上冊從宋代文化史的大背景系統闡釋北宋理學的學術傳統，指出理學是一種合道德與倫理為一的義理精神。第一部分分析宋代文化的特點（道統的確立、文官體制、崇儒的社會風氣、技術的發達、民間宗教興盛等），以及宋儒所 建構的義理傳統及其哲學特質。第二部分詳細討論北宋理學諸家的思想體系，點評其在中國思想史上的地位和理論貢獻，所涉學人包括周敦頤、張載、程顥、程頤、邵雍，並兼及討論南宋的朱熹和陸九淵。.

The explosion of multiomics data poses new challenges to existing data mining methods. Joint analysis of multiomics data can make the best of the complementary information that is provided by different types of data. Therefore, they can more accurately explore the biological mechanism of diseases. In this article, two forms of joint nonnegative matrix factorization based on the sparse and graph Laplacian regularization method are proposed. In the method, the graph regularization constraint can preserve the local geometric structure of data. (...) L 2,1 -norm regularization can enhance the sparsity among the rows and remove redundant features in the data. First, SG-jNMF1 projects multiomics data into a common subspace and applies the multiomics fusion characteristic matrix to mine the important information closely related to diseases. Second, multiomics data of the same disease are mapped into the common sample space by SG-jNMF2, and the cluster structures are detected clearly. Experimental results show that SG-jNMF can achieve significant improvement in sample clustering compared with existing joint analysis frameworks. SG-jNMF also effectively integrates multiomics data to identify co-differentially expressed genes. SG-jNMF provides an efficient integrative analysis method for mining the biological information hidden in heterogeneous multiomics data. (shrink)

A dam is a complex and important water-retaining structure. Once the dam is broken, the flood will cause immeasurable damage to the lives and properties of the downstream people, so it is particularly important to have the dam risk management. Since the dam-break flood is a severe-consequence low-frequency event, the corresponding fatalities caused by it are difficult to estimate due to the lack of relevant data and poor data continuity. This paper analyzes the direct and indirect factors affecting the risk (...) of life loss in dam failures and studies the characteristics, distribution rules, and membership functions of each factor. An adaptive differential evolution method is constructed through an optimization of the mutation factors and cross factors of the differential evolution method. This proposed evaluation method also combines with the fuzzy clustering iterative method that is capable of evaluating the similarity of life loss in dam accidents. The effectiveness of the proposed method is verified by 16 dam-break case studies. (shrink)

本书是研究马克思主义哲学的成果汇集。这些成果从马克思主义哲学的视域,对当代重大现实问题和理论问题进行了认真的研究.

Neurite orientation dispersion and density imaging is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging, these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The (...) whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index and orientation dispersion index, increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter. These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development. (shrink)

本书共五章,内容包括文化模式与思维结构、现代立宪思维的理性主义认识论、自然法哲学及其在欧洲政治文化中的演进等。.

本书包括:古希腊;古罗马;中世纪;文艺复兴;古典主义;启蒙运动;德国古典时期;浪漫主义等内容。.

BackgroundPublished data and practice recommendations on end-of-life care generally reflect Western practice frameworks; there are limited data on withdrawal of treatment for children in China.MethodsWithdrawal of treatment for children in the pediatric intensive care unit of a regional children’s hospital in eastern China from 2006 to 2017 was studied retrospectively. Withdrawal of treatment was categorized as medical withdrawal or premature withdrawal. The guardian’s self-reported reasons for abandoning the child’s treatment were recorded from 2011.ResultsThe incidence of withdrawal of treatment for children (...) in the PICU decreased significantly; for premature withdrawal the 3-year average of 15.1% in 2006–2008 decreased to 1.9% in 2015–2017. The overall incidence of withdrawal of care reduced over the time period, and withdrawal of therapy by guardians was the main contributor to the overall reduction. The median age of children for whom treatment was withdrawn increased from 14.5 months in 2006 to 40.5 months in 2017. Among the reasons given by guardians of children whose treatment was withdrawn in 2011–2017, “illness is too severe” ranked first, accounting for 66.3%, followed by “condition has been improved”. Only a few guardians ascribed treatment withdrawal to economic reasons.ConclusionsThe frequency of withdrawal of medical therapy has changed over time in this children’s hospital PICU, and parental decision-making has been a large part of the change. (shrink)

本书是一本研究建筑学的书籍, 全书包括有《现象学与建筑学的思考》、《建筑中的神性》等多篇文章.

[v. 1] Xian Qin juan -- [v. 2] Qin Han juan -- [v. 3] Wei Jin Nan Bei Chao juan -- [v. 4] Sui Tang Wu Dai juan -- [v. 5] Song Liao Xi Xia Jin Yuan juan -- [v. 6] Ming Qing juan.