Welcome to SOAR: Shocker Open Access Repository!

SOAR is the institutional repository of Wichita State University. Its primary purpose is to make the University’s digital scholarship available to a global audience and to serve as a reliable digital storage solution. SOAR functions dually as both a publication platform and a digital archive. University faculty and staff are encouraged to publish their research works, data, or documents in SOAR. For student submissions, a recommendation from their professors is required.

Recent Submissions

  • Item
    Journal of Management & Engineering Integration, v.17, no.1 (Summer)
    (Association for Industry, Engineering and Management Systems (AIEMS), 2024-06) Association for Industry, Engineering and Management Systems (AIEMS)
  • Item
    The application of the KANO model in education: A systematic literature review
    (Association for Industry, Engineering and Management Systems (AIEMS), 2024-06) Buffon, Cintia Zuccon; Cudney, Elizabeth A.; Elshennawy, Ahmad
    As markets become more competitive, customer satisfaction becomes one of the most critical factors of success for any organization, especially in the service industry, where the opportunity for customer delight can be limited. Customer requirements and their perceptions of quality can differ significantly from organizations' views. Therefore, employing tools to bridge this gap and gaining insights into the voice of customers is imperative for organizations. The Kano model provides a framework for understanding the impact of quality attributes on customer satisfaction. The Kano model has been used in many different industries over the years. Multiple case studies have applied the Kano model to various service industry sectors from healthcare to tourism. owever, the use of the Kano model in education remains limited. This study provides a systematic literature review of the use of the Kano model in education. This study aims to examine the extent to which the Kano model is applied as a quality improvement tool in the education sector and to identify research gaps in the literature for future research.
  • Item
    Matching workloads to systems with deep reinforcement learning
    (Association for Industry, Engineering and Management Systems (AIEMS), 2024-06) Hu, Bing; Mason, Nicholas
    Along with the evolution of computer microarchitecture over the years, the number of dies, cores, and embedded multi-die interconnect bridges has grown. Optimizing the workload running on a central processing unit (CPU) to improve the computer performance has become a challenge. Matching workloads to systems with optimal system configurations to achieve the desired system performance is an open challenge in both academic and industrial research. In this paper, we propose two reinforcement learning (RL) approaches, a deep reinforcement learning (DRL) approach and an evolutionary deep reinforcement learning (EDRL) approach, to find an optimal system configuration for a given computer workload with a system performance objective. The experimental results demonstrate that both approaches can determine the optimal system configuration with the desired performance objective. The comparison studies illustrate that the DRL approach outperforms the standard RL approaches. In the future, these DRL approaches can be leveraged in system performance auto-tuning studies.
  • Item
    A practical guide to ISO 25022: Measurement of software quality in-use
    (Association for Industry, Engineering and Management Systems (AIEMS), 2024-06) Jaradat, Bassam; Weheba, Gamal S.
    This research investigated the challenges in measuring software Quality in-use following the ISO/IEC 25022:2016 standard. By highlighting these implementation challenges, the research proposed an implementation guide to simplify the evaluation process and recommended adjustments for future iterations of the standard. These recommendations are geared towards enhancing the precision of software quality measurement practices.
  • Item
    Exploring Nvidia's evolution, innovations, and future stock trends
    (Association for Industry, Engineering and Management Systems (AIEMS), 2024-06) Wang, John; Hsu, Jeffrey; Qin, Zhaoqiong
    This paper undertakes a thorough examination of Nvidia's stock market performance, intertwining historical analysis with forward-looking projections to illuminate the dynamic trajectory of this semiconductor industry giant. Commencing with a retrospective review, the authors delve into pivotal milestones, technological innovations, and strategic maneuvers that have shaped Nvidia's stock evolution. Utilizing advanced machine learning algorithms, including Random Forest and Support Vector Regression (SVR), alongside traditional statistical forecasting methods, we forecast future patterns. Through the incorporation of emerging industry dynamics, technological advancements, and market forecasts, our goal is to furnish stakeholders with insights pivotal for strategic decision-making. This dual perspective, encompassing both historical retrospection and future outlook, weaves a holistic narrative capturing the essence of Nvidia's stock market journey. It serves as a valuable resource for avid readers navigating the ever-changing landscape of the semiconductor market, especially considering the expanding role of Nvidia's GPUs in Artificial Intelligence (AI) and deep learning applications.