I Cannot Tell a Lie: Emotional Intelligence as a Predictor of Deceptive Behavior

Research has identified that perceived acceptability and likelihood of lying depend on the type of lie and personality characteristics such as honesty, kindness, assertiveness, and Machiavellianism. However, this research has focused on individuals’ experiences of their own emotions and neglected to consider how an individual’s understanding of others and their emotions influences deceptive behavior. I expanded upon this research during the summer of 2018 by investigating the relationship between emotional intelligence, personal intelligence, and perceived acceptability and likelihood of telling four types of lies, which are distinguished from one another based on their motivation (altruistic, conflict avoidance, social acceptance, or self‐gain). Participants were 80 University of New Hampshire undergraduate students who completed an online survey consisting of both self‐report and ability‐based measures. Results suggest that scores on ability‐based tests of personal intelligence may be useful in predicting an individual’s likelihood of telling lies for the purpose of social acceptance. Results also indicate a significant negative correlation between self‐reported likelihood of telling social‐acceptance lies and levels of personal intelligence, indicating that those with higher personal intelligence are less likely to tell social‐acceptance lies

The Determinants of NFL Ticket Prices: What Managers May Consider when Pricing Tickets

Our purpose of this study is to determine what factors contribute to NFL ticket prices across teams and over time. After creating a theoretically sound model based on past economic studies, a panel data set was constructed based on the 32 NFL teams from the 2002 through the 2010 season. Results of this study show that a team’s previous season’s winning percentage, the average income of the area, the population of the area, and playing in a new stadium all have a positive, and significant, influence on ticket price. This study’s outcome allows fans and others to observe what team managers may consider when making price-changing decisions, and also by what percent a change in each factor will potentially change price. The study also shows what contributes to ticket price over time, an interesting observation given the rapid increase in the demand for football in the last decade

IMMIGRATION, MEAT PACKING, AND TRADE: IMPLICATIONS FOR IOWA

The paper examines changes in employment and wage patterns, industrial restructuring, and foreign competition that affect opportunities of recent immigrants to the non-metropolitan Midwest, especially to Iowa. The focus is on food and kindred products where meat and poultry packing and processing are major components. Although total employment in this industry declined between 1980 and 1990, a significant increase in employment of Hispanic- and Asian- origin persons occurred in Iowa. As unions weakened and the real wage rate declined sharply during 1980-85 in meat and poultry packing and processing, new job opportunities for recent immigrants became available. These jobs provided full-time year-round work at significantly above the minimum wage and made regular schooling for their children and frequently home ownership possible. Note: Tables are not included in the PDF file--contact the authors for more information.

The role of intratemporal adjustment costs in a multi-sector economy

Business cycles ; Employment (Economic theory)

The Effects of Cognitive Jamming on Wireless Sensor Networks used for Geolocation

The increased use of Wireless Sensor Networks (WSN) for geolocation has led to an increased reliance on this technology. Jamming, protecting jamming, and detecting jamming in a WSN are areas of study that have greatly increased in interest. This research uses simulations and data collected from hardware experiments to test the effects of jamming on a WSN. Hardware jamming was tested using a Universal Software Radio Peripheral (USRP) Version 2 to assess the effects of jamming on a cooperative network of Java Sun SPOTs. The research combines simulations and data collected from the hardware experiments to see the effects of jamming on cooperative and noncooperative geolocation

Analysis of multi-sensor data, 12 September - 11 December 1968

Analysis of multi-sensor data obtained by Earth Resources Aircraft Progra

PERSIANN-MSA: A precipitation estimation method from satellite-based multispectral analysis

Visible and infrared data obtained from instruments onboard geostationary satellites have been extensively used for monitoring clouds and their evolution. The Advanced Baseline Imager (ABI) that will be launched onboard the Geostationary Operational Environmental Satellite-R (GOES-R) series in the near future will offer a larger range of spectral bands; hence, it will provide observations of cloud and rain systems at even finer spatial, temporal, and spectral resolutions than are possible with the current GOES. In this paper, a new method called Precipitation Estimation from Remotely Sensed information using Artificial Neural Networks-Multispectral Analysis (PERSIANN-MSA) is proposed to evaluate the effect of using multispectral imagery on precipitation estimation. The proposed approach uses a self-organizing feature map (SOFM) to classify multidimensional input information, extracted from each grid box and corresponding textural features of multispectral bands. In addition, principal component analysis (PCA) is used to reduce the dimensionality to a few independent input features while preserving most of the variations of all input information. The above method is applied to estimate rainfall using multiple channels of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellite. In comparison to the use of a single thermal infrared channel, the analysis shows that using multispectral data has the potential to improve rain detection and estimation skills with an average of more than 50% gain in equitable threat score for rain/no-rain detection, and more than 20% gain in correlation coefficient associated with rain-rate estimation. © 2009 American Meteorological Society