TO LOCALISE OR TO BE LOCALISED WITH WIFI IN THE HUBEI MUSEUM?

ISPRS The first method detects the beacon frames send by smartphones, laptops and other WiFi enabled devices in range using Libelium Meshlium Xtreme monitors. Their MAC addresses and the signal strength is measured by the Meshlium Xtreme and stored on an external database. We call this method WiFi monitoring. The second method a Wifi enabled device, like a smartphone, measures the signal strength of multiple Wifi Access Points in range to localise itself based on a previously created radio map. This method is known as WiFi fingerprinting. Both methods have some advantages and disadvantages. Advantages of the common way of WiFi fingerprinting are that the implementation costs are relatively low, because it is usually possible to use (a part of) the existing WiFi AP infrastructure. WiFi fingerprinting can reach a relatively high accuracy in the order of magnitude of meters. Finally, the location granularity can be adjusted to what is necessary for the purpose of the indoor localisation. This makes it employable for a wide range of purposes. The question remains how suitable these methods are for a 3D indoor navigation system for the Hubei provincial museum. One important aspect is the localisation-granularity necessary for the application. In a museum it is not necessary to know the exact X,Y position of a user (such high accuracy is unnecessary), more important is to know in which room the user is located so the information on exhibitions can be presented and the starting point of the navigation can be determined. Both methods can track the user and tell the room he or she is located at. Although WiFi smartphone monitoring may have a low update frequency it is still suitable for a navigation system for a museum since visitors usually spend more than a couple of minutes within a room

The stabilisation of purified, reconstituted P-glycoprotein by freeze drying with disaccharides

The drug efflux pump P-glycoprotein (P-gp) (ABCB1) confers multidrug resistance, a major cause of failure in the chemotherapy of tumours, exacerbated by a shortage of potent and selective inhibitors. A high throughput assay using purified P-gp to screen and characterise potential inhibitors would greatly accelerate their development. However, long-term stability of purified reconstituted ABCB1 can only be reliably achieved with storage at -80 °C. For example, at 20 °C, the activity of ABCB1 was abrogated with a half-life of <1 day. The aim of this investigation was to stabilise purified, reconstituted ABCB1 to enable storage at higher temperatures and thereby enable design of a high throughput assay system. The ABCB1 purification procedure was optimised to allow successful freeze drying by substitution of glycerol with the disaccharides trehalose or maltose. Addition of disaccharides resulted in ATPase activity being retained immediately following lyophilisation with no significant difference between the two disaccharides. However, during storage trehalose preserved ATPase activity for several months regardless of the temperature (e.g. 60% retention at 150 days), whereas ATPase activity in maltose purified P-gp was affected by both storage time and temperature. The data provide an effective mechanism for the production of resilient purified, reconstituted ABCB1

Automatic update of road attributes by mining GPS tracks

Despite advances in cartography, mapping is still a costly process which involves a substantial amount of manual work. This article presents a method for automatically deriving road attributes by analyzing and mining movement trajectories (e.g. GPS tracks). We have investigated the automatic extraction of eight road attributes: directionality, speed limit, number of lanes, access, average speed, congestion, importance, and geometric offset; and we have developed a supervised classification method (decision tree) to infer them. The extraction of most of these attributes has not been investigated previously. We have implemented our method in a software prototype and we automatically update the OpenStreetMap (OSM) dataset of the Netherlands, increasing its level of completeness. The validation of the classification shows variable levels of accuracy, e.g. whether a road is a one- or a two-way road is classified with an accuracy of 99%, and the accuracy for the speed limit is 69%. When taking into account speed limits that are one step away (e.g. 60 km/h instead of the classified 50 km/h) the classification increases to 95%, which might be acceptable in some use-cases. We mitigate this with a hierarchical code list of attributes.Urban Data ScienceOLD Urban Desig