Results 201 to 210 of about 4,510,623 (383)

Accounting for peak shifting in traditional cost-benefit analysis [PDF]

open access: yes
When cost-benefit analysis fails to account for peak-shifting the benefits of road improvement options are miscalculated. Using theory from transportation economics, we derive a simple model that disaggregates the average daily equilibrium into peak ...
Axelsen, Dan, Snarr, Hal W.
core   +1 more source

Powder Metallurgy and Additive Manufacturing of High‐Nitrogen Alloyed FeCr(Si)N Stainless Steel

open access: yesAdvanced Engineering Materials, EarlyView.
The alloying element Nitrogen enhances stainless steel strength, corrosion resistance, and stabilizes austenite. This study develops austenitic FeCr(Si)N steel production via powder metallurgy. Fe20Cr and Si3N4 are hot isostatically pressed, creating an austenitic microstructure.
Louis Becker   +5 more
wiley   +1 more source

Cost-benefit analysis of the National Immunization Program in Spain. [PDF]

open access: yesHum Vaccin Immunother
Pérez A   +12 more
europepmc   +1 more source

Nanoparticle‐Coated X2CrNiMo17‐12‐2 Powder for Additive Manufacturing – Part I: Surface, Flowability, and Optical Properties of SiC, Si, and Si3N4 Coated Metal Powders

open access: yesAdvanced Engineering Materials, EarlyView.
Herein, silicon‐based nanoparticle coatings on X2CrNiMo17‐12‐2 metal powder are presented. The coating process scale, process parameters, nanoparticle size (65–200 nm) as well as the coating amount are discussed regarding powder properties. The surface roughness affects the flowability, while reflectance depends on the coating material and surface ...
Arne Lüddecke   +4 more
wiley   +1 more source

A Cost-Benefit Analysis Simulation for the Digitalisation of Cold Supply Chains. [PDF]

open access: yesSensors (Basel), 2023
Schiffmann O   +4 more
europepmc   +1 more source

Beyond Order: Perspectives on Leveraging Machine Learning for Disordered Materials

open access: yesAdvanced Engineering Materials, EarlyView.
This article explores how machine learning (ML) revolutionizes the study and design of disordered materials by uncovering hidden patterns, predicting properties, and optimizing multiscale structures. It highlights key advancements, including generative models, graph neural networks, and hybrid ML‐physics methods, addressing challenges like data ...
Hamidreza Yazdani Sarvestani   +4 more
wiley   +1 more source

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