APPLYING SITUATION-SERVICE FIT TO PHYSICAL ENVIRONMENTS ENHANCED BY UBIQUITOUS INFORMATION SYSTEMS

Ubiquitous Information Systems (UIS) embedded in everyday environments are required to provide means for supporting complex behavioral task requirements by users. With the concept of a situation, we propose a knowledge level that can be used by users for understanding UIS-enhanced environments. It is discussed how the concept of situation differs from the concept of context. Situations are used to identify supporting services. At the core of this paper, we present how the Situation-Service Fit construct is used in early design stages (study 1) and later during the prototype phase (study 2). In study 2 we introduce an instrument for three more detailed fit constructs, i.e., Behavior-Service Fit, Modality-Service Fit, and Spatial-Service Fit. By additional evaluation of constructs known from various technology acceptance theories, we provide an innovative instrument for investigating UIS during the whole design cycle

Anticipating Energy-driven Crises in Process Industry by AI-based Scenario Planning

Power outages and fluctuations represent serious crisis situations in energy-intensive process industry like glass and paper production, where substances such as oil, gas, wood fibers or chemicals are processed. Power disruptions can interrupt chemical reactions and produce tons of waste as well as damage of machine parts. But, despite of the obvious criticality, handling of outages in manufacturing focuses on commissioning of expensive proprietary power plants to protect against power outages and implicit gut feeling in anticipating potential disruptions. With AISOP, we introduce a model for AI-based scenario planning for predicting crisis situations. AISOP uses conceptual, well-defined scenario patterns to capture entities of crisis situations. Data streams are mapped onto these patterns for determining historic crisis scenarios and predicting future crisis scenarios by using inductive knowledge and machine learning. The model was exemplified within a proof of concept for energy-driven disruption prediction. We were able to evaluate the proposed approach by means of a set of data streams on weather and outages in Germany in terms of performance in predicting potential outages for manufacturers of paper industry with promising results

Cascading Scenario Technique Enabling Automated And Situation-based Crisis Management

Crises are becoming more and more frequent. Whether natural disasters, economic crises, political events, or a pandemic - the right action mitigates the impact. The PAIRS project plans to minimize the surprise effect of these and to recommend appropriate actions based on data using artificial intelligence (AI). This paper conceptualizes a cascading model based on scenario technique, which acts as the basic approach in the project. The long-term discipline of scenario technique is integrated into the discipline of crisis management to enable short-term and continuous crises management in an automated manner. For this purpose, a practical crisis definition is given and interpreted as a process. Then, a cascading model is derived in which crises are continuously thought through using the scenario technique and three types of observations are classified: Incidents, disturbances, and crises. The presented model is exemplified within a non-technical application of a use case in the context of humanitarian logistics and the COVID-19 pandemic. Furthermore, first technical insights from the field of AI are given in the form of a semantic description composing a knowledge graph. In summary, a conceptual model is presented to enable situation-based crisis management with automated scenario generation by combining the two disciplines of crisis management with scenario technique

Neonatal screening: identification of children with 11β-hydroxylase deficiency by second-tier testing

21-Hydroxylase deficiency (21-OHD) is the target disease of newborn screening for congenital adrenal hyperplasia (CAH). We describe the additional detection of patients suffering from 11β-hydroxylase deficiency (11-OHD) by second-tier testing.Over a period of 5 years, screening for CAH was done in a total of 986,098 newborns by time-resolved immunoassay (DELFIA®) for 17α-hydroxyprogesterone (17-OHP). Positive samples were subsequently analyzed in an LC-MS/MS second-tier test including 17-OHP, cortisol, 11-deoxycortisol, 4-androstenedione and 21-deoxycortisol.In addition to 78 cases of 21-OHD, 5 patients with 11-OHD were identified. Diagnostic parameters were a markedly elevated concentration of 11-deoxycortisol in the presence of a low level of cortisol. Androstenedione was also increased. In contrast to 21-OHD, concentrations of 21-deoxycortisol were normal.Steroid profiling in newborn blood samples showing positive results in immunoassays for 17-OHP allows for differentiating 21-OHD from 11-OHD. This procedure may not detect all cases of 11-OHD in the newborn population because there may be samples of affected newborns with negative results for 17-OHP in the immunoassay

Reduction in PA28αβ activation in HD mouse brain correlates to increased mHTT aggregation in cell models

Huntington’s disease is an autosomal dominant heritable disorder caused by an expanded CAG trinucleotide repeat at the N-terminus of the Huntingtin (HTT) gene. Lowering the levels of soluble mutant HTT protein prior to aggregation through increased degradation by the proteasome would be a therapeutic strategy to prevent or delay the onset of disease. Native PAGE experiments in HdhQ150 mice and R6/2 mice showed that PA28αβ disassembles from the 20S proteasome during disease progression in the affected cortex, striatum and hippocampus but not in cerebellum and brainstem. Modulating PA28αβ activated proteasomes in various in vitro models showed that PA28αβ improved polyQ degradation, but decreased the turnover of mutant HTT. Silencing of PA28αβ in cells lead to an increase in mutant HTT aggregates, suggesting that PA28αβ is critical for overall proteostasis, but only indirectly affects mutant HTT aggregation

On the assessment of landmark salience for human navigation

In this paper, we propose a conceptual framework for assessing the salience of landmarks for navigation. Landmark salience is derived as a result of the observer’s point of view, both physical and cognitive, the surrounding environment, and the objects contained therein. This is in contrast to the currently held view that salience is an inherent property of some spatial feature. Salience, in our approach, is expressed as a three-valued Saliency Vector. The components that determine this vector are Perceptual Salience, which defines the exogenous (or passive) potential of an object or region for acquisition of visual attention, Cognitive Salience, which is an endogenous (or active) mode of orienting attention, triggered by informative cues providing advance information about the target location, and Contextual Salience, which is tightly coupled to modality and task to be performed. This separation between voluntary and involuntary direction of visual attention in dependence of the context allows defining a framework that accounts for the interaction between observer, environment, and landmark. We identify the low-level factors that contribute to each type of salience and suggest a probabilistic approach for their integration. Finally, we discuss the implications, consider restrictions, and explore the scope of the framework

Blue Carbon Storage Capacity of Temperate Eelgrass (Zostera marina) Meadows

Despite the importance of coastal ecosystems for the global carbon budgets, knowledge of their carbon storage capacity and the factors driving variability in storage capacity is still limited. Here we provide an estimate on the magnitude and variability of carbon stocks within a widely distributed marine foundation species throughout its distribution area in temperate Northern Hemisphere. We sampled 54 eelgrass (Zostera marina) meadows, spread across eight ocean margins and 36° of latitude, to determine abiotic and biotic factors influencing organic carbon (Corg) stocks in Zostera marina sediments. The Corg stocks (integrated over 25‐cm depth) showed a large variability and ranged from 318 to 26,523 g C/m2 with an average of 2,721 g C/m2. The projected Corg stocks obtained by extrapolating over the top 1 m of sediment ranged between 23.1 and 351.7 Mg C/ha, which is in line with estimates for other seagrasses and other blue carbon ecosystems. Most of the variation in Corg stocks was explained by five environmental variables (sediment mud content, dry density and degree of sorting, and salinity and water depth), while plant attributes such as biomass and shoot density were less important to Corg stocks. Carbon isotopic signatures indicated that at most sites <50% of the sediment carbon is derived from seagrass, which is lower than reported previously for seagrass meadows. The high spatial carbon storage variability urges caution in extrapolating carbon storage capacity between geographical areas as well as within and between seagrass species