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IEEE International Conference on Image Processing

作者： Tsung-Ming Tai Yun-Jie Jhang Wen-Jyi Hwang Free University of Bozen-Bolzano NVIDIA AI Technology Center International Intercollegiate Ph.D. Program National Tsing Hua University Department of Computer Science and Information Engineering National Taiwan Normal University

Supervised learning of deep neural networks heavily relies on large-scale datasets annotated by high-quality labels. In contrast, mislabeled samples can significantly degrade the generalization of models and result in memorizing samples, further learning erroneous associations of data contents to incorrect annotations. To this end, this paper proposes an efficient approach to tackle noisy labels by learning robust feature representation based on unsupervised augmentation restoration and cluster regularization. In addition, progressive self-bootstrapping is introduced to minimize the negative impact of supervision from noisy labels. Our proposed design is generic and flexible in applying to existing classification architectures with minimal overheads. Experimental results show that our proposed method can efficiently and effectively enhance model robustness under severely noisy labels.

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Vitisin B, a resveratrol tetramer from Vitis thunbergii var. taiwaniana, ameliorates impaired glucose regulations in nicotinamide/streptozotocin-induced type 2 diabetic mice

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Journal of Traditional and Complementary Medicine 2023年第5期13卷 479-488页

作者： Liu, Yuh-Hwa Lin, Yin-Shiou Sie, Yi-Yan Wang, Ching-Chiung Chang, Chi-I Hou, Wen-Chi Department of General Medicine School of Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Division of Gastroenterology Shin Kong Wu Ho-Su Memorial Hospital Taipei 111 Taiwan Graduate Institute of Pharmacognosy Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Clinical Drug Development of Herbal Medicine College of Pharmacy Taipei Medical University Taipei Taiwan School of Pharmacy College of Pharmacy Taipei Medical University Taipei 110 Taiwan Department of Biological Science and Technology National Pingtung University of Science and Technology Pingtung 912 Taiwan

Background and aim: In Taiwan, Vitis thunbergii var. taiwaniana (VTT) is used in traditional medicine and as a local tea. VTT rich in resveratrol and resveratrol oligomers have been reported to exhibit anti-obesity and anti-hypertensive activities in animal models;however, no studies have investigated type 2 diabetes mellitus (T2dM) treatments. This study aimed to investigate the anti-T2dM effects of resveratrol tetramers isolated from the VTT in nicotinamide/streptozotocin (STZ)-induced Institute of Cancer Research (ICR) mice. Experimental procedure: The oral glucose tolerance test (OGTT) was used to imitate postprandial blood glucose (BG) regulations in mice by pre-treatment with VTT extracts, resveratrol tetramers of vitisin A, vitisin B, and hopeaphenol 30 min before glucose loads. Vitisin B (50 mg/kg) was administered to treat T2dM-ICR mice once daily for 28 days to investigate its hypoglycemic activity. Results and conclusion: Mice pre-treated with VTT-S-95EE, or vitisin B (100 mg/kg) 30-min before glucose loading showed significant reductions (P < 0.001) in the area under the curve at 120-min (BG-AUC_0-120) than those without pre-treatment with VTT-S-95 E E or vitisin B. Vitisin B-treated T2dM mice showed hypoglycemic activities via a reduction in plasma dipeptidyl peptidase (dPP)-IV activities to maintain insulin actions and differed significantly than those of untreated T2dM mice (P < 0.05), and also reduced BG-AUC_0-120 and insulin-AUC_0-120 in the OGTT. These in vivo results showed that VTT containing vitisin B would be beneficial for developing nutraceuticals and/or functional foods for glycemic control in patients with T2dM, which should be investigated further. © 2023 Center for Food and Biomolecules, National Taiwan University

关键词： and hopeaphenol Nutraceuticals Resveratrol tetramers of vitisin a Type 2 diabetes mellitus (T2dM) Vitis thunbergii var. taiwaniana (VTT) Vitisin B

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Removal of nitrogen and phosphorus pollutants from swine wastewater

Removal of nitrogen and phosphorus pollutants from swine was...

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2021 International Symposium on Intelligent Signal Processing and Communication Systems, ISPACS 2021

作者： Nguyen, Minh-Ky Lin, Chitsan Nhut, Huynh Tan Trung Hiep, Nguyen Hung, Nguyen T.Q. Huu Tuan, Tran National Kaohsiung University of Science and Technology College of Maritime Ph.D. Program In Maritime Science and Technology Kaohsiung Taiwan National Kaohsiung University of Science and Technology Department of Marine Environmental Engineering Kaohsiung Taiwan Nong Lam University of Ho Chi Minh City Faculty of Environment and Natural Resources Ho Chi Minh Viet Nam National Kaohsiung University of Science and Technology College of Maritime Kaohsiung Taiwan

ISBN: (纸本)9781665419512

This study investigated the simultaneous removal of nitrogen and phosphorus from swine wastewater by a magnesium ammonium phosphate crystallization. The precipitation of struvite was determined from swine wastewater, obtaining the optimal ph, Mg/N/P molar ratio, and reaction time were 9.0, 1.6/1/1.5 and 30 min, respectively. Ammonium and phosphate removal efficiencies were 98.1 ± 0.5% and 98.9 ± 0.4% under optimal conditions, and the highest crystallization mass obtained was 14.6 ± 0.6 g/L. Results illustrated that struvite precipitation is an efficient method for removing ammonium and phosphate in swine wastewater. © 2021 IEEE.

关键词： phosphorus

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Mesenchymal Stromal/Stem Cells Know Best:The Remarkable Complexities of Its InteractionsWith Polymorphonuclear Neutrophils

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Stem Cells 2024年第5期42卷 403-415页

作者： Wang, Li-Tzu Lee, Wei Liu, Ko-Jiunn Sytwu, Huey-Kang Yen, Men-Luh Linju Yen, B. Department of Obstetrics and Gynecology National Taiwan University (NTU) Hospital College of Medicine NTU Taipei Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University Taipei Taiwan Regenerative Medicine Research Group Institute of Cellular and System Medicine National Health Research Institutes (NHRI) Zhunan Taiwan Cell Therapy Center China Medical University Hospital Taichung Taiwan National Institute of Cancer Research NHRI Tainan Taiwan National Institute of Infectious Diseases and Vaccinology NHRI Zhunan Taiwan Department and Graduate Institute of Microbiology and Immunology National Defense Medical Center Taipei Taiwan

Polymorphonuclear neutrophils (PMNs), the predominant immune cell type in humans, have long been known as first-line effector cells against bacterial infections mainly through phagocytosis and production of reactive oxygen species (ROS). However, recent research has unveiled novel and pivotal roles of these abundant but short-lived granulocytes in health and disease. Human mesenchymal stromal/stem cells (MSCs), renowned for their regenerative properties and modulation of T lymphocytes from effector to regulatory phenotypes, exhibit complex and context-dependent interactions with PMNs. Regardless of species or source, MSCs strongly abrogate PMN apoptosis, a critical determinant of PMN function, except if PMNs are highly stimulated. MSCs also have the capacity to fine-tune PMN activation, particularly in terms of Cd11b expression and phagocytosis. Moreover, MSCs can modulate numerous other PMN functions, spanning migration, ROS production, and neutrophil extracellular trap (NET) formation/NETosis, but directionality is remarkably dependent on the underlying context: in normal nondiseased conditions, MSCs enhance PMN migration and ROS production, whereas in inflammatory conditions, MSCs reduce both these functions and NETosis. Furthermore, the state of the MSCs themselves, whether isolated from diseased or healthy donors, and the specific secreted products and molecules, can impact interactions with PMNs;while healthy MSCs prevent PMN infiltration and NETosis, MSCs isolated from patients with cancer promote these functions. This comprehensive analysis highlights the intricate interplay between PMNs and MSCs and its profound relevance in healthy and pathological conditions, shedding light on how to best strategize the use of MSCs in the expanding list of diseases with PMN involvement. © The Author(s) 2024. Published by Oxford University Press.

关键词： : polymorphonuclear neutrophils apoptosis Cd11b mesenchymal stromal/stem cells migration NETosis phagocytosis reactive oxygen species

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A Review on Analytical Approaches for Ambient Ozone Open data in Taiwan

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Current Pollution Reports 2024年第3期10卷 374-386页

作者： Nguyen, duy-Hieu Liao, Chih-Hsiang Bui, Xuan-Thanh Yuan, Chung-Shin Lin, Chitsan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung Taiwan (R.O.C.) Department of Environmental Engineering and Science Chia-Nan University of Pharmacy and Science Tainan Taiwan (R.O.C.) Key Laboratory of Advanced Waste Treatment Technology & Faculty of Environment and Natural Resources Ho Chi Minh City University of Technology (HCMUT) Ho Chi Minh City Vietnam Vietnam National University Ho Chi Minh City (VNU-HCM) Ho Chi Minh City Vietnam Institute of Environmental Engineering National Sun Yat-Sen University Kaohsiung Taiwan (R.O.C.) Department of Marine Environmental Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan (R.O.C.)

The air quality in Taiwan is significantly impacted by ambient ozone (O3) pollution, which poses a challenge in terms of control due to the involvement of precursors and influencing factors in its photochemical process. This review investigates the measures that have been implemented in Taiwan over the past two decades to address this issue and evaluate their effectiveness in reducing O3 concentrations. Furthermore, it highlights relevant studies that have employed advanced methods to examine the O3 problem. Comprehending the complex formation of O3 and its driving factors is crucial in efficiently managing O3 pollution. Nevertheless, accurately quantifying the impacts of these factors can be challenging due to their interconnections. To bridge this gap in knowledge, conducting a robust causality analysis becomes imperative to accurately quantify the causal influence of major factors. Furthermore, eliminating seasonal variations can improve the precision and accuracy of trend analyses concerning long-term changes in O3 concentrations. deep learning, in particular, holds significant advantages in predicting O3 concentrations as it can capture non-linear and long-term memory characteristics effectively. In summary, attaining acceptable O3 levels in Taiwan is challenging due to its geographical location, which makes it susceptible to air pollution from both local sources as well as distant upwind areas. The utilization of advanced methods is essential for comprehensively studying the evolution of O3 and formulating effective mitigation measures. While there is a necessity to develop new analytical methods, implementing existing robust methodologies can also provide valuable insights into the dynamics and impacts of O3. By leveraging these approaches, we can enhance our comprehension of O3 pollution in Taiwan and develop effective strategies to mitigate its harmful effects on air quality.

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Estimating the volume of penumbra in rodents using dTI and stack-based ensemble machine learning framework

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European Radiology Experimental 2024年第1期8卷 59页

作者： Kuo, duen-Pang Chen, Yung-Chieh Li, Yi-Tien Cheng, Sho-Jen Hsieh, Kevin Li-Chun Kuo, Po-Chih Ou, Chen-Yin Chen, Cheng-Yu Department of Medical Imaging Taipei Medical University Hospital No.250 Wu Hsing Street Taipei Taiwan Translational Imaging Research Center Taipei Medical University Hospital Taipei Taiwan Research Center for Neuroscience Taipei Medical University Taipei Taiwan Ph.D. Program in Medical Neuroscience College of Medical Science and Technology Taipei Medical University Taipei Taiwan Department of Computer Science National Tsing Hua University Hsinchu Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Department of Radiology National Defense Medical Center Taipei Taiwan

Background: This study investigates the potential of diffusion tensor imaging (dTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PdM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PdM at 30 and 90 min after occlusion. We used 11 dTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PdM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PdM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PdM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions: Our study confirmed that PV can be estimated using dTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PdM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement: The proposed dTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points: • Penumbral volume can be estimated by dTI combi

关键词： Animals diffusion tensor imaging Infarction (middle cerebral artery) Ischemic stroke Machine learning

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Identification of potential source regions and long-range transport routes/channels of marine PM2.5 at remote sites in East Asia

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science of the Total Environment 2024年 915卷

作者： Yen, Po-Hsuan Yuan, Chung-Shin Soong, Ker-Yea Jeng, Ming-Shiou Cheng, Wen-Hsi Institute of Environmental Engineering National Sun Yat-sen University Kaohsiung Taiwan Aerosol Science Research Center National Sun Yat-sen University Kaohsiung Taiwan Institute of Marine Biology National Sun Yat-sen University Kaohsiung City Taiwan Biodiversity Research Center Academia Sinica Nangang Taipei Taiwan Green Island Marine Research Station Biodiversity Research Center Academia Sinica Green Island Taitung Taiwan Ph.D. Program in Maritime Science and Technology College of Maritime National Kaohsiung University of Science and Technology Kaohsiung City Taiwan

Long-range transport (LRT) of air masses in East Asia and their impacts on marine PM2.5 were explored. Situated in the leeward region of East Asia, Taiwan Island marked by its elevated Central Mountain Range (CMR) separates air masses into two distinct air currents. This study aims to investigate the transport of PM2.5 from the north to the leeward region. Six transport routes (A-F) were identified and further classified them into three main channels (i.e. East, West, and South Channels) based on their transport routes and potential sources. Green Island (Site GR) and Hengchun Peninsula (Site HC) exhibited similarities in their transport routes, with Central China, North China, and Korean Peninsula being the major source regions of PM2.5, particularly during the Asian Northeastern Monsoons (ANMs). dongsha Island (Site dS) was influenced by both Central China and coastal regions of East China, indicating Asian continental outflow (ACO) as the major source of PM2.5. The positive matrix factorization (PMF) analysis of PM2.5 resolved that soil dust, sea salts, biomass burning, ship emissions, and secondary aerosols were the major sources. Northerly Channels (i.e. East and West Channels) were primarily influenced by ship emissions and secondary aerosols, while South Channel was dominated by oceanic spray and soil dust. The results of W-PSCF and W-CWT analysis indicated that three remote sites experienced significant contributions from Central China in the highest PM2.5 concentration range (75–100%). In contrast, PM2.5 in the 0–25% and 25–50% ranges primarily originated from the open seas, with ship emissions being the prominent source. It suggested that northern regions with heavy industrialization and urbanization have impacts on high PM2.5 concentrations, while open seas are the main sources of low PM2.5 concentrations. © 2024 Elsevier B.V.

关键词： Factorization

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Grinding wheel specification cybernetic recommendation with multi-task multi-imbalanced learning in smart manufacturing system

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Advanced Engineering Informatics 2024年 61卷

作者： Yao, Kuo-Ching Chen, Tzu-Li Chen, James C. Li, Chia-Ruei International Intercollegiate Ph.D. Program National Tsing Hua University Hsinchu30013 Taiwan Kinik Company New Taipei23942 Taiwan Graduate Institute of Intelligent Manufacturing Technology National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Industrial Engineering and Engineering Management National Tsing Hua University Hsinchu300044 Taiwan

Over the years, the grinding wheels industry has played a crucial role in mechanical engineering. Grinding wheel specification is composed of various factors such as abrasive type, wheel grade, bond type, and more, which are derived from customer workpiece operation information. Traditionally, the determination process of grinding wheel specifications is complicated. Experienced engineers need to check out the limitation and consider the interaction of each factor. When the new specification order comes, engineers have to spend a lot of time on historical data search and discussion. Recently, the application of artificial intelligence attracts more and more attention in the manufacturing industry. Through collecting historical data, the model has the ability to extract the domain knowledge, predict the specification that does not exist, and reduce the decision time. However, when training the model, this grinding wheel specification faced multiple imbalanced targets and multi-task issues. In the past, research efforts primarily focused on studying single-task learning (STL) algorithms. Seldom of them has a systematic way to discuss the model determination of multiple targets and correlation influence, especially on the tabular-type dataset. In this study, we proposed a multiple resampling chain multi-task learning framework to address the issue of data science. To tackle the multi-task imbalance resampling problem, we also proposed a resampling strategy called Multiple Resampling Chain. Moreover, we conducted a sensitivity analysis to examine the correlation effect on model improvement. Finally, we proposed a cyber-recommendation system that integrates the training model and interface to provide a more intuitive way of specification prediction. This system not only can help engineers to improve the time spent on making decisions, but also make the whole manufacturing process to be more intelligent. © 2024 Elsevier Ltd

关键词： Grinding (machining)

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What affects users to click social media ads and purchase intention? The roles of advertising value, emotional appeal and credibility

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Asia Pacific Journal of Marketing and Logistics 2022年第8期35卷 1900-1916页

作者： Wen-Kuo Chen Chia-Ju Ling Chien-Wen Chen Department of Money and Banking National Kaohsiung University of Science and Technology Kaohsiung Taiwan Ph.D. Program in Management Dayeh University Changhua Taiwan Department of Business Administration Feng Chia University College of Business Taichung Taiwan

Purpose This study is based on a web-based advertising value model, adding emotional appeal, advertising credibility, advertising clicks, product evaluation and incentive to examine the impact on consumers' product evaluation and purchase intention. design/methodology/approach In total, 313 valid online questionnaires were collected from those who had browsed the social media advertisement. Findings The results show that advertising credibility and emotional appeal have a higher impact on advertising attitude than informativeness and irritation in the original web-based advertising value model. Research limitations/implications The findings strengthen the literature on the antecedents of advertising attitude in social media context, confirming advertising clicks as a predictor of product evaluation, and determining that advertising attitude, product evaluation and incentive play key roles as predictors of consumer purchase intention. Practical implications The findings are particularly relevant to advertising marketing managers. In the context of social media advertising, the advertising credibility of consumers is the key to increasing consumers' willingness to purchase. Advertisers must establish a good advertising reputation to consumers in the social media context. Originality/value This study combines the web-based advertising value model, advertising clicks, product evaluation and incentive to investigate the factors that influence customers' purchase decisions in the context of social media.

关键词： Advertising value model Ad clicks Product evaluation Incentive Purchase intention

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ROBUST PARAMETER ESTIMATION FOR RATIONAL ORdINARY dIFFERENTIAL EQUATIONS

arXiv

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arXiv 2023年

作者： Bassik, Oren Berman, Yosef Go, Soo Hong, Hoon Ilmer, Ilia Ovchinnikov, Alexey Rackauckas, Chris Soto, Pedro Yap, Chee Ph.D. Program in Mathematics CUNY Graduate Center New YorkNY United States Ph.D. Program in Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics North Carolina State University RaleighNC United States Department of Mathematics CUNY Queens College United States Ph.D. Programs in Mathematics and Computer Science CUNY Graduate Center New YorkNY United States Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States Mathematical Institute University of Oxford Oxford United Kingdom Department of Computer Science Courant Institute of Mathematical Science New York University New YorkNY United States

We present a new approach for estimating parameters in rational OdE models from given (measured) time series data. In typical existing approaches, an initial guess for the parameter values is made from a given search interval. Then, in a loop, the corresponding outputs are computed by solving the OdE numerically, followed by computing the error from the given time series data. If the error is small, the loop terminates and the parameter values are returned. Otherwise, heuristics/theories are used to possibly improve the guess and continue the loop. These approaches tend to be non-robust in the sense that their accuracy depend on the search interval and the true parameter values;furthermore, they cannot handle the case where the parameters are locally identifiable. In this paper, we propose a new approach, which does not suffer from the above non-robustness. In particular, it does not require making good initial guesses for the parameter values or specifying search intervals. Instead, it uses differential algebra, interpolation of the data using rational functions, and multivariate polynomial system solving. We also compare the performance of the resulting software with several other estimation software packages. © 2023, CC BY-NC-SA.

关键词： Parameter estimation

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