Social Media, Privacy, and the Academic Classroom

We live in a world full of social media and portable technology that allows for the effortless access to, and sharing of, information. While this constant connection can be viewed as a benefit by some, there have been recent, sometimes embarrassing, instances throughout the world that show just how quickly any expectation of privacy can be destroyed. From pictures of poorly dressed shoppers at a grocery store to customers recording interactions with their servers at restaurants, the internet is full of media (all with the potential to go viral) created and posted without consent of all parties captured. This risk to privacy is not just limited to retail and restaurants, as being in any situation amongst people puts you at risk, including being in an academic classroom. Anyone providing in-class instruction, be they professor or librarian, can be at risk for this type of violation of privacy. In addition, the students in the class are also at risk for being unwittingly captured by their classmates. To combat this, colleges and universities are providing recommendations to faculty regarding this issue, such as including suggested syllabus statements about classroom recording by students. In some instances, colleges and universities have instituted formal policies with strict penalties for violators. An overview of current privacy law as it relates to an academic setting is discussed as well as recent, newsworthy instances of student recording in the classroom and the resulting controversies. Additionally, there is a discussion highlighting various recommendations and formal policies that have been issued and adopted by colleges and universities around the country. Finally, advice is offered about what librarians can do to educate students, faculty, and staff about the privacy rights of others and the potential harm that could come from posting to social media and the open web images and video of others without their consent.

Evaluation Of Reservoir Model Integration With Deterministic And Probabilistic Streamflow Forecasts

Due to the increase in water demand and hydropower energy, it is getting more important to operate hydraulic structures in an efficient manner while sustaining multiple demands. Especially, companies, governmental agencies, consultant offices require effective, practical integrated tools and decision support frameworks to operate reservoirs, cascades of run-of-river plants and related elements such as canals by merging hydrological and reservoir simulation/optimization models with various numerical weather predictions, radar and satellite data. The model performance is highly related with the streamflow forecast, related uncertainty and its consideration in the decision making. While deterministic weather predictions and its corresponding streamflow forecasts directly restrict the manager to single deterministic trajectories, probabilistic forecasts can be a key solution by including uncertainty in flow forecast scenarios for dam operation. The objective of this study is to compare deterministic and probabilistic streamflow forecasts on an earlier developed basin/reservoir model for short term reservoir management. The study is applied to the Yuvacık Reservoir and its upstream basin which is the main water supply of Kocaeli City located in the northwestern part of Turkey. The reservoir represents a typical example by its limited capacity, downstream channel restrictions and high snowmelt potential. Mesoscale Model 5 and Ensemble Prediction System data are used as a main input and the flow forecasts are done for 2012 year using HEC-HMS. Hydrometeorological rule-based reservoir simulation model is accomplished with HEC-ResSim and integrated with forecasts. Since EPS based hydrological model produce a large number of equal probable scenarios, it will indicate how uncertainty spreads in the future. Thus, it will provide risk ranges in terms of spillway discharges and reservoir level for operator when it is compared with deterministic approach. The framework is fully data driven, applicable, useful to the profession and the knowledge can be transferred to other similar reservoir systems.