Prediction of Airport Arrival Rates Using Data Mining Methods

This research sought to establish and utilize relationships between environmental variable inputs and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered using various statistical modeling methods within an overarching data mining protocol and the developed models were tested using historical data. Additionally, a selected model was deployed using real-time predictive weather information to estimate airport efficiency as a demonstration of potential operational usefulness. This work employed SAS® Enterprise Miner TM data mining and modeling software to train and validate decision tree, neural network, and linear regression models to estimate the importance of weather input variables in predicting Airport Arrival Rates (AAR) using the FAA’s Aviation System Performance Metric (ASPM) database. The ASPM database contains airport performance statistics and limited weather variables archived at 15-minute and hourly intervals, and these data formed the foundation of this study. In order to add more weather parameters into the data mining environment, National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) meteorological hourly station data were merged with the ASPM data to increase the number of environmental variables (e.g., precipitation type and amount) into the analyses. Using the SAS® Enterprise Miner TM, three different types of models were created, compared, and scored at the following ten airports: a) Hartsfield-Jackson Atlanta International Airport (ATL), b) Los Angeles International Airport (LAX), c) O’Hare International Airport (ORD), d) Dallas/Fort Worth International Airport (DFW), e) John F. Kennedy International Airport (JFK), f) Denver International Airport (DEN), g) San Francisco International Airport (SFO), h) Charlotte-Douglas International Airport (CLT), i) LaGuardia Airport (LGA), and j) Newark Liberty International Airport (EWR). At each location, weather inputs were used to estimate AARs as a metric of efficiency easily interpreted by FAA airspace managers. To estimate Airport Arrival Rates, three data sets were used: a) 15-minute and b) hourly ASPM data, along with c) a merged ASPM and meteorological hourly station data set. For all three data sets, the models were trained and validated using data from 2014 and 2015, and then tested using 2016 data. Additionally, a selected airport model was deployed using National Weather Service (NWS) Localized Aviation MOS (Model Output Statistics) Program (LAMP) weather guidance as the input variables over a 24-hour period as a test. The resulting AAR output predictions were then compared with the real-world AARs observed. Based on model scoring using 2016 data, LAX, ATL, and EWR demonstrated useful predictive performance that potentially could be applied to estimate real-world AARs. Marginal, but perhaps useful AAR prediction might be gleaned operationally at LGA, SFO, and DFW, as the number of successfully scored cases fall loosely within one standard deviation of acceptable model performance arbitrarily set at ten percent of the airport’s maximum AAR. The remaining models studied, DEN, CLT, ORD, and JFK appeared to have little useful operational application based on the 2016 model scoring results

Impact of Weather Factors on Airport Arrival Rates: Application of Machine Learning in Air Transportation

Weather is responsible for approximately 70% of air transportation delays in the National Airspace System, and delays resulting from convective weather alone cost airlines and passengers millions of dollars each year due to delays that could be avoided. This research sought to establish relationships between environmental variables and airport efficiency estimates by data mining archived weather and airport performance data at ten geographically and climatologically different airports. Several meaningful relationships were discovered from six out of ten airports using various machine learning methods within an overarching data mining protocol, and the developed models were tested using historical data

The Bermuda Triangle : the pragmatics, policies, and principles for data sharing in the history of the Human Genome Project

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Journal of the History of Biology 51 (2018): 693–805, doi:10.1007/s10739-018-9538-7.The Bermuda Principles for DNA sequence data sharing are an enduring legacy of the Human Genome Project (HGP). They were adopted by the HGP at a strategy meeting in Bermuda in February of 1996 and implemented in formal policies by early 1998, mandating daily release of HGP-funded DNA sequences into the public domain. The idea of daily sharing, we argue, emanated directly from strategies for large, goal-directed molecular biology projects first tested within the “community” of C. elegans researchers, and were introduced and defended for the HGP by the nematode biologists John Sulston and Robert Waterston. In the C. elegans community, and subsequently in the HGP, daily sharing served the pragmatic goals of quality control and project coordination. Yet in the HGP human genome, we also argue, the Bermuda Principles addressed concerns about gene patents impeding scientific advancement, and were aspirational and flexible in implementation and justification. They endured as an archetype for how rapid data sharing could be realized and rationalized, and permitted adaptation to the needs of various scientific communities. Yet in addition to the support of Sulston and Waterston, their adoption also depended on the clout of administrators at the US National Institutes of Health (NIH) and the UK nonprofit charity the Wellcome Trust, which together funded 90% of the HGP human sequencing effort. The other nations wishing to remain in the HGP consortium had to accommodate to the Bermuda Principles, requiring exceptions from incompatible existing or pending data access policies for publicly funded research in Germany, Japan, and France. We begin this story in 1963, with the biologist Sydney Brenner’s proposal for a nematode research program at the Laboratory of Molecular Biology (LMB) at the University of Cambridge. We continue through 2003, with the completion of the HGP human reference genome, and conclude with observations about policy and the historiography of molecular biology

Akerman-Teixeira Duo with St. Pius X High School Classical Guitar Orchestra

KSU School of Music presents the Akerman-Teixeira Duo with St. Pius X High School Classical Guitar Orchestra.https://digitalcommons.kennesaw.edu/musicprograms/1205/thumbnail.jp

A Stable Cranial Neural Crest Cell Line from Mouse

Cranial neural crest cells give rise to ectomesenchymal derivatives such as cranial bones, cartilage, smooth muscle, dentin, as well as melanocytes, corneal endothelial cells, and neurons and glial cells of the peripheral nervous system. Previous studies have suggested that although multipotent stem-like cells may exist during the course of cranial neural crest development, they are transient, undergoing lineage restriction early in embryonic development. We have developed culture conditions that allow cranial neural crest cells to be grown as multipotent stem-like cells. With these methods, we obtained 2 independent cell lines, O9-1 and i10-1, which were derived from mass cultures of Wnt1-Cre; R26R-GFP-expressing cells. These cell lines can be propagated and passaged indefinitely, and can differentiate into osteoblasts, chondrocytes, smooth muscle cells, and glial cells. Whole-genome expression profiling of O9-1 cells revealed that this line stably expresses stem cell markers (CD44, Sca-1, and Bmi1) and neural crest markers (AP-2α, Twist1, Sox9, Myc, Ets1, Dlx1, Dlx2, Crabp1, Epha2, and Itgb1). O9-1 cells are capable of contributing to cranial mesenchymal (osteoblast and smooth muscle) neural crest fates when injected into E13.5 mouse cranial tissue explants and chicken embryos. These results suggest that O9-1 cells represent multipotent mesenchymal cranial neural crest cells. The O9-1 cell line should serve as a useful tool for investigating the molecular properties of differentiating cranial neural crest cells

Tissue Origins and Interactions in the Mammalian Skull Vault

AbstractDuring mammalian evolution, expansion of the cerebral hemispheres was accompanied by expansion of the frontal and parietal bones of the skull vault and deployment of the coronal (fronto-parietal) and sagittal (parietal–parietal) sutures as major growth centres. Using a transgenic mouse with a permanent neural crest cell lineage marker, Wnt1-Cre/R26R, we show that both sutures are formed at a neural crest–mesoderm interface: the frontal bones are neural crest-derived and the parietal bones mesodermal, with a tongue of neural crest between the two parietal bones. By detailed analysis of neural crest migration pathways using X-gal staining, and mesodermal tracing by DiI labelling, we show that the neural crest–mesodermal tissue juxtaposition that later forms the coronal suture is established at E9.5 as the caudal boundary of the frontonasal mesenchyme. As the cerebral hemispheres expand, they extend caudally, passing beneath the neural crest–mesodermal interface within the dermis, carrying with them a layer of neural crest cells that forms their meningeal covering. Exposure of embryos to retinoic acid at E10.0 reduces this meningeal neural crest and inhibits parietal ossification, suggesting that intramembranous ossification of this mesodermal bone requires interaction with neural crest-derived meninges, whereas ossification of the neural crest-derived frontal bone is autonomous. These observations provide new perspectives on skull evolution and on human genetic abnormalities of skull growth and ossification

Overexpression of Smad2 Reveals Its Concerted Action with Smad4 in Regulating TGF- β-Mediated Epidermal Homeostasis

AbstractMembers of the transforming growth factor-β (TGF-β) superfamily are critical regulators for epithelial growth and can alter the differentiation of keratinocytes. Transduction of TGF-β signaling depends on the phosphorylation and activation of Smad proteins by heteromeric complexes of ligand-specific type I and II receptors. To understand the function of TGF-β and activin-specific Smad, we generated transgenic mice that overexpress Smad2 in epidermis under the control of keratin 14 promoter. Overexpression of Smad2 increases endogenous Smad4 and TGF-β1 expression while heterozygous loss of Smad2 reduces their expression levels, suggesting a concerted action of Smad2 and -4 in regulating TGF-β signaling during skin development. These transgenic mice have delayed hair growth, underdeveloped ears, and shorter tails. In their skin, there is severe thickening of the epidermis with disorganized epidermal architecture, indistinguishable basement membrane, and dermal fibrosis. These abnormal phenotypes are due to increased proliferation of the basal epidermal cells and abnormalities in the program of keratinocyte differentiation. The ectodermally derived enamel structure is also abnormal. Collectively, our study presents the first in vivo evidence that, by providing an auto-feedback in TGF-β signaling, Smad2 plays a pivotal role in regulating TGF-β-mediated epidermal homeostasis

Bermuda 2.0: Reflections from Santa Cruz

In February 1996, the genome community met in Bermuda to formulate principles for circulating genomic data. Although it is now 20 years since the Bermuda Principles were formulated, they continue to play a central role in shaping genomic and data-sharing practices. However, since 1996, “openness” has become an increasingly complex issue. This commentary seeks to articulate three core challenges data-sharing faces today

Cyclic dermal BMP signalling regulates stem cell activation during hair regeneration

In the age of stem cell engineering it is critical to understand how stem cell activity is regulated during regeneration. Hairs are mini-organs that undergo cyclic regeneration throughout adult life1, and are an important model for organ regeneration. Hair stem cells located in the follicle bulge2 are regulated by the surrounding microenvironment, or niche3. The activation of such stem cells is cyclic, involving periodic -catenin activity4, 5, 6, 7. In the adult mouse, regeneration occurs in waves in a follicle population, implying coordination among adjacent follicles and the extrafollicular environment. Here we show that unexpected periodic expression of bone morphogenetic protein 2 (Bmp2) and Bmp4 in the dermis regulates this process. This BMP cycle is out of phase with the WNT/-catenin cycle, thus dividing the conventional telogen into new functional phases: one refractory and the other competent for hair regeneration, characterized by high and low BMP signalling, respectively. Overexpression of noggin, a BMP antagonist, in mouse skin resulted in a markedly shortened refractory phase and faster propagation of the regenerative wave. Transplantation of skin from this mutant onto a wild-type host showed that follicles in donor and host can affect their cycling behaviours mutually, with the outcome depending on the equilibrium of BMP activity in the dermis. Administration of BMP4 protein caused the competent region to become refractory. These results show that BMPs may be the long-sought 'chalone' inhibitors of hair growth postulated by classical experiments. Taken together, results presented in this study provide an example of hierarchical regulation of local organ stem cell homeostasis by the inter-organ macroenvironment. The expression of Bmp2 in subcutaneous adipocytes indicates physiological integration between these two thermo-regulatory organs. Our findings have practical importance for studies using mouse skin as a model for carcinogenesis, intra-cutaneous drug delivery and stem cell engineering studies, because they highlight the acute need to differentiate supportive versus inhibitory regions in the host skin