Matching Items (182)
Description
The price based marketplace has dominated the construction industry. The majority of owners use price based practices of management (expectation and decision making, control, direction, and inspection.) The price based/management and control paradigm has not worked. Clients have now been moving toward the best value environment (hire contractors who know what they are doing, who preplan, and manage and minimize risk and deviation.) Owners are trying to move from client direction and control to hiring an expert and allowing them to do the quality control/risk management. The movement of environments changes the paradigm for the contractors from a reactive to a proactive, from a bureaucratic
on-accountable to an accountable position, from a relationship based
on-measuring to a measuring entity, and to a contractor who manages and minimizes the risk that they do not control. Years of price based practices have caused poor quality and low performance in the construction industry. This research identifies what is a best value contractor or vendor, what factors make up a best value vendor, and the methodology to transform a vendor to a best value vendor. It will use deductive logic, a case study to confirm the logic and the proposed methodology.
on-accountable to an accountable position, from a relationship based
on-measuring to a measuring entity, and to a contractor who manages and minimizes the risk that they do not control. Years of price based practices have caused poor quality and low performance in the construction industry. This research identifies what is a best value contractor or vendor, what factors make up a best value vendor, and the methodology to transform a vendor to a best value vendor. It will use deductive logic, a case study to confirm the logic and the proposed methodology.
ContributorsPauli, Michele (Author) / Kashiwagi, Dean (Thesis advisor) / Sullivan, Kenneth (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
ContributorsSoto-Berelov, Mariela (Author) / Fall, Patricia L. (Author) / Barton, C. Michael (Author) / Falconer, Steven E. (Author) / Sarjoughian, Hessam S. (Author) / Arrowsmith, J. Ramon (Author)
Created2007
Description
Though it is a widespread adaptation in humans and many other animals, parental care comes in a variety of forms and its subtle physiological costs, benefits, and tradeoffs related to offspring are often unknown. Thus, I studied the hydric, respiratory, thermal, and fitness dynamics of maternal egg-brooding behavior in Children's pythons (Antaresia childreni). I demonstrated that tight coiling detrimentally creates a hypoxic developmental environment that is alleviated by periodic postural adjustments. Alternatively, maternal postural adjustments detrimentally elevate rates of egg water loss relative to tight coiling. Despite ventilating postural adjustments, the developmental environment becomes increasingly hypoxic near the end of incubation, which reduces embryonic metabolism. I further demonstrated that brooding-induced hypoxia detrimentally affects offspring size, performance, locomotion, and behavior. Thus, parental care in A. childreni comes at a cost to offspring due to intra-offspring tradeoffs (i.e., those that reflect competing offspring needs, such as water balance and respiration). Next, I showed that, despite being unable to intrinsically produce body heat, A. childreni adjust egg-brooding behavior in response to shifts in nest temperature, which enhances egg temperature (e.g., reduced tight coiling during nest warming facilitated beneficial heat transfer to eggs). Last, I demonstrated that A. childreni adaptively adjust their egg-brooding behaviors due to an interaction between nest temperature and humidity. Specifically, females' behavioral response to nest warming was eliminated during low nest humidity. In combination with other studies, these results show that female pythons sense environmental temperature and humidity and utilize this information at multiple time points (i.e., during gravidity [egg bearing], at oviposition [egg laying], and during egg brooding) to enhance the developmental environment of their offspring. This research demonstrates that maternal behaviors that are simple and subtle, yet easily quantifiable, can balance several critical developmental variables (i.e., thermoregulation, water balance, and respiration).
ContributorsStahlschmidt, Zachary R (Author) / DeNardo, Dale F (Thesis advisor) / Harrison, Jon (Committee member) / McGraw, Kevin (Committee member) / Rutowski, Ronald (Committee member) / Walsberg, Glenn (Committee member) / Arizona State University (Publisher)
Created2011
Description
As global competition continues to grow more disruptive, organizational change is an ever-present reality that affects companies in all industries at both the operational and strategic level. Organizational change capabilities have become a necessary aspect of existence for organizations in all industries worldwide. Research suggests that more than half of all organizational change efforts fail to achieve their original intended results, with some studies quoting failure rates as high as 70 percent. Exasperating this problem is the fact that no single change methodology has been universally accepted. This thesis examines two aspect of organizational change: the implementation of tactical and strategic initiatives, primarily focusing on successful tactical implementation techniques. This research proposed that tactical issues typically dominate the focus of change agents and recipients alike, often to the detriment of strategic level initiatives that are vital to the overall value and success of the organizational change effort. The Delphi method was employed to develop a tool to facilitate the initial implementation of organizational change such that tactical barriers were minimized and available resources for strategic initiatives were maximized. Feedback from two expert groups of change agents and change facilitators was solicited to develop the tool and evaluate its impact. Preliminary pilot testing of the tool confirmed the proposal and successfully served to minimize tactical barriers to organizational change.
ContributorsLines, Brian (Author) / Sullivan, Kenneth T. (Thesis advisor) / Badger, William (Committee member) / Kashiwagi, Dean (Committee member) / Arizona State University (Publisher)
Created2011
Description
Internet sites that support user-generated content, so-called Web 2.0, have become part of the fabric of everyday life in technologically advanced nations. Users collectively spend billions of hours consuming and creating content on social networking sites, weblogs (blogs), and various other types of sites in the United States and around the world. Given the fundamentally emotional nature of humans and the amount of emotional content that appears in Web 2.0 content, it is important to understand how such websites can affect the emotions of users. This work attempts to determine whether emotion spreads through an online social network (OSN). To this end, a method is devised that employs a model based on a general threshold diffusion model as a classifier to predict the propagation of emotion between users and their friends in an OSN by way of mood-labeled blog entries. The model generalizes existing information diffusion models in that the state machine representation of a node is generalized from being binary to having n-states in order to support n class labels necessary to model emotional contagion. In the absence of ground truth, the prediction accuracy of the model is benchmarked with a baseline method that predicts the majority label of a user's emotion label distribution. The model significantly outperforms the baseline method in terms of prediction accuracy. The experimental results make a strong case for the existence of emotional contagion in OSNs in spite of possible alternative arguments such confounding influence and homophily, since these alternatives are likely to have negligible effect in a large dataset or simply do not apply to the domain of human emotions. A hybrid manual/automated method to map mood-labeled blog entries to a set of emotion labels is also presented, which enables the application of the model to a large set (approximately 900K) of blog entries from LiveJournal.
ContributorsCole, William David, M.S (Author) / Liu, Huan (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Candan, Kasim S (Committee member) / Arizona State University (Publisher)
Created2011
Description
Free/Libre Open Source Software (FLOSS) is the product of volunteers collaborating to build software in an open, public manner. The large number of FLOSS projects, combined with the data that is inherently archived with this online process, make studying this phenomenon attractive. Some FLOSS projects are very functional, well-known, and successful, such as Linux, the Apache Web Server, and Firefox. However, for every successful FLOSS project there are 100's of projects that are unsuccessful. These projects fail to attract sufficient interest from developers and users and become inactive or abandoned before useful functionality is achieved. The goal of this research is to better understand the open source development process and gain insight into why some FLOSS projects succeed while others fail. This dissertation presents an agent-based model of the FLOSS development process. The model is built around the concept that projects must manage to attract contributions from a limited pool of participants in order to progress. In the model developer and user agents select from a landscape of competing FLOSS projects based on perceived utility. Via the selections that are made and subsequent contributions, some projects are propelled to success while others remain stagnant and inactive. Findings from a diverse set of empirical studies of FLOSS projects are used to formulate the model, which is then calibrated on empirical data from multiple sources of public FLOSS data. The model is able to reproduce key characteristics observed in the FLOSS domain and is capable of making accurate predictions. The model is used to gain a better understanding of the FLOSS development process, including what it means for FLOSS projects to be successful and what conditions increase the probability of project success. It is shown that FLOSS is a producer-driven process, and project factors that are important for developers selecting projects are identified. In addition, it is shown that projects are sensitive to when core developers make contributions, and the exhibited bandwagon effects mean that some projects will be successful regardless of competing projects. Recommendations for improving software engineering in general based on the positive characteristics of FLOSS are also presented.
ContributorsRadtke, Nicholas Patrick (Author) / Collofello, James S. (Thesis advisor) / Janssen, Marco A (Thesis advisor) / Sarjoughian, Hessam S. (Committee member) / Sundaram, Hari (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT Facility managers have an important job in today's competitive business world by caring for the backbone of the corporation's capital. Maintaining assets and the support efforts cause facility managers to fight an uphill battle to prove the worth of their organizations. This thesis will discuss the important and flexible use of measurement and leadership reports and the benefits of justifying the work required to maintain or upgrade a facility. The task is streamlined by invoking accountability to subject experts. The facility manager must trust in the ability of his or her work force to get the job done. However, with accountability comes increased risk. Even though accountability may not alleviate total control or cease reactionary actions, facility managers can develop key leadership based reports to reassign accountability and measure subject matter experts while simultaneously reducing reactionary actions leading to increased cost. Identifying and reassigning risk that are not controlled to subject matter experts is imperative for effective facility management leadership and allows facility managers to create an accurate and solid facility management plan, supports the organization's succession plan, and allows the organization to focus on key competencies.
ContributorsTellefsen, Thor (Author) / Sullivan, Kenneth (Thesis advisor) / Kashiwagi, Dean (Committee member) / Badger, William (Committee member) / Arizona State University (Publisher)
Created2011
Description
Studies of animal contests often focus solely on a single static measurement of fighting ability, such as the size or the strength of the individual. However, recent studies have highlighted the importance of individual variation in the dynamic behaviors used during a fight, such as, assessment strategies, decision making, and fine motor control, as being strong predictors of the outcome of aggression. Here, I combined morphological and behavioral data to discover how these features interact during aggressing interactions in male virile crayfish, Faxonius virilis. I predicted that individual variation in behavioral skill for decision making (i.e., number of strikes thrown), would determine the outcome of contest success in addition to morphological measurements (e.g. body size, relative claw size). To evaluate this prediction, I filmed staged territorial interactions between male F. virilis and later analyzed trial behaviors (e.g. strike, pinches, and bout time) and aggressive outcomes. I found very little support for skill to predict win/loss outcome in trials. Instead, I found that larger crayfish engaged in aggression for longer compared to smaller crayfish, but that larger crayfish did not engage in a greater number of claw strikes or pinches when controlling for encounter duration. Future studies should continue to investigate the role of skill, by using finer-scale techniques such as 3D tracking software, which could track advanced measurements (e.g. speed, angle, and movement efficiency). Such studies would provide a more comprehensive understanding of the relative influence of fighting skill technique on territorial contests.
ContributorsNguyen, Phillip Huy (Author) / Angilletta, Michael (Thesis director) / McGraw, Kevin (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
A semiconductor supply chain modeling and simulation platform using Linear Program (LP) optimization and parallel Discrete Event System Specification (DEVS) process models has been developed in a joint effort by ASU and Intel Corporation. A Knowledge Interchange Broker (KIBDEVS/LP) was developed to broker information synchronously between the DEVS and LP models. Recently a single-echelon heuristic Inventory Strategy Module (ISM) was added to correct for forecast bias in customer demand data using different smoothing techniques. The optimization model could then use information provided by the forecast model to make better decisions for the process model. The composition of ISM with LP and DEVS models resulted in the first realization of what is now called the Optimization Simulation Forecast (OSF) platform. It could handle a single echelon supply chain system consisting of single hubs and single products In this thesis, this single-echelon simulation platform is extended to handle multiple echelons with multiple inventory elements handling multiple products. The main aspect for the multi-echelon OSF platform was to extend the KIBDEVS/LP such that ISM interactions with the LP and DEVS models could also be supported. To achieve this, a new, scalable XML schema for the KIB has been developed. The XML schema has also resulted in strengthening the KIB execution engine design. A sequential scheme controls the executions of the DEVS-Suite simulator, CPLEX optimizer, and ISM engine. To use the ISM for multiple echelons, it is extended to compute forecast customer demands and safety stocks over multiple hubs and products. Basic examples for semiconductor manufacturing spanning single and two echelon supply chain systems have been developed and analyzed. Experiments using perfect data were conducted to show the correctness of the OSF platform design and implementation. Simple, but realistic experiments have also been conducted. They highlight the kinds of supply chain dynamics that can be evaluated using discrete event process simulation, linear programming optimization, and heuristics forecasting models.
ContributorsSmith, James Melkon (Author) / Sarjoughian, Hessam S. (Thesis advisor) / Davulcu, Hasan (Committee member) / Fainekos, Georgios (Committee member) / Arizona State University (Publisher)
Created2012
Description
The complexity of the systems that software engineers build has continuously grown since the inception of the field. What has not changed is the engineers' mental capacity to operate on about seven distinct pieces of information at a time. The widespread use of UML has led to more abstract software design activities, however the same cannot be said for reverse engineering activities. The introduction of abstraction to reverse engineering will allow the engineer to move farther away from the details of the system, increasing his ability to see the role that domain level concepts play in the system. In this thesis, we present a technique that facilitates filtering of classes from existing systems at the source level based on their relationship to concepts in the domain via a classification method using machine learning. We showed that concepts can be identified using a machine learning classifier based on source level metrics. We developed an Eclipse plugin to assist with the process of manually classifying Java source code, and collecting metrics and classifications into a standard file format. We developed an Eclipse plugin to act as a concept identifier that visually indicates a class as a domain concept or not. We minimized the size of training sets to ensure a useful approach in practice. This allowed us to determine that a training set of 7:5 to 10% is nearly as effective as a training set representing 50% of the system. We showed that random selection is the most consistent and effective means of selecting a training set. We found that KNN is the most consistent performer among the learning algorithms tested. We determined the optimal feature set for this classification problem. We discussed two possible structures besides a one to one mapping of domain knowledge to implementation. We showed that classes representing more than one concept are simply concepts at differing levels of abstraction. We also discussed composite concepts representing a domain concept implemented by more than one class. We showed that these composite concepts are difficult to detect because the problem is NP-complete.
ContributorsCarey, Maurice (Author) / Colbourn, Charles (Thesis advisor) / Collofello, James (Thesis advisor) / Davulcu, Hasan (Committee member) / Sarjoughian, Hessam S. (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013