Exhibiting the Austro-Hungarian Empire: The Austrian Museum for Folk Culture in Vienna, 1895-1925

The Austrian Museum for Folk Culture (Österreichisches Museum für Volkskunde) was established in 1895 in Vienna, the capital of the Austro-Hungarian Empire. Initially founded as ‘monument of a state of nations [Völkerstaat]’ it acted on and facilitated larger imperial projects of statecraft, war and international diplomacy that spanned the Empire and its displacement in the interwar period (Schmidt 1960: 29). While much of the Museum’s collection was acquired in the years before the Empire’s collapse in 1918, I argue that it was only in the Empire’s afterlife that the Museum was able to perform its memory work for an entombed ‘state of nations’. The Museum projected this site of imperial memory initially onto a post-imperial pan-European map and then, following the rise of German nationalism in Germany and Austria, onto a pan-German vision of empire and nationhood

Industry clusters: An antidote for knowledge sharing and collaborative innovation?

This paper will focus on Industry Clusters and a rationale for why they may be considered an Antidote for Knowledge Sharing and Collaborative Innovation.The paper draws on data gathered during the course of research undertaken in Dubai

Modeling Adsorption of Molecular Semiconductors on an Ionic Substrate: PTCDA and CuPc on NaCl

Molecular adsorption can be accurately studied using computational chemistry methods. Experimental results suggest that molecular geometry and energies can be influenced by the presence of thin film substrates as well as surrounding molecules. In our study, Density Functional Theory (DFT) and Molecular Mechanics (MM) are used to model the configurations of the organic semiconducting materials, Perylene Tetracarboxylic Dianhydride, C24H8O6 (PTCDA), and Copper Phthalocyanine, C34H16CuN8 (CuPc), as adsorbed on single and double layer NaCl substrates of various dimensions and charge settings. After geometry and charge optimization of the molecules using DFT, the molecular geometries are optimized under different environments using computational calculations with specific force field settings in HyperChem software using MM. Energies and geometries of the molecules are then recorded and results are compared to experimental results as detailed in Burke et al, 2018. As we evaluate our computational findings, we can see that our results directly reflect those found experimentally by Burke et al, 2018. This supports the idea that this method of simulation can produce reliable models in the field of physical chemistry of molecular adsorption

Sensing behaviour in healthcare design

We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life

Needs Elicitation for Novel Pervasive Healthcare Technology

It is widely accepted that engaging with end-users to elicit their needs is beneficial when designing a new artefact. This can be particularly challenging, however, when end-users are limited in their ability to provide input. When there is broad variation in users' needs, a further challenge is to include the large number of users required to represent the entire population. Failure to do so may lead to a solution that is over specialised to fit the needs of only a small subset of users. Both challenges are common in healthcare applications in which the end-user is also care recipient (or patient). What if instead of trying to engage vastly many users in design activities, we could hear the voice of the patient by tapping into existing channels within the health care service system? Many interactions between healthcare providers and patients involve knowledge transfer. Observing these could inform designers about patients’ support needs and healthcare providers’ information needs. Healthcare professionals offer a wealth of knowledge based on a clinical understanding of the condition as well as experience listening to patients' problems. Especially where patients are in denial about their condition, their healthcare providers might offer more detailed information than the patient themselves regarding their needs. Since each patient knows only their own experience, whereas healthcare professionals encounter numerous patients, their perspective is more robust against inter-patient variation, and they are able to comment on trends, scale or proportions .We therefore explore how users' needs can be elicited by observing activities in which information is already being shared and discussed in the care process, and from the extensive knowledge of healthcare professionals. This is particularly relevant for pervasive healthcare technology, in which established methods for engaging users to elicit their needs can be difficult or even impossible to apply. In this paper we document our needs elicitation process in a relevant example as a method story, and present our findings and reflections on this as the key contribution of this work

Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia:Feasibility Case Series

Background: Mobile and wearable devices are increasingly being used to support our everyday lives and track our behavior. Since daily support and behavior tracking are two core components of cognitive rehabilitation, such personal devices could be employed in rehabilitation approaches aimed at improving independence and engagement among people with dementia. Objective: The aim of this work was to investigate the feasibility of using smartphones and smartwatches to augment rehabilitation by providing adaptable, personalized support and objective, continuous measures of mobility and activity behavior. Methods: A feasibility study comprising 6 in-depth case studies was carried out among people with early-stage dementia and their caregivers. Participants used a smartphone and smartwatch for 8 weeks for personalized support and followed goals for quality of life. Data were collected from device sensors and logs, mobile self-reports, assessments, weekly phone calls, and interviews. This data were analyzed to evaluate the utility of sensor data generated by devices used by people with dementia in an everyday life context; this was done to compare objective measures with subjective reports of mobility and activity and to examine technology acceptance focusing on usefulness and health efficacy. Results: Adequate sensor data was generated to reveal behavioral patterns, even for minimal device use. Objective mobility and activity measures reflecting fluctuations in participants’ self-reported behavior, especially when combined, may be advantageous in revealing gradual trends and could provide detailed insights regarding goal attainment ratings. Personalized support benefited all participants to varying degrees by addressing functional, memory, safety, and psychosocial needs. A total of 4 of 6 (67%) participants felt motivated to be active by tracking their step count. One participant described a highly positive impact on mobility, anxiety, mood, and caregiver burden, mainly as a result of navigation support and location-tracking tools. Conclusions: Smartphones and wearables could provide beneficial and pervasive support and monitoring for rehabilitation among people with dementia. These results substantiate the need for further investigation on a larger scale, especially considering the inevitable presence of mobile and wearable technology in our everyday lives for years to come

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant

Sensing behaviour in healthcare design

We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life