Mycoflora and Fumonisin Mycotoxins Associated with Cowpea [Vigna unguiculata (L.) Walp] Seeds

ArticleCowpea seed samples from South Africa and Benin were analyzed for seed mycoflora. Fusarium species detected were F. equiseti, F. chlamydosporum, F. graminearum, F. proliferatum, F. sambucinum, F. semitectum, and F. subglutinans. Cowpea seed from South Africa and Benin and F. proliferatum isolates from Benin, inoculated onto maize patty medium, were analyzed for fumonisin production. Samples were extracted with methanol/water and cleaned up on strong anion exchange solid phase extraction cartridges. HPLC with precolumn derivatization using o-phthaldialdehyde was used for the detection and quantification of fumonisins. Cowpea cultivars from South Africa showed the presence of fumonisin B1 at concentrations ranging between 0.12 and 0.61 μg/g, whereas those from Benin showed no fumonisins. This is believed to be the first report of the natural occurrence of FB1 on cowpea seed. Fumonisin B1, B2, and B3 were produced by all F. proliferatum isolates. Total fumonisin concentrations were between 0.8 and 25.30 μg/g, and the highest level of FB1 detected was 16.86 μg/g

The Transformation Towards Smart (er) Factories:Integration Requirements of the Digital Twin

The vision of the smart factory with its interconnected systems is based on the seamless (real-time) integration of data across the information system (IS) landscape. Yet, due to the existence of many legacy systems, this task is far from trivial. In industrial practice, the IS landscape comprises systems of different application functionality which can be characterized as technologically heterogeneous, e.g. transaction processing versus real-time systems. Integrating such systems has always been a major challenge and constraining force for many organizations. This problem is receiving renewed attention in the context of the implementation of the digital twin in manufacturing. Due to its central role in the IS landscape, the digital twin needs to communicate with a number of heterogeneous applications to achieve its full potential, i.e. achieving a complete virtual representation of an asset, process or product. This research analyses the integration requirements from the perspective of the digital twins’ application functionality. In particular, we provide an explicit mapping of the integrations needed between the digital twin and existing information systems (IS) in manufacturing, which serves as a basis to better understand integration issues. These findings provide an explanation for and a conceptualization of some of the challenges that emerge when transforming towards an interconnected smart factory

Digital Shadows as an Enabler for the Internet of Production

Part 5: Cyber-Physical Production Systems and Digital TwinsInternational audienceDue to increasing atomization, manufacturing companies generate increasing amounts of production data. Most of this data is domain-specific, heterogeneous and unstructured. This complicates the access, interpretation, analysis and usage for efficiency improvement, faster reaction to change and weaknesses identification. To overcome this challenge, the idea of an “internet of production” is to link all kind of production relevant data by a data lake. Based on this data lake, digital shadows aggregate data for a specific purpose. For example, digital shadows in production planning and control help to manage the dynamic changes like delays in production or machine break–downs. This paper examines the existing research in the field of digital twins and digital shadows in manufacturing and gives a brief overview of the historical development. In particular, the potential and possible applications of digital shadows in production planning and control are analyzed. A top–down–bottom–up approach is developed to support the design of digital shadows in production planning and control