5026 research outputs found
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Integration Through Redefinition: Revisiting the Role of Negotiators’ Goals
No full textEffective negotiation rests in part on generating integrative agreements, or agreements advancing parties’ interests through generating joint gains. Theorists have outlined multiple possibilities to achieve integrative agreements (Pruitt in Negotiation behaviour, Academic Press, New York, 1981; Carnevale in: Deutsch, Coleman, Marcus (eds) Handbook of conflict resolution: theory and practice, Jossey-Bass, San Francisco, 2006), but negotiation research relies disproportionately on studies of one method of integration—making efficient tradeoffs on existing issues. The current studies examine integration through redefinition—modifying the issues under discussion. Doing so encourages revisiting the role goals play in negotiation. Study 1 found that positive and negative bargaining zones are not just indicators of agreement rates, but also cues to consider redefining issues. Specifically, negative bargaining zones spurred attempts to create value that positive bargaining zones did not. Study 2 found that focusing on interests was useful for redefining issues, whereas focusing on ambitious targets was no better than focusing on reservation points. Implications for negotiation theory are discussed
Autoencoder-Inspired Convolutional Network-Based Super-Resolution Method in MRI
No full textObjective: To introduce an MRI in-plane resolution enhancement method that estimates High-Resolution (HR) MRIs from Low-Resolution (LR) MRIs. Method Materials: Previous CNN-based MRI super-resolution methods cause loss of input image information due to the pooling layer. An Autoencoder-inspired Convolutional Network-based Super-resolution (ACNS) method was developed with the deconvolution layer that extrapolates the missing spatial information by the convolutional neural network-based nonlinear mapping between LR and HR features of MRI. Simulation experiments were conducted with virtual phantom images and thoracic MRIs from four volunteers. The Peak Signal-to-Noise Ratio (PSNR), Structure SIMilarity index (SSIM), Information Fidelity Criterion (IFC), and computational time were compared among: ACNS; Super-Resolution Convolutional Neural Network (SRCNN); Fast Super-Resolution Convolutional Neural Network (FSRCNN); Deeply-Recursive Convolutional Network (DRCN). Results: ACNS achieved comparable PSNR, SSIM, and IFC results to SRCNN, FSRCNN, and DRCN. However, the average computation speed of ACNS was 6, 4, and 35 times faster than SRCNN, FSRCNN, and DRCN, respectively under the computer setup used with the actual average computation time of 0.15 s per pixels. Conclusion: The result of this study implies the potential application of ACNS to real-time resolution enhancement of 4D MRI in MRI guided radiation therapy
“The Spirit Is Willing”: A Study of School Climate, Bullying, Self-Efficacy, and Resilience in High-Ability Low-Income Youth
No full textPrior research has shown that high-ability students from low-income backgrounds are more likely to lose academic ground when compared to high-ability students from middle- and high-income backgrounds. Hierarchical cluster analysis was used to evaluate estimations of school climate, bullying, ostracism, self-efficacy, and resilience from four cohorts of high-ability low-income middle school youth to explore why this would occur. A two-cluster solution revealed that the confident school lovers (n = 63) and tentative school lovers (n = 42) were highly integrated in the social milieu of their schools and confident in their ability to make friends and resist negative peer pressure. Significant differences between the clusters potentially highlight the role that school chaos plays in eroding trust and undermining resilience, with tentative school lovers endorsing significantly lower school climate, including feeling less safe, and reported lower identification with school
Fexit: The effect of political and promotional communication from friends and family on Facebook exiting intentions
No full textFacebook enjoys worldwide popularity, but public trust in the site is waning. Some users are exiting Facebook while others are decreasing the intensity and frequency in which they engage with the site. Many brands rely on social media, but consumers’ changing behaviors, coupled with Facebook\u27s algorithm changes, may force brands to switch social media marketing strategies. This research uses exiting behavior, social capital, and closeness as theoretical lenses to explore why Facebook users decrease or eliminate their use of the site in the “post-trust era” of Facebook. A mixed-methods approach is used across three studies. Findings suggest that Facebook users feel freer to express themselves and are less likely to leave Facebook over their interactions with non-family than with family. While brand-focused and political posts negatively affect future Facebook use, there are important differences regarding aligned versus opposing political content from family and non-family. Theoretical and managerial implications are offered
Be Like Bernie
No full textThis poster is part of the Be Like Bernie campaign, which celebrated the beginning of the Spring 2021 semester at Columbus State University.https://csuepress.columbusstate.edu/marketing/1060/thumbnail.jp
Bernie Knows Its Cold Out But He is Eager to Start the New Semester
No full textThis poster uses the meme of Bernie Sanders in front of the Simon Schwob Memorial Library to celebrate the start of the Spring 2021 semester at Columbus State University.https://csuepress.columbusstate.edu/marketing/1058/thumbnail.jp
African American Portraits of Leadership: Kamau Marshall
No full textIn its African American Portraits of Leadership series, Columbus State University Libraries celebrated several African American leaders.
This poster highlights Kamau Marshall. Mr. Marshall helped Joe Biden win the White House as Biden\u27s Director of Strategic Communications.https://csuepress.columbusstate.edu/marketing/1063/thumbnail.jp
Global Higher Education: Examining Response to the COVID-19 Pandemic Using Agility and Adaptability
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A Comparative Study on Feature Extraction and Classification/Clustering of Fake News and Conspiracy Theories from Twitter Data
Get PDFFake news and conspiracy theories have become largely abundant in the expanding world of social media. They predominantly affect the beliefs and thoughts of the public, resulting in chaos. They have always existed throughout the last few decades. They have been linked to prejudice, revolutions and genocide across history. They have also been known to have propelled people to reject mainstream medicines to an extent where some diseases are recurring in some parts of the world. They impose a serious impact since they are capable of spreading very fast Thus, it is very important to find suitable ways to detect fake news and conspiracy theories in social media, which requires a thorough analysis of their features. This study presents a survey on the various techniques of feature extraction and classification that can be implemented to classify and detect fake news and conspiracy theories from twitter datasets. The results indicate that the tf-idf method of feature extraction, when implemented with the svm classification algorithm, yields the highest accuracy of 99.6% in comparison to the other algorithms i.e. multinomial naive bayes, logistic regression and decision tree. The Bag of Words model yields an accuracy of 52.3% for both multinomial naive bayes and logistic regression algorithms and a lower range of accuracies for the other two algorithms i.e. svm and decision tree . TF-IDF has thus performed better than Bag of Words