1 Smart Sensor Interface Model Using IoT for Pipeline Integrity Cathodic Protection , Mohammed Algarni Mohammed Zwawi   Mechanical Engineering Department, Faculty of Engineering King Abdulaziz University, Saudi Arabia   malgarni1@kau.edu.sa; mzwawi@kau.edu.sa
This study proposes a design of a smart sensor interface for monitoring Cathodic Protection (CP) in an Internet of Things (IoT) environment. The corrosion sensor is used to monitor voltage potential from a sacrificial anode to an underground pipe or pipelines. The past design of monitoring CP was via wired networking (for data transfer) or by onsite inspection. Although such inspection offers a very reliable monitoring method, it is expensive and inconvenient. This research proposes a new, advanced and straightforward method to monitor CP, for field engineers, by the advancement of IoT intelligence to provide suggestions and make online-orders for consumable parts. This research proposes a solution by the novel design of monitoring corrosion via CP using a smart sensor interface model in an IoT environment and smart-phone applications. The results of this system design show good monitoring performance and excellent analytical potential.
2 Big Data, Ethics, and Social Impact Theory – A Conceptual Framework , Gwen White, Thilini Ariyachandra, and David White1   Management and Information Systems and Psychology Xavier University   whiteg@xavier.edu
Decisions made using big data, impact ethical issues like privacy, security, ownership, and decision making. In addition, those same decisions can have a positive or negative social impact. This paper proposes a framework that explains how decisions made using big data impact ethics and social impact theory. A broad literature review explored how big data and ethics can have a social impact on society. The proposed framework of big data, ethics and social impact is illustrated through three examples. Insurance companies manipulate big data to impact sales. The Center for Disease Control examines big data to determine the location of the next outbreak. Companies analyze big data in predictive analytics to increase marketing or determine a new trend. It was found that these uses of big data directly affect ethics which has a positive or negative social impact. Simple decisions can change the outcome for one or millions.
3 Performance Enhancing Drug Usage: The Influence of Adverse Health Effects and Public Embarrassment , B. Andrew Cudmore and Sherry Jensen   Florida Institute of Technology   acudmore@fit.edu; sjensen@fit.edu
This research examines the impact of adverse health effects and public embarrassment as deterrents to the use of performance enhancing drugs (PEDs). Deterrence theory suggests that potential PED users execute a cost-benefit analysis before engaging in illicit drug use; any increase in perceived costs reduces the likelihood of drug use. In accordance with the deterrence theory, this study finds that social costs (public embarrassment) have a negative impact on attitudes toward PEDs. However, potential health costs, even extreme ones, do not deter amateur athletes from considering PEDs. Rationale is offered for why fear of social disapproval has a larger impact than adverse health outcomes on attitudes of potential PED users. Results provide guidance for the development of marketing communications designed to deter amateur athletes from considering PEDs.
4 An Overview of Very High Cycle Fatigue Behavior of Additively Manufactured Ti-6Al-4V , Palmer Frye and Jutima Simsiriwong   School of Engineering, College of Computing, Engineering & Construction University of North Florida   j.simsiriwong@unf.edu
This paper presents a brief review on the current state of knowledge of the very high cycle fatigue (VHCF) behavior of metallic parts fabricated using Additive Manufacturing (AM) processes. It has been shown that AM has significant potential to replace traditional manufacturing methods that impose geometric limitations to designs. Powder-based metallic AM methods allow for precise layerwise processing of complex net-shape parts without the use of special tooling or molds. Among various metals commonly used in AM processes, titanium (Ti) 6Al-4V alloy is currently of great interest especially in aerospace applications that contain parts with complex geometries (i.e., turbine blades in jet engines). To safely adapt AM Ti-6Al-4V parts in these applications, their mechanical properties and fatigue behavior must be understood. Various studies have identified AM processinduced defects (i.e., entrapped gas pores, lack of fusion defects between build layers, etc.) to be the main cause of fatigue failure of AM Ti-6Al-4V parts. However, there are limited studies relating to the effects of these defects on the behavior of AM metals in the VHCF regime (beyond 107 cycles). Knowledge of the VHCF behavior of AM Ti-6Al-4V is needed for the aforementioned applications due to the high loading frequencies and long service lives required for these parts.
5 Engineering Disasters: The Role of Engineering versus Management Cumulative Failure Risk Factor , Andrzej J. Gapinski   The Pennsylvania State University - Fayette   ajg2@psu.edu
The article investigates engineering disasters as failures of either engineering design, project management decisions, or management processes in general. The paper points out that more often than not the failures of engineering endeavors were due to shortcomings of project management and organizational culture irrespective of the area of engineering discipline involved. The cumulative failure risk factor is proposed to assess an overall project failure risk, which can assist in project failure risk assessment and consequently in identifying the shortcomings in an organization.
6 Lean in Education: Mistake-Proofing Methods Used by Teachers at a Magnet High School , Robert S. Keyser   Kennesaw State University   rkeyser@kennesaw.edu
High school teachers, like most people, make mistakes on a regular basis, but they have also developed mistake-proofing methods to negate the impact of these errors. There is currently no published research in the field of identifying common mistakes and employing mistake-proofing techniques among teachers in a high school environment. A qualitative, descriptive study describes the most common mistakes made by teachers at a magnet high school in the metro-Atlanta area as well as the mistake-proofing methods used to mitigate these mistakes. An online survey was distributed to all faculty at the high school and 30 responses were received within the four-week deadline. The most common error reported related to the grading of assignments. Teachers also struggled with personal time management, as well as classroom time management. Results indicate six common types of mistakes and their respective mistake-proofing methods that are discussed in this paper.
7 Review of Earned Value Management (EVM) Methodology, its Limitations, and Applicable Extensions , Anisulrahman Nizam and Ahmad Elshannaway   University of Central Florida   anizam@knights.ucf.edu 
Department of Defense (DoD) Instruction 5000.02 requires an Earned Value Management System (EVMS) compliant with ANSI/EIA-748 for all DoD cost or incentive contracts valued at or greater than $20M. Earned Value Management (EVM) integrates cost, schedule, and time to draw conclusions about current project status as well as make projections for future project status. Though EVM has been widely adopted on many projects, there are clear limitations indicated in the literature which ultimately inhibit the ability of EVM to become universally accepted as a best practice across all industries. In response, researchers have developed extensions such as Earned Schedule Management (ESM), Earned Duration Management (EDM), and Customer Earned Value (CEV). This paper addresses the evolution, limitations, and new extensions of EVM.
8 Exploratory Analysis of the Malcolm Baldrige National Quality Award Model , Tettey Anyama, Gholston Sampson, and Mesmer Bryan   University of Alabama in Huntsville   aht0005@uah.edu
This paper conducts exploratory statistical analysis to assess trends in scores for the Malcolm Baldrige National Performance Excellence Award Model (MBNQA). The analysis identifies significant differences and similarities across sectors of examiner scores for the award program. The paper makes use of real consensus and site visit scores data collected over 11 years period, 2007-2017, from a States’ Performance Excellence Award program to conduct the analysis. There are 2 parts to the study. In the first part, the authors use descriptive statistics and various univariate parametric procedures to observe differences/ similarities in variability and mean scores over the period of the study. The results show that the variability in the scoring approaches across sectors were not statistically different. The mean consensus scores, however, were statistically different from the mean site visit scores. The second part of the study uses multivariate analysis to assess any significant difference in means between award winners and non-award winners for the consensus and site visit scores. The results show a significant difference between award winners and non-award winners for all 7 category scores using the site visit data but a different pattern for the consensus scores. The study confirms examiner consistency and reliability, as well as the need for all applicants to be given a site visit tour during the award program.