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Synergistic effect of silicon availability and salinity on metal adsorption in a common estuarine diatom

Journal of environmental sciences 2025年第2期148卷 364-374页

作者： Fengyuan Chen Jie Ma Ke Pan SZU-HKUST Joint PhD Program in Marine Environmental Science Shenzhen UniversityShenzhen 518060China Shenzhen Key Laboratory of Marine Microbiome Engineering Institute for Advanced StudyShenzhen UniversityShenzhen 518060China Department of Ocean Science The Hong Kong University of Science and TechnologyHong Kong 999077China

Increasing nitrogen and phosphorus discharge and decreasing sediment input have made silicon(Si)a limiting element for diatoms in *** in nutrient structure and salinity fluctuation can greatly affect metal uptake by estuarine ***,the combined effects of Si and salinity on metal accumulation in these diatoms have not been *** this study,we aimed to investigate how salinity and Si availability combine to influence the adsorption of metals by a widely distributed diatom Phaeodactylum *** data indicate that replete Si and low salinity in seawater can enhance cadmium and copper adsorption onto the diatom *** the single-cell level,surface potential was a dominant factor determining metal adsorption,while surface roughness also contributed to the highermetal loading capacity at lower *** a combination of noninvasive micro-test technology,atomic force microscopy,X-ray photoelectron spectroscopy,and Fourier transform infrared spectroscopy,we demonstrate that the diversity and abundance of the functional groups embedded in diatom cell walls vary with salinity and Si *** results in a change in the cell surface potential and transient metal *** study provides novel mechanisms to explain the highly variable metal adsorption capacity of a model estuarine diatom.

关键词： Diatom Metal adsorption Silicon Salinity Functional group Surface potential

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Low-Temperature Methanolysis of Polycarbonate over Solid Base Sodium Aluminate

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Langmuir 2024年第10期40卷 5338-5347页

作者： Krisbiantoro, Philip Anggo Sato, Miyu Lin, Tzu-Ming Chang, Yu-Chia Peng, Tzu-Yun Wu, Yun-Chih Liao, Weisheng Kamiya, Yuichi Otomo, Ryoichi Wu, Kevin C.-W. Molecular Science and Technology Program Taiwan International Graduate Program Academia Sinica Taipei11529 Taiwan International Graduate Program of Molecular Science and Technology National Taiwan University Taipei10617 Taiwan Department of Chemical Engineering College of Engineering National Taiwan University Taipei10617 Taiwan Graduate School of Environmental Science Hokkaido University Nishi 5 Kita 10 Sapporo060-0810 Japan Faculty of Environmental Earth Science Hokkaido University Nishi 5 Kita 10 Sapporo060-0810 Japan Center of Atomic Initiative for New Materials National Taiwan University Taipei10617 Taiwan Department of Chemical Engineering and Materials Science Yuan Ze University Taoyuan320 Taiwan

Herein, a low-cost and readily available sodium aluminate (NaAlO2) was used as a solid base catalyst for the depolymerization of polycarbonate (PC) via methanolysis in the presence of tetrahydrofuran (THF) as a solvent. NaAlO2 was highly active for the reaction, and the performance was comparable to that of soluble strong base SrO and much higher than those of MgO and CaO. By the reaction over the catalyst, a highly pure and crystalline bisphenol A (BPA) was obtained. Among tested organic solvents, THF was the best in aiding PC methanolysis over NaAlO2 due to the polarity similar to PC according to Hansen solubility parameters (HSPs). At 60 °C, 98.1% PC conversion and 96.8% BPA yield were achieved within just 2 h. NaAlO2 was reusable without any severe catalyst deactivation in at least four runs. The mechanistic study revealed that the reaction proceeded via the methoxide pathway, with THF aiding the dissolution of PC. The reaction over NaAlO2 possessed a low apparent activation energy (Ea) of 75.1 kJ mol-1, which is the lowest ever reported so far for the reaction over solid catalysts. © 2024 The Authors. Published by American Chemical Society.

关键词： Sodium Aluminate

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New insights into chemical compositions and health benefits of white tea and development of new products derived from white tea

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Food science and Human Wellness 2024年

作者： Tharuka Wijesekara Yongxiang Liu Baojun Xu Food Science and Technology Program， Department of Life Sciences， BNU-HKBU United International College Department of Food Science and Agricultural Chemistry， Faculty of Agricultural and Environmental Sciences， McGill University

This study reviews the health benefits of white tea and the development of new products derived from white tea. The core processing procedures such as withering and drying play a pivotal role in shaping the unique characteristics of white tea. In the withering phase， water gradually diminishes， and endogenous polyphenol oxidase induces a slight oxidation of polyphenols which leads to a delicate fermentation process. This not only helps preserve the integrity of most components but also imparts an exclusive flavor and taste to white tea. Additionally， white tea stands out as a rich source of polyphenols， polysaccharides， and saponins. These bioactive components which can be extractable during brewing are conferring a myriad of health benefits. These benefits include antioxidant， anti-inflammatory， anti-aging， anti-radiation， anti-hyperglycemia， anti-hyperlipidemia， neuroprotection， and cardiovascular-protection properties. Innovative applications range from ready-to-drink infusions to skincare products. The review proposes white tea as a raw material for supplements， meal replacements， and confectionery. Various studies suggest its potential in enhancing gut health， supporting endurance， and contributing to photoprotection in skincare. The article concludes by highlighting white tea's adaptability， its role as a bridge between tradition and modernity， and its potential to shape the future of functional foods and beverages. Overall， white tea emerges as a versatile resource with applications extending beyond traditional consumption， offering a holistic approach to health and wellness.

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Dimensionality reduction and prediction of soil consolidation coefficient using random forest coupling with Relief algorithm

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Frontiers of Structural and Civil Engineering 2022年第2期16卷 224-238页

作者： Hai-Bang LY Huong-Lan Thi VU Lanh Si HO Binh Thai PHAM Department of Civil Engineering University of Transport TechnologyHanoi100000Vietnam Civil and Environmental Engineering Program Hiroshima UniversityHiroshima739-8527Japan Department of Science Technology and International CooperationUniversity of Transport TechnologyHanoi100000Vietnam

The consolidation coefficient of soil(C_(v))is a crucial parameter used for the design of structures leaned on soft *** general,the C_(v) is determined experimentally in the ***,the experimental tests are time-consuming as well as ***,researchers tried several ways to determine C_(v) via other simple soil *** this study,we developed a hybrid model of Random Forest coupling with a Relief algorithm(RF-RL)to predict the C_(v) of *** conduct this study,a database of soil parameters collected from a case study region in Vietnam was used for *** performance of the proposed models was assessed via statistical indicators,namely Coefficient of determination(R^(2)),Root Mean Squared Error(RMSE),and Mean Absolute Error(MAE).The proposal models were constructed with four sets of soil variables,including 6,7,8,and 13 *** results revealed that all models performed well with a high performance(R^(2)>0.980).Although the RF-RL model with 13 variables has the highest prediction accuracy(R^(2)=0.9869),the difference compared with other models was negligible(i.e.,R^(2)=0.9824,0.9850,0.9825 for the cases with 6,7,8 inputs,respectively).Thus,it can be concluded that the hybrid model of RF-RL can be employed to predict C_(v) based on the basic soil parameters.

关键词： soil consolidation coefficient machine learning random forest Relief

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DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND OPEN PIT COAL MINE DETECTION USING SATELLITE IMAGERY 44

DEEP LEARNING-BASED APPROACH FOR ANNUAL CHANGE DETECTION AND...

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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Prasetya, Koni Dwi Tsai, Fuan International PhD Program in Environmental Science and Technology National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan

ISBN: (纸本)9781713893646

This study introduces a novel deep learning-based method for detecting open pit coal mines in medium high (MH)resolution satellite imagery and analyzing annual variations in coal mining areas. The study aims to monitor and precisely identify coal mines, given their vital function as a primary energy source and a significant contributor to greenhouse gas emissions. The proposed method employs the U-Net architecture and ResNet 34 as its backbone for accurate detection and classification. The applied dataset consists of multispectral imagery from Sentinel-2 and synthetic aperture radar (SAR) imagery from Sentinel-1. Manual labelling of known coal mine locations using mining data as a reference, subdividing Sentinel image patches for training U-Net convolutional neural networks (CNNs) to classify coal mine and non-coal mine areas, and training and testing U-Net architecture and ResNet as a backbone deep learning model are the three essential steps involved in the process. The classification accuracy of the coal mining detection deep learning model is 97%, and the kappa value is 0.91. Preliminary results indicate that the investigation demonstrates the evolution of coal mining from 2016 to 2021, with an increase of over 40% in coal mining area in 2019. In 2017 and 2020, the area mined for coal will decrease. Variations in annual coal mining variations emphasize the significance of replanting efforts. The proposed method uses deep learning and satellite imagery to provide an accurate and efficient solution for detecting and monitoring open-pit coal mines. Incorporating artificial intelligence into dynamic coal mining activities yields valuable insights that aid in making informed decisions for sustainability. © 2023 ACRS. All Rights Reserved.

关键词： Artificial Intelligence Coal Mine Deep Learning Monitoring Multispectral SAR Sentinel Supervised Classification U-Net

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Universal nanocomposite coating with antifouling and redox capabilities for electrochemical affinity biosensing in complex biological fluids

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Nanoscale Horizons 2024年第5期9卷 843-852页

作者： Bharti, Aditya Manu Rakesh Kumar, R.K. Chuang, Cheng-Hsin Shaikh, Muhammad Omar International PhD Program for Science National Sun Yat-sen University Kaohsiung80424 Taiwan Institute of Medical Science and Technology National Sun Yat-sen University Kaohsiung80424 Taiwan Department of Chemical Engineering National Taiwan University Taipei City10617 Taiwan Institute of Biomedical Engineering and Nanomedicine National Healthcare Research Institutes Miaoli County350 Taiwan National Sun Yat-sen University Kaohsiung80424 Taiwan Sustainability Science and Management Program Tunghai University Taichung407224 Taiwan

Electrochemical affinity biosensors have the potential to facilitate the development of multiplexed point-of-care diagnostics in complex biological fluids. However, their commercial viability has been hindered by challenges such as electrode biofouling and the lack of inherent redox properties. To address this unmet need, we have developed a universal nanocomposite coating which is unique in its ability to not only allow oriented conjugation of the biorecognition element but also specific detection directly in complex biological fluids like serum and urine owing to its built-in antifouling and redox capabilities, thus improving suitability for point of care testing. This multifunctional coating comprises a 3D porous crosslinked bovine serum albumin matrix for oriented conjugation and antifouling properties with embedded graphene nanosheets modified with amino-ferrocene for enhanced conductivity and mediator-free biosensing. The coating showed minimal signal degradation despite prolonged exposure to 1% bovine serum albumin, artificial urine and untreated human serum for up to 30 days. To demonstrate its utility, we fabricated and tested proof-of-concept electrochemical immunosensors for bladder cancer protein biomarkers, specifically interleukin-8 (IL-8) and vascular endothelial growth factor (VEGF). The practical feasibility was highlighted by the excellent sensitivity and specificity observed for IL-8 and VEGF with a limit of detection of 41 pg mL−1 and 67 pg mL−1, respectively. Consequently, this universal nanocomposite-based electrochemical biosensing platform can be extended to the point of care testing of a broad spectrum of biomarkers present in complex biological fluids, thus enabling reliable and early diagnostics. © 2024 The Royal Society of Chemistry

关键词： Biomarkers

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Classification and prediction of student performance data using various machine learning algorithms

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Materials Today: Proceedings 2023年 80卷 3782-3785页

作者： Pallathadka, Harikumar Wenda, Alex Ramirez-Asís, Edwin Asís-López, Maximiliano Flores-Albornoz, Judith Phasinam, Khongdet Manipur International University Manipur Imphal India Department of Electrical Engineering Faculty of Science and Technology State Islamic University of Sultan Syarif Kasim Riau Indonesia Management and turism Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Computer Science and Engineering Science Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru PhD in Environmental Engineering Environmental Sciences Faculty Universidad Nacional Santiago Antúnez de Mayolo Huaraz Peru Faculty of Food and Agricultural Technology Pibulsongkram Rajabhat University Phitsanulok Thailand

In today's competitive world, it is critical for an institute to forecast student performance, classify individuals based on their talents, and attempt to enhance their performance in future tests. Students should be advised well in advance to concentrate their efforts in a specific area in order to improve their academic achievement. This type of analysis assists an institute in lowering its failure rates. Based on their prior performance in comparable courses, this study predicts students' performance in a course. Data mining is a collection of techniques used to uncover hidden patterns in massive amounts of existing data. These patterns may be valuable for analysis and prediction. Education data mining refers to the collection of data mining applications in the field of education. These applications are concerned with the analysis of data from students and teachers. The analysis might be used for categorization or prediction. Machine learning such as Nave Bayes, ID3, C4.5, and SVM are investigated. UCI machinery student performance data set is used in experimental study. Algorithms are analysed on certain parameters like- accuracy, error rate. © 2021

关键词： Forecasting

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Prospects for agribusiness in Ukraine over the next 5 years

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international Journal of environmental Studies 2023年第2期80卷 291-298页

作者： Buka, Stanislav Tkachuk, Vadym Kondratiuk, Vadym Tonkha, Oksana Slobodyanyuk, Nataliia Regional Economy and Economic Policy PhD programme Baltic International Academy Riga Latvia Department of Economics National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Animal Feeding and Feed Technologies National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Soil Science and Soil Protection National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Technology of Meat Fish and Seafood Products National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine

All-out war prompts an urgent search for food security. Looking ahead, we also seek ways and means of sustainable development for the agricultural sector. In reviewing promising activities for farmers that can be implemented within the next 5 years, each individual approach is considered from technological, financial, and legal angles, and guidelines are developed for the agricultural enterprises of the near future. Despite the daunting challenges, agribusiness in Ukraine has great prospects through adoption of precision agriculture, zero tillage, and diverse cropping systems;high-quality products in tune with international standards, including substantially more organically grown products;digital literacy and new technical solutions;and more resilient logistics. Of course, all depend on a secure knowledge base and proper financing. © 2022 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Food supply

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Seagrass Meadows under the Changing Climate:A Review of the Impacts of Climate Stressors

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Research in Ecology 2022年第1期4卷 27-36页

作者： Kuok Ho Daniel Tang Tony Hadibarata Environmental Science Program Division of Science and TechnologyBeijing Normal University-Hong Kong Baptist University United International CollegeZhuhaiGuangdong519087China Environmental Engineering Program Faculty of Engineering and ScienceCurtin University MalaysiaCDT 250MiriSarawak98009Malaysia

Seagrass meadows provide important ecological functions,particularly by serving as carbon sinks and breeding grounds for marine *** change has threatened seagrass communities,causing their replacement,loss and increased *** review examined scholarly articles published between 2010-2021 to comprehensively present the impacts of climate change on seagrass *** shows that ocean warming negatively affects seagrass communities by favouring communities of lower structuring capacities,thus reducing the effectiveness of their ecological *** warming also promotes the propagation and spread of invasive species,and changes the trophic structures leading to further loss of seagrasses of *** seawater temperature is associated with shoot mortality and retarded growth of certain *** level rise causes more wave energy to be received by coastal seagrass communities,thus,creating more damage to the *** sea limits light penetration and alters distribution of seagrass *** dioxide enrichment of seawater increases photosynthetic rate of seagrasses but ocean warming and acidification counteract this beneficial *** dioxide enrichment affects different seagrass species and different parts of a seagrass species differently,and,where beneficial to seagrass communities,could enhance their ecological *** extremes could kill seagrasses while marine heatwaves and flooding could act synergistically to increase carbon demand of certain seagrasses and unfavourably change their *** impacts are often aggravated by anthropogenic *** review calls for more studies and conservation efforts to understand the impacts of climate change on seagrass communities and future-proof them against the changing climate.

关键词： Climate change Extreme weather Seagrass Ocean warming Ocean acidification Vulnerability

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Assessment of air pollution emitted during cooking using biomass and cleaner fuels in the Shiselweni region of Eswatini (Swaziland)

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Clean Technologies and environmental Policy 2024年第9期26卷 3003-3020页

作者： Neumann, Melinda Dlamini, Wonder Nathi Sallah-Ud-Din, Rasham Berekute, Abiyu Kerebo Siregar, Sepridawati Getnet, Mastewal Endeshaw Maulana, Muchsin Pan, Wen-Chi Lung, Shih-Chun Candice Yu, Kuo-Pin International Health Program National Yang Ming Chiao Tung University No.155 Sec. 2 Li-Nong Street Taipei112304 Taiwan Institute of Environmental and Occupational Health Sciences National Yang Ming Chiao Tung University No.155 Sec. 2 Li-Nong Street Taipei112304 Taiwan International Ph.D. Program in Environmental Science and Technology National Yang Ming Chiao Tung University No.155 Sec. 2 Li-Nong Street Taipei112304 Taiwan Department of Chemistry College of Natural and Computational Sciences Arba Minch University Arbaminch Ethiopia Research Center for Environmental Changes Academia Sinica No. 128 Academia Road Section 2Nangang Taipei11529 Taiwan Faculty of Mineral Technology AKPRIND Institute of Science and Technology Yogyakarta Indonesia

In Eswatini, 62.3% of households still rely on solid fuel for cooking, especially wood (61.8%). Smoke emissions during biomass burning remain the primary source of indoor air pollution, comprising pollutants detrimental to health. This study provides a quantitative exposure assessment of fuels used in the Shiselweni region with the objectives of monitoring the carbon monoxide (CO) and carbon dioxide (CO2) concentrations during cooking events and evaluating cancer and noncancer risks due to exposure to particulate-bound polycyclic aromatic hydrocarbons (PAHs) during cooking events among cooking personnel in households that cook indoors by burning biomass. Real-time CO, CO2, and particulate matter (PM) monitoring was performed in seventeen kitchens during cooking events across the Shiselweni region using different cooking methods: biomass in open fires and stoves, liquefied petroleum gas (LPG), and electric stoves. PM2.5, PM10, and CO exceeded indoor exposure guidelines for biomass fuel-related homesteads. Particulate PAH intake concentrations were evaluated, and biomass fuel users exhibited high cancer risks and low embryo survival chances due to particulate PAH exposure. The average total cost of reducing the incremental lifetime cancer risk (ILCR) and hazard quotient (HQ) to acceptable levels was lower when shifting from biomass to LPG stoves than when shifting to electric stoves. Graphical abstract: (Figure presented.) © The Author(s) 2024.

关键词： Carbon dioxide

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