Retention as a Function of Competition in Learning

This study was undertaken to add to empirical evidence for use in drawing some sort of conclusions as to the significance of competition as a factor in retention of learning. This is necessary if teachers are to offer optimum learning conditions to their students

Claytonia virginica L.

https://thekeep.eiu.edu/herbarium_specimens_byname/20847/thumbnail.jp

Corallorhiza wisteriana Conrad

https://thekeep.eiu.edu/herbarium_specimens_byname/21251/thumbnail.jp

Corallorhiza odontorhiza Nutt.

https://thekeep.eiu.edu/herbarium_specimens_byname/21256/thumbnail.jp

Corallorhiza odontorhiza Nutt.

https://thekeep.eiu.edu/herbarium_specimens_byname/21256/thumbnail.jp

Psychological adjustment in mathematically gifted students

Abstract The Brief Symptom Inventory (BSI) was adminis were identified by a national talent search and attended a summer program in precalculus. Results indicated that these students were significantly bett e r a d j u s t e d t h a n t h e a d o l e s c e n t n o r m a t i v e g r o u p f o r the in str um ent Gender, grade level, and verb al abilities were not related to adjustment scores

The value of break crops in weed management

This publication discusses the effect on weed management of the inclusion of break crops (lupin, field pea, chickpea, faba bean, lentil and canola) in the cropping rotation compared to a rotation of continuous cereals. Including break crops in the cropping rotation allows weed management options unavailable or not suited in wheat. For example, growing field peas allows crop-topping/ desiccation for weed seed set control, crop topping cannot be used in wheat without severe yield loss. Problem weeds can be targeted through break crops, for example, grass weeds are generally more easily controlled in break crops than in cereals. Most weed management benefits attributed to break crops depend upon a well grown and well managed break crop.https://researchlibrary.agric.wa.gov.au/bulletins/1117/thumbnail.jp

PlasticNet: Deep Learning for Automatic Microplastic Recognition via FT-IR Spectroscopy

The recognition of microplastics (MPs) in environmental samples via FT-IR is challenging due to a plethora of factors can lead to significant variances in measured spectra. Conventional library search approaches compare the observed spectrum with spectra in reference libraries, which will lead to errors due the variance in spectra. Motivated to tackle this challenge, this study explores the feasibility of leveraging deep learning for automatic MP recognition via FT-IR spectroscopy. More specifically, a deep convolution neural network (CNN) architecture, referred to here as PlasticNet, is introduced for the purpose of automatic MP recognition. PlasticNet was trained on a large corpus of FT-IR spectra of different plastic types in order to learn discriminative spectral features characterizing each plastic type. Experimental results showed that PlasticNet was capable of recognizing between MPs in an effective way and at a faster speed compared with libary search

Acer saccharum Marshall

https://thekeep.eiu.edu/herbarium_specimens_byname/21779/thumbnail.jp

Acer saccharum Marshall

https://thekeep.eiu.edu/herbarium_specimens_byname/21779/thumbnail.jp