Flow chart of Fuzzy Logic based control system. by Gourab Saha (8987405)

“…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”

Block diagram for IoT-based irrigation system. by Gourab Saha (8987405)

“…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”

Flow chart of Average Value-based control system. by Gourab Saha (8987405)

“…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”

Hardware design for IoT-based irrigation system. by Gourab Saha (8987405)

“…Multi-spectral band images from Landsat-8 satellite images of a chosen land are employed from USGS Earth Resources Observation and Science (EROS) Center for extracting indices that are used for agricultural analysis, determining the vegetation index, water index, and salinity index of that land using K-means. Furthermore, crop yield is predicted using Linear Regression and Random Forest, achieving accuracies of 93.49% and 95.87%, respectively, while using RMSE (Root Mean Squared Error) as the loss function. …”

S1 File - by Soheila Nazarpour (11357538)

“…After completion of the forms, the created data in the Excel software in Google Drive were converted to SPSS 24 and then analyzed by using <i>t</i>-test, ANOVA, Pearson correlation, and multiple linear regression tests. The overall mean score of self-care was 71.84±17.81 percent. …”

Table_1_Multivariate piecewise linear regression model to predict radiosensitivity using the association with the genome-wide copy number variation.xlsx by Joanna Tobiasz (11816504)

“…The weighted mean relative errors (MRE) decreased with increasing the segments' number. …”

Disability scores rate changes and predictors among road traffic injury victims admitted at Gondar specialized comprehensive hospital northwest Ethiopia: A prospective follow-up st... by Zewditu Abdissa Denu (11654368)

“…Work status was assessed at each survey using a single question “are you back to work?” We employed a linear mixed model to identify disability scores changes over time and risk factors associated with disability. …”

Image_1_Alterations in brain structure associated with trigeminal nerve anatomy in episodic migraine.JPEG by Tiffani J. Mungoven (13113000)

“…Additionally, within the same brain areas, relationships of AX and RD with nerve FA were determined. We found that in both interictal migraine and control participants, decreasing trigeminal nerve FA was associated with significantly increased MD in brainstem regions including the spinal trigeminal nucleus and midbrain periaqueductal gray matter (PAG), and in higher brain regions such as the hypothalamus, insula, posterior cingulate, primary somatosensory and primary visual (V1) cortices. …”

Flow chart of research object screening. by Wenyao Xie (21567889)

“…Estradiol exhibited significant non-linear relationship with ALI (P = 0.027), with multiple inflection points suggesting concentration-dependent effects.…”

Table_1_Distribution patterns of fern species richness along elevations the Tibetan Plateau in China: regional differences and effects of climate change variables.docx by Muhammad Umair (4062583)

“…The richness of fern species has a log-linear relationship with moisture-related factors such as moisture index (MI), mean annual precipitation (MAP), and drought index (DI). …”

DataSheet_1_Distribution patterns of fern species richness along elevations the Tibetan Plateau in China: regional differences and effects of climate change variables.docx by Muhammad Umair (4062583)

“…The richness of fern species has a log-linear relationship with moisture-related factors such as moisture index (MI), mean annual precipitation (MAP), and drought index (DI). …”

Image_1_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.jpg by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Table_2_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.docx by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Table_3_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.docx by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Image_2_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.jpg by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Table_4_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.docx by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Image_3_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.jpg by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Table_1_Association of Age and Structural Brain Changes With Functional Connectivity and Executive Function in a Middle-Aged to Older Population-Based Cohort.docx by Maximilian Schulz (6505427)

“…We observed an independent association of higher age with decreased functional connectivity, while there was no significant association of functional connectivity with cortical thickness or PSMD. …”

Data_Sheet_1_Empiric Methods to Account for Pre-analytical Variability in Digital Histopathology in Frontotemporal Lobar Degeneration.docx by Lucia A. A. Giannini (6906953)

“…We found a significant difference in duplicate measurements from distinct staining batches in FTLD-Tau (mean difference: GM = 1.13 ± 0.44, WM = 1.28 ± 0.56; p < 0.001) and FTLD-TDP (GM = 0.95 ± 0.66, WM = 0.90 ± 0.77; p < 0.001), and these measurements were linearly related (R-squared [Rsq]: FTLD-Tau GM = 0.92, WM = 0.92; FTLD-TDP GM = 0.75, WM = 0.78; p < 0.001 all). …”

Validation and predictive accuracy of the cerebrovascular model, by Hadi Esfandi (21387211)

“…Next, we combined blood flow data from non-bifurcating capillaries at all tested ABNP levels (10 steps) into a single dataset and calculated the means and standard deviations across layers L1-L4, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0321053#pone.0321053.g006" target="_blank">Fig 6</a>(b-c). …”