125 research outputs found
What Drives Asian Descendent Studentsâ Motivation for Learning? Exploring the Key Ingredients to Nurture Achievement
This motivation project is part of a larger study of exploring the relationship between Asian parenting styles and childrenâs academic achievement. In light of the consistent Asian students PISA (Programme for International Student Assessment) data results and the media phenomenon created by Amy Chuaâs Battle Hymn of the Tiger Mother in 2011 in U.S., our research team found that high motivation has been a reoccurring theme in childrenâs academic achievement in the perceptions of Asian descendent parents. The purpose of this project is to examine the Asian descendent studentsâ motivations for learning through the parentsâ experiences and perceptions. The research methods include individual interviews and a focus group interview. Eighteen parents, including thirteen mothers and five fathers participated in this study. All participants had at least one child within the ages of 2-20 years old. Based on our findings, four themes have emerged. We learned that these parents nurtured and sustained childrenâs motivation for learning through the following four ingredients, including resources, communications, setting clear and high expectations, and the modeling of the parents. Implications for educators will be provided in this presentation.https://engagedscholarship.csuohio.edu/u_poster_2014/1003/thumbnail.jp
The impact of different sentiment in investment decisions: evidence from Chinaâs stock markets IPOs
In this study, we used data on Chinaâs initial public offerings (IPOs),
market volatility and macro environment before and after two
stock crashes during 2006â2016 to investigate how different
investor sentiment affects IPO first-day flipping. The empirical
results show that the expected returns of allocated investors are
affected by sentiment, with allocated investors having higher psychological
expectations of future returns during an optimistic bull
market and their optimism discouraging first-day flipping, while
higher risk-free interest rate levels and rising broad market indices
also discourage first-day flipping and tend to sell in the future. The
pessimistic bear market during which allocated investors have
lower psychological expectations of future returns, their pessimism
will promote first-day flipping, and the increase in the risk-free rate
level will also promote first-day flipping, which is the opposite of
the optimistic bull market, indicating that their risk aversion has
increased and they tend to sell on the same day. We also found an
anomaly that the greater the decline in the broad market index
during a pessimistic bear market, the more inclined the allocated
investors are to sell in the future when the broad market index rises
in an attempt to gain higher returns. These findings help explain
and understand the impact of market and macro index fluctuations
on investor behavior under different investor sentiments
Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum
An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum
Estimation of Dry Matter and N Nutrient Status of Choy Sum by Analyzing Canopy Images and Plant Height Information
The estimation accuracy of plant dry matter by spectra- or remote sensing-based methods tends to decline when canopy coverage approaches closure; this is known as the saturation problem. This study aimed to enhance the estimation accuracy of plant dry matter and subsequently use the critical nitrogen dilution curve (CNDC) to diagnose N in Choy Sum by analyzing the combined information of canopy imaging and plant height. A three-year experiment with different N levels (0, 25, 50, 100, 150, and 200 kg center dot ha(-1)) was conducted on Choy Sum. Variables of canopy coverage (CC) and plant height were used to build the dry matter and N estimation model. The results showed that the yields of N-0 and N-25 were significantly lower than those of high-N treatments (N-50, N-100, N-150, and N-200) for all three years. The variables of CC x Height had a significant linear relationship with dry matter, with R-2 values above 0.87. The good performance of the CC x Height-based model implied that the saturation problem of dry matter prediction was well-addressed. By contrast, the relationship between dry matter and CC was best fitted by an exponential function. CNDC models built based on CC x Height information could satisfactorily differentiate groups of N deficiency and N abundance treatments, implying their feasibility in diagnosing N status. N application rates of 50-100 kgN/ha are recommended as optimal for a good yield of Choy Sum production in the study region
Using crop intercepted solar radiation and vegetation index to estimate dry matter yield of Choy Sum
An accurate assessment of vegetable yield is essential for agricultural production and management. One approach to estimate yield with remote sensing is via vegetation indices, which are selected in a statistical and empirical approach, rather than a mechanistic way. This study aimed to estimate the dry matter of Choy Sum by both a causality-guided intercepted radiation-based model and a spectral reflectance-based model and compare their performance. Moreover, the effect of nitrogen (N) rates on the radiation use efficiency (RUE) of Choy Sum was also evaluated. A 2-year field experiment was conducted with different N rate treatments (0 kg/ha, 25 kg/ha, 50 kg/ha, 100 kg/ha, 150 kg/ha, and 200 kg/ha). At different growth stages, canopy spectra, photosynthetic active radiation, and canopy coverage were measured by RapidScan CS-45, light quantum sensor, and camera, respectively. The results reveal that exponential models best match the connection between dry matter and vegetation indices, with coefficients of determination (R2) all below 0.80 for normalized difference red edge (NDRE), normalized difference vegetation index (NDVI), red edge ratio vegetation index (RERVI), and ratio vegetation index (RVI). In contrast, accumulated intercepted photosynthetic active radiation (Aipar) showed a significant linear correlation with the dry matter of Choy Sum, with root mean square error (RMSE) of 9.4 and R2 values of 0.82, implying that the Aipar-based estimation model performed better than that of spectral-based ones. Moreover, the RUE of Choy Sum was significantly affected by the N rate, with 100 kg N/ha, 150 kg N/ha, and 200 kg N/ha having the highest RUE values. The study demonstrated the potential of Aipar-based models for precisely estimating the dry matter yield of vegetable crops and understanding the effect of N application on dry matter accumulation of Choy Sum
Early-detection and classification of live bacteria using time-lapse coherent imaging and deep learning
We present a computational live bacteria detection system that periodically
captures coherent microscopy images of bacterial growth inside a 60 mm diameter
agar-plate and analyzes these time-lapsed holograms using deep neural networks
for rapid detection of bacterial growth and classification of the corresponding
species. The performance of our system was demonstrated by rapid detection of
Escherichia coli and total coliform bacteria (i.e., Klebsiella aerogenes and
Klebsiella pneumoniae subsp. pneumoniae) in water samples. These results were
confirmed against gold-standard culture-based results, shortening the detection
time of bacterial growth by >12 h as compared to the Environmental Protection
Agency (EPA)-approved analytical methods. Our experiments further confirmed
that this method successfully detects 90% of bacterial colonies within 7-10 h
(and >95% within 12 h) with a precision of 99.2-100%, and correctly identifies
their species in 7.6-12 h with 80% accuracy. Using pre-incubation of samples in
growth media, our system achieved a limit of detection (LOD) of ~1 colony
forming unit (CFU)/L within 9 h of total test time. This computational bacteria
detection and classification platform is highly cost-effective (~$0.6 per test)
and high-throughput with a scanning speed of 24 cm2/min over the entire plate
surface, making it highly suitable for integration with the existing analytical
methods currently used for bacteria detection on agar plates. Powered by deep
learning, this automated and cost-effective live bacteria detection platform
can be transformative for a wide range of applications in microbiology by
significantly reducing the detection time, also automating the identification
of colonies, without labeling or the need for an expert.Comment: 24 pages, 6 figure
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