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This is a book on the practical approaches of reliability to electrotechnical devices and systems. It includes the electromagnetic effect, radiation effect, environmental effect, and the impact of the manufacturing process on electronic materials, devices, and boards.

Technology: general issues --- History of engineering & technology --- 3D-IC (three-dimensional integrated circuit) --- electromagnetic interference --- near field measurement --- SAC305 --- BGA --- low temperature --- fracture failure --- factorial design of experiment --- genetic algorithm optimization --- return loss --- multiple-input multiple-output (MIMO) --- single event effects --- linear energy transfer --- Monte Carlo simulation --- radiation hardness --- pressureless sintered micron silver joints --- deep space environment --- extreme thermal shocks --- reconstruction --- simulation --- elastic mechanical properties --- state of health --- remaining useful life --- electrochemistry based electrical model --- semi-empirical capacity fading model --- useful life distribution --- quality and reliability assurance --- single event effect --- microdosimetry --- lineal energy --- deconvolution --- gamma process --- lifetime --- measurement system analysis --- reliability estimation --- GaN --- operational amplifier --- proton therapy --- prompt gamma imaging --- 3D X-ray --- bias temperature-humidity reliability test --- conductive anodic filament (CAF) --- de-penalization --- finite element analysis --- 3D-IC (three-dimensional integrated circuit) --- electromagnetic interference --- near field measurement --- SAC305 --- BGA --- low temperature --- fracture failure --- factorial design of experiment --- genetic algorithm optimization --- return loss --- multiple-input multiple-output (MIMO) --- single event effects --- linear energy transfer --- Monte Carlo simulation --- radiation hardness --- pressureless sintered micron silver joints --- deep space environment --- extreme thermal shocks --- reconstruction --- simulation --- elastic mechanical properties --- state of health --- remaining useful life --- electrochemistry based electrical model --- semi-empirical capacity fading model --- useful life distribution --- quality and reliability assurance --- single event effect --- microdosimetry --- lineal energy --- deconvolution --- gamma process --- lifetime --- measurement system analysis --- reliability estimation --- GaN --- operational amplifier --- proton therapy --- prompt gamma imaging --- 3D X-ray --- bias temperature-humidity reliability test --- conductive anodic filament (CAF) --- de-penalization --- finite element analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This is a book on the practical approaches of reliability to electrotechnical devices and systems. It includes the electromagnetic effect, radiation effect, environmental effect, and the impact of the manufacturing process on electronic materials, devices, and boards.

3D-IC (three-dimensional integrated circuit) --- electromagnetic interference --- near field measurement --- SAC305 --- BGA --- low temperature --- fracture failure --- factorial design of experiment --- genetic algorithm optimization --- return loss --- multiple-input multiple-output (MIMO) --- single event effects --- linear energy transfer --- Monte Carlo simulation --- radiation hardness --- pressureless sintered micron silver joints --- deep space environment --- extreme thermal shocks --- reconstruction --- simulation --- elastic mechanical properties --- state of health --- remaining useful life --- electrochemistry based electrical model --- semi-empirical capacity fading model --- useful life distribution --- quality and reliability assurance --- single event effect --- microdosimetry --- lineal energy --- deconvolution --- gamma process --- lifetime --- measurement system analysis --- reliability estimation --- GaN --- operational amplifier --- proton therapy --- prompt gamma imaging --- 3D X-ray --- bias temperature-humidity reliability test --- conductive anodic filament (CAF) --- de-penalization --- finite element analysis --- n/a

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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- n/a

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• Reference Manager
• EndNote
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Tribology has been and continues to be one of the most relevant fields, being present in almost all aspects of our lives. The understanding of tribology provides us with solutions for future technical challenges. At the root of all advances made so far are multitudes of precise experiments and an increasing number of advanced computer simulations across different scales and multiple physical disciplines. Based upon this sound and data-rich foundation, advanced data handling, analysis and learning methods can be developed and employed to expand existing knowledge. Therefore, modern machine learning (ML) or artificial intelligence (AI) methods provide opportunities to explore the complex processes in tribological systems and to classify or quantify their behavior in an efficient or even real-time way. Thus, their potential also goes beyond purely academic aspects into actual industrial applications. To help pave the way, this article collection aimed to present the latest research on ML or AI approaches for solving tribology-related issues generating true added value beyond just buzzwords. In this sense, this Special Issue can support researchers in identifying initial selections and best practice solutions for ML in tribology.

Technology: general issues --- History of engineering & technology --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound --- artificial intelligence --- machine learning --- artificial neural networks --- tribology --- condition monitoring --- semi-supervised learning --- random forest classifier --- self-lubricating journal bearings --- reduced order modelling --- dynamic friction --- rubber seal applications --- tensor decomposition --- laser surface texturing --- texturing during moulding --- digital twin --- PINN --- reynolds equation --- triboinformatics --- databases --- data mining --- meta-modeling --- monitoring --- analysis --- prediction --- optimization --- fault data generation --- Convolutional Neural Network (CNN) --- Generative Adversarial Network (GAN) --- bearing fault diagnosis --- unbalanced datasets --- tribo-testing --- tribo-informatics --- natural language processing --- tribAIn --- BERT --- amorphous carbon coatings --- UHWMPE --- total knee replacement --- Gaussian processes --- rolling bearing dynamics --- cage instability --- regression --- neural networks --- random forest --- gradient boosting --- evolutionary algorithms --- rolling bearings --- remaining useful life --- feature engineering --- structure-borne sound

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An eminent philosopher explains why we owe it to future generations to take immediate action on global warmingClimate change is the supreme challenge of our time. Yet despite growing international recognition of the unfolding catastrophe, global carbon emissions continue to rise, hitting an all-time high in 2019. Unless humanity rapidly transitions to renewable energy, it may be too late to stop irreversible ecological damage. In The Pivotal Generation, renowned political philosopher Henry Shue makes an impassioned case for taking immediate, radical action to combat global warming.Shue grounds his argument in a rigorous philosophical analysis of climate change’s moral implications. Unlike previous generations, which didn’t fully understand the danger of burning carbon, we have the knowledge to comprehend and control rising carbon dioxide levels. And unlike future generations, we still have time to mitigate the worst effects of global warming. This generation has the power, and thus the responsibility, to save the planet. Shirking that responsibility only leaves the next generation with an even heavier burden—one they may find impossible to bear.Written in direct, accessible language, The Pivotal Generation approaches the latest scientific research with a singular moral clarity. It’s an urgently needed call to action for anyone concerned about the planet’s future.

Climate change mitigation --- Environmental policy - United States --- Environmental ethics --- Climatic changes - Forecasting --- Climate change mitigation. --- Climate mitigation --- Climatic changes --- Climatic mitigation --- Mitigation of climate change --- Environmental protection --- Mitigation --- Acid rain. --- Alternative energy. --- American Recovery and Reinvestment Act of 2009. --- Analogy. --- Arctic National Wildlife Refuge. --- BNP Paribas. --- Bank. --- Behalf. --- Biofuel. --- Bribery. --- Business plan. --- Carbon Energy. --- Carbon capture and storage. --- Chesapeake Energy. --- China Construction Bank. --- Climate change. --- Climate risk. --- Climate. --- Combustion. --- Competitiveness. --- Contempt. --- Core business. --- Criticism. --- Customer. --- Deep sea. --- Deforestation. --- Dodd–Frank Wall Street Reform and Consumer Protection Act. --- Economic cost. --- Economy and Society. --- Electricity generation. --- Energy development. --- Environmental impact of the coal industry. --- Ethane (data page). --- Every Nation. --- Externality. --- Filing (legal). --- Filing (metalworking). --- Financial Regulator. --- Fossil fuel. --- Frustration. --- Future generation. --- Geological formation. --- Global warming. --- Government. --- Gradualism. --- Grandparent. --- Greenhouse gas. --- Greenland ice sheet. --- Heat flux. --- Hedge fund. --- High-voltage direct current. --- Incentive. --- Infrastructure. --- Intermittency. --- International Energy Agency. --- Low-carbon economy. --- Market mechanism. --- Melting. --- Methane. --- Misinformation. --- National wealth. --- Natural gas. --- Norm (social). --- Occidental Petroleum. --- Occupational safety and health. --- Oil well. --- Ownership (psychology). --- Payment. --- Petroleum industry. --- Pipeline transport. --- Plastic pollution. --- Plastic. --- Political corruption. --- Pollution. --- Positive feedback. --- Requirement. --- Responsiveness. --- Saudi Arabia. --- Saving. --- Scale In. --- Scientist. --- Sea level rise. --- Sea level. --- Shorthand. --- Social disruption. --- Sociotechnical system. --- Soil. --- Sovereign state. --- Standard of living. --- Suggestion. --- Technology. --- Too big to fail. --- Useful Life. --- Vegetation. --- Water supply. --- Wealth. --- Window function. --- World economy. --- Year. --- ethylene. --- Environmental policy --- Environmental ethics. --- Forecasting.