Application of Machine Learning-Based Models to Understand and Predict Critical Flux of Oil-in-Water Emulsion in Crossflow Microfiltration by Henry J. Tanudjaja (12106715)

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EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things by Arash GhorbanniaDelavar (22563696)

“…By integrating intelligent batching, an <b>Intelligent Threshold Detector (ITD)</b>, and a cost-based fog selection mechanism, EDLIoT significantly reduces energy consumption and task execution delay.…”

NN analysis to assign the importance of variables. by Sulmaz Ghahramani (3387635)

Histograms of (A) distance measured in meters to nearest neighboring tree given bin widths of 2 m (<i>n</i> = 44), and (B) basal areas of all sampled trees (<i>n</i> = 58; bin widt... by Alexandra M. Campbell (3284094)

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The number of people who searched for a waste bin, added a waste bin to their shopping cart or purchased a waste bin. by Yaopei Wang (14046943)

IR-induced mutation profiles of DNA repair mutants significantly different from wild-type. by Bettina Meier (274833)

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A proposed working model of how the GmNN1/FT2a-GmNYFA-C-GmENOD40 module regulates nodulation in soybean. by Xinxin Li (281237)

Median ATR-FTIR absorbance spectra of malignant (<i>n = 88</i>) and benign (<i>n = 78</i>) breast tissue samples. by Rock Christian Tomas (12003669)

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Loss of Bin1 in neurons affects gene expression in microglia <i>in vivo</i>. by Kathleen M. McAvoy (7224863)

Cell-autonomous and non-cell autonomous effects of neuronal BIN1 loss <i>in vivo</i> by Kathleen M. McAvoy (7224863)

“…Furthermore, the level of neuronal-specific BIN1 isoform 1 protein is decreased in sporadic AD cases in parallel with neuronal loss, despite an overall increase in BIN1 total mRNA. …”

Genetic deletion of Bin1 in neurons alters neuronal activation <i>in vivo</i>. by Kathleen M. McAvoy (7224863)

Genetic deletion of Bin1 in neurons leads to a reduction in survival but not to neuronal loss in PS19 Hemi or Ncar mice. by Kathleen M. McAvoy (7224863)

The strength of the commute time-functional connectivity relationship demonstrates weak pathological significance and weak dependency on age. by Rostam M. Razban (22232522)

“…</b> Strengthening commute time-FC correlations shown in <b>B</b> seem to be driven by a decrease in average commute time across age. Note that all reported correlations are calculated by considering all data points, i.e., they are not calculated for the binned data.…”

Mean values and 95% confidence intervals for recovery time, Average NN Interval (AVNN); Standard Deviation of NN Intervals (SDNN), Low Frequency/ High Frequency Ratio (LF/HF); and... by Robert Trybulski (10998702)

Temporal profiles of the key BO-NN features. by Julia Berezutskaya (9080269)

“…The shifts associated with non-significant prediction accuracies are greyed-out. <b>(c)</b> The results of the MDS for the key BO-NN features. …”

Species assigned toe each time bin for text Fig 1. by Brendan M. Anderson (11234356)

Positional cloning of <i>qNN1</i> on chromosome 16. by Xinxin Li (281237)

Significance test results of the basic model. by Shanshan Lu (580920)

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The ten most significant features of the Random Forest model based on classification accuracy. by Nguyen Tat Thanh (10296398)

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MAE significance comparison between MSCALSTM and baseline models based on Energy dataset. by Zhifei Yang (14613332)

“…<p>MAE significance comparison between MSCALSTM and baseline models based on Energy dataset.…”