04625nam 2200709 a 450 991096699910332120230801222010.097866136291979781444361049144436104X9781280599354128059935997814443610321444361031(CKB)2670000000160759(EBL)871517(OCoLC)780445278(SSID)ssj0000622581(PQKBManifestationID)12215381(PQKBTitleCode)TC0000622581(PQKBWorkID)10642796(PQKB)11403306(MiAaPQ)EBC871517(Au-PeEL)EBL871517(CaPaEBR)ebr10540928(CaONFJC)MIL362919(Perlego)1002429(EXLCZ)99267000000016075920110825d2012 uy 0engur|n|---|||||txtccrArchitectural technology /Stephen Emmitt2nd ed.Hoboken, N.J. Wiley-Blackwell20121 online resource (265 p.)Description based upon print version of record.9781405194792 1405194790 Includes bibliographical references and index.Architectural Technology; Contents; Foreword; Introduction; 1 Fundamentals; Sensory engagement; Building innovation; Building characteristics; Enclosure and functional requirements; Philosophies and approaches; Further reading; 2 Physical Design Generators; The physical context: a sense of place; Micro climates and weathering; Structure and fabric; Materials; Services; Further reading; 3 Social Design Generators; The social context; Communication and language; Design decisions; Risk; Quality; Added value; Further reading; 4 Regulatory Design Generators; Town planning and development controlThe building regulationsStandards and codes of practice; Trade associations; Testing and research reports; Further reading; 5 Humane Design Generators; Perception of our buildings; Physiology and usability; Healthy environments; Safe environments; Secure environments; Fire safety; Further reading; 6 Physical Interfaces; Typologies; Transitions; Joints and connections; Tolerances; Further reading; 7 The Art of Detailing; Detailing principles; Environmental issues; Performance of the joint; Designing the details; Further reading; 8 The Art of Specifying; Specification methodsSelection criteria - fitness for purposeWriting the specification; Contents of a written specification; Further reading; 9 The Art of Informing; Media; Coordinated project information; Drawings; Physical models; Bills of quantities; Digital information and virtual details; Information flow and design changes; Further reading; 10 Assembling the Parts; The designer-contractor interface; Flows; Quality of work; Design changes; Practical completion and hand-over; Learning from building projects; Further reading; 11 Living with Buildings; Durability and decayPreservation, restoration, and conservationPrinciples of conservation, repair and maintenance; Upgrading existing buildings; Learning from buildings; Further reading; 12 Disassembly and Reuse; Reusing redundant buildings; Demolition and disassembly; Reclamation, reuse, and recycling; Stretching the tradition; Further reading; References; IndexSince the publication of the first edition of Architectural Technology, in 2002, there have been significant developments in the number of courses, the profile of the discipline as well as significant changes in the Construction sector. The Second edition of Architectural Technology addresses these challenges directly. Much greater emphasis is given to the three core themes of the book - Environmental Sustainability; Innovation; and Design. An increase in the visual material included reinforces the critical role of Design, aiding students to better translate conceptual designsArchitectureTechnological innovationsArchitecture and technologyArchitectural designTechniqueArchitectureTechnological innovations.Architecture and technology.Architectural designTechnique.720.1/05Emmitt Stephen856066MiAaPQMiAaPQMiAaPQBOOK9910966999103321Architectural technology3948134UNINA07254nam 22006495 450 99666847150331620250721130836.0981-9698-75-810.1007/978-981-96-9875-2(CKB)39698424000041(MiAaPQ)EBC32227343(Au-PeEL)EBL32227343(DE-He213)978-981-96-9875-2(EXLCZ)993969842400004120250721d2025 u| 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierAdvanced Intelligent Computing Technology and Applications 21st International Conference, ICIC 2025, Ningbo, China, July 26–29, 2025, Proceedings, Part VII /edited by De-Shuang Huang, Yijie Pan, Wei Chen, Bo Li1st ed. 2025.Singapore :Springer Nature Singapore :Imprint: Springer,2025.1 online resource (903 pages)Lecture Notes in Computer Science,1611-3349 ;15848981-9698-74-X -- Intelligent Data Analysis amp Prediction. -- MSFformer: Multi-Scale Spatiotemporal Fusion Transformer with Preserving Non Stationary Information for Time Series Forecasting. -- Quantifying the Impact of Coaching Effectiveness Based on the TEL Model. -- SSF-GCN: Sensor-Spatial Fusion Graph Network for RUL Prediction. -- AW-SARIMA: Efficient Hybrid Framework for Nonstationary Time Series Forecasting via DWT and Adaptive Thresholding. -- Research on Intelligent Evaluation Model Based on Large Models. -- Learner Empowered Knowledge Tracing Model. -- A Few-Shot Intrusion Detection Method Combining Model-Agnostic Meta-Learning And Siamese Neural Network. -- How to Use Social Media for Bitcoin Price Prediction: A Multi-Source Data Fusion Method Based on CSTNet. -- CMulti-Feature Fusion Method for Programming Exercise Difficulty Assessment Using CodeBERT. -- Scene Graph-based Semantic Enhancement for Multimodal Fake News Detection. -- IDMixer: Decomposition Spatial-Temporal Identity for Traffic Flow Forecasting. -- A Self-Attentive Temporal-Spatial Anomaly Detection Method for IoT Time Series. -- Adaptive Cross-Variable Spectral Filtering for Time Series Forecasting . -- Hybrid Car-Following Model with LSTM and RF in Driver Behavior. -- A Mixture-of-Experts Framework with Fake Review Detection for Robust Recommendation Systems. -- Multi-scale Dual-path Transformer Network for Multivariate Time Series Forecasting. -- BFKT: Enhancing Knowledge Tracing based on Forgetting Mechanisms and Multi Feature Fusion. -- Macro-Micro Feature Aware Transformer for Dissolved Oxygen Prediction. -- MTDTSN: Multi-Scale Spatiotemporal Networks with Exogenous Factors for Bus Passenger Flow Prediction. -- AMSformer: Adaptive Transformer with Convolutional Multi-scale Feature Interaction for Time Series Forecasting. -- A Time-Enhanced Data Disentanglement Network for Traffic Flow Forecasting. -- CycleKAN: Integrating Kolmogorov-Arnold Networks with Explicit Cycle Modeling for Efficient Urban Traffic Forecasting. -- Capability-Aware Knowledge Tracing for Learner’s Knowledge Mastery Modeling. -- AGTCN: An Adaptive Gating Approach in Spatiotemporal Convolutional Networks for Accurate Air Quality Prediction. -- TrafusionNet: Efficient Multi-Agent Trajectory Prediction with Temporal-Social Perception Mamba and Diffusion Model. -- FedVCP: Efficient Crowd Flow Prediction Employing Multi-Source External Factors in Vertical Federated Learning. -- Shareformer: A Patch Transformer Model with Shared Attention for Multivariate Time Series Forecasting. -- A Novel Random Sample Partition-Based Ensemble Algorithm for Credit Card Fraud Detection. -- StarHAR: A Lightweight and Low-latency Framework for Sensor-based Human Activity Recognition. -- Trend Prediction First, Personality Refinement After. KanPaTST: A KAN Fine tuned Patch Time Series Transformer for Public Opinion Popularity Forecasting. -- CEDMix: Contrast-Enhanced Dynamic Channel Mixing for Correlated Time Series Forecasting. -- NuPreX: Times Series Forecasting for The Nuclear Steam Supply System. -- Frequency-Aware Robust Multimodal Fake News Detection. -- Sample Spatial Distribution and Diffusion Time Deviation Based Anomaly Detection Method. -- Building Extraction from Remote Sensing Images Based on Dual-Stream Feature Fusion Network. -- Multi-Hop Aware Graph Convolutional Network and Collaborative Transformer for Traffic Flow Prediction. -- Multi-step Traffic Flow Prediction Based on Reinforced Attention Graph Convolutional Network. -- CDFNet: Collaborative Decomposition and Forecasting Network for Time Series. -- S³E-Net: Spatio-Temporal Selective State Expert Network for Traffic Flow Prediction. -- ECDformer: Enhanced Channel-Dependent Transformer for Multivariate Time Series Forecasting. -- Rule Generation for Anomalous Behaviors Detection in Enterprises: A Few-Shot Learning Approach via Chain-of-Thoughts. -- TVCorNet: Time-Variable Correlation Learning Enhancement Network for Multivariate Time Series Forecasting. -- BiMa-Former: A Dual-Token Hybrid Model with Bidirectional Mamba and Transformer for Temporal- Multivariate Decoupled Forecasting.This 20-volume set LNCS 15842-15861 constitutes - in conjunction with the 4-volume set LNAI 15862-15865 and the 4-volume set LNBI 15866-15869 - the refereed proceedings of the 21st International Conference on Intelligent Computing, ICIC 2025, held in Ningbo, China, during July 26-29, 2025. The total of 1206 regular papers were carefully reviewed and selected from 4032 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".Lecture Notes in Computer Science,1611-3349 ;15848Computational intelligenceComputer networksMachine learningApplication softwareComputational IntelligenceComputer Communication NetworksMachine LearningComputer and Information Systems ApplicationsComputational intelligence.Computer networks.Machine learning.Application software.Computational Intelligence.Computer Communication Networks.Machine Learning.Computer and Information Systems Applications.006.3Huang De-Shuang1732604Pan Yijie1758608Chen Wei636150Li Bo645181MiAaPQMiAaPQMiAaPQBOOK996668471503316Advanced Intelligent Computing Technology and Applications4408042UNISA