Matching Items (24)

Creation of an E-Book to Promote Time Management for College Students

Description

The purpose of this creative project is to make an E-Book that promotes time management for college students in a way that interests them. The author of this recognizes that

The purpose of this creative project is to make an E-Book that promotes time management for college students in a way that interests them. The author of this recognizes that there are many distractions to keep college students from sitting down and reading a textbook; that is why an E-Book featuring videos and interactive videos was chosen. The research questions presented below began my research and understanding of the topic. These questions are as follows: 1. What is a way to promote time management for college students? a) What are some mediums that will appeal to young people who want to do more than just read a book. 2. When figuring out how to manage their time, what are the areas of life students consider to be most important? 3. What perspectives to various facets of the world like, business, academia and the foreign community think about time management? 4. What perspective to millennials have on time management? By answering these questions above, the author hopes to understand what is good time management, and how to explore it in a way that will interest young people. The author is doing so by creating a series of narrative videos that he himself acted in portraying a fictitious student both engaging in and not practicing good time management techniques. The created nine videos, with three dedicated to a section each. The three sections were what students do wrong, how they can improve and how they can maintain their success. Within each section were three sub- sections that students must use time management skills for: mental techniques, physical well-being, and juggling work and personal commitments. See the attached documents (Appendix A) for a full collection of the scripts that were created for these videos. The author also created quizzes through the website Bookry, allowing him to make review questions for those reading the book. The quizzes were then made into widgets and inserted into the book. Each quiz was about 5 questions each and was at the end of each of the sub-sections, meaning there were 45 questions total. See the attached documents (Appendix B) for screenshots of each quiz question and the correct answer.

Contributors

Agent

Created

Date Created
  • 2016-05

SMART SCHEDULING FOR INSTRUCTIONAL MODULE DEVELOPMENT SYSTEM

Description

Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate

Many organizational course design methodologies feature general guidelines for the chronological and time-management aspects of course design development. Proper course structure and instructional strategy pacing has been shown to facilitate student knowledge acquisition of novel material. These course-scheduling details influencing student learning outcomes implies the need for an effective and tightly coupled component of an instructional module. The Instructional Module Development System, or IMODS, seeks to improve STEM, or ‘science, technology, engineering, and math’, education, by equipping educators with a powerful informational tool that helps guide course design by providing information based on contemporary research about pedagogical methodology and assessment practices. This is particularly salient within the higher-education STEM fields because many instructors come from backgrounds that are more technical and most Ph.Ds. in science fields have traditionally not focused on preparing doctoral candidates to teach. This thesis project aims to apply a multidisciplinary approach, blending educational psychology and computer science, to help improve STEM education. By developing an instructional module-scheduling feature for the Web-based IMODS, Instructional Module Development System, system, we can help instructors plan out and organize their course work inside and outside of the classroom, while providing them with relevant helpful research that will help them improve their courses. This article illustrates the iterative design process to gather background research on pacing of workload and learning activities and their influence on student knowledge acquisition, constructively critique and analyze pre-existing information technology (IT) scheduling tools, synthesize graphical user interface, or GUI, mockups based on the background research, and then implement a functional-working prototype using the IMODs framework.

Contributors

Agent

Created

Date Created
  • 2016-05

137357-Thumbnail Image.png

Software Solutions to Academic Resource Distribution and Management

Description

Academic resources at Arizona State University are vast and allow a student to maintain success through his/her university attendance. The distribution and management of these systems is arduous and manually

Academic resources at Arizona State University are vast and allow a student to maintain success through his/her university attendance. The distribution and management of these systems is arduous and manually done. A software solution for the distribution of academic resource information is a Dashboard system that utilizes information from the university, and is expandable. A solution for the management of academic centers utilizes a scheduling algorithm that allows quick scheduling of resources. Overall these solutions provide easier workflows than the current systems allow.

Contributors

Agent

Created

Date Created
  • 2013-12

137405-Thumbnail Image.png

Optimal Scheduling of the Refurbishment of Rotable Parts in an Aircraft Maintenance System

Description

The efficient refurbishment of rotable parts on an aircraft proves to be a main concern for airline carriers today. Airlines must be able to seamlessly rotate parts into and out

The efficient refurbishment of rotable parts on an aircraft proves to be a main concern for airline carriers today. Airlines must be able to seamlessly rotate parts into and out of the system for maintenance in accordance with FAA requirements while leaving daily operations uninterrupted. In this paper, we develop an airline maintenance scheduling model that constructs an optimal schedule for part maintenance over a given time horizon using deterministic forecasting techniques. The model generates a schedule that minimizes the total cost of a maintenance schedule solution while maximizing the utility of all parts in the system. The model is then tested against actual network data of three part types crucial to airline operations and used to investigate the current data collection processes of US Airways maintenance lead time metrics. Manual sensitivity analysis is performed to generate the marginal value of each parameter and potential model extensions are highlighted as a result of these conclusions.

Contributors

Agent

Created

Date Created
  • 2013-12

132730-Thumbnail Image.png

The Use of Simulation in a Foundry Setting

Description

Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has

Woodland/Alloy Casting, Inc. is an aluminum foundry known for providing high-quality molds to their customers in industries such as aviation, electrical, defense, and nuclear power. However, as the company has grown larger during the past three years, they have begun to struggle with the on-time delivery of their orders. Woodland prides itself on their high-grade process that includes core processing, the molding process, cleaning process, and heat-treat process. To create each mold, it has to flow through each part of the system flawlessly. Throughout this process, significant bottlenecks occur that limit the number of molds leaving the system. To combat this issue, this project uses a simulation of the foundry to test how best to schedule their work to optimize the use of their resources. Simulation can be an effective tool when testing for improvements in systems where making changes to the physical system is too expensive. ARENA is a simulation tool that allows for manipulation of resources and process while also allowing both random and selected schedules to be run through the foundry’s production process. By using an ARENA simulation to test different scheduling techniques, the risk of missing production runs is minimized during the experimental period so that many different options can be tested to see how they will affect the production line. In this project, several feasible scheduling techniques are compared in simulation to determine which schedules allow for the highest number of molds to be completed.

Contributors

Created

Date Created
  • 2019-05

152173-Thumbnail Image.png

Dynamic scheduling of stream programs on embedded multi-core processors

Description

Stream computing has emerged as an importantmodel of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core

Stream computing has emerged as an importantmodel of computation for embedded system applications particularly in the multimedia and network processing domains. In recent past several programming languages and embedded multi-core processors have been proposed for streaming applications. This thesis examines the execution and dynamic scheduling of stream programs on embedded multi-core processors. The thesis addresses the problem in the context of a multi-tasking environment with a time varying allocation of processing elements for a particular streaming application. As a solution the thesis proposes a two step approach where the stream program is compiled to gather key application information, and to generate re-targetable code. A light weight dynamic scheduler incorporates the second stage of the approach. The dynamic scheduler utilizes the static information and available resources to assign or partition the application across the multi-core architecture. The objective of the dynamic scheduler is to maximize the throughput of the application, and it is sensitive to the resource (processing elements, scratch-pad memory, DMA bandwidth) constraints imposed by the target architecture. We evaluate the proposed approach by compiling and scheduling benchmark stream programs on a representative embedded multi-core processor. We present experimental results that evaluate the quality of the solutions generated by the proposed approach by comparisons with existing techniques.

Contributors

Agent

Created

Date Created
  • 2013

152302-Thumbnail Image.png

Thermal aware scheduling in hadoop map reduce framework

Description

The energy consumption of data centers is increasing steadily along with the associ- ated power-density. Approximately half of such energy consumption is attributed to the cooling energy, as a result

The energy consumption of data centers is increasing steadily along with the associ- ated power-density. Approximately half of such energy consumption is attributed to the cooling energy, as a result of which reducing cooling energy along with reducing servers energy consumption in data centers is becoming imperative so as to achieve greening of the data centers. This thesis deals with cooling energy management in data centers running data-processing frameworks. In particular, we propose ther- mal aware scheduling for MapReduce framework and its Hadoop implementation to reduce cooling energy in data centers. Data-processing frameworks run many low- priority batch processing jobs, such as background log analysis, that do not have strict completion time requirements; they can be delayed by a bounded amount of time. Cooling energy savings are possible by being able to temporally spread the workload, and assign it to the computing equipments which reduce the heat recirculation in data center room and therefore the load on the cooling systems. We implement our scheme in Hadoop and performs some experiments using both CPU-intensive and I/O-intensive workload benchmarks in order to evaluate the efficiency of our scheme. The evaluation results highlight that our thermal aware scheduling reduces hot-spots and makes uniform temperature distribution within the data center possible. Sum- marizing the contribution, we incorporated thermal awareness in Hadoop MapReduce framework by enhancing the native scheduler to make it thermally aware, compare the Thermal Aware Scheduler(TAS) with the Hadoop scheduler (FCFS) by running PageRank and TeraSort benchmarks in the BlueTool data center of Impact lab and show that there is reduction in peak temperature and decrease in cooling power using TAS over FCFS scheduler.

Contributors

Agent

Created

Date Created
  • 2013

151324-Thumbnail Image.png

Stochastic optimization and real-time scheduling in cyber-physical systems

Description

A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems.

A principal goal of this dissertation is to study stochastic optimization and real-time scheduling in cyber-physical systems (CPSs) ranging from real-time wireless systems to energy systems to distributed control systems. Under this common theme, this dissertation can be broadly organized into three parts based on the system environments. The first part investigates stochastic optimization in real-time wireless systems, with the focus on the deadline-aware scheduling for real-time traffic. The optimal solution to such scheduling problems requires to explicitly taking into account the coupling in the deadline-aware transmissions and stochastic characteristics of the traffic, which involves a dynamic program that is traditionally known to be intractable or computationally expensive to implement. First, real-time scheduling with adaptive network coding over memoryless channels is studied, and a polynomial-time complexity algorithm is developed to characterize the optimal real-time scheduling. Then, real-time scheduling over Markovian channels is investigated, where channel conditions are time-varying and online channel learning is necessary, and the optimal scheduling policies in different traffic regimes are studied. The second part focuses on the stochastic optimization and real-time scheduling involved in energy systems. First, risk-aware scheduling and dispatch for plug-in electric vehicles (EVs) are studied, aiming to jointly optimize the EV charging cost and the risk of the load mismatch between the forecasted and the actual EV loads, due to the random driving activities of EVs. Then, the integration of wind generation at high penetration levels into bulk power grids is considered. Joint optimization of economic dispatch and interruptible load management is investigated using short-term wind farm generation forecast. The third part studies stochastic optimization in distributed control systems under different network environments. First, distributed spectrum access in cognitive radio networks is investigated by using pricing approach, where primary users (PUs) sell the temporarily unused spectrum and secondary users compete via random access for such spectrum opportunities. The optimal pricing strategy for PUs and the corresponding distributed implementation of spectrum access control are developed to maximize the PU's revenue. Then, a systematic study of the nonconvex utility-based power control problem is presented under the physical interference model in ad-hoc networks. Distributed power control schemes are devised to maximize the system utility, by leveraging the extended duality theory and simulated annealing.

Contributors

Agent

Created

Date Created
  • 2012

149478-Thumbnail Image.png

Optimization of surgery delivery systems

Description

Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several

Optimization of surgical operations is a challenging managerial problem for surgical suite directors. This dissertation presents modeling and solution techniques for operating room (OR) planning and scheduling problems. First, several sequencing and patient appointment time setting heuristics are proposed for scheduling an Outpatient Procedure Center. A discrete event simulation model is used to evaluate how scheduling heuristics perform with respect to the competing criteria of expected patient waiting time and expected surgical suite overtime for a single day compared to current practice. Next, a bi-criteria Genetic Algorithm is used to determine if better solutions can be obtained for this single day scheduling problem. The efficacy of the bi-criteria Genetic Algorithm, when surgeries are allowed to be moved to other days, is investigated. Numerical experiments based on real data from a large health care provider are presented. The analysis provides insight into the best scheduling heuristics, and the tradeoff between patient and health care provider based criteria. Second, a multi-stage stochastic mixed integer programming formulation for the allocation of surgeries to ORs over a finite planning horizon is studied. The demand for surgery and surgical duration are random variables. The objective is to minimize two competing criteria: expected surgery cancellations and OR overtime. A decomposition method, Progressive Hedging, is implemented to find near optimal surgery plans. Finally, properties of the model are discussed and methods are proposed to improve the performance of the algorithm based on the special structure of the model. It is found simple rules can improve schedules used in practice. Sequencing surgeries from the longest to shortest mean duration causes high expected overtime, and should be avoided, while sequencing from the shortest to longest mean duration performed quite well in our experiments. Expending greater computational effort with more sophisticated optimization methods does not lead to substantial improvements. However, controlling daily procedure mix may achieve substantial improvements in performance. A novel stochastic programming model for a dynamic surgery planning problem is proposed in the dissertation. The efficacy of the progressive hedging algorithm is investigated. It is found there is a significant correlation between the performance of the algorithm and type and number of scenario bundles in a problem instance. The computational time spent to solve scenario subproblems is among the most significant factors that impact the performance of the algorithm. The quality of the solutions can be improved by detecting and preventing cyclical behaviors.

Contributors

Agent

Created

Date Created
  • 2010

155821-Thumbnail Image.png

Wireless Sensor Data Transport, Aggregation and Security

Description

Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things

Wireless sensor networks (WSN) and the communication and the security therein have been gaining further prominence in the tech-industry recently, with the emergence of the so called Internet of Things (IoT). The steps from acquiring data and making a reactive decision base on the acquired sensor measurements are complex and requires careful execution of several steps. In many of these steps there are still technological gaps to fill that are due to the fact that several primitives that are desirable in a sensor network environment are bolt on the networks as application layer functionalities, rather than built in them. For several important functionalities that are at the core of IoT architectures we have developed a solution that is analyzed and discussed in the following chapters.

The chain of steps from the acquisition of sensor samples until these samples reach a control center or the cloud where the data analytics are performed, starts with the acquisition of the sensor measurements at the correct time and, importantly, synchronously among all sensors deployed. This synchronization has to be network wide, including both the wired core network as well as the wireless edge devices. This thesis studies a decentralized and lightweight solution to synchronize and schedule IoT devices over wireless and wired networks adaptively, with very simple local signaling. Furthermore, measurement results have to be transported and aggregated over the same interface, requiring clever coordination among all nodes, as network resources are shared, keeping scalability and fail-safe operation in mind. Furthermore ensuring the integrity of measurements is a complicated task. On the one hand Cryptography can shield the network from outside attackers and therefore is the first step to take, but due to the volume of sensors must rely on an automated key distribution mechanism. On the other hand cryptography does not protect against exposed keys or inside attackers. One however can exploit statistical properties to detect and identify nodes that send false information and exclude these attacker nodes from the network to avoid data manipulation. Furthermore, if data is supplied by a third party, one can apply automated trust metric for each individual data source to define which data to accept and consider for mentioned statistical tests in the first place. Monitoring the cyber and physical activities of an IoT infrastructure in concert is another topic that is investigated in this thesis.

Contributors

Agent

Created

Date Created
  • 2017