3 resultados para capacity planning and investment

em DigitalCommons@University of Nebraska - Lincoln


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The purpose of the current study is to identify the impact of teaching students to revise their stories on writing production (Total Words Written; TWW), writing accuracy (Percent Correct Writing Sequences; %CWS), number of critical story elements included in stories, and quality of writing. Three third-grade and one fourth-grade student who were experiencing difficulties in the area of writing were involved in the study. The students were first taught to plan their stories using the evidence-based program, Self-Regulated Strategy Development (SRSD), which has frequently been implemented to teach students to plan their stories. Students were then taught to revise their stories using SRSD procedures modified for instruction in revision strategies. Student progress was evaluated through a multiple-probe design across tasks and a multiple-probe design across participants, which allowed for experimental control over time and across story probes. In addition to the previously mentioned variables, student’s acceptability of the intervention and their attitudes toward writing were also assessed. Results indicated that instruction in revising increased student writing accuracy beyond the effects of instruction in planning. Additionally, although instruction in planning was shown to increase writing production, number of critical story elements, and quality of writing, instruction in revising produced additional improvement in these variables as well. Finally, results indicated that students liked the intervention and their attitudes toward writing generally increased. Implications for practice and future research directions will be discussed. Advisor: Merilee McCurdy

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Given the similar interests of United Way organizations and universities in planning, implementation, and evaluation of human services, the two social institutions could be extensively and effectively partnering with one another. However, there is little documentation that such cooperative efforts are taking place. This article describes one such collaboration in Lincoln, Nebraska. The purpose of the article is to show the potential of such collaboration to improve community-wide coordination and outcomes by following the principles of a community-engagement model, to generate more effective use of evaluative tools that can assist in developing evidence-based practices in community planning, and to connect areas of study within the university to United Way efforts.

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The next-generation SONET metro network is evolving into a service-rich infrastructure. At the edge of such a network, multi-service provisioning platforms (MSPPs) provide efficient data mapping enabled by Generic Framing Procedure (GFP) and Virtual Concatenation (VC). The core of the network tends to be a meshed architecture equipped with Multi-Service Switches (MSSs). In the context of these emerging technologies, we propose a load-balancing spare capacity reallocation approach to improve network utilization in the next-generation SONET metro networks. Using our approach, carriers can postpone network upgrades, resulting in increased revenue with reduced capital expenditures (CAPEX). For the first time, we consider the spare capacity reallocation problem from a capacity upgrade and network planning perspective. Our approach can operate in the context of shared-path protection (with backup multiplexing) because it reallocates spare capacity without disrupting working services. Unlike previous spare capacity reallocation approaches which aim at minimizing total spare capacity, our load-balancing approach minimizes the network load vector (NLV), which is a novel metric that reflects the network load distribution. Because NLV takes into consideration both uniform and non-uniform link capacity distribution, our approach can benefit both uniform and non-uniform networks. We develop a greedy loadbalancing spare capacity reallocation (GLB-SCR) heuristic algorithm to implement this approach. Our experimental results show that GLB-SCR outperforms a previously proposed algorithm (SSR) in terms of established connection capacity and total network capacity in both uniform and non-uniform networks.