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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

Technology: general issues --- plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti–Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure–property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- n/a --- Ti-Mn alloys --- structure-property relationship

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Third millennium engineering address new challenges in materials sciences and engineering. In particular, the advances in materials engineering combined with the advances in data acquisition, processing and mining as well as artificial intelligence allow for new ways of thinking in designing new materials and products. Additionally, this gives rise to new paradigms in bridging raw material data and processing to the induced properties and performance. This present topical issue is a compilation of contributions on novel ideas and concepts, addressing several key challenges using data and artificial intelligence, such as:- proposing new techniques for data generation and data mining;- proposing new techniques for visualizing, classifying, modeling, extracting knowledge, explaining and certifying data and data-driven models;- processing data to create data-driven models from scratch when other models are absent, too complex or too poor for making valuable predictions;- processing data to enhance existing physic-based models to improve the quality of the prediction capabilities and, at the same time, to enable data to be smarter; and- processing data to create data-driven enrichment of existing models when physics-based models exhibit limits within a hybrid paradigm.

Technology: general issues --- plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti-Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure-property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks --- plasticity --- machine learning --- constitutive modeling --- manifold learning --- topological data analysis --- GENERIC --- soft living tissues --- hyperelasticity --- computational modeling --- data-driven mechanics --- TDA --- Code2Vect --- nonlinear regression --- effective properties --- microstructures --- model calibration --- sensitivity analysis --- elasto-visco-plasticity --- Gaussian process --- high-throughput experimentation --- additive manufacturing --- Ti-Mn alloys --- spherical indentation --- statistical analysis --- Gaussian process regression --- nanoporous metals --- open-pore foams --- FE-beam model --- data mining --- mechanical properties --- hardness --- principal component analysis --- structure-property relationship --- microcompression --- nanoindentation --- analytical model --- finite element model --- artificial neural networks --- model correction --- feature engineering --- physics based --- data driven --- laser shock peening --- residual stresses --- data-driven --- multiscale --- nonlinear --- stochastics --- neural networks

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Research on intelligent agents and multi-agent systems has matured during the past decade, and many effective applications of this technology are currently being deployed. Although computational approaches for multi-agent systems have mainly emerged in the past few decades, scholars have been prolific with regard to the variety of methods proposed to solve this paradigm. Different communities have emerged with multi-agent systems as their main research topic. Multi-agent systems allow the development of distributed and intelligent applications in complex and dynamic environments. Systems of this kind play a crucial role in life, evidenced by the broad range of applied areas involved in their use, including manufacturing, management sciences, e-commerce, and biotechnology. There are many reasons for the interest of researchers in this new discipline. Firstly, computational systems have gradually shifted towards a distributed paradigm where heterogeneous entities with different goals can enter and leave the system dynamically and interact with each other. Secondly, new computational systems should be able to negotiate with one another, typically on the behalf of humans, in order to come to mutually acceptable agreements. As a consequence, autonomy, interaction, mobility, and openness are key concepts studied in the area. The purpose of this book is to document some of the advances made in this paradigm and attempt to show the current state of this technology by analyzing different aspects in addition its possible application in various domains. This review of the current state-of-the-art does not intend to make an exhaustive exploration of all the current existing works but, rather, to try to give an overview of the research in agent technology, showing the high level of activity of this area.

History of engineering & technology --- multi-robot --- consensus problem --- formation control --- noise --- time delay --- unmanned surface vehicles --- multi-agent system --- training system --- genetic-based fuzzy rule learning --- intelligent autonomous control --- modeling and simulation --- multi-agent systems --- smart city development --- spatiotemporal modeling --- actor–network theory --- geoparticipation --- social interactions --- simulation model --- photovoltaic energy --- parameter fine-tuning --- self-reported behaviour --- predictive model --- multi-agent planning and scheduling --- potential game --- equilibrium selection --- interoperability --- multiagent systems --- organizational models --- agent-based collective intelligence --- multi-agent complex systems --- scale-free properties --- power law distribution --- biologically inspired approaches and methods --- collective foraging --- physics-based simulation --- methodologies for agent-based systems --- multi-robot simulation --- discrete event simulator --- agent and multi-agent applications --- classification --- prediction --- multi-agent --- wisdom-of-crowds --- Hollywood --- feature-extension --- collective-intelligence --- swarm --- educational games --- game design --- situated psychological agents --- education --- competences --- decision support system --- agent based modeling and simulation --- production scheduling --- green coffee supply chain --- agent-based modeling --- agent-based simulation --- decision support

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Research on intelligent agents and multi-agent systems has matured during the past decade, and many effective applications of this technology are currently being deployed. Although computational approaches for multi-agent systems have mainly emerged in the past few decades, scholars have been prolific with regard to the variety of methods proposed to solve this paradigm. Different communities have emerged with multi-agent systems as their main research topic. Multi-agent systems allow the development of distributed and intelligent applications in complex and dynamic environments. Systems of this kind play a crucial role in life, evidenced by the broad range of applied areas involved in their use, including manufacturing, management sciences, e-commerce, and biotechnology. There are many reasons for the interest of researchers in this new discipline. Firstly, computational systems have gradually shifted towards a distributed paradigm where heterogeneous entities with different goals can enter and leave the system dynamically and interact with each other. Secondly, new computational systems should be able to negotiate with one another, typically on the behalf of humans, in order to come to mutually acceptable agreements. As a consequence, autonomy, interaction, mobility, and openness are key concepts studied in the area. The purpose of this book is to document some of the advances made in this paradigm and attempt to show the current state of this technology by analyzing different aspects in addition its possible application in various domains. This review of the current state-of-the-art does not intend to make an exhaustive exploration of all the current existing works but, rather, to try to give an overview of the research in agent technology, showing the high level of activity of this area.

History of engineering & technology --- multi-robot --- consensus problem --- formation control --- noise --- time delay --- unmanned surface vehicles --- multi-agent system --- training system --- genetic-based fuzzy rule learning --- intelligent autonomous control --- modeling and simulation --- multi-agent systems --- smart city development --- spatiotemporal modeling --- actor–network theory --- geoparticipation --- social interactions --- simulation model --- photovoltaic energy --- parameter fine-tuning --- self-reported behaviour --- predictive model --- multi-agent planning and scheduling --- potential game --- equilibrium selection --- interoperability --- multiagent systems --- organizational models --- agent-based collective intelligence --- multi-agent complex systems --- scale-free properties --- power law distribution --- biologically inspired approaches and methods --- collective foraging --- physics-based simulation --- methodologies for agent-based systems --- multi-robot simulation --- discrete event simulator --- agent and multi-agent applications --- classification --- prediction --- multi-agent --- wisdom-of-crowds --- Hollywood --- feature-extension --- collective-intelligence --- swarm --- educational games --- game design --- situated psychological agents --- education --- competences --- decision support system --- agent based modeling and simulation --- production scheduling --- green coffee supply chain --- agent-based modeling --- agent-based simulation --- decision support --- multi-robot --- consensus problem --- formation control --- noise --- time delay --- unmanned surface vehicles --- multi-agent system --- training system --- genetic-based fuzzy rule learning --- intelligent autonomous control --- modeling and simulation --- multi-agent systems --- smart city development --- spatiotemporal modeling --- actor–network theory --- geoparticipation --- social interactions --- simulation model --- photovoltaic energy --- parameter fine-tuning --- self-reported behaviour --- predictive model --- multi-agent planning and scheduling --- potential game --- equilibrium selection --- interoperability --- multiagent systems --- organizational models --- agent-based collective intelligence --- multi-agent complex systems --- scale-free properties --- power law distribution --- biologically inspired approaches and methods --- collective foraging --- physics-based simulation --- methodologies for agent-based systems --- multi-robot simulation --- discrete event simulator --- agent and multi-agent applications --- classification --- prediction --- multi-agent --- wisdom-of-crowds --- Hollywood --- feature-extension --- collective-intelligence --- swarm --- educational games --- game design --- situated psychological agents --- education --- competences --- decision support system --- agent based modeling and simulation --- production scheduling --- green coffee supply chain --- agent-based modeling --- agent-based simulation --- decision support

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This Special Issue, consisting of 14 papers, presents the latest findings concerning both numerical and experimental investigations. Their aim is to achieve a reduction in pollutant emissions, as well as an improvement in fuel economy and performance, for internal combustion engines. This will provide readers with a comprehensive, unbiased, and scientifically sound overview of the most recent research and technological developments in this field. More specific topics include: 3D CFD detailed analysis of the fuel injection, combustion and exhaust aftertreatments processes, 1D and 0D, semi-empirical, neural network-based control-oriented models, experimental analysis and the optimization of both conventional and innovative combustion processes.

History of engineering & technology --- homogeneous charge compression ignition (HCCI) --- exhaust gas recirculation (EGR) --- dual-fuel --- dimethyl ether (DME) --- exhaust emission --- co-combustion --- dual fuel --- combustion stability --- coefficient of variation of IMEP --- probability density of IMEP --- 0D model --- predictive model --- tumble --- turbulent intensity --- spark-ignition engine --- engine geometry --- AdBlue® injection --- large eddy simulation --- Eulerian–Lagrangian approach --- thermal decomposition --- wall–film formation --- conversion efficiency --- hybrid electric vehicle --- real driving emissions --- fuel consumption --- vehicle performance --- electric supercharger --- Lambda-1 engine --- 48 V Mild Hybrid --- electrically assisted turbocharger --- variable geometry turbocharger-exhaust gas recirculation --- oxygen concentration --- active disturbance rejection control --- model-based --- control --- diesel engine --- ANN --- physics-based model --- semi-empirical model --- CNG --- diesel fuel --- dual fuel engine --- rate of heat release --- ignition delay --- burn duration --- exhaust gas emission --- camless --- electromagnetic variable valve train --- magnetorheological buffer --- soft landing --- solenoid injectors --- indirect-acting piezoelectric injectors --- direct-acting piezoelectric injectors --- engine-out emissions --- combustion noise --- diesel engines --- pollutant emission reduction --- mixing process --- advanced injection strategy --- varying injection rate --- engine torque estimation --- GDI engines --- extended state observer --- online performance --- torque --- nitrogen oxide emissions --- model-based control --- engines --- numerical simulation --- pollutant emissions prediction --- computational fluid dynamics