排序方式： 共有113条查询结果，搜索用时 15 毫秒
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对我国澳门地区的幼儿园教育模式、入学考核、收生方式、评审内容、计划与政策等进行多角度分析与比较,有助于深入了解当地的幼儿教育制度,从而参照相关细节,使我国建立完善的教育体制模型,达到平等教育的核心理念。同时,为确保幼儿教育产业的长期发展,使其受到社会各界的重视,改变以往对幼儿阶段学习与培训的漠视,开启孩童迈入教育的漫长道路,从而有利于教育事业的规划、提高人民的素养。 相似文献
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曹叶 《孝感职业技术学院学报》2021,(1):94-98
客观归责理论是解决结果归属的重要理论,其在过失犯中应用居多。客观归责的两个步骤,首先是在结果和法所不允许的风险之间存在因果关系,其次是在这一因果流程未被阻断或改变的前提下,损害结果在规范目的的范围内,满足这两个条件的行为人才具有责任。客观归责理论所确立的归责流程致力于限制责任范围,将那些没有刑法规范意义的因果流程从责任范围内剔除出去,旨在通过一系列规则体系限制人们因自己的行为招致的外部风险承担。 相似文献
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在比较我国澳门与内地关于商品房预售的法律规定基础上提出,对澳门来说,可通过适当调整行政管理强度和方式弥补法律漏洞,从根源上遏制发展商一房二卖的行为;对内地来说,可参照我国《澳门民法典》,将预约合同规定于《合同法》总则或某个章节中,并明确规定预约合同具有一般合同的属性和效力,其立法应从预约合同在成立、效力、种类、合同责任、归责原则等方面与现行《合同法》中一般合同的规定存在的差异入手。 相似文献
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本文依托产业价值链的“微笑曲线”,结合供应链的“四流”价值流要素,构建供应链服务行业的价值链模型,接着结合商业模式创新的理论视角,应用探究性和案例性研究方法,对供应链企业从创新战略与创新路径两个维度研究商业模式创新机制,最后以实践案例进行分析。研究得出三点结论：在商业模式创新机制方面,企业可实施商流、物流、信息流和资金流四大价值流管理创新战略;企业可遵循专业性精深创新和综合性拓展创新两条路径;企业实施商业模式创新需要以其拥有的关键资源和能力以及依托其在所属行业的价值链模型作为参照基础。 相似文献
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[目的/意义] 以哮喘为例,对其相关手机应用程序的可信性及全面性作详细探讨。[方法/过程] 以中英文关键词"哮喘"asthma"肺功能"lung function"峰流量"peak flow"吸入器"inhaler",在App Store、Google Play及安卓市场上搜寻,并下载相关程序进行筛查及评估。根据英国BTS/SIGN、美国EPR-3、国际GINA及HONcode的医学及信息指引去统计哮喘疾病和相关治疗资料并评估其可信性及全面性。[结果/结论] 研究发现许多手机应用程序的内容与医学指引不一致,如果医护工作者以手机应用程序内容作学习用途并依此为患者提供医学意见,有可能对患者健康造成危害。目前在市场上较难找出优质的应用程序,应用程序的开发者及应用程序平台的管理者应作出改善。 相似文献
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采用文献资料法、特尔斐法、问卷调查法等,对澳门家庭体育现状进行调查与分析.在概述澳门家庭内部属性及家庭网的基础上,探讨澳门家庭体育人口特征、活动特征、消费特征、体育参与因素、社会支持等.建议:发展核心家庭的体育人口,开展家庭体育可因"类"而"宜",利用家庭网发展家庭体育,增加女性体育人口,促成影响因素的有效联动. 相似文献
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以战略性新兴产业民营A股上市公司的2014—2018年度财务报告数据为研究对象,探讨环境不确定性、会计稳健性对公司研发创新的影响。研究结果表明,会计稳健性对公司研发创新具有正向促进作用,环境不确定性对公司研发创新具有负向影响,会计稳健性是对环境不确定性的一种谨慎反映,可以抑制环境不确定性对公司研发创新的负面影响。通过区分环境不确定程度,进一步发现当环境高度不确定时,会计稳健性对公司研发创新的正面促进作用更加显著,当环境低度不确定时,会计稳健性对公司研发创新的作用并不显著。 相似文献
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《Information processing & management》2022,59(1):102764
Technology-intensive industries spend huge resources in the production of products to commercialize successful products. If the appetite on the market continues to change, the capacity to rapidly and cost-effectively refresh the offerings is an important competitive advantage. Even if components and designs need to be modified as new models are released, their underlying technology and designs can generally be reused to allow rapid economic development. Data are considered an important raw material that can influence multidisciplinary analysis, government, and business efficiency. In this paper, the Efficient data analytics (EDA) method has been suggested to fix societal challenges. The proposed methods aim to share the authors' views and perspectives on the emerging opportunities and challenges of the efficient data revolution.EDA provides four key aspects of technology reuse: strategy, method, culture, and information technology. The dimensions are further broken down into concepts supporting this technology reuse, including design on the technology platform and the reuse assessments. In practice, the system can evaluate an organization's existing reuse capabilities and offer an overall theoretical review of activities promoting technology reuse. To prove the system's concepts, industrial scenarios highlighting real questions of technological growth are used. Besides, the possible societal benefits of EDA in six ways are illustrated: enhanced decision management and incident prediction, data-informed technologies and innovative market models, direct social and climate benefits, community engagement, accountability, and public trust. Some best practice is suggested to capture these advantages. The experimental results suggested EDA increases reusability knowledge in the organization (96.3%), operational cost (95.1%), prediction ratio (97.4%), Community engagement ratio (94.1%), and public trust (98.5%). 相似文献
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《Information processing & management》2022,59(1):102790
Entity alignment is an important task for the Knowledge Graph (KG) completion, which aims to identify the same entities in different KGs. Most of previous works only utilize the relation structures of KGs, but ignore the heterogeneity of relations and attributes of KGs. However, these information can provide more feature information and improve the accuracy of entity alignment. In this paper, we propose a novel Multi-Heterogeneous Neighborhood-Aware model (MHNA) for KGs alignment. MHNA aggregates multi-heterogeneous information of aligned entities, including the entity name, relations, attributes and attribute values. An important contribution is to design a variant attention mechanism, which adds the feature information of relations and attributes to the calculation of attention coefficients. Extensive experiments on three well-known benchmark datasets show that MHNA significantly outperforms 12 state-of-the-art approaches, demonstrating that our approach has good scalability and superiority in both cross-language and monolingual KGs. An ablation study further supports the effectiveness of our variant attention mechanism. 相似文献
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《Information processing & management》2022,59(1):102816
Tourism has become a growing industry day by day with the developing economic conditions and the increasing communication and social interaction ability of the people. Forecasting tourism demand is not only important for tourism operators to maximize their revenues but also important for the formation of economic plans of the countries on a global scale. Based on the predictions countries are able to regulate the sectors that benefit economically from tourism locally. Therefore, it is crucial to accurately predict the demand in many weeks advance. In this study, we propose a new demand forecasting model for the hospitality industry that forecasts weekly hotel demand four weeks in advance through Attention-Long Short Term Memory (Attention-LSTM). Unlike most of the existing methods, the proposed method utilizes the time series demand data together with additional features obtained from K-Means Clustering findings such as Top 10 Hotel Features or Hotel Embeddings obtained using Neural Networks (NN). While creating our model, the clustering part was influenced by the fact that travelers choose their accommodation according to certain criteria, and the hotels meeting similar criteria may have similar demands. Therefore, before the clustering part, we also applied methods that would enable us to represent the features of the hotels more properly and we observed that 10-D Embedded Hotel Data representation with NN Embeddings came to the fore. In order to observe the performance of the proposed hotel demand forecasting model we used a real-world dataset provided by a tourism agency in Turkey and the results show that the proposed model achieves less mean absolute error and mean absolute percentage error (at worst % 3 and at most % 29 improvements) compared to the currently used machine learning and deep learning models. 相似文献