Results 141 to 150 of about 543,386 (314)

Price Premiums for Single‐Name and Compound‐Name Geographical Indications in Swiss Cheese Trade

open access: yesAgribusiness, EarlyView.
ABSTRACT Geographical indications (GIs) have become increasingly important in agri‐food markets, especially in Europe. For Swiss cheese imports and exports, we analyze whether GIs are associated with higher trade prices. We find that price premiums can be obtained for both exports and imports. However, this is only the case for cheeses with single name
Judith Irek
wiley   +1 more source

On Relative Stability for Strongly Mixing Sequences

open access: yesFoundations
We consider a class of strongly mixing sequences with infinite second moment. This class contains important GARCH processes that are applied in econometrics. We show the relative stability for such processes and construct a counterexample. We apply these
Adam Jakubowski   +1 more
doaj   +1 more source

Pricing Dynamics in the US Hemp Market: A Vertical Price Transmission Analysis of the Hemp Value Chain

open access: yesAgribusiness, EarlyView.
ABSTRACT The US hemp market is a new and nascent industry that has been devoid of research for about half a century. This study examined the effects of exogenous shock on price at each phase of the value chain—Farm (hemp biomass), and its impact on prices at other phases of the value chain—Intermediary Processor (crude cannabidiol hemp) and Final ...
Solomon Odiase   +2 more
wiley   +1 more source

3D investigation and modeling of the geometric effects on porosity in packed beds

open access: yesAIChE Journal, EarlyView.
Abstract In porous beds, physical boundaries restrict particle arrangement, leading to inhomogeneous porosity. This paper reports on the porosity profiles that are the result of geometric effects on monodisperse packed beds in cylindrical and cubic arrangements. Special focus is given to the influence of edges and corners in cubic geometries.
Bastian Oldach   +3 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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