This paper proposes a model-based optimization method for the production of automotive seals in an extrusion process. The high production throughput, coupled with quality constraints and the inherent uncertainty of the process, encourages the search for operating conditions that minimize nonconformities. The main uncertainties arise from the process variability and from the raw material itself. The proposed method, which is based on Bayesian optimization, takes these factors into account and obtains a robust set of process parameters. Due to the high computational cost and complexity of performing detailed simulations, a reduced order model is used to address the optimization. The proposal has been evaluated in a virtual environment, where it has been verified that it is able to minimize the impact of process uncertainties. In particular, it would significantly improve the quality of the product without incurring additional costs, achieving a 50% tighter dimensional tolerance compared to a solution obtained by a deterministic optimization algorithm.

Even though it has been established that a hyetograph\'s shape affects the results of hydrological simulations, common engineering practice does not always account for this fact. Instead, a single design storm is often considered sufficient for designing a urban drainage system. This study examines the impact that this design paradigm, combined with the uncertainty introduced by subjective choices made during the design process, has on the robustness of a designed system. To do so, we evaluated a set of individual designs created by engineering students using the same Chicago hyetograph as a design storm. We then created ensembles of hyetographs with the same precipitation volume and duration as the Chicago hyetograph and evaluated the designs\' hydrological responses. The results showed that designs, which performed equally well for the initial design storm, triggered varying responses for the storms in the ensembles and, consequently, showed different levels of robustness, hinting at a need to adapt the current design approach.

UNASSIGNED: MR-guided radiotherapy adds the precision of magnetic resonance imaging (MRI) to the therapeutic benefits of a linear accelerator. Prior to each therapeutic session, an MRI generates a significant volume of imaging data ripe for analysis. Radiomics stands at the forefront of medical imaging and oncology research, dedicated to mining quantitative imaging attributes to forge predictive models. However, the robustness of these models is often challenged.
UNASSIGNED: To assess the robustness of feature extraction, we conducted reproducibility studies using a 0.35 T MR-linac system, employing both a specialized phantom and patient-derived images, focusing on cases of pancreatic cancer. We extracted shape-based, first-order and textural features from patient-derived images and only first-order and textural features from phantom-derived images. The impact of the delay between simulation and first fraction images was also assessed with an equivalence test.
UNASSIGNED: From 107 features evaluated, 58 (54 %) were considered as non-reproducible: 18 were uniformly inconsistent across both phantom and patient images, 9 were specific to phantom-based analysis, and 31 to patient-derived data.
UNASSIGNED: Our findings show that a significant proportion of radiomic features extracted from this dual dataset were unreliable. It is essential to discard these non-reproducible elements to refine and enhance radiomic model development, particularly for MR-guided radiotherapy in pancreatic cancer.

Investigating U-Net model robustness in medical image synthesis against adversarial perturbations, this study introduces RobMedNAS, a neural architecture search strategy for identifying resilient U-Net configurations. Through retrospective analysis of synthesized CT from MRI data, employing Dice coefficient and mean absolute error metrics across critical anatomical areas, the study evaluates traditional U-Net models and RobMedNAS-optimized models under adversarial attacks. Findings demonstrate RobMedNAS\'s efficacy in enhancing U-Net resilience without compromising on accuracy, proposing a novel pathway for robust medical image processing. .

Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient\'s complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

The large number of different analytical choices used by researchers is partly responsible for the challenge of replication in neuroimaging studies. For an exhaustive robustness analysis, knowledge of the full space of analytical options is essential. We conducted a systematic literature review to identify the analytical decisions in functional neuroimaging data preprocessing and analysis in the emerging field of cognitive network neuroscience. We found 61 different steps, with 17 of them having debatable parameter choices. Scrubbing, global signal regression, and spatial smoothing are among the controversial steps. There is no standardized order in which different steps are applied, and the parameter settings within several steps vary widely across studies. By aggregating the pipelines across studies, we propose three taxonomic levels to categorize analytical choices: 1) inclusion or exclusion of specific steps, 2) parameter tuning within steps, and 3) distinct sequencing of steps. We have developed a decision support application with high educational value called METEOR to facilitate access to the data in order to design well-informed robustness (multiverse) analysis.

Superhydrophobic surfaces hold immense potential in underwater drag reduction. However, as the Reynolds number increases, the drag reduction rate decreases, and it may even lead to a drag increase. The reason lies in the collapse of the air mattress. To address this issue, this paper develops a pyramid-shaped robust superhydrophobic surface with wedged microgrooves, which exhibits a high gas fraction when immersed underwater and good ability to achieve complete spreading and recovery of the air mattress through air replenishment in the case of collapse of the air mattress. Pressure drop tests in a water tunnel confirm that with continuous air injection, the drag reduction reaches 64.8% in laminar flow conditions, substantially greater than 38.4% in the case without air injection, and can achieve 50.8% drag reduction in turbulent flow. This result highlights the potential applications of superhydrophobic surfaces with air mattress recovery for drag reduction.

Aposematic coloration offers an opportunity to explore the molecular mechanisms underlying canalization. In this study, the role of epigenetic regulation underlying robustness was explored in the aposematic coloration of the milkweed bug, Oncopeltus fasciatus. Polycomb (Pc) and Enhancer of zeste (E(z)), which encode components of the Polycomb repressive complex 1 (PRC1) and PRC2, respectively, and jing, which encodes a component of the PRC2.2 subcomplex, were knocked down in the fourth instar of O. fasciatus. Knockdown of these genes led to alterations in scutellar morphology and melanization. In particular, when Pc was knocked down, the adults developed a highly melanized abdomen, head and forewings at all temperatures examined. In contrast, the E(z) and jing knockdown led to increased plasticity of the dorsal forewing melanization across different temperatures. Moreover, jing knockdown adults exhibited increased plasticity in the dorsal melanization of the head and the thorax. These observations demonstrate that histone modifiers may play a key role during the process of canalization to confer robustness in the aposematic coloration.

An analytical, accurate, precise, specific, efficient and simple Ultra-Performance Liquid Chromatography method has been developed and validated for the determination of Pazopanib in bulk and was applied on marketed Pharmaceutical Dosage form. The mobile phase used for the chromatographic runs consisted of 0.1% OPA Buffer and Acetonitrile in the ratio of 30:70% v/v. The separation was achieved on a BHEL UPLC column using isocratic mode. Pazopanib Drug peak were well separated and were detected by a PDA detector at 256 nm. The developed method was linear at the concentration range 6-14 μg/ml for Pazopanib. The method has been validated according to ICH guidelines with respect to system suitability, specificity, precision, accuracy and robustness. The LOD and LOQ for the Pazopanib were found to be 0.5853 µg/ml and 1.7738µg/ml respectively. The developed method is simple, precise, specific, accurate and rapid, making it suitable for estimation of Pazopanib in bulk and marketed pharmaceutical dosage form dosage form.