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We investigate the motivations behind choice of Facebook profile picture. Users tend to choose profile pictures that make them look attractive. Profile picture has an influence on their choice of profile picture. Facebook profile picture reflects user's personality.
The proposed scheme also allows one to continuously change the frequency of the signal.
Prediction of profile changes by the cutting up of profile photographs is unlikely to given an accurate picture of the soft tissue profile response.
Our study supports the theory of collaborative learning through learning from peers as results shows that other's choice of profile pictures affects viewer's own future choice of profile picture.
We show that the signal provides an image very different from the actual surface profile.
Thus, the statistical analysis of profile data should yield a sharp and robust product picture.
Using original data collected among less than 35 years old Facebook users (N = 267), we find that besides the intensity of Facebook use, offline engagement in protest and boycotts has a significant and positive effect on the likelihood to change one's profile picture as part of a campaign.
The effect bears some resemblance to a profile ‘mode change’, but here the effect is gradual and episodic.
Results indicated that users who post a profile picture along with their LinkedIn profile are perceived as more socially attractive and more competent than users who do not post a picture.

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What are the most common reasons why students choose to pursue certain courses in college?
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Students choose to pursue certain courses in college based on a variety of reasons. Research indicates that students are motivated by factors such as course content relevance to future careers, the potential for higher grades, popularity of professors, recommendations from peers. Additionally, students consider structural constraints, normative expectations, and individual motivations when selecting courses, with STEM courses often chosen for career development, social sciences for interest, and humanities for intellectual broadening. Intrinsic motivations like personal preferences heavily influence course selection, while extrinsic factors such as family tradition or course ease have less impact. Moreover, students in exercise and sport science degrees often choose courses due to their love of sport, desire to help others, and career prospects in physiotherapy or sport science. Motivation, including needs for power, affiliation, and achievement, plays a significant role in students' program choices.
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How do cultural nuances affect the design and implementation of cross-cultural research studies?
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Cultural nuances significantly impact the design and implementation of cross-cultural research studies. Researchers emphasize the importance of considering cultural beliefs, traditions, and values when conducting cross-cultural studies to ensure the validity and reliability of findings. Methodological rigor is crucial in cross-cultural personality research, requiring careful consideration of sampling issues, test adaptations, and the integration of emic and etic approaches. Understanding cultural values and interaction patterns is essential, as small differences in initial conditions can lead to significant consequences in cross-cultural interactions. Moreover, the role of culture in shaping societal differences in game behavior highlights the complexity of attributing observed variations solely to cultural influences. Therefore, a comprehensive understanding of cultural nuances is vital for designing and executing effective cross-cultural research studies.
What are the advantages and disadvantages of using partitional and hierarchical clustering for trip analysis?
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Partitional and hierarchical clustering methods offer distinct advantages and disadvantages when applied to trip analysis, each catering to specific aspects of transportation and mobility research. Partitional clustering, as seen in the application of smart card data for inferring trip purposes, leverages temporal attributes to cluster passengers, showing considerable improvement in trip purpose inference by focusing on sequences of trips rather than individual trips. This method is advantageous for its ability to handle large datasets efficiently, as demonstrated in the analysis of trajectory clustering and path modelling, where it automatically determines the number of clusters, enhancing performance in trajectory analysis. However, a significant disadvantage is its sensitivity to the initial choice of clusters, which can lead to varying results and potentially overlook the nuanced spatial-temporal dynamics of trip data. Hierarchical clustering, on the other hand, excels in capturing the multi-layered structure of trip data, allowing for a detailed analysis of vehicle networks by establishing clusters of desired sizes, which improves information sharing and Quality of Service (QoS) in data-driven automobile networks. It is particularly effective in identifying spatial regions based on origin-destination pairs, offering insights into connectivity and travel patterns that support infrastructural decision-making. The hierarchical approach also facilitates the prediction of traffic in bike sharing systems by clustering stations to increase the stability of usage and prediction accuracy. However, its disadvantages include a higher computational cost, especially with large datasets, and the complexity of determining the optimal level of the hierarchy to cut the dendrogram for cluster formation. Both clustering methods contribute valuable insights into trip analysis, with partitional clustering offering efficiency and scalability, while hierarchical clustering provides depth and detailed structure. The choice between them depends on the specific requirements of the trip analysis task, including dataset size, the need for detailed structural insights, and computational resources.
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How have cultural and social trends impacted the purchasing decisions of millennials?
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What Is the effectiveness of home gardens in improving maternal nutrition and household security?
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