1,721,099 research outputs found
Risky health behaviors and behavioral differences of the US youth: quasi-evidence with empirical study: policy implications
No full textThe focus of this paper is to examine the determinants and analyze the effects of risky health behaviors of alcohol and illicit drug use on social violence (drunken driving, riding in a car driven by a drunken driver, and not wearing seatbelts) among youth in the United States. Alcohol and illicit drug use usually lead to social violence as well as a reduction in health status and earnings. Although it is illegal to drink and drive in the U.S., forty-five percent of the traffic accidents among the age group of 14-18 are alcohol-related. Alcohol is a leading factor in deaths related to motor vehicle accidents.
This research defines use of alcohol, tobacco, cocaine, and other illicit drug use as risky health behavior. The use of some substances tend to precede and increase the risk of initiating habitual use of substances among the youth. The data used for this project is drawn from the 1992 and 2017 National Youth Risk Behavior Survey to examine the behavioral difference between two periods. The study examines the relationship between alcohol and illicit drug use and three types of violent behaviors: (1) drunken driving, (2) occupying a car driven by someone who has been drinking, and (3) not wearing seatbelts.
The results show that there is a positive relationship between the risky health behaviors of alcohol and illicit drug uses and social violence (drunken driving, riding in a car driven by a drunken driver, and not wearing seatbelts) among youth. The results suggest that binge drinking, smoking habits, as well as illicit drug use will contribute to the escalation of habitual, high-risk behaviors such as: drunken driving and not using seatbelts, among youth. The results also indicate that youth attitudes toward drunken driving will become more sensitive to multi-consumption habits as they get old. Controlling the consumption of alcohol and drug use at an early age is indeed an important factor in reducing drunken-driving behavior later. Drunken driving behavior is more likely to be a habitual behavior, and to reduce this behavior, access to alcohol and illicit drugs must be restricted among early teens.This audio recording was presented at the first annual Celebration of Undergraduate Research and Creative Activity while the author was an undergraduate student at Rutgers University-Camden
Subduction initiation of Indochina and South China blocks: insight from the forearc ophiolitic peridotites of the Song Ma Suture Zone in Vietnam
No full textAbstract not availableThanh Xuan Ngo, M. Santosh, Hai Thanh Tran, Hieu Trung Pha
Figure 7 in Ecological niche overlap of two allopatric karst-adapted tiger geckos (Goniurosaurus) from northern Vietnam: microhabitat use and implications for conservation
No full textFigure 7. Anthropogenic impacts in natural habitats of Goniurosaurus huuliensis and Goniurosaurus luii: (a) quarrying for cement production; (b) timber logging. (Photographed by Hai N. Ngo.)Published as part of Ngo, Hai Ngoc, Nguyen, Huy Quoc, Tien, Phan Quang, Tran, Hieu Minh, Nguyen, Truong Quang, van Schingen-Khan, Mona & Ziegler, Thomas, 2022, Ecological niche overlap of two allopatric karst-adapted tiger geckos (Goniurosaurus) from northern Vietnam: microhabitat use and implications for conservation, pp. 1495-1511 in Journal of Natural History 56 (37-40) on page 1506, DOI: 10.1080/00222933.2022.2120437, http://zenodo.org/record/715663
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Earnings prediction using machine learning methods and analyst comparison
Full text linkIn the course of this dissertation we propose an experimental study on how technical,
macroeconomic, and financial variables, alongside analysts’ forecasts, can be used to
optimize the prediction for the subsequent quarter’s earnings results using machine learning,
comparing the performance of the models to analysts’ forecasts. The dissertation includes
three steps. In step one, an event study is conducted to test abnormal returns in firms’ stock
prices in the day following earnings announcement, grouped by earnings per share (EPS)
growth in classes of size 3, 6 and 9, computed for each quarter. In step two, several machine
learning models are built to maximize the accuracy of EPS predictions. In the last step,
investment strategies are constructed to take advantage of investors’ expectations, which are
closely correlated with analysts’ predictions. In the backdrop of an exhaustive analysis on
quarterly earnings predictions using machine learning methods, conclusions are drawn
related to the superiority of the CatBoost classifier. All machine learning models tested
underperform analyst predictions, which could be explained by the time and privileged
information at analysts’ disposal, as well as their selection of firms to cover. Regardless,
machine learning models can be used as a confirmation for analyst predictions, and
statistically significant investment strategies are pursued with those fundamentals.
Importantly, high confidence predictions by machine learning models are significantly more
accurate than the average accuracy of forecasts.No decorrer desta dissertação, realiza-se um estudo experimental sobre a forma como
análises técnicas, macroeconómicas, fundamentais e as previsões dos analistas podem ser
utilizadas em conjunto para otimizar a previsão dos resultados de lucros do próximo
trimestre de empresas A dissertação inclui três etapas. Na primeira etapa, é efetuado um
estudo de evento para testar os retornos anormais nas ações no dia seguinte aos anúncios de
lucros, sendo estes agrupados pelo crescimento do lucro por ação nas classes de 3, 6 e 9,
calculado para cada trimestre. Na etapa dois, vários modelos de machine learning (ML) são
concebidos para maximizar a precisão das previsões de crescimento de lucros de empresas.
Na última etapa, estratégias de investimento são construídas para tirar proveito das
expectativas do investidor, que estão relacionadas com as previsões dos analistas. Uma vez
que um dos projetos de pesquisa mais exaustivos sobre previsões de lucros para o próximo
trimestre, conclusões podem ser retiradas relacionadas com a superioridade do modelo
CatBoost nas previsões de lucros. Todos os modelos de testados apresentam desempenho
inferior às previsões dos analistas, o que pode ser explicado pelo tempo e pelas informações
privilegiadas a que os analistas têm acesso, bem como pela escolha da empresa sob a qual
as suas previsões incidem. Os modelos de podem ser utilizados como uma confirmação para
as previsões dos analistas criando estratégias de investimento estatisticamente significativas.
Além disso, as previsões com alta confiança por modelos de são mais precisas do que a
precisão média das previsões dos analistas
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Integrating emission indicators in investment decisions : an evaluation of OLS Regression, kNN and Gradient Boosting Classification approaches
Full text linkThis dissertation studies the application of ordinary least squares regressions and supervised
machine learning classification models on emission indicator integration on listed share
investments. A large set of emission and financial variables are gathered from STOXX600
constituents stretching 2011 - 2020. Implementing a backward elimination feature selection
narrow down 60 emission indicators to Internal Carbon Pricing and NOx and SOx Emissions
Reduction Initiatives showing statistically significant relations with next quarter returns. The
selected emission indicators are complemented by a set of control variables and used in three
approaches to forming investment portfolios. A comparative analysis of the approaches
through - a rolling window OLS regression, kNN classification and Gradient Boosting
classification - show that a kNN approach to forming percentile portfolios outperform both
the regression and Gradient Boosting approach. Both the kNN and Gradient Boosting
approaches provide next quarter Up/Down return signal prediction higher than 50%. No
approach outperforms a 1/N strategy composed of the source index constituents and only the
best ranked percentile portfolio shows statistically significant 3 and 5 factor model alphas in
all portfolio creation approaches.Esta dissertação estuda a aplicação de regressões de mínimos quadrados ordinários e modelos
de classificação de aprendizado de máquina supervisionado na integração de indicadores de
emissão em investimentos listados. Uma seleção de recursos de eliminação para trás restringe
60 indicadores de emissão para Preço interno de carbono e Iniciativas de redução de emissões
de NOx e SOx, mostrando uma relação estatisticamente significativa com os retornos do
próximo trimestre. Os indicadores de emissões significativas são complementados por um
conjunto de variáveis de controle e implementados em três estratégias de investimento. Uma
análise comparativa das estratégias de investimento formadas usando regressões OLS de
período de tempo rolando, classificação kNN e classificação Gradient Boosting mostram que
uma abordagem kNN para formar carteiras percentuais supera tanto a regressão quanto a
abordagem Gradient Boosting. Ambas as abordagens kNN e Gradient Boosting fornecem
previsão de sinal de retorno Up / Down para o próximo trimestre superior a 50%. Nenhuma
abordagem supera uma estratégia 1 / N composta pelos constituintes do índice de origem e
apenas o portfólio de percentil melhor classificado mostra alfas de modelo de 3 e 5 fatores
estatisticamente significativos em todas as abordagens de criação de portfóli
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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