Multi scale digital image correlation for automatic edge detection of ply cracks in composite laminates under quasi static and fatigue loading

Ply cracking is typically the first ply level damage mode in composite laminates under static and fatigue tensile loading. Ply cracks do not usually cause the final failure of a laminate, but may significantly degrade the effective properties of the composite and serve as a source for other damage modes initiation. However, the in-situ experimental detection and quantification of this damage mode is a challenging task specially under fatigue loading conditions without stopping the test. This work is focused on detection of ply cracking and calculation of crack density [1] in multidirectional symmetric composite laminates. The main objective of this work is to study whether the DIC technique [2] is reliable for automated crack detection and functional in calculation of crack density. Therefore, digital image correlation with 2D-DIC and 3D-DIC setups (Figure 1) at the edge and top surfaces of [02/902]s, [02/452]s and [902/02]s glass/epoxy laminates is utilized to detect crack density under uniaxial quasi-static and fatigue loading conditions. For fatigue, the maximum cyclic tensile stress of 90% of the first crack initiation stress, obtained from quasi-static tests, with load ratio σmin/σmax=0.1, frequency of 5 Hz and up to 200,000 cycles, is considered. An optimization analysis is implemented to evaluate the resolution and the standard uncertainty in DIC strain and displacement measurements. Next, a comparison is established between the discontinuities in both strain and displacement fields for crack detection. Consequently, the displacement field has proved to deliver better and more accurate results than strain fields in crack density calculation. The results obtained from DIC analysis are compared and validated by microscopic images which are acquired after performing each test (Figure 2). The study of the results shows that the developed DIC methodology are suited for automated crack detection in fatigue and quasi-static loadings, and as such extract the evolution of crack density vs. number of cycles

Exploring belonging in the documentary inclusief [inclusive] as a matter of care

In this article we study the concept of 'belonging' within the context of inclusive education. We see inclusion and belonging as two entangled concepts and focus on inclusion as an ethical process. We do this by analyzing the Flemish documentary Inclusief [Inclusive] to investigate the idea of belonging as building and finding roots. As a method we carry out a close reading of two episodes of the documentary and plug in theory to gain a deeper sense about the meaning of belonging in two students' life stories. In our writing process these fragments become tangible. Through the stories, we see how belonging emerges. We come to understand belonging in inclusive education as connected to the dynamic interplay with the context; listening with care; and radical relationality. By combining these elements schools can invest in becoming inclusive communities of care, and contribute to every student having a feeling of belonging

The (un)necessity of child portrayal in momfluencer content : exploring mothers’ perspectives on influencer sharenting through in-depth interviews

Momfluencers heavily disclose personal information and pictures of their children on their social media as they aim to share parenting experiences with their followers. Including their children in their content may make them more authentic and increase their celebrity capital. However, empirical research testing this assumption is scarce. Importantly, the portrayal of children in influencer sharenting has raised concern regarding the privacy of the portrayed child and the related consequences for the child's well-being. Through 20 in-depth interviews with followers of momfluencers, we assessed the perceptions of and attitudes towards child portrayal in momfluencer content and privacy-protective behaviors. The mothers in our study appeared to be highly concerned about the risks related to sharenting behaviors. While these mothers believed that portraying children in momfluencer content is essential to enhance their perceptions of credibility, authenticity, and intimacy; they emphasized that this can be achieved while protecting the child's privacy through the use of anti-sharenting techniques. Transparent communication about the choices momfluencers make regarding these techniques appeared to be essential for fostering meaningful relationships. In addition, momfluencers who employ anti-sharenting techniques and transparently explain their reasons for doing so may have the potential to influence changes in followers' own sharenting behavior. These findings emphasize the potential of anonymizing children in momfluencer content, enabling momfluencers to protect their children's online privacy while maintaining the affordances of sharenting

Extreme multi-label skill extraction training using large language models

Online job ads serve as a valuable source of information for skill requirements, playing a crucial role in labor market analysis and erecruitment processes. Since such ads are typically formatted in free text, natural language processing (NLP) technologies are required to automatically process them. We specifically focus on the task of detecting skills (mentioned literally, or implicitly described) and linking them to a large skill ontology, making it a challenging case of extreme multi-label classification (XMLC). Given that there is no sizable labeled (training) dataset are available for this specific XMLC task, we propose techniques to leverage general Large Language Models (LLMs). We describe a cost-effective approach to generate an accurate, fully synthetic labeled dataset for skill extraction, and present a contrastive learning strategy that proves effective in the task. Our results across three skill extraction benchmarks show a consistent increase of between 15 to 25 percentage points in RPrecision@5 compared to previously published results that relied solely on distant supervision through literal matche

Unsupervised transfer learning across different data modalities for bearing's speed identification

Recent advancements in transfer learning have revolutionized predictive maintenance, enabling cross-domain generalization for components with varying characteristics and operating under different conditions. While traditional transfer learning approaches require labeled data in both source and target domains, unsupervised transfer learning strives for a more cost-effective alternative for which only labels are available in the source domain. This study investigates adversarial transfer learning between two different sensor modalities: vibration and acoustic. The goal is to enable bearing monitoring using microphones, which are, in general terms, cheaper and easier to deploy than vibration sensors; and without the need to label data in the target domain. The research goal is to identify the operating speed of a bearing testbed. The source domain data correspond to vibration measurements taken from an attached sensor, while the target domain uses a microphone array at distance. Artificial Neural Networks are used as the base architecture. Transferability is assessed with two unsupervised adversarial learning techniques: gradient reversal and deep correlation alignment. Their performance is compared to traditional supervised transfer learning via fine-tuning. Experimental results demonstrate that gradient reversal outperforms deep correlation alignment and is able to achieve results similar to those obtained with supervised transfer learning. These findings highlight the feasibility of speed identification using a microphone array and establish a baseline for future condition monitoring research with such sensors

Clinical reasoning over tabular data and text with bayesian networks

Bayesian networks are well-suited for clinical reasoning on tabular data, but are less compatible with natural language data, for which neural networks provide a successful framework. This paper compares and discusses strategies to augment Bayesian networks with neural text representations, both in a generative and discriminative manner. This is illustrated with simulation results for a primary care use case (diagnosis of pneumonia) and discussed in a broader clinical context

Chemical perturbation of chloroplast Ca2+ dynamics in Arabidopsis thaliana suspension cell cultures and seedlings

Ca2+ signaling is part of universal signal transduction pathways to respond to external and internal stimuli or stress and in plants plays a central role in chloroplasts, such as in the regulation of photosynthetic enzymes or the transition from light to dark. Only recently, the underlying molecular machinery, e.g., transporters and channels that enable chloroplast Ca2+ fluxes, has started to be elucidated. However, chemical tools to specifically perturb these chloroplast Ca2+ fluxes are largely lacking. Here, we describe an efficient aequorin-based system in Arabidopsis thaliana suspension cell cultures to screen for chemicals that alter light-to-dark-induced chloroplast stroma Ca2+ signals. Subsequently, the effect of the hits on chloroplast Ca2+ signals is validated in Arabidopsis seedlings. The research lays a foundation for the identification of novel proteins involved in Ca2+ transport in chloroplast stroma under light-to-dark transition and for investigating the interaction of chloroplast Ca2+ signaling with photosynthesis in general

Could you not translate me? Sociability and performativity in romantic translation

Proposing a model through which to document Romantic translation that is focused on recovering the translators who organised its processes, the present article coordinates two recently prominent concepts; that is, sociability and performativity. It argues that these two terms should be studied in tandem, and that they require close attention to concrete material and technological practices and contexts of mediation in order to become fully tractable. The German-English historical novel Walladmor (1823-1824) is examined as a case study to demonstrate the potential of this article's proposals for reading Romantic translation. This text initially appears as a pseudotranslation, and is such a key example of that mode that it was the first pseudotranslation to be branded as such; however, it soon morphs into a pseudo-backtranslation upon its translation into its pseudo-original language, revealing layers of performativity and sociability in this involuted interlinguistic movement. Building on this case, the article offers a detailed discussion of sociability and performativity in Romantic contexts and beyond, showing that the presumption all Romantic translation is service translation ignores its potential as a wellspring of creativity and freedom