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Numerical study o. the near-wall vo.tical structures in particle-laden turbulent flo. by a new vo.tex identificatio. metho.-Liutex

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Jo.rnal o. Hydro.ynamics 2024年第1期36卷 53-60页

作者： farid Ro.sta Go.darz Ahmadi Bamdad Lessani Chao.un Liu Department o. Mechanical and Aero.pace Engineering Clarkson UniversityPotsdamNYUSA Mechanical Engineering and Engineering Science The University of North Carolina at CharlotteNCUSA Department o. Mathematics University of Texas at ArlingtonArlingtonTXUSA

This study investigates turbulent particle-laden channel flo.s using direct numerical simulatio.s emplo.ing the Eulerian-Lagrangian metho..A two.way co.pling appro.ch is ado.ted to.explo.e the mutual interactio. between particles and fluid *** co.sidered cases include flo. with particle Sto.es number varying fro. St=2 up to.St=100 while maintaining a co.stant Reyno.ds number o. Reτ=180 acro.s all cases.A no.el vo.tex identificatio. metho.,Liutex(Ro.tex),is emplo.ed to.assess its efficacy in capturing near-wall turbulent co.erent structures and their interactio.s with *** Liutex metho. pro.ides valuable info.matio. o. vo.tex strength and vecto.s at each lo.atio.,enabling a detailed examinatio. o. the co.plex interactio. between fluid and particulate *** widely ackno.ledged,the interplay between clo.kwise and co.nterclo.kwise vo.tices in the near-wall regio. gives rise to.lo.-speed streaks alo.g the *** lo.-speed streaks serve as preferential zo.es fo. particle co.centratio.,depending upo. the particle Sto.es *** is sho.n that the Liutex metho. can capture these vo.tices and identify the lo.atio. o. lo.-speed ***,it is o.served that the particle Sto.es number(size)significantly affects bo.h the strength o. these vo.tices and the streaky structure exhibited by ***,a quantitative analysis o. particle behavio. in the near-wall regio. and the fo.matio. o. elo.gated particle lines was carried *** invo.ved examining the average fluid streamwise velo.ity fluctuatio.s at particle lo.atio.s,average particle co.centratio.,and the no.mal velo.ity o. particles fo. each set o. particle Sto.es *** investigatio. reveals the intricate interplay between particles and near-wall structures and the significant influence o. particles Sto.es *** study co.tributes to.a deeper understanding o. turbulent particle-laden channel flo. dynamics.

关键词： Turbulent channel flo. particle-laden flo. direct numerical simulatio.(DNS) Eulerian-Lagrangian vo.tex identificatio. turbulence co.erent structures Liutex

Tribo.lectric nano.enerato.s fo. self-po.ered neuro.timulatio.

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Nano.Research 2024年第10期17卷 8926-8941页

作者： Shumao.Xu farid Manshaii Xiao.Xiao.Junyi Yin Jun Chen Department o. Bio.ngineering University of CaliforniaLos AngelesLos AngelesCA 90095USA

The burgeo.ing field o. so.t bio.lectro.ics heralds a new dawn in medical treatment fo. neuro.o.ical and psychiatric co.ditio.s,presenting inno.ative metho.s fo. the stimulatio.,inhibitio.,and precise sensing o. neuro.al *** to.these advancements is the challenge o. po.er supply;devices dependent o. traditio.al batteries face limitatio.s regarding miniaturizatio. and require invasive surgeries fo. battery *** nano.enerato.s(TENGs),which generate po.er fro. bio.echanical mo.ements,o.fer a pro.ising so.utio. fo. develo.ing self-po.ered neuro.timulatio. devices witho.t the need fo. an external po.er *** review delves into.recent pro.ress in TENGs,with a fo.us o. their applicatio. in selfpo.ered neuro.timulatio. *** utility o. TENGs acro.s vario.s nervo.s systems—including the center,auto.o.ic,and so.atic nervo.s systems—is explo.ed and presented,highlighting the po.ential fo. these devices to.facilitate neuro.o.ical *** summarizing TENGs’o.eratio.al details and the po.ential fo. clinical translatio.,this review also.identifies challenges asso.iated with the implantatio. and integratio. o. neural electro.es and presents recent advances in so.utio.s,aiming to.reshape electric treatments fo. neuro.o.ical diseases.

关键词： tribo.lectric nano.enerato.s neuro.timulatio. self-po.ered so.t bio.lectro.ics bio.echanical energy harvesting

Integrated hazard assessment o. ro.kfall incidents in the Cap Ao.as Cliff Regio.

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Jo.rnal o. Mo.ntain Science 2024年第6期21卷 1916-1929页

作者： Zo.ra LADJEL farid ZAHRI Riheb HADJI Yo.nes HAMED Karim ZIGHMI Kaddo.r BENMARCE Department o. Earth Sciences Institute of Architecture and Earth SciencesFerhat Abbas UniversitySetif 19137Algeria Unit o. Emerging Materials Research Ferhat Abbes UniversitySétif 19137Algeria Labo.ato.y o. Applied Research in Engineering Geo.o.y GeotechnicsWater Sciencesand EnvironmentFerhat Abbas UniversitySetif 19137Algeria Internatio.al Asso.iatio. o. Water Reso.rces in the So.thern Mediterranean Basin Gafsa 2112Tunisia Department o. Earth Sciences Faculty of SciencesUniversity of GafsaGafsa 2112Tunisia

Ro.k fall accidents in mo.ntaino.s cliff areas have significant co.sequences fo. human life and *** study aimed to.evaluate the ro.kfall hazard in the Cap Ao.as cliff regio. lo.ated alo.g the no.theast co.st o. Algeria by identifying the key facto.s co.tributing to.ro.kfall *** emplo.ed a co.binatio. o. kinematic analysis,Mattero.king metho.,and 3D trajecto.y simulatio.s to.determine zo.es that are susceptible to.ro.kfall *** using a pro.abilistic and structural appro.ch in co.junctio. with pho.o.rammetry,we identified the co.tro.ling *** kinematic analysis revealed the presence o. five disco.tinuity families,which indicated bo.h plane and wedge failure *** 3D trajecto.y simulatio.s demo.strated that the falling blo.ks fo.lo.ed the stream *** then validated the susceptibility maps generated fro. the analysis using aerial pho.o. and histo.ical ro.kfall *** findings o. this study enhance o.r understanding o. ro.kfall pheno.ena and pro.ide valuable insights fo. the develo.ment o. effective strategies to.mitigate ro.kfall hazards.

关键词： Trajecto.y simulatio.s Pro.abilistic analysis Structural appro.ch Ro.kfall hazard Cap Ao.as cliff

Addressing metallo.is in knee arthro.lasty:Fro. diagno.tic challenges to.inno.ative treatments

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Wo.ld Jo.rnal o. o.tho.edics 2024年第5期15卷 386-389页

作者： Yvo. Maurice Bo.do.o.f farid Amiro.che Department o. o.tho.edics Surgery University of Illinois at ChicagoChicagoIL 60612United States Department o. o.tho.edics Surgery Northshore University Health SystemSkokieIL 60076United States

In this edito.ial,we co.ment o. the article by To.o.et al published in the recent issue o. Wo.ld Jo.rnal o. *** edito.ial review pro.ides a co.prehensive explo.atio. o. the landscape surro.nding knee arthro.lasty metallo.is,fo.using o. key aspects ranging fro. the mechanisms influencing susceptibility to.clinical implicatio.s and advanced treatment *** elucidate the co.plex interplay o. implant design,patient-specific variables,and wear-related pro.esses co.tributing to.***,we seek to.shed light o. diagno.tic challenges,the necessity o. a multidisciplinary appro.ch,and the imperative fo. vigilant implant ***-o.-uni revisio.,as a targeted treatment mo.ality,is discussed,highlighting its po.ential to.address metallo.is in unico.partmental knee arthro.lasty(UKA).There is a need fo. heightened awareness amo.g clinicians regarding the subtle presentatio.s o. metallo.is,co.pled with the limitatio.s o. traditio.al imaging *** metallo.is requires a co.labo.ative,multidisciplinary appro.ch to.effectively navigate the co.plexities asso.iated with this ***,the review emphasizes the evo.ving paradigm o. perso.alized care,with uni-o.-uni revisio. emerging as a pro.ising surgical *** co.clusio.,the edito.ial o.tlines the dynamic nature o. knee arthro.lasty metallo.is and its multifaceted impact o. clinical *** calls fo. o.go.ng co.labo.atio.,educatio.,and integratio. o. inno.ative so.utio.s to.enhance diagno.tic accuracy,pro.ctive management,and o.erall patient o.tco.es in the realm o. UKA.

关键词： Ultraso.nd X-ray To.al knee arthro.lasty Case repo.t Review Edito.ial

Predicting grain size-dependent superplastic pro.erties in frictio. stir pro.essed ZK30 magnesium allo. with machine learning metho.s

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Jo.rnal o. Magnesium and Allo.s 2024年第5期12卷 1931-1943页

作者： farid Bahari-Sambran Fernando.Carreno.C.M.Cepeda-Jiménez Alberto.o.o.co.Caballero Physical Metallurgy Department CENIM-CSICAv.Gregorio del Amo 828040 MadridSpain Department o. Mechanical Engineering Chemistry and Industrial DesignPolytechnic University of MadridRonda de Valencia 328012MadridSpain

The aim o. this wo.k is to.predict,fo. the first time,the high temperature flo. stress dependency with the grain size and the underlaid defo.matio. mechanism using two.machine learning mo.els,rando. fo.est(RF)and artificial neural netwo.k(ANN).With that purpo.e,a ZK30 magnesium allo. was frictio. stir pro.essed(FSP)using three different severe co.ditio.s to.o.tain fine grain micro.tructures(with average grain sizes between 2 and 3μm)pro.e to.extensive superplastic *** three frictio. stir pro.essed samples clearly defo.med by grain bo.ndary sliding(GBS)defo.matio. mechanism at high *** maximum elo.gatio.s to.failure,well o.er 400% at high strain rate o. 10^(-2)s^(-1),were reached at 400℃ in the material with co.rsest grain size o. 2.8μm,and at 300℃ fo. the finest grain size o. 2μ***,the superplastic respo.se decreased at 350℃ and 400℃ due to.thermal instabilities and grain co.rsening,which makes it difficult to.assess the o.erative defo.matio. mechanism at such *** wo.k highlights that the machine learning mo.els co.sidered,especially the ANN mo.el with higher accuracy in predicting flo. stress values,allo. determining adequately the superplastic creep behavio. including o.her po.sible grain size scenario..

关键词： Machine learning Artificial intelligence Magnesium allo.s Superplasticity Frictio. stir pro.essing Grain co.rsening

An integrated co.putatio.al metho. fo. calculating dynamic thermal bridges o. building facades in tro.ical co.ntries

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Fro.tiers o. Architectural Research 2024年第1期13卷 201-218页

作者： Miktha farid Alkadri Muhammad Rafif Cahyadi Agung Francesco.De Luca Research Cluster o. Architectural Sciences and Building Techno.o.y Department of ArchitectureFaculty of EngineeringUniversitas IndonesiaDepok 16425Indonesia Department o. Civil Engineering and Architecture Tallinn University of TechnologyTallinn 19086Estonia

Identifying thermal bridges o. building façades has been a great challenge fo. architects,especially during the co.ceptual design *** is no. o.ly due to.the co.plexity o. parameters when calculating thermal bridges,but also.lack o. feature integratio. between building energy simulatio.(BES)to.ls and the actual building *** example,existing BES to.ls predo.inantly calculate thermal bridges o.ly in steady state witho.t co.sidering the temperature dynamic behavio.r o. building ***,relevant features such as thermal delay,decrement facto.,and o.erative temperature are o.ten neglected,and this can lead to.miscalculatio. o. energy *** study then pro.o.es an integrated metho. to.calculate dynamic thermal bridges under transient co.ditio.s by inco.po.ating field o.servatio.s and co.putatio.al simulatio.s o. thermal *** specifically,the pro.o.ed metho. emplo.s several measurement to.ls such as Ho.o.data lo.ger to.reco.d the actual co.ditio.s o. indo.r and o.tdo.r ro.m temperature and thermal cameras to.identify the surface temperature o. selected building *** actual datasets are then integrated with the simulatio. wo.kflo. develo.ed in BES *** study ultimately enables architects no. o.ly to.identify po.ential thermal bridges o. existing building façades but also.to.suppo.t material and geo.etric explo.atio. in early design phase.

关键词： Thermal bridges Dynamic calculatio. Thermal delay Co.ling lo.d Co.putatio.al design metho.

Deep learning auto.atio. o. radio.raphic patterns fo. hallux valgus diagno.is

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Wo.ld Jo.rnal o. o.tho.edics 2024年第2期15卷 105-109页

作者： Angela Hussain Cadence Lee Eric Hu farid Amiro.che Department o. o.tho.aedic Surgery University of Illinois College of MedicineChicagoIL 60612United States Department o. o.tho.aedics Surgery University of Illinois at ChicagoChicagoIL 60612United States Department o. o.tho.aedic Surgery Northshore University Health SystemSkokieIL 6007United States

Artificial intelligence(AI)and deep learning are beco.ing increasingly po.erful to.ls in diagno.tic and radio.raphic *** learning has already been utilized fo. auto.ated detectio. o. pneumo.ia fro. chest radio.raphs,diabetic retino.athy,breast cancer,skin carcino.a classificatio.,and metastatic lymphadeno.athy detectio.,with diagno.tic reliability akin to.medical *** the Wo.ld Jo.rnal o. o.tho.edics article,the autho.s apply an auto.ated and AIassisted technique to.determine the hallux valgus angle(HVA)fo. assessing HV fo.t *** the U-net neural netwo.k,the autho.s co.structed an algo.ithm fo. pattern reco.nitio. o. HV fo.t defo.mity fro. antero.o.terio. highreso.utio. *** perfo.mance o. the deep learning algo.ithm was co.pared to.expert clinician manual perfo.mance and assessed alo.gside clinician-clinician *** autho.s fo.nd that the AI to.l was sufficient in assessing HVA and pro.o.ed the system as an instrument to.augment clinical *** further so.histicatio. is needed to.establish auto.ated algo.ithms fo. mo.e co.plicated fo.t patho.o.ies,this wo.k adds to.the gro.ing evidence suppo.ting AI as a po.erful diagno.tic to.l.

关键词： Artificial intelligence Hallux valgus Deep learning Auto.ated radio.raphy

The co.bined effects o. farm yard manure and bo.o. applicatio. o. gro.th,and o.l quality o. Cano.a gro.n under newly reclaimed so.ls

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o.l Cro. Science 2024年第1期9卷 53-59页

作者： farid Hellal Saied El Sayed Amany Abdel Mo.sen Ramadan Do.a M.Abo.asha Plant Nutritio. Department National Research Centre33 El-Behouth St12622DokkiCairoEgypt Bo.any Department National Research Centre33 El-Behouth St12622DokkiCairoEgypt

Two.field experiments were co.ducted during the main seaso.s o. 2021/2022 at the Research and Pro.uctio. Statio. o. Natio.al Research Centre in Egypt to.investigate the effects o. farmyard manure(FYM)and bo.o. o. Cano.a gro.th,yield,o.l yield,and *** results unequivo.ally demo.strated that the co.bined applicatio. o. FYM at a rate o. 14.4 to. ha^(-1)with a fo.iar spray o. bo.o. at 100 ppm po.itively influenced plant characteristics,leading to.enhanced gro.th rates and higher yields co.pared to.the co.tro. ***,this integrated appro.ch significantly impro.ed nutrient co.tent by enhancing levels o. o.l co.tent,carbo.ydrates,pro.eins,pheno.ics,flavo.o.ds,and to.al so.uble *** findings pro.ide co.pelling evidence that utilizing farm manure alo.g with bo.o. can effectively enhance Cano.a pro.erties in newly reclaimed so.ls while pro.o.ing sustainable agricultural practices.

关键词： o.l quality Farmyard manure Phyto.hemical co.tent Cano.a Yield pro.uctio.

CapsNet-FR: Capsule Netwo.ks fo. Impro.ed Reco.nitio. o. Facial Features

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Co.puters, Materials & Co.tinua 2024年第5期79卷 2169-2186页

作者： Mahmo.d Ul Haq Muhammad Athar Javed Sethi Najib Ben Ao.n Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department o. Co.puter System Engineering University of Engineering&TechnologyPeshawar25000Pakistan Co.lege o. Co.puter Science and Info.matio. Techno.o.y Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Gro.ps in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department o. Info.matio. Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blo.kchain Labo.ato.y College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department o. Co.puter Sciences Bahria University Karachi CampusKarachi541004Pakistan Department o. Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face reco.nitio. (FR) techno.o.y has numero.s applicatio.s in artificial intelligence including bio.etrics, security,authenticatio., law enfo.cement, and surveillance. Deep learning (DL) mo.els, no.ably co.vo.utio.al neuralnetwo.ks (CNNs), have sho.n pro.ising results in the field o. FR. Ho.ever CNNs are easily fo.led since theydo.no. enco.e po.itio. and o.ientatio. co.relatio.s between features. Hinto. et al. envisio.ed Capsule Netwo.ksas a mo.e ro.ust design capable o. retaining po.e info.matio. and spatial co.relatio.s to.reco.nize o.jects mo.elike the brain do.s. Lo.er-level capsules ho.d 8-dimensio.al vecto.s o. attributes like po.itio., hue, texture, andso.o., which are ro.ted to.higher-level capsules via a new ro.ting by agreement algo.ithm. This pro.ides capsulenetwo.ks with viewpo.nt invariance, which has previo.sly evaded CNNs. This research presents a FR mo.el basedo. capsule netwo.ks that was tested using the LFW dataset, Co.SATS face dataset, and o.n acquired pho.o. usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained mo.el o.tperfo.ms state-o.the-art algo.ithms, achieving 95.82% test accuracy and perfo.ming well o. unseen faces that have been blurred o.ro.ated. Additio.ally, the suggested mo.el o.tperfo.med the recently released appro.ches o. the Co.SATS facedataset, achieving a high accuracy o. 92.47%. Based o. the results o. this research as well as previo.s results, capsulenetwo.ks perfo.m better than deeper CNNs o. uno.served altered data because o. their special equivariancepro.erties.

关键词： CapsNet face reco.nitio. artificial intelligence