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Antifungal activity and potential mechanism of paeonol against Fusarium graminearum and the application on wheat grains and steamed bread

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Grain and Oil Science and technology 2025年

作者： Zhang, Yiming Xiao, Hongying Poms, Roland Ernest Li, Qian Zhao, Renyong Grain Oil and Food Engineering Technology Research Center of the State Grain and Reserves Administration/Key Laboratory of Henan Province Henan University of Technology Zhengzhou China Henan Key Laboratory of Cereal and Oil Food Safety and Nutrition College of Food Science and Engineering Henan University of Technology Zhengzhou China Department of Nutrition and Health IU - International University of Applied Sciences Germany

Fusarium graminearum (F. graminearum) is a severe phytopathogen threatening agriculture production and food security. Paeonol, serves as a plant-derived natural component, is a promising antifungal agent. At a concentration of 0.3125 mg/mL, paeonol was adequate to fully inhibit the growth of F. graminearum mycelia within 3 days. Fourier-Transform Infrared Spectroscopy (FT-IR) analysis showed that paeonol had no impact on the outer surface of F. graminearum cell walls. While propidium iodide staining, extracellular conductivity, and pH value measurements demonstrated that paeonol disrupted the cell membrane. Furthermore, lipid oxidation and osmotic stress responses were observed in F. graminearum treated with paeonol, resulting in a 47.23% rise in malondialdehyde (MDA) levels and a 515.43% increase in glycerol levels. Moreover, on the 7th day after exposure to paeonol treatment, the deoxynivalenol (DON) level was significantly reduced, measuring only one-fifth of that in the control group. Finally, paeonol was shown to inhibit F. graminearum on wheat grains and steamed bread slices. These results, for the first time, revealed the inhibitory mode of action of paeonol against F. graminearum as reflected by disruption of cell membrane integrity, induction of lipid oxidation and osmotic pressure, as well as DON biosynthesis. Furthermore, this study provided scientific evidence for the potential applications of paeonol in agriculture and food industry. © 2024

关键词： Cell membranes

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Network extension planning towards resilient urban critical infrastructures using deep reinforcement learning

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applied Soft Computing 2025年 176卷

作者： Liu, Qiong Zhang, Limao Skibniewski, Miroslaw J. Department of Architecture and Civil Engineering City University of Hong Kong Hong Kong China School of Civil and Hydraulic Engineering National Center of Technology Innovation for Digital Construction Huazhong University of Science and Technology 1037 Luoyu Road Hongshan District Hubei Wuhan China Department of Civil & Environmental Engineering University of Maryland College ParkMD United States Chaoyang University of Technology Taichung Taiwan Polish Academy of Sciences Institute for Theoretical and Applied Informatics Gliwice Poland

As cities continue to grow, expanding metro networks becomes essential for optimizing urban transportation efficiency. Therefore, scientific strategic planning of metro networks is indispensable. This study proposes a deep reinforcement learning (DRL) approach to discover the optimal planning strategy for metro network extension. The model integrates multi-source data into the reward function while customizing the state and action spaces to reflect the unique characteristics of metro networks. A policy network is developed using an Encoder-Decoder framework, with the parameters being updated by an Actor-Critic framework based on policy gradient. A comprehensive performance index is proposed to evaluate the vulnerability and service capacity of planned networks. The proposed method is validated through a case study on the Hangzhou metro system. The results demonstrate that the proposed DRL can result in optimal planned networks that outperform the actually implemented network with a maximum Performance Improvement Percentage of 19.87 %. The DRL-based optimization framework proposed for metro network extension planning is anticipated to enhance adaptability towards urban development and increase resilience. © 2025 Elsevier B.V.

关键词： Deep reinforcement learning

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Application of fluidized-bed homogeneous crystallization technology to carbon sequestration and recovery from flue gas

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Sustainable Environment Research 2024年第1期34卷 1-16页

作者： Huang, Po-Chun Vilando, Anabella C. Ha, Thi-Hanh Lu, Ming-Chun Department of Environmental Engineering National Chung Hsing University Taichung City40227 Taiwan College of Engineering and Architecture Bicol State College of Applied Sciences and Technology Naga City4400 Philippines

The Earth’s energy balance produced by human activity is the main factor in the complex relationship between greenhouse gases and global warming. The Taiwan Environmental Protection Agency reports that carbon dioxide makes up more than 95% of Taiwan’s most recent greenhouse gas emissions. This study used fluidized-bed homogeneous crystallization (FBHC) technology to recover carbonate in a simulated CO2-enriched flue gas. It was specifically designed to determine how carbonate removal and crystallization efficiency were affected by carbonate surface loading, the influence of the source of calcium ions, and interfering substances. Results revealed that the best surface loading at 55 kg m− 2 h− 1 achieved 93% removal and 84% crystallization efficiency. At 50 mg L− 1 of sulfate ions, the presence of more interfering compounds tends to reduce carbonate removal to 97% and 91% crystallization. Regarding X-ray diffraction data, the recovered carbonate crystals resembled calcium carbonate crystals. It has been demonstrated that carbonate can be recovered using FBHC technology as a method of CO2 capture and storage. © The Author(s) 2024.

关键词： Calcium carbonate

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Forecasting maximal and minimal air temperatures using explainable machine learning: Shapley additive explanation versus local interpretable model-agnostic explanations

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Stochastic Environmental Research and Risk Assessment 2025年 1-31页

作者： Daif, Noureddine Di Nunno, Fabio Granata, Francesco Difi, Salah Kisi, Ozgur Heddam, Salim Kim, Sungwon Adnan, Rana Muhammad Zounemat-Kermani, Mohammad Faculty of Science Agronomy Department 20 August 1955 University of Skikda: Universite du 20 Août 1955 de Skikda Route El Hadaik BP 26 Skikda Algeria University of Cassino and Southern Lazio Via G. Di Biasio 43 Cassino Frosinone03043 Italy University of Hassiba Benbouali Chlef Algeria Department of Civil Engineering Lübeck University of Applied Sciences Lübeck23562 Germany Department of Civil Engineering School of Technology IIia State University Tbilisi0162 Georgia School of Civil Environmental and Architectural Engineering Korea University Seoul02841 Korea Republic of Faculty of Science Agronomy Department Hydraulics Division University 20 Août 1955 Skikda Route El Hadaik BP 26 Skikda Algeria Department of Railroad Construction and Safety Engineering Dongyang University Yeongju36040 Korea Republic of College of Architecture and Urban Planning Guangzhou University Guangzhou510006 China Center for global health research Saveetha Institute of Medical and Technical sciences Chennai600001 India Department of Water Engineering Shahid Bahonar University of Kerman Kerman Iran

This study investigates the performance of four boosting machine learning models, AdaBoost, XGBoost, CatBoost, and LightGBM, for forecasting maximal (Tmax) and minimal (Tmin) air temperatures at six lead times: the same day and 1, 7, 15, 21, and 30 days ahead. Daily temperature data from the USGS 02187010 weather station (South Carolina, USA) were used for model training and validation. To address the challenges posed by the non-linearity and complexity of climate data, the models were integrated with explainable artificial intelligence (XAI) techniques, specifically SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), which provide insights into the role of input features in shaping predictions. Results indicate that forecasting accuracy declines with increasing lead time. Among the tested models, CatBoost1 consistently exhibited the best performance. For Tmax forecasting on the validation set, CatBoost1 yielded a correlation coefficient (R) of 0.900, Nash–Sutcliffe efficiency (NSE) of 0.810, root mean squared error (RMSE) of 3.447 °C, mean absolute error (MAE) of 2.571 °C, Willmott’s Index (WI) of 0.947, Legates and McCabe Index (LM) of 0.615, explained variance score (EVS) of 0.810, and absolute percentage bias (APB) of 15.415%. For Tmin, CatBoost1 achieved R = 0.941, NSE = 0.885, RMSE = 2.618 °C, MAE = 1.952 °C, WI = 0.969, LM = 0.709, EVS = 0.885, and APB = 54.360%. These findings demonstrate that boosting models, when combined with explainable AI techniques, offer a robust and transparent framework for temperature forecasting, supporting their application in climate risk management, agriculture, and energy planning. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： AdaBoost CatBoost Forecasting LightGBM LIME SHAP Tmax Tmin XGBoost

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Intellectual property policy development: The case of a state college in the Philippines

Intellectual property policy development: The case of a stat...

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2021 IEEE technology and engineering Management Conference - Europe, TEMSCON-EUR 2021

作者： Montanez, John Joshua F. Bicol State College of Applied Sciences and Technology College of Engineering and Architecture Camarines Sur Naga City Philippines

ISBN: (纸本)9781665440912

The creation of the mind or Intellectual Property is an emerging concept among state Universities and colleges, and Private Higher Education Institutions and is the cornerstone of the innovation ecosystem among academic institutions aside from research activities. This study presents the level of Intellectual Property (IP) Awareness of the employees (Teaching and Non-Teaching Personnel) of the bicol state college of applied sciences and technology (BISCAST) and its impact on the IP policy development of BISCAST. The study used a mixed-method type of research incorporating quantitative and descriptive analysis. The quantitative analysis utilizes a survey instrument used to assess the level of IP awareness of BISCAST employees. The survey instrument is divided into two parts, namely: IP and IP rights. The study's descriptive component shows the impact of several initiatives of the BISCAST towards the development of the IP Policy. The survey was administered to 120 teaching personnel and 54 non-teaching personnel garnering a weighted mean of 2.85 and 1.94 interpreted as partially aware and less aware, respectively. The study reveals that an increase in utility model and industrial design applications is noted, despite partial awareness of the teaching personnel. This is attributed to the annual IP awareness seminar and workshops. The necessary factors were considered in the IP Policy's approval tailor-suited to the needs of BISCAST, like incentives and disputes. It gave birth to the BISCAST Intellectual Property Management Unit. The approval of the BISCAST IP policy sparks the innovation ecosystem within the institution. © 2021 IEEE.

关键词： Intellectual property

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Level identification of Soil pH and macronutrients using mobile application through image processing 2

Level identification of Soil pH and macronutrients using mob...

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2nd International Conference for Emerging technology, INCET 2021

作者： Montanez, John Joshua F. College of Engineering and Architecture Bicol State College of Applied Sciences and Technology Camarines Sur Naga City Philippines

ISBN: (纸本)9781728170299

Soil pH and macronutrient (Nitrogen, Phosphorus and Potassium) levels are the primary parameters for crop and fertilizer recommendations given to farmers to achieve soil productivity. Soil Test Kits (STK) use a colorimetric process where results are dependent on the colors produced. This study aims to develop a mobile application that gives crop and fertilizer recommendations after determining the soil pH level and NPK content of the soil through image processing. The OpenCV library is used for the image processing component, while Eclipse is used for its Android development component. Using a t-test for the pH level and Wilcoxon signed ranks test for NPK levels, the results show the mobile application can adequately determine the soil's pH level and NPK content and give crop and fertilizer recommendations. The use of the mobile application can save time, and it is cost-efficient among its users. © 2021 IEEE.

关键词： Crops

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Overlooking the effect of alcohol quenchers can mislead mechanistic understanding in ferrate-based advanced oxidation processes

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Separation and Purification technology 2025年 370卷

作者： Wang, Ying Chen, Liang Chen, Xin Yang, Chunyan Cheng, Xin Liu, Wen Tiraferri, Alberto Ma, Jun Liu, Baicang State Key Laboratory of Hydraulics and Mountain River Engineering College of Architecture and Environment Sichuan University Sichuan Chengdu610207 China Yibin Institute of Industrial Technology Sichuan University Yibin Park Section 2 Lingang Ave. Cuiping District Sichuan Yibin644000 China The Key Laboratory of Water and Sediment Sciences Ministry of Education College of Environmental Sciences and Engineering Peking University Beijing100871 China Department of Environment Land and Infrastructure Engineering Politecnico di Torino Turin10129 Italy State Key Laboratory of Urban Water Resource and Environment School of Municipal and Environmental Engineering Harbin Institute of Technology Harbin150090 China

Alcohols serve as effective quenchers and are widely employed to assess the role of reactive species in advanced oxidation processes (AOPs). However, this study reveals that frequently used alcohol quenchers, including tert-butanol (TBA), methanol (MA), ethanol (EA), and furfuryl alcohol (FFA), can substantially affect the ferrate (Fe(VI)) system. Firstly, the presence of alcohols was found to influence the decomposition of Fe(VI). In the pH range of 7–11, various alcohols promoted Fe(VI) decomposition, especially at pH 7–8, namely, the pH conditions often used in Fe(VI)-based advanced oxidation studies. The magnitude of Fe(VI) decomposition influenced by the alcohols was ranked as FFA > EA > MA > TBA. Secondly, alcohols may alter the formation and conversion of high-valent iron active species. Using the phenylmethyl sulfoxide (PMSO) probe, we observed that MA and TBA inhibited the removal of PMSO by Fe(VI), while FFA and EA substantially enhanced it. These results indicate that alcohols may not only influence the decomposition of Fe(VI) itself, but also affect the degradation of pollutants mediated by high-valent iron active species. These phenomena may lead to misconceptions about the mechanisms and efficacy of Fe(VI)-based AOPs. Overall, this work underscores the importance of carefully selecting quenchers in complex oxidation systems involving Fe(VI) to avoid misleading interpretations of Fe(VI) decomposition and oxidation behavior. © 2025 Elsevier B.V.

关键词： Advanced oxidation processes Alcohol Ferrate High-valent iron species Quenchers

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Human-centered GeoAI foundation models: where GeoAI meets human dynamics

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Urban Informatics 2025年第1期4卷 1-15页

作者： Ye, Xinyue Du, Jiaxin Li, Xinyu Shaw, Shih-Lung Fu, Yanjie Dong, Xishuang Zhang, Zhe Wu, Ling Department of Landscape Architecture and Urban Planning & Center for Geospatial Sciences Applications and Technology Texas A&M University College Station USA Department of Computer Science and Engineering Texas A&M University College Station USA College of Computing Grand Valley State University Allendale USA Department of Geography & Sustainability University of Tennessee Knoxville USA School of Computing and AI Arizona State University Tempe USA Department of Electrical & Computer Engineering Prairie View A&M University Prairie View USA Department of Geography Texas A&M University College Station USA Department of Justice Studies Prairie View A&M University Prairie View USA

This study examines the role of human dynamics within Geospatial Artificial Intelligence (GeoAI), highlighting its potential to reshape the geospatial research field. GeoAI, emerging from the confluence of geospatial technologies and artificial intelligence, is revolutionizing our comprehension of human-environmental interactions. This revolution is powered by large-scale models trained on extensive geospatial datasets, employing deep learning to analyze complex geospatial phenomena. Our findings highlight the synergy between human intelligence and AI. Particularly, the humans-as-sensors approach enhances the accuracy of geospatial data analysis by leveraging human-centric AI, while the evolving GeoAI landscape underscores the significance of human–robot interaction and the customization of GeoAI services to meet individual needs. The concept of mixed-experts GeoAI, integrating human expertise with AI, plays a crucial role in conducting sophisticated data analyses, ensuring that human insights remain at the forefront of this field. This paper also tackles ethical issues such as privacy and bias, which are pivotal for the ethical application of GeoAI. By exploring these human-centric considerations, we discuss how the collaborations between humans and AI transform the future of work at the human-technology frontier and redefine the role of AI in geospatial contexts.

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Synergistic mechanisms of fly ash reinforcement on the mechanical characteristics, deformation behaviour, grain refinement, and grain boundary in friction stir processed copper matrix composites

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Journal of Materials Research and technology 2025年 36卷 9831-9839页

作者： Harikishor Kumar Rabindra Prasad Parshant Kumar I Sri Phani Sushma Balram Yelamasetti Sagar Shelare Shubham Sharma A.I. Ismail Jasmina Lozanovic Deparment of Mechanical Engineering MLR Institute of technology Hyderabad Telangana India Department of Mechanical Engineering IIITDM Jabalpur India School of Mechanical Engineering Dr. Vishwanath Karad MIT World Peace University Pune 411038 India Department of Mechanical Engineering JNTUK University College of Engineering Narasaraopet Andhra Pradesh India-522601 Department of Mechanical Engineering Priyadarshini College of Engineering Nagpur 440019 Maharashtra India University Centre for Research and Development Chandigarh University Mohali Punjab 140413 India Department of Technical Sciences Western Caspian University Baku Azerbaijan Centre for Research Impact and Outcome Chitkara University Institute of Engineering and Technology Chitkara University Rajpura 140401 Punjab India Jadara University Research Center Jadara University Jordan Mechanical Engineering Department College of Engineering and Architecture UMM Alqura University Saudi Arabia University of Applied Sciences Campus Vienna Department of Engineering 1100 Vienna Austria

Fly ash is an attractive reinforcement for copper and its alloys. It is a by-product produced from the combustion of coal in thermal power plant. This solid waste produced massively across the globe is considered as a serious threat to the environment. Researchers across the globes are looking for its utilization. Copper matrix composites reinforced with fly ash instead of costly hard ceramics are one of those applications. However, it is technically challenging to incorporate fly ash in copper matrix uniformly and homogeneously via liquid-based processing techniques due to large density gradient. In the present study, efforts are being put to fabricate fly ash reinforced copper matrix composites by friction stir processing (FSP) and analyze its performance for microstructure and mechanical properties. Microstructural features were depicted by optical microscopy, scanning electron microscope and electron backscattered diffraction across cross-section. Hardness was observed across cross-section of the processed plate for plotting hardness profile. Tensile test was conducted on base and processed plate for strength. The composite exhibited uniform and homogeneous sputtering of fly ash particles in the copper matrix without agglomeration of particles irrespective of the position in stir zone which may be attributed to the solid state nature of the process. No disturbance in terms of voids or irregularities was detected at the interface which shows excellent integrity at the interface. Substantial grain refinement for the copper matrix along with fracture of the reinforced fly ash was observed. It was also observed that the hardness and tensile strength of the composite improved but ductility decreased. The mechanism responsible for hardness and strength improvement may be attributed to grain refinement and uniform dispersion of fly ash particles. Reduced ductility may be due to the presence of fly ash particles which brought brittleness to the composite.

关键词： Fly ash Copper Friction stir processing Copper matrix composite Microstructure Strength

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Ozone pollution induced-yield loss of major staple crops in China and effects from COVID-19

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Journal of Environmental sciences 2025年 157卷 804-820页

作者： Haiyang Liu Siyuan Wang Guangsheng Chen Zhaozhong Feng Di Liu Wenxiu Zhang Shufen Pan Hanqin Tian State Key Laboratory For Ecological Security of Regions and Cities Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing 100085 China University of Chinese Academy of Sciences Beijing 100049 China College of Environmental and Resource Sciences Zhejiang A&F University Hangzhou 311300 China School of Ecology and Applied Meteorology Nanjing University of Information Science and Technology Nanjing 210044 China Department of Engineering and Environmental Studies Program Boston College Chestnut Hill MA 02467-9991 USA Center for Earth System Science and Global Sustainability Schiller Institute for Integrated Science and Society Boston College Chestnut Hill MA 02467-9991 USA Department of Earth and Environmental Sciences Boston College Chestnut Hill MA 02467-9991 USA

Surface ozone (O 3 ) pollution showed a continuous increasing trend during the recent decades in China, posing an increasing threat to food security. A wide range of yield reductions have been reported and thus more studies are needed to narrow down the uncertainty resulting from spatiotemporal accuracy of O 3 metrics and extrapolation methods. Based on a high spatial resolution (0.1°) hourly surface O 3 data, here we analyzed the spatiotemporal O 3 pollution patterns and impacts on yield, production and economic losses for wheat, rice, and maize in China during 2005–2020. The accumulated O 3 exposure over a threshold of 40 ppb (AOT40) increased by 10 % during 2005–2019, and a decrease of 5.56 % was observed in 2020 due to the COVID-19 lockdowns. Rising O 3 pollution reduced national level wheat, rice and maize yields by 14.51 % ± 0.43 %, 11.10 % ± 0.6 %, and 3.99 % ± 0.11 %, respectively. A Business-As-Usual projection suggested that the relative yield loss (RYL) would potentially reach 8 %–18 % at the national scale by 2050 if no emission control is implemented. COVID-19 lockdowns in 2020 led to significantly reduced RYL for maize (0.52 %) and rice (2.17 %) but not for wheat (0.11 %), with the largest reduction (1.88 %–9.4 %) in North China Plain, highlighting the potential benefits of emission control. Our findings provided robust evidence that rising O 3 pollution has significantly affected China's crop yields, production and economic losses, underscoring the urgent need to curb O 3 pollution to safeguard food security, particularly in densely populated and industrialized regions.

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