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Title: Armour Tech News, February 28, 1933
Creator: Armour Institute of Technology
Date: 1933-02-28, 1933-02-28
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, March 14, 1933
Creator: Armour Institute of Technology
Date: 1933-03-14, 1933-03-14
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, March 21, 1933
Creator: Armour Institute of Technology
Date: 1933-03-21, 1933-03-21
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, April 11, 1933
Creator: Armour Institute of Technology
Date: 1933-04-11, 1933-04-11
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, March 07, 1933
Creator: Armour Institute of Technology
Date: 1933-03-07, 1933-03-07
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, April 04, 1933
Creator: Armour Institute of Technology
Date: 1933-04-04, 1933-04-04
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, March 28, 1933
Creator: Armour Institute of Technology
Date: 1933-03-28, 1933-03-28
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, November 21, 1933
Creator: Armour Institute of Technology
Date: 1933-11-21, 1933-11-21
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, December 19, 1933
Creator: Armour Institute of Technology
Date: 1933-12-19, 1933-12-19
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, January 23, 1934
Creator: Armour Institute of Technology
Date: 1934-01-23, 1934-01-23
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, October 17, 1933
Creator: Armour Institute of Technology
Date: 1933-10-17, 1933-10-17
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, October 18, 1928
Creator: Armour Institute of Technology
Date: 1928-10-18, 1928-10-18
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, January 17, 1929
Creator: Armour Institute of Technology
Date: 1929-01-17, 1929-01-17
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, September 27, 1928
Creator: Armour Institute of Technology
Date: 1928-09-27, 1928-09-27
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, January 24, 1929
Creator: Armour Institute of Technology
Date: 1929-01-24, 1929-01-24
Collection: Technology News Microfilm collection, 1928-1981

Title: Armour Tech News, October 25, 1928
Creator: Armour Institute of Technology
Date: 1928-10-25, 1928-10-25
Collection: Technology News Microfilm collection, 1928-1981

Title: A Hybrid Data-Driven Simulation Framework For Integrated Energy-Air Quality (iE-AQ) Modeling at Multiple Urban Scales
Creator: Ashayeri, Mehdi
Date: 2020
Description: To date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of...
Show moreTo date, limited work has been done to collectively incorporate two key urban challenges: climate change and air pollution for the design of sustainable and healthy built environments. Main limitations to doing so include the existence of large spatiotemporal gaps in local outdoor air pollution data and a lack of a formal theoretical framework to effectively integrate localized urban air pollution data into sustainable built environment design strategies such as natural ventilation in buildings. This work hypothesizes that emerging advanced computational modeling approaches, including artificial intelligence (AI) and machine learning (ML) techniques, along with big open data set initiatives, can be used to fill some of those gaps. This can be achieved if urban air quality explanatory factors are properly identified and effectively connected to the current building performance simulation workflows.Therefore, the primary objective of this dissertation is to develop a hybrid AI-based data-driven simulation framework for integrated Energy-Air Quality (iE-AQ) modeling to quantify the combined energy reduction profiles and health risks implications of sustainable built environment design. This framework (1) incorporates dynamic human-centered factors, including mobility and building occupancy among others into the model, (2) interlinks land use regression (LUR), inverse distance weighting (IDW), and building energy simulation (BES) approaches via the R computational platform for developing the model, and (3) develops a web-based platform and interactive tool for visualizing and communicating the results. A series of novel machine learning approaches are tested within the workflow to improve efficiency and accuracy of the simulation model. A multi-scale model of urban air quality (using PM2.5 concentrations as the end point) and weather localization model with high spatiotemporal resolution was developed for Chicago, IL using low-cost sensor data. The integrated energy and air quality model was tested for the prototype office building at multiple urban scales in Chicago through applying air pollution-adjusted natural ventilation suitable hours.Results showed that the proposed ML approaches improved model accuracy above traditional simulation and statistical modeling approaches and that incorporating dynamic building-related factors such as human activity patterns can further improve urban air quality prediction models. The results of integrated energy and air quality (iE-AQ) analysis highlight that the energy saving potentials for natural ventilation considering local ambient air pollution and micro-climate data vary from 5.2% to 17% within Chicago. The proposed framework and tool have the potential to aid architects, engineers, planners and urban health policymakers in designing sustainable cities and empowering analytical solutions for reducing human health risk.
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Title: A MULTIPLE CASE STUDY OF COLLEGE MATHEMATICS INSTRUCTORS’ TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) AND ITS RELATIONSHIP TO THE INTEGRATION OF INFORMATION AND COMMUNICATIONS TECHNOLOGY (ICT) IN THEIR TEACHING PRACTICES AND STUDENTS’ LEARNING
Creator: Alhejoj, Kawkab
Date: 2020
Description: This multiple-case study aimed to investigate the following essential aspects of instructors’ ICT integration in higher education: self...
Show moreThis multiple-case study aimed to investigate the following essential aspects of instructors’ ICT integration in higher education: self-reported technological pedagogical content knowledge (TPACK), level of ICT integration, and motivations-challenges to integrate specific ICT tools to teach particular mathematics concepts. Four college math instructors were selected purposefully from four community colleges. The TPACK conceptual framework was adopted through the use of the TPACK-M self-assessment survey to understand the perceived TPACK knowledge of the instructors. The model of Niess et al (2009), which describes the teacher’s level of practical ICT integration in the light of their TPACK, assisted in exploring the way college math instructors used ICT. Data collection involved surveys, semi-structured interviews, and direct classroom observations. Quantitative data was analyzed using descriptive statistics, while Atlas.ti software was applied for qualitative data. The findings showed that the total TPACK-M was rated high, with TPK the lowest among all the constructs. In terms of the ICT integration model, one instructor fitted into the recognizing level, another into the adapting level, and two others into the accepting level. There was a misalignment between the self-reported TPACK knowledge and the in-class level of ICT integration. Instructors need more support in developing practical TPACK abilities via effective PD and activating the “teacher model” to help college math instructors integrate ICT in creative and successful practice. Also, more research in higher education is recommended using a larger sample in the area of designing a TPACK instrument for college math instructors to help capture their perceptions and recognize any gap between what they know and what they do in higher education contexts.
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Title: CITIZENSHIP PRESSURE, JOB STRESS, AND WORK-TO-FAMILY CONFLICT: THE MODERATING ROLE OF FLEXIBILITY IDIOSYNCRATIC DEALS
Creator: Ahmed, Shujaat Farah
Date: 2020
Description: Organizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties...
Show moreOrganizational expectations of employee performance have been expanding over time from traditional core task behaviors to include extra duties which may not be out of volition (Bolino, Turnley, Gilstrap, & Suazo, 2010). However, this extra work comes at a price, as employees are exhausted (Bolino et al., 2010) which can have health implications. Yet, no previous studies have examined the mechanism by which citizenship pressure is related to work interfering with family (WIF) conflict. Consequently, this study investigated an underlying mechanism, job stress, through which citizenship pressure was hypothesized to be related to work-family conflict from the work perspective, i.e., WIF conflict. This study also sought to identify the moderating role of flexibility idiosyncratic deals (i-deals) in the relationship of citizenship pressure with a) job stress, and b) WIF conflict. Data were collected across two waves with a time separation of one month in between waves. A total of 323 workers (mean age = 36.2) across industries in the United States participated in the study. Sixty-three percent identified as women, and 37% were men. Regression analyses were used to test the first three hypotheses. PROCESS was used to test the remainder of the hypotheses. Results for the regressions indicated that citizenship pressure was related at job stress and WIF conflict. Further, job stress was related to WIF conflict. Subsequently, the mediation hypothesis was significant. However, the moderation, and moderated mediation models were not statistically significant. I conducted post-hoc analyses to determine other possible significant paths in the model. The indirect effect of WIF conflict through the citizenship pressure and job stress link was statistically significant, thereby supporting an alternate mediation hypothesis. Perceived flexibility i-deals significantly moderated citizenship pressure and WIF conflict at time 1 only. The implications of this study are: managers should focus on their employees’ stressor experiences, as extra work beyond one’s specified job role is increasingly expected of employees. By doing so, pressure may be reduced through improving perceptions that employees can negotiate flexibility i-deals. This is especially important in an era of scarce resources, as pressure to go the extra mile is linked to a number of negative outcomes, such as increased WIF conflict and job stress.
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Title: Unsupervised Learning of Visual Odometry Using Direct Motion Modeling
Creator: Andrei, Silviu Stefan
Date: 2020
Description: Data for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on...
Show moreData for supervised learning of ego-motion and depth from video is scarce and expensive to produce. Subsequently, recent work has focused on unsupervised learning methods and achieved remarkable results which surpass in some instances the accuracy of supervised methods. Many unsupervised approaches rely on predicted monocular depth and so ignore motion information. Moreover, unsupervised methods which do incorporate motion information do so only indirectly by designing the depth prediction network as an RNN. Hence, none of the existing methods model motion directly. In this work, we show that it is possible to achieve superior pose estimation results by modeling motion explicitly. Our method uses a novel learning-based formulation for depth propagation and refinement which transforms predicted depth maps from the current frame onto the next frame where it serves as a prior for predicting the next frame's depth map. Experimental results demonstrate that the proposed approach surpasses state of the art techniques for the pose prediction task while being better or on par with other methods for the depth prediction task.
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