Matching Items (201)
Description
Identifying the hindrances to performing effective talent acquisition within the science, technology, engineering, and mathematics field is an important topic for technical hiring managers. Top candidates have multiple options during highly competitive market conditions requiring managers to look for unique solutions which diverge from competition. Prior to this study there has been very little research considering national laboratory research and development challenges from a technical hiring manager’s talent acquisition perspective. Utilizing a unique combination of national laboratory multi-organization survey, pilot study, Human Resource (HR) tracking data and trust based business strategy to enhance partnering this research finds hiring managers can leverage out of the box techniques to improve internal processes while developing industry support to target highly qualified individuals. This methodology could be utilized by technical hiring managers across federal national laboratory enterprise to effectively capture next generation staff and leadership talent who align with their organization professionally as well as social culture.
ContributorsBane, Scott C. (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtado, Kristen (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2023
Description
Although Saudi Arabia is moving towards a sustainable future, Existing residential buildings in the country are extremely unsustainable. Therefore, there is a necessity for greening the existing residential building. Mostadam green rating systems was developed by the Saudi ministry of housing in 2019 to address the long-term sustainability vision in residential buildings in the country. By setting Mostadam requirements as an objective of the retrofit process, it will ensure that the building achieve sustainability. However, Mostadam is new and there is a lack of knowledge of implementing its requirements on existing buildings. The aim of this research is to develop a framework to green existing residential buildings in Saudi Arabia to achieve Mostadam energy and water minimum requirements. The framework was developed based on an extensive keyword-based search and an analysis of 92 relevant research. The process starts with assessing the building against the minimum requirements of energy and water of Mostadam. After that, optimization phase is conducted. Building information modelling is used in the optimization phase. Energy and water efficiency optimization measures are identified from the analysed literature. Revit is used in the base model authoring and Green building studio cloud is used to simulate the energy and water efficiency measures. Then, payback period is calculated for all the efficiency measured to assess the decision making. A case study of a villa in Riyadh, Saudi Arabia is provided. result shows that the implemented efficiency measures led to an increment of 37.5% in annual energy savings and 26.1% in the annual water savings. Results shows that the application of the proposed framework supports evaluating energy and water efficiency measures to implement it on the buildings to achieve Mostadam minimum energy and water requirements. Recommendations were made for future work to bridge the knowledge gap.
ContributorsMohamed, Sara Murad (Author) / Sullivan, Kenneth (Thesis advisor) / Chong, Oswald (Committee member) / Hurtado, Kristen (Committee member) / Arizona State University (Publisher)
Created2022
Description
ABSTRACT
Upon joining Arizona State University in July 2017, the author, a registered architect, inherited the oversight of the University Project Design Guidelines. During the following four years, revisions were made to the Project Design Guidelines and implemented for ongoing and future new construction and renovation work at all five Arizona State University campuses. During this time, it became evident that many projects were not following guidelines resulting in costly rework, or hastily submitted variance requests to avoid or replace the design guidelines, typically during, versus prior to, construction. Tracking of these variance requests began in Summer 2020 identifying some commonly requested variance items for discussion by the Project Guidelines Steering Committee. In June 2021, a progressive design-build solicitation was held for a new campus building. During the interview process it was evident that not all parties on the design-build team (owner, architect and general contractor) had the same understanding of the role, importance, or reasoning for project design guidelines. The confusion demonstrated during the variance and interview process made the author curious as to the overall sentiment of design standards in the industry. What areas of project guidelines are emphasized by universities? Is there a correlation between guideline information and the greatest/least amount of construction costs? Can universities be better served by focusing on a comprehensive understanding and implementation of project design guidelines that impact the greatest construction cost of the project?
ContributorsLisiewski II, Joseph Vincent (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtato, Kristen (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2022
Description
Facilities Management is a service that should follow economic concepts of “value added” and “cost-effectiveness.” Facilities sites and campuses can be divided into geographic facilities maintenance zones to improve response time, coordination of trades, customer service, and the ownership or accountability of technicians. Facilities zone maintenance teams of multi-trade technicians can work together in a dynamic partnership to significantly reduce costs and do more with less. Six months of field research, case studies, and crew balance analysis of primary quantitative data was used to deductively evaluate the effectiveness of the zone maintenance model. To fill gaps in skilled labor, reduce maintenance costs, and increase available skilled labor capacity the maintenance zone implemented a strategy to better utilize and schedule the labor of unskilled entry level maintenance technicians. A teamwork approach was also used to share the collective multi-trade workload and allow the zone maintenance crew to accomplish more than individual technicians could do alone.
A comprehensive literature review revealed an alarming lack of facilities management research and the vast disconnect between academic assumptions and practical real-world applications. It is evident from the case studies that more effective utilization of unskilled labor and harnessing the unique capacity of a multi-trade team are important competitive advantages of the facilities zone maintenance model. These intangible contributions and the value added to the organization can be measured and quantified through careful data collection and analysis. These studies are a reminder that significant maintenance cost savings can be achieved by eliminating labor waste and crew scheduling inefficiencies. Value can be added to the organization by reducing these and other intangible costs by focusing on continuous improvement, productivity, efficiency, and effective workflow.
ContributorsMathews, Paul (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtado, Kristen (Committee member) / Standage, Richard (Committee member) / Arizona State University (Publisher)
Created2022
Description新世纪以来中国电影的产业化改革与探索愈发呈现良好的态势，国产院线电影也在实践中努力赢得观众和票房市场。其中类型喜剧电影，最符合商业电影规律、最顺应影视市场需求、最能获得票房收益而备受影视创投机构、制作公司青睐。本论文研究对象聚焦类型喜剧电影，通过“欢声笑语里的财富”现象，探究类型喜剧电影内部本体构成要素与外部客观促成要素的关联；以通过分析自变量与因变量因素对中国电影票房之类型喜剧影响因素进行实证研究，为影视创投和影视制作总结并提供可靠建议。
本论文整体结构包括：第一部分为导论，包括研究背景、目的意义，相关文献综述与文献评述和论文创新性。第二部分聚焦类型喜剧本身，从电影学范畴的电影本体出发，探究“笑”的心理、社会与文化内涵，并分析将“笑”对经济领域的延伸。第三部分以影视投资、票房为依托，从现象和数据中探寻影响类型喜剧电影的因素，为展开中国电影票房之类型喜剧影响因素实证研究做好理论的铺垫。第四与第五部分则基于上述理论进行实证检验，选用2013-2020年电影样本，采用多元线性回归模型研究喜剧类型对票房的吸引力，以及不同种类型喜剧对电影票房的提振效果作用差异。研究发现喜剧电影对电影票房有显著的提振作用；以及研究电影的外部影响因素（续集效应）对电影票房的作用。发现续集电影有更好的票房表现，续集效应的票房提升作用在喜剧电影中表现的更加明显。
本论文研究成果最终将回归到“欢声笑语里的财富”本身；即“类型复合喜剧”对促进电影与金融产业的互动关联、实现更加可持续化发展，以及进而推动经济及文化业的发展。
ContributorsLiu, Yongqian (Author) / Shen, Wei (Thesis advisor) / Zhu, Ning (Thesis advisor) / Dong, Xiaodan (Committee member) / Arizona State University (Publisher)
Created2022
Description人口的老龄化不仅对养老事业提出更高的要求，也对养老服务产业人才的培养提出要求。但是青年学生选择涉老服务专业的意愿却非常低。因此，为了探究职业学院如何增强涉老服务专业吸引力这一问题，本文以学生为主体视角，利用相关理论，对于影响青年学生选择涉老服务专业的因素进行全面的分析，并结合深度访谈和调查法，提出并建构了相关的理论模型。首先，通过深度访谈和焦点小组讨论，结合对现有的文献的分析，本文提出了影响青年学生选择职业院校涉老服务专业的各种因素，主要包括：个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平。之后，本文通过调查法，基于社会认同理论构建了本文的研究模型，并通过结构方程模型对所构建的模型进行检查。
本文的研究结果表明：个人未来风险感知对学生专业认同度产生负面影响；家庭经济资本对学生专业认同度产生负面影响；社会信息评价对学生专业认同度产生正面影响；校企合作水平对学生专业认同度产生正面影；专业课程建设水平对学生专业认同度产生正面影响；学生激励水平对学生专业认同度产生正面影响；师资队伍建设水平对学生专业认同度产生正面影响；学生专业认同度对学生专业选择意愿产生正面影响。
基于上述研究结论，本文选取了个人未来风险感知、家庭经济资本、社会信息评价、校企合作水平、专业课程建设水平、学生激励水平、师资队伍建设水平等因素对于广东岭南职业技术学院涉老服务专业的现有吸引力进行了分析和评估，并从这些视角进一步了对如何提升招生吸引力问题进行探讨，为提高涉老服务专业对于青年学生的吸引力，得出了相关管理建议。
ContributorsZhou, Lanqing (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Pei, Ker-Wei (Committee member) / Arizona State University (Publisher)
Created2021
Description
By the evolution of technologies and computing power, it is possible to capture and save large amounts of data and then find patterns in large and complex datasets using data science and machine learning. This dissertation introduces machine-learning models and econometric models to use in infrastructure transportation projects. Among transportation infrastructure projects, the airline industry and highways are selected to implement the models.The first topic of this dissertation focuses on using machine-learning models in highway projects. The International Roughness Index (IRI) for asphalt concrete pavement is predicted based on the 12,637 observations in the Long-Term Pavement Performance (LTPP) dataset for 1,390 roads and highways in the 50 states of the United States and the District of Columbia from 1989 to 2018. The results show that XGBoost provides a better model fit in terms of mean absolute error and coefficient of determination than other studied models. Also, the most important factors in predicting the IRI are identified.
The second topic of this dissertation aims to develop machine-learning models to predict customer dissatisfaction in the airline industry. The relationship between measures of service failure (flight delay and mishandled baggage) and customer dissatisfaction is predicted by using longitudinal data from 2003 to 2019 from the U.S. airline industry. Data was obtained from the Air Travel Consumer Report (ATCR) published by the U.S. Department of Transportation. Flight delay is more important in low-cost airlines, while mishandled baggage is more important in legacy airlines. Also, the effect of the train-test split ratio on each machine-learning model is examined by running each model using four train-test splits. Results indicate that the train-test split ratio could influence the selection of the best model.
The third topic in this dissertation uses econometric analysis to investigate the relationship between customer dissatisfaction and two measures of service failure in the U.S. airline industry. Results are: 1) Mishandled baggage has more impact than flight delay on customer complaints. 2) The effect of an airline’s service failures on customer complaints is contingent on the category of the airline. 3) The effect of flight delay on customer complaints is lower for low-cost airlines compared to legacy airlines.
ContributorsDamirchilo, Farshid (Author) / Fini, Elham H (Thesis advisor) / Lamanna, Anthony J (Committee member) / Parast, Mahour M (Committee member) / Sullivan, Kenneth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Applications over a gesture-based human-computer interface (HCI) require a new user login method with gestures because it does not have traditional input devices. For example, a user may be asked to verify the identity to unlock a device in a mobile or wearable platform, or sign in to a virtual site over a Virtual Reality (VR) or Augmented Reality (AR) headset, where no physical keyboard or touchscreen is available. This dissertation presents a unified user login framework and an identity input method using 3D In-Air-Handwriting (IAHW), where a user can log in to a virtual site by writing a passcode in the air very fast like a signature. The presented research contains multiple tasks that span motion signal modeling, user authentication, user identification, template protection, and a thorough evaluation in both security and usability. The results of this research show around 0.1% to 3% Equal Error Rate (EER) in user authentication in different conditions as well as 93% accuracy in user identification, on a dataset with over 100 users and two types of gesture input devices. Besides, current research in this area is severely limited by the availability of the gesture input device, datasets, and software tools. This study provides an infrastructure for IAHW research with an open-source library and open datasets of more than 100K IAHW hand movement signals. Additionally, the proposed user identity input method can be extended to a general word input method for both English and Chinese using limited training data. Hence, this dissertation can help the research community in both cybersecurity and HCI to explore IAHW as a new direction, and potentially pave the way to practical adoption of such technologies in the future.
ContributorsLu, Duo (Author) / Huang, Dijiang (Thesis advisor) / Li, Baoxin (Committee member) / Zhang, Junshan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021
Description
Demand for processing machine learning workloads has grown incredibly over the past few years. Kubernetes, an open-source container orchestrator, has been widely used by public and private cloud providers for building scalable systems for meeting this demand. The data used to train machine learning workloads can be sensitive in nature, and organizations may prefer to be responsible for their data security and governance by housing it on on-premises systems. Hybrid cloud gives organizations the flexibility to use both on-premises and cloud infrastructure together, leveraging the advantages of both. While there is a long list of benefits, Kubernetes has limitations by design that limit a user’s abilities in a hybrid cloud environment. The Kubernetes control plane does not allow for the management of worker nodes across cloud providers. This boundary puts new responsibilities on the end-user when deploying a hybrid cloud workload. The end-user must create their clusters and specify which cluster the workload will be scheduled to ahead of time. The Kubernetes scheduler will not take the capacity of another cluster into account. To address these limitations, this thesis presents a new hybrid cloud Kubernetes scheduler that can create new clusters on-demand and burst machine learning workloads to a public cloud when on-premises resources are insufficient. Workloads begin scheduling on an on-premises Kubernetes cluster. When the on-premises cluster’s capacity is exhausted, a new Kubernetes cluster is created on-demand in a public cloud provider, and machine learning tasks waiting in the Kubernetes scheduling queue are dynamically migrated to the public cloud provider’s Kubernetes cluster. The public Kubernetes cluster is dynamically sized and auto scaled based on the pending tasks’ demand. When migrating tasks, the data dependencies among tasks are considered, and a region is dynamically chosen to reduce migration time and cost. The scheduler is experimentally evaluated with real-world machine learning workloads, including predicting if a subscriber will stay with a subscription service, predicting the discount needed to retain a subscription customer, predicting if a credit card transaction is fraudulent, and simulated real-world job arrival behavior in a real hybrid cloud environment. Results show that the scheduler can substantially reduce the workload execution time by dynamically migrating tasks from on-premises to public cloud and minimizing the cost by dynamically sizing and scaling the public cluster.
ContributorsKieley, James (Author) / Zhao, Ming (Thesis advisor) / Huang, Dijiang (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2021
Description
Many public agencies and/or private owners have no standards that help them to select the most suitable delivery method for their capital projects; hence, in some cases, this results in selecting the inappropriate project delivery method. This adversely impacts the project performance and leads to many negative consequences; starting with schedule growth, cost overrun, and may end up in an epic failure of the project. This research mainly focuses on developing a guideline to help owners make the decision on selecting the most appropriate delivery method for their capital projects. This research goes through three stages: Stage 1 - An extensive literature review of past research is conducted to conclude the selection factors considered in the decision-making process and the decision analysis technique and the project delivery methods; Stage 2 - This stage includes building up the selection model and setting out its guidelines; Stage 3 - This is the final stage of the research thread and includes the validation of the selection model through applying this model on some case study projects by industry practitioners, then evaluating the final results. The owner’s guideline for project delivery method selection, developed within this research, is designed to help owners increase the project success likelihood by selecting the suitable project delivery methods during the pre-construction phase (planning phase of the project life cycle).
ContributorsSallam, Omar Mohamed (Author) / Sullivan, Kenneth (Thesis advisor) / Hurtado, Kristen (Committee member) / Kutz, Barry (Committee member) / Arizona State University (Publisher)
Created2021