Posterpresentatie gegeven tijdens bezoek SBE (Samenwerkende Bedrijven Eemsdelta) aan de Hanzehogeschool
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PURPOSE: The purpose of this review article is to describe characteristics of auditory processing disorders (APD) by evaluating the literature in which children with suspected or diagnosed APD were compared with typically developing children and to determine whether APD must be regarded as a deficit specific to the auditory modality or as a multimodal deficit.METHOD: Six electronic databases were searched for peer-reviewed studies investigating children with (suspected) APD in comparison with typically developing peers. Relevant studies were independently reviewed and appraised by 2 reviewers. Methodological quality was quantified using the American Speech-Language-Hearing Association's levels of evidence.RESULTS: Fifty-three relevant studies were identified. Five studies were excluded because of weak internal validity. In total, 48 studies were included, of which only 1 was classified as having strong methodological quality. Significant dissimilarities were found between children referred with listening difficulties and controls. These differences relate to auditory and visual functioning, cognition, language, reading, and physiological and neuroimaging measures.CONCLUSIONS: Methodological quality of most of the incorporated studies was rated moderate due to the heterogeneous groups of participants, inadequate descriptions of participants, and the omission of valid and reliable measurements. The listening difficulties of children with APD may be a consequence of cognitive, language, and attention issues rather than bottom-up auditory processing.
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Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
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Currently, many novel innovative materials and manufacturing methods are developed in order to help businesses for improving their performance, developing new products, and also implement more sustainability into their current processes. For this purpose, additive manufacturing (AM) technology has been very successful in the fabrication of complex shape products, that cannot be manufactured by conventional approaches, and also using novel high-performance materials with more sustainable aspects. The application of bioplastics and biopolymers is growing fast in the 3D printing industry. Since they are good alternatives to petrochemical products that have negative impacts on environments, therefore, many research studies have been exploring and developing new biopolymers and 3D printing techniques for the fabrication of fully biobased products. In particular, 3D printing of smart biopolymers has attracted much attention due to the specific functionalities of the fabricated products. They have a unique ability to recover their original shape from a significant plastic deformation when a particular stimulus, like temperature, is applied. Therefore, the application of smart biopolymers in the 3D printing process gives an additional dimension (time) to this technology, called four-dimensional (4D) printing, and it highlights the promise for further development of 4D printing in the design and fabrication of smart structures and products. This performance in combination with specific complex designs, such as sandwich structures, allows the production of for example impact-resistant, stress-absorber panels, lightweight products for sporting goods, automotive, or many other applications. In this study, an experimental approach will be applied to fabricate a suitable biopolymer with a shape memory behavior and also investigate the impact of design and operational parameters on the functionality of 4D printed sandwich structures, especially, stress absorption rate and shape recovery behavior.
By transitioning from a fossil-based economy to a circular and bio-based economy, the industry has an opportunity to reduce its overall CO2 emission. Necessary conditions for effective and significant reductions of CO2-emissions are that effective processing routes are developed that make the available carbon in the renewable sources accessible at an acceptable price and in process chains that produce valuable products that may replace fossil based products. To match the growing industrial carbon demand with sufficient carbon sources, all available circular, and renewable feedstock sources must be considered. A major challenge for greening chemistry is to find suitable sustainable carbon that is not fossil (petroleum, natural gas, coal), but also does not compete with the food or feed demand. Therefore, in this proposal, we omit the use of first generation substrates such as sugary crops (sugar beets), or starch-containing biomasses (maize, cereals).
Electrohydrodynamic Atomization (EHDA), also known as Electrospray (ES), is a technology which uses strong electric fields to manipulate liquid atomization. Among many other areas, electrospray is currently used as an important tool for biomedical applications (droplet encapsulation), water technology (thermal desalination and metal recovery) and material sciences (nanofibers and nano spheres fabrication, metal recovery, selective membranes and batteries). A complete review about the particularities of this technology and its applications was recently published in a special edition of the Journal of Aerosol Sciences [1]. Even though EHDA is already applied in many different industrial processes, there are not many controlling tools commercially available which can be used to remotely operate the system as well as identify some spray characteristics, e.g. droplet size, operational mode, droplet production ratio. The AECTion project proposes the development of an innovative controlling system based on the electrospray current, signal processing & control and artificial intelligence to build a non-visual tool to control and characterize EHDA processes.
