Shade-Grown Coffee: Simulation and Policy Analysis for Coastal Oaxaca, Mexico

Shade-grown coffee provides a livelihood to many farmers, protects biodiversity, and creates environmental services. Many shade-coffee farmers have abandoned production in recent years, however, in response to declines in international coffee prices. This paper builds a farmer decision model under price uncertainty and uses simulation analysis of that model to examine the likely impact of various policies on abandonment of shade-coffee plantations. Using information from coastal Oaxaca, Mexico, this paper examines the role of various constraints in abandonment decisions, reveals the importance of the timing of policies, and characterizes the current situation in the study region.coffee farming, decision analysis, numerical modeling, Monte Carlo, price variability

Analysis of running unlubricated friction pairings under permanent slip with an emphasis on advanced ceramic-steel pairings

Running clutches under permanent slip offers multiple applications regarding vibration damping or torque distribution in powertrains, for instance. Advanced engineering ceramics show specific benefits in wear behaviour and thermal resistance and are therefore representing an interesting chance for running unlubricated clutches under permanent slip conditions, as well. The emphasis of this analysis is the characterization of the tribological behaviour of the non-oxide ceramic/steel friction pairing SSiC/C45E regarding friction coefficient and wear. As to influencing factors, the sliding speed and contact pressure between the friction surfaces, as well as the specific energy dissipation are varied and analysed. The analysis results of running advanced engineering ceramics under permanent slip are very promising concerning system based friction coefficient level and stability as well as wear behaviour

Revisiting the COUNTER Algorithms for List Update

COUNTER algorithms, a family of randomized algorithms for the list update problem, were introduced by Reingold, Westbrook and Sleator [7]. They showed that for any>0, there exist COUNTER algorithms that achieve a competitive ratio of p 3+. In this paper we use a mixture of two COUNTER algorithms to achieve a competitiveness of 12=7, which is less than p 3. Furthermore, we demonstrate that it is impossible to prove a competitive ratio smaller than 12=7 for any mixture of COUNTER algorithms using the typeofpotential function argument that has been used so far. We also provide new lower bounds for the competitiveness of COUNTER algorithms in the standard cost model, including a 1.625 lower bound for the variant BIT and a matching 12/7 lower bound for our algorithm.

Identifying used methods and datasets in scientific publications

Although it has become common to assess publications and researchers by means of their citation count (e.g., using the h-index), measuring the impact of scientific methods and datasets (e.g., using an h-index for datasets) has been performed only to a limited extent. This is not surprising because the usage information of methods and datasets is typically not explicitly provided by the authors, but hidden in a publication’s text. In this paper, we propose an approach to identifying methods and datasets in texts that have actually been used by the authors. Our approach first recognizes datasets and methods in the text by means of a domain-specific named entity recognition method with minimal human interaction. It then classifies these mentions into used vs. non-used based on the textual contexts. The obtained labels are aggregated on the document level and integrated into the Microsoft Academic Knowledge Graph modeling publications’ metadata. In experiments based on the Microsoft Academic Graph, we show that both method and dataset mentions can be identified and correctly classified with respect to their usage to a high degree. Overall, our approach facilitates method and dataset recommendation, enhanced paper recommendation, and scientific impact quantification. It can be extended in such a way that it can identify mentions of any entity type (e.g., task)

The Funder\u27s Point of View

This panel from the World Data System's 2023 Repository Sustainability Summit featured Ishwar Chandramouliswaran (NIH), Dr. Cerese Albers (NASA), Dr. Martin Halbert (NSF), Dr. Michael Nelson (Carnegie Endowment for International Peace), and Dr. Michael Cooke (DOE), and was moderated by Dr. David Castle (University of Victoria)