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Full-Text Articles in Engineering

Shoulder Muscular Fatigue From Static Posture Concurrently Reduces Cognitive Attentional Resources, Mitchell L. Stephenson, Alec G. Ostrander, Hamid Norasi, Michael C. Dorneich Jun 2019

Shoulder Muscular Fatigue From Static Posture Concurrently Reduces Cognitive Attentional Resources, Mitchell L. Stephenson, Alec G. Ostrander, Hamid Norasi, Michael C. Dorneich

Industrial and Manufacturing Systems Engineering Publications

Objective: The goal of this work is to determine whether muscular fatigue concurrently reduces cognitive attentional resources in technical tasks for healthy adults.

Background: Muscular fatigue is common in the workplace but often dissociated with cognitive performance. A corpus of literature demonstrates a link between muscular fatigue and cognitive function, but few investigations demonstrate that the instigation of the former degrades the latter in a way that may affect technical task completion. For example, laparoscopic surgery increases muscular fatigue, which may risk attentional capacity reduction and undermine surgical outcomes.

Method: A total of 26 healthy participants completed a dual-task cognitive ...


Evaluation Of An Intelligent Team Tutoring System For A Collaborative Two-Person Problem: Surveillance, Alec Ostrander, Desmond Bonner, Jamiahus Walton, Anna Slavina, Kaitlyn M. Ouverson, Adam Kohl, Stephen Gilbert, Michael Dorneich, Anne Sinatra, Eliot H. Winer Jan 2019

Evaluation Of An Intelligent Team Tutoring System For A Collaborative Two-Person Problem: Surveillance, Alec Ostrander, Desmond Bonner, Jamiahus Walton, Anna Slavina, Kaitlyn M. Ouverson, Adam Kohl, Stephen Gilbert, Michael Dorneich, Anne Sinatra, Eliot H. Winer

Industrial and Manufacturing Systems Engineering Publications

This paper describes the development and evaluation of an Intelligent Team Tutoring System (ITTS) for pairs of learners working collaboratively to monitor an area. In the Surveillance Team Tutor (STT), learners performed a surveillance task in a virtual environment, communicating to track hostile moving soldiers. This collaborative problem solving task required significant communication to achieve the common goal of perfect surveillance. In a pilot evaluation, 16 two-person teams performed the task within one of three feedback conditions (Individual, Team, or None) across four trials each. The STT used a unique approach to filtering feedback so that teams in both individual ...


Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham Jan 2019

Optimizing Ensemble Weights And Hyperparameters Of Machine Learning Models For Regression Problems, Mohsen Shahhosseini, Guiping Hu, Hieu Pham

Industrial and Manufacturing Systems Engineering Publications

Aggregating multiple learners through an ensemble of models aims to make better predictions by capturing the underlying distribution more accurately. Different ensembling methods, such as bagging, boosting and stacking/blending, have been studied and adopted extensively in research and practice. While bagging and boosting intend to reduce variance and bias, respectively, blending approaches target both by finding the optimal way to combine base learners to find the best trade-off between bias and variance. In blending, ensembles are created from weighted averages of multiple base learners. In this study, a systematic approach is proposed to find the optimal weights to create ...


Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis Jan 2019

Maize Yield And Nitrate Loss Prediction With Machine Learning Algorithms, Mohsen Shahhosseini, Rafael A. Martinez-Feria, Guiping Hu, Sotirios Archontoulis

Industrial and Manufacturing Systems Engineering Publications

Pre-season prediction of crop production outcomes such as grain yields and N losses can provide insights to stakeholders when making decisions. Simulation models can assist in scenario planning, but their use is limited because of data requirements and long run times. Thus, there is a need for more computationally expedient approaches to scale up predictions. We evaluated the potential of five machine learning (ML) algorithms as meta-models for a cropping systems simulator (APSIM) to inform future decision-support tool development. We asked: 1) How well do ML meta-models predict maize yield and N losses using pre-season information? 2) How many data ...