scholarly journals Predicting Shifts in Land Suitability for Maize Cultivation Worldwide Due to Climate Change: A Modeling Approach

Land ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 295
Author(s):  
Yuan Gao ◽  
Anyu Zhang ◽  
Yaojie Yue ◽  
Jing’ai Wang ◽  
Peng Su
Keyword(s):  
Climate Change ◽  
Crop Production ◽  
Large Scale ◽  
Land Suitability ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
Maize Cultivation ◽  
Large Scale Screening ◽  
Selection Of

Suitable land is an important prerequisite for crop cultivation and, given the prospect of climate change, it is essential to assess such suitability to minimize crop production risks and to ensure food security. Although a variety of methods to assess the suitability are available, a comprehensive, objective, and large-scale screening of environmental variables that influence the results—and therefore their accuracy—of these methods has rarely been explored. An approach to the selection of such variables is proposed and the criteria established for large-scale assessment of land, based on big data, for its suitability to maize (Zea mays L.) cultivation as a case study. The predicted suitability matched the past distribution of maize with an overall accuracy of 79% and a Kappa coefficient of 0.72. The land suitability for maize is likely to decrease markedly at low latitudes and even at mid latitudes. The total area suitable for maize globally and in most major maize-producing countries will decrease, the decrease being particularly steep in those regions optimally suited for maize at present. Compared with earlier research, the method proposed in the present paper is simple yet objective, comprehensive, and reliable for large-scale assessment. The findings of the study highlight the necessity of adopting relevant strategies to cope with the adverse impacts of climate change.

scholarly journals On the Treatment of Missing Item Responses in Educational Large-Scale Assessment Data: An Illustrative Simulation Study and a Case Study Using PISA 2018 Mathematics Data

2021 ◽  
Vol 11 (4) ◽  
pp. 1653-1687
Author(s):  
Alexander Robitzsch
Keyword(s):  
Large Scale ◽  
Model Fit ◽  
Large Scale Assessment ◽  
Missing Data Treatments ◽  
Scale Assessment ◽  
Scaling Models ◽  
Item Responses ◽  
Response Propensity ◽  
Missing Item

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

scholarly journals An Elastic Platform for Large-scale Assessment of Software Assignments for MOOCs (EPLASAM)

2016 ◽  
pp. 187-206 ◽  
Author(s):  
Michael Walker ◽  
Douglas C. Schmidt ◽  
Jules White
Keyword(s):  
Software Development ◽  
Software Design ◽  
Large Scale ◽  
Working Environment ◽  
Software Platform ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
Recent Advances ◽  
Linux Containers

To address this efficiency and the resulting inherent scalability problems of Learning-at-Scale, this chapter propose a platform called SPLAShED: Software Platform for Large-Scale Assessment of Software-Development for Education-at-a-Distance, that uses Linux containers to provide OS-level virtualization. This gives each desired service the equivalent of a Virtual Private Server (VPS) that creates a temporary private userspace on the server. Each VPS provides a separate working environment for each desired application, but does not incur the overhead of traditional virtualization techniques. Our SPLAShED platform applies recent advances in Linux container deployment automation, resources isolation, portability, and usability. These advances enable the SPLAShED platform to serve both as. This chapter explores assignments as a case study: an Android based software design assignment. This case study shows how the SPLAShED platform will able to accommodate and facilitate advanced Software Development courses with features and abilities currently not available.

scholarly journals Quantifying Solid Findings in Mathematics Education: Loss of Meaning for Algebraic Symbols

2021 ◽  
Vol 29 (1) ◽  
Author(s):  
Giorgio Bolondi ◽  
Federica Ferretti
Keyword(s):  
Mathematics Education ◽  
Educational Research ◽  
Large Scale ◽  
Research Approach ◽  
Research Issues ◽  
Large Scale Assessment ◽  
Scale Assessment ◽  
High Level ◽  
Assessment Results

We report an example of a research approach aimed at gathering quantitative pieces of evidence of solid findings in mathematics education. The main goal of this project is to provide an additional perspective on solid findings in education, to be used by teachers and by researchers in their work. As a case study, we present a situation of “loss of meaning” in algebra, exploring it with data coming from a large-scale assessment interpreted by means of theoretical lenses. We are able to give information about the extent of the phenomenon and to highlight how the phenomenon is relevant also for high-level students. This approach can provide a link between large-scale assessment results, educational research, and teachers’ practices, and suggests further research issues.

Using Web-based Testing for Large-Scale Assessment

2013 ◽  
Author(s):  
Laura S. Hamilton ◽  
Stephen P. Klein ◽  
William Lorie
Keyword(s):  
Large Scale ◽  
Web Based ◽  
Large Scale Assessment ◽  
Scale Assessment

scholarly journals Methodology for Determining the Location of River Ports on a Modernized Waterway Based on Non-Cost Criteria: A Case Study of the Odra River Waterway

2021 ◽  
Vol 13 (6) ◽  
pp. 3571
Author(s):  
Bogusz Wiśnicki ◽  
Dorota Dybkowska-Stefek ◽  
Justyna Relisko-Rybak ◽  
Łukasz Kolanda
Keyword(s):  
Large Scale ◽  
Multicriteria Decision Making ◽  
Research Process ◽  
Optimal Selection ◽  
Investment Projects ◽  
Odra River ◽  
Research Problems ◽  
Selection Of ◽  
Construction Works

The paper responds to research problems related to the implementation of large-scale investment projects in waterways in Europe. As part of design and construction works, it is necessary to indicate river ports that play a major role within the European transport network as intermodal nodes. This entails a number of challenges, the cardinal one being the optimal selection of port locations, taking into account the new transport, economic, and geopolitical situation that will be brought about by modernized waterways. The aim of the paper was to present an original methodology for determining port locations for modernized waterways based on non-cost criteria, as an extended multicriteria decision-making method (MCDM) and employing GIS (Geographic Information System)-based tools for spatial analysis. The methodology was designed to be applicable to the varying conditions of a river’s hydroengineering structures (free-flowing river, canalized river, and canals) and adjustable to the requirements posed by intermodal supply chains. The method was applied to study the Odra River Waterway, which allowed the formulation of recommendations regarding the application of the method in the case of different river sections at every stage of the research process.

scholarly journals BonMOLière: Small-Sized Libraries of Readily Purchasable Compounds, Optimized to Produce Genuine Hits in Biological Screens across the Protein Space

2021 ◽  
Vol 22 (15) ◽  
pp. 7773
Author(s):  
Neann Mathai ◽  
Conrad Stork ◽  
Johannes Kirchmair
Keyword(s):  
Large Scale ◽  
Computational Approach ◽  
Large Sets ◽  
Compound Libraries ◽  
Wide Range ◽  
Protein Space ◽  
High Chance ◽  
Large Scale Screening ◽  
Early Drug ◽  
Selection Of

Experimental screening of large sets of compounds against macromolecular targets is a key strategy to identify novel bioactivities. However, large-scale screening requires substantial experimental resources and is time-consuming and challenging. Therefore, small to medium-sized compound libraries with a high chance of producing genuine hits on an arbitrary protein of interest would be of great value to fields related to early drug discovery, in particular biochemical and cell research. Here, we present a computational approach that incorporates drug-likeness, predicted bioactivities, biological space coverage, and target novelty, to generate optimized compound libraries with maximized chances of producing genuine hits for a wide range of proteins. The computational approach evaluates drug-likeness with a set of established rules, predicts bioactivities with a validated, similarity-based approach, and optimizes the composition of small sets of compounds towards maximum target coverage and novelty. We found that, in comparison to the random selection of compounds for a library, our approach generates substantially improved compound sets. Quantified as the “fitness” of compound libraries, the calculated improvements ranged from +60% (for a library of 15,000 compounds) to +184% (for a library of 1000 compounds). The best of the optimized compound libraries prepared in this work are available for download as a dataset bundle (“BonMOLière”).

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