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Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Acta Oceanologica Sinica 2024年第7期43卷 93-111页

作者： Tianyu Zhang Mohammed Shokr Zhida Zhang Fengming Hui Xiao Cheng Zhilun Zhang Jiechen Zhao Chunlei Mi School of Geospatial Engineering and Science Sun Yat-Sen University/Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Zhuhai 519082China Key Laboratory of Comprehensive Observation of Polar Environment(Sun Yat-Sen University) Ministry of EducationZhuhai 519082China State Key Laboratory of Remote Sensing Science College of Global Change and Earth System ScienceBeijing Normal UniversityBeijing 100875China Science and Technology Branch Environment and Climate Change CanadaToronto M3H5T4Canada Qingdao Innovation and Development Base(Centre) Harbin Engineering UniversityQingdao 266500China State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchChinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input *** article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice ***,it estimates the uncertainty of the output in response to the uncertainties of the input *** parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow *** input parameters include air temperature,ice surface temperature,and wind *** the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice *** sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and *** retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved *** in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be *** resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement *** results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

关键词： Arctic sea ice 1-D thermodynamic ice model thin-ice thickness sea ice parameterization

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Efficacy of Eisenia bicyclis phlorotannins in the treatment of diabetes and reducing inflammation

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Food Bioscience 2023年第2期52卷 404-411页

作者： Rathinam Raja Shanmugam Hemaiswarya Kulanthaiyesu Arunkumar N.Mathiyazhagan Sabariswaran Kandasamy A.Arun P.Ramasamy Research and Development Wing Sree Balaji Medical College and Hospital(SBMCH)Bharath Institute of Higher Education and Research(BIHER)Chennai600044India Department of Biotechnology IIT MadrasChennai600036India Department of Plant Science Central University of KeralaPeriye671320KasaragodKeralaIndia Division of Research and Innovation Department of BiotechnologySaveetha School of EngineeringSaveetha Institute of Medical and Technical ScienceChennai600052India Water-Energy Nexus Laboratory Department of Environmental EngineeringUniversity of SeoulSeoul02504Republic of Korea Bioenergy and Bioremediation Laboratory Department of MicrobiologyAlagappa UniversityKaraikudiIndia

Crude phlorotannins from Eisenia bicyclis(CPEb),a common perennial brown seaweed were screened for pharmacological activities such as anti-inflammatory,antioxidant,and antidiabetic *** Phlorotannins(CPEb)studied by FT-IR,LC-MS,UHPLC,and UV chromatography showed the occurrence of dieckol,8,8’-bieckol,fucofuroeckol,and the carotenoid,zeaxanthin(0.5 mg/kg).LPS,an inflammatory mediator,enhanced the production of cytokines such IL-1(interleukin-1),IL-6(interleukin-6),IL-8(interleukin-8),IL-10(interleukin-10),TNF-α(tumour necrosis factor-α),chemokine CXCL10,and NF-κB(nuclear factor kappa-light-chain-enhancer of activated B cells)in differentiated THP1 macrophage *** differentiated human monocytic cell line LPS-induced THP1 cells,CPEb was found to have anti-inflammatory effects by reducing the expression of IL-1,IL-6,IL-8(1.2 fold),CXCL10,NF-κB,and TNF-α.CPEb promoted 2-NBDG absorption in differentiated C2C12 myotubes under both basal(16.08%)and insulin-stimulated(51.09%)*** increased IRS/AKT-dependent glucose absorption and also activates the AMPK ***-α-glucosidase,anti-inflammatory and antioxidant activity of CPEb indicate their role in treating hyperglycemia by scavenging ROS and inflammatory responses.

关键词： Seaweeds Eisenia bicyclis Antioxidant Antidiabetic Anti-inflammatory

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Identification of a dual-function peptide for the detection and haematopoietic potency assessment of type Ⅰ collagen hydrolysates

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Collagen and Leather 2025年第1期7卷 48-62页

作者： Guiya Deng Jiajun Wang Cuicui Men Keyi Cao Chengming Li Liang Zheng Haibin Liu Shangwei Guo Yaqin Huang Key Lab of Biomedical Materials of Natural Macromolecules(Beijing University of Chemical Technology) Ministry of EducationBeijing Laboratory of Biomedical MaterialsCollege of Materials Science and EngineeringBeijing University of Chemical TechnologyBeijing 100029China Dong’e Ejiao Co. Ltd.Liaocheng 252200China Shandong Key Laboratory of Gelatine TCM Research and DevelopmentLiaocheng 252200China Shandong Technology Innovation Center of Gelatin-Based Traditional Chinese Medicine Liaocheng 252200China National Engineering Technology Research Center for Gelatin-Based Traditional Chinese Medicine Liaocheng 252200China

Type Ⅰ collagen hydrolysate with haematopoietic activity has shown tremendous potential for application in the functional food *** identification of peptide with haematopoietic activity to ensure the efficiency of functional foods remains challenging because of the vast combinatorial potential of short peptides from collagen *** the present study,we identified a novel marker peptide(the I-1 peptide,GAAGLpGPK,p:hydroxyproline)for type I collagen with haematopoietic capabilities via theoretical sequence analysis and computational prediction of *** I-1 peptide was shown to increase the survival rate of haematopoietic stem cells following injury induced by 5-fluorouracil(5-FU),increasing the survival rate from 65.40 to 105.28%,thereby exhibiting significant haematopoietic *** importantly,the mechanism underlying the haematopoietic activity of the I-1 peptide was analogous to activate the JAK/STAT signalling pathway mediated by granulocyte‒macrophage colony‒stimulating *** this basis,a high-precision method for the detection of type I collagen hydrolysates was developed using the I-1 peptide(relative standard deviation=1.57%).This study not only provides novel perspectives for the exploration of haematopoietic peptides but also offers a new methodology for controlling the bioactivity of collagen-based food products.

关键词： TypeⅠcollagen Marker peptide Identification Haematopoiesis

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Life cycle assessment of HFC-134a production by calcium carbide acetylene route in China

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Chinese Journal of Chemical engineering 2022年第2期35卷 236-244页

作者： Suisui Zhang Jingying Li Yan Nie Luyao Qiang Boyang Bai Zhiwei Peng Xiaoxun Ma School of Chemical Engineering Northwest UniversityXi’an 710069China Chemical Engineering Research Center of the Ministry of Education(MOE)for Advanced Use Technology of Shanbei Energy Xi’an 710069China Shaanxi Research Center of Engineering Technology for Clean Coal Conversion Xi’an 710069China Collaborative Innovation Center for Development of Energy and Chemical Industry in Northern Shaanxi Xi’an 710069China International Scientific and Technological Cooperation Base of the Ministry of Science and Technology(MOST)for Clean Utilization of Hydrocarbon Resources Xi’an 710069China

HFC-134a is a widely used environment-friendly *** present,China is the largest producer of HFC-134a in the *** production of HFC-134a in China mainly adopts the calcium carbide acetylene ***,the production route has high resource and energy consumption and large waste emission,and few of the studies addressed on the environmental performance of its production *** study quantified the environmental performance of HFC-134a production by calcium carbide route via carrying out a life cycle assessment(LCA)using the CML 2001 *** uncertainty analysis by Monte-Carlo simulation was also carried *** results showed that electricity had the most impact on the environment,followed by steam,hydrogen fluoride and chlorine,and the impact of direct CO_(2) emissions in calcium carbide production stage on the global warming effect also could not be ***,the clean energy(e.g.,wind,solar,biomass,and natural gas)was used to replace coal-based electricity and coal-fired steam in this study,showing considerable environmental *** the same time,the use of advanced production technologies could also improve environmental benefits,and the environmental impact of the global warming category could be reduced by 4.1%via using CO_(2) capture and purification *** Chinese database of HFC-134a production established in this study provides convenience for the relevant study of *** the production of HFC-134a,this study helps to better identify the specific environmental hotspots and proposes useful ways to improve the environmental benefits.

关键词： HFC-134a Calcium carbide Life cycle assessment Environmental impact Carbon capture

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Deep Learning-Based Model for Detection of Brinjal Weed in the Era of Precision Agriculture

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Computers, Materials & Continua 2023年第10期77卷 1281-1301页

作者： Jigna Patel Anand Ruparelia Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabad382481India Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Centre for Inter-Disciplinary Research and Innovation University of Petroleum and Energy StudiesDehradun248001India Doctoral School University Politehnica of BucharestBucharest060042Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm ValceaRamnicu Valcea240050Romania Power Engineering Department Gheorghe Asachi Technical University of IasiIasi700050Romania

The overgrowth of weeds growing along with the primary crop in the fields reduces crop *** solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used *** application of herbicide is effective but causes environmental and health ***,Precision Agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary *** by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this *** key objective of this study is to detect plant and non-plant(weed)parts from crop *** the help of object detection,the precise location of weeds from images can be *** dataset is collected manually from a private farm in Gandhinagar,Gujarat,*** combined approach of classification and object detection is applied in the proposed *** Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object *** have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed *** outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

关键词： Precision Agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN

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Short-term degradation hybrid prognostics for dynamically operating fuel cells 4

Short-term degradation hybrid prognostics for dynamically op...

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4th International Conference on Energy, Power and Electrical engineering, EPEE 2024

作者： Hu, Guichi Lv, Zhigang Wang, Peng Li, Xiaoyan Di, Ruohai Wang, Chu Xi'an Technological University School of Weapon Science and Technology Xi'an710021 China Xi'an Technological University School of Electronic Information Engineering Xi'an710021 China Institute for Interdiscilinary Innovation Research Xi'an Technological University Xi'an710021 China Xi'an Technological University Office of Development and Planning Xi'an China

ISBN: (纸本)9798331518066

Recently proton exchange membrane fuel cells (PEMFC) are widely used in various commercialization scenarios such as automobiles. However, there are still challenges in extracting the health indicators of PEMFC under dynamic operating conditions and then realizing accurate degradation prediction. In response to the problem, a hybrid prognostic framework is proposed in this paper. Specifically, health indicators are extracted based on a degradation mechanism model. Then, short-term degradation prediction is realized by utilizing a Bi-directional Long and Short-term Memory Network. To confirm the accuracy of the proposed method, a vehicle-oriented PEMFC durability test dataset is employed. The experimental results show that the proposed model can provide reconstructed stack voltages with up to 99% accuracy. In turn, a short-term prediction with no more than 5% relative error is realized. © 2024 IEEE.

关键词： Electronic health record

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Optimizing Resource Allocation in Healthcare Systems for Efficient Pandemic Management using Machine Learning and Artificial Neural Networks

Optimizing Resource Allocation in Healthcare Systems for Eff...

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Advancements in Smart, Secure and Intelligent Computing (ASSIC), International Conference on

作者： S. Kaliappan Ramya Maranan Avinash Malladi Nagendar Yamsani Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Research and Innovation SIMATS Saveetha School of Engineering Chennai Tamil Nadu India Faculty of Science and Technology (lcfaitech) Mechatronics Engineering ICFAI Foundation for Higher Education (IFHE) Hyderabad School of Computer Science & Artificial Intelligence SR University Warangal Telangana

Pandemics pose significant challenges to healthcare systems, particularly in resource allocation and patient care. Traditional methods for resource allocation often face limitations in handling complex data, adapting to changing conditions, and providing real-time decision support. To address these challenges, we propose a novel method for optimizing resource allocation in healthcare systems using machine learning (ML) and artificial neural networks (ANN s). Traditional resource allocation methods, often based on historical data and manual decision-making, may not be adequate to handle the dynamic and complex nature of pandemics. These methods often fail to account for real-time changes in disease patterns, patient needs, and resource availability. The proposed method integrates predictive modeling, optimization algorithms, and real-time decision support to enhance resource utilization and improve patient outcomes during pandemics.

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Integration of the grey relational analysis with machine learning for sucrose anaerobic hydrogen production prediction

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International Journal of Hydrogen Energy 2024年 68卷 388-397页

作者： Wang, Xianze Wang, Yifan Tseng, Yu-Yao Gao, Yidi Li, Keqing Wang, Ming-Hung Wu, Jinghui Lay, Chyi-How Huo, Mingxin Engineering Research Center of Low-Carbon Treatment and Green Development of Polluted Water in Northeast China Ministry of Education School of Environment Northeast Normal University Changchun130117 China Science and Technology Innovation Center for Municipal Wastewater Treatment and Water Quality Protection Northeast Normal University Changchun130117 China Key Laboratory of Songliao Aquatic Environment Ministry of Education Jilin Jianzhu University Changchun130118 China Master's Program of Green Energy Science and Technology Feng Chia University Taichung40724 Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Chiayi County Taiwan

Anaerobic fermentation for hydrogen production is influenced by various environmental factors that can limit microbial activity. However, machine learning (ML) shows significant potential in explaining the complexity of biological processes. This study focuses on the application of ML in predicting hydrogen production rates (HPR) during anaerobic fermentation of biomass energy. Grey relation analysis was employed to investigate the correlation between operational parameters and HPR. Five ML algorithms, namely decision tree (DT), random forest (RF), extreme gradient boosting (XGBoost), and K-nearest neighbor (KNN), were utilized in conjunction with operating conditions and digestate characteristics as features, with HPR as the target variable. The evaluation of the models was conducted using mean squared error (MSE) and R squared (R2). Notably, butyrate, oxidation-reduction potential (ORP), and volatile suspended solids (VSS) were identified as key factors influencing hydrogen production during sucrose anaerobic fermentation. Among the ML algorithms tested, XGBoost achieved the highest accuracy, with an R2 value of 0.91 and an MSE of 0.0052. © 2024 Hydrogen Energy Publications LLC

关键词： Machine learning

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Improving service accessibility and equity for sustainable development goals without newly facilities by rural settlement reconstruction

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Geography and Sustainability 2025年第1期6卷 105-117页

作者： Caihui Cui Zhigang Han Feng Liu Jingru Ma Haiying Wang Xiang Chen College of Geographical Sciences Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China College of Remote Sensing and Geoinformatics Engineering Faculty of Geographical Science and EngineeringHenan UniversityZhengzhou 450046China Henan Industrial Technology Academy of Spatiotemporal Big Data Henan UniversityZhengzhou 450046China Henan Technology Innovation Center of Spatio-Temporal Big Data Henan UniversityZhengzhou 450046China Key Research Institute of Yellow River Civilization and Sustainable Development Henan UniversityKaifeng 475004China Department of Geography University of ConnecticutStorrsCT 06269USA

Ensuring the provision of accessible,affordable,and high-quality public services to all individuals aligns with one of the paramount aims of the United Nations’Sustainable development Goals(SDGs).In the face of esca lating urbanization and a dwindling rural populace in China,reconstructing rural settlements to enhance public service accessibility has become a fundamental strategy for achieving the SDGs in rural ***,few stud ies have examined the optimal methods for rural settlement reconstruction that ensure accessible and equitable public services while considering multiple existing facilities and service *** paper focuses on rural settlement reconstruction in the context of the SDGs,employing an inverted MCLP-CC(maximal coverage loca tion problem for complementary coverage)model to identify optimal rural settlements and a rank-based method for their *** in Changyuan,a county-level city in Henan Province,China,this study observed significant enhancements in both accessibility and equity following rural settlement reconstruction by utilizing the MH3SFCA(modified Huff 3-step floating catchment area)and the spatial Lorenz curve ***,these improvements were achieved without the addition of new facilities,with the accessibility increasing by 44.21%,4.97%,and 3.11%;Gini coefficients decreasing by 19.53%,1.64%,and 3.18%;Ricci-Schutz coef-ficients decreasing by 21.09%,2.09%,and 4.33%for educational,medical,and cultural and sports facilities,*** indicated that rural settlement reconstruction can bolster the accessibility and equity of public ser-vices by leveraging existing *** paper provides a new framework for stakeholders to better reconstruct rural settlements and promote sustainable development in rural areas in China.

关键词： SDGs Equity Accessibility Rural settlement reconstruction Spatial optimization

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Salinity Stress Deteriorates Grain Yield and Increases 2-Acetyl-1-Pyrroline Content in Rice

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Rice science 2024年第4期31卷 371-374,I0015-I0021页

作者： WEI Huanhe MA Weiyi ZHANG Xiang ZUO Boyuan GENG Xiaoyu WANG Lulu ZHU Wang CHEN Yinglong HUO Zhongyang XU Ke MENG Tianyao DAI Qigen Jiangsu Key Laboratory of Crop Cultivation and Physiology/Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops/Key Laboratory of Saline-Alkali Soil Reclamation and Utilization in Coastal Areas the Ministry of Agriculture and Rural Affairs of China/East China Branch of National Center of Technology Innovation for Saline-Alkali Tolerant Rice/Research Institute of Rice Industrial Engineering TechnologyYangzhou UniversityYangzhou 225009China Institutes of Agricultural Science and Technology Development/Joint International Research Laboratory of Agriculture and Agri-Product Safety the Ministry of Education of ChinaYangzhou UniversityYangzhou 225009China

Salinity stress greatly impacts rice grain yield and quality, as well as the 2-acetyl-1-pyrroline(2-AP) content in grains. The present study was conducted with Nanjing 9108(NJ9108, conventional japonica rice) and Wenliangyou 669(WLY669, indica hybrid rice) in the fields with non-salinity(NS), low salinity(LS), and high salinity(HS) stresses in 2021 and 2022.

关键词： salinity Yield erior

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