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BackgroundDepression is a prevalent mental health disorder and the fourth leading cause of disability in the world as per the World Health Organization. Use of antidepressants can lead to adverse drug events (ADEs), defined as any injury resulting from medication use. This study aimed to examine changes in hospital admissions due to antidepressant-related ADEs (ArADEs) among different socio-demographic groups and changes in lengths of stay (LOS) and hospital charges in ArADE admissions from 2001 to 2011.MethodsThe Healthcare Cost and Utilization Project database was used. ArADE admissions in different socio-demographic groups were examined including characteristics such as age, gender, rural/urban, and income. LOS and hospital charges for ArADE cases were compared between 2001 and 2011. Chi-square test and t test were used for statistical analyses.ResultsThere were 17,375 and 20,588 ArADE related admissions in 2001 and 2011, respectively. There was a 17.6% increase among the group of 18 to 64 years old and a 64.8% increase among the group of 65 years or older while the other age groups experienced decreased admission rates. Males and females had similar increases. Patients from the lower income areas experienced a two-fold increase while those from the higher income areas experienced a decrease. The mean LOS for all ArADE related admissions increased from 2.18 to 2.81 days and mean hospital charges increased from $8,456.2 to $21,572.5.ConclusionsThere was an increase in ArADE hospital admissions. The greater increase in ArADE admissions among elderly, urban or low-income patients should be noted and addressed by practitioners and policy makers. The large increase in hospital charges needs further research.

Encryption systems have been developed for image viewing applications using the Hill Cipher algorithm. This study aims to evaluate the image encryption quality of the Hill Cipher algorithm. Several traditional metrics are used to evaluate the quality of the encryption scheme. Three of such metrics have been selected for this study. These include, the Colour Histogram, the Maximum Deviation (comparing the original image) and the Entropy Analysis of the encrypted image. Encryption quality results from all three schemes using a variety of images show that a plain Hill Cipher approach gives a good result for all kinds of images but is more suited for colour dense images.

Abstract-Attendance records play a vital role in the educational sector. It is so vital that students are not allowed to sit for examinations if they do not meet the class attendance benchmark. But students, instead of making sure they attend classes regularly, devise cunny ways of committing attendance fraud. This unpleasant trend has made it necessary to develop systems that can take accurate class attendance records and minimize fraud. The use of biometrics to develop attendance taking systems is becoming quite popular. One of such biometrics is The Face. In this paper, a facial recognition algorithm known as Fisherfaces or Fisher Discriminant Analysis (FDA) which is not sensitive to substantial variation in facial look and illumination is used to develop the facial recognition attendance taking system. The system implemented has a training database of Ten (10) students. Ten (10) facial images of each student are taken with different composures, looks and under different levels of illumination. Tests on nine (9) students in the database yielded accuracies of as low as 70% and as high as 90%. This validates the proof that the more the number of training facial image in the database, the higher the accuracy of Fisherfaces approach. The simple mail transfer protocol (SMTP) was interfaced with the database to send identification messages (name of student identified with time and date of identification) to the email address of the administrator (in this case the lecturer) in realtime to effectively monitor the attendance. The result was found capable of eliminating attendance fraud.

A new approach that offers the potential for local drug delivery to the inner ear is a 3D printed, patient individualized, drug-loaded implant that precisely fits into the round window niche (RWN). Anatomically correct digital light processing (DLP) 3D printed implant prototypes are beneficial for preoperative planning and rehearsal of implantation techniques due to tactile feedback. The aim is to define desired mechanical material properties for future RWN implants. For this purpose, RWN implant prototypes (RWN-IPs) were DLP 3D printed using commercially available E-Shell 500 and E-Shell 600 materials (Envisiontec GmbH, Gladbeck, Germany) and a selfestablished PEGDA700 composition. These photopolymers are suitable for 3D printing RWN-IPs that feature different mechanical characteristics. The (1) mechanical properties (tensile test) were investigated, (2) the implantation feasibility and (3) fitting accuracy in human cadaver RWN were evaluated. As a result, E-Shell 500 has relatively high stretchability (ɛm ~ 60%) while E-Shell 600 and PEGDA700 are brittle and PEGDA700 has low strength. The E-Shell 500 material performs by far the best at handling and insertion. EShell 600 has adequate strength but is hard to handle because of rigid material behavior. PEGDA700 enables high 3D printing accuracy but lacks adequate mechanical behavior for adequate insertion of implant prototypes in RWN.