University of Dayton
  • Dayton, OH, United States
Recent publications
The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. New modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.
The rate of college student mental health difficulties has been climbing, leading to overburdened college counseling centers. We propose the Holistic Prevention & Intervention Model (HPIM) as one solution in which campus and community resources work collaboratively to support students experiencing psychological distress and alleviate clinical demands. The HPIM moves from autonomous solutions to organizational-based strategies on a continuum of proactive to reactive interventions. We discuss how this model can be tailored and implemented for college campuses across the United States, including examining the resources available to the campus, the overall campus culture, and college demographics that affect risk and protective factors.
Dynamic business environments can cause unexpected disruptions that can force firms to embrace new ways of working. In such situations, salesforce change agility can be a key to success. However, scant research has examined the factors that promote the development of change agility, or the consequences thereof. We address these notable omissions by using a sample of 237 B2B salespeople to explore the effect of idiosyncratic deals on work absorption, change agility, behavioral performance, and sales performance. The findings indicate that a salesperson’s idiosyncratic work arrangements (i.e. Flexibility and Development Idiosyncratic Deals) enhance work absorption, which in turn increases salesperson change agility. Moreover, we find that while development idiosyncratic deals are positively related to change agility, flexibility idiosyncratic deals do not impact change agility. Finally, our analysis also shows that change agility is positively related to behavioral performance, with subsequent downstream effects on sales performance. Collectively, these findings offer a glimpse into the importance of agile behaviors as salespeople deal with the changes impacting the sales function.
Current studies of diversity in teams and organizations highlight the importance of examining activated, rather than just dormant, differences on a team. In this study, we contribute to organizational diversity theories by arguing that the activation of differences is a communicative process whereby how teams talk about their differences matters in how the activated differences affect team outcomes. Drawing on an in-depth qualitative study of real-life scientific teams, we examine the relationship between how team members activate and frame differences and how those communicative frames affect the team’s collective work. We find that how teams frame their differences affects the relationship between activated differences and team outcomes. We give practical and theoretical recommendations for the communicative management of differences on teams and in organizations.
Researchers have been increasingly taking advantage of the stochastic actor-oriented modeling framework as a method to analyze the evolution of network ties. Although the framework has proven to be a useful method to model longitudinal network data, it is designed to analyze a sample of one bounded network. For group and team researchers, this can be a significant limitation because such researchers often collect data on more than one team. This paper presents a nontechnical and hands-on introduction for a meta-level technique for stochastic actor-oriented models in RSIENA where researchers can simultaneously analyze network drivers from multiple samples of teams and groups. Moreover, we follow up with a multilevel Bayesian version of the model when it is appropriate. We also provide a framework for researchers to understand what types of research questions and theories could be examined and tested.
Lignin@Fe3O4 nanoparticles adsorb at oil-water interfaces, form Pickering emulsions, induce on-demand magnetic responses to break emulsions, and can sequester oil from water. Lignin@Fe3O4 nanoparticles were prepared using a pH-induced precipitation method and were fully characterized. These were used to prepare Pickering emulsions with castor oil/Sudan red G dye and water at various oil/water volume ratios and nanoparticle concentrations. The stability and demulsification of the emulsions under different magnetic fields generated with permanent magnets (0-540 mT) were investigated using microscopy images and by visual inspection over time. The results showed that the Pickering emulsions were more stable at the castor oil/water ratio of 50/50 and above. Increasing the concentration of lignin@Fe3O4 improved the emulsion stability and demulsification rates with 540 mT applied magnetic field strength. The adsorption of lignin@Fe3O4 nanoparticles at the oil/water interface using 1-pentanol evaporation through Marangoni effects was demonstrated, and magnetic manipulation of a lignin@Fe3O4 stabilized castor oil spill in water was shown. Nanoparticle concentration and applied magnetic field strengths were analyzed for the recovery of spilled oil from water; it was observed that increasing the magnetic strength increased oil spill motion for a lignin@Fe3O4 concentration of up to 0.8 mg mL −1 at 540 mT. Overall, this study demonstrates the potential of lignin-magnetite nanocomposites for rapid on-demand magnetic responses to externally induced stimuli.
A wing’s response to periodic vortical gusts of different frequencies and magnitudes was investigated and controlled using a PID based closed loop control system. All experiments were conducted in the University of Dayton Water Tunnel (UD-WaT). The vortical gust was generated using an oscillating flat plate pitched about its quarter chord. The frequency of the vortical gusts was controlled by the oscillation frequency of the flat plate. Simultaneous Time Resolved Particle Image Velocimetry (TR-PIV), and force measurements were collected to capture the vortical gust and its interaction with a downstream flat plate at a fixed angle of attack. The force histories were correlated with the flow physics to determine the cause of force transients. A PID-based closed loop control system was then implemented to mitigate the periodic vortical gusts. The flow field responsible for successful gust mitigation was studied using TR-PIV. The closed loop controller was able to achieve up to 63.32% average mitigation. PIV data revealed the effective angle of attack history for gust alleviation, and the error in 𝐂𝐋 was higher at higher 𝛂̇ 𝐞𝐟𝐟.
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3,300 members
Phu H. Phung
  • Department of Computer Science
Madhuri Kango-Singh
  • Department of Biology
Erick Vasquez
  • Department of Chemical and Materials Engineering
Md. Zahangir Alom
  • Department of Electrical and Computer Engineering
Mrigendra Rajput
  • Department of Biology
300 College Park, 45469, Dayton, OH, United States