Personal Wayfinding Assistance

We are traveling many different routes every day. In familiar environments it is easy for us to find our ways. We know our way from bedroom to kitchen, from home to work, from parking place to office, and back home at the end of the working day. We have learned these routes in the past and are now able to find our destination without having to think about it. As soon as we want to find a place beyond the demarcations of our mental map, we need help. In some cases we ask our friends to explain us the way, in other cases we use a map to find out about the place. Mobile phones are increasingly equipped with wayfinding assistance. These devices are usually at hand because they are handy and small, which enables us to get wayfinding assistance everywhere where we need it. While the small size of mobile phones makes them handy, it is a disadvantage for displaying maps. Geographic information requires space to be visualized in order to be understandable. Typically, not all information displayed in maps is necessary. An example are walking ways in parks for car drivers, they are they are usually no relevant route options. By not displaying irrelevant information, it is possible to compress the map without losing important information. To reduce information purposefully, we need information about the user, the task at hand, and the environment it is embedded in. In this cumulative dissertation, I describe an approach that utilizes the prior knowledge of the user to adapt maps to the to the limited display options of mobile devices with small displays. I focus on central questions that occur during wayfinding and relate them to the knowledge of the user. This enables the generation of personal and context-specific wayfinding assistance in the form of maps which are optimized for small displays. To achieve personalized assistance, I present algorithmic methods to derive spatial user profiles from trajectory data. The individual profiles contain information about the places users regularly visit, as well as the traveled routes between them. By means of these profiles it is possible to generate personalized maps for partially familiar environments. Only the unfamiliar parts of the environment are presented in detail, the familiar parts are highly simplified. This bears great potential to minimize the maps, while at the same time preserving the understandability by including personally meaningful places as references. To ensure the understandability of personalized maps, we have to make sure that the names of the places are adapted to users. In this thesis, we study the naming of places and analyze the potential to automatically select and generate place names. However, personalized maps only work for environments the users are partially familiar with. If users need assistance for unfamiliar environments, they require complete information. In this thesis, I further present approaches to support uses in typical situations which can occur during wayfinding. I present solutions to communicate context information and survey knowledge along the route, as well as methods to support self-localization in case orientation is lost

Augmented reality and GIS: On the possibilities and limits of markerless AR

Ponencias, comunicaciones y pósters presentados en el 17th AGILE Conference on Geographic Information Science "Connecting a Digital Europe through Location and Place", celebrado en la Universitat Jaume I del 3 al 6 de junio de 2014.The application of Augmented Reality (AR) in the geo-spatial domain offers huge potentials: AR can visualize invisible properties of spatial entities, can display historic data for them, or can help in finding places. Whatever the application is, AR in the geo-spatial domain will often be purely sensor based, thus without the help of visual or sensory markers. In this paper we analyse the achievable accuracy of AR projections under everyday conditions with consumer hardware. We can show that AR can be applied in applications in smaller geographic scale, but is not sufficient if it comes to the preciseness required when inspecting infrastructural data of small scale

Variable Renewable Energy in Modeling Climate Change Mitigation Scenarios

This paper addresses the issue of how to account for short‐term temporal variability of renewable energy sources and power demand in long‐term climate change mitigation scenarios in energy‐economic models. An approach that captures in a stylized way the major challenges to the integration of variable renewable energy sources into power systems has been developed. As a first application this approach has been introduced to REMIND‐D, a hybrid energy‐economy model of Germany. An approximation of the residual load duration curve is implemented. The approximating function endogenously changes depending on the penetration and mix of variable renewable power. The approach can thus be used to account for variability and correlations between different sources of renewable supply and power demand within the intertemporal optimization of long‐term (energy system) investment decisions in climate change mitigation scenarios. Moreover, additional constraints are introduced to account for flexibility requirements concerning loadfollowing and ancillary services. The parameterization is validated with MICOES a highly resolved dispatch model. Model results show that significant changes are induced when the new residual load duration curve methodology is implemented. With variability, scenarios show that the German power sector is no longer fully decarbonized because natural gas combined‐cycle plants are built to complement renewable energy generation. The mitigation costs increase by about 20% compared to a model version in which variability is not taken into account

On the Economics of Renewable Energy Sources

With the global expansion of renewable energy (RE) technologies, the provision of optimal RE policy packages becomes an important task. We review pivotal aspects regarding the economics of renewables that are relevant to the design of an optimal RE policy, many of which are to date unresolved. We do so from three interrelated perspectives that a meaningful public policy framework for inquiry must take into account. First, we explore different social objectives justifying the deployment of RE technologies, including potential co-benefits of RE deployment, and review modelbased estimates of the economic potential of RE technologies, i.e. their socially optimal deployment level. Second, we address pivotal market failures that arise in the course of implementing the economic potential of RE sources in decentralized markets. Third, we discuss multiple policy instruments curing these market failures. Our framework reveals the requirements for an assessment of the relevant options for real-world decision makers in the field of RE policies. This review makes it clear that there are remaining white areas on the knowledge map concerning consistent and socially optimal RE policies

Artificial intelligence for clinical decision support for monitoring patients in cardiovascular ICUs: a systematic review

Background: Artificial intelligence (AI) and machine learning (ML) models continue to evolve the clinical decision support systems (CDSS). However, challenges arise when it comes to the integration of AI/ML into clinical scenarios. In this systematic review, we followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA), the population, intervention, comparator, outcome, and study design (PICOS), and the medical AI life cycle guidelines to investigate studies and tools which address AI/ML-based approaches towards clinical decision support (CDS) for monitoring cardiovascular patients in intensive care units (ICUs). We further discuss recent advances, pitfalls, and future perspectives towards effective integration of AI into routine practices as were identified and elaborated over an extensive selection process for state-of-the-art manuscripts. Methods: Studies with available English full text from PubMed and Google Scholar in the period from January 2018 to August 2022 were considered. The manuscripts were fetched through a combination of the search keywords including AI, ML, reinforcement learning (RL), deep learning, clinical decision support, and cardiovascular critical care and patients monitoring. The manuscripts were analyzed and filtered based on qualitative and quantitative criteria such as target population, proper study design, cross-validation, and risk of bias. Results: More than 100 queries over two medical search engines and subjective literature research were developed which identified 89 studies. After extensive assessments of the studies both technically and medically, 21 studies were selected for the final qualitative assessment. Discussion: Clinical time series and electronic health records (EHR) data were the most common input modalities, while methods such as gradient boosting, recurrent neural networks (RNNs) and RL were mostly used for the analysis. Seventy-five percent of the selected papers lacked validation against external datasets highlighting the generalizability issue. Also, interpretability of the AI decisions was identified as a central issue towards effective integration of AI in healthcare

Reassessment of CXCR4 Chemokine Receptor Expression in Human Normal and Neoplastic Tissues Using the Novel Rabbit Monoclonal Antibody UMB-2

BACKGROUND: The CXCR4 chemokine receptor regulates migration and homing of cancer cells to specific metastatic sites. Determination of the CXCR4 receptor status will provide predictive information for disease prognosis and possible therapeutic intervention. However, previous attempts to localize CXCR4 using poorly characterized mouse monoclonal or rabbit polyclonal antibodies have produced predominant nuclear and occasional cytoplasmic staining but did not result in the identification of bona fide cell surface receptors. METHODOLOGY/PRINCIPAL FINDINGS: In the present study, we extensively characterized the novel rabbit monoclonal anti-CXCR4 antibody (clone UMB-2) using transfected cells and tissues from CXCR4-deficient mice. Specificity of UMB-2 was demonstrated by cell surface staining of CXCR4-transfected cells; translocation of CXCR4 immunostaining after agonist exposure; detection of a broad band migrating at M(r) 38,000-43,000 in Western blots of homogenates from CXCR4-expressing cells; selective detection of the receptor in tissues from CXCR4+/+ but not from CXCR4-/- mice; and abolition of tissue immunostaining by preadsorption of UMB-2 with its immunizing peptide. In formalin-fixed, paraffin-embedded human tumor tissues, UMB-2 yielded highly effective plasma membrane staining of a subpopulation of tumor cells, which were often heterogeneously distributed throughout the tumor. A comparative analysis of the mouse monoclonal antibody 12G5 and other frequently used commercially available antibodies revealed that none of these was able to detect CXCR4 under otherwise identical conditions. CONCLUSIONS/SIGNIFICANCE: Thus, the rabbit monoclonal antibody UMB-2 may prove of great value in the assessment of the CXCR4 receptor status in a variety of human tumors during routine histopathological examination

Changes in physical activity during the retirement transition: a series of novel n-of-1 natural experiments.

BACKGROUND: Existing evidence about the impact of retirement on physical activity (PA) has primarily focused on the average change in PA level after retirement in group-based studies. It is unclear whether findings regarding the direction of PA change after retirement from group-based studies apply to individuals. This study aimed to explore changes in PA, PA determinants and their inter-relationships during the retirement transition at the individual level. METHODS: A series of n-of-1 natural experiments were conducted with seven individuals who were aged 55-76 years and approaching retirement. PA was measured by tri-axial accelerometry. Twice-daily self-report and ecological momentary assessments of evidence- and theory-based determinants of PA (e.g. sleep length/quality, happiness, tiredness, stress, time pressure, pain, intention, perceived behavioural control, priority, goal conflict and goal facilitation) were collected via a questionnaire for a period of between 3 and 7 months, which included time before and after the participant's retirement date. A personalised PA determinant was also identified by each participant and measured daily for the duration of the study. Dynamic regression models for discrete time binary data were used to analyse data for each individual participant. RESULTS: Two participants showed a statistically significant increase in the probability of engaging in PA bouts after retirement and two participants showed a significant time trend for a decrease and increase in PA bouts over time during the pre- to post-retirement period, respectively. There was no statistically significant change in PA after retirement for the remaining participants. Most of the daily questionnaire variables were significantly associated with PA for one or more participants but there were no consistent pattern of PA predictors across participants. For some participants, the relationship between questionnaire variables and PA changed from pre- to post-retirement. CONCLUSIONS: The findings from this study demonstrate the impact of retirement on individual PA trajectories. Using n-of-1 methods can provide information about unique patterns and determinants of individual behaviour over time, which has been obscured in previous research. N-of-1 methods can be used as a tool to inform personalised PA interventions for individuals within the retirement transition

Personal Wayfinding Assistance

We are traveling many different routes every day. In familiar environments it is easy for us to find our ways. We know our way from bedroom to kitchen, from home to work, from parking place to office, and back home at the end of the working day. We have learned these routes in the past and are now able to find our destination without having to think about it. As soon as we want to find a place beyond the demarcations of our mental map, we need help. In some cases we ask our friends to explain us the way, in other cases we use a map to find out about the place. Mobile phones are increasingly equipped with wayfinding assistance. These devices are usually at hand because they are handy and small, which enables us to get wayfinding assistance everywhere where we need it. While the small size of mobile phones makes them handy, it is a disadvantage for displaying maps. Geographic information requires space to be visualized in order to be understandable. Typically, not all information displayed in maps is necessary. An example are walking ways in parks for car drivers, they are they are usually no relevant route options. By not displaying irrelevant information, it is possible to compress the map without losing important information. To reduce information purposefully, we need information about the user, the task at hand, and the environment it is embedded in. In this cumulative dissertation, I describe an approach that utilizes the prior knowledge of the user to adapt maps to the to the limited display options of mobile devices with small displays. I focus on central questions that occur during wayfinding and relate them to the knowledge of the user. This enables the generation of personal and context-specific wayfinding assistance in the form of maps which are optimized for small displays. To achieve personalized assistance, I present algorithmic methods to derive spatial user profiles from trajectory data. The individual profiles contain information about the places users regularly visit, as well as the traveled routes between them. By means of these profiles it is possible to generate personalized maps for partially familiar environments. Only the unfamiliar parts of the environment are presented in detail, the familiar parts are highly simplified. This bears great potential to minimize the maps, while at the same time preserving the understandability by including personally meaningful places as references. To ensure the understandability of personalized maps, we have to make sure that the names of the places are adapted to users. In this thesis, we study the naming of places and analyze the potential to automatically select and generate place names. However, personalized maps only work for environments the users are partially familiar with. If users need assistance for unfamiliar environments, they require complete information. In this thesis, I further present approaches to support uses in typical situations which can occur during wayfinding. I present solutions to communicate context information and survey knowledge along the route, as well as methods to support self-localization in case orientation is lost

Customizing qualitative spatial and temporal calculi

Qualitative spatial and temporal calculi are usually formulated on a particular level of granularity and with a particular domain of spatial or temporal entities. If the granularity or the domain of an existing calculus doesn't match the requirements of an application, it is either possible to express all information using the given calculus or to customize the calculus. In this paper we distinguish the possible ways of customizing a spatial and temporal calculus and analyze when and how computational properties can be inherited from the original calculus. We present different algorithms for customizing calculi and proof techniques for analyzing their computational properties. We demonstrate our algorithms and techniques on the Interval Algebra for which we obtain some interesting results and observations. We close our paper with results from an empirical analysis which shows that customizing a calculus can lead to a considerably better reasoning performance than using the non-customized calculus