Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul

Deploying deep learning-based applications in specialized domains like the aircraft production industry typically suffers from the training data availability problem. Only a few datasets represent non-everyday objects, situations, and tasks. Recent advantages in research around Vision Foundation Models (VFM) opened a new area of tasks and models with high generalization capabilities in non-semantic and semantic predictions. As recently demonstrated by the Segment Anything Project, exploiting VFM's zero-shot capabilities is a promising direction in tackling the boundaries spanned by data, context, and sensor variety. Although, investigating its application within specific domains is subject to ongoing research. This paper contributes here by surveying applications of the SAM in aircraft production-specific use cases. We include manufacturing, intralogistics, as well as maintenance, repair, and overhaul processes, also representing a variety of other neighboring industrial domains. Besides presenting the various use cases, we further discuss the injection of domain knowledge

Synthesis and in Vitro Evaluation of Stabilized and Selective Neuromedin U-1 Receptor Agonists

Neuromedin U (NMU) is a multifunctional neuropeptide which is characterized by a high conservation through all species. Herein, we describe the synthesis of a novel set of NMU-analogs based on the truncated NMU-8. Through combination of previously reported modifications, an elaborate structure-activity relationship study was performed aiming for the development of peptides with an increased selectivity toward NMU receptor 1 (NMUR1). Compound 7 possessed the highest NMUR1 selectivity (IC50 = 0.54 nM, selectivity ratio = 5313) together with an increased potency (EC50 = 3.7 nM), an 18% increase of the maximal effect at NMUR1, and a higher resistance against enzymatic degradation as compared to the native NMU-8. The development of a potent NMUR1 agonist with extended half-life could represent an attractive tool to further unveil the role of NMUR1 in NMU signaling

Reproducibility in the absence of selective reporting: An illustration from large‐scale brain asymmetry research

The problem of poor reproducibility of scientific findings has received much attention over recent years, in a variety of fields including psychology and neuroscience. The problem has been partly attributed to publication bias and unwanted practices such as p‐hacking. Low statistical power in individual studies is also understood to be an important factor. In a recent multisite collaborative study, we mapped brain anatomical left–right asymmetries for regional measures of surface area and cortical thickness, in 99 MRI datasets from around the world, for a total of over 17,000 participants. In the present study, we revisited these hemispheric effects from the perspective of reproducibility. Within each dataset, we considered that an effect had been reproduced when it matched the meta‐analytic effect from the 98 other datasets, in terms of effect direction and significance threshold. In this sense, the results within each dataset were viewed as coming from separate studies in an “ideal publishing environment,” that is, free from selective reporting and p hacking. We found an average reproducibility rate of 63.2% (SD = 22.9%, min = 22.2%, max = 97.0%). As expected, reproducibility was higher for larger effects and in larger datasets. Reproducibility was not obviously related to the age of participants, scanner field strength, FreeSurfer software version, cortical regional measurement reliability, or regional size. These findings constitute an empirical illustration of reproducibility in the absence of publication bias or p hacking, when assessing realistic biological effects in heterogeneous neuroscience data, and given typically‐used sample sizes

Health and Animal Agriculture in Developing Countries

VII, 424p. 47 illus., 35 illus. in color.online r