Towards Context Driven Modularization of Large Biomedical Ontologies

Formal knowledge about human anatomy, radiology or diseases is necessary to support clinical applications such as medical image search. This machine processable knowledge can be acquired from biomedical domain ontologies, which however, are typically very large and complex models. Thus, their straightforward incorporation into the software applications becomes difficult. In this paper we discuss first ideas on a statistical approach for modularizing large medical ontologies and we prioritize the practical applicability aspect. The underlying assumption is that the application relevant ontology fragments, i.e. modules, can be identified by the statistical analysis of the ontology concepts in the domain corpus. Accordingly, we argue that most frequently occurring concepts in the domain corpus define the application context and can therefore potentially yield the relevant ontology modules. We illustrate our approach on an example case that involves a large ontology on human anatomy and report on our first manual experiments

Pure Business for a better World?

The pressure on businesses to “give something back to society” has increased considerably in the last decades and concepts like Corporate Social Responsibility gained enormous populari-ty. While for many years companies have been distributing cash and goods to charity, attach-ing to the CSR agenda a philanthropic nuance, many companies have recently started to shift away from philanthropic giving towards a more business-like CSR policy approach. This so-called strategic CSR is increasingly seen as the solution to achieve development goals. The purpose of this study was to investigate the shift away from philanthropic CSR using the ex-ample of two multinational oil companies operating in Uganda, where oil has been recently discovered. The companies’ strategies were analysed drawing on eight core economic multi-pliers. Furthermore the potential effect of stakeholders on the multipliers was scrutinized and the characteristics of strategic CSR mapped. The findings outline that the two companies have shifted to a strategic approach, whereas not all multipliers are manifested equally. It can be seen that each company interprets strategic CSR differently, which challenges the positive impact on society, whereas best practices confirm the potential of strategic CSR to create long term economic opportunities

Improving Environmental Aspects of Perovskite Solar Cells by Lead Alternatives

Die Reduktion von Treibhausgasen ist essentiell um die negativen Auswirkungen des Klimawandels zu limitieren. Um dies zu erzielen, bedarf es der Entwicklung neuer, regenerativer Technologien zur Energiewandlung. Perowskitsolarzellen sind hierfür eine vielversprechende Photovoltaiktechnologie, welche in den letzten Jahren enorme Effizienzsteigerungen erreichen konnten. Die Kommerzialisierung und gesellschaftliche Akzeptanz dieser Technologie wird jedoch durch das dabei eingesetzte Blei erschwert. Um Auswirkungen auf die menschliche Gesundheit zu reduzieren und die Umweltverträglichkeit zu verbessern, werden in dieser Arbeit zwei chemische Elemente als Alternativen zu Blei in Perowskitverbindungen und deren Potential in stabilen, effizienten Perowskitsolarzellen erforscht. Zinn ist dabei ein besonders vielversprechender Bleiersatz. Dies begründet sich vor allem in seiner Verträglichkeit für Mensch und Umwelt sowie den hohen Solarzellwirkungsgraden. In dieser Arbeit wurde eine Strategie für 1:1 Zinn-Blei gemischte Perowskitsolarzellen entwickelt, die ungünstige Löchertransportschicht PEDOT:PSS (Polyethylendioxythiophen- Polystyrolsulfonat) und den Methylammonium (MA+)-haltigen Perowskitabsorber zu ersetzen. Dies legt die Grundlage für eine stabilere Solarzellarchitektur. Des Weiteren, wurden neue Erkenntnisse zum Mechanismus des häufig eingesetzten Zinnfluoride (SnF₂) Additives in Zinn- Perowskitsolarzellen erlangt. Es wurde eine bevorzugte Akkumulation des SnF₂ an der PEDOT:PSS Grenzfläche nachgewiesen. Darüber hinaus reagiert das akkumulierte SnF₂ mit dem PEDOT:PSS unter Bildung einer Zinnsulfid (SnS) Zwischenschicht, wodurch ein positiver Einfluss auf die Solarzellleistung erwartet wird, da SnS ein p-Halbleiter ist. Durch umfangreiche Optimierung der Grenzschichten und des Perowskitabsorbers zur Reduktion von Rekombinationsverlusten, konnten Solarzelleffizienzen von bis zu 6,6% für Zinn-Perowskitsolarzellen mit hohen Leerlaufspannungen von bis zu 670mV erzielt werden. Stabilitätsanalysen von Zinnhaltigen Perowskitsolarzellen unter atmosphärischen Umgebungsbedingungen haben deren hohen Oxidationsempfindlichkeit verifiziert, wodurch deren Herstellung und Operation unter inerten Bedingung notwendig ist. Des Weiteren, scheint die Solarzelldegradation in atmosphärischer Umgebung und bei Hitze durch die Verschlechterung der Solarzellschichten dominiert zu werden und nicht durch die Degradation des Perowskitabsorbers selbst. Da der Einfluss von Zinn-Perowskiten auf Mensch und Umwelt kontrovers diskutiert wird, wurde ein weiteres Substitutionselement, Bismut, untersucht. Die Verwendung dieses in Perowskitartigen Verbindungen zeichnet sich durch deren hohen Verträglichkeit für Mensch und Umwelt aus. In dieser Arbeit wurde eine Strategie verfolgt, um homogene, kompakte Cs₃Bi₂I₉-basierte Filme mit variablen Bandlücken abzuscheiden. Mittels der hergestellten Solarzellen konnten Solarzelleigenschaften dieses Materials nachgewiesen werden. Diese Arbeit betrachtet das Spannungsfeld zwischen der Verträglichkeit der verwendeten Perowskitabsorber für Mensch und Umwelt und der Leistung von Solarzellen. Zinn-haltige Perowskitabsorber erzielen hohe Wirkungsgrade, erhöhen jedoch lediglich die Verträglichkeit für Mensch und Umwelt. Wohingegen, Bismut basierte Absorber verträglich für Mensch und Umwelt sind, aber herausfordernd geringe Solarzellleistungen aufweisen. Diese Arbeit reiht sich in das allgemeine Verständnis bleifreier Perowskitabsorber ein. Neue Erkenntnisse wurden hinsichtlich einer Strategie für PEDOT:PSS- und MA+-freier Bauelemente, dem Verständnis des SnF₂-Mechanismus und neuer Ansätze zur Reduktion von Rekombinationsverlusten mittels Grenzflächen- und Morphologieoptimierungen gewonnen

Influences on the activity and lying behavior of lactating dairy cows with particular attention to lameness

Lameness, which is usually the cause of a painful claw disease, continues to be the third most common reason for culling on dairy-producing farms in Germany. Claw diseases are not only painful, affecting both health and well-being of the animals, they are also often diagnosed too late. In order to counteract resulting financial and performance losses and additionally increase animal welfare, the earliest possible detection of lameness and subsequent treatment of the cause of lameness are essential. Despite more than 70% of all farmers being willing to eliminate this malady and improve hoof health, lameness prevalence is often underestimated. In order to support the farmer as good as possible and at the same time meet the requirements of ever-advancing herd management, sensor-assisted lameness detection should be ensured, because the earliest possible detection and treatment of lameness can significantly reduce the costs of a dairy farm and benefit individual animal welfare. In the first presented study data on the lying behavior of dairy cows with regard to animal-physiological, environmental and management-based influences were analyzed in order to make it available for further lameness research. It was found that above all the daily lying time was influenced by the lactation number, the lactation status, the oestrus and the milking frequency. Therefore, these factors should be taken into account in future models as well. A second study has shown that there is a causal relationship between the walking speed of dairy cows and hoof health. Lame cows had a significantly slower walking speed than non-lame animals, so the integration of running speed into a predictive model is considered meaningful. Numerous sensor systems enable an accurate and continuous monitoring of the health status of the dairy cows. The combination of data from different sensor systems enables the farmer to accurately monitor the health status of each individual animal in real time. In this way, the farmer is able to meet the demands of society for increased animal welfare in modern dairy farms

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiolog

A Linguistic Approach to Aligning Representations of Human Anatomy and Radiology

To realize applications such as semantic medical image search different domain ontologies are necessary that provide complementary knowledge about human anatomy and radiology. Consequently, integration of these different but nevertheless related types of medical knowledge from disparate domain ontologies becomes necessary. Ontology alignment is one way to achieve this objective. Our approach for aligning medical ontologies has three aspects: (a) linguistic-based, (b) corpus-based, and (c) dialogue-based. We briefly report on the linguistic alignment (i.e. the first aspect) using an ontology on human anatomy and a terminology on radiology

AI Hazard Management: A framework for the systematic management of root causes for AI risks

Recent advancements in the field of Artificial Intelligence (AI) establish the basis to address challenging tasks. However, with the integration of AI, new risks arise. Therefore, to benefit from its advantages, it is essential to adequately handle the risks associated with AI. Existing risk management processes in related fields, such as software systems, need to sufficiently consider the specifics of AI. A key challenge is to systematically and transparently identify and address AI risks' root causes - also called AI hazards. This paper introduces the AI Hazard Management (AIHM) framework, which provides a structured process to systematically identify, assess, and treat AI hazards. The proposed process is conducted in parallel with the development to ensure that any AI hazard is captured at the earliest possible stage of the AI system's life cycle. In addition, to ensure the AI system's auditability, the proposed framework systematically documents evidence that the potential impact of identified AI hazards could be reduced to a tolerable level. The framework builds upon an AI hazard list from a comprehensive state-of-the-art analysis. Also, we provide a taxonomy that supports the optimal treatment of the identified AI hazards. Additionally, we illustrate how the AIHM framework can increase the overall quality of a power grid AI use case by systematically reducing the impact of identified hazards to an acceptable level

Navigating AI innovation ecosystems in manufacturing: Shaping factors and their implications

Manufacturers often encounter challenges when implementing artificial intelligence (AI) in their manufacturing operations. Similar challenges with other digital transformation technologies have resulted in the emergence of innovation ecosystems. In this paper, we aim to demonstrate the emergence of AI innovation ecosystems and highlight the factors that influence their structure in manufacturing. To achieve this, we conducted a qualitative study of ten manufacturing case studies, analyzing different value propositions, activities, actors, and modules in AI ecosystems in the manufacturing sector. We first visualize the AI innovation ecosystems to showcase their structure and then discuss factors such as trustworthiness, scalability, simulation, and cloud that impact the ecosystem structure. Our study provides practitioners with a better understanding of the structure of AI ecosystems and their influencing factors. For researchers, we introduce influencing factors as a new part of the ecosystem-as-structure concept, which can lead to new research opportunities

Detection, Explanation and Filtering of Cyber Attacks Combining Symbolic and Sub-Symbolic Methods

Machine learning (ML) on graph-structured data has recently received deepened interest in the context of intrusion detection in the cybersecurity domain. Due to the increasing amounts of data generated by monitoring tools as well as more and more sophisticated attacks, these ML methods are gaining traction. Knowledge graphs and their corresponding learning techniques such as Graph Neural Networks (GNNs) with their ability to seamlessly integrate data from multiple domains using human-understandable vocabularies, are finding application in the cybersecurity domain. However, similar to other connectionist models, GNNs are lacking transparency in their decision making. This is especially important as there tend to be a high number of false positive alerts in the cybersecurity domain, such that triage needs to be done by domain experts, requiring a lot of man power. Therefore, we are addressing Explainable AI (XAI) for GNNs to enhance trust management by exploring combining symbolic and sub-symbolic methods in the area of cybersecurity that incorporate domain knowledge. We experimented with this approach by generating explanations in an industrial demonstrator system. The proposed method is shown to produce intuitive explanations for alerts for a diverse range of scenarios. Not only do the explanations provide deeper insights into the alerts, but they also lead to a reduction of false positive alerts by 66% and by 93% when including the fidelity metric.Comment: arXiv admin note: text overlap with arXiv:2105.0874