A Social Science Approach Using Big Data for City Planning

Cities bring about economic dynamism through positive economic externalities; however, the concentration of people in dense locations has its costs — epidemics, social unrest, pollution, and congestion are some of the ills of cities. As cities evolve, they experience stress, and fault lines appear; the ability to pulse a city and provide early warning of these fault lines can prove advantageous for policymakers in managing and planning for cities. This paper outlines a research program that developed a city scanning tool to measure cities and detect aberrations as they surface. We aggregated data from various industry partners, governmental agencies, and public online sources to develop the measurement metric and applied social science theories to analyze and interpret the results. The results of this study contribute to information system (IS) research by showcasing the role IS research in city planning and for societal good

Deformation Clustering Methods for Topologically Optimized Structures under Crash Load based on Displacement Time Series

Multi-objective Topology Optimization has been receiving more and more attention in structural design recently. It attempts to maximize several performance objectives by redistributing the material in a design space for a given set of boundary conditions and constraints, yielding many Paretooptimal solutions. However, the high number of solutions makes it difficult to identify preferred designs. Therefore, an automatic way of summarizing solutions is needed for selecting interesting designs according to certain criteria, such as crashworthiness, deformation, and stress state. One approach for summarization is to cluster similar designs and obtain design representatives based on a suitable metric. For example, with Euclidean distance of the objective functions as the metric, design groups with similar performance can be identified and only the representative designs from different clusters may be analyzed. However, previous research has not dealt with the deformation-related time-series data of structures with different topologies. Since the non-linear dynamic behavior of designs is important in various fields such as vehicular crashworthiness, a clustering method based on time-dependent behavior of structures is proposed here. To compare the time-series displacement data of selected nodes in the structure and to create similarity matrices of those datasets, euclidean metrics and Dynamic Time Warping (DTW) are introduced. This is combined with clustering techniques such as k-medoids and Ordering Points To Identify the Clustering Structure (OPTICS), and we investigate the use of unsupervised learning methods to identify and group similar designs using the time series of nodal displacement data. In the first part, we create simple time-series datasets using a mass-spring system to validate the proposed methods. Each dataset has predefined clusters of data with distinct behavior such as different periods or modes. Then, we demonstrate that the combination of metrics for comparison of time series (Euclidean and DTW) and the clustering method (k-medoids and OPTICS) can identify the clusters of similar behavior accurately. In the second part, we apply these methods to a more realistic, engineering dataset of nodal displacement time series describing the crash behavior of topologically-optimized designs. We identify similar structures and obtain representative designs from each cluster. This reveals that the suggested method is useful in analyzing dynamic crash behavior and supports the designers in selecting representative structures based on deformation data at the early stages of the design process

Unexpected deviation from diene behaviour of uracil amidine: Towards synthesis of some pyrido[2,3-d]pyrimidine derivatives

Condensation products obtained from the treatment of uracil amidine with preformed or in situ generated suitably substituted olefins unexpectedly undergo intramolecular cyclisation during silica gel chromatography to generate pyrido[2,3-d]pyrimidines. Various reaction conditions arestudiedandthealterednatureoftheuracilamidinemoleculeisfurtherexploredbyreactingitwithdifferentsuitably substituted alkene

An economic model of early marriage

To explain female adolescent marriage patterns around the world, we develop a marriage market model with asymmetric information about prospective marriage partners, and a noisy signal about the brideís quality during an engagement. In equilibrium, there is a negative relationship between the age and perceived quality of women on the marriage market and, consistent with available evidence, older brides make higher net marriage payments. The model also implies path dependence in the evolution of adolescent marriage practices over time and persistent e§ects on marriage practices from transitory shocks. Model simulations show interventions which increase the opportunity cost of early marriage attenuates the association between bride quality and age, triggering a virtuous cycle of marriage postponement

Detecting positional vertigo using an ensemble of 2D convolutional neural networks

The aim of the work presented here was to develop a system that can automatically identify attacks of dizziness occurring in patients suffering from positional vertigo, which occurs when sufferers move their head into certain positions. We used our novel medical device, CAVA, to record eye- and head-movement data continually for up to 30 days in patients diagnosed with a disorder called Benign Paroxysmal Positional Vertigo. Building upon our previous work, we describe a novel ensemble of five 2D Convolutional Neural Networks, using composite recognition features, including eye-movement data and three-channel accelerometer data. We achieve an F1 score of 0.63 across an 11-fold cross-fold validation experiment, demonstrating that the system can detect a few seconds of motion provoked dizziness from within over a 100 h of normal eye-movement data. We show that the system outperforms our previous 1D Neural Network approach, and that our ensemble classifier is superior to each of the individual networks it contains. We also demonstrate that our composite recognition features provide improved performance over results obtained using the individual data sources independently

Cloning, Expression, and Purification of Histidine-Tagged Escherichia coli Dihydrodipicolinate Reductase

This work was supported in part by funds from an Oklahoma State Regent Grant for Higher Education (021606), P20RR016478 grant from the National Center for Research Resources (NCRR) a component of National Institute of Health (NIH), and a grant from the University of Central Oklahoma office of Research and Grants to L.C.The enzyme dihydrodipicolinate reductase (DHDPR) is a component of the lysine biosynthetic pathway in bacteria and higher plants. DHDPR catalyzes the NAD(P)H dependent reduction of 2,3-dihydrodipicolinate to the cyclic imine L-2,3,4,5,-tetrahydropicolinic acid. The dapB gene that encodes dihydrodipicolinate reductase has previously been cloned, but the expression of the enzyme is low and the purification is time consuming. Therefore the E. coli dapB gene was cloned into the pET16b vector to improve the protein expression and simplify the purification. The dapB gene sequence was utilized to design forward and reverse oligonucleotide primers that were used to PCR the gene from Escherichia coli genomic DNA. The primers were designed with NdeI or BamHI restriction sites on the 5’and 3’ terminus respectively. The PCR product was sequenced to confirm the identity of dapB. The gene was cloned into the expression vector pET16b through NdeI and BamHI restriction endonuclease sites. The resulting plasmid containing dapB was transformed into the bacterial strain BL21 (DE3). The transformed cells were utilized to grow and express the histidine-tagged reductase and the protein was purified using Ni-NTA affinity chromatography. SDS/PAGE gel analysis has shown that the protein was 95% pure and has approximate subunit molecular weight of 28 kDa. The protein purification is completed in one day and 3 liters of culture produced approximately 40–50 mgs of protein, an improvement on the previous protein expression and multistep purification.Yeshttp://www.plosone.org/static/editorial#pee

Genomic analysis of a large set of currently—and historically—important human adenovirus pathogens

Human adenoviruses (HAdVs) are uniquely important “model organisms” as they have been used to elucidate fundamental biological processes, are recognized as complex pathogens, and are used as remedies for human health. As pathogens, HAdVs may effect asymptomatic or mild and severe symptomatic disease upon their infection of respiratory, ocular, gastrointestinal, and genitourinary systems. High-resolution genomic data have enhanced the understanding of HAdV epidemiology, with recombination recognized as an important and major pathway in the molecular evolution and genesis of emergent HAdV pathogens. To support this view and to actualize an algorithm for identifying, characterizing, and typing novel HAdVs, we determined the DNA sequence of 95 isolates from archives containing historically important pathogens and collections housing currently circulating strains to be sequenced. Of the 85 samples that were completely sequenced, 18 novel recombinants within species HAdV-B and D were identified. Two HAdV-D genomes were found to contain novel penton base and fiber genes with significant divergence from known molecular types. In this data set, we found additional isolates of HAdV-D53 and HAdV-D58, two novel genotypes recognized recently using genomics. This supports the thesis that novel HAdV genotypes are not limited to “one-time” appearances of the prototype but are of importance in HAdV epidemiology. These data underscore the significance of lateral genomic transfer in HAdV evolution and reinforce the potential public health impact of novel genotypes of HAdVs emerging in the population