Several notable issues arise from overcrowding in an emergency department (ED) for both patients and staff. Longer wait times, higher ambulance diversion rates, longer stays, higher incidence of medical errors, higher...Several notable issues arise from overcrowding in an emergency department (ED) for both patients and staff. Longer wait times, higher ambulance diversion rates, longer stays, higher incidence of medical errors, higher rates of patient mortality, and greater harm to hospitals due to financial losses are some of these problems. Collaboration is crucial in the healthcare industry since it determines the team’s hourly performance in managing patient care. By using Walker and Avant’s (2011) concept analysis method, the author reviewed the literature to better understand ED crowding, to ensure that patients receive safe treatment in a timely manner, and to highlight best practices that can be identified through concept analysis and practice evaluations. In conducting this concept analysis, Walker and Avant’s framework was applied to examine the nature of the findings selected for the advancement of the concept. Everyone working in the ED, from those who determine policy to those on the front lines continually encounter new obstacles, but has little or no time to formulate fresh concepts or reconsider how ED treatment is provided. Overcrowding occurs when the number of patients requiring attention, awaiting transfer, or undergoing diagnosis and treatment exceeds the physical capacity of ED staff. If a clear plan is not in place to increase and improve services in proportion to a growing population, this situation will persist.展开更多
针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层...针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层的调制模式所确定的三维空间内选择最佳的速率。通过实际测试验证,结果表明在不同的信道环境下,该算法的吞吐率性能均优于Atheros MIMO RA、Minstrel等速率自适应算法,有效提高了网络吞吐量性能和利用率。展开更多
智能制造的蓬勃发展奠定了新一代工业革命的基石,而智能化生产需要实时采集现场工业数据,并通过大数据分析和计算,实现智能决策与控制。特别是随着视觉技术的迅猛发展,大量的视觉数据回传,对工业网络提出巨大的挑战。从工业场景入手,分...智能制造的蓬勃发展奠定了新一代工业革命的基石,而智能化生产需要实时采集现场工业数据,并通过大数据分析和计算,实现智能决策与控制。特别是随着视觉技术的迅猛发展,大量的视觉数据回传,对工业网络提出巨大的挑战。从工业场景入手,分析智能制造对工业网络,特别是吞吐量方面的性能需求,进而提出基于第五代移动通信技术(5th generation mobile communication technology,5G)高频系统的无线解决方案,并完成5G高频网络的性能测试。测试结果表明,5G高频解决方案满足智能制造上行高吞吐量的需求。展开更多
Multi-cell multi-user multiple-input multiple-output （MC-MU-MIMO） is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the lar...Multi-cell multi-user multiple-input multiple-output （MC-MU-MIMO） is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing （MCP） systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit （CU）. Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.展开更多
文摘Several notable issues arise from overcrowding in an emergency department (ED) for both patients and staff. Longer wait times, higher ambulance diversion rates, longer stays, higher incidence of medical errors, higher rates of patient mortality, and greater harm to hospitals due to financial losses are some of these problems. Collaboration is crucial in the healthcare industry since it determines the team’s hourly performance in managing patient care. By using Walker and Avant’s (2011) concept analysis method, the author reviewed the literature to better understand ED crowding, to ensure that patients receive safe treatment in a timely manner, and to highlight best practices that can be identified through concept analysis and practice evaluations. In conducting this concept analysis, Walker and Avant’s framework was applied to examine the nature of the findings selected for the advancement of the concept. Everyone working in the ED, from those who determine policy to those on the front lines continually encounter new obstacles, but has little or no time to formulate fresh concepts or reconsider how ED treatment is provided. Overcrowding occurs when the number of patients requiring attention, awaiting transfer, or undergoing diagnosis and treatment exceeds the physical capacity of ED staff. If a clear plan is not in place to increase and improve services in proportion to a growing population, this situation will persist.
文摘针对超高速IEEE 802.11ac网络中速率自适应算法的效率是决定系统性能的关键因素,本文提出了一种基于信息统计的高效速率自适应算法(AMRA)。该算法采用发送和接收相结合的方式精确地估计当前信道状况,并在由发送带宽、空间流数、物理层的调制模式所确定的三维空间内选择最佳的速率。通过实际测试验证,结果表明在不同的信道环境下,该算法的吞吐率性能均优于Atheros MIMO RA、Minstrel等速率自适应算法,有效提高了网络吞吐量性能和利用率。
文摘智能制造的蓬勃发展奠定了新一代工业革命的基石,而智能化生产需要实时采集现场工业数据,并通过大数据分析和计算,实现智能决策与控制。特别是随着视觉技术的迅猛发展,大量的视觉数据回传,对工业网络提出巨大的挑战。从工业场景入手,分析智能制造对工业网络,特别是吞吐量方面的性能需求,进而提出基于第五代移动通信技术(5th generation mobile communication technology,5G)高频系统的无线解决方案,并完成5G高频网络的性能测试。测试结果表明,5G高频解决方案满足智能制造上行高吞吐量的需求。
文摘Multi-cell multi-user multiple-input multiple-output （MC-MU-MIMO） is a promising technique to eliminate inter-user interference and inter-cell cochannel interference in wireless telecommunication systems. As the large number of users in the system and the limited number of simultaneously supportable users with MC-MU-MIMO, it is necessary to select a subset of users to maximize the total throughput. However, the fully centralized user selection algorithms used in single cell system, which will incur high complexity and backhaul load in multi-cell cooperative processing （MCP） systems, are not suitable to MC-MU-MIMO systems. This article presents a two cascaded user selection method for MCP systems with multi-cell block diagonalization. In this paper, a local optimal subset of users, which can maximize the local sum capacity, is first chosen by the greedy method in every cooperative base station in parallel. Then, all the cooperative base stations report their local optimal users to the central unit （CU）. Finally, the global optimal users, which can maximize the global sum capacity of MCP systems, are selected from the aggregated local optimal users at the CU. The simulation results show that the proposed method performs closely to the optimal and centralized algorithm. Meanwhile, the complexity and backhaul load are reduced dramatically.
