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Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Research publications and data nowadays should be publicly available on the internet and, theoretically, usable for everyone to develop further research, products, or services. The long-term accessibility of research data is, therefore, fundamental in the economy of the research production process. However, the availability of data is not sufficient by itself, but also their quality must be verifiable. Measures to ensure reuse and reproducibility need to include the entire research life cycle, from the experimental design to the generation of data, quality control, statistical analysis, interpretation, and validation of the results. Hence, high-quality records, particularly for providing a string of documents for the verifiable origin of data, are essential elements that can act as a certificate for potential users (customers). These records also improve the traceability and transparency of data and processes, therefore, improving the reliability of results. Standards for data acquisition, analysis, and documentation have been fostered in the last decade driven by grassroot initiatives of researchers and organizations such as the Research Data Alliance (RDA). Nevertheless, what is still largely missing in the life science academic research are agreed procedures for complex routine research workflows. Here, well-crafted documentation like standard operating procedures (SOPs) offer clear direction and instructions specifically designed to avoid deviations as an absolute necessity for reproducibility. Therefore, this paper provides a standardized workflow that explains step by step how to write an SOP to be used as a starting point for appropriate research documentation.
Institutionelle Bildung ist für autistische Lernende mit vielgestaltigen und spezifischen Hindernissen verbunden. Dies gilt insbesondere im Zusammenhang mit Inklusion, deren Relevanz nicht zuletzt durch das Übereinkommen der Vereinten Nationen über die Rechte von Menschen mit Behinderung gegeben ist.
Diese Arbeit diskutiert zahlreiche lernrelevante Besonderheiten im Kontext von Autismus und zeigt Diskrepanzen zu den nicht immer ausreichend angemessenen institutionellen Lehrkonzepten. Eine zentrale These ist hierbei, dass die ungewöhnlich intensive Aufmerksamkeit von Autist*innen für ihre Spezialinteressen dafür genutzt werden kann, das Lernen mit fremdgestellten Inhalten zu erleichtern. Darauf aufbauend werden Lösungsansätze diskutiert, welche in einem neuartigen Konzept für ein digitales mehrgerätebasiertes Lernspiel resultieren.
Eine wesentliche Herausforderung bei der Konzeption spielbasierten Lernens besteht in der adäquaten Einbindung von Lerninhalten in einen fesselnden narrativen Kontext. Am Beispiel von Übungen zur emotionalen Deutung von Mimik, welche für das Lernen von sozioemotionalen Kompetenzen besonders im Rahmen von Therapiekonzepten bei Autismus Verwendung finden, wird eine angemessene Narration vorgestellt, welche die störungsarme Einbindung dieser sehr speziellen Lerninhalte ermöglicht.
Die Effekte der einzelnen Konzeptionselemente werden anhand eines prototypisch entwickelten Lernspiels untersucht. Darauf aufbauend zeigt eine quantitative Studie die gute Akzeptanz und Nutzerfreundlichkeit des Spiels und belegte vor allem die
Verständlichkeit der Narration und der Spielelemente. Ein weiterer Schwerpunkt liegt in der minimalinvasiven Untersuchung möglicher Störungen des Spielerlebnisses durch den Wechsel zwischen verschiedenen Endgeräten, für die ein innovatives Messverfahren entwickelt wurde.
Im Ergebnis beleuchtet diese Arbeit die Bedeutung und die Grenzen von spielbasierten Ansätzen für autistische Lernende. Ein großer Teil der vorgestellten Konzepte lässt sich auf andersartige Lernszenarien übertragen. Das dafür entwickelte technische Framework zur Realisierung narrativer Lernpfade ist ebenfalls darauf vorbereitet, für weitere Lernszenarien, gerade auch im institutionellen Kontext, Verwendung zu finden.
The soft error rate (SER) due to heavy-ion irradiation of a clock tree is investigated in this paper. A method for clock tree SER prediction is developed, which employs a dedicated soft error analysis tool to characterize the single-event transient (SET) sensitivities of clock inverters and other commercial tools to calculate the SER through fault-injection simulations. A test circuit including a flip-flop chain and clock tree in a 65 nm CMOS technology is developed through the automatic ASIC design flow. This circuit is analyzed with the developed method to calculate its clock tree SER. In addition, this circuit is implemented in a 65 nm test chip and irradiated by heavy ions to measure its SER resulting from the SETs in the clock tree. The experimental and calculation results of this case study present good correlation, which verifies the effectiveness of the developed method.
The aim of our project design space exploration with answer set programming is to develop a general framework based on Answer Set Programming (ASP) that finds valid solutions to the system design problem and simultaneously performs Design Space Exploration (DSE) to find the most favorable alternatives. We leverage recent developments in ASP solving that allow for tight integration of background theories to create a holistic framework for effective DSE.
The Potsdam answer set solving collection, or Potassco for short, bundles various tools implementing and/or applying answer set programming. The article at hand succeeds an earlier description of the Potassco project published in Gebser et al. (AI Commun 24(2):107-124, 2011). Hence, we concentrate in what follows on the major features of the most recent, fifth generation of the ASP system clingo and highlight some recent resulting application systems.
Answer Set Programming faces an increasing popularity for problem solving in various domains. While its modeling language allows us to express many complex problems in an easy way, its solving technology enables their effective resolution. In what follows, we detail some of the key factors of its success. Answer Set Programming [ASP; Brewka et al. Commun ACM 54(12):92–103, (2011)] is seeing a rapid proliferation in academia and industry due to its easy and flexible way to model and solve knowledge-intense combinatorial (optimization) problems. To this end, ASP offers a high-level modeling language paired with high-performance solving technology. As a result, ASP systems provide out-off-the-box, general-purpose search engines that allow for enumerating (optimal) solutions. They are represented as answer sets, each being a set of atoms representing a solution. The declarative approach of ASP allows a user to concentrate on a problem’s specification rather than the computational means to solve it. This makes ASP a prime candidate for rapid prototyping and an attractive tool for teaching key AI techniques since complex problems can be expressed in a succinct and elaboration tolerant way. This is eased by the tuning of ASP’s modeling language to knowledge representation and reasoning (KRR). The resulting impact is nicely reflected by a growing range of successful applications of ASP [Erdem et al. AI Mag 37(3):53–68, 2016; Falkner et al. Industrial applications of answer set programming. K++nstliche Intelligenz (2018)]