12x12 Multiplier unit. by Ayesha Waris (21368446)

“…<div><p>CRYSTALS-Kyber has been standardized by the National Institute of Standards and Technology (NIST) as a quantum-resistant algorithm in the post-quantum cryptography (PQC) competition. …”

NTT operations in MDC4NIP. by Ayesha Waris (21368446)

“…<div><p>CRYSTALS-Kyber has been standardized by the National Institute of Standards and Technology (NIST) as a quantum-resistant algorithm in the post-quantum cryptography (PQC) competition. …”

6x6 LUT-based multiplication. by Ayesha Waris (21368446)

“…<div><p>CRYSTALS-Kyber has been standardized by the National Institute of Standards and Technology (NIST) as a quantum-resistant algorithm in the post-quantum cryptography (PQC) competition. …”

Immunoassay of key anoikis-related genes. by Yufeng He (5673119)

Expression analysis of lncRNAs between the MCAO and sham groups. by Meimei Xu (744867)

The code for sample size calculation. by Ying Zhou (25031)

“…We collected basic clinical data and multimodal ultrasound data from these patients as predictive features, with clinical pregnancy as the predictive label, for model training. …”

Preprocessed Spatial Dynamic Wind Power Forecasting (SDWPF) Dataset from Baidu KDD Cup 2022 Challenge by Yuan Shi (22779782)

Breakdown of respondents. by Qunita Brown (19751520)

“…High quality data from Africa will afford diversity to global data sets, reducing bias in algorithms built for artificial intelligence technologies in healthcare. …”

Integrating drought warning water level with analytical hedging for reservoir water supply operation by Wenhua Wan (8051543)

“…</p><p dir="ltr">2. R codes for the HR-based DP algorithm, the processes deriving seasonal DWWL, and the statistical performance of HR with DWWL during typical drought years.…”

Linear mixed-effect model results. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

Visualizations of three clusters. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

Summary of three preparatory reading clusters. by Shirong Chen (22127046)

“…Additionally, we found three distinct preparatory reading patterns: <i><i>Fast Surface-level Preparatory Reading, Systematic Deep-level Preparatory Reading,</i></i> and <i><i>Extended Iterative Preparatory Reading,</i></i> each reflecting a distinct combination of cognitive investment and reading speed. …”

LSTM model’s equations. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Parameter’s interpretation. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

The models’ training parameters. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Model’s measure methods. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Association point and relationship. by Songsong Wang (8088293)

“…The findings indicate that the LSTM model, when integrated with the watershed-internal KG and LLM, can effectively incorporate critical elements influencing water level changes, the accuracy of the LLM-KG-LSTM model is enhanced by 3% compared to the standard LSTM model, and the LSTM series outperforms both RNN and GRU models, Our method will guide future research from the perspective of focusing on forecasting algorithms to the perspective of focusing on the relationship between multi-dimensional disaster data and algorithm parallelism.…”

Periodic Table’s Properties Using Unsupervised Chemometric Methods: Undergraduate Analytical Chemistry Laboratory Exercise by Adrian Gabriel Pereira de Quental (20382423)

“…The unsupervised algorithms were able to find “natural” clustering from the periodic table using the data structure without any prior knowledge of the class assignment of the samples. …”

Periodic Table’s Properties Using Unsupervised Chemometric Methods: Undergraduate Analytical Chemistry Laboratory Exercise by Adrian Gabriel Pereira de Quental (20382423)

“…The unsupervised algorithms were able to find “natural” clustering from the periodic table using the data structure without any prior knowledge of the class assignment of the samples. …”

S1 File - by Xiaojie Feng (6873953)