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An approximation method to solve Atangana-Baleanu FPDEs

AIP Conference Proceedings 2024年第1期3219卷

作者： Rasool Shah Hassan K. Jassim Hijaz Ahmad Muslim Yusif Zayir Safaa H. Mahdi Ali T. Salman Ali K. Jassim Sahib A. Sachit Hussein G. Taher Hossein A. Eaued Mohammed A. Hussein Tarig M. Elzaki Mohamed R. Ali Kadhim A. Jabbar Saad A. Khafif Department of Mathematics Abdul Wali Khan University Mardan Mardan - Pakistan Department of Mathematics University of Thi-Qar Thi-Qar-Iraq Near East University Operational Research Center in Healthcare Nicosia-Turkey Department of Computer Science and Mathematics Lebanese American University Beirut-Lebanon Section of Mathematics International Telematic University Uninettuno Roma-Italy Educational Directorate of Thi-Qar Nasiriyah-Iraq Scientific Research Center AL-Ayen University Nassreya-Iraq Mathematics Department Faculty of Sciences and Arts University of Jeddah Jeddah-Saudi Arabia Department of Mathematics Benha Faculty of Engineering Benha University Benha-Egypt College of Technical Engineering National University of Science and Technology Thi-Qar-Iraq Educational Directorate of Diwaniyah Diwaniyah - Iraq

In this study, the homotopy perturbation approach (HPM) combined with the natural transform (NT) is used to obtain analytical solutions to fractional differential equations (FDEs) involving the Atangana-Baleanu deriva...

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Information Needs for Opioid Use Disorder Treatment Using Buprenorphine Product: Qualitative Analysis of Suboxone-Focused Reddit Data

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Journal of medical Internet research 2025年 27卷 e68886页

作者： Madhusudan Basak Omar Sharif Sarah E Lord Jacob T Borodovsky Lisa A Marsch Sandra A Springer Edward V Nunes Charles D Brackett Luke J Archibald Sarah M Preum Department of Computer Science Dartmouth College Hanover NH United States. Department of Computer Science and Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh. Center for Technology and Behavioral Health Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Biomedical Data Science Geisel School of Medicine Dartmouth College Lebanon NH United States. Department of Psychiatry Dartmouth Health Lebanon NH United States. Department of Psychiatry Geisel School of Medicine Dartmouth College Lebanon NH United States. Section of Infectious Disease Department of Internal Medicine Yale School of Medicine New Haven CT United States. Center for Interdisciplinary Research on AIDS Yale University School of Public Health New Haven CT United States. Section of Infectious Disease Department of Internal Medicine Veterans Administration Connecticut Healthcare System West Haven CT United States. Department of Psychiatry Division on Substance Use Columbia University Irving Medical Center and New York State Psychiatric Institute New York NH United States. Department of Medicine Geisel School of Medicine Dartmouth College Hanover NH United States. Department of Internal Medicine Dartmouth Hitchcock Medical Center Lebanon NH United States.

BACKGROUND:Buprenorphine is a Food and Drug Administration-approved medication for opioid use disorder. However, individuals with opioid use disorder often report information needs regarding buprenorphine treatment on social media platforms such as Reddit. The field lacks a systematic approach to organizing these data and characterizing treatment information needs that may be unique and unavailable elsewhere. OBJECTIVE:In this study, we curated and analyzed large-scale data from social media to characterize self-reported buprenorphine treatment information needs using a qualitative analysis. METHODS:We collected 15,253 Reddit posts from the subreddit r/Suboxone. Following a standard protocol and guidance from clinical experts, we first identified 5 main themes from the data and then manually coded 6000 posts based on these themes. Finally, we determined the most frequently appearing topics within each theme by analyzing samples from each group. RESULTS:Among the 6000 posts, 2417 (40.3%) contained a single theme, 2160 (36%) contained two themes, and 834 (13.9%) contained three themes. The most frequent topics found across these themes included reports of psychological and physical effects during recovery, complexities in accessing buprenorphine, and significant information gaps regarding medication administration, tapering, and substance use at different stages of recovery. Moreover, self-treatment strategies and peer-driven advice revealed potential rumors and misinformation. CONCLUSIONS:Our findings can guide the design of interventions to improve patient education and communication. They also help address knowledge gaps and misinformation related to treatment. Additionally, the results can support hypothesis generation for future clinical research on medication for opioid use disorder treatment.

关键词： MOUD Reddit access buprenorphine medication for opioid use disorder opioid use disorder patient experience social media substance use tapering treatment information needs

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Realizing flexible bioelectronic medicines for accessing the peripheral nerves – technology considerations

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Bioelectronic Medicine 2018年第1期4卷 1页

作者： Giagka, Vasiliki Serdijn, Wouter A. Section Bioelectronics Department of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Technologies for Bioelectronics Group Department of System Integration and Interconnection Technologies Fraunhofer Institute for Reliability and Microintegration IZM Berlin Germany

Patients suffering from conditions such as paralysis, diabetes or rheumatoid arthritis could in the future be treated in a personalised manner using bioelectronic medicines (BEms) (Nat Rev Drug Discov 13:399–400, 2013, Proc Natl Acad Sci USA 113:8284–9, 2016, J Intern Med 282:37–45, 2017). To deliver this personalised therapy based on electricity, BEms need to target various sites in the human body and operate in a closed-loop manner. The specific conditions and anatomy of the targeted sites pose unique challenges in the development of BEms. With a focus on BEms based on flexible substrates for accessing small peripheral nerves, this paper discusses several system-level technology considerations related to the development of such devices. The focus is mainly on miniaturisation and long-term operation. We present an overview of common substrate and electrode materials, related processing methods, and discuss assembly, miniaturisation and long-term stability issues. © 2018, The Author(s).

关键词： Electrodes

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Performance Comparison of Several Feature Selection Techniques for Offline Handwritten Character Recognition 3

Performance Comparison of Several Feature Selection Techniqu...

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3rd IEEE International Conference on Research in Intelligent and Computing in engineering, RICE 2018

作者： Kumar, Munish Jindal, M.K. Sharma, R.K. Ranijindal, Simpel Department of Computational Sciences Maharaja Ranjit Singh Punjab Technical University Bathinda Punjab India Department of Computer Science and Applications Panjab University Regional Centre Muktsar Punjab India Department of Computer Science Engineering Thapar University Patiala Punjab India Computer Science Engineering Section Yadavindera College of Engineering Talwandi Sabo Bathinda Punjab India

ISBN: (纸本)9781538625996

This paper presents a performance comparison of various feature selection techniques for offline handwritten Gurmukhi character recognition. Research on offline handwritten character recognition of Gurmukhi script is very difficult due to the complex structural properties of the script that are not matter-of-fact in most other scripts. Gurmukhi is the script used for writing the Punjabi language, which is the official language of Punjab state in India. We have Present. a feature extraction technique for offline handwritten Gurmukhi character recognition based on the boundary extent of the character image and used various feature selection techniques, to reduce the dimensionality of feature vectors. We have also compared their recognition performances using two different classifiers, namely, Nearest Neighbours (NN) and Support Vector Machine (SVM) with linear kernel. Different classification schemes measures are used for the performance analysis of different feature selection techniques. Results obtained using Present. feature extraction technique show that Chi Squared Attribute (CSA) feature selection technique performs better than other feature selection techniques using NN and SVM with linear kernel classifier for character recognition. In this work, we have obtained zone wise maximum recognition accuracy of 88.3%, 95.2% and 91.3% for upper zone, middle zone and lower zone of Gurmukhi script, respectively. © 2018 IEEE.

关键词： Support vector machines

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HSV Color Space Based Robot Grasping for Personalized Manufacturing Services

HSV Color Space Based Robot Grasping for Personalized Manufa...

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International Conference on Information and Communication Technology Convergence (ICTC)

作者： Hyunchul Kang Hyonyoung Han Heechul Bae Eunseo Lee Mingi Kim Jiyeon Son Hyun Kim Young-Kuk Kim ICT-enabled Intelligent Manufacturing Research Section Electronics and Telecommunications Research Institute Daejeon Korea Dept. of Computer Science & Engineering Chungnam National University Daejeon Korea

Recently, with the development of artificial intelligence technology, robots have been utilized not only in manufacturing but also in many industrial fields. The needs of customers have been diversified and the trend of future manufacturing has been changing from the conventional mass production to the multiproduct small volume production system. In a smart factory environment for custom manufacturing, the robot must be manually programmed for each work instruction every time, especially when a production product is changed. Robot programming takes a lot of time and effort of people. In this paper, we propose a method to automatically measure the product information(color, size) automatically which are 3D printed out based on HSV (hue, saturation, value) Color Space and to control the auto grasping of robots. Also, we applied this control system to the FaaS(Factory As a Service) testbed for functional verification.

关键词： Grasping FAA Image color analysis Manufacturing Service robots Three-dimensional displays

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Intelligent Backpropagated Neural Networks for Numerical Computations for MHD Squeezing Fluid Suspended by Nanoparticles between Two Parallel Plates

Research Square

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Research Square 2021年

作者： Shoaib, Muhammad Zahoor Raja, Muhammad Asif Farhat, Imrana Shah, Zahir Kumam, Poom Department of Mathematics COMSATS University Islamabad Attock Campus Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section .3 Douliou Yunlin64002 Taiwan Department of Electrical and Computer Engineering COMSATS University Islamabad Attock Campus Pakistan Department of Mathematics University of Lakki Marwat Lakki Marwat Khyber Pakhtunkhwa 28420 Pakistan 126 Pracha Uthit Rd. Bang Mod Thung Khru Bangkok10140 Thailand 126 Pracha Uthit Rd. Bang Mod Thung Khru Bangkok10140 Thailand

In the present research, artificial intelligence based backpropagated neural networks with Levenberg-Marquardt algorithm (BNN-LMA) are utilized to interpret the numerical computation for squeezing 2D magneto-hydrodynamic (MHD) nanofluid flow between two parallel plates. The non-linear system of ODEs represents the magneto-hydrodynamic, squeezing nanofluidic flow model (MHD-SNFM). A reference dataset for BNN-LMA is formulated by utilizing Adam numerical solver for different scenarios of MHD-SNFM by variation of squeezing number, Hartmann Number and heat source parameter. The validation, testing and training processes of BNN-LMA are exploited to analyze the approximate solution of MHD-SNFM for different scenarios and correctness of proposed BNN-LMA is verified by comparison of reference outcomes. The performance of BNN-LMA to solve the MHD-SNFM is validated through regression analysis, histogram studies and mean square error (MSE). © 2021, CC BY.

关键词： Regression analysis

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Investigating and Exploiting Image Resolution for Transfer Learning-based Skin Lesion Classification

arXiv

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arXiv 2020年

作者： Mahbod, Amirreza Ecker, Rupert Schaefer, Gerald Dorffner, Georg Wang, Chunliang Ellinger, Isabella Institute for Pathophysiology and Allergy Research Medical University of Vienna Vienna Austria Research and Development Department TissueGnostics GmbH Vienna Austria Department of Computer Science Loughborough University Loughborough United Kingdom Section for Artificial Intelligence Decision Support Medical University of Vienna Vienna Austria Department of Biomedical Engineering and Health Systems KTH Royal Institute of Technology Stockholm Sweden

Skin cancer is among the most common cancer types. Dermoscopic image analysis improves the diagnostic accuracy for detection of malignant melanoma and other pigmented skin lesions when compared to unaided visual inspection. Hence, computer-based methods to support medical experts in the diagnostic procedure are of great interest. Fine-tuning pre-trained convolutional neural networks (CNNs) has been shown to work well for skin lesion classification. Pre-trained CNNs are usually trained with natural images of a fixed image size which is typically significantly smaller than captured skin lesion images and consequently dermoscopic images are downsampled for fine-tuning. However, useful medical information may be lost during this transformation. In this paper, we explore the effect of input image size on skin lesion classification performance of fine-tuned CNNs. For this, we resize dermoscopic images to different resolutions, ranging from 64 × 64 to 768 × 768 pixels and investigate the resulting classification performance of three well-established CNNs, namely DenseNet-121, ResNet-18, and ResNet-50. Our results show that using very small images (of size 64×64 pixels) degrades the classification performance, while images of size 128×128 pixels and above support good performance with larger image sizes leading to slightly improved classification. We further propose a novel fusion approach based on a three-level ensemble strategy that exploits multiple fine-tuned networks trained with dermoscopic images at various sizes. When applied on the ISIC 2017 skin lesion classification challenge, our fusion approach yields an area under the receiver operating characteristic curve of 89.2% and 96.6% for melanoma classification and seborrheic keratosis classification, respectively, outperforming state-of-the-art algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Dermatology

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Computationally Assessing Diamond as an Ultrafast Pulse Shaper for High Power Ultrawide Band Radar

arXiv

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arXiv 2020年

作者： Herrmann, Christopher S. Croman, Joseph Baryshev, Sergey V. Department of Electrical and Computer Engineering Michigan State University 428 S. Shaw Ln. East LansingMI48824 United States High Power Microwave Section Code 5745 Naval Research Laboratory WashingtonDC20375 United States Department of Chemical Engineering and Materials Science Michigan State University 428 S. Shaw Ln. East LansingMI48824 United States

Diamond holds promise to reshape ultrafast and high power electronics. One such solid-state device is the diode avalanche shaper (DAS), which functions as an ultrafast closing switch where closing is caused by the formation of the streamer traversing the diode much faster than 107 cm/s. One of the most prominent applications of DAS is in ultrawide band (UWB) radio/radar. Here we simulate a diamond-based DAS and compare the results to a silicon-based DAS. All DAS were simulated in mixed mode as ideal devices using the drift-diffusion model. The simulations show that diamond DAS promises to outperform Si DAS when sharpening kilovolt nanosecond input pulse. The breakdown field and streamer velocity (∼10 times larger in diamond as compared to those in Si) are likely to be the major reasons enabling kV sub-50 ps switching using diamond DAS. © 2020, CC BY-NC-SA.

关键词： Diamonds

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A feedforward artificial neural network model for classification and detection of type 2 diabetes

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Journal of Physics: Conference Series 2021年第1期1734卷

作者： Enoch A. Frimpong Ariyo Oluwasanmi Edward Y. Baagyere Qin Zhiguang School of Information and Software Engineering. University of Electronic Science and Technology of China Chengdu China No.4 Section 2 North Jianshe Road 610054 Chengdu P. R. China Department of Computer Science University for Development Studies Box TL 1350 Tamale Ghana

Efforts to enhance accuracy in medical diagnostics in molecular medicine have contributed to the wide use of artificial neural network (ANN) algorithms for disease detection due to its ability to process large medical datasets and integrate them into characterized outputs to avoid misdiagnosis. Typically, the application of ANNs have proven useful in sample analyses of patients with diabetes and in decision support systems. Over the years, various ANN models have been utilized in medical diagnostics; however, these approaches still maintain certain levels of error and have lesser training and testing accuracies in disease detection. In this study, we propose a Feedforward Artificial Neural Network (FFANN) model with a dense neural network architecture suitable for processing numeric and textual dataset. We carefully designed our model structure to have the ability to maximize the number of layers and nodes required to learn every feature of the dataset and also to perform effective computations but avoiding model under fitting and overfitting which occurs when less or more layers are used respectively. This approach puts our model ahead of other state-of-the-art prediction models which have been proposed in terms of performance as it achieved 97.27% and 96.09% training and testing accuracies, respectively, for type 2 diabetes detection on Pima Indian Diabetes dataset.

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KLF2 maintains lineage fidelity and suppresses CD8 T cell exhaustion during acute LCMV infection

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science 2025年第6735期387卷 eadn2337页

作者： Fagerberg, Eric Attanasio, John Dien, Christine Singh, Jaiveer Kessler, Emily A. Abdullah, Leena Shen, Jian Hunt, Brian G. Connolly, Kelli A. De Brouwer, Edward He, Jiaming Iyer, Nivedita R. Buck, Jessica Borr, Emily R. Damo, Martina Foster, Gena G. Giles, Josephine R. Huang, Yina H. Tsang, John S. Krishnaswamy, Smita Cui, Weiguo Joshi, Nikhil S. Department of Immunobiology Yale University School of Medicine New Haven CT United States Program in Computational Biology & Bioinformatics Yale University New Haven CT United States Department of Microbiology and Immunology Geisel School of Medicine at Dartmouth NH United States Department of Pathology Feinberg School of Medicine at Northwestern University Chicago IL United States Department of Genetics and Computer Science Yale University School of Medicine New Haven CT United States Department of Medicine University of Chicago Chicago IL United States Section of Hematology Department of Internal Medicine Yale University School of Medicine New Haven CT United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania Philadelphia PA United States Institute for Immunology Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Parker Institute for Cancer Immunotherapy Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Yale Center for Systems and Engineering Immunology Yale University School of Medicine New Haven CT United States New Haven CT United States Department of Biomedical Engineering Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University CT USA New Haven United States Applied Math Program Yale University New Haven CT United States

Naïve CD8 T cells have the potential to differentiate into a spectrum of functional states during an immune response. How these developmental decisions are made and what mechanisms exist to suppress differentiation toward alternative fates remains unclear. We employed in vivo CRISPR-Cas9–based perturbation sequencing to assess the role of ~40 transcription factors (TFs) and epigenetic modulators in T cell fate decisions. Unexpectedly, we found that knockout of the TF Klf2 resulted in aberrant differentiation to exhausted-like CD8 T cells during acute infection. KLF2 was required to suppress the exhaustion-promoting TF TOX and to enable the TF TBET to drive effector differentiation. KLF2 was also necessary to maintain a polyfunctional tumor-specific progenitor state. Thus, KLF2 provides effector CD8 T cell lineage fidelity and suppresses the exhaustion program. © 2025 American Association for the Advancement of science. All rights reserved.

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