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IEEE International Conference on Big data

作者： Biao Yin Thomas A. Considine Fatemeh Emdad John V. Kelley Robert E. Jensen Elke A. Rundensteiner Data Science Program Worcester Polytechnic Institute Worcester MA USA Weapons and Materials Research Directorate US Army Research Laboratory MD USA Weapons and Materials Research Directorate ARL Northeast Regional Extended Site Burlington MA USA

ISBN: (数字)9781728162515

ISBN: (纸本)9781728162522

Material scientists study corrosion degradation of metallic structures due to its heavy economic and maintenance burdens. Assessing corrosion is both time consuming and labor intensive when utilizing outdoor tests under natural exposure conditions. Accelerated indoor corrosion tests are conducted in laboratory settings by material scientists to gage performance in a shorter period of time than outdoor tests. However, these indoor tests do not always correlate well with the actual performance in outdoor environments. Thus, there is a need to apply data-science methodologies to analyze and establish quantitative associations between indoor accelerated and outdoor exposure assessments to optimize artificially accelerated methods. We work with material experimental records including images, notes, meta-data and human-rated assessments collected over years. We apply data mining methods, such as Canonical Correlation Analysis (CCA) and its variants, to extract latent associations with corresponding feature mappings between the indoor and outdoor assessments in a projected data subspace. We find that CCA provides not only reliable quantitative associations but also interpretive mappings between the indoor and outdoor assessments. Further, three methods applied to control bias due to distinctive coating system stack up yield Pearson's correlation coefficients ranging from 0.70 to 0.83 in the optimized CCA subspaces, respectively. Moreover, predicting outdoor assessments from the CCA-projected indoor test data compared to using the original indoor data is shown to result in an increase in accuracy from 88% to 90% - confirming the effectiveness of our approach. Lastly, our results facilitate interesting domain-relevant discovery that could potentially lead to experimentalists better understanding corrosion resistance.

关键词： Corrosion Life estimation Feature extraction data mining Coatings Reliability Testing

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Correction: Tracing the Geography of NGOs: Exploring Where Canadian Development Organizations Work and Why

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VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations 2023年第1期35卷 47-47页

作者： Dicks, Heather Paras, Andrea Martel, Andréanne Johnson, Craig Davis, John-Michael Department of Sociology Memorial University of Newfoundland and Labrador St. John’s Canada Department of Political Science University of Guelph Guelph Canada Spur Change Program Montreal Canada Department of Integrative and Global Studies Worcester Polytechnic Institute Worcester USA

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The evolution of lithium-ion battery recycling

Nature Reviews Clean Technology

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Nature Reviews Clean Technology 2025年第1期1卷

作者： Xiaotu Ma Zifei Meng Yan Wang Marilena Velonia Bellonia Jeffrey Spangenberger Gavin Harper Eric Gratz Elsa Olivetti Renata Arsenault Mechanical and Materials Engineering Department Worcester Polytechnic Institute Worcester MA USA Technology and Policy Program Institute for Data Systems and Society Massachusetts Institute of Technology Cambridge MA USA ReCell Center Argonne National Laboratory Lemont IL USA School of Metallurgy and Materials University of Birmingham Birmingham UK The Faraday Institution Harwell Science and Innovation Campus Didcot UK Ascend Elements Inc. Westborough MA USA Department of Materials Science and Engineering Massachusetts Institute of Technology Cambridge MA USA Ford Ion Park Battery Center of Excellence Romulus MI USA

Demand for lithium-ion batteries (LIBs) is increasing owing to the expanding use of electrical vehicles and stationary energy storage. Efficient and closed-loop battery recycling strategies are therefore needed, which will require recovering materials from spent LIBs and reintegrating them into new batteries. In this Review, we outline the current state of LIB recycling, evaluating industrial and developing technologies. Among industrial technologies, pyrometallurgy can be broadly applied to diverse electrode materials but requires operating temperatures of over 1,000 °C and therefore has high energy consumption. Hydrometallurgy can be performed at temperatures below 200 °C and has material recovery rates of up to 93% for lithium, nickel and cobalt, but it produces large amounts of wastewater. Developing technologies such as direct recycling and upcycling aim to increase the efficiency of LIB recycling and rely on improved pretreatment processes with automated disassembly and cleaner mechanical separation. Additionally, the range of materials recovered from spent LIBs is expanding from the cathode materials recycled with established methods to include anode materials, electrolytes, binders, separators and current collectors. Achieving an efficient recycling ecosystem will require collaboration between recyclers, battery manufacturers and electric vehicle manufacturers to aid the design and automation of battery disassembly lines.

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Calibrating multi-dimensional complex ODE from noisy data via deep neural networks

arXiv

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arXiv 2021年

作者： Li, Kexuan Wang, Fangfang Liu, Ruiqi Yang, Fan Shang, Zuofeng Global Analytics and Data Sciences Biogen Inc United States Department of Mathematical Science Worcester Polytechnic Institute United States Department of Mathematics and Statistics Texas Tech University United States Eli Lilly and Company United States Department of Mathematical Sciences New Jersey Institute of Technology United States

Ordinary differential equations (ODEs) are widely used to model complex dynamics that arise in biology, chemistry, engineering, finance, physics, etc. Calibration of a complicated ODE system using noisy data is generally challenging. In this paper, we propose a two-stage nonparametric approach to address this problem. We first extract the de-noised data and their higher order derivatives using boundary kernel method, and then feed them into a sparsely connected deep neural network with rectified linear unit (ReLU) activation function. Our method is able to recover the ODE system without being subject to the curse of dimensionality and the complexity of the ODE structure. We have shown that our method is consistent if the ODE possesses a general modular structure with each modular component involving only a few input variables, and the network architecture is properly chosen. Theoretical properties are corroborated by an extensive simulation study that also demonstrates the effectiveness of the proposed method in finite samples. Finally, we use our method to simultaneously characterize the growth rate of COVID-19 cases from the 50 states of the United States. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

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Transplantation of human kidney organoids elicited a robust allogeneic response in a humanized mouse model

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Kidney International 2025年第6期107卷 1011-1016页

作者： Mon-Wei Yu, Samuel Choi, John Yongjoon Kady, Jamie De Cos, Marina Wong, Jenny S. King, Emily Younis, Nour Al Rahy, Nadim Al Chaar, Soltan Djebli, Houda Eskandari, Siawosh Samuel, Stephen Merhej, Tamara Sukumar, Nivedha Lin, Jia-Ren Chen, Jia-Yun Xu, Yilin Santagata, Sandro Weins, Astrid Yeung, Melissa Cravedi, Paolo Campbell, Kirk N. Lemos, Dario Bonventre, Joseph V. Brehm, Michael A. Azzi, Jamil Department of Medicine Nephrology Division Mount Sinai Hospital New York NY United States Department of Medicine Renal Division Brigham and Women's Hospital Harvard Medical School Boston MA United States Program in Molecular Medicine Diabetes Center of Excellence University of Massachusetts Chan Medical School Worcester MA United States Doctoral School of Science and Technology Lebanese University Beirut Lebanon Institute of Biological Science Faculty of Medical Sciences Mohammed VI Polytechnic University Benguerir Morocco Department of Biotechnology Northeastern University Boston MA United States Department of Systems Biology Harvard Medical School Boston MA United States Department of Pathology Brigham and Women's Hospital Harvard Medical School Boston MA United States Harvard Stem Cell Institute Cambridge MA United States Department of Medicine Division of Engineering in Medicine Brigham and Women's Hospital Harvard Medical School Boston MA United States

Human kidney organoids derived from embryonic stem cells (ESCs) or induced pluripotent stem cells (iPSCs) have become novel tools for studying various kidney pathologies. Here, we transplanted ESC-derived kidney organoids into humanized mice with a mature human adaptive immune system developed through thymic education. As judged by histology and immunophenotyping, the transplanted HLA-mismatched kidney organoids trigged a robust alloimmune response, characterized by a dense immune cell infiltrate and enhanced memory T cell phenotype in the allograft 30 days post-transplantation. Multiplexed immunofluorescence revealed expression of functional markers of various immune cell infiltrates in response to organoid allografts, mimicking the T cell-mediated rejection process in humans. This validated our model as a novel platform to study various therapeutic strategies to control alloimmunity. Splenocytes isolated from organoid-transplanted hosts showed an alloantigen-specific memory response against 2D kidney organoids ex vivo. Overall, our study indicates that transplanting kidney organoids in humanized mice may be a valuable tool for studying human allogeneic immunity. © 2025 International Society of Nephrology

关键词： human kidney organoids humanized mice model kidney transplantation

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IoT environmental analyzer using sensors and machine learning for migraine occurrence prevention 18

IoT environmental analyzer using sensors and machine learnin...

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18th IEEE International Conference on Machine Learning and Applications, ICMLA 2019

作者： Day, Rosemarie J. Salehi, Hassan Javadi, Mahsa Data Science Program Worcester Polytechnic Institute WorcesterMA United States Department of Electrical and Computer Engineering California State University Chico ChicoCA United States Division of Genetic Research University of Connecticut Health Center FarmingtonCT United States

ISBN: (纸本)9781728145495

Everyday migraines are affecting more than one billion people worldwide. This headache disorder is classified as the sixth most disabling disease in the world. Migraines are just one chronic illness affected by environmental triggers due to changes that occur inside the home. Migraines share this characteristic with sinus headaches and thus are often misdiagnosed. In this research work, an iOS-based environmental analyzer was designed, implemented and evaluated for migraine sufferers with the use of sensors. After the data collection and cleaning, five machine learning model were used to estimate prediction accuracy of migraines in terms of the environment. The data was evaluated against the models using K-Fold cross validation. The algorithm accuracy comparison showed that Linear Discriminant Analysis (LDA) produced highest accuracy for the testing data at a mean of 0.938. Preliminary results demonstrate the feasibility of using machine learning algorithms to perform the automated recognition of migraine trigger areas in the environment. © 2019 IEEE.

关键词： Internet of things

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Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning

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Bone 2023年第1期171卷 116743-116743页

作者： Jones, Brandon C. Wehrli, Felix W. Kamona, Nada Deshpande, Rajiv S. Vu, Brian-Tinh Duc Song, Hee Kwon Lee, Hyunyeol Grewal, Rasleen Kaur Chan, Trevor Jackson Witschey, Walter R. MacLean, Matthew T. Josselyn, Nicholas J. Iyer, Srikant Kamesh al Mukaddam, Mona Snyder, Peter J. Rajapakse, Chamith S. Department of Radiology Perelman School of Medicine University of Pennsylvania 1 Founders Building 3400 Spruce St Philadelphia 19104 PA United States Department of Bioengineering School of Engineering and Applied Sciences University of Pennsylvania St Philadelphia 19104 PA United States School of Electronics Engineering Kyungpook National University 80 Daehakro Bukgu Daegu 41566 South Korea Department of Medicine Division of Endocrinology Perelman School of Medicine University of Pennsylvania Perelman Center for Advanced Medicine 3400 Civic Center Boulevard Philadelphia 19104 PA United States Department of Data Science Worcester Polytechnic Institute 100 Institute Road Worcester 01609 MA United States

Background: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times. Purpose: To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity. Methods: In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosi

关键词： Cortical bone Deep learning MRI Osteoporosis Porosity Suppression Ratio

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Location embedding based pairwise distance learning for fine-grained diagnosis of urinary stones

arXiv

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arXiv 2024年

作者： Jin, Qiangguo Huang, Jiapeng Sun, Changming Cui, Hui Xuan, Ping Su, Ran Wei, Leyi Wu, Yu-Jie Wu, Chia-An Duh, Henry B.L. Lu, Yueh-Hsun School of Software Northwestern Polytechnical University Shaanxi China Yangtze River Delta Research Institute of NPU Suzhou China CSIRO Data61 Sydney Australia Department of Computer Science and Information Technology La Trobe University Melbourne Australia School of Engineering Shantou University Guangdong China College of Intelligence and Computing Tianjin University Tianjin China School of Information Xiamen University Xiamen China AIDD Faculty of Applied Science Macao Polytechnic University China Department of Radiology Shuang-Ho Hospital Taipei Medical University Taipei Taiwan Master of Public Health Program National Yang Ming Chiao Tung University Taipei Taiwan School of Design The Hong Kong Polytechnic University Hong Kong Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

The precise diagnosis of urinary stones is crucial for devising effective treatment strategies. The diagnostic process, however, is often complicated by the low contrast between stones and surrounding tissues, as well as the variability in stone locations across different patients. To address this issue, we propose a novel location embedding based pairwise distance learning network (LEPD-Net) that leverages low-dose abdominal X-ray imaging combined with location information for the fine-grained diagnosis of urinary stones. LEPD-Net enhances the representation of stone-related features through context-aware region enhancement, incorporates critical location knowledge via stone location embedding, and achieves recognition of fine-grained objects with our innovative fine-grained pairwise distance learning. Additionally, we have established an in-house dataset on urinary tract stones to demonstrate the effectiveness of our proposed approach. Comprehensive experiments conducted on this dataset reveal that our framework significantly surpasses existing state-of-the-art methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Embeddings

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Classifying Depression in Imbalanced datasets Using an Autoencoder- Based Anomaly Detection Approach

Classifying Depression in Imbalanced Datasets Using an Autoe...

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International Conference on Semantic Computing

作者： Walter Gerych Emmanuel Agu Elke Rundensteiner Data Science and Computer Science Programs Worcester Polytechnic Institute Worcester MA USA

Depression is the most prevalent mental health ailment in the United States, affecting 15% of the population. Untreated depression can significantly decrease quality of life, physical health, and has significant economic and societal costs. The traditional method of diagnosing depression requires the patient to respond to medical questionnaires and is subjective. Passive methods to autonomously detect depression are desirable. Prior work on smartphone sensing of depression has utilized machine learning classification of smartphone sensor data. However, as with many ailments, the percentage of afflicted users in most populations is small compared with those unaffected, leading to severe class imbalance. In this work, we explore anomaly detection methods as a method for mitigating class imbalance for depression detection. Our approach adopts a multi-stage machine learning pipeline. First, using autoen-coders, we project the mobility features of the majority class (undepressed users). Thereafter, the trained autoencoder then classifies a test set of users as either depressed (anomalous) or not depressed (inliers) using a One Class SVM algorithm. Our method, when applied to the real-world StudentLife data set shows that even with an extremely imbalanced dataset, our method is able to detect individuals with depression symptoms with an AUC-ROC of 0.92, significantly outperforming traditional machine learning classification approaches.

关键词： Feature extraction Anomaly detection Machine learning Support vector machines Standards Machine learning algorithms Entropy

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Deep Kernel Coherence Encoder

Deep Kernel Coherence Encoder

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Asilomar Conference on Signals, Systems & Computers

作者： Haitao Liu Randy Paffenroth Louis Scharf Fangzheng Sun Data Science Worcester Polytechnic Institute Worcester MA USA Mathematical Sciences Computer Science Data Science Worcester Polytechnic Institute Worcester MA USA Department of Mathematics Colorado State University Fort Collins CO USA

ISBN: (数字)9781728143002

ISBN: (纸本)9781728143019

Here we propose a non-linear generalization of Kernel Canonical Correlation Analysis (KCCA) using deep neural networks, which we call a Deep Kernel Coherence Encoder (DKCE). While classic KCCA only provides a few parameters that can be trained using data, DKCE works by optimizing over many parameters encoded in a combination of a radial basis function kernel and a deep neural network. This novel structure simultaneously provides a large number of parameters, through the deep neural network, and an infinite dimensional feature space, by way of the radial basis function kernel and Mercer's theorem. Moreover, we provide an efficient computational back-propagation method based on entry-wise coherence maximization that can be used in high-dimensional settings and implemented using standard software libraries. Lastly, we provide preliminary, but suggestive, results of our proposed method on the MNIST and Fashion MNIST data sets, and provide directions for future research.

关键词： Kernel Neural networks Coherence Covariance matrices Correlation Principal component analysis Matrix decomposition

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