The Impact of Instagram Usage and Other Social Factors on Self-Esteem Scores

Instagram has more than 400 million monthly active users and 80 million shared photos with 3.5 billion likes daily. On Instagram, many people post their entire lives for others to see and comment on. This leads to people judging, commenting, and even trying to emulate others they see on social media. This constant comparing to others can lead to a host of psychological issues such as depression, anxiety, and low self-esteem. As social media becomes more of a staple in people\u27s lives, it is important to study and understand the possible pitfalls to the culture it perpetuates. The purpose of this quantitative study was to use cognitive dissonance and attribution theories as the theoretical foundation to examine if there is a connection between Instagram usage and self-esteem by looking at the variables of length of a person\u27s marriage, gender, happiness in marriage, age, and culture. Participants were married men and woman between the ages of 18 and 80 who actively use Instagram. They completed both the Marriage Happiness Scale and the Rosenberg Self-Esteem Test offered in person and via Survey Monkey. The data were transferred to SPSS where multiple regression was used for data analysis. Through this research, the intention was to help people navigate social media better and create healthier peer relationships. In all the variables identified, only gender was a significant predictor of self-esteem. The positive social change for this study was that people would be more mindful of their own social media interactions to avoid their recreational use of a public platform to cause others to experience stress, depression, or other psychological harm

On the dissipation of seismic energy from source to surface

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Geology, 1958.Vita.Includes bibliographical references (leaves [157]-162).by Sven Treitel.Ph.D

Electronic Structures of Ligand Bridged Ruthenium and Cobalt Binuclear Complexes

Bridged superoxo and peroxodecaamminedicobalt complexes have been investigated using electronic room and low temperature spectroscopy. Assignments for these spectra have been proposed. The most important feature in the superoxo spectra is a low energy metal ligand, Co → O2-, charge transfer transition of moderate intensity. Both the superoxide and peroxide ions have been assigned positions in the spectrochemical series. The Dq of superoxide is very close to ammonia, while the Dq of peroxied is between NCS- and H2O. These results have been used to eliminate Fe(III) - O2- as possible model for oxyhemoglobin. Cyano bridged dicobalt and mixed iron-cobalt dimers have been looked at, and their spectra assigned as simple super-positions of their component parts. A series of 4+, 5+ and 6+ µ-pyrazinedecaamminediruthenium compounds have been investigated. Magnetic susceptibilities of the 5+ and 6+ compounds were measured and analyzed, assuming a tetragonally distorted d5 ion. Values for the tetragonal field, delocalization, and spin-orbit coupling parameters have been obtained. The 5+ compound gives an ESR signal at room temperature, a result not usually obtained for d5 Ru(III) salts. Electronic spectra were looked at for the ruthenium pyrazine dimers. The interesting 1570 nm band was found to be temperature independent, indicating an orbitally allowed transition. The origin of this band is discussed. A molecular orbital description of these compounds is suggested. The near IR transition is explained as a b3u (xz + xz) → b2g (xz - xz) d-d transition. The applicability of the Marcus Hush theory of electron transfer to the 5+ cation is discussed. The crystal structure of µ-nitrogendecaamminediruthemium(II) was determined. The Ru-N-N-Ru linkage is linear, and the N-N distance was found to be 1.124 Å, - only slightly longer than that in free nitrogen. An approximate molecular orbital scheme is given which assumes back donation of electrons from ruthenium d orbitals to the π*N2 orbital.</p

To be or not to be an auctioneer: Some thoughts on the legal nature of online eBay auctions and the protection of consumers

This paper discusses the legal classification of online “eBay” auctions. The discussion has key implications on the scope of consumer protection law as sale by auctions are, for example, excluded from the scope of the Consumer Protection (Distance Selling) Regulations 2000. The paper uncovers that online “eBay” auctions cannot always be considered as traditional auctions and that eBay, as an intermediary, is not to be considered as an auctioneer. This creates difficulties associated with a distributive application of consumer protection laws such as the Consumer Protection (Distance Selling) Regulations 2000. Another set of difficulties is associated with a lenient legal regime applicable to the liability of eBay under the Electronic Commerce (EC Directive) Regulations 2002 . The paper concludes that there is an urgent need to clarify the legal classification of online auctions and to rethink the liability of online auction sites to better protect consumers

Bayesian Neural Networks for Geothermal Resource Assessment: Prediction with Uncertainty

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (known resources or active power plants) and negative training sites (known drill sites with unsuitable geothermal conditions) and use these to constrain and optimize artificial neural networks for this classification task. The main objective is to predict the geothermal resource potential at unknown sites within a large geographic area where the defining features are known. These predictions could be used to target promising areas for further detailed investigations. We describe the evolution of our work from defining a specific neural network architecture to training and optimization trials. Upon analysis we expose the inevitable problems of model variability and resulting prediction uncertainty. Finally, to address these problems we apply the concept of Bayesian neural networks, a heuristic approach to regularization in network training, and make use of the practical interpretation of the formal uncertainty measures they provide.Comment: 27 pages, 12 figure