关键词： 医疗机构   专科医院   综合医院   基本医疗卫生   医院信息化   妇幼保健院   促进法   安全和质量  

摘要： 第一章总则第一条为促进智慧医院建设发展,适应医院信息化工作需要,规范医疗机构临床决策支持系统(clinical decision support system,CDSS)应用管理,提升医疗安全和质量,保证医患双方合法权益,依据《中华人民共和国基本医疗卫生与健康促进法》《医疗机构管理条例》等,制定本规范。第二条实施CDSS的二级以上综合医院、专科医院、妇幼保健院,其CDSS的建设、应用、安全和管理等适用本规范。其他级别、类别的医疗机构实施CDSS,可参照本规范执行。

关键词： 水旱灾害防御   工作体系   责任落实   决策支持   调度指挥  

摘要： 深入贯彻落实习近平总书记关于防汛抗旱工作重要指示精神,按照党的二十届三中全会决策部署,水利部办公厅印发了《加快构建水旱灾害防御工作体系的实施意见》,指导各级水利部门加快构建责任落实、决策支持、调度指挥“三位一体”的水旱灾害防御工作体系。解读了《实施意见》编制背景与重要意义、主要内容和特点及责任落实、决策支持、调度指挥三大方面的内涵,并提出《实施意见》印发后的落实安排,推动水旱灾害防御工作体系落地见效。

关键词： 国家发展改革委   企业生产经营   纺织原料   价格指数   大数据采集   决策支持   监测中心   价格变化趋势  

摘要： 7月18日,在国家发展和改革委员会价格监测中心网站“价格指数”——“社会机构价格指数”板块中,首次发布了上海棉交中心上棉指数。该指数多维度反映国内棉花市场价格变化,将为国内涉棉产业链相关企业生产经营提供价格参考与决策支持。上棉指数由上海国际棉花交易中心编制,通过大数据采集、分析、应用,以全新、直观的表现形式,准确客观反映国内重要大宗纺织原料价格变化趋势。

关键词： 决策支持系统   企业管理   信息系统应用  

摘要： 本文从决策支持系统的概念与构成出发,根据决策支持系统的性质不同,将其分为结构化决策、非结构化决策与半结构化决策并进行详细介绍,同时结合决策支持系统的功能与特征,对其在企业管理中的应用进行深入的探究.

关键词： Hydroelectric generators  

摘要： Traditionally, maintenance in the hydropower industry has been a labour-intensive and time-consuming process. It often relies on scheduled inspections and manual intervention. E-maintenance in hydropower plants can help to address this challenge by allowing remote monitoring and control of plant equipment, enabling timely detection and diagnosis of potential problems. This paper presents a case study of the implementation of an e-maintenance strategy for hydropower infrastructure at one of the largest generation companies in the Colombian electricity market. A machine learning model, implemented by Enel Colombia, is fed with recorded data on turbine bearing temperature and active power generation to predict problems in hydropower generators. The results show how e-maintenance can reduce operating costs and avoid breakdowns in hydro generation. © 2024, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： family separation   migration   Latina/o/x/e   relational maintenance   primary caregiver  

摘要： Latina/o/x/e families who experience migration-related separation face the heart-wrenching decision to live apart from each other, often to obtain better life opportunities for the entire family. In these situations, children live in a country separate from one or more parents, while a primary caregiver (e.g., the other parent, a grandmother, an aunt) looks after the children. Utilizing semi-structured interview data with 20 family triads (i.e., separated parent, separated child, primary caregiver) and drawing from the long-distance relational maintenance model (LDRMM), this study explores how primary caregivers help or impede separated parents and children's relational maintenance, while living apart in two different countries. Prospectively, caregivers were usually tasked with the responsibility of informing the child and helping them understand the upcoming family separation. Introspectively, most primary caregivers facilitated relational maintenance by directly connecting the child with the parent, providing the resources for the two parties to communicate (e.g., cellphone, WIFI), and emphasizing the benefits of the separation. Retrospectively, primary caregivers helped the children understand the separated parents' return;however, this often came at the expense of the primary caregiver feeling forgotten or overlooked by the child.

摘要： 为深入贯彻落实党中央、国务院决策部署,近日,商务部、中国人民银行、金融监管总局、国家外汇局联合印发《关于加强商务和金融协同 更大力度支持跨境贸易和投资高质量发展的意见》(以下简称《意见》),指导地方商务主管部门和中国人民银行分支机构、金融监管局、外汇局及金融机构加强协作联动,形成支持跨境贸易和投资高质量发展的更大合力.

关键词： 应急救援   指挥体系   优化   现代化   决策支持  

摘要： 应急救援是保障公众生命和财产安全的关键领域.在应急救援中,有效的指挥体系起着至关重要的作用.随着社会和技术的不断变化,现有的指挥体系需要不断优化和现代化改造.本文深入研究了应急救援指挥体系的现状、面临的挑战和机遇,以及如何借助先进技术和最佳实践来实现指挥体系的优化和现代化,探讨了指挥体系的组织结构、决策支持工具、通信技术、培训需求以及协调与合作机制.通过深入分析和案例研究,本文提供了提高应急救援指挥体系效率、灵活性和响应能力的具体建议.

关键词： Bike-sharing   Predictive maintenance   Survival analysis   Forecasting  

摘要： Bike-sharing systems have emerged as a significant element of urban mobility, providing an environmentally friendly transportation alternative. With the increasing integration of electric bikes alongside mechanical bikes, it is crucial to illuminate distinct usage patterns and their impact on maintenance. Accordingly, this research aims to develop a comprehensive understanding of mobility dynamics, distinguishing between different mobility modes, and introducing a novel predictive maintenance system tailored for bikes. By utilising a combination of trip information and maintenance data from Barcelona's bike-sharing system, Bicing, this study conducts an extensive analysis of mobility patterns and their relationship to failures of bike components. To accurately predict maintenance needs for essential bike parts, this research delves into various mobility metrics and applies statistical and machine learning survival models, including deep learning models. Due to their complexity, and with the objective of bolstering confidence in the system's predictions, interpretability techniques explain the main predictors of maintenance needs. The analysis reveals marked differences in the usage patterns of mechanical bikes and electric bikes, with a growing user preference for the latter despite their extra costs. These differences in mobility were found to have a considerable impact on the maintenance needs within the bike-sharing system. Moreover, the predictive maintenance models proved effective in forecasting these maintenance needs, capable of operating across an entire bike fleet. Despite challenges such as approximated bike usage metrics and data imbalances, the study successfully showcases the feasibility of an accurate predictive maintenance system capable of improving operational costs, bike availability, and security.

关键词： 恶意软件分类   灰度图   小样本学习   卷积神经网络   注意力机制   原型网络   联合损失函数  

摘要： 为解决小样本条件下恶意软件分类准确率低的问题,提出一种基于CBAM(convolutional block attention module)和原型网络的恶意软件分类模型。利用图像转换算法将恶意软件可执行文件转换为灰度图像;将残差连接和CBAM引入模型的特征嵌入模块,从通道和空间两个维度上增强关键特征表达,使得到的特征更具分辨性;提出联合损失函数,在距离交叉熵损失的基础上加入原型损失,通过减小类内距离的方式进一步扩增类间距离,使模型在样本数量有限的情况下取得良好的分类效果。实验结果表明,在每类恶意软件仅有5个样本的情况下,模型的分类准确率仍可达到83.12%。