Modeling method used. by Claire Teillet (18986264)

“…The most influential variables for predicting larval presence were the mean of Normalized Difference Vegetation Index (NDVI), texture indices from both NDVI, brightness index (BI), and the panchromatic image. Urban vegetation significantly influences larval presence, although higher vegetation index values correlate with a decreased probability of larval occurrence. …”

Some examples of selected Chinese characters. by Weijia Zhu (65481)

“…To address these challenges, we propose esFont, a novel guided Diffusion framework. …”

Ferroptosis Induction by a New Pyrrole Derivative in Triple Negative Breast Cancer and Colorectal Cancer by Domiziana Masci (4224451)

“…Furthermore, lactoperoxidase, malondialdehyde, and Fe­(II) levels significantly increased in <b>12</b>-treated tissues, whereas superoxide dismutase concentrations decreased. …”

Ferroptosis Induction by a New Pyrrole Derivative in Triple Negative Breast Cancer and Colorectal Cancer by Domiziana Masci (4224451)

“…Furthermore, lactoperoxidase, malondialdehyde, and Fe­(II) levels significantly increased in <b>12</b>-treated tissues, whereas superoxide dismutase concentrations decreased. …”

Summary of correlations in previous studies. by Dan Kuang (136225)

“…Furthermore, the infection rates of SARS-CoV-2, as estimated by the model, ranged from 0.012% (P5-P95: 0.004% - 0.020%) at the lowest baseline to 3.27% (P5-P95: 1.23% - 5.69%) at the peak of the epidemic, with 15.1% (P5-P95: 5.65% - 26.2%) of individuals infected during the epidemic wave between March 4th and June 15th. Additionally, we did not observe any COVID-19 outbreaks or cluster infections at the Chengdu 2023 FISU World University Games village, and there was no significant difference in the concentrations of SARS-CoV-2 in athletes before and after check-in at the village.…”

The characteristics of nine WWTPs. by Dan Kuang (136225)

“…Furthermore, the infection rates of SARS-CoV-2, as estimated by the model, ranged from 0.012% (P5-P95: 0.004% - 0.020%) at the lowest baseline to 3.27% (P5-P95: 1.23% - 5.69%) at the peak of the epidemic, with 15.1% (P5-P95: 5.65% - 26.2%) of individuals infected during the epidemic wave between March 4th and June 15th. Additionally, we did not observe any COVID-19 outbreaks or cluster infections at the Chengdu 2023 FISU World University Games village, and there was no significant difference in the concentrations of SARS-CoV-2 in athletes before and after check-in at the village.…”

Primers for RT-qPCR. by Jiaoyang Li (2862269)

“…To elucidate the function of NSP2 during PRRSV infection, we identified SH3KBP1 as an NSP2-interacting host protein using mass spectrometry. …”

Oligonucleotide primers for gene expression. by Cian Reid (17274007)

“…This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. …”

Calf health information. by Cian Reid (17274007)

“…This hyper-induction of IL-6 was observed most significantly in response to TLR1/2 stimulation in TUS positive calves. …”

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). …”

Mean parameter values for the selected crops. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Performance comparison of ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Comparative data of different soil samples. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Confusion matrix of random forest model. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Sensor value scenario for fuzzy logic algorithm. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Evaluation metrics of selected ML models. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Block diagram of the proposed system. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Chart for applicable amount of fertilizers. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Cost analysis of irrigation controller unit. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”

Run times of two algorithms. by Gourab Saha (8987405)

“…In this article, we present a complete integrated design of a smart IoT-based suitable agricultural land and crop selection, along with an irrigation system using agricultural mapping, machine learning, and fuzzy logic for precision agriculture. …”