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Already for decades it has been known that the winds of massive stars are inhomogeneous (i.e. clumped). To properly model observed spectra of massive star winds it is necessary to incorporate the 3-D nature of clumping into radiative transfer calculations. In this paper we present our full 3-D Monte Carlo radiative transfer code for inhomogeneous expanding stellar winds. We use a set of parameters to describe dense as well as the rarefied wind components. At the same time, we account for non-monotonic velocity fields. We show how the 3-D density and velocity wind inhomogeneities strongly affect the resonance line formation. We also show how wind clumping can solve the discrepancy between P v and H alpha mass-loss rate diagnostics.
Increasing demand for analytical processing capabilities can be managed by replication approaches. However, to evenly balance the replicas' workload shares while at the same time minimizing the data replication factor is a highly challenging allocation problem. As optimal solutions are only applicable for small problem instances, effective heuristics are indispensable. In this paper, we test and compare state-of-the-art allocation algorithms for partial replication. By visualizing and exploring their (heuristic) solutions for different benchmark workloads, we are able to derive structural insights and to detect an algorithm's strengths as well as its potential for improvement. Further, our application enables end-to-end evaluations of different allocations to verify their theoretical performance.
A Landscape for Case Models
(2019)
Case Management is a paradigm to support knowledge-intensive processes. The different approaches developed for modeling these types of processes tend to result in scattered models due to the low abstraction level at which the inherently complex processes are therein represented. Thus, readability and understandability is more challenging than that of traditional process models. By reviewing existing proposals in the field of process overviews and case models, this paper extends a case modeling language - the fragment-based Case Management (fCM) language - with the goal of modeling knowledge-intensive processes from a higher abstraction level - to generate a so-called fCM landscape. This proposal is empirically evaluated via an online experiment. Results indicate that interpreting an fCM landscape might be more effective and efficient than interpreting an informationally equivalent case model.
General intelligence has a substantial genetic background in children, adolescents, and adults, but environmental factors also strongly correlate with cognitive performance as evidenced by a strong (up to one SD) increase in average intelligence test results in the second half of the previous century. This change occurred in a period apparently too short to accommodate radical genetic changes. It is highly suggestive that environmental factors interact with genotype by possible modification of epigenetic factors that regulate gene expression and thus contribute to individual malleability. This modification might as well be reflected in recent observations of an association between dopamine-dependent encoding of reward prediction errors and cognitive capacity, which was modulated by adverse life events.
Rapid advances in location-acquisition technologies have led to large amounts of trajectory data. This data is the foundation for a broad spectrum of services driven and improved by trajectory data mining. However, for hybrid transactional and analytical workloads, the storing and processing of rapidly accumulated trajectory data is a non-trivial task. In this paper, we present a detailed survey about state-of-the-art trajectory data management systems. To determine the relevant aspects and requirements for such systems, we developed a trajectory data mining framework, which summarizes the different steps in the trajectory data mining process. Based on the derived requirements, we analyze different concepts to store, compress, index, and process spatio-temporal data. There are various trajectory management systems, which are optimized for scalability, data footprint reduction, elasticity, or query performance. To get a comprehensive overview, we describe and compare different exciting systems. Additionally, the observed similarities in the general structure of different systems are consolidated in a general blueprint of trajectory management systems.
Industry 4.0 and the Internet of Things are recent developments that have lead to the creation of new kinds of manufacturing data. Linking this new kind of sensor data to traditional business information is crucial for enterprises to take advantage of the data’s full potential. In this paper, we present a demo which allows experiencing this data integration, both vertically between technical and business contexts and horizontally along the value chain. The tool simulates a manufacturing company, continuously producing both business and sensor data, and supports issuing ad-hoc queries that answer specific questions related to the business. In order to adapt to different environments, users can configure sensor characteristics to their needs.
Working in iterations and repeatedly improving team workflows based on collected feedback is fundamental to agile software development processes. Scrum, the most popular agile method, provides dedicated retrospective meetings to reflect on the last development iteration and to decide on process improvement actions. However, agile methods do not prescribe how these improvement actions should be identified, managed or tracked in detail. The approaches to detect and remove problems in software development processes are therefore often based on intuition and prior experiences and perceptions of team members. Previous research in this area has focused on approaches to elicit a team's improvement opportunities as well as measurements regarding the work performed in an iteration, e.g. Scrum burn-down charts. Little research deals with the quality and nature of identified problems or how progress towards removing issues is measured. In this research, we investigate how agile development teams in the professional software industry organize their feedback and process improvement approaches. In particular, we focus on the structure and content of improvement and reflection meetings, i.e. retrospectives, and their outcomes. Researching how the vital mechanism of process improvement is implemented in practice in modern software development leads to a more complete picture of agile process improvement.
alt'ai is an agent-based simulation inspired by aesthetics, culture and environmental conditions of the Altai mountain region on the borders between Russia, Kazakhstan, China and Mongolia. It is set into a scenario of a remote automated landscape populated by sentient machines, where biological species, machines and environments autonomously interact to produce unforeseeable visual outputs. It poses a question of designing future machine-to-machine authentication protocols that are based on the use of images encoding agent behavior. Also, the simulation provides rich visual perspective on this challenge. The project pleads for a heavily aestheticized approach to design practice and highlights the importance of productively inefficient and information redundant systems.
Mobile sensing technology allows us to investigate human behaviour on a daily basis. In the study, we examined temporal orientation, which refers to the capacity of thinking or talking about personal events in the past and future. We utilise the mksense platform that allows us to use the experience-sampling method. Individual's thoughts and their relationship with smartphone's Bluetooth data is analysed to understand in which contexts people are influenced by social environments, such as the people they spend the most time with. As an exploratory study, we analyse social condition influence through a collection of Bluetooth data and survey information from participant's smartphones. Preliminary results show that people are likely to focus on past events when interacting with close-related people, and focus on future planning when interacting with strangers. Similarly, people experience present temporal orientation when accompanied by known people. We believe that these findings are linked to emotions since, in its most basic state, emotion is a state of physiological arousal combined with an appropriated cognition. In this contribution, we envision a smartphone application for automatically inferring human emotions based on user's temporal orientation by using Bluetooth sensors, we briefly elaborate on the influential factor of temporal orientation episodes and conclude with a discussion and lessons learned.
High-dimensional data is particularly useful for data analytics research. In the healthcare domain, for instance, high-dimensional data analytics has been used successfully for drug discovery. Yet, in order to adhere to privacy legislation, data analytics service providers must guarantee anonymity for data owners. In the context of high-dimensional data, ensuring privacy is challenging because increased data dimensionality must be matched by an exponential growth in the size of the data to avoid sparse datasets. Syntactically, anonymising sparse datasets with methods that rely of statistical significance, makes obtaining sound and reliable results, a challenge. As such, strong privacy is only achievable at the cost of high information loss, rendering the data unusable for data analytics. In this paper, we make two contributions to addressing this problem from both the privacy and information loss perspectives. First, we show that by identifying dependencies between attribute subsets we can eliminate privacy violating attributes from the anonymised dataset. Second, to minimise information loss, we employ a greedy search algorithm to determine and eliminate maximal partial unique attribute combinations. Thus, one only needs to find the minimal set of identifying attributes to prevent re-identification. Experiments on a health cloud based on the SAP HANA platform using a semi-synthetic medical history dataset comprised of 109 attributes, demonstrate the effectiveness of our approach.
Audit - and then what?
(2019)
Current trends such as digital transformation, Internet of Things, or Industry 4.0 are challenging the majority of learning factories. Regardless of whether a conventional learning factory, a model factory, or a digital learning factory, traditional approaches such as the monotonous execution of specific instructions don‘t suffice the learner’s needs, market requirements as well as especially current technological developments. Contemporary teaching environments need a clear strategy, a road to follow for being able to successfully cope with the changes and develop towards digitized learning factories. This demand driven necessity of transformation leads to another obstacle: Assessing the status quo and developing and implementing adequate action plans. Within this paper, details of a maturity-based audit of the hybrid learning factory in the Research and Application Centre Industry 4.0 and a thereof derived roadmap for the digitization of a learning factory are presented.
Bridging the Gap
(2019)
The recent restructuring of the electricity grid (i.e., smart grid) introduces a number of challenges for today's large-scale computing systems. To operate reliable and efficient, computing systems must adhere not only to technical limits (i.e., thermal constraints) but they must also reduce operating costs, for example, by increasing their energy efficiency. Efforts to improve the energy efficiency, however, are often hampered by inflexible software components that hardly adapt to underlying hardware characteristics. In this paper, we propose an approach to bridge the gap between inflexible software and heterogeneous hardware architectures. Our proposal introduces adaptive software components that dynamically adapt to heterogeneous processing units (i.e., accelerators) during runtime to improve the energy efficiency of computing systems.
New Public Governance (NPG) as a paradigm for collaborative forms of public service delivery and Blockchain governance are trending topics for researchers and practitioners alike. Thus far, each topic has, on the whole, been discussed separately. This paper presents the preliminary results of ongoing research which aims to shed light on the more concrete benefits of Blockchain for the purpose of NPG. For the first time, a conceptual analysis is conducted on process level to spot benefits and limitations of Blockchain-based governance. Per process element, Blockchain key characteristics are mapped to functional aspects of NPG from a governance perspective. The preliminary results show that Blockchain offers valuable support for governments seeking methods to effectively coordinate co-producing networks. However, the extent of benefits of Blockchain varies across the process elements. It becomes evident that there is a need for off-chain processes. It is, therefore, argued in favour of intensifying research on off-chain governance processes to better understand the implications for and influences on on-chain governance.
Cardiovascular drift response over two different constant-load exercises in healthy non-athletes
(2019)
Cardiovascular drift (CV-d) is a steady increase in heart rate (HR) over time while performing constant load moderate intensity exercise (CME) > 20 min. CV-d presents problems for the prescription of exercise intensity by means of HR, because the work rate (WR) during exercise must be adjusted to maintain target HR, thus disturbing the intended effect of the exercise intervention. It has been shown that the increase in HR during CME is due to changes in WR and not to CV-d.