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Performance of evolutionary optimized machine learning for modeling total organic carbon in core samples of shale gas fields

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Petroleum 2024年第1期10卷 150-164页

作者： Leonardo Goliatt c.m.Saporetti L.c.Oliveira E.Pereira Department of computational and Applied mechanics Federal University of Juiz de ForaJuiz de ForaBrazil Department of computational modeling Polytechnic InstituteRio de Janeiro State UniversityNova FriburgoBrazil Petrobras Rio de JaneiroBrazil Department of Stratigraphy and Paleontology Rio de Janeiro State UniversityRio de JaneiroBrazil

Rock samples'TOc content is the best indicator of the organic matter in source *** origin rock samples’analysis is used to calculate it manually by *** method requires time and resources because it relies on samples from many well intervals in source ***,research has been done to aid this *** learning algorithms can estimate total organic carbon instead of well logs and stratigraphic *** light of these efforts,the current work present a study on automating the total organic carbon estimation using machine learning approaches improved by an evolutionary methodology to give the model flexibility and *** algorithms,differential evolution,particle swarm optimization,grey wolf optimization,artificial bee colony,and evolution strategies were used to improve machine learning models to predict *** six metaheuristics were integrated into four machine learning methods:extreme learning machine,elastic net linear model,linear support vector regression,and multivariate adaptive regression *** samples from the YuDong-Nan shale gas field,located in the Sichuan basin,were used to evaluate the hybrid *** findings show that combining machine learning models with an evolutionary algorithms in a hybrid fashion produce flexible models that accurately predict *** results show that,independent of the metaheuristic used to guide the model selection,optimized extreme learning machines attained the best performance scores according to six *** hybrid models can be used in exploratory geological research,particularly for unconventional oil and gas resources.

关键词： Total organic carbon Hybrid models Automated machine learning Evolutionary algorithms Geology

An improved pulse coupled neural networks model for semantic IoT

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Digital communications and Networks 2024年第3期10卷 557-567页

作者： Rong ma Zhen Zhang Yide ma Xiping Hu Edith c.H.Ngai Victor c.m.Leung School of Information Science and Engineering Lanzhou UniversityLanzhou 730000China School of medical Technology Beijing Institute of TechnologyBeijing100081China Department of Electrical and Electronic Engineer The University of Hong KongHong Kong 999077China college of computer Science and Software Engineering Shenzhen UniversityShenzhen 518060China Department of Electrical and computer Engineering the University of British ColumbiaVancouverBC V6T 1Z4Canada

In recent years,the Internet of Things(IoT)has gradually developed applications such as collecting sensory data and building intelligent services,which has led to an explosion in mobile data ***,with the rapid development of artificial intelligence,semantic communication has attracted great attention as a new communication ***,for IoT devices,however,processing image information efficiently in real time is an essential task for the rapid transmission of semantic *** the increase of model parameters in deep learning methods,the model inference time in sensor devices continues to *** contrast,the Pulse coupled Neural Network(PcNN)has fewer parameters,making it more suitable for processing real-time scene tasks such as image segmentation,which lays the foundation for real-time,effective,and accurate image ***,the parameters of PcNN are determined by trial and error,which limits its *** overcome this limitation,an Improved Pulse coupled Neural Networks(IPcNN)model is proposed in this *** IPcNN constructs the connection between the static properties of the input image and the dynamic properties of the neurons,and all its parameters are set adaptively,which avoids the inconvenience of manual setting in traditional methods and improves the adaptability of parameters to different types of *** segmentation results demonstrate the validity and efficiency of the proposed self-adaptive parameter setting method of IPcNN on the gray images and natural images from the matlab and Berkeley Segmentation *** IPcNN method achieves a better segmentation result without training,providing a new solution for the real-time transmission of image semantic information.

关键词： Internet of things(IoT) Semantic information Real-time application Improved pulse coupled neural network Image segmentation

Semi-analytical investigation of heat transfer in a porous convective radiative moving longitudinal fin exposed to magnetic field in the presence of a shape-dependent trihybrid nanofluid

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Applied mathematics and mechanics(English Edition) 2024年第1期45卷 197-216页

作者： c.G.PAVITHRA B.J.GIREESHA m.L.KEERTHI Department of PG Studies and Research in mathematics Kuvempu UniversityShankaraghatta 577451India Department of mathematics and Statistics M.S.Ramaiah University of Applied SciencesBengaluru 560058India

The thermal examination of a non-integer-ordered mobile fin with a magnetism in the presence of a trihybrid nanofluid(Fe_3O_4-Au-Zn-blood) is carried out. Three types of nanoparticles, each having a different shape, are considered. These shapes include spherical(Fe_3O_4), cylindrical(Au), and platelet(Zn) configurations. The combination approach is utilized to evaluate the physical and thermal characteristics of the trihybrid and hybrid nanofluids, excluding the thermal conductivity and dynamic viscosity. These two properties are inferred by means of the interpolation method based on the volume fraction of nanoparticles. The governing equation is transformed into a dimensionless form, and the Adomian decomposition Sumudu transform method(ADSTm) is adopted to solve the conundrum of a moving fin immersed in a trihybrid nanofluid. The obtained results agree well with those numerical simulation results, indicating that this research is reliable. The influence of diverse factors on the thermal overview for varying noninteger values of γ is analyzed and presented in graphical representations. Furthermore, the fluctuations in the heat transfer concerning the pertinent parameters are studied. The results show that the heat flux in the presence of the combination of spherical, cylindrical, and platelet nanoparticles is higher than that in the presence of the combination of only spherical and cylindrical nanoparticles. The temperature at the fin tip increases by 0.705 759% when the value of the Peclet number increases by 400%, while decreases by 11.825 13% when the value of the Hartman number increases by 400%.

关键词： convection radiation moving longitudinal fin Adomian decomposition Sumudu transform method(ADSTm) trihybrid nanofluid magnetic field

Optimization of a photovoltaic-battery system using deep reinforcement learning and load forecasting

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Energy and AI 2024年第2期16卷 168-183页

作者： António corte Real G.Pontes Luz J.m.c.Sousa m.c.Brito S.m.Vieira IDmEc Instituto Superior TécnicoUniversidade de LisboaLisboa1049-001Portugal Instituto Dom Luiz Faculdade de CiênciasUniversidade de LisboaCampo Grande1749-016LisboaPortugal

Home Energy management Systems(HEmS)are increasingly relevant for demand-side management at the residential level by collecting data(energy,weather,electricity prices)and controlling home appliances or storage *** control can be performed with classical models that find optimal solutions,with high real-time computational cost,or data-driven approaches,like Reinforcement Learning,that find good and flexible solutions,but depend on the availability of load and generation data and demand high computational resources for *** this work,a novel HEmS is proposed for the optimization of an electric battery operation in a real,online and data-driven environment that integrates state-of-the-art load forecasting combining cNN and LSTm neural networks to increase the robustness of *** Reinforcement Learning agents are trained with different algorithms(Double DQN,Dueling DQN,Rainbow and Proximal Policy Optimization)in order to minimize the cost of electricity purchase and to maximize photovoltaic self-consumption for a PV-Battery residential *** show that the best Reinforcement Learning agent achieves a 35%reduction in total cost when compared with an optimization-based agent.

关键词： Energy management system Deep reinforcement learning Demand-side management Load forecasting Deep learning Energy storage systems

cO oxidation under lean and stoichiometric conditions over ceriazirconia with very low metal contents(cu,co,Ag and Pt)

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Journal of Rare Earths 2024年第9期42卷 1669-1681,I0002页

作者： J.c.martínez-munuera m.P.Yeste A.García-García mcmA Group Department of Inorganic Chemistry and Institute of MaterialsUniversity of AlicanteCarretera de San Vicente del Raspeig s/n03690San Vicente del RaspeigAlicanteSpain Solid chemistry and catalysis Group Department of Materials ScienceMetallurgic Engineering and Inorganic ChemistryInstitute of Electronic Microscopy and Materials ResearchUniversity of CádizAp.40E-11510Puerto RealCádizSpain

In this work,several ceria-zirconia based catalysts with very lo w(and equimolar) metal contents were prepared,characterised and tested for the cO oxidatio n reaction(under lean and stoichiometric conditions),trying to emulate those conditions found in a diesel oxidation catalyst(DOc) system from a diesel engine and those encountered under gasoline exhaust(λ=1).The metals chosen are cu,co,Ag and Pt(as a reliable benchmark).The results reveal enormous differences among reducibility and catalytic activity despite quite similar structural and textural properties of the catalysts,showing differences among dispersion(Ag-catalyst seems to present a low level of dispersion).This catalyst seems to be characterised,as well,by a strong electronic interaction between ce and Ag centres which is suggested to yield an improved reducibility under H_(2)-TPR ***,the order in catalytic activity(cu>Ag>co≈Pt>>support) seems not to follow the order found in reducibility and the cu-catalyst seems to be the most active independently on the reaction conditions,yielding nearly overlapped cO oxidation catalytic *** restingly,a stro ng correlation between the catalytic activity under the two conditions tested and the OSc values of the ce_(0.8)Zr_(0.2)O_(2)-supported metal catalysts is ***,OSc parameter measured at 150℃ can be used as a relevant descriptor to evaluate the cO oxidation activity at low and medium conversions for the investigated catalysts,much better than the H_(2)-TPR measurements.

关键词： cO oxidation Low metal content ceria-zirconia copper species OSc parameter Rare earths

Effect of radiative loss mechanisms on FIR thermometric parameters of Nd^(3+)-doped lithium tellurite glasses

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Journal of Rare Earths 2024年第7期42卷 1250-1257,I0002页

作者： c.Y.morassuti A.P.L Silva L.A.O.Nunes S.m.Lima L.H.c.Andrade Optical and Photothermal Spectroscopy Group(GEOF) Center for Studies in Natural Resources(CERNA)State University of Mato Grosso do Sul(UEMS)C.P.351DouradosMSBrazil Renato Archer Information Technology center Dom Pedro I Highway(SP-65)Km 1436-Chácaras Campos dos AmaraisCampinasSPBrazil Institute of Physics of São carlos University of São PauloSão CarlosSPBrazil

Nd^(3+)-doped tellurite glasses are promising materials for thermometers based on the fluorescence intensity ratio(FIR)***,at high Nd^(3+)concentrations,energy transfer(ET)processes such as optical reabsorption and cross-relaxation can affect the Nd^(3+)emission,which has been little explored in the ***,the present work investigated the use of Nd^(3+)-doped tellurite glass(samples doped with Nd^(3+)at 0.2 mol%,0.5 mol%,2.0 mol%,and 4.0 mol%)in fluorescence thermometers,in the temperature range from 299 to 371 *** results indicate a strong dependence of the FIR parameters on the Nd^(3+)concentration,due to changes in the emission band profiles caused by optical reabsorption of the Nd^(3+)emissions and cross-relaxation processes.A decrease of the relative sensitivity of the ratio^(4)F_(5/2)→^(4)I_(9/2)/^(4)F_(3/2)→^(4)I_(9/2)is observed for samples doped with higher amounts of Nd^(3+).The maximum relative sensitivity at 299 K is 3.00%/K,which is the highest value among the reported Nd^(3+)ions.

关键词： Nd^(3+)-doped tellurite glass Fluorescence intensity ratio Optical thermometry Judd-Ofelt Rare earths

Radiative decays D*(s)→ D(s)γ in covariant confined quark model

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chinese Physics c 2024年第2期48卷 45-56页

作者： c.T.Tran m.A.Ivanov P.Santorelli Q.c.Vo Department of Physics HCMC University of Technology and EducationVo Van Ngan 1700000 Ho Chi Minh CityVietnam Bogoliubov Laboratory of Theoretical Physics Joint Institute for Nuclear Research141980 DubnaRussia Dipartimento di Fisica"E.Pancini" Universitàdi Napoli Federico ⅡComplesso Universitario di Monte S.AngeloVia CintiaEdificio 680126 NapoliItaly Istituto Nazionale di Fisica Nucleare Sezione di Napoli80126 NapoliItaly University of Science 700000 Ho Chi Minh CityVietnam

Radiative decays D((s))*→D((s))γare revisited in light of new experimental data from the BaBar and BESⅢ*** radiative couplings gD*Dγencoding nonperturbative QcD effects are calculated in the framework of the covariant confined quark model developed by *** compare our results with other theoretical studies and experimental *** couplings(in GeV-1)|g(D*+D+γ)|=0.45(9)and|g(D*0D0γ)|=1.72(34)calculated in our model agree with the corresponding experimental data|g(D*+D+γ)|=0.47(7)and|g(D*0D0γ)|=1.77(16).The most interesting case is the decay Ds*→Dsγ,for which a recent prediction based on light-cone sum rules at next-to-leading order|gDs*Dsγ|=0.60(19)deviates from the first(and only to date)lattice QcD result|gDs*Dsγ|=0.11(2)at nearly3σ.Our calculation yields|gDs*Dsγ|=0.29(6),which falls somehow between the two mentioned results,although it is larger than those predicted in other studies using quark models or QcD sum rules.

关键词： covariant quark model radiative decay charmed meson

magnesium oxide(mgO)nanoadsorbents in wastewater treatment:A comprehensive review

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Journal of magnesium and Alloys 2024年第5期12卷 1709-1773页

作者： H.c.S.Perera V.Gurunanthanan Anoop Singh m.m.m.G.P.G.mantilaka G.Das Sandeep Arya Department of Physics Khalifa UniversityAbu Dhabi12788United Arab Emirates Postgraduate Institute of Science University of PeradeniyaPeradeniyaSri Lanka Department of Humanities and Sciences Guru Nanak Institutions Technical CampusIbrahimpatnamHyderabad501506India centre for Global Health Research Saveetha Medical College and HospitalsSaveetha Institute of Medical and Technical SciencesChennai602105Tamil NaduIndia Department of Physics University of JammuJammu and Kashmir180006India

Wastewater contamination by heavy metals and synthetic dyes presents a significant environmental challenge,necessitating effective and sustainable separation *** review article provides a detailed examination of magnesium oxide(mgO)nanoparticles as an innovative nanoadsorbent for wastewater treatment,with a specific focus on heavy metal and dye *** review comprehensively explores various aspects of mgO nanoparticles,including their structural characteristics and synthesis *** article delves into the morphology and crystallographic arrangement of mgO nanoparticles,offering insights into their structural *** the complexity of adsorption processes,the review identifies and analyzes parameters influencing the adsorption efficiency of mgO nanoparticles,such as temperature,pH,contact time,initial concentration,and co-existing *** interplay between these parameters and the adsorption capability of mgO nanoparticles emphasizes the importance of optimizing operational ***,the review assesses various synthesis methods for mgO nanoparticles,including sol-gel,hydrothermal,precipitation,green synthesis,solvothermal,and template-assisted *** discusses the advantages,limitations,and resulting nanoparticle characteristics of each method,enabling readers to grasp the implications of synthesis processes on adsorption *** comprehensive review consolidates current insights into the effectiveness of mgO nanoparticles as a potent nanoadsorbent for removing heavy metals and dyes from wastewater covering a wide spectrum of aspects related to mgO ***,there is a need to investigate the use of mgO in the treatment of actual wastewater or river water,in order to leverage its cost-effectiveness and high efficiency for practical water treatment applications in real-time.

关键词： mgO nanoparticles Nanoadsorbent Wastewater treatment Heavy metals Dye separation Adsorption Synthesis methods

Laser-induced graphene-coated wearable smart textile electrodes for biopotentials signal monitoring

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Frontiers of materials Science 2024年第1期18卷 95-111页

作者： c.m.Vidhya Yogita maithani Sakshi Kapoor J.P.Singh Department of Physics Indian Institute of Technology DelhiHauz KhasNew Delhi 110016India

This paper describes how to produce a wearable dry electrode at a reasonable cost and how to use it for the monitoring of biopotentials in *** textiles in wearable technologies have made a great advancement in the health care management and living standards of *** was manufactured using the low-cost single-step process,laser ablation of polyimide,a commercial *** dispersions were made using solvent isopropyl alcohol which has low boiling point,nontoxicity,and environmental *** successive coating of the graphene dispersion on the cotton fabric to make it conductive,the sheet resistance of the resulting fabric dropped to 3% of its initial *** laser-induced graphene(LIG)cotton dry electrodes thus manufactured are comparable to Ag/Agcl wet electrodes in terms of the skin-to-electrode impedance,measuring between 78.0 and 7.2 kΩ for the frequency between 40 Hz and 1 *** LIG cotton electrode displayed a signal-to-noise ratio of 20.17 *** to its comfort,simplicity,and good performance over a longer period of time,the textile electrode appears suited for medical applications.

关键词： biopotential electrocardiogram electromyogram flexible electrode textile porous graphene