Paired t-test for detecting the difference between DMFDEM errors and other types of DEM errors. by Qinghua Li (398885)

Seasonal adjustment of time series observed at mixed frequencies using singular value decomposition with wavelet thresholding by Shiyuan He (11282556)

“…<p>In this paper, we propose a novel seasonal adjustment method that accommodates time series observed at mixed frequencies and possessing possibly multiple abrupt changes in seasonality. …”

Cases with clonal MMR deficiencies. by Yuqian Liu (1413763)

“…During the process of deconvolution, the optimized division of each sub-clone is attained by a heuristic algorithm, aligning with clone proportions that adhere optimally to the sample’s clonal structure. …”

Table 4_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 3_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 2_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

Table 1_The potential of short-wave infrared hyperspectral imaging and deep learning for dietary assessment: a prototype on predicting closed sandwiches fillings.docx by Esther Kok (716077)

“…</p>Methods<p>Individual spectra were selected from 24 hyperspectral images of assembled closed sandwiches, measured in a spectral range of 1116.14 nm to 1670.62 nm over 108 bands, pre-processed with Standard Normal Variate filtering, derivatives, and subsampling, and fed into multiple algorithms, among which PLS-DA, multiple classifiers, and a simple neural network.…”

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

“…After extraction of this second pellet, the supernatant was mixed with the first supernatant obtained, resulting in an extraction of ~1 mL of 25 % MeOH.Betalain detection was tested with a structured subset of samples (545 samples). …”

Additional file 1 of The origin and evolution of cultivated rice and genomic signatures of heterosis for yield traits in super-hybrid rice by Yiyong Zhao (21466062)

“…This table delineates the results of gene expression clustering using the MFUZZ algorithm for three super-hybrid rice varieties, namely LYP9, Y900, and XLY900, along with their progenitor strains. …”