24 research outputs found
Optimizing the B.O.B.
The Kennesaw State University Department of Transportation manages the Big Owl Bus (B.O.B.). The B.O.B. has 9 routes which provide transportation around and between the Kennesaw and Marietta campuses, as well as to select off-campus apartment complexes and shopping centers. We utilized a number of methodologies to recommend improvements to the efficiency and accessibility of the B.O.B. We first used the vehicle routing problem to develop a binary integer linear programming model. This allowed us to determine a new set of routes that minimize total travel time across the routes. Next, we developed an integer non-linear programming model to assign a set of buses to each of the new routes, with the goal of minimizing overall cost. Performing a sensitivity analysis of our solution allowed our recommendations to be more robust. We then created a simulation model to verify our solution. We performed a financial analysis and conducted student surveys and driver interviews to inform our recommendations
ManyDogs Project: A Big Team Science Approach to Investigating Canine Behavior and Cognition
Dogs have a special place in human history as the first domesticated species and play important roles in many cultures around the world. However, their role in scientific studies has been relatively recent. With a few notable exceptions (e.g., Darwin, Pavlov, Scott, and Fuller), domestic dogs were not commonly the subject of rigorous scientific investigation of behavior until the late 1990s. Although the number of canine science studies has increased dramatically over the last 20 years, most research groups are limited in the inferences they can draw because of the relatively small sample sizes used, along with the exceptional diversity observed in dogs (e.g., breed, geographic location, experience). To this end, we introduce the ManyDogs Project, an international consortium of researchers interested in taking a big team science approach to understanding canine behavioral science. We begin by discussing why studying dogs provides valuable insights into behavior and cognition, evolutionary processes, human health, and applications for animal welfare. We then highlight other big team science projects that have previously been conducted in canine science and emphasize the benefits of our approach. Finally, we introduce the ManyDogs Project and our mission: (a) replicating important findings, (b) investigating moderators that need a large sample size such as breed differences, (c) reaching methodological con-sensus, (d) investigating cross-cultural differences, and (e) setting a standard for replication studies in general. In doing so, we hope to address previous limitations in individual lab studies and previous big team science frameworks to deepen our understanding of canine behavior and cognition
Scholarly Impact of Law School Faculties: Extending the Leiter Rankings to the Top 70 (2010)
Defining Disease, Diagnosis, and Translational Medicine within a Homeostatic Perturbation Paradigm: The National Institutes of Health Undiagnosed Diseases Program Experience.
Traditionally, the use of genomic information for personalized medical decisions relies on prior discovery and validation of genotype-phenotype associations. This approach constrains care for patients presenting with undescribed problems. The National Institutes of Health (NIH) Undiagnosed Diseases Program (UDP) hypothesized that defining disease as maladaptation to an ecological niche allows delineation of a logical framework to diagnose and evaluate such patients. Herein, we present the philosophical bases, methodologies, and processes implemented by the NIH UDP. The NIH UDP incorporated use of the Human Phenotype Ontology, developed a genomic alignment strategy cognizant of parental genotypes, pursued agnostic biochemical analyses, implemented functional validation, and established virtual villages of global experts. This systematic approach provided a foundation for the diagnostic or non-diagnostic answers provided to patients and serves as a paradigm for scalable translational research. Front Med (Lausanne) 2017 May 26; 4:62