Establishing a support service for educational technology within a university

This paper covers some of the issues associated with the support of learning technology within an institution. It describes the activities of a working support service, and highlights approaches to some of the academic, technological, political and management issues that have helped it perform effectively over a four‐year period

A system for patient management based discrete-event simulation and hierarchical clustering

Hospital Accident and Emergency (A&E) departments in England have a 4 hour target to treat 98% of patients from arrival to discharge, admission or transfer. Managing resources to meet the target and deliver care across the range of A&E services is a huge challenge for A&E managers. This paper develops an intelligent patient management tool to help managers and clinicians better understand patient length of stay and resources within an A&E area. The developed discrete-event simulation model gives a highlevel representation of ambulance arrivals into A&E. The model facilitates analysis in the following ways: visually interactive software showing patient length of stay in the A&E area; patient activity broken down into sub-groups so that intelligence might be gathered on how sub-groups affect the overall length of stay; understanding the number of patient treatment places and nurse resources required. To support ease of inputs for scenario and sensitivity testing, data is entered into the simulation model (Simul8) via Excel spreadsheets. The model discussed in this paper used patient length of stay grouped by A&E diagnosis codes and was limited to ambulance arrivals. The analysis was derived from A&E attendance in 2004 from an English hospital

What's wrong with the murals at the Mogao Grottoes : a near-infrared hyperspectral imaging method

Although a significant amount of work has been performed to preserve the ancient murals in the Mogao Grottoes by Dunhuang Cultural Research, non-contact methods need to be developed to effectively evaluate the degree of flaking of the murals. In this study, we propose to evaluate the flaking by automatically analyzing hyperspectral images that were scanned at the site. Murals with various degrees of flaking were scanned in the 126th cave using a near-infrared (NIR) hyperspectral camera with a spectral range of approximately 900 to 1700 nm. The regions of interest (ROIs) of the murals were manually labeled and grouped into four levels: normal, slight, moderate, and severe. The average spectral data from each ROI and its group label were used to train our classification model. To predict the degree of flaking, we adopted four algorithms: deep belief networks (DBNs), partial least squares regression (PLSR), principal component analysis with a support vector machine (PCA + SVM) and principal component analysis with an artificial neural network (PCA + ANN). The experimental results show the effectiveness of our method. In particular, better results are obtained using DBNs when the training data contain a significant amount of striping noise

Investigating porcine parvoviruses genogroup 2 infection using in situ polymerase chain reaction

Abstract Background Porcine parvovirus 2 (PPV2) was detected in swine serum without showing any relationship with disease. The emergence of the virus seemed to be a unique event until other genetically highly similar parvoviruses were identified in China and, later in 2012, the presence of the virus was also described in Europe. PPV2 is widely distributed in pig populations where it is suspected to be involved in respiratory conditions, based on its frequent detection in lung samples. In order to investigate the potential pathogenic involvement of PPV2, 60 dead pigs were examined from two farms. They were necropsied and tested for PPV2 and PCV2 (Porcine circovirus type 2) by PCR; by Brown and Brenn (B&B) staining for bacteria; by immunohistochemistry (IHC) to detect CD3, Swine leukocyte antigen class II DQ (SLAIIDQ), lysozyme, porcine reproductive and respiratory syndrome virus (PRRSV), swine influenza (SIV), Mycoplasma hyopneumoniae (Mhyo); and by in situ hybridization (ISH) to detect ssDNA and dsDNA of PCV2. PPV2 positive samples were subjected to in situ polymerase chain reaction (IS-PCR) including double staining method to detect PPV2 and host cell markers. To calculate statistical difference we used GENMOD or LOGISTIC procedures in Statistical Analysis System (SAS®). Results We found that the PPV2 was localized mostly in lymphocytes in lungs, lymph nodes and liver. Neither CD3 antigen nor lysozyme was expressed by these infected cells. In contrast, low levels of SLAIIDQ were expressed by infected cells, suggesting that PPV2 may have a specific tropism for immature B lymphocytes and/or NK lymphocytes though possibly not T lymphocytes. Conclusion The overall conclusion of this study indicates that PPV2 may contribute to the pathogenesis of pneumonia