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Reconstructing Suspended Sediment Concentrations in the Mekong River Basin Via Semi-Supervised-Based Deep Neural Networks

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SSRN

SSRN 2024年

作者： Nguyen, Thi-Thu-Ha Vu, Duc Quang Doan, Ngoc Phu Thi Khanh Chi, Huynh Li, Peixin Van Binh, Doan An, Yimeng Tuan Dung, Pham Hoang, Tuan A. Thai Son, Mai Laboratory of Environmental Sciences and Climate Change Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City70000 Viet Nam Faculty of Environment School of Technology Van Lang University Ho Chi Minh City70000 Viet Nam Department of Computer Science and Information System Thai Nguyen University of Education Viet Nam School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast United Kingdom College of Economics and Management Nanjing University of Aeronautics and Astronautics China Master program in Water Technology Reuse and Management Faculty of Engineering Vietnamese-German University Binh Duong Province Ben Cat Town820000 Viet Nam

The Mekong River Basin (MRB) is one of the highest biodiversity areas in the world and is the vital source of livelihoods for more than 60 millions of people over six countries in Southeast Asia. However, it is suffering from a severe sediment starvation problem caused by climate change and human activities, particularly dam constructions. Understanding long-term sediment changes thus plays a crucial role for management and contingency plans. Current sediment concentration data in the MRB are extremely sporadic, making it a non-trivial task to analyze them. This paper focuses on the sediment reconstruction to provide long-term reliable suspended sediment concentration (SSC) data using Machine Learning (ML) perspectives. In particular, a novel semi-supervised ML model, which can effectively deal with data sparsity, is introduced to predict/reconstruct missing sediment data. The key idea of this approach is to exploit abundant available hydroclimate data to reduce training overfitting rather than solely relying on sediment concentration data, thus enhancing the accuracy of the employed ML models. Extensive experiments on data obtained from 1979 to 2019 at the three main stations (i.e., Chiang Saen, Nong Khai, and Mukdahan) are conducted to demonstrate the performances of the proposed method compared to other state-of-the-art supervised techniques and traditional simulation methods commonly used in previous sediment forecasting/reconstruction studies. To the best of our knowledge, this approach is the first semi-supervised method to reconstruct sediment data in the field. Though this study focuses on the MRB, sediment sparsity is a very common problem in other river systems worldwide. Thus, this method can be widely applied for them for more effective sediment reconstruction/prediction in general. © 2024, The Authors. All rights reserved.

关键词： Biodiversity

Structural, optical, and thermal properties of BN thin films grown on diamond via pulsed laser deposition

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arXiv 2023年

作者： Biswas, Abhijit Alvarez, Gustavo A. Li, Tao Christiansen-Salameh, Joyce Jeong, Eugene Puthirath, Anand B. Iyengar, Sathvik Ajay Li, Chenxi Gray, Tia Zhang, Xiang Pieshkov, Tymofii S. Kannan, Harikishan Elkins, Jacob Vajtai, Robert Birdwell, A. Glen Neupane, Mahesh R. Garratt, Elias J. Pate, Bradford B. Ivanov, Tony G. Zhao, Yuji Tian, Zhiting Ajayan, Pulickel M. Department of Materials Science and Nanoengineering Rice University HoustonTX77005 United States Sibley School of Mechanical and Aerospace Engineering Cornell University IthacaNY14853 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States DEVCOM Army Research Laboratory RF Devices and Circuits AdelphiMD20783 United States Naval Research Laboratory Chemistry Division WashingtonDC20375 United States

Heterostructures based on ultrawide-bandgap (UWBG) semiconductors (bandgap >4.0 eV), such as boron nitride (BN) and diamond, hold significant importance for the development of high-power electronics in the next generation. However, achieving in-situ hetero-epitaxy of BN/diamond or vice versa remains exceptionally challenging due to the complex growth kinetics involved. In this study, we have grown BN thin film on (100) single crystal diamonds using pulsed laser deposition and investigated its structural, magnetic, optical, and thermal properties. The structural analyses confirmed the growth of BN films, which exhibited diamagnetic behavior at room temperature. Notably, the film demonstrated anisotropic refractive index characteristics within the visible-to-near-infrared wavelength range. The room temperature cross-plane thermal conductivity of BN is 1.53 (± 0.77) W/mK, while the thermal conductance of the BN/diamond interface is 20 (± 2) MW/m2K. These findings have significant implications for a range of device applications based on UWBG BN/diamond heterostructures. © 2023, CC BY.

关键词： Diamonds

AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data Science Training Platform to Increase Researcher Diversity and Advance Health Equity

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AIM-AHEAD Connect: Online Collaboration, Mentoring, and Data...

IEEE International Conference on Healthcare Informatics (ICHI)

作者： Toufeeq Ahmed Aidan Hoyal Katie Stinson Jay Johnson Zainab Latif Legand L. Burge Alexander Libin Guodong Gordon Gao Nawar Shara Jamboor K. Vishwanatha Department of Biomedical Informatics Vanderbilt University Medical Center Nashville United States Department of Electrical Engineering and Computer Science Howard University Washington D.C. United States MedStar Health Research Institute Georgetown University Washington D.C. United States Carey Business School Johns Hopkins University Baltimore United States Institute for Health Disparities University of North Texas Health Science Center at Fort Worth Fort Worth United States

AIM-AHEAD is an NIH-funded consortium whose goal is to advance health equity and researcher diversity via artificial intelligence and machine learning. In our first year of operations, we launched AIM-AHEAD Connect, a centralized platform for the consortium stakeholders containing mentoring, networking, and professional development resources. This paper describes the resources available in AIM-AHEAD Connect as well as collaborative efforts to provide tools and training for the entire consortium.

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Ensemble of Convolutional Neural Networks to diagnose Acute Lymphoblastic Leukemia from microscopic images

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Informatics in Medicine Unlocked 2021年 27卷

作者： Mondal, Chayan Hasan, Md. Kamrul Ahmad, Mohiuddin Awal, Md. Abdul Jawad, Md. Tasnim Dutta, Aishwariya Islam, Md. Rabiul Moni, Mohammad Ali Department of Electrical and Electronic Engineering (EEE) Khulna University of Engineering & Technology Khulna 9203 Bangladesh Department of Biomedical Engineering (BME) Khulna University of Engineering & Technology Khulna 9203 Bangladesh Electronics and Communication Engineering (ECE) Discipline Khulna University Khulna 9208 Bangladesh Department of Electrical and Electronic Engineering (EEE) Bangabandhu Sheikh Mujibur Rahman Science & Technology University Gopalganj 8100 Bangladesh Department of Computer Science Engineering (CSE) Pabna University of Science & Technology Pabna 6600 Bangladesh

Acute Lymphoblastic Leukemia (ALL) is a blood cell cancer characterized by the presence of excess immature lymphocytes., Even though automation in ALL prognosis is essential for cancer diagnosis, it remains a challenge due to the morphological correlation between malignant and normal cells. The traditional ALL classification strategy demands that experienced pathologists read cell images carefully, which is arduous, time-consuming, and often hampered by interobserver variation. This article has automated the ALL recognition task by employing deep Convolutional Neural Networks (CNNs). The weighted ensemble of deep CNNs is explored to recommend a better ALL cell classifier. The weights are estimated from ensemble candidates’ corresponding metrics, such as F1-score, area under the curve (AUC), and kappa values. Various data augmentations and pre-processing are incorporated to achieve a better generalization of the network. Our proposed model was trained and evaluated utilizing the C-NMC-2019 ALL dataset. The proposed weighted ensemble model has outputted a weighted F1-score of 89.7%, a balanced accuracy of 88.3%, and an AUC of 0.948 in the preliminary test set. The qualitative results displaying the gradient class activation maps confirm that the introduced model has a concentrated learned region. In contrast, the ensemble candidate models, such as Xception, VGG-16, DenseNet-121, MobileNet, and InceptionResNet-V2, separately exhibit coarse and scatter learned areas in most cases. Since the proposed ensemble yields a better result for the aimed task, it can support clinical decisions to detect ALL patients in an early stage. © 2021 The Authors

关键词： Acute Lymphoblastic Leukemia C-NMC-2019 dataset Deep Convolutional Neural Networks Ensemble image classifiers Transfer learning

RCMNet: A deep learning model assists CAR-T therapy for leukemia

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arXiv 2022年

作者： Zhang, Ruitao Han, Xueying Gul, Ijaz Zhai, Shiyao Liu, Ying Zhang, Yongbing Dong, Yuhan Ma, Lan Yu, Dongmei Zhou, Jin Qin, Peiwu Institute of Biomedical and Health Engineering Tsinghua Shenzhen International Graduate School China Precision Medicine and Public Health Tsinghua-Berkeley Shenzhen Institute United States The First Affiliated Hospital of Harbin Medical University China Food Inspection & Quarantine Center Shenzhen Custom China Department of Computer Science Harbin Insitute of Technology Shenzhen China School of Mc chanical Electrical & Information Engineering Shandong University China

Acute leukemia is a type of blood cancer with a high mortality rate. Current therapeutic methods include bone marrow transplantation, supportive therapy, and chemotherapy. Although a satisfactory remission of the disease can be achieved, the risk of recurrence is still high. Therefore, novel treatments are demanding. Chimeric antigen receptor-T (CAR-T) therapy has emerged as a promising approach to treat and cure acute leukemia. To harness the therapeutic potential of CAR-T cell therapy for blood diseases, reliable cell morphological identification is crucial. Nevertheless, the identification of CAR-T cells is a big challenge posed by their phenotypic similarity with other blood cells. To address this substantial clinical challenge, herein we first construct a CAR-T dataset with 500 original microscopy images after staining. Following that, we create a novel integrated model called RCMNet (ResNet18 with CBAM and MHSA) that combines the convolutional neural network (CNN) and Transformer. The model shows 99.63% top-1 accuracy on the public dataset. Compared with previous reports, our model obtains satisfactory results for image classification. Although testing on the CAR-T cells dataset, a decent performance is observed, which is attributed to the limited size of the dataset. Transfer learning is adapted for RCMNet and a maximum of 83.36% accuracy has been achieved, which is higher than other SOTA models. The study evaluates the effectiveness of RCMNet on a big public dataset and translates it to a clinical dataset for diagnostic applications. Copyright © 2022, The Authors. All rights reserved.

关键词： T-cells

A novel near-infrared light responsive 4D printed nanoarchitecture with dynamically and remotely controllable transformation

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Nano Research 2019年第6期12卷 1381-1388页

作者： Haitao Cui Shida Miao Timothy Esworthy Se-jun Lee Xuan Zhou Sung Yun Hann Thomas J.Webster Brent T.Harris Lijie Grace Zhang Department of Mechanical and Aerospace Engineering The George Washington UniversityWashingtonDC 20052USA Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Department of Neurology and Pathology Georgetown UniversityWashingtonDC 20007USA Departments of Electrical and Computer Engineering The George Washington UniversityWashingtonDC 20052USA Department of Biomedical Engineering The George Washington UniversityWashingtonDC 20052USA Department of Medicine The George Washington UniversityWashingtonDC 20052USA

Four-dimensional (4D) printing is an emerging and highly innovative additive manufacturing process by which to fabricate pre-designed,self-assembly structures with the ability to transform over ***,one of the critical challenges of 4D printing is the lack of advanced 4D printing systems that not only meet all the essential requirements of shape change but also possess smart,dynamic capabilities to spatiotemporally and instantly control the shape-transformation ***,we present a facile 4D printing platform which incorporates nanomaterials into the conventional stimuli-responsive polymer,allowing the 4D printed object to achieve a dynamic and remote controlled,on-time and position shape transformation.A proof-of-concept 4D printed brain model was created using near-infrared light (NIR) responsive nanocomposite to evaluate the capacity for controllable 4D transformation,and the feasibility of photothermal stimulation for modulating neural stem cell *** novel 4D printing strategy can not only be used to create dynamic 3D patterned biological structures that can spatiotemporally control their shapes or behaviors of transformation under a human benign stimulus (NIR),but can also provide a potential method for building complex self-morphing objects for widespread applications.

关键词： near-infrared light responsive four-dimensional (4D) printing dynamically and remotely controllable graphene brain neural stem cell

ADAPTIVE AGGREGATION WEIGHTS FOR FEDERATED SEGMENTATION OF PANCREAS MRI

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arXiv 2024年

作者： Pan, Hongyi Durak, Gorkem Zhang, Zheyuan Taktak, Yavuz Keles, Elif Aktas, Halil Ertugrul Medetalibeyoglu, Alpay Velichko, Yury Spampinato, Concetto Schoots, Ivo Bruno, Marco J. Keswani, Rajesh N. Tiwari, Pallavi Bolan, Candice Gonda, Tamas Goggins, Michael G. Wallace, Michael B. Xu, Ziyue Bagci, Ulas Department of Radiology Northwestern University ChicagoIL United States Department of Radiology Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Internal Medicine Istanbul Faculty of Medicine Istanbul University Istanbul Turkey Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy Department of Radiology and Nuclear Medicine Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Erasmus Medical Center Rotterdam Netherlands Department of Gastroenterology and Hepatology Northwestern University ChicagoIL United States Department of Biomedical Engineering and Radiology University of Wisconsin MadisonWI United States Division of Gastroenterology and Hepatology Mayo Clinic JacksonvilleFL United States Division of Gastroenterology and Hepatology New York University New YorkNY United States Division of Gastroenterology and Hepatology Johns Hopkins University BaltimoreMD United States NVIDIA BethesdaMD United States

Federated learning (FL) enables collaborative model training across institutions without sharing sensitive data, making it an attractive solution for medical imaging tasks. However, traditional FL methods, such as Federated Averaging (FedAvg), face difficulties in generalizing across domains due to variations in imaging protocols and patient demographics across institutions. This challenge is particularly evident in pancreas MRI segmentation, where anatomical variability and imaging artifacts significantly impact performance. In this paper, we conduct a comprehensive evaluation of FL algorithms for pancreas MRI segmentation and introduce a novel approach that incorporates adaptive aggregation weights. By dynamically adjusting the contribution of each client during model aggregation, our method accounts for domain-specific differences and improves generalization across heterogeneous datasets. Experimental results demonstrate that our approach enhances segmentation accuracy and reduces the impact of domain shift compared to conventional FL methods while maintaining privacy-preserving capabilities. Significant performance improvements are observed across multiple hospitals (centers). © 2024, CC BY.

关键词： Federated learning

Comparative analysis of hysteresis modulation, adaptive and PWM based sliding mode control of a buck converter

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Comparative analysis of hysteresis modulation, adaptive and ...

2019 IASTED International Conference on Control and Optimization of Renewable Energy Systems, CORES 2019

作者： Shourov, Erfan C. Sabzehgar, Reza Rasouli, Mohammad Fajri, Poria Department of Electrical and Computer Engineering San Diego State University San DiegoCA United States Department of Electrical and Computer Engineering Penn State Behrend EriePA United States Department of Electrical and Biomedical Engineering University of Nevada RenoNV United States

ISBN: (纸本)9780889869967

In this paper, three Sliding Mode Control (SMC) strategies namely the Hysteresis Modulation (HM), the adaptive, and the Pulse Width Modulation (PWM) techniques are designed and utilized to accomplish output voltage regulation of a DC-DC power converter. Simulation studies are conducted to evaluate the performance of each controller, and to demonstrate which controller is better suited for the output voltage control of DC-DC power converter in the presence of sudden changes to both load and input voltage level. © Proceedings of the IASTED International Conference on Control and Optimization of Renewable Energy Systems, CORES 2019. All rights reserved.

关键词： Sliding mode control

Analysis of growing tumor on the flow velocity of cerebrospinal fluid in human brain using computational modeling and fluid-structure interaction

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arXiv 2021年

作者： Uzair-Ul-Haq, Muhammad Ahmed, Ali Mustansar, Zartasha Shaukat, Arslan Margetts, Lee Waris, Asim Nadeem, Faizan Department of Biomedical Engineering & Sciences School of Mechanical & Manufacturing Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Computational Engineering Research center of Modelling and Simulation National University of Sciences and Technology Islamabad44000 Pakistan Department of Computer & Software Engineering College of Electrical & Mechanical Engineering National University of Sciences and Technology Islamabad44000 Pakistan Department of Mechanical Aerospace and Civil Engineering University of Manchester United Kingdom Nuclear Medicine specialist associated with INMOL cancer hospital Lahore Pakistan

Cerebrospinal fluid (CSF) plays a pivotal role in normal functioning of Brain. Intracranial compartments such as blood, brain and CSF are incompressible in nature. Therefore, if a volume imbalance in one of the aforenoted compartments is observed, the other reaches out to maintain net change to zero. Whereas, CSF has higher compliance over long term. However, if the CSF flow is obstructed in the ventricles, this compliance may get exhausted early. Brain tumor on the other hand poses a similar challenge towards destabilization of CSF flow by compressing any section of ventricles thereby ensuing obstruction. To avoid invasive procedures to study effects of tumor on CSF flow, numerical-based methods such as Finite element modeling (FEM) are used which provide excellent description of underlying pathological interaction. A 3D fluid-structure interaction (FSI) model is developed to study the effect of tumor growth on the flow of cerebrospinal fluid in ventricle system. The FSI model encapsulates all the physiological parameters which may be necessary in analyzing intraventricular CSF flow behavior. Findings of the model show that brain tumor affects CSF flow parameters by deforming the walls of ventricles in this case accompanied by a mean rise of 74.23% in CSF flow velocity and considerable deformation on the walls of ventricles. © 2021, CC BY-SA.

关键词： Brain