应用于智能交互领域.     

文件头中存储加密标识技术的研究与实现

为了解决目前文件安全方面存在的问题,进一步提高对文件数据的保护强度,对基于文件系统过滤驱动的文件透明加/解密技术进行了研究,针对传统方法在加密标识识别方面存在的不足,提出了文件头保存文件加密标识的方法。该方法通过过滤驱动为密文文件添加一个包含加密标识的文件头,在进行文件操作时通过该加密标识识别密文文件。同时过滤驱动在处理文件的读写操作时,通过设置文件读写偏移跳过文件头,完成正确的读写操作。结合实例,对测试结果进行了分析,验证了该设计的准确性、有效性和可行性。     

基于IMEMS传感器的汽车行驶姿态检测系统设计

为了给汽车安全控制系统提供准确的行驶姿态数据,设计了基于集成微机电系统(IMEMS)传感器的虚拟仪器检测系统。系统采用IMEMS传感器采集车身姿态参数,在虚拟仪器平台下对数据进行分析处理,使用卡尔曼滤波器对测量值进行估计,并在实车工况下对检测系统进行测试。结果表明:系统能够很好地满足汽车行驶姿态检测的要求,具有可靠的实时性。     

Exploration of artificial neural network to predict morphology of TiO2 nanotube

Artificial neural network (ANN) was developed to predict the morphology of TiO₂ nanotube prepared by anodization. The collected experimental data was simplified in an innovative approach and used as training and validation data, and the morphology of TiO₂ nanotube was considered as three parameters including the degree of order, diameter and length. Applying radial basis function neural network to predict TiO₂ nanotube degree of order and back propagation artificial neural network to predict the nanotube diameter and length were emphasized in this paper. Some important problems such as the selection of training data, the structure and parameters of the networks were discussed in detail. It was proved in this paper that ANN technique was effective in the prediction work of TiO₂nanotube fabrication process.     

Performance comparison of artificial neural network and logistic regression model for differentiating lung nodules on CT scans

PurposeTo compare the diagnostic performances of artificial neural networks (ANNs) and multivariable logistic regression (LR) analyses for differentiating between malignant and benign lung nodules on computed tomography (CT) scans.MethodsThis study evaluated 135 malignant nodules and 65 benign nodules. For each nodule, morphologic features (size, margins, contour, internal characteristics) on CT images and the patient’s age, sex and history of bloody sputum were recorded. Based on 200 bootstrap samples generated from the initial dataset, 200 pairs of ANN and LR models were built and tested. The area under the receiver operating characteristic (ROC) curve, Hosmer–Lemeshow statistic and overall accuracy rate were used for the performance comparison.ResultsANNs had a higher discriminative performance than LR models (area under the ROC curve: 0.955 ± 0.015 (mean ± standard error) and 0.929 ± 0.017, respectively, p < 0.05). The overall accuracy rate for ANNs (90.0 ± 2.0%) was greater than that for LR models (86.9 ± 1.6%, p < 0.05). The Hosmer–Lemeshow statistic for the ANNs was 8.76 ± 6.59 vs. 6.62 ± 4.03 (p > 0.05) for the LR models.ConclusionsWhen used to differentiate between malignant and benign lung nodules on CT scans based on both objective and subjective features, ANNs outperformed LR models in both discrimination and clinical usefulness, but did not outperform for the calibration.     

Decentralized fault detection and diagnosis via sparse PCA based decomposition and Maximum Entropy decision fusion

This paper proposes an approach for decentralized fault detection and diagnosis in process monitoring sensor networks. The sensor network is decomposed into multiple, potentially overlapping, blocks using the Sparse Principal Component Analysis algorithm. Local predictions are generated at each block using Support Vector Machine classifiers. The local predictions are then fused via a Maximum Entropy algorithm. Empirical studies on the benchmark Tennessee Eastman Process data demonstrated that the proposed decentralized approach achieves accuracy comparable to that of the fully centralized approach, while offering benefits in terms of fault tolerance, reusability, and scalability.