民商事合同中的 “过度履行”研究

合同是契约精神的产物,从该角度来讲,合同之约定应当被双方所严格履行。但是实践中会出现一种情况,合同之债务人因种种原因而履行了优于原约定的商品或行为。在这种情况下,对于这种“过度履行”应如何定性,法律又应对其如何评价,该问题深值得探讨。本文拟在通过对合同及契约精神的介绍,结合不同学者对该问题的认识,来对合同的“过度履行”做定性分析。同时为了让本文之研究结果可以更好的回归实践,本文后部分亦有对研究结论中的适用条件之举证责任做补充研究。对日后有关过度履行的相关研究及立法有很大意义。</jats:p

ATF-DF-project.zip

这项研究提出了ATF-DF-VS的文本分类算法，通过提取类典型特征向量和利用向量稀疏性来实现文本分类。该算法分为两个阶段：首先提取类别特征向量，然后利用这些特征向量进行文本分类计算。实验结果显示，该算法在提高文本分类准确率的同时，显著降低了计算复杂度并加快了计算速度。这个压缩包里包含了VS算法代码文件夹，KNN算法代码文件夹，以及十四类训练和测试数据集文件夹，其中VS和KNN文件夹里的README文件为代码说明。</p

Article Data

The package contains 14 categories of class typical dictionaries, class typical feature vector, class typical feature vector DF value and Sort the words by weight value in descending order.This paper proposes an Average Term frequency-Document frequency-Wavelet analysis (ATF-DF-WA) algorithm for text classification. The algorithm leverages the inherent characteristics of big data text samples, which are furnished with pre-existing category labels. It employs wavelet analysis on these samples and subsequently performs classification through waveform similarity computation. The algorithm is structured into two distinct stages. The first stage is the typical feature extraction stage. Before text classification, the ATF-DF text category feature extraction algorithm is initially proposed drawing upon statistical principles of big data. The ATF-DF algorithm is employed for the extraction of features from multi-category large-scale textual data, utilizing the existing category labels to derive class-specific feature vectors. These vectors are subsequently transformed into waveforms, and the wavelet analysis is employed to derive the class typical feature layer waveform. The second stage is the text classification stage. Based on the class's typical feature vector, the feature vector for the sample to be classified is derived. Subsequently, wavelet analysis (WA) is conducted to extract the waveform from the feature layer. Text classification is accomplished by quantifying the similarity between the obtained waveform and the waveform of the class's characteristic feature layer. The ATF-DF-WA algorithm leverages the statistical advantages inherent in big data and effectively employs wavelet analysis tools, thus offering distinctive benefits. Experimental outcomes indicate that, in comparison with conventional text classification algorithms, the ATF-DF-WA algorithm precisely extracts class-representative feature vectors from diverse text types. This precision is attributed to the application of wavelet analysis and waveform similarity computations, which substantially enhance the accuracy, recall rate, and F1 score of text classification.</p

train test data

This package contains the training data set as well as the test data set。</p

Article Data

The package contains 14 categories of class typical dictionaries, class typical feature vector, class typical feature vector DF value and Sort the words by weight value in descending order.Here's a description of the study:In this study, we propose the ATF-DF-VS algorithm for text classification. The algorithm aims to extract class-typicalfeature vectors from labeled text samples, andbased on these extractedvectors, the classification process by leveraging vector sparsity isfinally completed. The algorithm is structured into two distinct stages. The first stage is the typical feature extraction stage. In this part, an ATF-DF algorithm is introduced to extract the distinctive features of large text datasets with predefined categories. The ATF-DF weight values for all terms in the characteristic dictionary are calculated. Following a DF descending order and data compression procedure, the feature terms with the highest values are selected as the class-typicalfeature vectors. The second stage is the text classification stage. The classification samples are vectorized using the class typical feature vectors, and several vectors of the samples to be classified are obtained. Text classification is completedby calculating the sparsity of the vectors. This algorithm leverages the advantages of big data sample statistics and has a unique advantage in text classification calculations. The experimental findings indicate that the ATF-DF-VS algorithm outperforms traditional text classification algorithms in accurately extracting class- typicalfeature vectors from various types of text. This enhancement leads to a marked improvement in text classification accuracy, a substantial reduction in computational requirements for text classification, and an increase in processing speed.</p

伊犁河谷春秋草场草地生态调查及其恢复对策

针对新疆伊犁河谷春秋草场严重退化的现状,通过对退化草场、围栏封育并实施灌溉的草场、单纯围栏的草场和灌溉但不封牧草场样方的对比调查,从地表植物的生物量、植物种类、优质牧草所占比例和草地植物多样性指数等的变化规律和特征方面,探讨了伊犁河谷地区春秋草场退化的原因和恢复措施。结果表明:适当灌溉后草地生物量为1540.5g.m-2,远高于不灌溉的草地(生物量为188.13g.m-2),表明干旱是本区草地植被生长一个重要制约因素;而仅改变草地的水分条件,在超载率达123%的大环境下,草场中优质牧草的比例由封育的62.38%下降到未封育的2.08%。因此,适度放牧并辅以灌溉措施是本区天然草场恢复的2个必要条件