LEADER 00957nam0-2200253 --450 001 9910628194103321 005 20221129110749.0 100 $a20221129d1924----kmuy0itay5050 ba 101 0 $aita 102 $aIT 105 $a 001yy 200 1 $aRelazione sul progetto Ruffini per la proprietà scientifica fatta dal s. c. prof. S. Solazzi nell'adunanza del 10 aprile 1924 210 $a[S.l.$cs.n.]$d1924 215 $a[347]-350 p.$d25 cm 300 $aEstratto da: Rendiconti del R. Istituto lombardo di scienze e lettere, vol. 57., fasc. 6.-10., 1924. 676 $a340.54$v23$zita 700 1$aSolazzi,$bSiro$0236351 801 0$aIT$bUNINA$gREICAT$2UNIMARC 901 $aBK 912 $a9910628194103321 952 $aBibl. Solazzi Busta S 260$b64375$fFGBC 959 $aFGBC 996 $aRelazione sul progetto Ruffini per la proprietà scientifica fatta dal s. c. prof. S. Solazzi nell'adunanza del 10 aprile 1924$92967535 997 $aUNINA LEADER 01814aam 2200433I 450 001 9910711382703321 005 20160420032119.0 024 8 $aGOVPUB-C13-5cf4458e11c6a36e3f09a6da119478b6 035 $a(CKB)5470000002482201 035 $a(OCoLC)947005721 035 $a(EXLCZ)995470000002482201 100 $a20160420d2015 ua 0 101 0 $aeng 181 $2rdacontent 182 $2rdamedia 183 $2rdacarrier 200 12$aA review of test methods for determining protective capabilities of fire fighter protective clothing from steam /$fShonali Nazare, Daniel Madrzykowski 210 1$aGaithersburg, MD :$cU.S. Dept. of Commerce, National Institute of Standards and Technology,$d2015. 215 $a1 online resource (viii + 28 pages) $cillustrations (black and white) 225 1 $aNIST technical note ;$v1861 300 $aContributed record: Metadata reviewed, not verified. Some fields updated by batch processes. 300 $aFebruary 2015. 300 $aTitle from PDF title page (viewed December 31, 2015). 320 $aIncludes bibliographical references. 606 $aFire fighters$xUniforms 606 $aPersonal protective equipment$xFire testing 615 0$aFire fighters$xUniforms. 615 0$aPersonal protective equipment$xFire testing. 700 $aNazare?$b Shonali$01400409 701 $aMadrzykowski$b Daniel$01387650 701 $aNazare?$b Shonali$01400409 712 02$aNational Institute of Standards and Technology (U.S.).$bEngineering Laboratory.$bFire Research Division. 801 0$bNBS 801 1$bNBS 801 2$bGPO 906 $aBOOK 912 $a9910711382703321 996 $aA review of test methods for determining protective capabilities of fire fighter protective clothing from steam$93500862 997 $aUNINA LEADER 05106nam 2200781 a 450 001 9911020342503321 005 20200520144314.0 010 $a9783527659142 010 $a3527659145 010 $a9783527659166 010 $a3527659161 010 $a9781299476011 010 $a1299476015 010 $a9783527659173 010 $a352765917X 035 $a(CKB)2550000001020410 035 $a(EBL)1170128 035 $a(SSID)ssj0001255470 035 $a(PQKBManifestationID)12486590 035 $a(PQKBTitleCode)TC0001255470 035 $a(PQKBWorkID)11244719 035 $a(PQKB)11353079 035 $a(SSID)ssj0001034066 035 $a(PQKBManifestationID)11565567 035 $a(PQKBTitleCode)TC0001034066 035 $a(PQKBWorkID)11006196 035 $a(PQKB)11591627 035 $a(MiAaPQ)EBC1170128 035 $a(OCoLC)843196372 035 $a(PPN)183849787 035 $a(Perlego)1012601 035 $a(EXLCZ)992550000001020410 100 $a20130424d2013 uy 0 101 0 $ager 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aWaldboden $eein Bildatlas der wichtigsten Bodentypen aus Osterreich, Deutschland und der Schweiz /$fherausgegeben von Ernst Leitgeb ... [et al.] 205 $a1. Aufl. 210 $aWeinheim $cWiley-VCH$d2013 215 $a1 online resource (413 p.) 300 $aDescription based upon print version of record. 311 08$a9783527327133 311 08$a3527327134 320 $aIncludes bibliographical references and index. 327 $a2.1.1 Waldo?kologische Naturra?ume2.1.2 Potenzielle natu?rliche Waldgesellschaft; 2.1.3 Allgemeine Lageparameter; 2.1.4 Ausgangsmaterial; 2.1.5 Wasserhaushalt; 2.1.6 Klimadiagramme; 2.1.7 O?kologische Netzdiagramme; 2.2 Bodenprobenahme und Analytik; 2.2.1 Probenahme und Probenvorbereitung; 2.2.2 Analytische Parameter pH-Wert; 2.2.3 Bestimmungsgrenzen; 2.2.4 Abgeleitete Parameter; 2.2.5 Klassifikation und Tiefenverlauf wichtiger Bodenparameter; 2.3 Bodensystematik und Gliederung der Bo?den; 2.3.1 Gruppe: Fels-Auflagehumusbo?den bzw. O/C-Bo?den und Terrestrische Rohbo?den 327 $a2.3.2 Gruppe: Terrestrische Humusbo?den (ausgenommen Fels-Auflagehumusbo?den) bzw. Ah/C-Bo?den und Schwarzerden2.3.3 Gruppe: Braunerden und Lessive?s; 2.3.4 Gruppe: Podsole und Semipodsole; 2.3.5 Gruppe: Kalklehme bzw. Terrae calcis; 2.3.6 Gruppe: Pelosole; 2.3.7 Gruppe: Kolluvisole; 2.3.8 Gruppe: Pseudogleye bzw. Stauwasserbo?den; 2.3.9 Gruppe: Aubo?den bzw. Auenbo?den; 2.3.10 Gruppe: Gleye; 2.3.11 Gruppe: Moore und Anmoore; 2.4 Horizontierung der Bo?den; 2.4.1 Bodenhorizonte nach O?BS und KA5; 2.4.2 Merkmale zur Abgrenzung von Bodenhorizonten; 3 Auswahl der Bodenprofile 327 $aTeil 2: Bodenprofile aus O?sterreich, Deutschland und der Schweiz4 Fels-Auflagehumusbo?den bzw. O/C-Bo?den und Terrestrische Rohbo?den; 4.1 Fels-Auflagehumusboden auf Carbonatgestein (Beispiel I); 4.2 Fels-Auflagehumusboden auf Carbonatgestein (Beispiel II); 4.3 Fels-Auflagehumusboden auf Carbonatgestein (Beispiel III); 4.4 Carbonatfreier Textur-Substratboden/Typischer Rohhumus; 5 Terrestrische Humusbo?den (ausgenommen Fels-Auflagehumusbo?den) bzw. Ah/C-Bo?den und Schwarzerden; 5.1 Typischer Ranker/Moderartiger Mull; 5.2 Typischer Ranker/Rohhumusartiger Moder 327 $a5.3 Brauner Typischer Ranker/Typischer Mull5.4 Verbraunte Mull-Pararendzina/Typischer Mull; 5.5 Typischer Tschernosem/Typischer Mull; 5.6 Verbraunter Typischer Tschernosem/Moderartiger Mull; 5.7 Moder-Rendzina/Rohhumusartiger Moder; 5.8 Moder-Rendzina/Kalkmoder; 5.9 Kalklehm-Rendzina/Mullartiger Moder; 5.10 Kalklehm-Rendzina/Typischer Mull (Beispiel I); 5.11 Kalklehm-Rendzina/Typischer Mull (Beispiel II); 6 Braunerden und Lessive?s; 6.1 Entkalkte Typische Braunerde/Typischer Mull; 6.2 Pseudovergleyte entkalkte Typische Braunerde/Typischer Mull 327 $a6.3 Carbonatfreie Typische Braunerde/Typischer Moder 330 $aEin einzigartiger Bildband reich an Beispielen der maßgeblichen Bodentypen. Im Fokus: die Waldgebiete O?sterreichs, Deutschlands und der Schweiz. Zu jedem Bodenprofil sind umfassende Daten zu u?ber 40 Bodenmerkmalen angefu?hrt, die anschaulich aufbereitet und interpretiert sind. Zusammen mit Kommentaren zum Baumwachstum und zur Waldbewirtschaftung liefern diese Bodendokumentationen wertvolle Hinweise fu?r die Praxis.Die zum Teil speziell fu?r dieses Buch aufgenommenen Bodenprofile (Auflagehumus und Mineralboden) sind eine gute Unterstu?tzung fu?r die Bodenansprache vor Ort. Vereinheitlichte b 606 $aForest soils$zAustria$vAtlases 606 $aForest soils$zGermany$vAtlases 606 $aForest soils$zSwitzerland$vAtlases 615 0$aForest soils 615 0$aForest soils 615 0$aForest soils 676 $a631.4 676 $a631.44 701 $aLeitgeb$b Ernst$01837604 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911020342503321 996 $aWaldboden$94416358 997 $aUNINA LEADER 12817nam 22007095 450 001 9910879579203321 005 20251225202054.0 010 $a981-9755-88-3 024 7 $a10.1007/978-981-97-5588-2 035 $a(CKB)33992542300041 035 $a(DE-He213)978-981-97-5588-2 035 $a(MiAaPQ)EBC31694999 035 $a(Au-PeEL)EBL31694999 035 $a(EXLCZ)9933992542300041 100 $a20240813d2024 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvanced Intelligent Computing Technology and Applications $e20th International Conference, ICIC 2024, Tianjin, China, August 5?8, 2024, Proceedings, Part III /$fedited by De-Shuang Huang, Zhanjun Si, Yijie Pan 205 $a1st ed. 2024. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2024. 215 $a1 online resource (XVIII, 519 p. 268 illus., 184 illus. in color.) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v14864 311 08$a981-9755-87-5 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- Organization -- Contents - Part III -- Neural Networks -- Trust Evaluation with Deep Learning in Online Social Networks: A State-of-the-Art Review -- 1 Introduction -- 2 Analysis and Comparison of Related Work -- 2.1 Graph-Based Neural Networks -- 2.2 Other Deep Learning Method -- 3 Challenges and Open Problems -- 4 Research Direction -- 4.1 Trust Evaluation Based on Ensemble Learning with DNN in OSNs -- 4.2 Cross-Origin Trust Evaluation Based on Deep Learning with a Hyperparameter Auto Optimizer in OSNs -- 5 Conclusion -- References -- Deep Neural Network-Based Intrusion Detection in Internet of Things: A State-of-the-Art Review -- 1 Introduction -- 2 Analysis and Comparison of Related Work -- 2.1 Network Traffic-Based Intrusion Detection -- 2.2 Device Behavior-Based Intrusion Detection -- 3 Challenges and Open Problems -- 4 Research Direction -- 4.1 An IDS Based on Federated Learning and Transfer Learning -- 4.2 An IDS Based on Explainable DNNs -- 5 Conclusion -- References -- CNN-SENet: A Convolutional Neural Network Model for Audio Snoring Detection Based on Channel Attention Mechanism -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Convolutional Neural Networks -- 3.2 SENet -- 4 Experiment -- 4.1 Baseline -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Evaluation Indicators -- 4.5 Experimental Results -- 5 Summary -- References -- Selecting Effective Triplet Contrastive Loss for Domain Alignment -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation and Notations -- 3.2 Triplet Contrastive Loss -- 3.3 Model Structure -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Results and Discussion -- 5 Conclusion -- References -- RCSnet--Flower Classification Network Design Based on Transfer Learning and Channel Attention Mechanism -- 1 Introduction -- 2 Related Work. 327 $a3 Methods -- 3.1 Residual Networks -- 3.2 SENet -- 3.3 Transfer Learning -- 4 Experiment -- 4.1 Baseline -- 4.2 Datasets -- 4.3 Data Preprocessing -- 4.4 Evaluation Indicators -- 4.5 Experimental Results -- 5 Summary -- References -- MDGCL: Message Dropout Graph Contrastive Learning for Recommendation -- 1 Introduction -- 2 Preliminaries -- 2.1 Graph Collaborative Filtering -- 2.2 Graph Contrastive Learning -- 3 Methodology -- 3.1 Analysis of MessageDropout -- 3.2 A Simplified Architecture for GCL -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Experimental Results -- 5 Conclusion -- References -- Improved CNN Model Using Innovative Adaptive-DropMessage for Gomoku Game -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Adaptive-DropMessage Method -- 3.2 Dilated Depthwise Separable Convolution -- 3.3 Residual Dense Block and SENet Module -- 3.4 Spatial Attention Module -- 3.5 Gomoku-Specific Output Layer -- 4 Experiments and Analysis -- 4.1 Experimental Setup -- 4.2 Game Experiment Results and Analysis -- 5 Conclusion -- References -- A Survey: Feature Fusion Method for Object Detection Field -- 1 Introduction -- 2 Feature Fusion Methods -- 2.1 Simple Topology Fusion Structure -- 2.2 Complex Topology Fusion Structure -- 2.3 Analysis and Summary -- 3 Datasets and Evaluation Indicators -- 3.1 Datasets -- 3.2 Evaluation Indicators -- 4 Trends and Challenges -- 5 Conclusion -- References -- Dual-Branch Collaborative Learning for Visual Question Answering -- 1 Introduction -- 2 Related Work -- 2.1 Visual Question Answering -- 2.2 Graph-Based Visual Relationship Reasoning -- 2.3 Collaborative Learning -- 3 Method -- 3.1 Overview -- 3.2 Question Representation and Image Representation -- 3.3 Relational Reasoning Branch -- 3.4 Attention Branch -- 3.5 Dual-Branch Collaborative Learning -- 3.6 Joint Predict -- 4 Experiments. 327 $a4.1 Datasets and Implementation Detail -- 4.2 Ablation Experiment -- 4.3 Comparison with SOTAs -- 4.4 Qualitative Analysis -- 5 Conclusion -- References -- SCAI: A Spectral Data Classification Framework with Adaptive Inference for Rapid and Portable Identification of Chinese Liquors -- 1 Introduction -- 2 Methodology -- 2.1 SCAI: Early-Exit with Self-distillation -- 2.2 SCAI+: SCAI with PA-ResNet -- 3 Experiment -- 3.1 Experiment Instrument -- 3.2 Datasets -- 3.3 Baselines -- 3.4 Common Performance Result -- 3.5 Application Performance Result -- 3.6 Self-distillation Training Analysis -- 4 Conclusion -- References -- EfficientPose: A Lightweight and Efficient Model with Transformer for Human Pose Estimation -- 1 Introduction -- 2 Related Work -- 2.1 Human Pose Estimation -- 2.2 Atrous Convolution and ASPP -- 3 Methods -- 3.1 EfficientPose Architecture -- 3.2 Efficient Bottleneck Block (EBB) -- 3.3 Transformer Encoder -- 3.4 Iterative Training Strategy -- 4 Experiments -- 4.1 Implementation Results -- 4.2 Ablations -- 5 Conclusion -- References -- Enhanced Chinese Named Entity Recognition with Transformer-Based Multi-feature Fusion -- 1 Introduction -- 2 Related Work -- 2.1 Chinese NER Based on Lexical Enhancement -- 2.2 Chinese NER with Fusion of Glyph Features -- 2.3 Feature Fusion in Chinese NER -- 3 Method -- 3.1 Text Representation Layer -- 3.2 Sequence Encoding Layer -- 3.3 Sequence Decoding Layer -- 4 Experiments -- 4.1 Results and Analysis -- 5 Conclusion -- References -- YOLO-Fire: A Fire Detection Algorithm Based on YOLO -- 1 Introduction -- 2 YOLO-Fire -- 2.1 SimpleC3 -- 2.2 Dynamic Upsampler -- 2.3 Focal WIoU-Loss -- 3 Experiments -- 3.1 Implementation Details -- 3.2 Ablation Studies -- 3.3 Experimental Results Analysis -- 4 Conclusion -- References -- From Vision to Sound: The Application of ViT-LSTM in Music Sequence -- 1 Introduction. 327 $a2 Related Work -- 3 Methods -- 3.1 Dataset Definition -- 3.2 Model Construction -- 4 Experiment -- 4.1 Data Processing -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- Graph Convolution Recommendation Algorithm Integrating Multi-relationship Preferences -- 1 Introduction -- 2 Graph Convolution Recommendation Algorithm Integrating Multi-relationship Preferences -- 2.1 User Feature Embedding Propagation -- 2.2 Item Feature Embedding Propagation -- 2.3 Predict -- 2.4 Model Optimization -- 3 Experimental Design and Result Analysis -- 3.1 Experimental Data Set -- 3.2 Evaluation Indicators -- 3.3 Experimental Settings -- 3.4 Experimental Results -- 3.5 Ablation Analysis -- 4 Conclusion -- References -- Temporal Sequential Wave Neural Network for Solving the Optimal Cognitive Subgraph Query Problem -- 1 Introduction -- 2 Definition of the Problem -- 3 Temporal Sequential Wave Neural Network -- 3.1 Design of Temporal Sequential Wave Neural Network -- 3.2 TSWNN Algorithm -- 3.3 Time Complexity Analysis of TSWNN Algorithm -- 4 Example of TSWNN Algorithm -- 5 Experimentation -- 5.1 Experimental Results with Different Number of Nodes -- 5.2 Experimental Results with Different Number of Sides -- 5.3 Experimental Results with Different Limiting Times -- 6 Conclusion -- References -- Joint Prior Relation Enhancement and Non-autoregressive Decoding for Document-Level Event Extraction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Entity Mention Recognition -- 3.2 Prior Relation Enhancement Encoding -- 3.3 Event Argument Combination Recognition -- 3.4 Event Type Detection -- 3.5 Event Record Extraction -- 4 Experiments -- 4.1 Datasets -- 4.2 Baselines and Metric -- 4.3 Main Results -- 4.4 Ablation Study -- 5 Conclusions -- References -- Intrusion Detection System Based on ViTCycleGAN and Rules -- 1 Introduction -- 2 Related Work. 327 $a3 Methodologies -- 3.1 Cycle-Consistent Generative Adversarial Network (CycleGAN) -- 3.2 Vision Transformer -- 3.3 Intrusion Detection System: ViTCycleGAN -- 4 Experiments -- 4.1 Dataset -- 4.2 Data Preprocessing -- 4.3 Performance Evaluation of ViTCyleGAN Intrusion Detection System -- 5 Conclusion -- References -- DFT-3DLaneNet: Dual-Frequency Domain Enhanced Transformer for 3D Lane Detection -- 1 Introduction -- 2 Related Work -- 2.1 2D Lane Detection -- 2.2 3D Lane Detection -- 2.3 Frequency Domain Image Processing -- 3 Method -- 3.1 Frequency Domain Feature Extraction -- 3.2 Dual-Channel High-Frequency Feature Enhancement -- 3.3 Cross-channel Low-Frequency Attention -- 3.4 Dual-Frequency Domain Deformable Attention -- 3.5 Prediction and Loss -- 4 Experiment -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 4.3 Comparisons with State-of-the-Arts -- 4.4 Ablation Studies -- 5 Conclusions -- References -- Correlation Matters: A Stock Price Predication Model Based on the Graph Convolutional Network -- 1 Introduction -- 2 Related Work -- 2.1 Stock Price Prediction -- 2.2 Graph Convolutional Network -- 3 Problem Formulation -- 4 StockGCN -- 4.1 Graph Construction -- 4.2 Framework of StockGCN -- 5 Experiments -- 5.1 Datasets -- 5.2 Preprocessing -- 5.3 Baselines -- 5.4 Result Analysis -- 5.5 Ablation Study -- 6 Conclusion -- References -- Coformer: Collaborative Transformer for Medical Image Segmentation -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Encoder -- 3.2 Multi-scale Representation Fusion Module (MRF) -- 3.3 Decoder -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Implementation Details -- 4.3 Evaluation Results -- 5 Conclusion -- References -- FRMM: Feature Reprojection for Exemplar-Free Class-Incremental Learning -- 1 Introduction -- 2 Methods -- 2.1 Problem Statement and Framework -- 2.2 Feature Reprojection. 327 $a2.3 Ensemble of Two Experts. 330 $aThis 13-volume set LNCS 14862-14874 constitutes - in conjunction with the 6-volume set LNAI 14875-14880 and the two-volume set LNBI 14881-14882 - the refereed proceedings of the 20th International Conference on Intelligent Computing, ICIC 2024, held in Tianjin, China, during August 5-8, 2024. The total of 863 regular papers were carefully reviewed and selected from 2189 submissions. This year, the conference concentrated mainly on the theories and methodologies as well as the emerging applications of intelligent computing. Its aim was to unify the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. Therefore, the theme for this conference was "Advanced Intelligent Computing Technology and Applications". Papers that focused on this theme were solicited, addressing theories, methodologies, and applications in science and technology. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v14864 606 $aComputational intelligence 606 $aComputer networks 606 $aMachine learning 606 $aApplication software 606 $aComputational Intelligence 606 $aComputer Communication Networks 606 $aMachine Learning 606 $aComputer and Information Systems Applications 615 0$aComputational intelligence. 615 0$aComputer networks. 615 0$aMachine learning. 615 0$aApplication software. 615 14$aComputational Intelligence. 615 24$aComputer Communication Networks. 615 24$aMachine Learning. 615 24$aComputer and Information Systems Applications. 676 $a006.3 702 $aHuang$b De-Shuang 702 $aSi$b Zhanjun 702 $aPan$b Yijie 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910879579203321 996 $aAdvanced Intelligent Computing Technology and Applications$94410353 997 $aUNINA