An experiment in blended career development: the University of Derby’s social media internship programme

It is possible to describe the capability of an individual to use the online environment to pursue their career as their digital career literacy. It is comprised of a range of different skills including the ability to: search; evaluate resources; communicate; network with other people; develop your reputation; and utilise an ever growing range of tools and environments as part of your career building. In another article in this edition of the NICEC journal Hooley (2012) has defined digital career literacy as encompassing changing, collecting, critiquing, connecting, communicating, creating and curating. This requires both the translation of offline skills and the development of new online ones. This article sets out the experience of running the social media internship programme (SMIP), an intervention to develop students’ digital career literacy at the University of Derby.University of Derby Teaching Informed by Research programm

Building online employability: a guide for academic departments

This guide will help academic departments to support students to think about their careers and to use the online environment wisely. Used badly the array of social media and online technologies can seriously disadvantage a students’ career development, but if used well they can support students to find out about and transition into their future career.This work was funded by the University of Derby’s Research for Teaching and Learning programme

Psychophysical Research in Development of a Fiber-optic Helmet Mounted Display

The Fiber Optic Helmet Mounted Display (FOHMD) was conceived as an innovative solution to existing flight simulator display deficiencies. An initial (breadboard) version of the system was fabricated to permit experimentation which would help define design requirements for a more refined engineering prototype. A series of visual/human factors studies are being conducted at the USAF Human Resources Laboratory (AFHRL) Operations Training Division, Williams AFB, Arizona to determine the optimum fit of human observer operating characteristics and fiber optic helmet mounted display technology. Pilot performance within a variety of high resolution insert/binocular overlap combinations is being assessed in two classes of environment. The first two of four studies planned incorporate an air-to-air combat environment, whereas the second two studies will use a low level environment with air to ground weapons delivery

The construction and evaluation of a test of ability to recognize certain aspects of excellence in written composition in grades 10, 11, and 12

Thesis (M.A.)--Boston Universit

Guns Considered as Thermodynamic Machines.

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Critical Cochlea/Vestibular Interactions

There is a close interaction of the gravity detecting balance organs, the maculae of the saccule and utricle of the inner ear, with the hearing system of the inner ear. The need for this is that although they detect the sensations specific to their own function there is interference with this function due to overlap of wavelengths used by both systems resulting in extraordinary stimulation of the other system for both hearing and balance

Is acupuncture an effective treatment for menorrhagia? : systematic review, exploratory randomised trial, qualitative investigation and GP survey

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

An Assessment of Supervised and Unsupervised Machine Learning Applications Toward Predicting Gulf of Mexico Coastal Hypoxia

Observations of dissolved oxygen, salinity, temperature, and six different nutrient concentrations of the waters on the TXLA Shelf in the months of March – September in 2003 – 2014 were used in unsupervised and supervised machine learning techniques to identify driving processes of hypoxia and examine the performance of classification algorithms on predicting hypoxia on the TXLA Shelf. Unsupervised machine learning techniques, principal component analysis, and K-means clustering, successfully identified variability patterns that were associated with previously known drivers and processes of hypoxia in the region such as vertical stratification of the water column and the Mississippi River plume. The performance of eight classification algorithms (i.e., logistic regression, LDA, QDA, naïve bayes, KNN, SVM, decision tree, and random forest) on predicting hypoxia with the observations on TXLA Shelf were compared. Results showed that naïve bayes performed best on classifying hypoxia with high recall and low false positive rates. Balancing the class distribution in the training set of each algorithm significantly increased performance, indicating that classifier performance was strongly dependent on input training data. This study establishes that straightforward machine learning techniques can aid in identification of known main drivers of hypoxia and their characteristics and that those characteristics can be used to predict hypoxia on the TXLA Shelf. Thees techniques have the potential to evaluate hypoxia presence or absence in hydrographic data where DO is missing and can be a powerful tool used in water quality and resource management in the region. While the approaches presented in this study were specifically for the TXLA Shelf, the methodology is applicable to other coastal systems and locations with similar datasets