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The article focuses on the findings of endurance tests on thrust bearings. In addition to the mechanical load (axial load: 10 ≤ C0/P ≤ 19, lubrication gap: 0.33 µm ≤ h0 ≤ 1.23 µm), these bearings are also exposed to electrical loads (voltage: 20 Vpp ≤ U0 ≤ 60 Vpp, frequency 5 kHz and 20 kHz), such as those generated by modern frequency converters. In a previous study, the focus was on the chemical change in the lubricant and the resulting wear particles. In contrast, this article focuses on the changes occurring in the metallic contact partners. Therefore, the changes in the surface topography are analysed using Abbott–Firestone curves. These findings show that tests with an additional electrical load lead to a significant reduction in roughness peaks. A correlation to acceleration measurements is performed. Moreover, it is shown that the electrical load possibly has an effect on the light load hardness. An increase in the occurring wear could not be detected during the test series. Also, a comparison with mechanical reference tests is made. The article finally provides an overview of different measurement values and their sensitivity to additional electrical loads in roller bearings. [ABSTRACT FROM AUTHOR]

Quantitative susceptibility mapping (QSM) has attracted considerable interest for tissue characterization (e.g., iron and calcium accumulation, myelination, venous vasculature) in the human brain and relies on extensive data processing of gradient-echo MRI phase images. While deep learning-based field-tosusceptibility inversion has shown great potential, the acquisition parameters applied in clinical settings such as image resolution or image orientation with respect to the magnetic field have not been fully accounted for. Furthermore, the lack of comprehensive training data covering a wide range of acquisition parameters further limits the current QSM deep learning approaches. Here, we propose the integration of a priori information of imaging parameters into convolutional neural networks with our approach, adaptive convolution, that learns the mapping between the additional presented information (acquisition parameters) and the changes in the phase images associated with these varying acquisition parameters. By associating a-priori information with the network parameters itself, the optimal set of convolution weights is selected based on data-specific attributes, leading to generalizability towards changes in acquisition parameters. Moreover, we demonstrate the feasibility of pretraining on synthetic data and transfer learning to clinical brain data to achieve substantial improvements in the computation of susceptibility maps. The adaptive convolution 3D U-Net demonstrated generalizability in acquisition parameters on synthetic and in-vivo data and outperformed models lacking adaptive convolution or transfer learning. Further experiments demonstrate the impact of the side information on the adaptive model and assessed susceptibility map computation on simulated pathologic data sets and measured phase data. [ABSTRACT FROM AUTHOR]

The article explores the issue of bias in artificial intelligence (AI) systems and its potential impact on various fields such as healthcare, criminal justice, and employment. It emphasizes the need for thoughtful implementation of AI to address and mitigate bias in order to ensure fair and equitable outcomes. The article discusses different types of bias, including systemic bias, statistical bias, and human bias, and explains how bias can manifest in machine learning algorithms. It also highlights specific examples of bias in AI systems, such as racial bias in facial recognition technology and gender bias in job recruitment AI. The article calls for regulations to address algorithmic bias and mentions recent executive orders and legislation in the US and the European Union as steps towards regulating AI. It proposes a regulatory framework that includes principles of fairness, transparency, accountability, and sustainability to combat algorithmic bias. The article also discusses the importance of fairness, transparency, accountability, and sustainability in the use of automated decision systems in criminal justice and other contexts. It provides recommendations for assessing and evaluating the impact of AI systems and emphasizes the need for periodic testing, impact assessments, and model updates to ensure sustainability and fairness over time. The article concludes with a success story of a predictive-analytics algorithm implemented in Allegheny County, Pennsylvania, which improved the identification of families in need of intervention for child abuse and neglect, highlighting the importance of trustworthy AI systems. [Extracted from the article]

Within collective epistemology, there is a class of theories that understand the epistemic status of collective attitude ascriptions, such as "the college union knows that the industrial action is going to plan" as saying that a sufficient subset of group member attitudes has the relevant epistemic status. I will demonstrate that these summativist approaches to collective epistemology are incompatible with epistemic permissivism, the doctrine that a single body of evidence may rationalize multiple doxastic attitudes. In particular, we can use epistemic permissivism to generate so-called divergence cases, which demonstrate situations in which rationality requires group-level and member-level attitudes to diverge. [ABSTRACT FROM AUTHOR]

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Competing Interests: The authors declare that they have no conflict of interest.