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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
○ ○ Article ⓘ Two-pathway spatiotemporal representation learning for extreme water temperature prediction ⓘ Accurate predictions of extreme water temperatures are criticalto understanding the variability of the marine environment and reducing marine disasters maximized by global warming. In this study, we propose a twopathway framework with separated spatial and temporal encoders for accurate prediction of water temperature, especially extremely high water temperature, through effective spatiotemporal representation learning. The spatial and temporal encoder networks based on the Transformer's self-attention mechanism performs the task of predicting the water temperature time series at the 16 coastal locations around the Korean Peninsula for the seven consecutive days ahead at daily intervals with various combinations of patch embedding methods, positional embedding for spatial features. Comparative experiments with conventional deep convolutional and recurrent networks are also conducted for comparison. By comparing and assessing these results, the proposed two-pathway framework can improve the predictability of extremely high coastal water temperature by better capturing spatiotemporal interrelationships and long-range dependencies from open ocean and regional sea, and further determines the optimal architectural details of self-attention-based spatial and temporal encoders. Furthermore, to examine the explainability of the proposed model and its consistency with domain knowledge, spatial and temporal attention maps are visualized and analyzed that represents weights for spatiotemporal input sequences that are more relevant to predict for future predictions. ⓘ Kim, Jinah; Kim, Taekyung; Kim, Jaeil ⓘ Korea Inst Ocean Sci & Technol, Coastal Disaster Res Ctr, Busan, South Korea; Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea ⓘ ⓘ 55720345100; 59471665700; 57211615348 ⓘ jakim@kiost.ac.kr;tkkim@kiost.ac.kr;jaeilkim@knu.ac.kr; ⓘ ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE ⓘ ENG APPL ARTIF INTEL ⓘ 0952-1976 1873-6769 131 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ENGINEERING, ELECTRICAL & ELECTRONIC;ENGINEERING, MULTIDISCIPLINARY ⓘ 2024 8 2.6 2.26 2025-05-07 ⓘ 7 7 Two -pathway representation learning; Spatiotemporal representation learning; Self -attention mechanism; Multi -step -ahead prediction; Sea surface temperature; Marine heatwaves ⓘ SEA-SURFACE TEMPERATURE; EAST/JAPAN SEA; LSTM MODEL; CURRENTS ⓘ Marine heatwaves; Multi-step-ahead prediction; Sea surface temperature; Self-attention mechanism; Spatiotemporal representation learning; Two-pathway representation learning ⓘ Domain Knowledge; Embeddings; Forecasting; Global warming; Oceanography; Recurrent neural networks; Signal encoding; Surface waters; Attention mechanisms; Heatwaves; Marine heatwave; Multi-step-ahead predictions; Sea surface temperature; Sea surfaces; Self-attention mechanism; Spatiotemporal representation learning; Surface temperatures; Two-pathway representation learning; Surface temperature; Water temperature ⓘ English 2024 2024-05 10.1016/j.engappai.2023.107718 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ VisNet: Spatiotemporal self-attention-based U-Net with multitask learning for joint visibility and fog occurrence forecasting ⓘ To provide skillful prediction of horizontal visibility and fog occurrence over consecutive 12-h ahead forecasts with hourly time interval, a spatiotemporal self-attention-based U-Net architecture with multitask learning is proposed and applied to the overall Korean Peninsula. The proposed spatiotemporal learning framework facilitates the capture of multiple spatiotemporal teleconnections and lags between multiple variables from numerical reanalysis grid data over the Korean Peninsula and in-situ measurements at the 155 automatic weather station locations. In addition, multitask learning, which simultaneously performs a regression task for predicting visibility distance and a classification task for predicting fog occurrence, is applied to overcome the data imbalance problem presented by the occurrence of hazardous events by sharing the representation of the tasks used to characterize low visibility and fog occurrence and further generalize the prediction performance. Extensive ablation studies and comparative experiments with state-of-the-art (SOTA) models are conducted to determine the combination of input variables, input/output sequence lengths, data source, spatial resolution of the dataset, level of joint learning of multiple tasks, and network architecture necessary to obtain the optimal model architecture and experimental conditions. Moreover, three-dimensional analysis of geographical location, land-use purpose, and temporal parameters such as season, horizontal visibility distance threshold, and weather code classes is performed using various evaluation metrics suitable for regression and classification tasks of predicting low visibility and fog. Furthermore, the reliability of the model was examined through trained attention maps and probability calculations for predicted fog events compared to actual fog occurrences. Compared to SOTA, the proposed model achieved an average root-mean-square error improvement of about 380 m for the horizontal visibility distance prediction and an improvement in fog occurrence classification accuracy of about 6% when predicting for 1-h ahead forecast. ⓘ Kim, Jinah; Cha, Jieun; Kim, Taekyung; Lee, Hyesook; Yu, Ha-Yeong; Suh, Myoung-Seok ⓘ Korea Inst Ocean Sci & Technol, Coastal Disaster Res Ctr, Busan, South Korea; Korea Meteorol Adm, Natl Inst Meteorol Sci, AI Meteorol Res Div, Jeju, South Korea; Kyungpook Natl Univ, Sch Comp Sci & Engn, Daegu, South Korea; Kongju Natl Univ, Dept Atmospher Sci, Gongju, South Korea ⓘ Suh, Myoung-Seok/AAI-6915-2020 ⓘ 55720345100; 59226354300; 59471665700; 36118148900; 57215419115; 8439180500 ⓘ jakim@kiost.ac.kr;jecha0326@korea.kr;hyesook.lee01@korea.kr;hakkk96@smail.kongju.ac.kr;sms416@kongju.ac.kr; ⓘ ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE ⓘ ENG APPL ARTIF INTEL ⓘ 0952-1976 1873-6769 136 SCIE AUTOMATION & CONTROL SYSTEMS;COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE;ENGINEERING, ELECTRICAL & ELECTRONIC;ENGINEERING, MULTIDISCIPLINARY ⓘ 2024 8 2.6 0 2025-05-07 ⓘ 0 0 Atmospheric visibility prediction; Fog detection; Multi-step-ahead forecasts; Multitask learning ⓘ PREDICTION; EVENTS; MODEL ⓘ Atmospheric visibility prediction; Fog detection; Multi-step-ahead forecasts; Multitask learning; Spatiotemporal self-attention-based U-net ⓘ Land use; Learning systems; Mean square error; Network architecture; Visibility; Weather forecasting; Weather information services; Atmospheric visibility; Atmospheric visibility prediction; Classification tasks; Fog detection; Horizontal visibility; Multi-step-ahead forecast; Multisteps; Multitask learning; Spatiotemporal self-attention-based U-net; Visibility prediction; Fog ⓘ English 2024 2024-10 10.1016/j.engappai.2024.108967 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Efficient lithium recovery from simulated brine using a hybrid system: Direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX) ⓘ Seawater reverse osmosis (SWRO) brine is a readily available resource hub in many countries, fulfilling the country's freshwater need by SWRO, yet lower in a concentration of high-demand elements like Li. This study outlines developing a novel hybrid system that combines direct contact membrane distillation (DCMD) and electrically switched ion exchange (ESIX) to facilitate simultaneous SWRO brine enrichment followed by selective Li recovery. The DCMD process concentrates the SWRO brine utilizing electrospun nanofiber membranes (ENMs) composed of polyvinylidene fluoride (PVDF). Incorporating reduced graphene oxide (rGO) nanoparticles augments the morphological, thermal, and mechanical stability of the PVDF ENMs. The water contact angle (WCA) of the 1-rGO/PVDF ENM stands at 142.08 degrees, a testament to an enhanced hydrophobic property which resulted in a 12 % freshwater recovery from simulated SWRO brine and a 2.4-fold increase in Li+ concentration. The durability of the 1-rGO/PVDF ENM is evident in its minimal 11 % reduction in WCA after 15 h of brine concentration. In the context of hybrid operation, a Li-selective LiAlO2 electrode, coupled with an activated carbon counter electrode, demonstrated remarkable Li recovery for Li capture solutions enriched by the rGOPVDF membrane in the DCMD phase. Compared to the Li concentration in the DCMD feed, sequential Li capture and release cycles recovered 91.8 % of Li, thereby underlining the critical role of the hybrid mode operation in concentrating Li from simulated brine solutions. ⓘ Gulied, Mona; Zavahir, Sifani; Elmakki, Tasneem; Park, Hyunwoong; Gago, Guillermo Hijos; Shon, Ho Kyong; Han, Dong Suk ⓘ Qatar Univ, Ctr Adv Mat, Doha, Qatar; Qatar Univ, Coll Arts & Sci, Mat Sci & Technol Program, Doha, Qatar; Kyungpook Natl Univ, Sch Energy Engn, Daegu 41566, South Korea; Acciona Agua, O&M Desalinat Middle East& Oceania, Doha, Qatar; Univ Technol Sydney, ARC Res Hub Nutrients Circular Econ, Ctr Technol Water & Wastewater, Sch Civil & Environm Engn,Fac Engn & IT, Ultimo, NSW 2007, Australia; Qatar Univ, Coll Engn, Dept Chem Engn, Doha, Qatar ⓘ Shon, Ho Kyong/P-7057-2015; Shon, Ho/P-7057-2015; Han, Dong SuK/AAX-9333-2021 ⓘ 57204041429; 56493120500; 57221280532; 7601565583; 57978054400; 6701629946; 36139213900 ⓘ dhan@qu.edu.qa; ⓘ DESALINATION ⓘ DESALINATION ⓘ 0011-9164 1873-4464 572 SCIE ENGINEERING, CHEMICAL;WATER RESOURCES ⓘ 2024 9.8 2.7 4.04 2025-05-07 ⓘ 13 13 Membrane distillation; Electrospun nanofiber membrane; Electrochemically switchable ion exchange; Brine management; Lithium recovery ⓘ ZERO LIQUID DISCHARGE; ELECTROSPUN NANOFIBER MEMBRANE; FUNCTIONALIZED GRAPHENE OXIDE; PVDF MEMBRANES; REVERSE ELECTRODIALYSIS; DESALINATION; WATER; RO; CRYSTALLIZATION; REDUCTION ⓘ Brine management; Electrochemically switchable ion exchange; Electrospun nanofiber membrane; Lithium recovery; Membrane distillation ⓘ Activated carbon; Aluminum compounds; Distillation; Electrodes; Fluorine compounds; Graphene; Ion exchange; Ions; Lithium; Mechanical stability; Membranes; Nanofibers; Recovery; Brine management; Direct contact membrane distillation; Electrochemically switchable ion exchange; Electrospun nanofiber membrane; Electrospun nanofibers; Lithium recoveries; Membrane distillation; Nanofiber membrane; Seawater reverse osmosis; Switchable; brine; distillation; hybrid; ion exchange; lithium; membrane; nanoparticle; reverse osmosis; Hybrid systems ⓘ English 2024 2024-03-01 10.1016/j.desal.2023.117127 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Review ⓘ Enhanced lithium separation from brines using nanofiltration (NF) technology: A review ⓘ This study investigates the dynamics of the lithium (Li) market, focusing particularly on the use of nanofiltration (NF) membranes for Li extraction from various brines. It covers a range of aqueous resources, including brines from seawater desalination, oil- and gas-produced waters, salt lakes, and geothermal aquifers, emphasizing the value of repurposing leftover brines. The research compares current and emerging brine-based Li recovery methods, underlining their advantages over traditional sources. A critical aspect of the study is a comprehensive review of recent advancements in Li-selective NF membranes, exploring materials design principles and the development of membranes with enhanced Li+ selectivity. The integration of NF systems in Li recovery, as supported by various studies, appears promising. This review also discusses the practical challenges and potential advancements in designing targeted Li+ ion-selective membranes, aiming to spur continued research in this crucial area. Ultimately, the study provides an extensive analysis of Li extraction from various brine sources using NF technology, positioning it as an effective strategy in the field. ⓘ Sajna, M. S.; Elmakki, Tasneem; Zavahir, Sifani; Tariq, Haseeb; Abdulhameed, Adil; Park, Hyunwoong; Shon, Ho Kyong; Han, Dong Suk ⓘ Qatar Univ, Ctr Adv Mat CAM, POB 2713, Doha, Qatar; Qatar Univ, Coll Arts & Sci, Mat Sci & Technol Master Program, POB 2713, Doha, Qatar; Qatar Univ, Coll Engn, Dept Chem Engn, POB 2713, Doha, Qatar; Kyungpook Natl Univ, Sch Energy Engn, Daegu 41566, South Korea; Univ Technol Sydney UTS, Ctr Technol Water & Wastewater CTWW, Sch Civil & Environm Engn, Ultimo, NSW, Australia ⓘ Shon, Ho/P-7057-2015; Han, Dong SuK/AAX-9333-2021 ⓘ 56019426600; 57221280532; 56493120500; 57922243000; 57869714300; 7601565583; 6701629946; 36139213900 ⓘ dhan@qu.edu.qa; ⓘ DESALINATION ⓘ DESALINATION ⓘ 0011-9164 1873-4464 592 SCIE ENGINEERING, CHEMICAL;WATER RESOURCES ⓘ 2024 9.8 2.7 1.18 2025-05-07 ⓘ 10 10 Resource recovery; Lithium; Nanofiltration (NF); Brine; Selective separation ⓘ SALT-LAKE BRINES; HIGH MG2+/LI+ RATIO; CRITICAL METALS; MEMBRANE; RECOVERY; WATER; MAGNESIUM; OPPORTUNITIES; EXTRACTION; LI ⓘ Brine; Lithium; Nanofiltration (NF); Resource recovery; Selective separation ⓘ Ion selective membranes; Lithium deposits; 'current; Lithium extraction; Lithium recoveries; Nanofiltration; Oil and gas; Repurposing; Resource recovery; Salt lakes; Seawater desalination; Selective separation; aquifer; brine; desalination; filtration; lithium; membrane; separation; Nafion membranes ⓘ English 2024 2024-12-21 10.1016/j.desal.2024.118148 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Hypothalamic neuronal activation in non-human primates drives naturalistic goal-directed eating behavior ⓘ Maladaptive feeding behavior is the primary cause of modern obesity. While the causal influence of the lateral hypothalamic area (LHA) on eating behavior has been established in rodents, there is currently no primatebased evidence available on naturalistic eating behaviors. We investigated the role of LHA GABAergic (LHA GABA ) neurons in eating using chemogenetics in three macaques. LHA GABA neuron activation significantly increased naturalistic goal-directed behaviors and food motivation, predominantly for palatable food. Positron emission tomography and magnetic resonance spectroscopy validated chemogenetic activation. Resting-state functional magnetic resonance imaging revealed that the functional connectivity (FC) between the LHA and frontal areas was increased, while the FC between the frontal cortices was decreased after LHA GABA neuron activation. Thus, our study elucidates the role of LHA GABA neurons in eating and obesity therapeutics for primates and humans. ⓘ Ha, Leslie Jaesun; Yeo, Hyeon-Gu; Kim, Yu Gyeong; Baek, Inhyeok; Baeg, Eunha; Lee, Young Hee; Won, Jinyoung; Jung, Yunkyo; Park, Junghyung; Jeon, Chang-Yeop; Kim, Keonwoo; Min, Jisun; Song, Youngkyu; Park, Jeong-Heon; Nam, Kyung Rok; Son, Sangkyu; Yoo, Seng Bum Michael; Park, Sung-hyun; Choi, Won Seok; Lim, Kyung Seob; Choi, Jae Yong; Cho, Jee-Hyun; Lee, Youngjeon; Choi, Hyung Jin ⓘ Seoul Natl Univ, Neurosci Res Inst, Wide River Inst Immunol, Dept Biomed Sci,Coll Med, Seoul, South Korea; Korea Res Inst Biosci & Biotechnol KRIBB, Natl Primate Res Ctr, Cheongju, South Korea; Korea Natl Univ Sci & Technol, KRIBB Sch Biosci, Daejeon, South Korea; Korea Res Inst Biosci & Biotechnol KRIBB, Futurist Anim Resource & Res Ctr, Cheongju, South Korea; Kyungpook Natl Univ, Sch Life Sci, Plus KNU Creat Biores Grp BK21, Daegu, South Korea; Korea Basic Sci Inst, Ctr Bioimaging & Translat Res, Cheongju, South Korea; Korea Inst Radiol & Med Sci, Div Appl RI, Seoul, South Korea; Korea Natl Univ Sci & Technol, Radiol & Med Oncol Sci, Seoul, South Korea; Inst Basic Sci, Ctr Neurosci Imaging Res, Suwon, South Korea; Sungkyunkwan Univ, Dept Biomed Engn, Suwon, South Korea; Sungkyunkwan Univ, Dept Intelligent Precis Healthcare Convergence, Suwon, South Korea; Incheon Natl Univ, Dept Nanobioengn, Incheon, South Korea; Incheon Natl Univ, Ctr Brain Machine Interface, Incheon, South Korea; Seoul Natl Univ, Dept Brain & Cognit Sci, Seoul, South Korea ⓘ Yoo, Seng Bum/GLS-9760-2022; Choi, Won-Seok/IZP-8016-2023; Park, Junghyung/KXQ-7522-2024; Lee, Youngjeon/LZH-8969-2025 ⓘ 58507191200; 56263762800; 57219109450; 57572456200; 6504508711; 57194560863; 56018670200; 58507422900; 55671747100; 56522472100; 57204572034; 58507641700; 55494114900; 58508311300; 42661925500; 57224213304; 57201909274; 57219112045; 57215086073; 36470521900; 57138980800; 36805120300; 57199022088; 55724357700 ⓘ smhany@kirams.re.kr;jhcho@kbsi.re.kr;neurosci@kribb.re.kr;hjchoi@snu.ac.kr; ⓘ NEURON ⓘ NEURON ⓘ 0896-6273 1097-4199 112 13 SCIE NEUROSCIENCES ⓘ 2024 15 2.7 1.92 2025-05-07 ⓘ 3 4 ⓘ LATERAL HYPOTHALAMUS; CHEMOGENETIC DISCONNECTION; CONSUMMATORY BEHAVIORS; HAND DEXTERITY; MOTIVATION; CIRCUITS; RECEPTOR; CORTEX; REWARD; PET ⓘ chemogenetics; GABAergic neuron; lateral hypothalamus area; motivation; MRS; naturalistic eating behaviors; non-human primates; obesity; PET; Rs-fMRI ⓘ Animals; Feeding Behavior; Female; GABAergic Neurons; Goals; Hypothalamic Area, Lateral; Hypothalamus; Macaca mulatta; Magnetic Resonance Imaging; Male; Neurons; Positron-Emission Tomography; 4 aminobutyric acid receptor; atropine; clozapine; enrofloxacin; flumazenil; gadolinium; ketamine; ketoprofen; 4 aminobutyric acid receptor; animal experiment; animal model; animal tissue; Article; behavior; behavior assessment; body weight; brain region; cell activation; computer assisted tomography; controlled study; deep learning; feeding behavior; female; food intake; functional connectivity; functional magnetic resonance imaging; gene expression; genetic analysis; heart rate; histology; hypothalamus; image analysis; immunofluorescence; immunohistochemistry; immunoreactivity; latent period; locomotion; nerve cell; nerve cell network; nonhuman; nuclear magnetic resonance spectroscopy; obesity; positron emission tomography; primate; proton nuclear magnetic resonance; radiochemistry; viral gene delivery system; vital sign; animal; diagnostic imaging; hypothalamus; lateral hypothalamus; male; motivation; nerve cell; nuclear magnetic resonance imaging; physiology; rhesus monkey ⓘ English 2024 2024-07-03 10.1016/j.neuron.2024.03.029 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Integrated seawater hub: A nexus of sustainable water, energy, and resource generation ⓘ This review paper explores the potential for seawater desalination plants to operate as integrated hubs for addressing the increasing demand for water, energy, mineral resources, and foods, particularly in resource-scarce regions. The integrated seawater hub (ISH) utilizes seawater as a common input, provides multipurpose facilities that can cater to freshwater and agricultural requirements, brine processing for salt and minerals extraction, promotes energy recovery, and mitigates greenhouse gas emissions by employing renewable and alternative energy technologies, thereby bolstering sustainable development. Capitalizing on seawater, the most abundant resource on our planet, these plants can contribute significantly to the sustainability sector. This study delves into the essential aspects of integrating mainly the seawater reverse osmosis (SWRO) desalination process to create a portfolio of clean, sustainable water supplies, energy sources, and other valuable products. Furthermore, this paper seeks to offer a comprehensive analysis within a unified framework, incorporating various established technologies that demonstrate the multifaceted capabilities of desalination plants. This includes the delivery of a freshwater supply and effectively repurposing the brine, the primary liquid waste product from these facilities. Emphasizing the potential to achieve a circular economy centered on brine management, our review presents an environmentally friendly approach to urban development. The study also explores emerging research domains where seawater desalination plants utilize renewable energy sources like solar, wind, and biomass to produce clean water and green hydrogen. It suggests that further research and investment in the realm of integrated seawater resource hubs could yield significant benefits for both local communities and the wider global community. ⓘ Sajna, M. S.; Elmakki, Tasneem; Schipper, Kira; Ihm, Seungwon; Yoo, Youngwook; Park, Byungsung; Park, Hyunwoong; Shon, Ho Kyong; Han, Dong Suk ⓘ Qatar Univ, Ctr Adv Mat, POB 2713, Doha, Qatar; Qatar Univ, Coll Arts & Sci, Mat Sci & Technol Master Program, POB 2713, Doha, Qatar; Qatar Univ, Coll Arts & Sci, Ctr Sustainable Dev, Algal Technol Program, POB 2713, Doha, Qatar; Saline Water Convers Corp SWCC, Water Technol Innovat Inst & Res Advancement WTIIR, POB 8284, Al Jubail 31951, Saudi Arabia; Kyungpook Natl Univ, Sch Energy Engn, Daegu 41566, South Korea; Univ Technol, Sch Civil & Environm Engn, POB 129, Broadway, Sydney, NSW 2007, Australia; Qatar Univ, Coll Engn, Dept Chem Engn, POB 2713, Doha, Qatar ⓘ Ihm, Seungwon/JCN-7268-2023; Shon, Ho/P-7057-2015; Schipper, Kira/HJH-4433-2023; Han, Dong SuK/AAX-9333-2021; Shon, Ho Kyong/P-7057-2015; Yoo, Youngwook/JLM-6603-2023 ⓘ 58668273500; 57221280532; 55445734500; 56304111000; 58668273600; 55902077700; 7601565583; 6701629946; 36139213900 ⓘ dhan@qu.edu.qa; ⓘ DESALINATION ⓘ DESALINATION ⓘ 0011-9164 1873-4464 571 SCIE ENGINEERING, CHEMICAL;WATER RESOURCES ⓘ 2024 9.8 2.7 4.34 2025-05-07 ⓘ 36 38 Integrated seawater hub; Seawater reverse osmosis; Brine; Renewable energy; Hydrogen; Biomass ⓘ PRESSURE RETARDED OSMOSIS; METAL-IONS CS; OF-THE-ART; REVERSE-OSMOSIS; FLOATING PHOTOBIOREACTOR; FRESH-WATER; DESALINATION; BRINE; ELECTRODIALYSIS; RECOVERY ⓘ Biomass; Brine; Hydrogen; Integrated seawater hub; Renewable energy; Seawater reverse osmosis ⓘ Desalination; Economics; Gas emissions; Greenhouse gases; Renewable energy resources; Reverse osmosis; Seawater; Solar power generation; Sustainable development; Urban growth; Water conservation; Water supply; Integrated seawater hub; Renewable energies; Seawater desalination plants; Seawater reverse osmosis; Sustainable energy; Sustainable resources; Sustainable water; Water energy; Water generation; Waters resources; alternative energy; biomass; brine; desalination; hydrogen; reverse osmosis; seawater; sustainability; water resource; Investments ⓘ English 2024 2024-02-01 10.1016/j.desal.2023.117065 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Predicting and optimizing forward osmosis membrane operation using machine learning ⓘ Forward osmosis (FO) utilizes a draw solution to transport water across a semipermeable membrane, offering energy-efficient water treatment and resource recovery. This study explores machine learning models to predict FO performance at pilot scale, overcoming the limitations of traditional mathematical models in terms of computational load and time. By analyzing data from FO pilot experiments, we compare various algorithms, including multiple linear regression (MLR), support vector machine (SVM), k-nearest neighbor (KNN), decision tree, and artificial neural networks (ANNs). Among these, ANN was evaluated as most suitable and further optimized with input features of the permeate flux, membrane area, feed and draw solution flow rates, and feed and draw solution concentrations. The optimized ANN model demonstrated high accuracy for water flux prediction, with R-2 values of 0.9886 and RMSE values of 0.3498 Lm(-2) h(-1). Additionally, an ANN model is developed to predict operating pressures under various FO operation conditions. By integrating FO water flux and operating pressure predictions, our model identifies optimal operating conditions that balance specific energy consumption and water recovery. Our findings offer insights and practical guidance for process engineers to efficiently design and operate FO systems to minimize energy consumption and maximize recovery. ⓘ Nurhayati, Mita; Jeong, Kwanho; Lee, Haelyong; Park, Jongkwan; Hong, Bum Ui; Kang, Ho Geun; Shon, Ho Kyong; Lee, Sungyun ⓘ Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, 2559 Gyeongsang-daero, Sangju 37224, South Korea; Indonesia Univ Educ, Dept Chem, Setiabudhi 229, Bandung 40154, Indonesia; Chosun Univ, Dept Environm Engn, Gwangju 61452, South Korea; Changwon Natl Univ, Dept Environm & Energy Engn, Chang Won 51140, Gyeongsangnamdo, South Korea; Inst Adv Engn, Bio Resource Ctr, Yongin 17180, South Korea; BIN TECH KOREA Co Ltd, A 3S52,158-10 Sajik Daero 361 Beon Gil, Cheongju, Chungcheongbuk, South Korea; Univ Technol Sydney, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia; Kyungpook Natl Univ, Dept Environm & Safety Engn, 2559 Gyeongsang Daero, Sangju Si 37224, South Korea ⓘ Shon, Ho/P-7057-2015; Nurhayati, Mita/GOJ-8523-2022 ⓘ 57222139830; 56659062100; 59236666200; 56969869700; 56910662400; 58303400900; 6701629946; 36438267000 ⓘ sungyunlee@knu.ac.kr; ⓘ DESALINATION ⓘ DESALINATION ⓘ 0011-9164 1873-4464 592 SCIE ENGINEERING, CHEMICAL;WATER RESOURCES ⓘ 2024 9.8 2.7 1.01 2025-05-07 ⓘ 3 5 Forward osmosis; Machine learning; Performance prediction; Operation optimization; Specific energy consumption ⓘ PLATE-AND-FRAME; WATER; PERFORMANCE; MODEL; SCALE; DIFFUSION; SYSTEMS ⓘ Forward osmosis; Machine learning; Operation optimization; Performance prediction; Specific energy consumption ⓘ Osmosis membranes; Prediction models; Artificial neural network modeling; Draw solutions; Feed solution; Forward osmosis; Machine-learning; Neural-networks; Operations optimization; Performance prediction; Specific energy consumption; Water flux; artificial neural network; energy efficiency; energy use; machine learning; membrane; numerical model; osmosis; support vector machine; Adversarial machine learning ⓘ English 2024 2024-12-21 10.1016/j.desal.2024.118154 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Updated measurement method for transparent exopolymer particles (TEPs) and their precursors with insights into efficient monitoring ⓘ Transparent exopolymer particles (TEPs) and their precursors play a critical role in various environmental processes, including contaminant transport and membrane fouling. This study presents an improved Alcian blue (AB) staining-based TEP measurement method and investigates spectroscopic proxies to enhance analysis convenience and reproducibility. The proposed method employs deionized (DI) water and sonication to extract particulate TEPs (TEP0.4 mu m) and particulate TEPs and their precursors (TEP10kDa), thus eliminating the need for concentrated sulfuric acid and simplifying the procedure with an integrated standard curve. This AB-DI method had a low detection limit of 5 mu g Xeq/L for a 1 L sample volume, allowing precise measurements to be made across a broad concentration range. When used in wastewater treatment and microfiltration membrane filtrates, the AB-DI method provided insights into the removal mechanisms of TEPs and their precursors. This study also identified effective spectroscopic proxies by correlating TEP concentrations with optical properties, achieving remarkable estimation accuracy using the UV254 absorbance of TEP-extracted solutions. The UV254 and fluorescence intensities of the protein-like peaks in raw water samples demonstrated significant potential as proxies for TEPs and their precursors. The proposed AB-DI method represents a viable approach for the real-time TEP monitoring for desalination and water reuse processes. ⓘ Nguyen, Hoang Dung; Nurhayati, Mita; Pham, Thi Thuy Trang; Lee, Byung Joon; Park, Jongkwan; Shon, Ho Kyong; Lee, Sungyun ⓘ Kyungpook Natl Univ, Dept Adv Sci & Technol Convergence, 2559 Gyeongsang Daero, Sangju 37224, South Korea; Indonesia Univ Educ, Dept Chem, Setiabudhi 229, Bandung 40154, Indonesia; Kyungpook Natl Univ, Dept Environm & Safety Engn, 2559 Gyeongsang Daero, Sangju 37224, South Korea; Changwon Natl Univ, Dept Environm & Energy Engn, Chang Won 51140, South Korea; Univ Technol Sydney, Sch Civil & Environm Engn, Sydney, NSW 2007, Australia ⓘ Nurhayati, Mita/GOJ-8523-2022; Shon, Ho/P-7057-2015 ⓘ 59157101700; 57222139830; 57850478200; 56016052400; 56969869700; 6701629946; 36438267000 ⓘ sungyunlee@knu.ac.kr; ⓘ DESALINATION ⓘ DESALINATION ⓘ 0011-9164 1873-4464 591 SCIE ENGINEERING, CHEMICAL;WATER RESOURCES ⓘ 2024 9.8 2.7 0.67 2025-05-07 ⓘ 3 3 Transparent exopolymer particle; Alcian blue; TEP 0.4 mu m; TEP 10kDa; Spectroscopic proxy ⓘ SEAWATER REVERSE-OSMOSIS; ORGANIC-MATTER; REMOVAL EFFICIENCY; WATER-TREATMENT; MARINE; FRESH; ULTRAFILTRATION; BIOFILM; INDICATOR; MEMBRANES ⓘ Alcian blue; Spectroscopic proxy; TEP<sub>0.4μm</sub>; TEP<sub>10kDa</sub>; Transparent exopolymer particle ⓘ Deionized water; Desalination; Membrane fouling; Microfiltration; Optical properties; Spectroscopic analysis; Wastewater reclamation; Wastewater treatment; Water conservation; Alcian blues; Efficient monitoring; Measurement methods; Particulates; Spectroscopic proxy; Transparent exopolymer particle0.4μm; Transparent exopolymer particle10kda; Transparent exopolymer particles; monitoring; particulate matter; polymer; spectroscopy; sulfuric acid; Water filtration ⓘ English 2024 2024-12-11 10.1016/j.desal.2024.117975 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ A Hybrid Transformer Framework for Efficient Activity Recognition Using Consumer Electronics ⓘ In the field of research on wireless visual sensor networks, human activity recognition (HAR) using consumer electronics is now an emerging research area in both the academic and industrial sectors, with a diverse range of applications. However, the implementation of HAR through computer vision methods is highly challenging on consumer electronic devices, due to their limited computational capabilities. This means that mainstream approaches in which computationally complex contextual networks and variants of recurrent neural networks are used to learn long-range spatiotemporal dependencies have achieved limited performance. To address these challenges, this paper presents an efficient framework for robust HAR for consumer electronics devices, which is divided into two main stages. In the first stage, convolutional features from the multiply₁₇ layer of a lightweight MobileNetV3 are employed to balance the computational complexity and extract the most salient contextual features ( $7\times 7\times 576\times 30$ ) from each video. In the second stage, a sequential residual transformer network (SRTN) is designed in a residual fashion to effectively learn the long-range temporal dependencies across multiple video frames. The temporal multi-head self-attention module and residual strategy of the SRTN enable the proposed method to discard non-relevant features and to optimise the spatiotemporal feature vector for efficient HAR. The performance of the proposed model is evaluated on three challenging HAR datasets, and is found to yield high levels of accuracy of 76.1428%, 96.6399%, and 97.3130% on the HMDB51, UCF101, and UCF50 datasets, respectively, outperforming a state-of-the-art method for HAR. ⓘ Hussain, Altaf; Khan, Samee Ullah; Khan, Noman; Bhatt, Mohammed Wasim; Farouk, Ahmed; Bhola, Jyoti; Baik, Sung Wook ⓘ Sejong Univ, Seoul 143747, South Korea; Kyungpook Natl Univ, Sch Elect Engn, Daegu 41566, South Korea; Model Inst Engn & Technol, Dept Comp Sci & Engn, Jammu 181122, India; South Valley Univ, Fac Comp & Artificial Intelligence, Dept Comp Sci, Hurghada 83523, Egypt; Chitkara Univ, Inst Engn & Technol, Rajpura 140401, India ⓘ Khan, Noman/ABI-3725-2020; Farouk, Ahmed/F-7874-2015; Khan, Dr. Noman/ABI-3725-2020; Khan, Samee/AAA-3302-2019; HUSSAIN, ALTAF/JCN-7515-2023; Farouk, Ahmed/JKI-2398-2023; Baik, Sung/ABC-3969-2022; Bhatt, Mohammed wasim/HJP-7481-2023 ⓘ 57212425772; 59045429700; 57219205088; 57222961220; 56814623800; 26421594300; 7102833923 ⓘ a.hussain@ieee.org;samee@ieee.org;nomank3797@ieee.org;wasimmohammad71@gmail.com;ahmed.farouk@sci.svu.edu.eg;jyoti.1189@chitkara.edu.in;sbaik@sejong.ac.kr; ⓘ IEEE TRANSACTIONS ON CONSUMER ELECTRONICS ⓘ IEEE T CONSUM ELECTR ⓘ 0098-3063 1558-4127 70 4 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS ⓘ 2024 10.9 2.9 3.51 2025-04-16 ⓘ 8 14 Feature extraction; Consumer electronics; Human activity recognition; Computational modeling; Transformers; Visualization; Computer architecture; Human action recognition; wireless visual sensor networks; consumer electronics; video classification; surveillance system; transformer network ⓘ NEURAL-NETWORKS; FEATURES; LSTM ⓘ consumer electronics; Human action recognition; surveillance system; transformer network; video classification; wireless visual sensor networks ⓘ Industrial research; Pattern recognition; Recurrent neural networks; Security systems; Wireless sensor networks; Activity recognition; Consumer electronic devices; Human activity recognition; Human-action recognition; Learn+; Performance; Surveillance systems; Transformer network; Video classification; Wireless visual sensors networks; Complex networks ⓘ English 2024 2024-11 10.1109/tce.2024.3373824 ⓘ 바로가기 바로가기 바로가기 바로가기
○ Article ⓘ Deep Reinforcement Learning-based Physical Layer Security Framework for Internet of Medical Things ⓘ The Internet of Medical Things (IoMT) is transforming modern healthcare information systems by connecting a diverse array of medical devices and sensors. However, significant security and privacy challenges arise when handling confidential medical data during transmission. This paper addresses these challenges by proposing a Physical Layer Security (PLS) framework integrated with Cell-Free Massive Multiple Input Multiple Output (CF-mMIMO) to enhance security in IoMT environments. The framework introduces a safe zone (SZ), a protected area surrounding legitimate healthcare devices to prevent access by eavesdroppers. This spatial segmentation enables precise beamforming within the SZ while amplifying artificial noise (AN) outside it, significantly boosting the secrecy rate. Additionally, the framework dynamically selects communication devices based on channel quality and orthogonality, optimizing network resources, reducing inter-user interference, and ensuring high-quality communication in densely deployed healthcare settings. Simulation results confirm that our approach adapts to and leverages the spatial dynamics of eavesdroppers, maintaining high secrecy rates even in scenarios with increased eavesdropper presence, thus keeping sensitive medical data secure and unreadable to unauthorized entities. © 1975-2011 IEEE. ⓘ Razaq, Mian Muaz; Jiao, Yan; Peng, Limei; Ho, Pin-Han; Chen, Yuguang; Dong, Fangjie ⓘ Kyungpook National University, School of Computer Science and Engineering, Daegu, 41566, South Korea; University of Electronic Science and Technology of China, Shenzhen Institute for Advanced Study, Guangdong, China; Kyungpook National University, School of Computer Science and Engineering, Daegu, 41566, South Korea, University of Electronic Science and Technology of China, Shenzhen Institute for Advanced Study, Guangdong, China; University of Electronic Science and Technology of China, Shenzhen Institute for Advanced Study, Guangdong, China, Department of Electrical and Computer Engineering, University of Waterloo, ON, Canada; Shenzhen Unicom, Innovation Business Capability Center, China; National Health Commission's Center for Statistics and Information, China ⓘ ⓘ 57221661906; 59488542400; 7201574271; 7402211578; 59489615100; 59489819900 ⓘ ⓘ IEEE Transactions on Consumer Electronics ⓘ IEEE T CONSUM ELECTR ⓘ 0098-3063 1558-4127 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS ⓘ 2024 10.9 2.9 0.32 2025-05-07 ⓘ 1 ⓘ ⓘ Cell-Free Massive MIMO; Deep Reinforcement Learning; Internet of Medical Things; Physical Layer Security; Secrecy Rate Maximization ⓘ ⓘ English Article in press 2024 10.1109/tce.2024.3521386 ⓘ 바로가기 바로가기 바로가기
○ ○ Article ⓘ Effect of atractylenolide III on zearalenone-induced Snail1-mediated epithelial-mesenchymal transition in porcine intestinal epithelium ⓘ Background The intestinal epithelium performs essential physiological functions, such as nutrient absorption, and acts as a barrier to prevent the entry of harmful substances. Mycotoxins are prevalent contaminants found in animal feed that exert harmful effects on the health of livestock. Zearalenone (ZEA) is produced by the Fusarium genus and induces gastrointestinal dysfunction and disrupts the health and immune system of animals. Here, we evaluated the molecular mechanisms that regulate the effects of ZEA on the porcine intestinal epithelium.Results Treatment of IPEC-J2 cells with ZEA decreased the expression of E-cadherin and increased the expression of Snai1 and Vimentin, which induced Snail1-mediated epithelial-to-mesenchymal transition (EMT). In addition, ZEA induces Snail-mediated EMT through the activation of TGF-beta signaling. The treatment of IPEC-J2 cells with atractylenolide III, which were exposed to ZEA, alleviated EMT.Conclusions Our findings provide insights into the molecular mechanisms of ZEA toxicity in porcine intestinal epithelial cells and ways to mitigate it. ⓘ Kim, Na Yeon; Kim, Myoung Ok; Shin, Sangsu; Kwon, Woo-Sung; Kim, Bomi; Lee, Joon Yeop; In Lee, Sang ⓘ Kyungpook Natl Univ, Dept Anim Sci & Biotechnol, Sangju 37224, Gyeongsangbug, South Korea; Kyungpook Natl Univ, Res Inst Innovat Anim Sci, Sangju 37224, Gyeongsangbug, South Korea; Natl Inst Korean Med Dev, Gyeonsan 38540, South Korea ⓘ Kwon, Woo-Sung/J-6731-2019 ⓘ 59884547500; 8934745900; 55490360000; 54383715800; 57783738900; 57209263499; 57203597336 ⓘ silee78@knu.ac.kr; ⓘ JOURNAL OF ANIMAL SCIENCE AND BIOTECHNOLOGY ⓘ J ANIM SCI BIOTECHNO ⓘ 1674-9782 2049-1891 15 1 SCIE AGRICULTURE, DAIRY & ANIMAL SCIENCE ⓘ 2024 6.5 2.9 0 2025-05-07 ⓘ 1 1 Atractylenolide III; Epithelial-mesenchymal transition; IPEC-J2 cells; Snail; TGF-beta signaling; Zearalenone ⓘ EMT ⓘ Atractylenolide III; Epithelial–mesenchymal transition; IPEC-J2 cells; Snail; TGF-beta signaling; Zearalenone ⓘ ⓘ English 2024 2024-06-07 10.1186/s40104-024-01038-z ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Emissions of HFC-23 do not reflect commitments made under the Kigali Amendment ⓘ HFC-23 (trifluoromethane) is a potent greenhouse gas released to the atmosphere primarily as a by-product of HCFC-22 (chlorodifluoromethane) synthesis. Since 2020, the Kigali Amendment to the Montreal Protocol has required Parties to destroy their HFC-23 emissions to the extent possible. Here, we present updated HFC-23 emissions estimated from atmospheric observations. Globally, emissions fell to 14.0 +/- 0.9 Gg yr-1 in 2023 from their maximum in 2019 of 17.3 +/- 0.8 Gg yr-1, but remained five times higher than reported in 2021. Atmospheric observation-based emissions for eastern China, the world's largest HCFC-22 producer, were also found to be substantially higher than 2020-2022 reported emissions. We estimate that potential HFC-23 sources not directly linked to HCFC-22 production explain only a minor, albeit highly uncertain, fraction of this discrepancy. Our findings suggest that HFC-23 emissions have not been destroyed to the extent reported by the Parties since the implementation of the Kigali Amendment. ⓘ Adam, Ben; Western, Luke M.; Muhle, Jens; Choi, Haklim; Krummel, Paul B.; O'Doherty, Simon; Young, Dickon; Stanley, Kieran M.; Fraser, Paul J.; Harth, Christina M.; Salameh, Peter K.; Weiss, Ray F.; Prinn, Ronald G.; Kim, Jooil; Park, Hyeri; Park, Sunyoung; Rigby, Matt ⓘ UNIV BRISTOL, SCH CHEM, BRISTOL, England; Univ Calif San Diego, Scripps Inst Oceanog, La Jolla, CA USA; Kyungpook Natl Univ, Kyungpook Inst Oceanog, Daegu, South Korea; CSIRO Environm, Aspendale, Vic, Australia; MIT, Ctr Global Change Sci, Cambridge, MA 02139 USA; Kyungpook Natl Univ, Dept Oceanog, Daegu, South Korea ⓘ ; Krummel, Paul/A-4293-2013; Fraser, Paul/D-1755-2012; Young, Dickon/AFO-7065-2022 ⓘ 58946895400; 56730761600; 55917306500; 57215186877; 6602579613; 57120195000; 22837436400; 36134921000; 7202782061; 8878471400; 6602378882; 7404027402; 7005942405; 36142937600; 57217629478; 57085459500; 38762109000 ⓘ benjamin.adam@bristol.ac.uk;matt.rigby@bristol.ac.uk; ⓘ COMMUNICATIONS EARTH & ENVIRONMENT ⓘ COMMUN EARTH ENVIRON ⓘ 2662-4435 5 1 SCIE ENVIRONMENTAL SCIENCES;GEOSCIENCES, MULTIDISCIPLINARY;METEOROLOGY & ATMOSPHERIC SCIENCES ⓘ 2024 8.9 2.9 0 2025-05-07 ⓘ 0 0 ⓘ ATMOSPHERIC GASES; EASTERN ASIA; HCFC-22; SYSTEM; TRENDS; CHF3 ⓘ ⓘ Kigali; Rwanda; byproduct; estimation method; greenhouse gas; uncertainty analysis ⓘ English 2024 2024-12-21 10.1038/s43247-024-01946-y ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Enabling built-in electric fields on rhenium-vacancy-rich heterojunction interfaces of transition-metal dichalcogenides for pH-universal efficient hydrogen and electric energy generation ⓘ Most advanced hydrogen evolution reaction (HER) catalysts show high activity under alkaline conditions. However, the performance deteriorates at a natural and acidic pH, which is often problematic in practical applications. Herein, a rhenium (Re) sulfide-transition-metal dichalcogenide heterojunction catalyst with Re-rich vacancies (NiS2-ReS2-V) has been constructed. The optimized catalyst shows extraordinary electrocatalytic HER performance over a wide range of pH, with ultralow overpotentials of 42, 85, and 122 mV under alkaline, acidic, and neutral conditions, respectively. Moreover, the two-electrode system with NiS2-ReS2-V-1 as the cathode provides a voltage of 1.73 V at 500 mA cm(-2), superior to industrial systems. Besides, the open-circuit voltage of a single Zn-H2O cell with NiS2-ReS2-V-1 as the cathode can reach an impressive 90.9% of the theoretical value, with a maximum power density of up to 31.6 mW cm(-2). Moreover, it shows remarkable stability, with sustained discharge for approximately 120 h at 10 mA cm(-2), significantly outperforming commercial Pt/C catalysts under the same conditions in all aspects. A series of systematic characterizations and theoretical calculations demonstrate that Re vacancies on the heterojunction interface would generate a stronger built-in electric field, which profoundly affects surface charge distribution and subsequently enhances HER performance. ⓘ Wang, Benzhi; Wang, Lixia; Lee, Ji Hoon; Isimjan, Tayirjan Taylor; Jeong, Hyung Mo; Yang, Xiulin ⓘ Guangxi Normal Univ, Sch Chem & Pharmaceut Sci, Guangxi Key Lab Low Carbon Energy Mat, Guilin 541004, Peoples R China; Sungkyunkwan Univ, Sch Mech Engn, Dept Smart Fab Technol, Suwon 16419, South Korea; Kyungpook Natl Univ, Sch Mat Sci & Engn, Daegu, South Korea; King Abdullah Univ Sci & Technol KAUST, Saudi Arabia Basic Ind Corp SABIC, Thuwal 239556900, Saudi Arabia ⓘ Isimjan, Tayirjan/KOC-6235-2024; Yang, Xiulin/I-2704-2015; Lee, Ji/AAU-7285-2021; Benzhi, Wang/AGZ-2552-2022 ⓘ 57205606723; 57224361413; 55689885200; 35299481100; 42061388000; 56002252900 ⓘ isimjant@sabic.com;hmjeong@skku.edu;xlyang@gxnu.edu.cn; ⓘ CARBON ENERGY ⓘ CARBON ENERGY ⓘ 2637-9368 6 9 SCIE CHEMISTRY, PHYSICAL;ENERGY & FUELS;MATERIALS SCIENCE, MULTIDISCIPLINARY;NANOSCIENCE & NANOTECHNOLOGY ⓘ 2024 24.2 2.9 3.54 2025-05-07 ⓘ 16 14 built-in electric field; electrocatalysts; hydrogen evolution reaction; self-powered system; water splitting; Zn-H2O cell ⓘ EFFECTIVE STRATEGY; EVOLUTION; CATALYST; PERFORMANCE; RECONSTRUCTION; NANOSHEETS; RES2 ⓘ built-in electric field; electrocatalysts; hydrogen evolution reaction; self-powered system; water splitting; Zn–H<sub>2</sub>O cell ⓘ Alkalinity; Catalyst activity; Cathodes; Electric discharges; Heterojunctions; Nickel compounds; Open circuit voltage; Rhenium compounds; Sulfur compounds; Transition metals; Alkaline conditions; Built-in electric fields; Dichalcogenides; Heterojunction interfaces; Hydrogen evolution reactions; Reaction performance; Self-powered systems; Water splitting; Zn–H2O cell; ]+ catalyst; Electrocatalysts ⓘ English 2024 2024-09 10.1002/cey2.526 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Establishment of a chicken intestinal organoid culture system to assess deoxynivalenol-induced damage of the intestinal barrier function ⓘ BackgroundDeoxynivalenol (DON) is a mycotoxin that has received recognition worldwide because of its ability to cause growth delay, nutrient malabsorption, weight loss, emesis, and a reduction of feed intake in livestock. Since DON-contaminated feedstuff is absorbed in the gastrointestinal tract, we used chicken organoids to assess the DON-induced dysfunction of the small intestine.ResultsWe established a culture system using chicken organoids and characterized the organoids at passages 1 and 10. We confirmed the mRNA expression levels of various cell markers in the organoids, such as KI67, leucine-rich repeat containing G protein-coupled receptor 5 (Lgr5), mucin 2 (MUC2), chromogranin A (CHGA), cytokeratin 19 (CK19), lysozyme (LYZ), and microtubule-associated doublecortin-like kinase 1 (DCLK1), and compared the results to those of the small intestine. Our results showed that the organoids displayed functional similarities in permeability compared to the small intestine. DON damaged the tight junctions of the organoids, which resulted in increased permeability.ConclusionsOur organoid culture displayed topological, genetic, and functional similarities with the small intestine cells. Based on these similarities, we confirmed that DON causes small intestine dysfunction. Chicken organoids offer a practical model for the research of harmful substances. ⓘ Kang, Tae Hong; Lee, Sang In ⓘ Kyungpook Natl Univ, Dept Anim Sci & Biotechnol, Sangju 37224, Gyeongsangbuk D, South Korea; Kyungpook Natl Univ, Res Inst Innovat Anim Sci, Sangju 37224, Gyeongsangbug D, South Korea ⓘ ⓘ 57903443800; 57203597336 ⓘ silee78@knu.ac.kr; ⓘ JOURNAL OF ANIMAL SCIENCE AND BIOTECHNOLOGY ⓘ J ANIM SCI BIOTECHNO ⓘ 1674-9782 2049-1891 15 1 SCIE AGRICULTURE, DAIRY & ANIMAL SCIENCE ⓘ 2024 6.5 2.9 2.56 2025-05-07 ⓘ 8 8 Barrier function; Deoxynivalenol; Organoids ⓘ STEM-CELL; PERMEABILITY; IMPACT ⓘ Barrier function; Deoxynivalenol; Organoids ⓘ ⓘ English 2024 2024-02-18 10.1186/s40104-023-00976-4 ⓘ 바로가기 바로가기 바로가기 바로가기
○ ○ Article ⓘ Feature Selection Graph Neural Network for Optimized Node Categorization ⓘ Graph Neural Networks (GNNs) is one of the most essential tools for learning from graph-structured data. A wide range of tasks has demonstrated their usefulness in graph-structured data. Engineering configuration has progressed fundamentally, further developing execution on different forecast errands. Utilizing learnable weight matrices, these neural networks typically incorporate feature transformation and node feature aggregation in the same layer. The articulateness of the layers of neural network and the significance of node information gathered from various hops are complicated to analyze. Because diverse graph datasets exhibit changeable degrees of heterophily and homophily in class label dissemination, it is necessary to recognize which features are crucial for the forecast tasks without preceding knowledge. The proposed work focus on demonstrating that the GNN model's efficiency can be harmed by frequently employing less informative features and that not all aggregation process features are beneficial. The experimental outcomes validated that the performance can be enhanced on a wide range of datasets by learning specific subsets of these features. Based on the observations, several significant design concepts for neural graph networks are being introduce. More specifically, L2-Normalization is employed over GNN layers using SoftMax as a regularize and a "soft-selector" of characteristics gathered from neighbors at various hop distances. The Feature Selection Graph Neural Network (FSGNN), a straightforward and shallow model, is framed by combining these approaches. Nine standard datasets for the node classification task empirically exhibit that the proposed model outperforms current GNN models, with significant improvements of up to 50.80%. ⓘ Preethaa, K. R. Sri; Wadhwa, Gitanjali; Natarajan, Yuvaraj; Paul, Anand ⓘ Kyungpook Natl Univ, Dept Robot & Smart Syst Engn, Daegu 41566, South Korea; KPR Inst Engn & Technol, Dept Comp Sci & Engn, Coimbatore 641407, India; Kyungpook Natl Univ, Dept Comp Sci & Engn, Daegu 41566, South Korea ⓘ raj, yuva/GWV-2080-2022; Paul, Anand/V-6724-2017 ⓘ 57214320928; 57219654131; 57204528689; 56650522400 ⓘ k.r.sripreethaa@kpriet.ac.in;gitanjaliwadhwa@kpriet.ac.in;yuvaraj.n@kpriet.ac.in;anand@knu.ac.kr; ⓘ IEEE TRANSACTIONS ON CONSUMER ELECTRONICS ⓘ IEEE T CONSUM ELECTR ⓘ 0098-3063 1558-4127 70 1 SCIE ENGINEERING, ELECTRICAL & ELECTRONIC;TELECOMMUNICATIONS ⓘ 2024 10.9 2.9 0 2025-05-07 ⓘ 1 1 Graph neural networks; Predictive models; Feature extraction; Data models; Task analysis; Social networking (online); Computational modeling; Graph neural network; feature selection; neural network; node classification ⓘ ENSEMBLE; ALGORITHM; MODEL ⓘ feature selection; Graph neural network; neural network; node classification ⓘ Feature Selection; Graph neural networks; Graphic methods; Job analysis; Multilayer neural networks; Network layers; Social networking (online); Computational modelling; Features extraction; Features selection; Graph neural networks; Neural-networks; Node classification; Predictive models; Social networking (online); Task analysis; Classification (of information) ⓘ English 2024 2024-02 10.1109/tce.2023.3345390 ⓘ 바로가기 바로가기 바로가기 바로가기
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