科创中国

目的 探讨门诊疾病管理信息系统在系统性红斑狼疮患者中的应用效果.方法 成立课题组,制订管理流程,包括采集病例及建立管理档案、评估患者、实施护理干预及效果评价.结果 利用门诊疾病管理信息系统管理后患者对疾病的认知得分为(79.24±1.58)分;按时复诊依从性由56.25%提高到91.25%,服药依从性由71.25%提高到95.00%,患者对医护人员服务满意度山90.00%提高到100.00%.结论 门诊疾病管理信息系统是一种行之有效的门诊患者管理模式,适合对非住院系统红斑狼疮患者进行长期持续的管理.

基于互动媒体、数据库及利害关系管理的互动式整合营销传播(Interactive IMC)已经成为非营利组织创造价值、获得资源的重要战略手段.在阐述互动式营销传播的基础上,引入了供应链的价值模式,最后详细分析了互动式整合营销在供应链管理中的运用.

Insect source-sink dynamics are vital to ecologically intensive pest management. Maintaining sink plant hosts,or "trap crops",and destroying alternate hosts or breeding places adjacent to the field crop are effective pest management strategies for some arthropods. However,determining whether a host acts as a source or a sink is challenging, especially when the pest species is highly mobile and polyphagous. The western tarnished plant bug,Lygus hesperus,is highly polyphagous,and can utilize >300 hosts. Its presence has been documented in 26 roadside weed hosts in the Texas High Plains.Previous studies demonstrated that L. hesperus prefer alfalfa over cotton and several alternate weed hosts.A four-year project involved surveying and sampling for L. hesperus in the agricultural landscapes of several sub-regions of the southwestern United States,including the Texas High Plains. In Texas,geographic information of the landscape vegetation complex was compiled from a 150 km radius in the Texas High Plains. In one study,fifty irrigated cotton fields representing the crop diversity within this region were sampled via sweep-net for 10 weeks. This effort also included sampling of up to six noncotton insect habitats within a 3 km radius of each field. Seasonal average L. hesperus abundance data were regressed with 27 field characteristics( variables),including habitat-specific land cover,distance between focal cotton fields and non-cotton habitats,longitude,latitude,elevation,habitat heterogeneity index,and several environmental / ecological variables. Significant variables were selected using a stepwise regression at 15% probability rate. A 10-parameter linear model explained 93% of the variation in the data. Major parameters contributing significantly to variation in L. hesperus abundance in cotton were corn and sunflower acreages,focal cotton field distances from several non-cotton hosts,and habitat heterogeneity index. In addition,field marking-and-capture studies were conducted using protein markers and enzyme-linked immunosorbent assays to characterize L. hesperus intercrop movement behavior. The field marking-and-capture approach can be used to study the effects of various crop management practices on L. hesperus intercrop movement and can potentially be applied to other pests and cropping systems.

论述了河北省地下水数据库管理应用系统的设计思想,该系统在地理信息系统的支撑下实现了数据入库、查询和统计,结合河北省地下水业务需求,自动计算地下水通报相关内容并生成成果,有效提高了工作效率.

物流行业的飞速发展迫使物流公司有强烈的愿望通过上市进行资本融资,进而产生盈余管理的行为.首先对物流公司IPO过程中盈余管理的手段进行分析,进而又分析了其进行盈余管理的特点、识别方法及测度其盈余管理幅度的模型.

阐述了推进精益化仓储管理的重要意义,分析了当前仓储管理的现状与存在的主要问题,提出了推进精益化仓储管理的对策措施.

文章对工作家庭冲突的定义、常用测量工具和国内外研究现状进行综述,并总结了工作家庭冲突的影响因素,结合我国护理专业特点提出平衡工作家庭关系的措施.

项目管理预算是项目管理投资控制的重要环节,结合南水北调天津干线工程建设管理工作实践,从编制方法和编制原则方面论述了概算和项目管理预算不同的编制过程,从南水北调投资价差编制和投资控制考评及奖惩等不同的角度反映了项目管理预算的重要性.事实证明,有效的利用项目管理预算与项目工程价款结算对比,能够对投资有效控制,减少投资风险.有利于提高管理水平,加强成本控制,提高经济效益.

目的 探讨严格液体管理对重症肺部感染患者心肺保护作用及其可能的机制.方法 将157例重症肺部感染患者随机分为液体管理组和对照组,用脉搏指示连续心输出量监测(PiCCO)的方法检测血流动力学变化指标[心输出量(CO)、心指数(CI)、胸腔内血管容量( ITBVI)、血管外肺水(EVLWI)]并指导液体管理;实验前后测定患者血浆脑型利钠肽(BNP),评估液体管理对患者心功能的影响;测定纤支镜肺灌洗液中白细胞介素(IL)-6的浓度,以评估肺组织局部炎症情况,监测动脉血气分析;实验前后行胸部CT检查评价肺部病变恢复情况.结果 与对照组比较,液体管理组血浆脑型利钠肽、血流动力学指标( CO、CI、EVLWI)和肺组织局部IL-6水平均明显下降(P<0.05),动脉血气氧分压升高(P<0.05),肺泡-动脉氧分压差下降(P<0.05),肺部病变和肺功能明显改善.结论 积极液体管理可保护重症肺部感染患者的心肺功能,其原因可能与降低炎症反应及改善氧合有关.

Cyber physical systems(CPS)recently emerge as a new technology which can provide promising approaches to demand side management(DSM),an important capability in industrial power systems.Meanwhile,the manufacturing center is a typical industrial power subsystem with dozens of high energy consumption devices which have complex physical dynamics.DSM,integrated with CPS,is an effective methodology for solving energy optimization problems in manufacturing center.This paper presents a prediction-based manufacturing center self-adaptive energy optimization method for demand side management in cyber physical systems.To gain prior knowledge of DSM operating results,a sparse Bayesian learning based componential forecasting method is introduced to predict24-hour electric load levels for specific industrial areas in China.From this data,a pricing strategy is designed based on short-term load forecasting results.To minimize total energy costs while guaranteeing manufacturing center service quality,an adaptive demand side energy optimization algorithm is presented.The proposed scheme is tested in a machining center energy optimization experiment.An AMI sensing system is then used to measure the demand side energy consumption of the manufacturing center.Based on the data collected from the sensing system,the load prediction-based energy optimization scheme is implemented.By employing both the PSO and the CPSO method,the problem of DSM in the manufacturing center is solved.The results of the experiment show the self-adaptive CPSO energy optimization method enhances optimization by 5%compared with the traditional PSO optimization method.