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Journal of Natural gas Chemistry 2008年第2期17卷 175-178页

作者： Francisco E.M.Farias fernando g.sales Fabiano A.N.Fernandes Universidade Federal do Ceará Departamento de Engenharia QuímicaCampus do PiciBloco 70960455-760 Fortaleza-CEBrazil

The dependencies of Fischer-Tropsch synthesis liquid hydrocarbon product distribution on operating pressure and temperature have been studied over three potassium-promoted iron catalysts with increasing potassium molar content. The study followed an experimental planning and the results were analyzed based on surface response methodology. The effects of different operating conditions and potassium contents on the liquid product distribution were compared based on number average carbon number and dispersion. Results showed that high pressures （25 to 30 arm） favored the production of waxes that could be converted into liquid fuels through hydrocracking, while greater direct selectivity towards diesel was favored by low pressure （20 arm） using catalysts with low potassium to iron molar ratios. The liquid product distribution produced using an iron catalyst with high potassium content presented higher number-average number of carbons when compared to the distribution obtained using an iron catalyst with low potassium content.

关键词： Fischer-Tropsch synthesis liquid fuels iron-based catalyst potassium

E-Commerce Fraud Detection Based on Machine Learning Techniques:Systematic Literature Review

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Big Data Mining and Analytics 2024年第2期7卷 419-444页

作者： Abed Mutemi fernando Bacao NOVA Information Management School(NOVA IMS) Universidade Nova de LisboaCampus de CampolideLisboa 1070-312Portugal

The e-commerce industry’s rapid growth,accelerated by the COVID-19 pandemic,has led to an alarming increase in digital fraud and associated *** establish a healthy e-commerce ecosystem,robust cyber security and anti-fraud measures are ***,research on fraud detection systems has struggled to keep pace due to limited real-world *** in artificial intelligence,Machine Learning(ML),and cloud computing have revitalized research and applications in this *** ML and data mining techniques are popular in fraud detection,specific reviews focusing on their application in e-commerce platforms like eBay and Facebook are lacking *** reviews provide broad overviews but fail to grasp the intricacies of ML algorithms in the e-commerce *** bridge this gap,our study conducts a systematic literature review using the Preferred Reporting Items for Systematic reviews and Meta-Analysis(PRISMA)*** aim to explore the effectiveness of these techniques in fraud detection within digital marketplaces and the broader e-commerce *** the current state of the literature and emerging trends is crucial given the rising fraud incidents and associated *** our investigation,we identify research opportunities and provide insights to industry stakeholders on key ML and data mining techniques for combating e-commerce *** paper examines the research on these techniques as published in the past *** the PRISMA approach,we conducted a content analysis of 101 publications,identifying research gaps,recent techniques,and highlighting the increasing utilization of artificial neural networks in fraud detection within the industry.

关键词： E-commerce Machine Learning(ML) systematic review fraud detection organized retail fraud

Exploring multifaceted factors in chronic kidney disease risk: A comprehensive analysis of biochemistry, lifestyle, and inflammation in elderly Chinese individuals

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World Journal of Clinical Cases 2024年第5期12卷 1033-1035页

作者： fernando Cardona Unitat de genètica Molecular Instituto de Biomedicina de Valencia–CSICValencia 46010Spain Centro de investigación Biomédica en Red Enfermedades Neurodegenerativas(CIBERNED) Instituto de Salud Carlos IIIMadrid 28029Spain Departamento de Biotecnología Escuela Técnica Superior de Ingeniería Agronómica y del Medio Natural(ETSIAMN)Univeristat Politècnica de ValenciaValencia 46022Spain

This letter praises a recent article in the World Journal of Clinical Cases(Roles of biochemistry data,lifestyle,and inflammation in identifying abnormal renal function in old Chinese),examining factors affecting abnormal renal function in elderly Chinese using advanced machine *** highlights the importance of uric acid,age,hemoglobin,body mass index,sport hours,and systolic blood *** study's holistic approach,integrating lifestyle and inflammation,offers a nuanced understanding of chronic kidney disease risk *** letter suggests exploring mechanistic pathways of hyperuricemia,the link between anemia and renal function,and the connection between body mass index and estimated glomerular filtration *** advocates investigating physical activity's impact on renal health and the independent effects of blood *** study significantly contributes to chronic kidney disease understanding,proposing avenues for further exploration and *** are extended to the authors and the journal.

关键词： Biochemistry data Lifestyle Machine learning Renal function

Biotransformation of 6:2 fluorotelomer sulfonate(6:2 FTS)in sulfur-rich media by Trametopsis cervina

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Frontiers of Environmental Science & Engineering 2024年第9期18卷 17-26页

作者： Felix grimberg Thomas M Holsen Sujan fernando Siwen Wang Department of Civil and Environmental Engineering Clarkson UniversityPotsdamNew York 13699USA

Biotransformation of 6:2 fluorotelomer sulfonate(6:2 FTS)by two species of white-rot fungi,Pleurotus ostreatus(***)and Trametopsis cervina(***),was investigated in a sulfurrich medium designed to stimulate production of lignin-degrading *** of 6:2 FTS was observed by *** over the study period of 30 d,but not by *** rates were comparable to those found in other studies investigating mixed culture degradation in nonsulfur limiting media,with approximately 50 mol%of applied 6:2 FTS removed after 30 *** transformation products were short-chain perfluorocarboxylic acids(PFCAs),including PFHxA(2.27 mol%),PFPeA(0.24 mol%),and PFBA(0.28 mol%).The main intermediate products include 5:2 sFTOH(16.3 mol%)and 5:3 FTCA(2.99 mol%),while 6:2 FTCA,6:2 FTuCA,and 5:2 ketone were also identified at low *** 60 mol%of detected products were assigned to the major pathway to 5:2 ketone,and 40 mol%were assigned to the minor pathway to 5:3 *** overall molar balance was found to decrease to 75 mol%by Day 30,however,was closed to near 95 mol%with a theoretical estimation for the volatile intermediates in the headspace,5:2 ketone and 5:2 *** different capabilities of the two white-rot fungal species for 6:2 FTS biotransformation in sulfur-rich media suggest that the enzyme processes of *** to de-sulfonate 6:2 FTS may be unrelated to sulfur metabolism.

关键词： White-rot fungus 6:2 fluorotelomer sulfonate(6:2 FTS) Biotransformation Sulfur-rich medium Intermediate products

Boosting efficiency in state estimation of power systems by leveraging attention mechanism

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Energy and AI 2024年第2期16卷 438-449页

作者： Elson Cibaku fernando gama SangWoo Park Industrial Engineering New Jersey Institute of TechnologyNewarkNJUnited States Pendulum Systems DenverCOUnited States

Ensuring stability and reliability in power systems requires accurate state estimation, which is challenging due to the growing network size, noisy measurements, and nonlinear power-flow equations. In this paper, we introduce the graph Attention Estimation Network (gAEN) model to tackle power system state estimation (PSSE) by capitalizing on the inherent graph structure of power grids. This approach facilitates efficient information exchange among interconnected buses, yielding a distributed, computationally efficient architecture that is also resilient to cyber-attacks. We develop a thorough approach by utilizing graph Convolutional Neural Networks (gCNNs) and attention mechanism in PSSE based on Supervisory Control and Data Acquisition (SCADA) and Phasor Measurement Unit (PMU) measurements, addressing the limitations of previous learning architectures. In accordance with the empirical results obtained from the experiments, the proposed method demonstrates superior performance and scalability compared to existing techniques. Furthermore, the amalgamation of local topological configurations with nodal-level data yields a heightened efficacy in the domain of state estimation. This work marks a significant achievement in the design of advanced learning architectures in PSSE, contributing and fostering the development of more reliable and secure power system operations.

关键词： Power grids State estimation Attention mechanism graph neural networks Distributed computation grid cyber-security

Pioneering the direct large-scale laser printing of flexible“graphenic silicon”self-standing thin films as ultrahigh-performance lithium-ion battery anodes

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Carbon Energy 2024年第7期6卷 26-40页

作者： Avinash Kothuru Adam Cohen gil Daffan Yonatan Juhl fernando Patolsky School of Chemistry Faculty of Exact SciencesTel Aviv UniversityTel AvivIsrael Department of Materials Science and Engineering The Iby and Aladar Fleischman Faculty of EngineeringTel Aviv UniversityTel AvivIsrael

Recent technological advancements,such as portable electronics and electric vehicles,have created a pressing need for more efficient energy storage ***-ion batteries(LIBs)have been the preferred choice for these applications,with graphite being the standard anode material due to its ***,graphite falls short of meeting the growing demand for higher energy density,possessing a theoretical capacity that lags *** address this,researchers are actively seeking alternative materials to replace graphite in commercial *** promising avenue involves lithiumalloying materials like silicon and phosphorus,which offer high theoretical ***-silicon composites have emerged as a viable option,showing improved capacity and performance over traditional graphite or pure silicon ***,the existing methods for synthesizing these composites remain complex,energy-intensive,and costly,preventing widespread adoption.A groundbreaking approach is presented here:the use of a laser writing strategy to rapidly transform common organic carbon precursors and silicon blends into efficient“graphenic silicon”composite thin *** films exhibit exceptional structural and energy storage *** resulting three-dimensional porous composite anodes showcase impressive attributes,including ultrahigh silicon content,remarkable cyclic stability(over 4500 cycles with∼40%retention),rapid charging rates(up to 10 A g^(-1)),substantial areal capacity(>5.1 mAh cm^(-2)),and excellent gravimetric capacity(>2400 mAh g^(-1) at 0.2 A g^(-1)).This strategy marks a significant step toward the scalable production of high-performance LIB *** widely available,cost-effective precursors,the laser-printed“graphenic silicon”composites demonstrate unparalleled performance,potentially streamlining anode production while maintaining exceptional *** innovation not only paves the way for advanced LIBs but also sets a precedent for

关键词： 4D printing energy storage fast-charging laser-induced graphene lithium-ion silicon carbon composite anodes

Potential of Bamboo Species guadua trinii and guadua angustifolia for Nanocellulose Production

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Journal of Renewable Materials 2024年第9期12卷 1541-1555页

作者： Agatha E.R.Prado gárate fernando E.Felissia María Cristina Area Teresa Suirezs María Evangelina Vallejos Círculo de Investigación del Bambú Universidad Nacional Agraria La Molina(UNALM)Lima15026Perú Instituto de Materiales de Misiones(IMAM) Universidad Nacional de Misiones(UNaM)Consejo Nacional de Investigaciones Científicas y Técnicas(CONICET)Facultad de Ciencias ExactasQuímicas y Naturales(FCEQYN)Programa de Celulosa y Papel(PROCyP)Posadas3300Argentina Instituto de Materiales de Misiones(IMAM) Universidad Nacional de Misiones(UNaM)Consejo Nacional de Investigaciones Científicas y Técnicas(CONICET)Facultad de Ciencias Forestales(FCF)Eldorado3380Argentina

Non-traditional lignocellulosic materials are a significant resource for producing high-value products,including *** work studied the nanocellulose obtention from chemical pulps of the two fast-growing bamboo species,guadua trinii,and guadua *** pulps were produced by soda-anthraquinone(S)pulping from both autohydrolysis-pretreated(H)and unpretreated bamboo ***-pretreated(SHP)and unpretreated soda-anthraquinone(AQ)(SP)pulps were characterized by yield,Kappa number,alpha,beta,and gamma cellulose,degree of polymerization,water retention value,and *** nanocellulose was produced by a sequential chemical oxidation treatment(2,2,6,6-tetramethylpiperidine-1-oxyl,TEMPO reagent)and mechanical *** was characterized by carboxylic group content and *** results revealed that autohydrolysis pretreatment resulted in a higher hemicelluloses extraction in *** contrast,the pulping yield of unpretreated samples was higher for ***,and the soda-AQ pulps from this species exhibited better delignification than the autohydrolysis-pretreated *** crystallinity index values of the obtained pulps were high(>80%),and the alpha-cellulose contents were *** viscosities of the aqueous nanocellulose suspensions were higher for the nanocellulose solutions obtained from the unpretreated soda-AQ ***,nanocellulose from *** unpretreated soda-AQ pulps had a higher rate of carboxylic *** results of this work are significant in assessing the potential of both bamboo species as a source of high-value products within the biorefinery framework because the viscosities of the aqueous nanocellulose suspensions depend on the size and shape of *** has significant importance for industrial unit operations and potential applications.

关键词： Natural fiber guadua angustifolia guadua trinii soda-anthraquinone pulp nanocellulose

Time-Series Embeddings from Language Models:A Tool for Wind Direction Nowcasting

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Journal of Meteorological Research 2024年第3期38卷 558-569页

作者： Decio ALVES Fabio MENDONCA Sheikh Shanawaz MOSTAFA fernando MORgADO-DIAS University of Madeira Campus Universitário da Penteada9020-105FunchalPortugal Interactive Technologies Institute/Laboratory for Robotics and Engineering Systems and Agência Regional para o Desenvolvimento da Investigação Tecnologia e InovaçãoEdif.Madeira TecnopoloCaminho da Penteada piso-29020-105FunchalPortugal

Wind direction nowcasting is crucial in various sectors,particularly for ensuring aviation operations and *** this context,the TELMo(Time-series Embeddings from Language Models)model,a sophisticated deep learning architecture,has been introduced in this work for enhanced wind-direction *** by using three years of data from multiple stations in the complex terrain of an international airport,TELMo incorporates the horizontal u(east-west)and v(north-south)wind components to significantly reduce forecasting *** a day with high wind direction variability,TELMo achieved mean absolute error values of 5.66 for 2-min,10.59 for 10-min,and 14.79 for 20-min forecasts,processed within a swift 9-ms/step *** degree-based analysis,in comparison,yielded lower performance,emphasizing the effectiveness of the u and v *** contrast,a Vanilla neural network,representing a shallow-learning approach,underperformed in all analyses,highlighting the superiority of deep learning methodologies in wind direction *** is an efficient model,capable of accurately forecasting wind direction for air traffic operations,with an error less than 20°in 97.49%of the predictions,aligning with recommended international *** model design enables its applicability across various geographical locations,making it a versatile tool in global aviation meteorology.

关键词： wind nowcasting wind components wind direction time series prediction deep learning

‘Jelly to Joule’:Direct laser writing of sustainable jellyfish-based ‘graphenic silicon’ anodes for environmentally remediating high-performance lithium-ion batteries

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Journal of Energy Chemistry 2024年第10期97卷 553-565,I0012页

作者： gil Daffan Adam Cohen Yuval Sharaby Roman Nudelman Shachar Richter fernando Patolsky Department of Materials Science and Engineering the Iby and Aladar Fleischman Faculty of EngineeringTel Aviv UniversityTel Aviv 69978Israel school of Chemistry Faculty of Exact SciencesTel Aviv UniversityTel Aviv 69978Israel

The ramifications of global climate change and resource scarcities have made it imperative to re-examine the definition of sustainable energy-storage *** is crucial to recognize that not all renewable resources are inherently sustainable,and their full impact on the environment must be *** the proliferation of invasive jellyfish species wreaking havoc on marine ecosystems and economies worldwide,utilizing overabundant jellyfish as a carbon source presents an opportunity to create energy-storage systems that are both financially beneficial and environmentally ***,a comprehensive approach to sustainability also requires eco-friendly solutions throughout the entire lifecycle,from material sourcing to battery production,without compromising highperformance ***,most electrode syntheses for lithium-ion batteries(LIBs) employed are energy-intensive,multiple-steps,complex,and *** response,this work pioneers the straightforward use of low-energy laser irradiation of a jellyfish biomass/silicon nanoparticle blend to encapsulate the silicon nanoparticles in-situ within the as-forming conductive carbonized matrix,creating sustainable and additive-free composite *** self-standing anode is directly synthesized under ambient conditions and requires no ***,a laser-synthesized conductive threedimensional porous carbon/silicon composite anode from raw jellyfish biomass for LIBs is presented,displaying outstanding cyclic stability(>1000 cycles),excellent capacity retention(>50% retention after1000 cycles),exceptional coulombic efficiency(>99%),superb reversible gravimetric capacity(>2000 mAh/g),and high rate performance capability(>1.6 A/g),paving a new path to future sustainable energy production.

关键词： Laser Silicon Carbon Jellyfish Sustainable Lithium Biomass

Developing a two-decadal time-record of rice field maps using Landsat-derived multi-index image collections with a random forest classifier:A google Earth Engine based approach

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Information Processing in Agriculture 2024年第2期11卷 260-275页

作者： W.Ashane M.fernando I.P.Senanayake Department of Nano Science Technology Wayamba University of Sri LankaKuliyapitiya 60200Sri Lanka Dchoolf Engineing CllegeoEngineeringSience and EnionmentThe UniversyewcastleCallaghanW2Austraia

Historic maps showing the temporal distribution of rice fields are important for precision agriculture,irrigation optimisation,forecasting crop yields,land use management and formulating ***,mapping rice felds using traditional ground surveys is impractical when high cost,time and labour requirements are considered,and the availability of such detailed records is *** satellite remote sensing appears to be a viable solution,conventional segmentation and classification methods with spectral bands are often unable to contrast the distinct characteristics between rice fields and other vegetation *** this end,we explored a novel,google Earth Engine(gEE)based multiindex random forest(RF)classification approach to map rice fields over two *** images from 2000 to 2020 of two Sri Lankan rice cultivation districts were extracted from gEE and a multi-index RF classification algorithm was applied to distinguish the rice *** results showed above 80%accuracy for both training and validation,when compared against high spatial resolution google Earth *** essence,multi-index sampling and RF together synergised the compelling classifcation accuracy by effectively capturing vegetation,water(ponding)and soil characteristics unique to the rice felds using a single-click *** maps developed in this study were further compared against the MODIS land cover type product(MCD12Q1)and the corresponding superior statistics on rice fields demonstrated the robustness of the proposed *** work seeking effective index combinations is recommended,and this approach can potentially be extended to other crop analyses elsewhere.

关键词： google Earth Engine(gEE) Image classification Random forest Mapping rice fields Time series analysis Vegetation index