"case western reserve"

With the rapid development of smart manufacturing, data-driven deep learning (DL) methods are widely used for bearing fault diagnosis. Aiming at the problem of model training crashes when data are imbalanced and the difficulty of traditional signal analysis methods in effectively extracting fault features, this paper proposes an intelligent fault diagnosis method of rolling bearings based on Gramian Angular Difference Field (GADF) and Improved Dual Attention Residual Network (IDARN). The original vibration signals are encoded as 2D-GADF feature images for network input; the residual structures will incorporate dual attention mechanism to enhance the integration ability of the features, while the group normalization (GN) method is introduced to overcome the bias caused by data discrepancies; and then the model is trained to complete the classification of faults...

Traditional approaches to the intelligent fault diagnosis of rolling bearings have predominantly relied on manual expertise for feature extraction, a practice that compromises robustness. In addition, the existing convolutional neural network (CNN) is characterized by an overabundance of parameters and a substantial requirement for training samples. To address these limitations, this study introduces a novel fault diagnosis algorithm for rolling bearings, integrating a one-dimensional convolutional neural network (1DCNN) with a support vector machine (SVM) to form an enhanced 1DCNN-SVM model...

A rolling bearing fault diagnosis method based on Recursive Quantitative Analysis (RQA) combined with time domain feature extraction and Whale Optimization Algorithm Support Vector Machine (WOA-SVM) is proposed. Firstly, the recurrence graph of the vibration signal is drawn, and the nonlinear feature parameters in the recurrence graph combined with Standard Deviation (STD) are extracted by recursive quantitative analysis method to generate feature vectors; after that, in order to construct the optimal support vector machine model, the Whale Optimization Algorithm is used to optimize the c and g parameters...

BACKGROUND: The historical diversity gap in the neurosurgical workforce persists to this day. Women, despite constituting over half of the yearly total of medical school graduates, comprise only 6% of certified practicing neurosurgeons in the United States. Furthermore, Black Americans make up under 4% of U.S. neurosurgeons, despite making up around 14% of the national population. OBJECTIVE: The purpose of this account is to highlight the life and career of Dr. Maxine Deborrah Hyde and illustrate the importance and necessity of diversity and inclusivity in advancing the field of neurosurgery...

As an essential component of mechanical equipment, the fault diagnosis of rolling bearings may not only guarantee the systematic operation of the equipment, but also minimize any financial losses caused by equipment shutdowns. Fault diagnosis algorithms based on convolutional neural networks (CNN) have been widely used. However, traditional CNNs have limited feature representation capabilities, thereby making it challenging to determine their hyperparameters. This paper proposes a fault diagnosis method that combines a 1D-CNN with an attention mechanism and hyperparameter optimization to overcome the aforementioned limitations; this method improves the search speed for optimal hyperparameters of CNN models, improves the diagnostic accuracy, and enhances the representation of fault feature information in CNNs...

INTRODUCTION: Obesity is a multifactorial chronic disease and a major contributor to numerous health conditions. Despite the high prevalence, costs, and health effects of obesity, physicians are largely unprepared to treat it. Most medical students and residents lack sufficient training in obesity and obesity management. METHODS: We evaluated a two-part team-based learning seminar (TBL) on obesity pathogenesis and treatment for first-year medical students at Case Western Reserve University School of Medicine (CWRU SOM)...

Rolling element bearings (REBs) are an essential part of rotating machinery. A localised defect in a REB typically results in periodic impulses in vibration signals at bearing characteristic frequencies (BCFs), and these are widely used for bearing fault detection and diagnosis. One of the most powerful methods for BCF detection in noisy signals is envelope analysis. However, the selection of an effective band-pass filtering region presents significant challenges in moving towards automated bearing fault diagnosis due to the variable nature of the resonant frequencies present in bearing systems and rotating machinery...

Dr Charles L Christian arrived in New York City in 1953, having grown up in Wichita, Kansas, and graduating from medical school at Case Western Reserve in Cleveland, Ohio. In New York, Dr Christian embarked on training in internal medicine at Columbia's Presbyterian Hospital where he met an individual who would shape the course of his career, Dr Charles Ragan, a founder of the Arthritis Foundation. Dr Christian, or Chuck as he was usually called, went on to shape the developing field of rheumatology, advancing understanding of our most complex diseases as an investigator, master clinician, mentor, and academic leader...

To address the challenges associated with nonlinearity, non-stationarity, susceptibility to redundant noise interference, and the difficulty in extracting fault feature signals from rolling bearing signals, this study introduces a novel combined approach. The proposed method utilizes the variational mode decomposition (VMD) and K-singular value decomposition (K-SVD) algorithms to effectively denoise and enhance the collected rolling bearing signals. Initially, the VMD method is employed to separate the overall noise into intrinsic mode functions (IMFs), reducing the noise content within each IMF...

In cases where a client suffers from completely unlabeled data, unsupervised learning has difficulty achieving an accurate fault diagnosis. Semi-supervised federated learning with the ability for interaction between a labeled client and an unlabeled client has been developed to overcome this difficulty. However, the existing semi-supervised federated learning methods may lead to a negative transfer problem since they fail to filter out unreliable model information from the unlabeled client. Therefore, in this study, a dynamic semi-supervised federated learning fault diagnosis method with an attention mechanism (SSFL-ATT) is proposed to prevent the federation model from experiencing negative transfer...

This study sought to assess perceptions towards and reasons for participation in research bronchoscopy studies in a high TB burden urban setting. Additionally, the study aimed to identify areas of pre- and post-procedural concern among healthy adults approached to participate in research bronchoscopy. A cross sectional qualitative study was undertaken at the Uganda-Case Western Reserve University Collaboration Tuberculosis Research Project Clinic at Mulago National Referral Hospital in Kampala, Uganda. In-depth interviews were conducted with participants at their pre-bronchoscopy visit (n = 17) and after they had undergone bronchoscopy (n = 23) to examine their perceptions and experiences with the procedure...

BACKGROUND: Education about the prevalent chronic disease of obesity is still minimal and variable in medical school curricula. In a student-led effort with faculty support, the authors designed and implemented an obesity medicine elective at Case Western Reserve University School of Medicine (CWRU). The 10-week elective, taught by seven physicians and one dietitian, was offered in January 2023 to medical students and included: weekly lectures, an interactive session with a patient, shadowing in obesity medicine practices, attendance at a distance-learning intensive behavioral lifestyle program, student presentations, and a final written reflection...

The vibration signals of rolling bearings are complex and changeable, and extracting meaningful features is difficult. Currently, the commonly used empirical mode decomposition (EMD) algorithms have the problem of mode aliasing. In this paper, a new feature extraction method based on the improved complete ensemble empirical mode decomposition with adapted noise (ICEEMDAN) and permutation entropy is proposed. In this method, the ICEEMDAN algorithm is first improved and optimized to enable a self-selection function The vibration signal is then decomposed into several intrinsic modal functions using this algorithm, and the permutation entropy is extracted as the fault feature of rolling bearings, which improves the accuracy of fault classification and realizes the intelligent feature extraction of different fault states...

BACKGROUND: Case Western Reserve University (CWRU)/University Hospitals Cleveland Medical Center in Cleveland, OH, and the University of Pittsburgh (Pitt) in Pittsburgh, PA, forged a strategic alliance to form the Rustbelt Center for AIDS Research. The Rustbelt Center for AIDS Research developed a National Institutes of Health-supported diversity, equity, and inclusion pathway initiative termed the "Rustbelt Investigators for the Next Generation (RING) Program" that provides research training experiences for Puerto Rican students that will help them pursue a biomedical research career in HIV...

In bearing fault diagnosis, machine learning methods have been proven effective on the basis of the heterogeneous features extracted from multiple domains, including deep representation features. However, comparatively little research has been performed on fusing these multi-domain heterogeneous features while dealing with the interrelation and redundant problems to precisely discover the bearing faults. Thus, in the current study, a novel diagnostic method, namely the method of incorporating heterogeneous representative features into the random subspace, or IHF-RS, is proposed for accurate bearing fault diagnosis...

Federated learning (FL) is an effective method when a single client cannot provide enough samples for multiple condition fault diagnosis of bearings since it can combine the information provided by multiple clients. However, some of the client's working conditions are different; for example, different clients are in different stages of the whole life cycle, and different clients have different loads. At this point, the status of each client is not equal, and the traditional FL approach will lead to some clients' useful information being ignored...

Programmatic assessment is a systematic approach used to document and assess learner performance. It offers learners frequent formative feedback from a variety of contexts and uses both high- and low-stakes assessments to determine student progress. Existing research has explored learner and faculty perceptions of programmatic assessment, reporting favorable impact on faculty understanding of the importance of assessment stakes and feedback to learners while students report the ability to establish and navigate towards goals and reflect on their performance...

This study targets the low accuracy and efficiency of the support vector machine (SVM) algorithm in rolling bearing fault diagnosis. An improved grey wolf optimizer (IGWO) algorithm was proposed based on deep learning and a swarm intelligence optimization algorithm to optimize the structural parameters of SVM and improve the rolling bearing fault diagnosis. A nonlinear contraction factor update strategy was also proposed. The variable coefficient changes with the shrinkage factor α . Thus, the search ability was balanced at different early and late stages by controlling the dynamic changes of the variable coefficient...

The goal of this project was to assess the current state of racial and ethnic presentation in medical pedagogy using the pre-clerkship curriculum at Case Western Reserve University School of Medicine (CWRU SOM). We systematically reviewed 20,630 slides across the basic sciences curriculum from 2020 to 2022 for references to race, ethnicity, or photos of people of color. Results showed that race and ethnicity are overwhelmingly used as biological constructs and references lack appropriate historical context...

We propose a new fault diagnosis model for rolling bearings based on a hybrid kernel support vector machine (SVM) and Bayesian optimization (BO). The model uses discrete Fourier transform (DFT) to extract fifteen features from vibration signals in the time and frequency domains of four bearing failure forms, which addresses the issue of ambiguous fault identification caused by their nonlinearity and nonstationarity. The extracted feature vectors are then divided into training and test sets as SVM inputs for fault diagnosis...