Droplet-based microfluidics can be used to model clustered bacterial growth. by Justin Greene (306625)

T3SS induction slows growth, but cells can support high levels of S10 expression. by Justin Greene (306625)

Results of longitudinal analysis of mental health in HCWs. by A. H. Ayesha Lavell (16734002)

Surgical changes through ISIW implantation. by Sayaka Kimura-Uchida (22793666)

Results of the LMM analysis for IOP change. by Sayaka Kimura-Uchida (22793666)

“…The mean GMS was 2.46 ± 1.33 preoperatively, and decreased to 1.32 ± 1.31 at 3 months, and 1.60 ± 1.41 at 12 months postoperatively. …”

Results of the LMM analysis for GMS change. by Sayaka Kimura-Uchida (22793666)

“…The mean GMS was 2.46 ± 1.33 preoperatively, and decreased to 1.32 ± 1.31 at 3 months, and 1.60 ± 1.41 at 12 months postoperatively. …”

The performance of eigenvectors of the covariance matrix in a model with more complex population structure. by Joshua G. Schraiber (8027060)

Correlation in site-level DNA methylation estimates between TMS and RRBS data after permutation. by Amy Longtin (21409957)

Cross-sectional dependence results. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Autocorrelation test results. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Pesaran’s CADF test results for Model I. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Descriptive statistics of related variables. by Ozlem Kutlu Furtuna (20308206)

“…Moreover, FDI and the non-linear form of FDI have no significant influence on ecological footprint. …”

Performance comparison of ML models. 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. …”

Comparative data of different soil samples. 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. …”

Confusion matrix of random forest model. 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. …”

Sensor value scenario for fuzzy logic algorithm. 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. …”

Evaluation metrics of selected ML models. 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 of the proposed 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. …”

Chart for applicable amount of fertilizers. 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. …”

Cost analysis of irrigation controller unit. 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. …”