Research and Education in Computational Science and Engineering

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.Comment: Major revision, to appear in SIAM Revie

Predicting attrition of new Student Affairs professionals through perceptions of work-related quality of life, synergistic supervision, and executive servant leadership

Abstract New professional attrition in Student Affairs has been established as a concern for the field (Bender, 1980; Lorden, 1998; Renn & Hodges, 2007; Marshall, Gardner, Hughes, & Lowery, 2016). The debilitating impacts on university finances, productivity, organizational stability, team disruption, and innovation as a result of this problem creates urgency for the field to understand its predictors. The current study reviewed the impact of new professionals’ work-related quality of life, their perception of the use of synergistic supervision by their direct supervisors, and their perception of the use of executive servant leadership by divisional leaders as potential predictors of attrition. Using logistic regression, several models were examined to determine the isolated influence of each of these study variables and the cumulative impact. Counter to hypotheses, the perceptions of style in both the supervisor and divisional leaders were not statistically significant. As hypothesized, the predictive value of work-related quality of life for new professionals proved to be significant, demonstrating that as new professionals increase in their level of work-related quality of life, the odds of them intending to leave the field decrease. The factors of work-related quality of life were further explored for their predictive value. New professionals’ job and career satisfaction and their general well-being were important predictors with both demonstrating that as they increased, the odds of new professionals intending to leave the field decreased. Control at work was a significant predictor as well with increases in the perceived level of control leading to increases in the odds of new professionals intending to leave Student Affairs. These findings provide insight on new professional attrition for Student Affairs supervisors, divisional leaders, and national organizations. Recommendations for Student Affairs leadership and suggestions for further research are discussed

A Predictive Model using Machine Learning Algorithm in Identifying Student's Probability on Passing Semestral Course

Purpose: The used of an integrated academic information system in higher education has been proven in improving quality education which results to generates enormous data that can be used to discover new knowledge through data mining concepts, techniques, and machine learning algorithm. This study aims to determine a predictive model to learn students' probability to pass their courses taken at the earliest stage of the semester. Method: To successfully discover a good predictive model with high acceptability, accurate, and precision rate which delivers a useful outcome for decision making in education systems, in improving the processes of conveying knowledge and uplifting student's academic performance, the proponent applies and strictly followed the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This study employs classification for data mining techniques, and decision tree for algorithm. Results: With the utilization of the newly discovered predictive model, the prediction of students' probabilities to pass the current courses they take gives 0.7619 accuracy, 0.8333 precision, 0.8823 recall, and 0.8571 f1 score, which shows that the model used in the prediction is reliable, accurate, and recommendable. Conclusion: Considering the indicators and the results, it can be noted that the prediction model used in this study is highly acceptable. The data mining techniques provides effective and efficient innovative tools in analyzing and predicting student performances. The model used in this study will greatly affect the way educators understand and identify the weakness of their students in the class, the way they improved the effectiveness of their learning processes gearing to their students, bring down academic failure rates, and help institution administrators modify their learning system outcomes. Recommendations: Full automation of prediction results accessible by the students, faculty, and institution administrators for fast management decision making should take place. Further study for the inclusion of some student`s demographic information, vast amount of data within the dataset, automated and manual process of predictive criteria indicators where the students can regulate to which criteria, they must improve more for them to pass their courses taken at the end of the semester as early as midterm period are highly needed

Enhancing Software Project Outcomes: Using Machine Learning and Open Source Data to Employ Software Project Performance Determinants

Many factors can influence the ongoing management and execution of technology projects. Some of these elements are known a priori during the project planning phase. Others require real-time data gathering and analysis throughout the lifetime of a project. These real-time project data elements are often neglected, misclassified, or otherwise misinterpreted during the project execution phase resulting in increased risk of delays, quality issues, and missed business opportunities. The overarching motivation for this research endeavor is to offer reliable improvements in software technology management and delivery. The primary purpose is to discover and analyze the impact, role, and level of influence of various project related data on the ongoing management of technology projects. The study leverages open source data regarding software performance attributes. The goal is to temper the subjectivity currently used by project managers (PMs) with quantifiable measures when assessing project execution progress. Modern-day PMs who manage software development projects are charged with an arduous task. Often, they obtain their inputs from technical leads who tend to be significantly more technical. When assessing software projects, PMs perform their role subject to the limitations of their capabilities and competencies. PMs are required to contend with the stresses of the business environment, the policies, and procedures dictated by their organizations, and resource constraints. The second purpose of this research study is to propose methods by which conventional project assessment processes can be enhanced using quantitative methods that utilize real-time project execution data. Transferability of academic research to industry application is specifically addressed vis-à-vis a delivery framework to provide meaningful data to industry practitioners

Center for Research on Sustainable Forests 2018 Annual Report

The Center for Research on Sustainable Forests (CRSF) was founded in 2006 to build on a rich history of leading forest research and to enhance our understanding of Maine’s forest resources in an increasingly complex world. CRSF brings together the natural and social sciences with an appreciation for the importance of the relationship between people and our ecosystems. We conduct research and inform stakeholders about how to balance the wise-use of our resources while conserving our natural world for future generations. Our mission is to conduct and promote leading interdisciplinary research on issues affecting the management and sustainability of northern forest ecosystems and Maine’s forest-based economy

Center for Research on Sustainable Forests 2017 Annual Report

Ongoing development within the CRSF to be the region’s research data portal and geospatial observatory for forests of the Northeastern US. In addition to updating the CRSF home website, we continue to support three online tools for forest resources professionals and the public: Northeast Forest Information System (NEFIS) – an online, opensource, web portal for applied forestry information (http://www.nefismembers.org). More than 1,000 documents were uploaded over the year on a wide range of topics, user numbers have doubled, and monthly page views have reached nearly 5,000. Maine Forest Spatial Tool – displays a wide variety of geospatial data on forest resources across the State of Maine for both forest resource professionals and the public (http://mfst.acg.maine.edu). Maine Forest Dashboard – The Dashboard was launched in Spring 2017 and can be accessed at http://www.maineforestdashboard.com. The site provides customizable forest statistics and changes using long-term data from the Maine Forest Service and has had nearly 100 page views since its release in early May. CRSF scientists continue to provide a strong return for every dollar provided by the Maine Economic Improvement Fund (MEIF) to support CRSF research. In the past year, there has been over $21 in return for every$1 invested in

Behavioral Responses of Willow Flycatchers, \u3ci\u3eEmpidonax traillii\u3c/i\u3e, to a Heterogeneous Environment

Spatial heterogeneity impacts population and community-level dynamics including species-level dispersal patterns, the use and availability of refugia, predator/prey dynamics, and reproductive fitness. Understanding how wild animal populations respond to environmental heterogeneity is essential for their proper management and conservation. In this study, I examine the responses of Willow Flycatchers to spatial heterogeneity in the distribution of their food and habitat resources. Over the course of three breeding seasons, I radio- tracked Willow Flycatchers at Fish Creek in Manti-La Sal National Forest in Utah, recorded detailed behavior data at each radio location, and collected fecal, feather and insect samples. I formulated individual and population-level Bayesian spatial resource selection functions to model Willow Flycatcher foraging and vocalization behavior on multiple scales. These models indicate that vocalization and foraging behavior are spatially segregated within the home ranges of Willow Flycatchers. Further, Willow Flycatchers were found to use mature riparian habitat for vocalizing while they used a variety of habitat types for foraging. The insect samples were used to identify distinct carbon and nitrogen stable isotope signatures for the aquatic and terrestrial insect communities at Fish Creek. In conjunction with the fecal samples, I used the stable isotope signatures to determine the contribution of aquatic versus terrestrial insects to the Willow Flycatcher diet. Aquatic insects comprised a larger proportion of the diet of adult than nestling Willow Flycatchers. This suggests that adult flycatchers consume a diet that is distinct from the one they feed to their nestlings. Finally, I compared space use characteristics in two populations of Willow Flycatchers: a population of the endangered Southwestern Willow Flycatcher at Roosevelt Lake, Arizona and another belonging to a non-endangered subspecies of Willow Flycatcher at Fish Creek, Utah. Differences in space use were found largely among breeding flycatchers while space use characteristics in non-breeding Willow Flycatchers did not differ across populations. This suggests that space use patterns in non-breeding Southwestern Willow Flycatchers may be generalizable to non-breeding flycatchers from non-endangered populations. This study expands our understanding of how Willow Flycatchers respond to spatial heterogeneity while its key findings have management and conservation implications for the species

Improving Traffic Safety And Drivers\u27 Behavior In Reduced Visibility Conditions

This study is concerned with the safety risk of reduced visibility on roadways. Inclement weather events such as fog/smoke (FS), heavy rain (HR), high winds, etc, do affect every road by impacting pavement conditions, vehicle performance, visibility distance, and drivers’ behavior. Moreover, they affect travel demand, traffic safety, and traffic flow characteristics. Visibility in particular is critical to the task of driving and reduction in visibility due FS or other weather events such as HR is a major factor that affects safety and proper traffic operation. A real-time measurement of visibility and understanding drivers’ responses, when the visibility falls below certain acceptable level, may be helpful in reducing the chances of visibility-related crashes. In this regard, one way to improve safety under reduced visibility conditions (i.e., reduce the risk of visibility related crashes) is to improve drivers’ behavior under such adverse weather conditions. Therefore, one of objectives of this research was to investigate the factors affecting drivers’ stated behavior in adverse visibility conditions, and examine whether drivers rely on and follow advisory or warning messages displayed on portable changeable message signs (CMS) and/or variable speed limit (VSL) signs in different visibility, traffic conditions, and on two types of roadways; freeways and two-lane roads. The data used for the analyses were obtained from a self-reported questionnaire survey carried out among 566 drivers in Central Florida, USA. Several categorical data analysis techniques such as conditional distribution, odds’ ratio, and Chi-Square tests were applied. In addition, two modeling approaches; bivariate and multivariate probit models were estimated. The results revealed that gender, age, road type, visibility condition, and familiarity with VSL signs were the significant factors affecting the likelihood of reducing speed following CMS/VSL instructions in reduced visibility conditions. Other objectives of this survey study were to determine the content of messages that iv would achieve the best perceived safety and drivers’ compliance and to examine the best way to improve safety during these adverse visibility conditions. The results indicated that Caution-fog ahead-reduce speed was the best message and using CMS and VSL signs together was the best way to improve safety during such inclement weather situations. In addition, this research aimed to thoroughly examine drivers’ responses under low visibility conditions and quantify the impacts and values of various factors found to be related to drivers’ compliance and drivers’ satisfaction with VSL and CMS instructions in different visibility and traffic conditions. To achieve these goals, Explanatory Factor Analysis (EFA) and Structural Equation Modeling (SEM) approaches were adopted. The results revealed that drivers’ satisfaction with VSL/CMS was the most significant factor that positively affected drivers’ compliance with advice or warning messages displayed on VSL/CMS signs under different fog conditions followed by driver factors. Moreover, it was found that roadway type affected drivers’ compliance to VSL instructions under medium and heavy fog conditions. Furthermore, drivers’ familiarity with VSL signs and driver factors were the significant factors affecting drivers’ satisfaction with VSL/CMS advice under reduced visibility conditions. Based on the findings of the survey-based study, several recommendations are suggested as guidelines to improve drivers’ behavior in such reduced visibility conditions by enhancing drivers’ compliance with VSL/CMS instructions. Underground loop detectors (LDs) are the most common freeway traffic surveillance technologies used for various intelligent transportation system (ITS) applications such as travel time estimation and crash detection. Recently, the emphasis in freeway management has been shifting towards using LDs data to develop real-time crash-risk assessment models. Numerous v studies have established statistical links between freeway crash risk and traffic flow characteristics. However, there is a lack of good understanding of the relationship between traffic flow variables (i.e. speed, volume and occupancy) and crashes that occur under reduced visibility (VR crashes). Thus, another objective of this research was to explore the occurrence of reduced visibility related (VR) crashes on freeways using real-time traffic surveillance data collected from loop detectors (LDs) and radar sensors. In addition, it examines the difference between VR crashes to those occurring at clear visibility conditions (CV crashes). To achieve these objectives, Random Forests (RF) and matched case-control logistic regression model were estimated. The results indicated that traffic flow variables leading to VR crashes are slightly different from those variables leading to CV crashes. It was found that, higher occupancy observed about half a mile between the nearest upstream and downstream stations increases the risk for both VR and CV crashes. Moreover, an increase of the average speed observed on the same half a mile increases the probability of VR crash. On the other hand, high speed variation coupled with lower average speed observed on the same half a mile increase the likelihood of CV crashes. Moreover, two issues that have not explicitly been addressed in prior studies are; (1) the possibility of predicting VR crashes using traffic data collected from the Automatic Vehicle Identification (AVI) sensors installed on Expressways and (2) which traffic data is advantageous for predicting VR crashes; LDs or AVIs. Thus, this research attempts to examine the relationships between VR crash risk and real-time traffic data collected from LDs installed on two Freeways in Central Florida (I-4 and I-95) and from AVI sensors installed on two vi Expressways (SR 408 and SR 417). Also, it investigates which data is better for predicting VR crashes. The approach adopted here involves developing Bayesian matched case-control logistic regression using the historical VR crashes, LDs and AVI data. Regarding models estimated based on LDs data, the average speed observed at the nearest downstream station along with the coefficient of variation in speed observed at the nearest upstream station, all at 5-10 minute prior to the crash time, were found to have significant effect on VR crash risk. However, for the model developed based on AVI data, the coefficient of variation in speed observed at the crash segment, at 5-10 minute prior to the crash time, affected the likelihood of VR crash occurrence. Argument concerning which traffic data (LDs or AVI) is better for predicting VR crashes is also provided and discussed