LEADER 05235nam 2200649 a 450 001 9910459158203321 005 20200520144314.0 010 $a0-444-53881-X 035 $a(CKB)2660000000011017 035 $a(EBL)1313395 035 $a(OCoLC)854975385 035 $a(SSID)ssj0001058827 035 $a(PQKBManifestationID)11602561 035 $a(PQKBTitleCode)TC0001058827 035 $a(PQKBWorkID)11070335 035 $a(PQKB)10086153 035 $a(MiAaPQ)EBC1313395 035 $a(PPN)179258842 035 $a(Au-PeEL)EBL1313395 035 $a(CaPaEBR)ebr10733199 035 $a(EXLCZ)992660000000011017 100 $a20130730d2013 uy 0 101 0 $aeng 135 $aurcn||||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBatteries, hydrogen storage and fuel cells$b[electronic resource] /$fedited by Steven L. Suib 210 $aAmsterdam $cElsevier$d2013 215 $a1 online resource (551 p.) 225 0$aNew and future developments in catalysis 300 $aDescription based upon print version of record. 311 $a0-444-53880-1 320 $aIncludes bibliographical references and index. 327 $aHalf Title; Title Page; Copyright; Contents; Introduction; Contributors; 1 Catalytic Batteries; 1.1 Introduction; 1.2 Metal-Air Batteries; 1.2.1 Catalytic Materials in Metal-Air Cells; 1.2.2 Aluminum-Air Batteries; 1.2.3 Lithium-Air Batteries; 1.2.4 Magnesium-Air Batteries; 1.2.5 Zinc-Air Batteries; 1.3 Environmental Conditions for Catalysts; 1.4 Safety Concerns for Metal-Air Battery Experimentation; 1.5 Future of Catalysts in Metal-Air Batteries; References; 2 A Novel Enzymatic Technology for Removal of Hydrogen Sulfide from Biogas; 2.1 Introduction; 2.2 Experimental 327 $a2.3 Results and Discussion 2.3.1 Effect of Enzyme Concentration; 2.3.2 Effect of Gas Flow Rate; 2.3.3 Effect of Enzyme Replenishment; 2.3.3.1 Replenishment at Saturation Point; 2.3.3.2 Replenishment at H2S Breakthrough; 2.3.4 Effect of Packing Material; 2.3.5 Sulfur Components Recovery; 2.4 Conclusions; Acknowledgments; References; 3 Electrocatalysts for the Electrooxidation of Ethanol; 3.1 Introduction; 3.2 Electrooxidation of Ethanol on Polycrystalline Pt, Pt (hkl) Electrodes and Pt/C Electrodes. Identification and Oxidation of Ethanol Adsorbate(s) 327 $a3.2.1 Electrochemical Studies of the Electrooxidation of Ethanol in Acid Medium 3.2.2 Identification of Ethanol Adsorbate and Oxidation Products by EC-FTIR and DEMS on Polycrystalline Pt and Pt/C Electrodes; 3.2.3 Adsorption and Electrooxidation of Acetic Acid; 3.2.4 Adsorption and Electrooxidation of Acetaldehyde; 3.3 Reaction Pathways and Mechanism of the Electrooxidation of Ethanol; 3.4 Designing of Supported Electrocatalysts for the Electrooxidation of Ethanol; 3.5 Fuel Cell Studies; 3.6 Summary; Acronyms and Symbols; References 327 $a4 Catalytic Processes Using Fuel Cells, Catalytic Batteries, and Hydrogen Storage Materials 4.1 Introduction; 4.2 Catalytic Processes in Fuel Cells; 4.2.1 Low-Temperature PEMFCs; 4.2.1.1 Hydrogen/Air(Oxygen) Fuel Cells; 4.2.1.1.1 Precious Metal-Based Catalysts; 4.2.1.1.2 Non-Precious Metal Catalysts; 4.2.1.2 Catalytic Processes in DMFCs; 4.2.1.2.1 Mechanism of Methanol Electrooxidation; 4.2.1.2.2 Precious Metal-Based Catalysts; 4.2.1.2.3 Non-Precious Metal Catalysts for Methanol Electrooxidation; 4.2.2 Solid Oxide Fuel Cells; 4.2.2.1 Methane Steam Reforming 327 $a4.3 Catalytic Processes in Batteries 4.3.1 Metal/Air Batteries; 4.3.1.1 Aqueous Electrolyte Metal/Air Batteries; 4.3.1.2 Non-Aqueous Electrolyte Li-Air Batteries; 4.3.2 Li-Water Batteries; 4.4 Catalytic Processes in Hydrogen Storage Materials; 4.4.1 Catalysis in Metal Hydrides; 4.4.2 Catalysts in Metal Organic Frameworks; 4.5 Summary; Acknowledgments; References; 5 Hydrogen Storage Materials; 5.1 Introduction; 5.2 Essential Properties of Hydrogen in Metals; 5.2.1 Thermodynamics; 5.2.2 Kinetics of Hydrogen Absorption and Desorption; 5.3 Hydride; 5.3.1 Ionic Hydride; 5.3.2 Covalent Hydride 327 $a5.3.3 Metallic Hydride (Interstitial Hydride) 330 $aNew and Future Developments in Catalysis is a package of seven books that compile the latest ideas concerning alternate and renewable energy sources and the role that catalysis plays in converting new renewable feedstock into biofuels and biochemicals. Both homogeneous and heterogeneous catalysts and catalytic processes will be discussed in a unified and comprehensive approach. There will be extensive cross-referencing within all volumes. Batteries and fuel cells are considered to be environmentally friendly devices for storage and production of electricity, and they are gaining considerable 606 $aFuel cells 606 $aHydrogen$xStorage 606 $aCatalysis 608 $aElectronic books. 615 0$aFuel cells. 615 0$aHydrogen$xStorage. 615 0$aCatalysis. 676 $a621.3124 701 $aSuib$b Steven L$021705 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910459158203321 996 $aBatteries, hydrogen storage and fuel cells$91988393 997 $aUNINA LEADER 08490nam 22006975 450 001 9910512188503321 005 20251225181933.0 010 $a3-030-92238-3 024 7 $a10.1007/978-3-030-92238-2 035 $a(CKB)5100000000152613 035 $a(MiAaPQ)EBC6857383 035 $a(Au-PeEL)EBL6857383 035 $a(DE-He213)978-3-030-92238-2 035 $a(PPN)259384968 035 $a(EXLCZ)995100000000152613 100 $a20211204d2021 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNeural Information Processing $e28th International Conference, ICONIP 2021, Sanur, Bali, Indonesia, December 8?12, 2021, Proceedings, Part III /$fedited by Teddy Mantoro, Minho Lee, Media Anugerah Ayu, Kok Wai Wong, Achmad Nizar Hidayanto 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (724 pages) 225 1 $aTheoretical Computer Science and General Issues,$x2512-2029 ;$v13110 311 08$a3-030-92237-5 327 $aCognitive Neurosciences -- A Novel Binary BCI Systems Based on Non-oddball Auditory and Visual Paradigms -- A Just-In-Time Compilation Approach for Neural Dynamics Simulation -- STCN-GR: Spatial-Temporal Convolutional Networks for Surface-Electromyography-Based Gesture Recognition -- Gradient descent learning algorithm based on spike selection mechanism for multilayer spiking neural networks -- Learning to Coordinate via Multiple Graph Neural Networks -- A Reinforcement Learning Approach for Abductive Natural Language Generation -- DFFCN: Dual Flow Fusion Convolutional Network for Micro Expression Recognition -- AUPro: Multi-label Facial Action Unit Proposal Generation for Sequence-level Analysis -- Deep kernelized network for fine-grained recognition -- Semantic Perception Swarm Policy with Deep Reinforcement Learning -- Reliable, Robust, and Secure Machine Learning Algorithms Open-Set Recognition with Dual Probability Learning -- How Much Do Synthetic Datasets Matter In Handwritten Text Recognition -- PCMO: Partial Classification from CNN-Based Model Outputs -- Multi-branch Fusion Fully Convolutional Network for Person Re-Identification -- Fast Organization of Objects Spatial Positions in Manipulator Space from Single RGB-D Camera -- EvoBA: An Evolution Strategy as a Strong Baseline for Black-Box Adversarial Attacks -- A Novel Oversampling Technique for Imbalanced Learning Based on SMOTE and Genetic Algorithm -- Dy-Drl2Op: Learning Heuristics for TSP on the Dynamic Graph via Deep Reinforcement Learning -- Multi-label classification of hyperspectral images based on label-specific feature fusion -- A Novel Multi-Scale Key-Point Detector Using Residual Dense Block and Coordinate Attention -- Alleviating Catastrophic Interference in Online Learning via Varying Scale of Backward Queried Data -- Construction and Reasoning for Interval-Valued EBRB Systems -- Theory and Applications of Natural Computing Paradigms -- Brain-mimetic Kernel: A Kernel Constructed from Human fMRI Signals Enabling aBrain-mimetic Visual Recognition Algorithm -- Predominant Sense Acquisition with a Neural Random Walk Model -- Processing-response dependence on the on-chip readout positions in spin-wave reservoir computing -- Advances in deep and shallow machine learning algorithms for biomedical data and imaging -- A Multi-Task Learning Scheme for Motor Imagery Signal Classification -- An End-to-End Hemisphere Discrepancy Network for Subject-Independent Motor Imagery Classification -- Multi-domain Abdomen Image Alignment Based on Joint Network of Registration and Synthesis -- Coordinate Attention Residual Deformable U-Net for Vessel Segmentation -- Gated Channel Attention Network for Cataract Classification on AS-OCT Image -- Overcoming Data Scarcity for Coronary Vessel Segmentation Through Self-Supervised Pre-Training -- Self-Attention Long-Term Dependency Modelling in Electroencephalography Sleep Stage Prediction -- ReCal-Net: Joint Region-Channel-Wise Calibrated Network for Semantic Segmentation in Cataract Surgery Videos -- Enhancing Dermoscopic Features Classification in Images Using Invariant Dataset Augmentation and Convolutional Neural Networks -- Ensembles of Randomized Neural Networks for Pattern-based Time Series Forecasting -- Grouped Echo State Network with Late Fusion for Speech Emotion Recognition -- Applications -- MPANet: Multi-level Progressive Aggregation Network for Crowd Counting -- AFLLC: A Novel Active Contour Model based on Adaptive Fractional Order Differentiation and Local Linearly Constrained Bias Field -- DA-GCN: A Dependency-Aware Graph Convolutional Network for Emotion Recognition in Conversations -- Semi-Supervised Learning with Conditional GANs for Blind Generated Image Quality Assessment -- Uncertainty-Aware Domain Adaptation for Action Recognition -- Free-Form Image Inpainting with Separable Gate Encoder-decoder Network -- BERTDAN: Question-Answer Dual Attention Fusion Networks With Pre-trained Models for Answer Selection -- Rethinking the Effectiveness of Selective Attention in Neural Networks -- An Attention Method to Introduce Prior Knowledge in Dialogue State Tracking -- Effect of Input Noise Dimension in GANs -- Wiper Arm Recognition using YOLOv4 -- Context Aware Joint Modeling of Domain Classification, Intent Detection and Slot Filling with Zero-shot Intent Detection Approach -- Constrained Generative Model for EEG Signals Generation -- Top-Rank Learning Robust to Outliers -- Novel GAN Inversion Model with Latent Space Constraints for Face Reconstruction -- Edge Guided Attention Based Densely Connected Network for Single Image Super-Resolution -- An Agent-Based Market Simulator for Back-testing Deep Reinforcement Learning Based Trade Execution Strategies -- Looking beyond the haze: A Pyramid Fusion Approach -- DGCN-rs: a Dilated Graph Convolutional Networks Jointly Modelling Relation and Semantic for Multi-Event Forecasting -- Training Graph Convolutional Neural Network against Label Noise -- An LSTM-based Plagiarism Detection via Attention Mechanism anda Population-based Approach for Pre-Training Parameters with imbalanced Classes. 330 $aThe four-volume proceedings LNCS 13108, 13109, 13110, and 13111 constitutes the proceedings of the 28th International Conference on Neural Information Processing, ICONIP 2021, which was held during December 8-12, 2021. The conference was planned to take place in Bali, Indonesia but changed to an online format due to the COVID-19 pandemic. The total of 226 full papers presented in these proceedings was carefully reviewed and selected from 1093 submissions. The papers were organized in topical sections as follows: Part I: Theory and algorithms; Part II: Theory and algorithms; human centred computing; AI and cybersecurity; Part III: Cognitive neurosciences; reliable, robust, and secure machine learning algorithms; theory and applications of natural computing paradigms; advances in deep and shallow machine learning algorithms for biomedical data and imaging; applications; Part IV: Applications. 410 0$aTheoretical Computer Science and General Issues,$x2512-2029 ;$v13110 606 $aPattern recognition systems 606 $aMachine learning 606 $aEducation$xData processing 606 $aComputer engineering 606 $aComputer networks 606 $aSocial sciences$xData processing 606 $aAutomated Pattern Recognition 606 $aMachine Learning 606 $aComputers and Education 606 $aComputer Engineering and Networks 606 $aComputer Application in Social and Behavioral Sciences 615 0$aPattern recognition systems. 615 0$aMachine learning. 615 0$aEducation$xData processing. 615 0$aComputer engineering. 615 0$aComputer networks. 615 0$aSocial sciences$xData processing. 615 14$aAutomated Pattern Recognition. 615 24$aMachine Learning. 615 24$aComputers and Education. 615 24$aComputer Engineering and Networks. 615 24$aComputer Application in Social and Behavioral Sciences. 676 $a006.32 702 $aMantoro$b Teddy 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910512188503321 996 $aNeural Information Processing$92554499 997 $aUNINA