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Nonlinear vibration analysis of cantilever beams: Analytical, numerical and experimental perspectives

Partial Differential Equations in Applied Mathematics 2025年 13卷

作者： Abbas, Qamar Nawaz, Rab Yaqoob, Haseeb Ali, Hafiz Muhammad Jamil, Muhammad Musaddiq Department of Mechanical Engineering King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Center for Applied Mathematics and Bioinformatics (CAMB) Gulf University for Science and Technology Hawally 32093 Kuwait Department of Mathematics COMSATS University Islamabad Park Road Islamabad Tarlai Kalan 45550 Pakistan Interdisciplinary Research Center for Sustainable Energy Systems (IRC-SES) King Fahd University of Petroleum & Minerals Dhahran 31261 Saudi Arabia Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg 91054 Germany

This study investigates the vibrational behavior of a mild steel cantilever beam with and without an attached end mass using experimental, numerical, analytical, and computational methods. Vibrational frequencies were determined using LABVIEW, MATLAB, and SOLIDWORKS, with analytical results derived from Euler-Bernoulli beam (EBB) theory. Experimental results closely matched MATLAB simulations, with an average percentage error of 1.44%, but showed a 14.44% deviation from analytical results due to neglected accelerometer mass. Findings highlight the importance of precise modeling, accounting for factors like damping and mass effects, to achieve accurate results. The study underscores the significance of resonant frequency identification in mitigating vibration failures in engineering systems. © 2025 The Author(s)

关键词： Analytical Cantilever beams Experimental Nonlinear vibration analysis Numerical

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Multimodal Doctor-in-the-Loop: A Clinically-Guided Explainable Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer

arXiv

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arXiv 2025年

作者： Caragliano, Alice Natalina Tacconi, Claudia Greco, Carlo Nibid, Lorenzo Ippolito, Edy Fiore, Michele Perrone, Giuseppe Ramella, Sara Soda, Paolo Guarrasi, Valerio Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Research Unit of Radiation Oncology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy Research Unit of Anatomical Pathology Department of Medicine and Surgery Università Campus Bio-Medico di Roma Rome Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

This study proposes a novel approach combining Multimodal Deep Learning with intrinsic eXplainable Artificial Intelligence techniques to predict pathological response in non-small cell lung cancer patients undergoing neoadjuvant therapy. Due to the limitations of existing radiomics and unimodal deep learning approaches, we introduce an intermediate fusion strategy that integrates imaging and clinical data, enabling efficient interaction between data modalities. The proposed Multimodal Doctor-in-the-Loop method further enhances clinical relevance by embedding clinicians’ domain knowledge directly into the training process, guiding the model’s focus gradually from broader lung regions to specific lesions. Results demonstrate improved predictive accuracy and explainability, providing insights into optimal data integration strategies for clinical applications. © 2025, CC BY-NC-ND.

关键词： Deep learning

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Increasing the Uptake of Breast and Cervical Cancer Screening Via the MAwar Application: Stakeholder-Driven Web Application Development Study

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JMIR Formative research 2025年 9卷 e65542页

作者： Nasrudin, Nurfarhana Sazlina, Shariff-Ghazali Cheong, Ai Theng Lee, Ping Yein Teo, Soo-Hwang Aneesa, Abdul Rashid Teo, Chin Hai Rokhani, Fakhrul Zaman Haron, Nuzul Azam Harrun, Noor Harzana Ho, Bee Kiau Isa, Salbiah Mohamed Department of Family Medicine Faculty of Medicine and Health Sciences Universiti Putra Malaysia Serdang Malaysia UM eHealth Unit Faculty of Medicine University of Malaya Kuala Lumpur Malaysia Cancer Research Malaysia Subang Jaya Malaysia Department of Primary Care Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Computer and Communication Systems Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia Department of Civil Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Malaysia Klinik Kesihatan Pandamaran Ministry of Health Malaysia Klang Malaysia Klinik Kesihatan Bandar Botanic Ministry of Health Malaysia Klang Malaysia

Background: Digital health interventions such as web health applications significantly enhance screening accessibility and uptake, particularly for individuals with low literacy and income levels. By involving stakeholders—including health care professionals, patients, and technical experts—an intervention can be tailored to effectively meet the users’ needs, ensuring contextual relevance for better acceptance and impact. Objective: The aim of this study is to prioritize the content and user interface appropriate for developing a web health application, known as the MAwar app, to promote breast and cervical cancer screening. Methods: A cross-sectional study for stakeholder engagement was conducted to develop a web-based application known as the MAwar app as part of a larger study entitled “The Effectiveness of an Interactive Web Application to Motivate and Raise Awareness on Early Detection of Breast and Cervical Cancers (The MAwar study)”. The stakeholder engagement process was conducted in a public health district that oversees 12 public primary care clinics with existing cervical and breast cancer screening programs. We purposively selected the stakeholders for their relevant roles in breast and cervical cancer screening (health care staff, patients, and public representatives), as well as expertise in software and user interface design (technology experts). The Quality Function Deployment method was used to reflect the priorities of diverse stakeholders (health care, technology experts, patients, and public representatives) in its design. The Quality Function Deployment method facilitated the translation of stakeholder perspectives into app features. Stakeholders rated features on a scale from 1 (least important) to 5 (most important), ensuring the app’s design resonated with user needs. The correlations between the “WHATs” (user requirements) and the “HOWs” (technical requirements) were scored using a 3-point ordinal scale, with 1 indicating weak correlation, 5

关键词： cancer screening Quality Function Deployment stakeholder engagement web health app

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Hybrid PET/MRI Analysis in the Juvenile Huntington Disease Evaluation

Hybrid PET/MRI Analysis in the Juvenile Huntington Disease E...

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Biomedical Applications, Technologies and Sensors (BATS), International Workshop on

作者： Maria Eugenia Caligiuri Patrizia Vizza Ferdinando Squitieri Giuseppe Lucio Cascini Umberto Sabatini Pierangelo Veltri Department of Surgical and Medical Sciences Magna Graecia University Catanzaro Italy Huntington and Rare Diseases Unit IRCCS Casa Sollievo della Sofferenza/CSS-Mendel San Giovanni Rotondo Italy Italian League for Research on Huntington (LIRH Foundation) Rome Italy Department of Experimental and Clinical Medicine Magna Graecia University Catanzaro Italy Department of Computer Science Modeling Electronics and Systems Engineering University of Calabria Rende Italy

Hybrid PET/MRI is an emerging approach to simultaneously investigate the connection of intrinsic metabolic with functional brain changes. In this work, we propose an image analysis workflow for longitudinal PET/MRI study in Juvenile-onset Huntington's Disease(JoHD) aiming to evaluate progressive disease-related volumetric, functional, and metabolic alterations. This examination/methodology could be very useful in the characterization of the rapidly worsening course of this degenerative pathology. Also, our main contribution lays in the multimodal hybrid PET/MRI data-fusion approach to identify features as potential markers of JoHD progression, and to support future trials in test drugs and treatments.

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Multiple Sclerosis Atlas: A Molecular Map of Brain Lesion Stages in Progressive Multiple Sclerosis

Network and Systems Medicine

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Network and systems Medicine 2020年第1期3卷 122-129页

作者： Frisch, Tobias Elkjaer, Maria L. Reynolds, Richard Michel, Tanja Maria Kacprowski, Tim Burton, Mark Kruse, Torben A. Thomassen, Mads Baumbach, Jan Illes, Zsolt Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Neurology Research Unit Department of Neurology Odense University Hopsital Odense Denmark Department of Clinical Research University of Southern Denmark Odense Denmark Division of Brain Science Imperial College London United Kingdom Department of Psychiatry University of Southern Denmark Odense Denmark Research Group Computational Systems Medicine Chair of Experimental Bioinformatics TUM School of Life Sciences Technical University of Munich Munich Germany Department of Clinical Genetics Odense University Hospital Odense Denmark Chair of Experimental Bioinformatics TUM School of Life Sciences Technical University of Munich Munich Germany

Introduction: Multiple sclerosis (MS) is a chronic disorder of the central nervous system with an untreatable late progressive phase. Molecular maps of different stages of brain lesion evolution in patients with progressive multiple sclerosis (PMS) are missing but critical for understanding disease development and to identify novel targets to halt progression. Materials and Methods: The MS Atlas database comprises comprehensive high-quality transcriptomic profiles of 98 white matter (WM) brain samples of different lesion types (normal-appearing WM [NAWM], active, chronic active, inactive, remyelinating) from ten progressive MS patients and 25 WM areas from five non-neurological diseased cases. Results: We introduce the first MS brain lesion atlas (***), developed to address the current challenges of understanding mechanisms driving the fate on a lesion basis. The MS Atlas gives means for testing research hypotheses, validating biomarkers and drug targets. It comes with a user-friendly web interface, and it fosters bioinformatic methods for de novo network enrichment to extract mechanistic markers for specific lesion types and pathway-based lesion type comparison. We describe examples of how the MS Atlas can be used to extract systems medicine signatures and demonstrate the interface of MS Atlas. Conclusion: This compendium of mechanistic PMS WM lesion profiles is an invaluable resource to fuel future MS research and a new basis for treatment development. © Tobias Frisch et al., 2020;Published by Mary Ann Liebert, Inc. 2020.

关键词： Medicine

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Pyramid Pixel Context Adaption Network for Medical Image Classification with Supervised Contrastive Learning

arXiv

引用

arXiv 2023年

作者： Zhang, Xiaoqing Xiao, Zunjie Wu, Xiao Chen, Yanlin Zhao, Jilu Hu, Yan Liu, Jiang Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Center for High Performance Computing Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen518055 China Singapore Eye Research Institute 169856 Singapore

Spatial attention mechanism has been widely incorporated into deep neural networks (DNNs), significantly lifting the performance in computer vision tasks via long-range dependency modeling. However, it may perform poorly in medical image analysis. Unfortunately, existing efforts are often unaware that long-range dependency modeling has limitations in highlighting subtle lesion regions. To overcome this limitation, we propose a practical yet lightweight architectural unit, Pyramid Pixel Context Adaption (PPCA) module, which exploits multiscale pixel context information to recalibrate pixel position in a pixel-independent manner dynamically. PPCA first applies a well-designed cross-channel pyramid pooling to aggregate multiscale pixel context information, then eliminates the inconsistency among them by the well-designed pixel normalization, and finally estimates per pixel attention weight via a pixel context integration. By embedding PPCA into a DNN with negligible overhead, the PPCANet is developed for medical image classification. In addition, we introduce supervised contrastive learning to enhance feature representation by exploiting the potential of label information via supervised contrastive loss. The extensive experiments on six medical image datasets show that PPCANet outperforms state-of-the-art attention-based networks and recent deep neural networks. We also provide visual analysis and ablation study to explain the behavior of PPCANet in the decision-making process. © 2023, CC BY.

关键词： Pixels

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DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

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Intelligence-Based Medicine 2025年 11卷 100192-100192页

作者： Amin, Mohammad Nahar, Khalid M.O. Gharaibeh, Hasan Nasayreh, Ahmad Alsalmanc, Neda'a Alomar, Alaa Malkawi, Majd Alqasem, Noor Smerat, Aseel Zitar, Raed Abu Nusier, Shawd Ezugwu, Absalom E. Abualigah, Laith Department of Information Technology and Computer Sciences Yarmouk University Irbid 211633 Jordan Faculty of Computer Studies Arab Open University P.O.Box 84901 Riyadh 11681 Saudi Arabia Department of Computer Science Faculty of Information Technology Jordan University of Science and Technology Irbid 22110 Jordan Department of Medicine Faculty of Medicine Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Systems and Informatics Engineering Hijjawi Faculty for Engineering Technology Yarmouk University Irbid 21163 Jordan Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai 602105 India Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk Tabuk 71491 Saudi Arabia Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates Department of Health and Clinical Outcomes Research Saint Louis University St. Louis MO United States Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom 2520 South Africa Computer Science Department Al al-Bayt University Mafraq 25113 Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman 19328 Jordan

Early and accurate diagnosis of brain tumors is crucial to improving patient outcomes and optimizing treatment strategies. Long-term brain injury results from aberrant proliferation of either malignant or nonmalignant tissues in the brain. MRIs, or magnetic resonance imaging, are one of the most used approaches for detecting brain tumors. Professionals physically evaluate people after they have had MRI filtering, the process of enhancing MRI scans for radiologist interpretation, to establish if they have a brain tumor. Because different specialists use different frames to make judgments on the same MRI image, their analyses may yield contradictory results. Furthermore, simply detecting a tumor is insufficient. Inconsistent diagnoses can lead to delays in treatment, impacting survival rates and quality of care. It is also crucial to diagnose the patient's tumor so that treatment can begin as soon as possible. In this research, we investigate the multi-class classification of brain tumors utilizing a cutting-edge methodology that includes feature extraction from pictures using the DieT Transformer model, dimensionality reduction with PCA, and feature selection using the ADE algorithm. The proposed model, known in the publication as ADE_DieT, obtained an accuracy of 96.09 %. In addition, this article analyzes the performance of various pre-trained models, including MobileNetV3, NasNet, ResNet50, VGG16, VGG19, and DeiT. The proposed approach shortens the time required for manual diagnosis by clinicians by assisting in the rapid and accurate identification of brain tumors using MRI data. In oncology, this is important since it allows for early treatment. Integrating ADE_DieT into clinical workflows can support radiologists by reducing diagnosis time and enhancing diagnostic consistency. © 2024 The Authors

关键词： Brain tumor Medical image analysis MRI Vision Transformer

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RadioPathomics: Multimodal Learning in Non-Small Cell Lung Cancer for Adaptive Radiotherapy

arXiv

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arXiv 2022年

作者： Tortora, Matteo Cordelli, Ermanno Sicilia, Rosa Nibid, Lorenzo Ippolito, Edy Perrone, Giuseppe Ramella, Sara Soda, Paolo Unit of Computer Systems & Bioinformatics Department of Engineering University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Anatomical Pathology Department of Medicine University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy Radiation Oncology Department of Medicine University Campus Bio-Medico of Rome Via Alvaro del Portillo 21 Rome00128 Italy

The current cancer treatment practice collects multimodal data, such as radiology images, histopathology slides, genomics and clinical data. The importance of these data sources taken individually has fostered the recent raise of radiomics and pathomics, i.e. the extraction of quantitative features from radiology and histopathology images routinely collected to predict clinical outcomes or to guide clinical decisions using artificial intelligence algorithms. Nevertheless, how to combine them into a single multimodal framework is still an open issue. In this work we therefore develop a multimodal late fusion approach that combines hand-crafted features computed from radiomics, pathomics and clinical data to predict radiation therapy treatment outcomes for non-small-cell lung cancer patients. Within this context, we investigate eight different late fusion rules (i.e. product, maximum, minimum, mean, decision template, Dempster-Shafer, majority voting, and confidence rule) and two patient-wise aggregation rules leveraging the richness of information given by computer tomography images and whole-slide scans. The experiments in leave-one-patient-out cross-validation on an in-house cohort of 33 patients show that the proposed multimodal paradigm with an AUC equal to 90.9% outperforms each unimodal approach, suggesting that data integration can advance precision medicine. As a further contribution, we also compare the handcrafted representations with features automatically computed by deep networks, and the late fusion paradigm with early fusion, another popular multimodal approach. In both cases, the experiments show that the proposed multimodal approach provides the best results. © 2022, CC BY-NC-ND.

关键词： Data integration

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Matching Using Sufficient Dimension Reduction for Heterogeneity Causal Effect Estimation 24

Matching Using Sufficient Dimension Reduction for Heterogene...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Haoran Zhang, Yinghao Cheng, Debo Li, Chen Feng, Zaiwen College of Science Huazhong Agricultural University Wuhan430070 China College of Computer Science and Engineering Guangxi Normal University Guilin541000 China University of South Australia Stem Australia Kyushu Institute of Technology Department of Computer Science and Systems Engineering Iizuka820-8502 Japan College of Informatics Huazhong Agricultural University Wuhan430070 China Huazhong Agricultural University Hubei Key Laboratory of Agricultural Bioinformatics Wuhan430070 China Huazhong Agricultural University Hubei Hongshan Laboratory Wuhan430070 China Huazhong Agricultural University National Key Laboratory of Crop Genetic Improvement Wuhan430070 China Wuhan University State Key Laboratory of Hybrid Rice 299 Bayi Rd Wuhan430070 China Macro Agricultural Research Institute Huazhong Agricultural University Wuhan430070 China

ISBN: (纸本)9798350319934

Causal inference plays an important role in understanding the underlying mechanisation of the data generation process across various domains. It is challenging to estimate the average causal effect and individual causal effects from observational data with high-dimensional covariates due to the curse of dimension and the problem of data sufficiency. The existing matching methods can not effectively estimate individual causal effect or solve the problem of dimension curse in causal inference. To address this challenge, in this work, we prove that the reduced set by sufficient dimension reduction (SDR) is a balance score for confounding adjustment. Under the theorem, we propose to use an SDR method to obtain a reduced representation set of the original covariates and then the reduced set is used for the matching method. In detail, a non-parametric model is used to learn such a reduced set and to avoid model specification errors. The experimental results on real-world datasets show that the proposed method outperforms the compared matching methods. Moreover, we conduct an experiment analysis and the results demonstrate that the reduced representation is enough to balance the imbalance between the treatment group and control group individuals. © 2022 IEEE.

关键词： Causal Inference Individual Causal Effect Matching Sufficient Dimension Reduction

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Toward Enhanced Clinical Decision Support for Patients Undergoing a Hip or Knee Replacement: Focus Group and Interview Study With Surgeons

JMIR Perioperative Medicine

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JMIR Perioperative Medicine 2023年第1期6卷 e36172页

作者： Grant, Sabrina Tonkin, Emma Craddock, Ian Blom, Ashley Holmes, Michael Judge, Andrew Masullo, Alessandro Nieto, Miquel Perello Song, Hao Whitehouse, Michael Flach, Peter Gooberman-Hill, Rachael Musculoskeletal Research Unit University of Bristol Southmead Hospital Bristol Medical School Bristol United Kingdom Digital Health Faculty of Engineering Bristol United Kingdom Faculty of Medicine Dentistry and Health University of Sheffield Sheffield United Kingdom Intelligent Systems Laboratory Department of Computer Science University of Bristol Bristol United Kingdom

Background: The current assessment of recovery after total hip or knee replacement is largely based on the measurement of health outcomes through self-report and clinical observations at follow-up appointments in clinical settings. Home activity-based monitoring may improve assessment of recovery by enabling the collection of more holistic information on a continuous basis. Objective: This study aimed to introduce orthopedic surgeons to time-series analyses of patient activity data generated from a platform of sensors deployed in the homes of patients who have undergone primary total hip or knee replacement and understand the potential role of these data in postoperative clinical decision-making. Methods: Orthopedic surgeons and registrars were recruited through a combination of convenience and snowball sampling. Inclusion criteria were a minimum required experience in total joint replacement surgery specific to the hip or knee or familiarity with postoperative recovery assessment. Exclusion criteria included a lack of specific experience in the field. Of the 9 approached participants, 6 (67%) orthopedic surgeons and 3 (33%) registrars took part in either 1 of 3 focus groups or 1 of 2 interviews. Data were collected using an action-based approach in which stimulus materials (mock data visualizations) provided imaginative and creative interactions with the data. The data were analyzed using a thematic analysis approach. Results: Each data visualization was presented sequentially followed by a discussion of key illustrative commentary from participants, ending with a summary of key themes emerging across the focus group and interview data set. Conclusions: The limitations of the evidence are as follows. The data presented are from 1 English hospital. However, all data reflect the views of surgeons following standard national approaches and training. Although convenience sampling was used, participants’ background, skills, and experience were considered heterogeneous.

关键词： arthroplasty clinical decision-making hip replacement home monitoring knee replacement orthopedic surgery postoperative follow-up video wearables

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