Rice blast and brown spot are two of the most significant fungal diseases affecting rice production.Although the leaf symptoms of both diseases are distinct,they manifest as similar brown-black lesions,complicating di...Rice blast and brown spot are two of the most significant fungal diseases affecting rice production.Although the leaf symptoms of both diseases are distinct,they manifest as similar brown-black lesions,complicating differ-entiation and effective management.Traditional diagnostic methods predominantly rely on DNA-based molecular techniques,which are not well-suited for rapid,large-scale applications.This study aimed to identify reliable chlorophyll fluorescence(ChlF)indicators for diagnosing these diseases at the pre-symptomatic stage using a pulse-amplitude modulation fluorometer.Changes in ChlF parameters were measured following fungal infection in 120 leaves and 750 spots across five time points in detached leaf assays.Diagnostic indicators were selected through machine learning and fold-change value comparisons,then validated using 374 pre-symptomatic spots induced under different infection conditions in whole plants.Fifteen ChlF diagnostic parameters were identified,nine of which were specifically associated with rice blast.Pre-symptomatic lesions in both diseases caused sig-nificant decreases in non-photochemical quenching parameters(NPQ_D1/D2 and qN_D1/D2/D3),while simul-taneous increases in photochemical quenching parameters(qL_L2/L3,qP_L2/L3/L4,and QY_L2/L3/L4)were observed only in rice blast.The vitality index R_(fd)_L2 exhibited a notable increase in both diseases.However,a consistent decrease in F_(t)_L2,necessary for calculating R_(fd)_L2,was observed only in rice blast,indicating different pathogenic mechanisms despite similar visible symptoms.Our results suggest that these ChlF parameters can serve as powerful diagnostic indicators for rice fungal diseases,and that ChlF imaging offers a straightforward approach for plant disease diagnosis.展开更多
To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex chal...To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.展开更多
Idiopathic pulmonary fibrosis(IPF)is a chronic inflammatory and fibrotic response-driven lung disease that is difficult to cure because it manifests excessive profibrotic cytokines(e.g.,TGF-β),activated myofibroblast...Idiopathic pulmonary fibrosis(IPF)is a chronic inflammatory and fibrotic response-driven lung disease that is difficult to cure because it manifests excessive profibrotic cytokines(e.g.,TGF-β),activated myofibroblasts,and accumulated extracellular matrix(ECM).In an attempt to develop an inhalation formulation with enhanced antifibrotic efficacy,we sought to fabricate unique aerosolizable inhaled microgels(μGel)that contain nintedanib-poly(lactic-co-glycolic acid)(PLGA)nanoparticles(NPs;n-PN)and pirfenidone-liposomes(p-LP).The aero-μGel was~12μm,resisted phagocytosis by alveolar macrophages in vitro and in vivo,and protected inner-entrapped n-PN and p-LP.The n-PN/p-LP@aero-μGel caused enhanced/extended antifibrotic efficacy in a bleomycin-induced pulmonary fibrosis mouse presumably due to prolonged lung residence.Consequently,the results obtained by intratracheal aerosol insufflation of our n-PN/p-LP@aero-μGel twice a week were much better than those by as many as seven doses of single or mixed applications of n-PN or p-LP.The antifibrotic/pharmacokinetic results for the n-PN/p-LP@aero-μGel included reduced fibrosis progression,restored lung physiological functions,deactivated myofibroblasts,inhibited TGF-βprogression,and suppressed ECM component production(collagen I andα-SMA)along with prolonged lung retention time.We believe that our n-PN/p-LP@aero-μGel increased the local availability of both nintedanib and pirfenidone due to evasion of alveolar macrophage phagocytosis and prolonged lung retention with reduced systemic distribution.Through this approach,our inhalation formulation subsequently attenuated fibrosis progression and improved lung function.Importantly,these results hold profound implications in the therapeutic potential of our n-PN/p-LP@aero-μGel to serve as a clinically promising platform,providing significant advancements for improved treatment of many respiratory diseases including IFP.展开更多
Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean en...Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.展开更多
基金supported by Rural Development Administration of Korea(RS-2022-RD010059)Creative-Pioneering Researchers Program by Seoul National University.
文摘Rice blast and brown spot are two of the most significant fungal diseases affecting rice production.Although the leaf symptoms of both diseases are distinct,they manifest as similar brown-black lesions,complicating differ-entiation and effective management.Traditional diagnostic methods predominantly rely on DNA-based molecular techniques,which are not well-suited for rapid,large-scale applications.This study aimed to identify reliable chlorophyll fluorescence(ChlF)indicators for diagnosing these diseases at the pre-symptomatic stage using a pulse-amplitude modulation fluorometer.Changes in ChlF parameters were measured following fungal infection in 120 leaves and 750 spots across five time points in detached leaf assays.Diagnostic indicators were selected through machine learning and fold-change value comparisons,then validated using 374 pre-symptomatic spots induced under different infection conditions in whole plants.Fifteen ChlF diagnostic parameters were identified,nine of which were specifically associated with rice blast.Pre-symptomatic lesions in both diseases caused sig-nificant decreases in non-photochemical quenching parameters(NPQ_D1/D2 and qN_D1/D2/D3),while simul-taneous increases in photochemical quenching parameters(qL_L2/L3,qP_L2/L3/L4,and QY_L2/L3/L4)were observed only in rice blast.The vitality index R_(fd)_L2 exhibited a notable increase in both diseases.However,a consistent decrease in F_(t)_L2,necessary for calculating R_(fd)_L2,was observed only in rice blast,indicating different pathogenic mechanisms despite similar visible symptoms.Our results suggest that these ChlF parameters can serve as powerful diagnostic indicators for rice fungal diseases,and that ChlF imaging offers a straightforward approach for plant disease diagnosis.
基金This research was supported by Study on Diagnostic and Prognostic of Lithium-Ion Battery for Electric Vehicle funded by Xynergypower Co.,Ltd.(UNIST-2.200733.01)also supported by the Hydrogen Energy Innovation Technology Development Program of the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT(MSIT))(NRF-2019M3E6A1064290).
文摘To ensure smooth and reliable operations of battery systems,reliable prognosis with accurate prediction of State of Health of lithium ion batteries is of utmost importance.However,battery degradation is a complex challenge involving many electrochemical reactions at anode,separator,cathode and electrolyte/electrode interfaces.Also,there is significant effect of the operating conditions on the battery degradation.Various machine learning tech-niques have been applied to estimate the capacity and State of Health of lithium ion batteries to ensure reliable operation and timely maintenance.In this paper,we study the Gaussian Process Regression(GPR)and Support Vector Machine(SVM)model-based approaches in estimating the capacity and State of Health of batteries.Bat-tery capacity and State of Health estimations are carried out using GPR and SVM models and the predictions comparatively studied for accuracy based on RMSE values.The prediction accuracy is further compared with re-spect to single sensor and multi sensor data.Further,a combined multi battery data set model is used to improve the prediction accuracy.Combining the data of multiple batteries with similar operating conditions for training a model resulted in higher prediction accuracy.
基金supported by a National Research Foundation of Korea(NRF)grant funded by the Korean government(MSITNo.NRF-2019R1A5A2027340)by the Bio&Medical Technology Development Program of the NRF funded by the Korean government(MSIT,No.NRF-2022M3A9G8017220).
文摘Idiopathic pulmonary fibrosis(IPF)is a chronic inflammatory and fibrotic response-driven lung disease that is difficult to cure because it manifests excessive profibrotic cytokines(e.g.,TGF-β),activated myofibroblasts,and accumulated extracellular matrix(ECM).In an attempt to develop an inhalation formulation with enhanced antifibrotic efficacy,we sought to fabricate unique aerosolizable inhaled microgels(μGel)that contain nintedanib-poly(lactic-co-glycolic acid)(PLGA)nanoparticles(NPs;n-PN)and pirfenidone-liposomes(p-LP).The aero-μGel was~12μm,resisted phagocytosis by alveolar macrophages in vitro and in vivo,and protected inner-entrapped n-PN and p-LP.The n-PN/p-LP@aero-μGel caused enhanced/extended antifibrotic efficacy in a bleomycin-induced pulmonary fibrosis mouse presumably due to prolonged lung residence.Consequently,the results obtained by intratracheal aerosol insufflation of our n-PN/p-LP@aero-μGel twice a week were much better than those by as many as seven doses of single or mixed applications of n-PN or p-LP.The antifibrotic/pharmacokinetic results for the n-PN/p-LP@aero-μGel included reduced fibrosis progression,restored lung physiological functions,deactivated myofibroblasts,inhibited TGF-βprogression,and suppressed ECM component production(collagen I andα-SMA)along with prolonged lung retention time.We believe that our n-PN/p-LP@aero-μGel increased the local availability of both nintedanib and pirfenidone due to evasion of alveolar macrophage phagocytosis and prolonged lung retention with reduced systemic distribution.Through this approach,our inhalation formulation subsequently attenuated fibrosis progression and improved lung function.Importantly,these results hold profound implications in the therapeutic potential of our n-PN/p-LP@aero-μGel to serve as a clinically promising platform,providing significant advancements for improved treatment of many respiratory diseases including IFP.
基金This research was supported by the Hydrogen Energy Innovation Technology Development Program of the National Research Foundation of Korea(NRF)funded by the Korean government(Ministry of Science and ICT(MSIT))(NRF-2019M3E6A1064290)supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(NRF-2019M1A2A2065614).
文摘Recently,considerable attention has been paid to the installation of renewable energy capacity to mitigate global CO_(2) emissions.H_(2) produced using water electrolysis and renewable energy is regarded as a clean energy carrier,generating electricity without CO_(2) emissions,called‘Green H 2’.In this paper,a prognostics and health man-agement model for an alkaline water electrolyzer was proposed to predict the load voltage on the electrolyzer to obtain the state of health information.The prognostics and health management model was developed by training historical operating data via machine learning models,support vector machine and gaussian process regression,showing the root mean square error of 1.28×10^(−3) and 8.03×10^(−6).In addition,a techno-economic analysis was performed for a green H_(2) production system,composed of 1 MW of photovoltaic plant and 1 MW of alkaline water electrolyzer,to provide economic insights and feasibility of the system.A levelized cost of H_(2) of$6.89 kgH_(2)−1 was calculated and the potential to reach the levelized cost of H_(2) from steam methane reforming with carbon capture and storage was shown by considering the learning rate of the photovoltaic module and elec-trolyzer.Finally,the replacement of the alkaline water electrolyzer at around 10 years was preferred to increase the net present value from the green H_(2) production system when capital expenditure and replacement cost are low enough.
