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关键词： 文献资料   生态中心主义   共同价值观   生态伦理学   政治学解读   人类中心主义   人与自然   如何看待  

摘要： 从政治学解读“绿色”的概念,范畴包括政党、政策、竞选组织、社会运动等,是一个明确的观念,包含一套特定的共同价值观。简单说,是如何看待人与自然之间关系的生态伦理学相关的价值判断。回溯不同领域绿色发展的文献资料,大致可分为人类中心主义和生态中心主义两种立场。由于两种主义存在对自然不同程度的尊重,引申出来的“绿色”就有“深绿”和“浅绿”之分。

关键词： 张枣   在森林中   元诗  

摘要： 张枣的诗通常有一种辩驳的张力充斥其中,他的《在森林中》以"森林"作为"诗学之旅"的出发点,在旅行的过程中涉及了他对"虚/实"、"时间/空间"、"语言/想象"等诸多关系的探讨和追寻,从而在诗学层面上回应了其所持有的"元诗"理论。在诗歌文本话语的多重纠葛之外,张枣的诗歌与其诗论也构建出了"辩驳"的张力空间。

关键词： 农民专业合作社   社会责任   利益相关者理论   AHP-熵值法  

摘要： 农民专业合作社具有"社会性"与"营利性"双重属性。一些合作社除为成员提供产品和服务之外,还承担了一些社会责任。特殊的产权结构和利益分配机制使合作社社会责任有别于传统的企业社会责任。合作社在自身能力、经营模式和外部因素的影响下,社会责任表现差异显著。基于利益相关者理论和广义企业社会责任观,首先分析了合作社社会责任范畴与特殊性;其次构建了社会责任评价体系,利用"层次分析法+熵值法"确定指标权重;最后基于398家种植类农民专业合作社的调研数据对评价体系进行应用与检验。研究结果表明,样本合作社对社员、社区、商业伙伴、消费者的社会责任履行程度较高,合作社社会责任评价指标体系具有适用性,对我国现行示范社评定体系具有一定的补充完善作用。

关键词： 护士   患者   隐私保护   认知  

摘要： 目的 调查护士对＂患者隐私＂保护范畴的认识情况,为护士进行＂患者隐私保护＂相关知识的培训及相关法律的健全完善提供参考依据.方法 采用问卷法进行调查.综合运用方便抽样与分层抽样,于2015年11月—2016年1月,对成都市两所三甲医院按科室隐私泄露风险的高低分层抽取科室,并整体纳入所抽取科室共600余名护士作为研究对象.发放自行设计的＂一般资料调查表＂及＂护士对＇患者隐私＇范畴的认识调查表＂.结果 共发放问卷672份,回收631份,其中有效问卷616份.调查显示护士对患者隐私保护范畴调查内容认同度较高的前五项为：身体的隐私部位（609人,98.86%）、接受医疗服务时因诊疗需要暴露个人信息的空间场所（586人,95.13%）、个人秘密（583人,94.64%）、生理缺陷（572人,92.86%）及个人收入（570人,92.53%）.认同度较低的五项为：性别（351人,56.98%）、姓名（399人,64.77%）、年龄（442人,71.75%）、心理活动（467人,75.81%）及女性的月经史（501人,81.33%）.结论 护理人员对患者隐私保护范畴的认识在个别项目上存在差异,护患双方的认识也存在明显差异.因此,需要进一步明确患者隐私保护的真正需求,不断强化护理人员对隐私相关知识的学习、完善医院相关管理制度,同时加强相关研究和立法,以切实保障患者的隐私权,提高患者的就医满意度.

关键词： CHRONIC myeloid leukemia   MYELODYSPLASTIC syndromes  

关键词： category learning   entropy   reasoning   representational distance   same-different learning  

摘要： The ability to form relational categories for objects that share few features in common is a hallmark of human cognition. For example, anything that can play a preventative role, from a boulder to poverty, can be a "barrier." However, neurobiological research has focused solely on how people acquire categories defined by features. The present functional magnetic resonance imaging study examines how relational and feature-based category learning compare in well-matched learning tasks. Using a computational model-based approach, we observed a cluster in left rostrolateral prefrontal cortex (rlPFC) that tracked quantitative predictions for the representational distance between test and training examples during relational categorization. Contrastingly, medial and dorsal PFC exhibited graded activation that tracked decision evidence during both feature-based and relational categorization. The results suggest that rlPFC computes an alignment signal that is critical for integrating novel examples during relational categorization whereas other PFC regions support more general decision functions.

关键词： MYELODYSPLASTIC syndromes   PATIENTS   TUMOR proteins   PROTEIN expression  

关键词： BRAIDED structures   TOPOLOGY   SYMMETRY (Mathematics)   ABELIAN groups   COHOMOLOGY theory  

摘要： A finite bosonic or fermionic symmetry can be described uniquely by a symmetric fusion category . In this work, we propose that 2+1D topological/SPT orders with a fixed finite symmetry are classified, up to quantum Hall states, by the unitary modular tensor categories over and the modular extensions of each . In the case , we prove that the set of all modular extensions of has a natural structure of a finite abelian group. We also prove that the set of all modular extensions of , if not empty, is equipped with a natural -action that is free and transitive. Namely, the set is an -torsor. As special cases, we explain in detail how the group recovers the well-known group-cohomology classification of the 2+1D bosonic SPT orders and Kitaev's 16 fold ways. We also discuss briefly the behavior of the group under the symmetry-breaking processes and its relation to Witt groups.

关键词： Dust   Maximum exposure limit   FTIR   FE-SEM   Particle size analyser   Risk  

摘要： This paper presents dust exposure study of 69 workers engaged in 11 categories of jobs over seven coalmines of Jharia Coalfields. Dust samples were analysed for dust concentration, maximum exposure limit (MEL), free silica and other minerals present, and particle size and shape. Study reveals that workers engaged in vicinity of coal/rock cutting operation, are exposed to higher dust concentration (50% samples exceeding MEL), and contain more fine particles (d(50) < 5 mu m) with sharp edges. Samples exceeding MEL are classified as high-risk category which needs special attention for taking preventive and protective measure like use of personal protective equipments, job rotation and reduction in dust generation through engineering control using appropriate technology of dust suppression and dust extraction as per their applicability. The study also suggests presence of kaolinite and asbestos along with quartz which make the dust more harmful in nature necessitating further investigation and careful control measures.

关键词： Weaning decision making   Association rule   Network analysis   Category weighted naive Bayes  

摘要： Mechanical ventilation is an invasive intervention commonly used in the intensive care unit to assist patients' respirations. Physicians' decisions to wean patients from ventilation are critical: Effective weaning decisions improve patient care and well-being, but ineffective decisions can create serious severe consequences and complications. Data-driven approaches, enabled by appropriate data mining techniques, can support physicians' weaning decisions. A review of the existing techniques reveals several gaps. Specifically, most techniques assume that a feature can contribute equally to different outcome classes, overlook the "fuzzy region" issue, and assess the importance of individual features holistically rather than scrutinize the discriminant power of distinctive categories of a feature toward each decision outcome class. To address these backdrops, we propose an association rule network-based feature category-weighted naive Bayes method capable of dealing with the inherent challenges in weaning decision making. Our method analyzes feature category weights for each decision outcome by incorporating association rule learning with weighted network analysis, then applies a category-weighted naive Bayes model that can assign differential weights to various feature categories. The results of our empirical evaluation, including several prevalent techniques-artificial neural network (ANN), ANN with backward feature selection, support vector machine (SVM), and SVM with logistical regression based feature selection-indicate that the proposed method consistently outperforms all the benchmark techniques in terms of accuracy, precision, recall and F measure. Published by Elsevier B.V.