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| WoS | SCOPUS | Document Type | Document Title | Abstract | Authors | Affiliation | ResearcherID (WoS) | AuthorsID (SCOPUS) | Author Email(s) | Journal Name | JCR Abbreviation | ISSN | eISSN | Volume | Issue | WoS Edition | WoS Category | JCR Year | IF | JCR (%) | FWCI | FWCI Update Date | WoS Citation | SCOPUS Citation | Keywords (WoS) | KeywordsPlus (WoS) | Keywords (SCOPUS) | KeywordsPlus (SCOPUS) | Language | Publication Stage | Publication Year | Publication Date | DOI | JCR Link | DOI Link | WOS Link | SCOPUS Link |
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| ○ | ○ | Article | Circular RNA circSMAD4 regulates cardiac fibrosis by targeting miR-671-5p and FGFR2 in cardiac fibroblasts | Heart failure is a leading cause of death and is often accompanied by activation of quiescent cardiac myofibroblasts, which results in cardiac fibrosis. In this study, we aimed to identify novel circular RNAs that regulate cardiac fibrosis. We applied transverse aortic constriction (TAC) for 1, 4, and 8 weeks in mice. RNA sequencing datasets were obtained from cardiac fibroblasts isolated by use of a Langendorff apparatus and then further processed by use of selection criteria such as differential expression and conservation in species. CircSMAD4 was upregulated by TAC in mice or by transforming growth factor (TGF)-b1 in primarily cultured human cardiac fibroblasts. Delivery of si-circSMAD4 attenuated myofibroblast activation and cardiac fibrosis in mice treated with isoproterenol (ISP). si-circSmad4 significantly reduced cardiac fibrosis and remodeling at 8 weeks. Mechanistically, circSMAD4 acted as a sponge against the microRNA miR-671-5p in a sequence-specific manner. miR-671-5p was downregulated during myofibroblast activation and its mimic form attenuated cardiac fibrosis. miR-671-5p mimic destabilized fibroblast growth factor receptor 2 (FGFR2) mRNA in a sequence-specific manner and interfered with the fibrotic action of FGFR2. The circSMAD4-miR-671-5p-FGFR2 pathway is thereby the development of cardiac fibrosis. | Jeong, Anna; Lim, Yongwoon; Kook, Taewon; Kwon, Duk-Hwa; Cho, Young Kuk; Ryu, Juhee; Lee, Yun-Gyeong; Shin, Sera; Choe, Nakwon; Kim, Yong Sook; Cho, Hye Jung; Kim, Jeong Chul; Choi, Yoonjoo; Lee, Su-Jin; Kim, Hyung-Seok; Kee, Hae Jin; Nam, Kwang-Il; Ahn, Youngkeun; Jeong, Myung Ho; Park, Woo Jin; Kim, Young-Kook; Kook, Hyun | Chonnam Natl Univ, Res Inst Med Sci, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ, Biomed Sci Grad Program BMSGP, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ, Basic Res Lab Vasc Remodeling, Med Sch, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ, Med Sch, Dept Pharmacol, Hwasun 58128, Jeollanamdo, South Korea; Gwangju Inst Sci & Technol GIST, Coll Life Sci, Gwangju, South Korea; Chosun Univ, Dept Pediat, Sch Med, Gwangju, South Korea; Kyungpook Natl Univ, Collage Pharm, Daegu, South Korea; Kyungpook Natl Univ, Res Inst Pharmaceut Sci, Daegu, South Korea; Chonnam Natl Univ Hosp, Heart Res Ctr, Dept Cardiol, Gwangju, South Korea; Chonnam Natl Univ, Med Sch, Dept Anat, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ Hosp, Dept Surg, Gwangju, South Korea; Chonnam Natl Univ, Combinatorial Tumor Immunotherapy Med Res Ctr, Med Sch, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ Hosp, Biomed Res Inst, Gwangju, South Korea; Chonnam Natl Univ, Med Sch, Dept Forens Med, Hwasun 58128, Jeollanamdo, South Korea; Chonnam Natl Univ, Med Sch, Dept Biochem, Hwasun 58128, Jeollanamdo, South Korea | Choi, Yoonjoo/C-4859-2011; Choi, Youn/AAS-3301-2021; Nam, Kwang/C-4580-2019; Nam, Kwang Il/C-4580-2019; Kook, Hyun/AAR-5405-2021; Kim, Young-Kook/AAE-8306-2020 | 57221964286; 57211203241; 57090616700; 54392133700; 59079228400; 57208255566; 57205530858; 14422691900; 15847530800; 36062269800; 57190298202; 37106971000; 58699824800; 58101594200; 57218341796; 57205335989; 7203003231; 56937721300; 56485157500; 14058468000; 57208862490; 7006255524 | ykk@jnu.ac.kr;kookhyun@jnu.ac.kr; | MOLECULAR THERAPY NUCLEIC ACIDS | MOL THER NUCL ACIDS | 2162-2531 | 34 | SCIE | MEDICINE, RESEARCH & EXPERIMENTAL | 2023 | 6.5 | 13.0 | 1.95 | 2025-06-25 | 14 | 13 | HEART-FAILURE; MOUSE MODEL; HYPERTROPHY; PROTECTS | cardiac fibrosis; circSMAD4; circular RNA; FGFR2; miR-671-5p; MT: Non-coding RNAs; transverse aortic constriction | bromethol; circular ribonucleic acid; circular RNA circsmad4; fibroblast growth factor receptor 2; isoprenaline; microRNA; microRNA 671 5p; small interfering RNA; transforming growth factor beta1; unclassified drug; adult; animal cell; animal experiment; animal model; animal tissue; aortic constriction; Article; bioinformatics; cell activation; cell differentiation; cell isolation; controlled study; differential gene expression; down regulation; fibroblast culture; gene function; gene overexpression; gene targeting; heart fibroblast; heart muscle fibrosis; human; human cell; male; mouse; nonhuman; nonviral gene delivery system; nonviral gene therapy; RNA sequencing; upregulation | English | 2023 | 2023-12-12 | 10.1016/j.omtn.2023.102071 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Development of DNA aptamers for visualization of glial brain tumors and detection of circulating tumor cells | Here, we present DNA aptamers capable of specific binding to glial tumor cells in vitro, ex vivo, and in vivo for visualization diagnostics of central nervous system tumors. We selected the aptamers binding specifically to the postoperative human glial primary tumors and not to the healthy brain cells and meningioma, using a modified process of systematic evolution of ligands by exponential enrichment to cells; sequenced and analyzed ssDNA pools using bioinformatic tools and identified the best aptamers by their binding abilities; determined threedimensional structures of lead aptamers (Gli-55 and Gli-233) with small-angle X-ray scattering and molecular modeling; isolated and identified molecular target proteins of the aptamers by mass spectrometry; the potential binding sites of Gli-233 to the target protein and the role of post-translational modifi-cations were verified by molecular dynamics simulations. The anti-glioma aptamers Gli-233 and Gli-55 were used to detect circulating tumor cells in liquid biopsies. These aptamers were used for in situ, ex vivo tissue staining, histopathological analyses, and fluorescence-guided tumor and PET/CT tumor visualization in mice with xenotransplanted human astrocytoma. The aptamers did not show in vivo toxicity in the preclinical animal study. This study demonstrates the potential applications of aptamers for precise diagnostics and fluores-cence-guided surgery of brain tumors. | Kichkailo, Anna S.; Narodov, Andrey A.; Komarova, Maria A.; Zamay, Tatiana N.; Zamay, Galina S.; Kolovskaya, Olga S.; Erakhtin, Evgeniy E.; Glazyrin, Yury E.; Veprintsev, Dmitry V.; Moryachkov, Roman, V; Zabluda, Vladimir V.; Shchugoreva, Irina; Artyushenko, Polina; Mironov, Vladimir A.; Morozov, Dmitry I.; Khorzhevskii, Vladimir A.; Gorbushin, Anton, V; Koshmanova, Anastasia A.; Nikolaeva, Elena D.; Grinev, Igor P.; Voronkovskii, Ivan I.; Grek, Daniil S.; Belugin, Kirill, V; Volzhentsev, Alexander A.; Badmaev, Oleg N.; Luzan, Natalia A.; Lukyanenko, Kirill A.; Peters, Georgy; Lapin, Ivan N.; Kirichenko, Andrey K.; Konarev, Petr, V; Morozov, Evgeny, V; Mironov, Gleb G.; Gargaun, Ana; Muharemagic, Darija; Zamay, Sergey S.; Kochkina, Elena, V; Dymova, Maya A.; Smolyarova, Tatiana E.; Sokolov, Alexey E.; Modestov, Andrey A.; Tokarev, Nikolay A.; Shepelevich, Nikolay, V; Ozerskaya, Anastasia, V; Chanchikova, Natalia G.; Krat, Alexey, V; Zukov, Ruslan A.; Bakhtina, Varvara I.; Shnyakin, Pavel G.; Shesternya, Pavel A.; Svetlichnyi, Valery A.; Petrova, Marina M.; Artyukhov, Ivan P.; Tomilin, Felix N.; Berezovski, Maxim, V | Prof VF Voino Yasenetsky Krasnoyarsk State Med Uni, 1 Partizana Zheleznyaka, Krasnoyarsk 660022, Russia; Russian Acad Sci, Siberian Branch, Fed Res Ctr Krasnoyarsk Sci Ctr, 50 Akademgorodok, Krasnoyarsk 660036, Russia; Krasnoyarsk Inter Dist Ambulance Hosp, 17 Kurchatova, Krasnoyarsk 660062, Russia; Kirensky Inst Phys, Lab Phys Magnet Phenomena, 50-38 Akademgorodok, Krasnoyarsk 660036, Russia; Siberian Fed Univ, 79 Svobodny Pr, Krasnoyarsk 660041, Russia; Russian Acad Sci, Dept Mol Elect, Fed Res Ctr Krasnoyarsk Sci Ctr, Siberian Branch, 50 Akademgorodok, Krasnoyarsk 660036, Russia; Natl Res Ctr Kurchatov Inst, 1 Akad Kurchatova, Moscow 123182, Russia; Tomsk State Univ, Lab Adv Mat & Technol, Siberian Phys Tech Inst, 36 Lenina, Tomsk 634050, Russia; Krasnoyarsk Reg Pathol Anat Bur, 3d Partizana Zheleznyaka, Krasnoyarsk 660022, Russia; Kyungpook Natl Univ, Dept Chem, 80 Daehak Ro, Daegu 702701, South Korea; Univ Jyvaskyla, Nanosci Ctr, POB 35, Jyvaskyla 40014, Finland; Univ Jyvaskyla, Dept Chem, POB 35, Jyvaskyla 40014, Finland; Federal Sci Res Ctr Crystallog & Photon RAS, AV Shubnikov Inst Crystallog, 59 Leninsky Pr, Moscow 119333, Russia; Fed Med Biol Agcy, Fed Siberian Res Clin Ctr, Krasnoyarsk, Russia; Krasnoyarsk Reg Clin Canc Ctr, 16 1 Ya Smolenskaya, Krasnoyarsk 660133, Russia; Russian Acad Sci, Inst Chem & Chem Technol SB RAS, Branch Fed Res Ctr Krasnoyarsk Sci Ctr, Siberian Branch, Krasnoyarsk 660036, Russia; Univ Ottawa, Dept Chem & Biomol Sci, 10 Marie Curie, Ottawa, ON K1N6N5, Canada; Russian Acad Sci, Inst Chem Biol & Fundamental Med, Siberian Branch, 8 Lavrentyev Ave, Novosibirsk 630090, Russia; Prof VF Voino Yasenetsky Krasnoyarsk State Med Uni, Lab Biomol & Med Technol, 1 Partizana Zheleznyaka, Krasnoyarsk 660022, Russia | Mironov, Vladimir/I-4712-2012; Lapin, Ivan/M-7677-2014; Zamay, Tatiana/D-8443-2014; Koshmanova, Anastasia/HKE-1356-2023; Dymova, Maya/E-8240-2011; Belugin, Kirill/HMW-0483-2023; Voronkovskii, Ivan/KBC-0783-2024; Kolovskaya, Olga/E-8066-2014; Gorbushin, Anton/ABA-9070-2022; Kichkailo, Anna/H-2079-2012; Svetlichnyi, Valery/O-1286-2013; Nikolaeva, Elena/I-8679-2014; Lukyanenko, Kirill/K-7124-2017; Berezovski, Maxim/MZQ-3758-2025; Petrova, Marina/L-5623-2014; Shesternya, Pavel/M-3898-2014; Smolyarova, Tatyana/C-6974-2019; Glazyrin, Yury/P-1135-2014; Voronkovskiy, Ivan/KBC-0783-2024; Peters, Georgy/O-9797-2015; Morozov, Dmitry/Q-2433-2017; Zukov, Ruslan/A-8193-2016; Zamay, Galina/E-8076-2014; Tomilin, Felix/F-3763-2014; Moryachkov, Roman/L-8949-2015; Sokolov, Alexey/B-9923-2014; Artyushenko, Polina/AAO-8226-2020; Kich, Annо/H-2079-2012; Konarev, Petr/W-1380-2017 | 8504768600; 6504325791; 56712526200; 8504768500; 37462346700; 37461620200; 57224568883; 55613657900; 57191412312; 57210322419; 6602148689; 57206772345; 26429141000; 16203310700; 26536517500; 28267763100; 57224568345; 57496329600; 57222529761; 55516438100; 57224572466; 57224130929; 57200599703; 57328214600; 57224125249; 56347942700; 57191378723; 57189001357; 7102202067; 56589607900; 6601921039; 55565541800; 41561851000; 56254439500; 54960964400; 8858514300; 58171636700; 37055492300; 57195672292; 56809521900; 57189344615; 57200599507; 57200596745; 57200601681; 57200600945; 56713024700; 6508181003; 56528471400; 57200245947; 35797340900; 35514642000; 23987271200; 56094993300; 6602246772; 6508080466 | annazamay@yandex.ru; | MOLECULAR THERAPY-NUCLEIC ACIDS | MOL THER NUCL ACIDS | 2162-2531 | 32 | SCIE | MEDICINE, RESEARCH & EXPERIMENTAL | 2023 | 6.5 | 13.0 | 2.43 | 2025-06-25 | 11 | 15 | NUCLEIC-ACID; POSTTRANSLATIONAL MODIFICATIONS; WEB SERVER; EXPRESSION; PROTEIN; PROLIFERATION; GLIOBLASTOMA; PROMOTES; BIOPSY; GLIOMA | astrocytoma; DNA aptamers; fluid biopsy; glial brain tumor; molecular modeling; MT: Oligonucleotides: Diagnostics and Biosensors; oligonucleotide tertiary structure; PET/CT; SELEX; tumor imaging | aptamer; circulating tumor DNA; cyclophosphamide; cyclosporine; epithelial cell adhesion molecule; ketoconazole; myelin basic protein; telomerase; triacylglycerol lipase; adult; aged; animal cell; animal experiment; animal model; animal tissue; Article; axoneme; binding site; brain cell; brain tissue; circular dichroism; circulating tumor cell; clinical article; comparative study; controlled study; DNA library; DNA sequence; ex vivo study; female; flow cytometry; glioblastoma; high throughput sequencing; histopathology; human; immunohistochemistry; in vivo study; laser microscopy; male; middle aged; molecular dynamics; molecular model; mouse; nonhuman; nuclear magnetic resonance imaging; primary tumor; protein secondary structure; three dimensional cell culture; tumor diagnosis; tumor xenograft; X ray crystallography; xenotransplantation; young adult | English | 2023 | 2023-06-13 | 10.1016/j.omtn.2023.03.015 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Development of Static and Dynamic Colorimetric Analysis Techniques Using Image Sensors and Novel Image Processing Software for Chemical, Biological and Medical Applications | Colorimetric sensing techniques for point(s), linear and areal array(s) were developed using image sensors and novel image processing software for chemical, biological and medical applications. Monitoring and recording of colorimetric information on one or more specimens can be carried out by specially designed image processing software. The colorimetric information on real-time monitoring and recorded images or video clips can be analyzed for point(s), line(s) and area(s) of interest for manual and automatic data collection. Ex situ and in situ colorimetric data can be used as signals for process control, process optimization, safety and security alarms, and inputs for machine learning, including artificial intelligence. As an analytical example, video clips of chromatographic experiments using different colored inks on filter papers dipped in water and randomly blinking light-emitting-diode-based decorative lights were used. The colorimetric information on points, lines and areas, with different sizes from the video clips, were extracted and analyzed as a function of time. The video analysis results were both visualized as time-lapse images and RGB (red, green, blue) color/intensity graphs as a function of time. As a demonstration of the developed colorimetric analysis technique, the colorimetric information was expressed as static and time-series combinations of RGB intensity, HSV (hue, saturation and value) and CIE L*a*b* values. Both static and dynamic colorimetric analysis of photographs and/or video files from image sensors were successfully demonstrated using a novel image processing software. | Yoo, Woo Sik; Kim, Jung Gon; Kang, Kitaek; Yoo, Yeongsik | WaferMasters Inc, Dublin, CA 94568 USA; Kyungpook Natl Univ, Inst Humanities Studies, Daegu 41566, South Korea; Dankook Univ, Coll Liberal Arts, Yongin 16890, South Korea | 55665974300; 48161606000; 9638686700; 57205391310 | woosik.yoo@wafermasters.com; | TECHNOLOGIES | TECHNOLOGIES | 2227-7080 | 11 | 1 | ESCI | ENGINEERING, MULTIDISCIPLINARY | 2023 | 4.2 | 13.0 | 0.89 | 2025-06-25 | 5 | 5 | pH indicator; litmus paper; color sensing; colorimetric quantification; dynamic analysis; photograph; video; image processing software | SONG-OF-ENLIGHTENMENT; METAL-TYPE; KOREA | color sensing; colorimetric quantification; dynamic analysis; image processing software; litmus paper; pH indicator; photograph; video | English | 2023 | 2023-02 | 10.3390/technologies11010023 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Generative Adversarial Network-Based Image Conversion Among Different Computed Tomography Protocols and Vendors: Effects on Accuracy and Variability in Quantifying Regional Disease Patterns of Interstitial Lung Disease | Objective: To assess whether computed tomography (CT) conversion across different scan parameters and manufacturers using a routable generative adversarial network (RouteGAN) can improve the accuracy and variability in quantifying interstitial lung disease (ILD) using a deep learning-based automated software.Materials and Methods: This study included patients with ILD who underwent thin-section CT. Unmatched CT images obtained using scanners from four manufacturers (vendors A-D), standard-or low-radiation doses, and sharp or medium kernels were classified into groups 1-7 according to acquisition conditions. CT images in groups 2-7 were converted into the target CT style (Group 1: vendor A, standard dose, and sharp kernel) using a RouteGAN. ILD was quantified on original and converted CT images using a deep learning-based software (Aview, Coreline Soft). The accuracy of quantification was analyzed using the dice similarity coefficient (DSC) and pixel-wise overlap accuracy metrics against manual quantification by a radiologist. Five radiologists evaluated quantification accuracy using a 10-point visual scoring system.Results: Three hundred and fifty CT slices from 150 patients (mean age: 67.6 +/- 10.7 years; 56 females) were included. The overlap accuracies for quantifying total abnormalities in groups 2-7 improved after CT conversion (original vs. converted: 0.63 vs. 0.68 for DSC, 0.66 vs. 0.70 for pixel-wise recall, and 0.68 vs. 0.73 for pixel-wise precision; P < 0.002 for all). The DSCs of fibrosis score, honeycombing, and reticulation significantly increased after CT conversion (0.32 vs. 0.64, 0.19 vs. 0.47, and 0.23 vs. 0.54, P < 0.002 for all), whereas those of ground-glass opacity, consolidation, and emphysema did not change significantly or decreased slightly. The radiologists' scores were significantly higher (P < 0.001) and less variable on converted CT. Conclusion: CT conversion using a RouteGAN can improve the accuracy and variability of CT images obtained using different scan parameters and manufacturers in deep learning-based quantification of ILD. | Hwang, Hye Jeon; Kim, Hyunjong; Seo, Joon Beom; Ye, Jong Chul; Oh, Gyutaek; Lee, Sang Min; Jang, Ryoungwoo; Yun, Jihye; Kim, Namkug; Park, Hee Jun; Lee, Ho Yun; Yoon, Soon Ho; Shin, Kyung Eun; Lee, Jae Wook; Kwon, Woocheol; Sun, Joo Sung; You, Seulgi; Chung, Myung Hee; Gil, Bo Mi; Lim, Jae-Kwang; Lee, Youkyung; Hong, Su Jin; Choi, Yo Won | Univ Ulsan, Asan Med Ctr, Dept Radiol, Coll Med, 86 Olymp Ro 43 Gil, Seoul 05505, South Korea; Univ Ulsan, Res Inst Radiol, Asan Med Ctr, Coll Med, 86 Olymp Ro 43 Gil, Seoul 05505, South Korea; Korea Adv Inst Sci & Technol KAIST, Robot Program, Daejeon, South Korea; Korea Adv Inst Sci & Technol KAIST, Kim Jaechul Grad Sch AI, Daejeon, South Korea; Korea Adv Inst Sci & Technol KAIST, Dept Bio & Brain Engn, Daejeon, South Korea; Coreline Soft Co Ltd, Seoul, South Korea; Sungkyunkwan Univ, Sch Med, Samsung Med Ctr, Dept Radiol, Republic, Seoul, South Korea; Sungkyunkwan Univ, Ctr Imaging Sci, Samsung Med Ctr, Sch Med, Seoul, South Korea; Sungkyunkwan Univ, Dept Hlth Sci & Technol, SAIHST, Seoul, South Korea; Seoul Natl Univ, Seoul Natl Univ Hosp, Coll Med, Dept Radiol, Seoul, South Korea; Soonchunhyang Univ, Bucheon Hosp, Dept Radiol, Bucheon, South Korea; Ewha Womans Univ, Dept Radiol, Seoul Hosp, Seoul, South Korea; Yonsei Univ, Dept Radiol, Wonju Coll Med, Wonju, South Korea; Ajou Univ, Dept Radiol, Sch Med, Suwon, South Korea; Catholic Univ Korea, Bucheon St Marys Hosp, Coll Med, Dept Radiol, Seoul, South Korea; Kyungpook Natl Univ, Dept Radiol, Sch Med, Daegu, South Korea; Hanyang Univ, Coll Med, Dept Radiol, Guri Hosp, Guri, South Korea; Hanyang Univ, Coll Med, Dept Radiol, Seoul Hosp, Seoul, South Korea | Seo, Joon/AAQ-5445-2021; Gil, Bomi/P-7293-2018; Yoon, Soon/AAL-1640-2020; Lee, Ho Yun/D-6086-2012; Kim, Hyun-Jong/X-3662-2019; Ye, Jong/C-1623-2011; Oh, Gyutaek/LGZ-8138-2024; Lee, Sang/B-1029-2013; Lee, Jaewoong/IQS-0514-2023 | 35975085000; 57217029241; 55512425800; 7403237499; 57217029219; 58097828800; 57213153173; 57204474501; 16550058300; 57204474178; 57192502540; 57219956574; 59076156800; 57196139526; 57027170100; 8503993100; 56768845000; 55728272600; 57200688423; 55515341400; 23051270300; 56764862100; 58141134900 | seojb@amc.seoul.kr; | KOREAN JOURNAL OF RADIOLOGY | KOREAN J RADIOL | 1229-6929 | 2005-8330 | 24 | 8 | SCIE | RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING | 2023 | 4.4 | 13.0 | 2.58 | 2025-06-25 | 13 | 12 | Interstitial lung disease; Computed tomography; Quantification; Artificial intelligence | IDIOPATHIC PULMONARY-FIBROSIS; QUANTITATIVE CT INDEXES; HIGH-RESOLUTION CT; AUTOMATED QUANTIFICATION; PNEUMONIA; DIAGNOSIS; SURVIVAL; HRCT | Artificial intelligence; Computed tomography; Interstitial lung disease; Quantification | Aged; Emphysema; Female; Humans; Lung; Lung Diseases, Interstitial; Middle Aged; Pulmonary Emphysema; Tomography, X-Ray Computed; accuracy; adult; aged; algorithm; Article; artificial intelligence; bronchiolitis obliterans organizing pneumonia; chronic hypersensitivity pneumonitis; computer assisted tomography; convolutional neural network; deep learning; emphysema; female; fibrosis; generative adversarial network; ground glass opacity; human; image analysis; interstitial lung disease; interstitial pneumonia; lung cancer; machine learning; major clinical study; male; multicenter study; radiation dose; radiologist; recall; retrospective study; scoring system; thorax radiography; diagnostic imaging; emphysema; interstitial lung disease; lung; lung emphysema; middle aged; procedures; x-ray computed tomography | English | 2023 | 2023-08 | 10.3348/kjr.2023.0088 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |
| ○ | ○ | Article | Heuristic Weight Initialization for Diagnosing Heart Diseases Using Feature Ranking | The advent of Artificial Intelligence (AI) has had a broad impact on life to solve various tasks. Building AI models and integrating them with modern technologies is a central challenge for researchers. These technologies include wearables and implants in living beings, and their use is known as human augmentation, using technology to enhance human abilities. Combining human augmentation with artificial intelligence (AI), especially after the recent successes of the latter, is the most significant advancement in their applicability. In the first section, we briefly introduce these modern applications in health care and examples of their use cases. Then, we present a computationally efficient AI-driven method to diagnose heart failure events by leveraging actual heart failure data. The classifier model is designed without conventional models such as gradient descent. Instead, a heuristic is used to discover the optimal parameters of a linear model. An analysis of the proposed model shows that it achieves an accuracy of 84% and an F-1 score of 0.72 with only one feature. With five features for diagnosis, the accuracy achieved is 83%, and the F-1 score is 0.74. Moreover, the model is flexible, allowing experts to determine which variables are more important than others when implementing diagnostic systems. | Lolaev, Musulmon; Naik, Shraddha M.; Paul, Anand; Chehri, Abdellah | Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu 41566, South Korea; Royal Mil Coll Canada, Dept Math & Comp Sci, Kingston, ON, Canada | Chehri, Abdellah/X-9516-2019; Lolaev, Musulmon/ADF-0781-2022; Paul, Anand/V-6724-2017; Naik, Shraddha/AAX-9472-2020 | 57226384015; 57200942684; 56650522400; 55666436200 | paul.editor@gmail.com; | TECHNOLOGIES | TECHNOLOGIES | 2227-7080 | 11 | 5 | ESCI | ENGINEERING, MULTIDISCIPLINARY | 2023 | 4.2 | 13.0 | 0.18 | 2025-06-25 | 0 | 1 | Artificial Intellience; data mining; gradient decent; feature selection; heart disease | Artificial Intellience; data mining; feature selection; gradient decent; heart disease | English | 2023 | 2023-10 | 10.3390/technologies11050138 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Article | Image-Based Quantification of Color and Its Machine Vision and Offline Applications | Image-based colorimetry has been gaining relevance due to the wide availability of smart phones with image sensors and increasing computational power. The low cost and portable designs with user-friendly interfaces, and their compatibility with data acquisition and processing, are very attractive for interdisciplinary applications from art, the fashion industry, food science, medical science, oriental medicine, agriculture, geology, chemistry, biology, material science, environmental engineering, and many other applications. This work describes the image-based quantification of color and its machine vision and offline applications in interdisciplinary fields using specifically developed image analysis software. Examples of color information extraction from a single pixel to predetermined sizes/shapes of areas, including customized regions of interest (ROIs) from various digital images of dyed T-shirts, tongues, and assays, are demonstrated. Corresponding RGB, HSV, CIELAB, Munsell color, and hexadecimal color codes, from a single pixel to ROIs, are extracted for machine vision and offline applications in various fields. Histograms and statistical analyses of colors from a single pixel to ROIs are successfully demonstrated. Reliable image-based quantification of color, in a wide range of potential applications, is proposed and the validity is verified using color quantification examples in various fields of applications. The objectivity of color-based diagnosis, judgment and control can be significantly improved by the image-based quantification of color proposed in this study. | Yoo, Woo Sik; Kang, Kitaek; Kim, Jung Gon; Yoo, Yeongsik | WaferMasters Inc, Dublin, CA 94568 USA; Kyungpook Natl Univ, Inst Humanities Studies, Daegu 41566, South Korea; Dankook Univ, Coll Liberal Arts, Yongin 16890, South Korea | 55665974300; 9638686700; 48161606000; 57205391310 | woosik.yoo@wafermasters.com;ysyoophd@dankook.ac.kr; | TECHNOLOGIES | TECHNOLOGIES | 2227-7080 | 11 | 2 | ESCI | ENGINEERING, MULTIDISCIPLINARY | 2023 | 4.2 | 13.0 | 0.53 | 2025-06-25 | 2 | 3 | color sensing; colorimetry; image processing; image analysis; machine vision; offline analysis | SONG-OF-ENLIGHTENMENT; METAL-TYPE; KOREA | color sensing; colorimetry; image analysis; image processing; machine vision; offline analysis | English | 2023 | 2023-04 | 10.3390/technologies11020049 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||||
| ○ | ○ | Review | Recent Technological Progress of Fiber-Optical Sensors for Bio-Mechatronics Applications | Bio-mechatronics is an interdisciplinary scientific field that emphasizes the integration of biology and mechatronics to discover innovative solutions for numerous biomedical applications. The broad application spectrum of bio-mechatronics consists of minimally invasive surgeries, rehabilitation, development of prosthetics, and soft wearables to find engineering solutions for the human body. Fiber-optic-based sensors have recently become an indispensable part of bio-mechatronics systems, which are essential for position detection and control, monitoring measurements, compliance control, and various feedback applications. As a result, significant advancements have been introduced for designing and developing fiber-optic-based sensors in the past decade. This review discusses recent technological advancements in fiber-optical sensors, which have been potentially adapted for numerous bio-mechatronic applications. It also encompasses fundamental principles, different types of fiber-optical sensors based on recent development strategies, and characterizations of fiber Bragg gratings, optical fiber force myography, polymer optical fibers, optical tactile sensors, and Fabry-Perot interferometric applications. Hence, robust knowledge can be obtained regarding the technological enhancements in fiber-optical sensors for bio-mechatronics-based interdisciplinary developments. Therefore, this review offers a comprehensive exploration of recent technological advances in fiber-optical sensors for bio-mechatronics. It provides insights into their potential to revolutionize biomedical and bio-mechatronics applications, ultimately contributing to improved patient outcomes and healthcare innovation. | Abdhul Rahuman, Mohomad Aqeel; Kahatapitiya, Nipun Shantha; Amarakoon, Viraj Niroshan; Wijenayake, Udaya; Silva, Bhagya Nathali; Jeon, Mansik; Kim, Jeehyun; Ravichandran, Naresh Kumar; Wijesinghe, Ruchire Eranga | Univ Sri Jayewardenepura, Fac Technol, Dept Mat & Mech Technol, Pitipana 10200, Homagama, Sri Lanka; Univ Sri Jayewardenepura, Fac Engn, Dept Comp Engn, Nugegoda 10250, Sri Lanka; Kyungpook Natl Univ, Coll IT Engn, Sch Elect & Elect Engn, 80 Daehak Ro, Daegu 41566, South Korea; Korea Basic Sci Inst, Ctr Sci Instrumentat, 169-148 Gwahak Ro, Daejeon 34133, South Korea; Sri Lanka Inst Informat Technol, Dept Elect & Elect Engn, Fac Engn, Malabe 10115, Sri Lanka | Silva, Bhagya/AAW-1014-2021; Kim, Jinhyun/AAK-3695-2020; RAVICHANDRAN, NARESH KUMAR/D-2190-2017; Kahatapitiya, Nipun Shantha/IYJ-5055-2023; Wijesinghe, Ruchire/K-3797-2016; Wijenayake, Udaya/AAY-8146-2021 | 58782451500; 58781581100; 58782675000; 55547801900; 57192304387; 24171094000; 7601373350; 57125825900; 56018152300 | aqeelabdhulrahuman@gmail.com;nipunshantha@gmail.com;aavniroshan@gmail.com;udayaw@sjp.ac.lk;nathali.slv@sjp.ac.lk;msjeon@knu.ac.kr;jeehk@knu.ac.kr;nareshr9169@kbsi.re.kr;eranga.w@sliit.lk; | TECHNOLOGIES | TECHNOLOGIES | 2227-7080 | 11 | 6 | ESCI | ENGINEERING, MULTIDISCIPLINARY | 2023 | 4.2 | 13.0 | 0.78 | 2025-06-25 | 12 | 17 | bio-mechatronics; fiber-optical sensors; force myography; polymer optical fiber; optical tactile sensors; Fabry-Perot interferometry | FABRY-PEROT-INTERFEROMETER; OPTICAL COHERENCE TOMOGRAPHY; MINIMALLY INVASIVE SURGERY; STRAIN; ROBOT; SYSTEM; FORCES | bio-mechatronics; Fabry–Perot interferometry; fiber-optical sensors; force myography; optical tactile sensors; polymer optical fiber | English | 2023 | 2023-12 | 10.3390/technologies11060157 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | |||
| ○ | ○ | Article | A Geospatial Analysis-Based Method for Railway Route Selection in Marine Glaciers: A Case Study of the Sichuan-Tibet Railway Network | Marine glaciers play a significant role in shaping landforms due to their erosive nature coupled with their surrounding environment. During this process, they pose a natural hazard threat to man-made infrastructure. The dynamic nature of these glaciers poses a particular threat, especially to railway infrastructure constructed in remote areas with glacial activity. Substantial research has been undertaken on the role of threats posed by marine glaciers to railway infrastructure. However, a detailed study of favorable glacier landforms prior to railway construction has yet to be explored. In this study, we propose a geospatial analysis-based method to determine the favorable most landforms shaped by marine glaciers for railway network route selection. This study provides a novel approach by first analyzing the availability of four major favorable landforms shaped by marine glaciers (glacier canyons, valley shoulders, moraine terraces, and ancient dammed lake basins), then proposes a railway route selection method for marine glacier distribution areas involving three steps. First, it is necessary to understand the basic situation of regional glaciers; then, to determine a feasible location for the railway based on judgment of the direct and indirect action areas of glaciers; and finally, through a thematic study of glacial geomorphology, to devise corresponding strategies for using glacial landforms to optimize the railway route. In order to verify the feasibility of the proposed method, it was implemented in the Palong Zangbo watershed of the Sichuan-Tibet railway network. Utilizing the power function method, the glacier basin areas of 22 glacier canyons along the Sichuan-Tibet railway line were identified and the maximum annual average velocity of 75 glaciers over the past ten years was calculated by offset tracking technology. The results indicate that the proposed optimization strategies utilizing glacier canyons for a short and straight route scheme and leveraging moraine terraces for a high-line scheme can provide comprehensive guidance for railway route selection in marine glacial areas. | Deng, Tao; Sharafat, Abubakar; Wie, Young Min; Lee, Ki Gang; Lee, Euiong; Lee, Kang Hoon | Hanyang Univ, Dept Civil & Environm Engn, Seoul 04763, South Korea; Kyungpook Natl Univ, Sch Architectural Civil Environm & Energy Engn, Daegu 41566, South Korea; Kyonggi Univ, Dept Mat Engn, Suwon 16227, South Korea; Daegu Univ, Dept Environm Engn, 201 Daegudae Ro, Gyongsan Si 38453, South Korea; Catholic Univ Korea, Dept Energy & Environm Engn, 43 Jibong Ro, Bucheon Si 14662, South Korea | ; Sharafat, Abubakar/ITW-2048-2023 | 58569526800; 57204630290; 23471529200; 7501503555; 58569424400; 55623492300 | diasyong@catholic.ac.kr; | REMOTE SENSING | REMOTE SENS-BASEL | 2072-4292 | 15 | 17 | SCIE | ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY;REMOTE SENSING | 2023 | 4.2 | 13.2 | 0.75 | 2025-06-25 | 5 | 5 | GIS; glacier shape landforms; marine glaciers; railway line selection method; route plan optimization | LAKE OUTBURST FLOODS; DAMMED LAKE; VELOCITY; PLATEAU; VALLEY | GIS; glacier shape landforms; marine glaciers; railway line selection method; route plan optimization | Geographic information systems; Geomorphology; Glacial geology; Railroad transportation; Railroads; Glacier shape landform; Line selections; Marine glacier; Optimisations; Railway line; Railway line selection method; Railway route selection; Route plan; Route plan optimization; Selection methods; Landforms | English | 2023 | 2023-09 | 10.3390/rs15174175 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | A Novel Framework for Correcting Satellite-Based Precipitation Products for Watersheds with Discontinuous Observed Data, Case Study in Mekong River Basin | Satellite-based precipitation (SP) data are gaining scientific interest due to their advantage in producing high-resolution products with quasi-global coverage. However, since the major reliance of precipitation data is on the distinctive geographical features of each location, they remain at a considerable distance from station-based data. This paper examines the effectiveness of a convolutional autoencoder (CAE) architecture in pixel-by-pixel bias correction of SP products for the Mekong River Basin (MRB). Two satellite-based products (TRMM and PERSIANN-CDR) and a gauge-based product (APHRODITE) are gridded rainfall products mined in this experiment. According to the estimated statistical criteria, the CAE model was effective in reducing the gap between SP products and benchmark data both in terms of spatial and temporal correlations. The two corrected SP products (CAETRMM and CAECDR) performed competitively, with CAE TRMM appearing to have a slight advantage over CAE CDR, however, the difference was minor. This study's findings proved the effectiveness of deep learning-based models (here CAE) for bias correction of SP products. We believe that this technique will be a feasible alternative for delivering an up-to-current and reliable dataset for MRB studies, given that the sole available gauge-based dataset for this area has been out of date for a long time. | Lee, Giha; Nguyen, Duc Hai; Le, Xuan-Hien | Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, 2559 Gyeongsang daero, Sangju 37224, South Korea; Thuyloi Univ, Fac Water Resources Engn, 175 Tay Son, Hanoi 10000, Vietnam; Kyungpook Natl Univ, Disaster Prevent Emergency Management Inst, 2559 Gyeongsang daero, Sangju 37224, South Korea | ; Le, Xuan-Hien/AAZ-9166-2021; Nguyen, Hai/AAD-8210-2020 | 35069799400; 57215097506; 57209735659 | hienlx@knu.ac.kr; | REMOTE SENSING | REMOTE SENS-BASEL | 2072-4292 | 15 | 3 | SCIE | ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY;REMOTE SENSING | 2023 | 4.2 | 13.2 | 1.05 | 2025-06-25 | 5 | 7 | APHRODITE; Mekong River basin; PERSIANN-CDR; precipitation bias correction; satellite precipitation; TRMM | BIAS-CORRECTION; DENSE NETWORK; MICROWAVE; CMORPH | APHRODITE; Mekong River basin; PERSIANN-CDR; precipitation bias correction; satellite precipitation; TRMM | Clock and data recovery circuits (CDR circuits); Pixels; Rain; Rain gages; Rivers; Satellites; Watersheds; APHRODITE; Auto encoders; Bias correction; Mekong river basins; PERSIANN; PERSIANN-CDR; Precipitation bias correction; Precipitation products; Satellite precipitation; TRMM; Deep learning | English | 2023 | 2023-02 | 10.3390/rs15030630 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Review | Aftermath of nanomaterials on lipid profile of microalgae as a radical fuel supplement-A review | Traditional fossil fuels used today have innumerable adverse effects on man and environment. Fuel derived microalgae is reported to be quite a healthier and eco-friendly alternative, as it contains lesser sulphur contents. This current review focuses on the application of nanoparticles in conjugation with microalgae for fuel quality production based on their effect on algal biomass and lipid profile. Processing of the microalgae leads to fuel generation of different viability and precision. Cultivation mode used, harvesting technique utilized and extraction procedure followed will determine the fuel quality and cost associated with it. Nanoparticles can complement any nutritional deficiencies pertaining to growing microalgae in wastewater or ponds. They can stimulate rapid absorption of nutrients and lipid accumulation with antibacterial properties. Aluminium nano -particles have been reported to enhance the growth in Chlorella sp. by 19 % in a span of 4 days. Lower con-centrations seem to favour the growth and biomass quality in microalgae. Iron nanoparticles incorporated in nanofibers of polymer are reported to have higher capture of gas molecules as well as an increase in biomass (794 mg/L) in Chlorella fusca. However, the limit of nanoparticles administration varies from species to species. Iron and zinc nanoparticles have been administered in maximum dosages up to 1000 mg/L till date. The current review highlights the possible application of nanomaterials on microalgal growth and their effect on lipid accumulation, which determines the quality of fuel generated by these strains. | Dey, Nibedita; Vickram, Sundaram; Thanigaivel, S.; Manikandan, S.; Subbaiya, R.; Karmegam, Natchimuthu; Kim, Woong; Govarthanan, Muthusamy | Saveetha Inst Med & Tech Sci SIMATS, Saveetha Sch Engn, Dept Biotechnol, Chennai 602105, Tamil Nadu, India; SRM Inst Sci & Technol, Fac Sci & Humanities, Dept Biotechnol, Kattankulathur 603203, Tamil Nadu, India; Copperbelt Univ, Sch Math & Nat Sci, Dept Biol Sci, Jambo Dr,POB 21692, Kitwe, Zambia; Govt Arts Coll Autonomous, PG & Res Dept Bot, Salem 636007, Tamil Nadu, India; Kyungpook Natl Univ, Dept Environm Engn, Daegu 41566, South Korea | Muthusamy, Govarthanan/C-1491-2014; Natchimuthu, Karmegam/J-4745-2019; S, Manikandan/GZM-7135-2022; Subbaiya, R/AAR-2948-2021; S, Vickram/ABG-9459-2020; dey, nibedita/AAG-6776-2021; Govarthanan, Muthusamy/C-1491-2014; Karmegam, Natchimuthu/J-4745-2019 | 57216549253; 55257689800; 55624925900; 55213168500; 55263515700; 6506043230; 55581636400; 54881927600 | kanishkarmegam@gmail.com;elshine@knu.ac.kr;gova.muthu@gmail.om; | FUEL | FUEL | 0016-2361 | 1873-7153 | 333 | SCIE | ENERGY & FUELS;ENGINEERING, CHEMICAL | 2023 | 6.7 | 13.2 | 7.01 | 2025-06-25 | 29 | 61 | Microalgae; Nanomaterials; Algal biomass; Lipid accumulation; Biofuel generation | OXIDE NANOPARTICLES; CHLORELLA-VULGARIS; FRESH-WATER; CHLAMYDOMONAS-REINHARDTII; SILVER NANOPARTICLES; GREEN-ALGAE; TOXICITY; MEMBRANE; CYTOTOXICITY; FEASIBILITY | Algal biomass; Biofuel generation; Lipid accumulation; Microalgae; Nanomaterials | Biofuels; Biomass; Cultivation; Fossil fuels; Iron; Microorganisms; Nanoparticles; Nanostructured materials; 'current; Adverse effect; Algal biomass; Biofuel generation; Eco-friendly; Fuel quality; Iron nanoparticles; Lipid accumulations; Lipid profile; Micro-algae; Microalgae | English | 2023 | 2023-02-01 | 10.1016/j.fuel.2022.126444 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Review | Artificial intelligence driven hydrogen and battery technologies-A review | The world has recognized the importance of renewable energy and is moving towards a rapid transition to renewable energy and energy efficiency. Advances in electrolysis and cost reductions, as well as the availability of renewable energy sources, have paved the way for the creation of green hydrogen, a completely carbon-free fuel, making it a real contender to revolutionize the energy market. The recent incorporation of artificial intelligence into the energy sector has provided a major breakthrough for the industry. Artificial intelligence algorithms and models such as artificial neural networks, machine learning, support vector regression, and fuzzy logic models can greatly contribute to improving hydrogen energy production, storage, and transportation. They play an important role in predicting various parameters, safety protocols and management of hydrogen production. Furthermore, advances in artificial intelligence are expected to bring huge state-of-the-art technologies and tools for hydrogen and battery technology that could help solve the current energy-oriented crises and problems. This review provides insight into the feasibility of state-of-the-art artificial intelligence for hydrogen and battery technology. The primary focus is to demonstrate the contribution of various AI techniques, its algorithms and models in hydrogen energy industry, as well as smart battery manufacturing, and optimization. Meanwhile, AI models integrated into battery technology play a key role in material discovery, battery design, improved battery manufacturing, diagnostic tools, and optimal battery management systems for smart batteries. With improved performance and longer life, these smart batteries will be integrated into modern robotics, electric vehicles, aerospace and other fields. | Ramesh, A. Sai; Vigneshwar, S.; Vickram, Sundaram; Manikandan, S.; Subbaiya, R.; Karmegam, N.; Kim, Woong | Vel Tech High Tech Dr Rangarajan Dr Sakunthala Eng, Chennai 600062, Tamil Nadu, India; Brandenburg Tech Univ Cottbus Senftenberg, D-01968 Senftenberg, Germany; Saveetha Inst Med & Tech Sci SIMATS, Saveetha Sch Engn, Dept Biotechnol, Chennai 602105, Tamil Nadu, India; Copperbelt Univ, Sch Math & Nat Sci, Dept Biol Sci, Jambo Dr,POB 21692, Kitwe, Zambia; Govt Arts Coll, Dept Bot, Salem 636007, Tamil Nadu, India; Kyungpook Natl Univ, Dept Environm Engn, Daegu 41566, South Korea | Natchimuthu, Karmegam/J-4745-2019; S, Manikandan/GZM-7135-2022; Subbaiya, R/AAR-2948-2021; S, Vickram/ABG-9459-2020; A, Sai Ramesh/B-6641-2012; Karmegam, Natchimuthu/J-4745-2019; A, SAI RAMESH/KIL-2469-2024 | 55750747700; 57992345200; 55257689800; 55213168500; 55263515700; 6506043230; 55581636400 | ramsubbubio@gmail.com;kanishkarmegam@gmail.com;elshine@knu.ac.kr; | FUEL | FUEL | 0016-2361 | 1873-7153 | 337 | SCIE | ENERGY & FUELS;ENGINEERING, CHEMICAL | 2023 | 6.7 | 13.2 | 8.17 | 2025-06-25 | 47 | 73 | AI models; Artificial intelligence; Battery technology; Hydrogen technology; Renewable energy | RENEWABLE ENERGY; NEURAL-NETWORK; FUEL-CELL; SUSTAINABLE ENERGY; MANAGEMENT-SYSTEM; OPTIMIZATION; STATE; CONSUMPTION; PERFORMANCE; GENERATION | AI models; Artificial intelligence; Battery technology; Hydrogen technology; Renewable energy | Battery management systems; Cost reduction; Energy efficiency; Energy policy; Fuzzy logic; Fuzzy neural networks; Hydrogen production; Hydrogen storage; Machine learning; Secondary batteries; AI model; Battery manufacturing; Battery technology; Carbon-free; Costs reduction; Hydrogen Energy; Hydrogen technologies; Rapid transitions; Renewable energies; Renewable energy source; Renewable energy resources | English | 2023 | 2023-04-01 | 10.1016/j.fuel.2022.126862 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Assessment of Active Ground Subsidence in the Dibrugarh and Digboi Areas of Assam, Northeast India, Using the PSInSAR Technique | Ground deformation on a regional to local scale is the consequence of a wide range of natural processes such as tectonic and anthropogenic activities. Globally, the over-extraction of groundwater and hydrocarbon exploitation are the primary causes of ground subsidence. The current study demonstrates regional scale ground subsidence analysis of the Dibrugarh and Digboi regions of Brahmaputra alluvial plain, Assam, Northeast India. To understand the ongoing surface deformation satellite base, the RADAR technique has been applied using SENTINEL-1A data, which were acquired between 15 October 2015 to 25 January 2022. The assessment carried out via the time series analysis of the radar data suggests that the Dibrugarh area is subsiding at a rate of similar to 5 mm/yr, whereas the Digboi is deforming at a much faster rate (+/- 22 mm/yr) than Dibrugarh. The presence of active faults in the subsurface and associated deformation is another reason for active ground subsidence. The outcomes of the current study validate that the study area is currently undergoing active subsurface deformation caused by both endogenic as well as exogenic processes. Furthermore, our Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) and satellite-based analysis suggest that the over-exploitation of the natural resources is enhancing the rate of deformation in the Brahmaputra alluvial plain in the northeast of India. | Lakhote, Abhishek; Kothyari, Girish Ch; Patidar, Atul Kumar; Changmai, Jayshree; Borgohain, Rashmi; Choudhury, Tanupriya; Um, Jung-Sup | Acad Sinica, Inst Biomed Sci, Taipei 11529, Taiwan; Univ Petr & Energy Studies, Dept Petr Engn & Earth Sci, Dehra Dun 248007, Uttarakhand, India; KSKV Kachchh Univ, Dept Earth & Environm Sci, Bhuj 370001, Gujarat, India; Symbiosis Int Univ, Symbiosis Inst Technol, CSE Dept, Lavale Campus, Pune 412115, Maharashtra, India; Kyungpook Natl Univ, Coll Social Sci, Dept Geog, Daegu 37224, South Korea | Choudhury, Tanupriya/AAB-8947-2020; Kothyari, Girish/AAS-3076-2020; Um, Jung-Sup/F-5351-2018 | 57218395500; 12792682800; 8434240100; 58672852700; 58672058900; 57193140084; 35173565000 | abhirl@earth.sinica.edu.tw;kothyarigirish_k@rediffmail.com;apatidar@ddn.upes.ac.in;ayshree.113629@stu.upes.ac.in;rashmiborgohain321@gmail.com;atulpatidar@gmail.com;tanupriya.choudhury@sitpune.edu.in;jsaeom@knu.ac.kr; | REMOTE SENSING | REMOTE SENS-BASEL | 2072-4292 | 15 | 20 | SCIE | ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY;REMOTE SENSING | 2023 | 4.2 | 13.2 | 0.75 | 2025-06-25 | 4 | 5 | Digboi; Dibrugarh; subsidence; persistent scatterer interferometric synthetic aperture radar (PSInSAR); Brahmaputra alluvial plain | BRAHMAPUTRA RIVER; SEDIMENT DISCHARGE; DEFORMATION; INSAR; VALLEY; MECHANISM; HIMALAYA; SYSTEM; HILLS | Brahmaputra alluvial plain; Dibrugarh; Digboi; persistent scatterer interferometric synthetic aperture radar (PSInSAR); subsidence | Faulting; Groundwater; Interferometry; Synthetic aperture radar; Time series analysis; 'current; Alluvial plains; Brahmaputra; Brahmaputra alluvial plain; Dibrugarh; Digboi; Interferometric synthetic aperture radars; Northeast india; Persistent scatterer interferometric synthetic aperture radar; Persistent scatterers; Subsidence | English | 2023 | 2023-10 | 10.3390/rs15204963 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Construction Site Multi-Category Target Detection System Based on UAV Low-Altitude Remote Sensing | On-site management of construction sites has always been a significant problem faced by the construction industry. With the development of UAVs, their use to monitor construction safety and progress will make construction more intelligent. This paper proposes a multi-category target detection system based on UAV low-altitude remote sensing, aiming to solve the problems of relying on fixed-position cameras and a single category of established detection targets when mainstream target detection algorithms are applied to construction supervision. The experimental results show that the proposed method can accurately and efficiently detect 15 types of construction site targets. In terms of performance, the proposed method achieves the highest accuracy in each category compared to other networks, with a mean average precision (mAP) of 82.48%. Additionally, by applying it to the actual construction site, the proposed system is confirmed to have comprehensive detection capability and robustness. | Liang, Han; Cho, Jongyoung; Seo, Suyoung | Kyungpook Natl Univ, Dept Civil Engn, Daegu 41566, South Korea | ; Seo, Suyoung/AAB-8465-2020; Liang, Han/HPC-7877-2023 | 57222620902; 58162455600; 35198914000 | syseo@knu.ac.kr; | REMOTE SENSING | REMOTE SENS-BASEL | 2072-4292 | 15 | 6 | SCIE | ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY;REMOTE SENSING | 2023 | 4.2 | 13.2 | 0.45 | 2025-06-25 | 5 | 5 | object detection; attention mechanism; remote sensing; UAV inspection system | CRANE PRODUCTIVITY; EQUIPMENT; SAFETY; WORKERS; IDENTIFICATION; NETWORKS | attention mechanism; object detection; remote sensing; UAV inspection system | Aircraft detection; Construction industry; Object recognition; Remote sensing; Unmanned aerial vehicles (UAV); Attention mechanisms; Construction sites; Detection system; Inspection system; Low altitudes; Objects detection; Remote-sensing; Site management; Targets detection; UAV inspection system; Object detection | English | 2023 | 2023-03 | 10.3390/rs15061560 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Evaluation of Numerous Kinetic Energy-Rainfall Intensity Equations Using Disdrometer Data | Calculating rainfall erosivity, which is the capacity of rainfall to dislodge soil particles and cause erosion, requires the measurement of the rainfall kinetic energy (KE). Direct measurement of KE has its own challenges, owing to the high cost and complexity of the measuring instruments involved. Consequently, the KE is often approximated using empirical equations derived from rainfall intensity (I-r) inputs in the absence of such instruments. However, the KE-I-r equations strongly depend on local climate patterns and measurement methods. Therefore, this study aims to compare and evaluate the efficacy of 27 KE-I-r equations with observed data. Based on a re-analysis, we also propose an exponential KE-I-r equation for the entire Korean site, and the spatial distribution of its parameter in the equation is also discussed. In this investigation, we used an optical disdrometer (OTT Parsivel(2)) to gather data in Sangju City (Korea) between June 2020 and December 2021. The outputs of this study are shown as follows: (1) The statistically most accurate estimates of KE expenditure and KE content in Sangju City are obtained using power-law equations given by Sanchez-Moreno et al. and exponential equations published by Lee and Won, respectively. (2) The suggested KE-I-r equation applied to the entire Korean site exhibits a comparable general correlation with the observed data. The parameter maps indicate a high variance in geography. | Van, Linh Nguyen; Le, Xuan-Hien; Nguyen, Giang V.; Yeon, Minho; Do, May-Thi Tuyet; Lee, Giha | Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, 2559 Gyeongsang Daero, Sangju 37224, South Korea; Kyungpook Natl Univ, Disaster Prevent Emergency Management Inst, 2559 Gyeongsang Daero, Sangju 37224, South Korea; Thuyloi Univ, Fac Water Resources Engn, 175 Tay Son, Hanoi 10000, Vietnam | Le, Xuan-Hien/AAZ-9166-2021; Nguyen, Giang/GQZ-4595-2022 | 57297359100; 57209735659; 57297771000; 57223436971; 58059176800; 35069799400 | leegiha@knu.ac.kr; | REMOTE SENSING | REMOTE SENS-BASEL | 2072-4292 | 15 | 1 | SCIE | ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;IMAGING SCIENCE & PHOTOGRAPHIC TECHNOLOGY;REMOTE SENSING | 2023 | 4.2 | 13.2 | 0.9 | 2025-06-25 | 7 | 7 | disdrometer; rainfall kinetic energy; rainfall intensity; Korea | RAINDROP SIZE DISTRIBUTIONS; SOIL LOSSES; EROSION; DETACHMENT; PARAMETERS; RADAR | disdrometer; Korea; rainfall intensity; rainfall kinetic energy | Kinetics; Rain; Disdrometer data; Disdrometers; Intensity equation; Korea; Measurements of; Observed data; Rainfall erosivity; Rainfall intensity; Rainfall kinetic energy; Soil particles; Kinetic energy | English | 2023 | 2023-01 | 10.3390/rs15010156 | 바로가기 | 바로가기 | 바로가기 | 바로가기 | ||
| ○ | ○ | Article | Guiding the optimization of membraneless microfluidic fuel cells via explainable artificial intelligence: Comparative analyses of multiple machine learning models and investigation of key operating parameters | Membraneless microfluidic fuel cells (MMFCs) offer great potential for clean energy production, but their expense and tedious optimization process have limited their wider use. Machine learning (ML) algorithms have shown promise in improving the optimization of MMFCs, but the "black box" nature of these models has caused uncertainty and limited their adoption. To address this, we conducted the first study that implements the explainable artificial intelligence (XAI) approach to gain in-depth insights into multiple ML optimization models' predictions and the impacts of operating parameters on MMFCs' performance. Among 27 investigated models that are generated based on nine ML and three bio-inspired evolutionary algorithms, the combination of a decision tree and the particle swarm optimization algorithm is the most effective model that satisfied all criteria of high time efficiency (execution time = 3.194 s), accuracy (squared correlation coefficient = 0.999), and optimum power density identification (0.278 mWcm 2). The ML-optimized power density is 239.024% increased, which is 3.4 times higher than the average power density obtained without optimization. We further utilized the XAI model to comprehensively examine the impacts of operating parameters, providing valuable decision aids for the researchers, technicians, and engineers in manufacturing optimal power MMFCs at a significantly lower cost and faster rate. | Nguyen, Dang Dinh; Tanveer, Muhammad; Mai, Hang-Nga; Pham, Thinh Quy Duc; Khan, Haroon; Park, Cheol Woo; Kim, Gyu Man | Kyungpook Natl Univ, Sch Mech Engn, Daegu 41566, South Korea; Natl Res Inst Mech Engn, 4 Pham Dong St, Hanoi, Vietnam; Hanoi Univ Business & Technol, 29A Vinh Tuy, Hanoi, Vietnam; Univ Liege, MSM Unit, Allee Decouverte 9 B52-3, B-4000 Liege, Belgium | Tanveer, Muhammad/AAO-9360-2021; Mai, Hang-Nga/Q-9865-2018 | 57447274900; 23486949200; 56964780900; 57215673039; 14521310700; 7408416474; 55664733000 | gyuman.kim@knu.ac.kr; | FUEL | FUEL | 0016-2361 | 1873-7153 | 349 | SCIE | ENERGY & FUELS;ENGINEERING, CHEMICAL | 2023 | 6.7 | 13.2 | 1.52 | 2025-06-25 | 13 | 13 | Membraneless microfluidic fuel cells; Artificial intelligence; Optimization; Shapley additive explanations; Black -box interpretation | PERFORMANCE; ELECTRODES; CHANNEL | Artificial intelligence; Black-box interpretation; Membraneless microfluidic fuel cells; Optimization; Shapley additive explanations | Biomimetics; Decision support systems; Fuel cells; Machine learning; Microfluidics; Particle swarm optimization (PSO); Black boxes; Black-box interpretation; Machine-learning; Membraneless; Membraneless microfluidic fuel cell; Microfluidic fuel cell; Operating parameters; Optimisations; Shapley; Shapley additive explanation; Decision trees | English | 2023 | 2023-10-01 | 10.1016/j.fuel.2023.128742 | 바로가기 | 바로가기 | 바로가기 | 바로가기 |
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