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Chanda Simfukwe,1 Young Chul Youn,1 Min-Jae Kim,2 Joonki Paik,2 Su-Hyun Han1 1Department of Neurology, Chung-Ang University College of Medicine, Seoul, South Korea; 2Department of Image, Chung-Ang University, Seoul, South KoreaCorrespondence: Young Chul Youn; Su-Hyun Han, Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea, Email neudoc@cau.ac.kr; freesu84@cau.ac.krPurpose: Electroencephalogram (EEG) signals give detailed information on the electrical brain activities occurring in the cerebral cortex. They are used to study brain-related disorders such as mild cognitive impairment (MCI) and Alzheimer’s disease (AD). Brain signals obtained using an EEG machine can be a neurophysiological biomarker for early diagnosis of dementia through quantitative EEG (qEEG) analysis. This paper proposes a machine learning methodology to detect MCI and AD from qEEG time-frequency (TF) images of the subjects in an eyes-closed resting state (ECR).Participants and Methods: The dataset consisted of 16,910 TF images from 890 subjects: 269 healthy controls (HC), 356 MCI, and 265 AD. First, EEG signals were transformed into TF images using a Fast Fourier Transform (FFT) containing different event-rated changes of frequency sub-bands preprocessed from the EEGlab toolbox in the MATLAB R2021a environment software. The preprocessed TF images were applied in a convolutional neural network (CNN) with adjusted parameters. For classification, the computed image features were concatenated with age data and went through the feed-forward neural network (FNN).Results: The trained models’, HC vs MCI, HC vs AD, and HC vs CASE (MCI + AD), performance metrics were evaluated based on the test dataset of the subjects. The accuracy, sensitivity, and specificity were evaluated: HC vs MCI was 83%, 93%, and 73%, HC vs AD was 81%, 80%, and 83%, and HC vs CASE (MCI + AD) was 88%, 80%, and 90%, respectively.Conclusion: The proposed models trained with TF images and age can be used to assist clinicians as a biomarker in detecting cognitively impaired subjects at an early stage in clinical sectors.Keywords: neurodegenerative diseases, electroencephalography, supervised machine learning, regression analysis

Ships and ship-shaped offshore installations are commonly equipped with pneumatic or solid rubber fenders to absorb kinetic energy during collisions, thereby reducing structural damage. The energy absorption capacity of these fenders must be precisely evaluated via reliable experimental, analytical, and numerical methods to ensure that the fenders are capable of preventing collision-associated structural failures. Accordingly, this study developed computational modelling techniques for analysing the energy absorption capacity of solid rubber fenders. Modelling was performed using LS-DYNA, a commercial software package for finite element simulation, and the developed models were validated by comparison with data from crushing tests on two types of solid rubber fender models - circular tube and V-shaped rubber fenders. The validated computational models were applied to examine a case study of a low-speed collision between a Suezmax class shuttle tanker and a very-large-crude-oil-carrier (VLCC) class ship-shaped offshore installation working side-by-side in offloading operation, with the latter equipped or not equipped with V-shaped rubber fenders, respectively. The results revealed that such fenders were effective protection against structural damage in such a collision scenario. [ABSTRACT FROM AUTHOR]

This research program investigates whether representational level of information underlying initial beliefs (individual vs. category) and disconfirming information (individual vs. category) influence the magnitude of belief and attitude change regarding categories of objects. In 3 experiments, 2 key effects emerged. A main effect of type of disconfirming information indicated that category-level information produced more belief and attitude change than did individual-level information. Also, a significant interaction between type of information at formation and disconfirmation indicated a relative matching effect, with category-level disconfirmation producing substantially more belief and attitude change than individual-level disconfirmation when initial beliefs were based on category-level information but only slightly greater change when initial beliefs were based on individual-level information. Keywords: attitudes; attitude change; belief change; persuasion; stereotype change

B Introduction: b Bruton tyrosine kinase inhibitors (BTKi) block B-cell receptor pathway signaling, leading to growth inhibition and cell death in malignant B-cells. Here, we characterized the overall safety/tolerability of zanu and compared it with ibr in patients (pts) with B-cell malignancies using the zanu clinical safety database. Zanubrutinib safety/tolerability profile and comparison with ibrutinib profile in B-cell malignancies: Post hoc analysis of a large clinical trial safety database. [Extracted from the article]