A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution util...A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution utilizes the reflection technology of .NET and design pattern. A typical application of the solution demonstrates that the new solution of data access layer performs better than the current N-tier architecture. More importantly, the application suggests that the new solution of data access layer can be reused effectively.展开更多
As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabri...As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.展开更多
高校自主招生考试考务编排系统是基于.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计和实现的C/S应用程序,将考生报名数据信息进行分类统计,分别设计考务编排系统的表现...高校自主招生考试考务编排系统是基于.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计和实现的C/S应用程序,将考生报名数据信息进行分类统计,分别设计考务编排系统的表现层(UI)、业务逻辑层(BLL)和数据访问层(DAL),从而实现考生的准考证编排、考试时间、监考老师和考场的安排、考生准考证、监考老师通知单、考场名单及考场信息的打印及考生成绩的录入和成绩查询等功能。展开更多
高校自主招生考试网上报名系统是基于ASP.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计B/S应用程序,分别实现网上报名系统的表现层(UI)、业务逻辑层(BLL)和数据访问层(D...高校自主招生考试网上报名系统是基于ASP.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计B/S应用程序,分别实现网上报名系统的表现层(UI)、业务逻辑层(BLL)和数据访问层(DAL)的设计,从而实现考生网上个人信息的注册、专业志愿的选报,考生报名信息的确认、打印、考试时间的查询、准考证的打印和考试成绩的查询功能。展开更多
首先,基于云计算应用模式,提出一种能有效利用云存储架构的双层缓存技术.通过在客户端和服务器端建立分布式缓存,能有效避免用户频繁访问远端数据,为用户构建轻量级的客户端,解决了目前地学数据可视化软件大量占用用户本地存储容量的问...首先,基于云计算应用模式,提出一种能有效利用云存储架构的双层缓存技术.通过在客户端和服务器端建立分布式缓存,能有效避免用户频繁访问远端数据,为用户构建轻量级的客户端,解决了目前地学数据可视化软件大量占用用户本地存储容量的问题.同时服务器端也避免了多次访问云存储文件系统,减少了大量的数据检索与加载时间.其次,提出一种ARLS(association rule last successor)访问预测算法,根据用户的历史访问记录,利用关联规则挖掘用户的访问模式,对其访问行为进行预测,进而提前加载数据,提高缓存命中率,解决了用户在可视化过程中不断移动兴趣区域,频繁更换渲染数据的问题,能有效应对用户具有多种访问模式的情况,提高了预测准确率.实验结果表明,该云存储架构显著减少了本地资源消耗,访问预测算法的准确率在最差情形下可达47.59%,平均准确率达91.3%,分布式缓存的平均缓存命中率达95.61%,可有效支持云端大规模地震数据的快速可视化.展开更多
基金the Foundation for Key Teachers of Chongqing University (200209055).
文摘A new design solution of data access layer for N-tier architecture is presented. It can solve the problems such as low efficiency of development and difficulties in transplantation, update and reuse. The solution utilizes the reflection technology of .NET and design pattern. A typical application of the solution demonstrates that the new solution of data access layer performs better than the current N-tier architecture. More importantly, the application suggests that the new solution of data access layer can be reused effectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.61006003 and 61674038)the Natural Science Foundation of Fujian Province,China(Grant Nos.2015J01249 and 2010J05134)+1 种基金the Science Foundation of Fujian Education Department of China(Grant No.JAT160073)the Science Foundation of Fujian Provincial Economic and Information Technology Commission of China(Grant No.83016006)
文摘As an industry accepted storage scheme, hafnium oxide（HfO_x） based resistive random access memory（RRAM）should further improve its thermal stability and data retention for practical applications. We therefore fabricated RRAMs with HfO_x/ZnO double-layer as the storage medium to study their thermal stability as well as data retention. The HfO_x/ZnO double-layer is capable of reversible bipolar switching under ultralow switching current（〈 3 μA） with a Schottky emission dominant conduction for the high resistance state and a Poole–Frenkel emission governed conduction for the low resistance state. Compared with a drastically increased switching current at 120℃ for the single HfO_x layer RRAM, the HfO_x/ZnO double-layer exhibits excellent thermal stability and maintains neglectful fluctuations in switching current at high temperatures（up to 180℃）, which might be attributed to the increased Schottky barrier height to suppress current at high temperatures. Additionally, the HfO_x/ZnO double-layer exhibits 10-year data retention @85℃ that is helpful for the practical applications in RRAMs.
文摘高校自主招生考试考务编排系统是基于.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计和实现的C/S应用程序,将考生报名数据信息进行分类统计,分别设计考务编排系统的表现层(UI)、业务逻辑层(BLL)和数据访问层(DAL),从而实现考生的准考证编排、考试时间、监考老师和考场的安排、考生准考证、监考老师通知单、考场名单及考场信息的打印及考生成绩的录入和成绩查询等功能。
文摘高校自主招生考试网上报名系统是基于ASP.NET4.0的三层构架设计原理,以SQL Server2008作为后台数据库平台,用Visual Studio 2010的C#语言作为设计工具设计B/S应用程序,分别实现网上报名系统的表现层(UI)、业务逻辑层(BLL)和数据访问层(DAL)的设计,从而实现考生网上个人信息的注册、专业志愿的选报,考生报名信息的确认、打印、考试时间的查询、准考证的打印和考试成绩的查询功能。
文摘首先,基于云计算应用模式,提出一种能有效利用云存储架构的双层缓存技术.通过在客户端和服务器端建立分布式缓存,能有效避免用户频繁访问远端数据,为用户构建轻量级的客户端,解决了目前地学数据可视化软件大量占用用户本地存储容量的问题.同时服务器端也避免了多次访问云存储文件系统,减少了大量的数据检索与加载时间.其次,提出一种ARLS(association rule last successor)访问预测算法,根据用户的历史访问记录,利用关联规则挖掘用户的访问模式,对其访问行为进行预测,进而提前加载数据,提高缓存命中率,解决了用户在可视化过程中不断移动兴趣区域,频繁更换渲染数据的问题,能有效应对用户具有多种访问模式的情况,提高了预测准确率.实验结果表明,该云存储架构显著减少了本地资源消耗,访问预测算法的准确率在最差情形下可达47.59%,平均准确率达91.3%,分布式缓存的平均缓存命中率达95.61%,可有效支持云端大规模地震数据的快速可视化.
