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American Society for photogrammetry and Remote Sensing, ASPRS 2020 Annual Conference

作者： Gebrehiwot, Asmamaw Hashemi-Beni, Leila Applied Science and Technology Ph.D. Program Department of Built Environment North Carolina A&T State University United States Geomatics Program Department of Built Environment North Carolina A&T State University United States

High-resolution remote sensing imagery has been increasingly used for flood applications. different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction;however, these two-dimensional (2d) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3d) from UAV data by integrating 2d deep learning-based flood map and 3d cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2d inundation map. The 2d classified map was overlaid on the SfM-based 3d point cloud to create a 3d flood map. The floodwater depth was estimated by subtracting a pre-flood digital Elevation Model (dEM) from the SfM-based dEM. The results show that the proposed method is efficient in creating a 3d flood extent map to support emergency response and recovery activates during a flood event. © 2020 International Society for photogrammetry and Remote Sensing. All rights reserved.

关键词： Remote sensing

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The design and implementation of a mobile location-aware digital signage system

The design and implementation of a mobile location-aware dig...

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International Conference on Mobile Ad-hoc and Sensor Networks

作者： Yu, Kun-Ming Yu, Cheng-Yan Yeh, Bo-Han Hsu, Ching-Hsien Hsieh, Hung-Nien Department of Computer Science and Information Engineering Chung Hua University Taiwan Ph.D. Program of Technology Management Chung Hua University Taiwan Department of Architecture and Urban Planning Chung Hua University Taiwan

ISBN: (纸本)9780769543154

The worldwide digital Signage market has been getting increasingly popular in recent years. Nevertheless, for service providers, the digital Signage business is still not easy to manage and time-consuming to operate. In this paper, based on the GPS and wireless infrastructure, we present a mobile location-aware digital signage system (LdSS). Through the centralized system architecture, the applications for the digital Signage can be expanded to various advertising vehicles including buses and other mobile advertising vehicles. Also, we present an advertisement recommendation algorithm, by which the advertisement can be broadcasted in the right markets more effectively. Evidenced by simulated experiments and analysis, compared with traditional region trigger advertising, sequential advertising and random advertising, our advertisement recommendation algorithm can effectively reach to the key target audiences in planned regions, while show excellent ability to differentiate markets in unplanned regions reaching to the target consumer groups as well. © 2010 IEEE.

关键词： Marketing

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deep learning for remote sensing image classification for agriculture applications

Deep learning for remote sensing image classification for ag...

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American Society for photogrammetry and Remote Sensing, ASPRS 2020 Annual Conference

作者： Hashemi-Beni, Leila Gebrehiwot, Asmamaw Geomatics Program Department of Built Environment North Carolina A&T State University United States Applied Science and Technology Ph.D. Program Department of Built Environment North Carolina A&T State University United States

This research examines the ability of deep learning methods for remote sensing image classification for agriculture applications. U-net and convolutional neural networks are fine-tuned, utilized and tested for crop/weed classification. The dataset for this study includes 60 top-down images of an organic carrots field, which was collected by an autonomous vehicle and labeled by experts. FCN-8s model achieved 75.1% accuracy on detecting weeds compared to 66.72% of U-net using 60 training images. However, the U-net model performed better on detecting crops which is 60.48% compared to 47.86% of FCN-8s. © 2020 International Society for photogrammetry and Remote Sensing. All rights reserved.

关键词： Crops

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Bioeconomic potential of lignocellulosic by-products: Steam explosion-based fractionation into solid and liquid phases for fiber and methane production

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Bioresource technology 2025年 433卷 132723页

作者： Fabian Orth Benedikt Hülsemann Jayen Aris Kriswantoro Ting-Wu Ko Chen-Yeon Chu State Institute of Agricultural Engineering and Bioenergy University of Hohenheim Garbenstraße 9 70599 Stuttgart Germany Ph.D. Program of Mechanical and Aeronautical Engineering Feng Chia University Wenhua 100 407 Taichung Taiwan School of Life Sciences and Technology Institut Teknologi Bandung Ganesa 10 40132 Bandung Indonesia Institute of Green Products Feng Chia University Wenhua 100 407 Taichung Taiwan Master’s Program of Green Energy Science and Technology Feng Chia University Wenhua 100 407 Taichung Taiwan

This study examines lignocellulosic by-products from agriculture and food industry for methane production. Banana peels, cacao bean shells, rice straw, tomato plant residues, and wheat straw underwent steam explosion treatment (SE) at 160 °C and 10 min followed by solid–liquid separation. Specific methane yield increased between 8.6 % to 19.6 % for rice and wheat straw and cacao bean shells after SE treatment, while decreasing for banana peels (14.1 %). The liquid phase demonstrated high specific methane yield potential, with SMY between 227 and 336 L CH4 kg OdM −1 . Meanwhile, the fiber-rich solid phase offers potential for both energy applications and material utilization. The process supports both bioeconomic strategies and greenhouse gas reduction efforts through sustainable valorization of agricultural waste streams in circular economy models. However, further research is needed to optimize SE conditions and investigate other applications for the solid such as peat substitution.

关键词： Feedstock Biogas Separation Specific Methane Yield Treatment

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Computer-aided drug discovery of a dual-target inhibitor for ovarian cancer: therapeutic intervention targeting CdK1/TTK signaling pathway and structural insights in the NCI-60

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Computers in Biology and Medicine 2025年 193卷 110445页

作者： Sung-Ling Tang Maryam Rachmawati Sumitra Yu-Cheng Kuo Han-Lin Hsu Muhamad Ansar Sheng-Liang Huang Shih-Yu Lee Hong-Jaan Wang Bashir Lawal Alexander T.H. Wu Lung-Ching Chen Hsu-Shan Huang School of Pharmacy National Defense Medical Center Taipei 11490 Taiwan PhD Program for Cancer Molecular Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Graduate Institute of Cancer Biology and Drug Discovery College of Medical Science and Technology Taipei Medical University Taipei 11031 Taiwan Department of Pharmacology School of Medicine College of Medicine Taipei Medical University Taipei 11031 Taiwan School of Post-Baccalaureate Chinese Medicine College of Chinese Medicine China Medical University Taichung 40604 Taiwan Graduate Institute of Clinical Medicine Taipei Medical University Taipei 11031 Taiwan Division of Pulmonary Medicine Department of Internal Medicine Wanfang Hospital Taipei Medical University Taipei 11031 Taiwan Ph.D. Program in the Clinical Drug Development of Herbal Medicine Taipei Medical University Taipei 110301 Taiwan Graduate Institute of Aerospace and Undersea Medicine National Defense Medical Center Taipei 11490 Taiwan UPMC Hillman Cancer Center University of Pittsburgh Pittsburgh PA USA Ph.D. Program of Translational Medicine College of Science and Technology Taipei Medical University Taipei 11031 Taiwan Division of Cardiology Department of Internal Medicine Shin Kong Wu Ho-Su Memorial Hospital Taipei 11101 Taiwan Ph.D. Program in Biotechnology Research and Development College of Pharmacy Taipei Medical University Taipei 11031 Taiwan

Ovarian cancer remains the third most prevalent and deadliest gynecologic malignancy worldwide, with most patients eventually developing resistance to platinum-based chemotherapy. This highlights a critical unmet need for innovative multitargeted therapies to address current treatment challenges. In this study, we identified 35 differentially expressed genes (dEGs) through integrated analysis of four GEO ovarian cancer datasets, with validation using TCGA data. Gene Ontology (GO) and KEGG enrichment analyses highlighted key tumor-associated pathways, and protein–protein interaction (PPI) network modeling prioritized CdK1 and TTK as high-value therapeutic targets. We evaluated the association between molecular genomic features and drug responses across the NCI-60 ovarian cancer cell line panel (IGROV1, OVCAR-3, OVCAR-4, OVCAR-5, OVCAR-8, NCI/AdR-RES, and SK-OV-3), using a series of salicylanilide-derived compounds and four FdA-approved drugs (cabozantinib, paclitaxel, rapamycin, and niclosamide) from the NCI developmental Therapeutics program (dTP). Among these, NSC765690 (MCC22) emerged as the most promising candidate. It demonstrated potent antiproliferative activity, high target selectivity, and strong binding affinity to both CdK1 and TTK. Multi-omics integration, combined with AI-driven network modeling, further elucidated NSC765690's mechanism of action and its relevance to ovarian cancer pathogenesis. Additionally, AdMET and pharmacokinetic profiling confirmed its favorable drug-like properties and low predicted toxicity. Collectively, these findings establish NSC765690 as a potent dual-target inhibitor and exemplify a rational, data-driven drug discovery pipeline for overcoming chemotherapy resistance in ovarian cancer.

关键词： Ovarian cancer dual-target small molecule inhibitor Molecular docking NCI-60 panel Machine learning Molecular dynamics simulations OC ovarian cancer ML machine learning NLP natural language processing dEGs differentially Expressed Genes PPI Protein-Protein Interaction GEO Gene Expression Omnibus GO Gene Ontology IPA Ingenuity Pathway Analysis TCGA The Cancer Genome Atlas KEGG Kyoto Encyclopedia of Genes and Genomes GO-BP Gene Ontology-Biological Process dTP developmental Therapeutics program NCI National Cancer Institute CC Cellular Component MF Molecular Functions IHC immunohistochemical FdR false discovery rate BBB Blood brain barrier 3d three-dimensional MdS molecular dynamics simulation analysis RMSF analysis Root Mean Square Fluctuation analysis CdKs cyclin-dependent kinases TTK threonine and tyrosine kinase CNS central nervous system OS overall survival CSC cancer stem cells AI artificial intelligence SRB sulforhodamine B assay AdMET absorption, distribution, metabolism, excretion, and toxicity HPA human protein atlas WES whole exome sequencing MdR multidrug resistance HGSOC high-grade serous ovarian cancer TKI tyrosine kinase inhibitor SAC spindle assembly checkpoint HRR homologous recombination repair HRd homologous recombination deficiency

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An integrative systems-biology approach defines mechanisms of Alzheimer’s disease neurodegeneration

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Nature Communications 2025年第1期16卷 1-21页

作者： Leventhal, Matthew J. Zanella, Camila A. Kang, Byunguk Peng, Jiajie Gritsch, david Liao, Zhixiang Bukhari, Hassan Wang, Tao Pao, Ping-Chieh danquah, Serwah Benetatos, Joseph Nehme, Ralda Farhi, Samouil Tsai, Li-Huei dong, Xianjun Scherzer, Clemens R. Feany, Mel B. Fraenkel, Ernest MIT Ph.D. Program in Computational and Systems Biology Cambridge MA United States Department of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Broad Institute of Harvard and MIT Cambridge MA United States Department of Pathology Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Spatial Technology Platform Broad Institute of Harvard and MIT Cambridge MA United States Precision Neurology Program Brigham and Women’s Hospital and Harvard Medical school Boston MA United States APDA Center for Advanced Parkinson’s Disease Research Brigham and Women’s Hospital and Harvard Medical School Boston MA United States Picower Institute for Learning and Memory Massachusetts Institute of Technology Cambridge MA United States Department of Brain and Cognitive Sciences Massachusetts Institute of Technology Cambridge MA United States Stanley Center for Psychiatric Research Broad Institute of Harvard and MIT Cambridge MA United States School of Computer Science Northwestern Polytechnical University Xi’an China Stephen and Denise Adams Center of Yale School of Medicine New Haven CT United States

despite years of intense investigation, the mechanisms underlying neuronal death in Alzheimer’s disease, remain incompletely understood. To define relevant pathways, we conducted an unbiased, genome-scale forward genetic screen for age-associated neurodegeneration in drosophila. We also measured proteomics, phosphoproteomics, and metabolomics in drosophila models of Alzheimer’s disease and identified Alzheimer’s genetic variants that modify gene expression in disease-vulnerable neurons in humans. We then used a network model to integrate these data with previously published Alzheimer’s disease proteomics, lipidomics and genomics. Here, we computationally predict and experimentally confirm how HNRNPA2B1 and MEPCE enhance toxicity of the tau protein, a pathological feature of Alzheimer’s disease. Furthermore, we demonstrated that the screen hits CSNK2A1 and NOTCH1 regulate dNA damage in drosophila and human stem cell-derived neural progenitor cells. Our study identifies candidate pathways that could be targeted to ameliorate neurodegeneration in Alzheimer’s disease. © The Author(s) 2025.

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Humming-to-Instrument Conversion based on CycleGAN

Humming-to-Instrument Conversion based on CycleGAN

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

作者： Lai, Wen-Hsing Wang, Siou-Lin Xu, Zhi-Yao National Kaohsiung University of Science and Technology Dept. of Computer and Communication Engineering Kaohsiung Taiwan National Kaohsiung University of Science and Technology Ph.D. Program In Engineering Science and Technology College of Engineering Kaohsiung Taiwan

ISBN: (纸本)9781665419512

In this research, we propose a humming-to-instrument conversion system based on cycle-consistent adversarial networks (CycleGAN) to convert human humming to the sound of viola. This research adjusts the weight of cycle-consistency loss and identity loss to successfully convert the sound. From the experimental results of objective RMSE, the converted audio is more similar to viola compared to the similarity to humming. From the results of subjective MOS, the quality of the converted sound is fair to listeners. © 2021 IEEE.

关键词： Communication systems Signal processing

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A disordered linker in the Polycomb protein Polyhomeotic tunes phase separation and oligomerization

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Molecular Cell 2025年

作者： Tim M. Gemeinhardt Roshan M. Regy Tien M. phan Nanu Pal Jyoti Sharma Olga Senkovich Andrea J. Mendiola Heather J. Ledterman Amy Henrickson daniel Lopes Utkarsh Kapoor Ashish Bihani djamouna Sihou Young C. Kim david Jeruzalmi Borries demeler Chongwoo A. Kim Jeetain Mittal Nicole J. Francis Montreal Clinical Research Institute (IRCM) Montreal QC Canada Division of Experimental Medicine McGill University Montreal QC Canada Department of Chemistry and Biochemistry The University of Lethbridge Lethbridge AB Canada Artie McFerrin Department of Chemical Engineering Texas A&M University College Station TX USA Department of Biochemistry and Molecular Medicine University of Montreal Montreal QC Canada Department of Biochemistry and Molecular Genetics Midwestern University Glendale AZ USA Department of Chemistry and Biochemistry City College of New York New York NY USA Department of Chemical and Biomedical Engineering University of Wyoming Laramie WY USA Center for Materials Physics and Technology Naval Research Laboratory Washington DC USA Ph.D. Program in Biochemistry The Graduate Center of the City University of New York New York NY USA Ph.D. Program in Biology The Graduate Center of the City University of New York New York NY USA Ph.D. Program in Chemistry The Graduate Center of the City University of New York New York NY USA Department of Chemistry and Biochemistry University of Montana Missoula MT USA Department of Chemistry Texas A&M University College Station TX USA Interdisciplinary Graduate Program in Genetics and Genomics Texas A&M University College Station TX USA

Biomolecular condensates are increasingly recognized as key regulators of chromatin organization, yet how their formation and properties arise from protein sequences remains incompletely understood. Cross-species comparisons can reveal both conserved functions and significant evolutionary differences. Here, we integrate in vitro reconstitution, molecular dynamics simulations, and cell-based assays to examine how drosophila and human variants of Polyhomeotic (ph)—a subunit of the PRC1 chromatin regulatory complex—drive condensate formation through their sterile alpha motif (SAM) oligomerization domains. We identify divergent interactions between SAM and the disordered linker connecting it to the rest of ph. These interactions enhance oligomerization and modulate both the formation and properties of reconstituted condensates. Oligomerization influences condensate dynamics but minimally impacts condensate formation. Linker-SAM interactions also affect condensate formation in drosophila and human cells and growth in drosophila imaginal discs. Our findings show how evolutionary changes in disordered linkers can fine-tune condensate properties, providing insights into sequence-function relationships.

关键词： sterile alpha motif oligomerization molecular dynamics simulations phase separation gene regulation Polycomb intrinsically disordered regions biomolecular condensates analytical ultracentrifugation biochemistry CRISPR drosophila

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Research on Key Barriers and drivers for Bicycle Industry to Implement Reverse Logistics

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Journal of Quality 2023年第6期30卷 407-442页

作者： Chang, An-Yuan Lai, Po-Yen Department of Industrial Management National Formosa University Taiwan Ph.D. Program in Smart Industry Technology Research and Design National Formosa University Taiwan

Various countries are formulating stricter laws and regulations to restrict the environmental pollution caused by various pollution. At the same time, enterprises also need to find new ways to reduce production costs. In recent years, reverse logistics (RL) have attracted the attention of enterprises and professionals, and some scholars have pointed out that if enterprises use RL technology properly, in addition to reducing operating costs, it can also gain huge benefits. Many literatures have pointed out that the implementation of RL can fully and effectively utilize resources and achieve the purpose of environmental protection. In order to implement RL smoothly, it is necessary to understand what resistance is faced and what assistance is needed. Although the implementation of RL can bring many benefits, and there are many related foreign literature studies, RL has not been popularized in the bicycle industry. Also, the discussion and research on the key barriers and key drivers are still very limited in domestic literature research. This research aims to use the multiple attribute decision making method to find out the resistance and assistance faced by Taiwan’s bicycle industry in the implementation of RL, so as to fill this knowledge gap. Through the distribution of questionnaires, experts are invited to provide data with reference to their own experience. After data integration, the fuzzy delphi method (FdM) is used to screen out important barriers and drivers, and the results are designed and distributed in a second questionnaire. Later on, the research combines with analytic hierarchy process (AHP) to get the key barriers. Finally, technique for order preference by similarity to ideal solution (TOPSIS) is used to obtain the key drivers, and the key factors are sorted in sequence. © 2023, Chinese Society for Quality. All rights reserved.

关键词： Operating costs

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Application of a Self-Assembly Sensing Layer to Grating Sensor Cabinets for Biomedical Sensing of Rheumatoid Arthritis Antigen-Antibodies

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IEEE Sensors Journal 2024年第7期24卷 10039-10047页

作者： Wen, Hsin-Yi Hsu, Hsiang-Cheng Feng, Wen-Kai Jhan, Kai-Siang Yeh, Yao-Tsung Chiang, Chia-Chin National Kaohsiung University of Science and Technology Department of Chemical and Materials Engineering Kaohsiung80778 Taiwan National Kaohsiung University of Science and Technology College of Maritime Department of Ph.D. Program in Maritime Science and Technology Kaohsiung807618 Taiwan National Kaohsiung University of Science and Technology Department of Mechanical Engineering Kaohsiung80778 Taiwan Fooyin University Department of Medical Laboratory Science and Biotechnology Kaohsiung83102 Taiwan

In this article, we harnessed biomedical applications in laser-assisted etching long-period fiber gratings (LLPFGs) sensors, subsequently deploying them within the realm of biomedical applications. The primary aim of this study was to devise a sensitive method for the detection of antigen-antibodies associated with rheumatoid arthritis (RA) using LLPFGs. Sensing the antigen binding took place on the surface of the optical fiber through a self-assembling process. Given the inherent limitations of conventional rheumatoid factor (RF) testing in terms of specificity, we introduced an innovative serum-based approach geared toward the identification of anti-cyclic citrullinated peptide (Anti-CCP) antibodies. This method exhibited an impressive diagnostic accuracy rate of approximately 80% for RA cases, coupled with an exceptional specificity level of 98% for individuals without RA. The incorporation of LLPFGs in the detection of RA antigen-antibodies presents a highly promising avenue for enhancing the precision and reliability of RA diagnosis. Furthermore, this research aligns closely with the United Nations Sustainable development Goal (SdG)3, which strives to promote well-being across all age groups and advance sustainable development by addressing health-related challenges, including the accurate diagnosis of diseases like RA. © 2023 IEEE.

关键词： Antibodies

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