LEADER 01012nam0-22003251i-450- 001 990003046020403321 010 $a0-8213-2514-0 035 $a000304602 035 $aFED01000304602 035 $a(Aleph)000304602FED01 035 $a000304602 100 $a20000920d1993----km-y0itay50------ba 101 0 $aita 102 $aIT 200 1 $aDesigning a System of Labor Market Statistics and Information$fRobert S. Goldfarb, Arvil V. Adams. 210 $aWashington$dD.C.$cThe World Bank$d1993. 215 $aVIII, 54 p.$d29 cm 225 1 $aWorld Bank discussion papers$v205 610 0 $aPaesi in via di sviluppo$aMercato del lavoro$aModelli statistici 676 $aG/2.1 702 1$aAdams,$bArvil V. 702 1$aGoldfarb,$bRobert S. 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990003046020403321 952 $aA/6.0 GOL$b14032$fSES 959 $aSES 996 $aDesigning a System of Labor Market Statistics and Information$9462603 997 $aUNINA DB $aING01 LEADER 05460nam 2200649 a 450 001 9910141633103321 005 20200520144314.0 010 $a3-527-65323-6 010 $a3-527-65321-X 010 $a3-527-65324-4 035 $a(CKB)2670000000342883 035 $a(EBL)1156974 035 $a(OCoLC)831115268 035 $a(SSID)ssj0000904696 035 $a(PQKBManifestationID)11530015 035 $a(PQKBTitleCode)TC0000904696 035 $a(PQKBWorkID)10922872 035 $a(PQKB)10171479 035 $a(MiAaPQ)EBC1156974 035 $a(Au-PeEL)EBL1156974 035 $a(CaPaEBR)ebr10677741 035 $a(CaONFJC)MIL484612 035 $a(PPN)183789512 035 $a(EXLCZ)992670000000342883 100 $a20130403d2013 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAirborne measurements for environmental research$b[electronic resource] $emethods and instruments /$fedited by Manfred Wendisch and Jean-Louis Brenguier 210 $aWeinheim $cWiley-VCH$d2013 215 $a1 online resource (689 p.) 225 0 $aWiley series in atmospheric physics and remote sensing 300 $aDescription based upon print version of record. 311 $a3-527-40996-3 320 $aIncludes bibliographical references and index. 327 $aAirborne Measurements for Environmental Research; Contents; Preface; A Tribute to Dr. Robert Knollenberg; List of Contributors; 1 Introduction to Airborne Measurements of the Earth Atmosphere and Surface; 2 Measurement of Aircraft State and Thermodynamic and Dynamic Variables; 2.1 Introduction; 2.2 Historical; 2.3 Aircraft State Variables; 2.3.1 Barometric Measurement of Aircraft Height; 2.3.2 Inertial Attitude, Velocity, and Position; 2.3.2.1 System Concepts; 2.3.2.2 Attitude Angle Definitions; 2.3.2.3 Gyroscopes and Accelerometers; 2.3.2.4 Inertial-Barometric Corrections 327 $a2.3.3 Satellite Navigation by Global Navigation Satellite Systems2.3.3.1 GNSS Signals; 2.3.3.2 Differential GNSS; 2.3.3.3 Position Errors and Accuracy of Satellite Navigation; 2.3.4 Integrated IMU/GNSS Systems for Position and Attitude Determination; 2.3.5 Summary, Gaps, Emerging Technologies; 2.4 Static Air Pressure; 2.4.1 Position Error; 2.4.1.1 Tower Flyby; 2.4.1.2 Trailing Sonde; 2.4.2 Summary; 2.5 Static Air Temperature; 2.5.1 Aeronautic Definitions of Temperatures; 2.5.2 Challenges of Airborne Temperature Measurements; 2.5.3 Immersion Probe; 2.5.4 Reverse-Flow Sensor 327 $a2.5.5 Radiative Probe2.5.6 Ultrasonic Probe; 2.5.7 Error Sources; 2.5.7.1 Sensor; 2.5.7.2 Dynamic Error Sources; 2.5.7.3 In-Cloud Measurements; 2.5.8 Calibration of Temperature Sensors; 2.5.9 Summary, Gaps, Emerging Technologies; 2.6 Water Vapor Measurements; 2.6.1 Importance of Atmospheric Water Vapor; 2.6.2 Humidity Variables; 2.6.3 Dew or Frost Point Hygrometer; 2.6.4 Lyman-? Absorption Hygrometer; 2.6.5 Lyman-? Fluorescence Hygrometer; 2.6.6 Infrared Absorption Hygrometer; 2.6.7 Tunable Laser Absorption Spectroscopy Hygrometer; 2.6.8 Thin Film Capacitance Hygrometer 327 $a2.6.9 Total Water Vapor and Isotopic Abundances of 18O and 2H2.6.10 Factors Influencing In-Flight Performance; 2.6.10.1 Sticking of Water Vapor at Surfaces; 2.6.10.2 Sampling Systems; 2.6.11 Humidity Measurements with Dropsondes; 2.6.12 Calibration and In-Flight Validation; 2.6.13 Summary and Emerging Technologies; 2.7 Three-Dimensional Wind Vector; 2.7.1 Airborne Wind Measurement Using Gust Probes; 2.7.1.1 True Airspeed (TAS) and Aircraft Attitude; 2.7.1.2 Wind Vector Determination; 2.7.1.3 Baseline Instrumentation; 2.7.1.4 Angles of Attack and Sideslip; 2.7.2 Errors and Flow Distortion 327 $a2.7.2.1 Parameterization Errors2.7.2.2 Measurement Errors; 2.7.2.3 Timing Errors; 2.7.2.4 Errors due to Incorrect Sensor Configuration; 2.7.3 In-Flight Calibration; 2.8 Small-Scale Turbulence; 2.8.1 Hot-Wire/Hot-Film Probes for High-Resolution Flow Measurements; 2.8.2 Laser Doppler Anemometers; 2.8.3 Ultrasonic Anemometers/Thermometers; 2.8.4 Measurements of Atmospheric Temperature Fluctuations with Resistance Wires; 2.8.5 Calibration of Fast-Response Sensors; 2.8.6 Summary, Gaps, and Emerging Technologies; 2.9 Flux Measurements; 2.9.1 Basics; 2.9.2 Measurement Errors 327 $a2.9.3 Flux Sampling Errors 330 $aThis first comprehensive review of airborne measurement principles covers all atmospheric components and surface parameters. It describes the common techniques to characterize aerosol particles and cloud/precipitation elements, while also explaining radiation quantities and pertinent hyperspectral and active remote sensing measurement techniques along the way. As a result, the major principles of operation are introduced and exemplified using specific instruments, treating both classic and emerging measurement techniques.The two editors head an international community of eminent scientists 410 0$aWiley Series in Atmospheric Physics and Remote Sensing 606 $aAtmosphere$xMeasurement 615 0$aAtmosphere$xMeasurement. 676 $a551.511028 701 $aWendisch$b Manfred$0958799 701 $aBrenguier$b Jean-Louis$0958800 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910141633103321 996 $aAirborne measurements for environmental research$92172472 997 $aUNINA LEADER 05325nam 22007215 450 001 996673180503316 005 20250806175355.0 010 $a3-032-00891-3 024 7 $a10.1007/978-3-032-00891-6 035 $a(MiAaPQ)EBC32256191 035 $a(Au-PeEL)EBL32256191 035 $a(CKB)40138064800041 035 $a(DE-He213)978-3-032-00891-6 035 $a(OCoLC)1534195138 035 $a(EXLCZ)9940138064800041 100 $a20250806d2026 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aModeling Decisions for Artificial Intelligence $e22nd International Conference, MDAI 2025, València, Spain, September 15?18, 2025, Proceedings /$fedited by Vicenç Torra, Yasuo Narukawa, Josep Domingo-Ferrer 205 $a1st ed. 2026. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2026. 215 $a1 online resource (669 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15957 311 08$a3-032-00890-5 327 $a -- Decision making and uncertainty. -- Measurable Closure of a Finitely-Additive Measure Space: An Analysis of Spaces Similar to Stone Spaces. -- Ecological Inference for Electoral Analysis: A Computational Perspective on Human Decision-Making. -- Dimensionality reduction with entropies from f-divergences. -- ChessFormer - Modeling human decision making in chess. -- Simulating Electoral Behavior. -- Multi-criteria Assessment of Clustering Procedures in E-Commerce. -- Automated Decision-Making via Reinforcement Learning from Demonstrations. -- Decision Analysis with the Hurwicz Decision Map under a Set of Interval Pri- ority Weight Vectors. -- An Investigation of Alternative Methods for the Inference of Probabilistic-Fuzzy Systems. -- Triangular Fuzzy Rescaling Distance. -- Data privacy. -- The differentially private d-Choquet integral: an extension of differentially pri- vate Choquet integrals. -- Defenses Against Membership Inference Attacks on Unlearned Data. -- Differential Private Risk Factors Analysis of Polypharmacy. -- Towards Lightning Network Channel Randomization. -- Assessing Privacy Requirements for Controlled Query Evaluation in OBDA. -- Machine learning. -- On Sharma-Mittal divergence-regularized Fuzzy c-Means Clustering and its Alternative. -- Probabilistic-Fuzzy Inference with Piecewise Linear Quantile Regression. -- Positive Unlabeled Classification Methods with Logistic Regression Revisited: An Evaluation of Optimization Techniques. -- Kacper Paczutkowski, Konrad Furma´nczyk Comparing Transformer Models for Stock Selection in Quantitative Trading. -- Data science. -- Decision Rules for Replicating the Visual Learning of the Blackboard in Digital Presentations. -- Dual Focus: Transforming Negatives into Knowledge. -- Testing monotonicity of similarity functions based on embeddings. -- Hybrid Transformer-ANFIS Architecture for Sentiment Analysis. -- Comparing Qualitative Object Descriptors using a Visual Similarity Measure. -- Improving Machine Understanding of Czech Medical Text Using Self-Supervised and Rule-Based Data Augmentation. -- Refining Community Detection in Social Networks: Agglomerative and Divisive Methods with Size Constraints. -- Comparing Graph Neural Networks for Single and Multi-Layer Brain Connec- tivity Analysis in Multiple Sclerosis. -- Enhancing Ultra-Low-Bit Quantization of Large Language Models Through Saliency-Aware Partial Retraining. 330 $aThis book constitutes the refereed proceedings of the 22nd International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2025, held in Valencia, Spain, during September 15-18, 2025. The 28 full papers were carefully reviewed and selected from 58 submissions. They are organized in topical sections as follows: Decision making and uncertainty; Data privacy; Machine learning and Data science. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v15957 606 $aArtificial intelligence 606 $aComputer systems 606 $aComputer networks 606 $aData structures (Computer science) 606 $aInformation theory 606 $aComputer science 606 $aArtificial Intelligence 606 $aComputer System Implementation 606 $aComputer Communication Networks 606 $aData Structures and Information Theory 606 $aTheory of Computation 615 0$aArtificial intelligence. 615 0$aComputer systems. 615 0$aComputer networks. 615 0$aData structures (Computer science) 615 0$aInformation theory. 615 0$aComputer science. 615 14$aArtificial Intelligence. 615 24$aComputer System Implementation. 615 24$aComputer Communication Networks. 615 24$aData Structures and Information Theory. 615 24$aTheory of Computation. 676 $a006.3 700 $aTorra$b Vicenc?$0848974 701 $aNarukawa$b Yasuo$01255058 701 $aDomingo-Ferrer$b Josep$01751715 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996673180503316 996 $aModeling Decisions for Artificial Intelligence$94428587 997 $aUNISA