Search results for "algorithms"

showing 10 items of 1716 documents

Ferro- and Antiferromagnetic Interactions in Oxalato-Centered Inverse Hexanuclear and Chain Copper(II) Complexes with Pyrazole Derivatives.

2021

Two novel copper(II) complexes of formulas {[Cu(4-Hmpz)4][Cu(4-Hmpz)2(µ3-ox-κ2O1,O2:κO2′:κO1′)(ClO4)2]}n (1) and {[Cu(3,4,5-Htmpz)4]2[Cu(3,4,5-Htmpz)2(µ3-ox-κ2O1,O2:κO2′:κO1′)(H2O)(ClO4)]2[Cu2(3,4,5-Htmpz)4(µ-ox-κ2O1,O2:κ2O2′,O1′)]}(ClO4)4·6H2O (2) have been obtained by using 4-methyl-1H-pyrazole (4-Hmpz) and 3,4,5-trimethyl-1H-pyrazole (3,4,5-Htmpz) as terminal ligands and oxalate (ox) as the polyatomic inverse coordination center. The crystal structure of 1 consists of perchlorate counteranions and cationic copper(II) chains with alternating bis(pyrazole)(µ3-κ2O1,O2:κO2′:κO1′-oxalato)copper(II) and tetrakis(pyrazole)copper(II) fragments. The crystal structure of 2 is made up of perchlorat…

crystal structurePharmaceutical Sciencechemistry.chemical_elementCrystal structurePyrazoleOxalateArticleAnalytical ChemistryPerchloratechemistry.chemical_compoundQD241-441TheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYDrug DiscoveryAntiferromagnetismPhysical and Theoretical Chemistrypolynuclear complexesOrganic ChemistryPolyatomic ioninverse coordination chemistryoxalatoCopperpyrazoleCrystallographycoordination polymerschemistryChemistry (miscellaneous)Intramolecular forcecopperMolecular Medicinemagnetic propertiesMolecules (Basel, Switzerland)
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How can algorithms help in segmenting users and customers? : A systematic review and research agenda for algorithmic customer segmentation

2023

What algorithm to choose for customer segmentation? Should you use one algorithm or many? How many customer segments should you create? How to evaluate the results? In this research, we carry out a systematic literature review to address such central questions in customer segmentation research and practice. The results from extracting information from 172 relevant articles show that algorithmic customer segmentation is the predominant approach for customer segmentation. We found researchers employing 46 different algorithms and 14 different evaluation metrics. For the algorithms, K-means clustering is the most employed. For the metrics, separation-focused metrics are slightly more prevalent…

customer segmentationmachine learningkoneoppiminenAIalgoritmittekoälyalgorithmssystemaattiset kirjallisuuskatsauksetasiakassegmentointi
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Algorytmy — nowy wymiar nadzoru i kontroli nad świadczącym pracę

2020

Autor wskazuje, że algorytmy stają się kluczową technologią władzy nad świadczącym pracę. Pozwalają na sformatowanie zarówno samych pracowników, jak i wzajemnych oddziaływań między nimi zasadniczo w jednym celu — optymalizacji procesów pracy służących zwiększeniu wydajności. Z tej perspektywy pracownik jest cyfrowym modelem zbudowanym z danych i informacji. Oznacza to, że wszelkie jego ekspresje ujawniane w środowisku pracy będą mogły być mierzalne, i to na rożne sposoby. Algorytmy rzucają również nowe światło na zagadnienie podporządkowania w zatrudnieniu. A wszystko dzięki ,,wtapianiu się” ich w środowisko danych biometrycznych osób świadczących pracę. W pewien sposób przejmują one własno…

dane biometrycznepodporządkowanie technologicznetechnological subordinationbiometric dataPolitical sciencealgorytmy uczenia głębokiegodeep learning algorithmsalgorithmic enterprisesAlgorithmprzedsiębiorstwa algorytmiczneinformacjainformationPraca i Zabezpieczenie Społeczne
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Track finding at Belle II

2021

Computer physics communications 259, 107610 (2021). doi:10.1016/j.cpc.2020.107610

data analysis methodPhysics - Instrumentation and DetectorsComputer scienceReal-time computingFOS: Physical sciencesGeneral Physics and AstronomyBELLETrack (rail transport)01 natural sciences530programming010305 fluids & plasmasHigh Energy Physics - ExperimentTracking algorithmsHigh Energy Physics - Experiment (hep-ex)Tracking detectorsSoftware0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Belle II; Tracking algorithms; Tracking detectorsBelle IIddc:530[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]010306 general physicsSpurious relationshipSelection (genetic algorithm)Event reconstructionbusiness.industrytrack data analysisInstrumentation and Detectors (physics.ins-det)Modular designResolution (logic)charged particleHardware and Architecturebusinessperformance
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Col : A Data Collection Protocol for Vanet

2012

International audience; In this paper, we present a protocol to collect data within a vehicular ad hoc network (VANET). In spite of the intrinsic dynamic of such network, our protocol simultaneously offers three relevant properties: (1) It allows any vehicle to collect data beyond its direct neighborhood (i.e., vehicles within direct communication range) using vehicle-to-vehicle communications only (i.e., the infrastructure is not required); (2) It tolerates possible network partitions; (3) It works on demand and stops when the data collection is achieved. To the best of our knowledge, this is the first collect protocol having these three characteristics. All that is chiefly obtained thanks…

data collection protocolVehicular communication systemsVANETCorrectnessnetwork operator antComputer sciencedata acquisitionDistributed computing[ INFO.INFO-NI ] Computer Science [cs]/Networking and Internet Architecture [cs.NI]self-stabilization area02 engineering and technologyVehicle dynamics[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI]Transient analysis[SPI]Engineering Sciences [physics]0203 mechanical engineeringvehicular ad hoc networkvehicle to vehicle communication0202 electrical engineering electronic engineering information engineeringAlgorithm design and analysisHeuristic algorithmsCOL algorithmProtocol (object-oriented programming)ComputingMilieux_MISCELLANEOUSAirplug software distributionVehicular ad hoc networkData collectionbusiness.industryNetwork partitionnetwork partition020206 networking & telecommunications020302 automobile design & engineeringVehiclesSoftware distributionRoadsAlgorithm designvehicular ad hoc networksbusinessProtocolsComputer network
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UAV-Assisted Data Collection in Wireless Sensor Networks: A Comprehensive Survey

2021

Wireless sensor networks (WSNs) are usually deployed to different areas of interest to sense phenomena, process sensed data, and take actions accordingly. The networks are integrated with many advanced technologies to be able to fulfill their tasks that is becoming more and more complicated. These networks tend to connect to multimedia networks and to process huge data over long distances. Due to the limited resources of static sensor nodes, WSNs need to cooperate with mobile robots such as unmanned ground vehicles (UGVs), or unmanned aerial vehicles (UAVs) in their developments. The mobile devices show their maneuverability, computational and energy-storage abilities to support WSNs in mul…

data collection scenariosTK7800-8360Computer Networks and CommunicationsProcess (engineering)Computer scienceDistributed computingComputerApplications_COMPUTERSINOTHERSYSTEMSwireless sensor networks (WSNs)mobile robotsenergy consumptionComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMSunmanned aerial vehicles (UAVs)Electrical and Electronic EngineeringData collectionControl algorithmcommunication structuresCommunication structures Control algorithms Data collection scenarios Energy consumption Mobile robots Unmanned aerial vehicles (UAVs) Wireless sensor networks (WSNs)Mobile robotEnergy consumptionSettore ING-IND/31 - ElettrotecnicaHardware and ArchitectureControl and Systems EngineeringSignal Processingcontrol algorithmsElectronicsMobile deviceWireless sensor networkLimited resourcesElectronics
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Data-Driven Evolutionary Optimization: An Overview and Case Studies

2019

Most evolutionary optimization algorithms assume that the evaluation of the objective and constraint functions is straightforward. In solving many real-world optimization problems, however, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for fitness evaluations. In more extreme cases, only historical data are available for performing optimization and no new data can be generated during optimization. Solving evolutionary optimization problems driven by data collected in simulations, physical experiments, production processes, or daily life are termed data-driven evolutionary optimization. In this…

data-driven optimizationMathematical optimizationOptimization problemmodel managementevoluutiolaskenta02 engineering and technologymatemaattinen optimointiEvolutionary computationTheoretical Computer ScienceData modelingData-drivenModel managementkoneoppiminenComputational Theory and MathematicsdatatiedeoptimointiTaxonomy (general)Constraint functionsalgoritmit0202 electrical engineering electronic engineering information engineeringProduction (economics)020201 artificial intelligence & image processingsurrogateevolutionary algorithmsSoftware
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Social Media as an Opinion Formulator: A Study on Implications and Recent Developments

2019

Social media has a great influence on how information reaches us and how we form an opinion based on it. For many users, social media increases the variety of information and ensure its accessibility across different platforms. However, recent years have seen an exponential increase in the power of social media. Most often, the forwarding and sharing of journalistic articles serve the recommendation from the user's point of view, but recently, these platforms have been developed into an echo chamber to transport additional information or even a critical attitude. In addition, the uncontrolled influx of social media in different parts of the world favors phenomena such as fake news and socia…

democratic societiesfake newsFacebookbusiness.industryMediation (Marxist theory and media studies)media_common.quotation_subjectSocial changeInternet privacyconnected audiencesosiaalinen mediaSincerityyleinen mielipideDemocracyVariety (cybernetics)social media.Power (social and political)demokratiavaikuttaminenPolitical scienceThe InternetSocial mediaFacebook algorithmsbusinessvaleuutisetmedia_common2019 2nd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET)
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Performance of $b$-Jet Identification in the ATLAS Experiment

2016

We thank CERN for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of ANPCyT, Argentina; YerPhI, Armenia; ARC, Australia; BMWFW and FWF, Austria; ANAS, Azerbaijan; SSTC, Belarus; CNPq and FAPESP, Brazil; NSERC, NRC and CFI, Canada; CERN; CONICYT, Chile; CAS, MOST and NSFC, China; COLCIENCIAS, Colombia; MSMT CR, MPO CR and VSC CR, Czech Republic; DNRF, DNSRC and Lundbeck Foundation, Denmark; IN2P3-CNRS, CEA-DSM/IRFU, France; GNSF, Georgia; BMBF, HGF, and MPG, Germany; GSRT, Greece; RGC, Hong Kong SAR, China; ISF, I-CORE and Benoziyo Center, Israel; INFN, Italy; MEXT an…

detector-systems performancePerformance of High Energy Physics Detectorsecondary [vertex]Elementary particle01 natural sciencesPARTONlaw.inventionSubatomär fysikCHANNELcluster findingscattering [p p]impact parameterGeneralLiterature_REFERENCE(e.g.dictionariesencyclopediasglossaries)протон-протонные столкновенияQBLarge detector-systems performanceHigh energy physics detectorLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics Detectors; Instrumentation; Mathematical Physicstrack data analysisQUARK PAIR PRODUCTIONbottom [jet]CERN LHC CollPattern recognition cluster finding calibration and fitting method7000 GeV-cmscolliding beams [p p]performanceHADRONIC COLLISIONSCiências Naturais::Ciências FísicasLarge detectorFitting methodHigh energy physicATLAS LHC High Energy Physics510 MathematicsmuonDISTRIBUTIONSUncertainty analysis Astroparticle physicHigh Energy Physics010306 general physicsSystematic uncertainties AlgorithmsAstroparticle physicsCalibration and fitting methodsScience & Technology010308 nuclear & particles physicsLarge detector systems for particle and astroparticle physicsParticle acceleratorRangingPerformance of High Energy PhysicsCOLLIDERScorrelationExperimental High Energy PhysicsPerformance of High Energy Physics DetectorshadronATLAS детекторБольшой адронный коллайдерcharm [jet]Elementary particleHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)lawSubatomic Physics[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Detectors and Experimental TechniquesInstrumentationUncertainty analysisMathematical PhysicsPhysicsPattern recognition cluster finding calibration and fitting methods4. EducationATLAS experimentSettore FIS/01 - Fisica SperimentaleDetectorsflavor [jet]calibration and fitting methodsATLASLarge Hadron ColliderLarge detector systems for particle and astroparticle physics; Large; detector-systems performance; Pattern recognition cluster finding; calibration and fitting methods; Performance of High Energy Physics; Detectors; PRODUCTION CROSS-SECTION; QUARK PAIR PRODUCTION; ROOT-S=7 TEV; PARTON; DISTRIBUTIONS; HADRONIC COLLISIONS; MATRIX-ELEMENTS; LHC; COLLIDERS; DETECTOR; CHANNEL8. Economic growthCalibrationparticle identification [bottom]LHCImpact parameterParticle Physics - ExperimentParticle physicsdata analysis method530 Physics:Ciências Físicas [Ciências Naturais]FOS: Physical sciences530MATRIX-ELEMENTSparticle identification [charm]on-line [trigger]Pattern recognition0103 physical sciencesComplementary methodddc:610DETECTORROOT-S=7 TEVCluster findingFísicaLarge detector systems for particle and astroparticle physics; Large detector-systems performance; Pattern recognition cluster finding calibration and fitting methods; Performance of High Energy Physics DetectorsPattern recognition systemcalibrationtracksPRODUCTION CROSS-SECTIONefficiencyHadronLarge detector systems for particle and astroparticle physicLargeHigh Energy Physics::ExperimentStatistical correlationstatisticalexperimental results
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Measurement of the spin-dependent structure function g1(x) of the deuteron

1993

We report on the first measurement of the spin-dependent structure function g1d of the deuteron in the deep inelastic scattering of polarised muons off polarised deuterons, in the kinematical range 0.006<x<0.6, 1 GeV2<Q2<30 GeV2. The first moment, Γ1d=sh{phonetic}01 g1d dx=0.023±0.020 (stat.) ± 0.015 (syst.), is smaller than the prediction of the Ellis-Jaffe sum rules. Using earlier measurements of g1p, we infer the first moment of the spin-dependent neutron structure function g1n. The difference Γ1p-Γ1n=0.20 ±0.05 (stat.) ± 0.04 (syst.) agrees with the prediction of the Bjorken sum rule, Γ1p-Γ1n=0.191 ±0.002.

deuteron: polarized targetNuclear and High Energy PhysicsINELASTIC E-P; POLARIZED PROTONS; SUM-RULE; SCATTERING; ELECTROPRODUCTION; ASYMMETRYINELASTIC E-PProtonpolarized target: deuterondeep inelastic scattering: muon deuteronstructure function: spinmuon deuteron: deep inelastic scatteringSUM-RULE530Nuclear physicsINELASTIC E-P; POLARIZED PROTONS; SUM-RULE; SCATTERING; ELECTROPRODUCTION; ASYMMETRY; MODELTheoryofComputation_ANALYSISOFALGORITHMSANDPROBLEMCOMPLEXITYSCATTERINGNeutronpolarized beam: muonSpin-½PhysicsQuantum chromodynamicsspin: structure functionMuonScatteringdeuteron: structure functionELECTROPRODUCTIONnucleon: structure functionCERN SPSDeep inelastic scatteringmomentmagnetic spectrometer: experimental resultsPOLARIZED PROTONSapprox. 100 GeVASYMMETRYSum rule in quantum mechanicsmuon: polarized beamParticle Physics - ExperimentPhysics Letters B
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