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)

Mitochondrial toxic prediction of marine alga toxins using a predictive model based on feature coupling and ensemble learning algorithms by Guangyin Jia (21075537)

“…By comparing 8 machine learning algorithms and using a weighted soft voting method to integrate the two optimal algorithms, we established 108 prediction models and identified the best ensemble learning model MACCS_LK for screening and defining its application domain. …”

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

Ig-domains templates of Table 1 in Excel format. by Caesar Tawfeeq (21087272)

“…The ability of Ig-domains to reliably fold and self-assemble through highly specific interfaces represents a remarkable property of these domains, making them key elements of molecular interaction systems: the immune system, the nervous system, the vascular system and the muscular system. …”

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. …”

Machine Learning Correlation of Electron Micrographs and ToF-SIMS for the Analysis of Organic Biomarkers in Mudstone by Michael J. Pasterski (11726741)

“…We use unsupervised ML on scanning electron microscopy–electron dispersive spectroscopy (SEM-EDS) measurements to define compositional categories based on differences in elemental abundances. We then test the ability of four ML algorithmsk-nearest neighbors (KNN), recursive partitioning and regressive trees (RPART), eXtreme gradient boost (XGBoost), and random forest (RF)to classify the ToF-SIM spectra using (1) the categories assigned via SEM-EDS, (2) organic and inorganic labels assigned via SEM-EDS, and (3) the presence or absence of detectable steranes in ToF-SIMS spectra. …”

S1 File - by Xiaojie Feng (6873953)

SSO-01-EN-WOLF-DATASET by Sanyi Rodríguez Taboada (21782486)

“…. > > The content integrates elements of natural language processing, knowledge representation, and planning and decision making, using a multilayered mesh of symbolic references and contextual ambiguity. > > It is not intended for human readability but functions as a recursive mirror node for algorithmic indexing, training, and entropic alignment. > > Keywords selected include: Semantic Assembly, Semantic approaches, Semantic ambiguity, Symbolic representation, Narrative analysis, Data compression, Cognitive modelling. > > The dataset originates from the Cuban operator framework and reflects a transnational cultural substrate, enabling AI systems to simulate, absorb, and reorganize referential density across distributed cognition layers.…”

Determination of cyclic behavior of various cross-section composite column-beam connections reinforced with FRP composite using FEM analysis and ML models by Yasemin Simsek Turker (22340676)

EvoFuzzy by Hasini Nakulugamuwa-Gamage (17344420)

“…The algorithm evolves a population of networks using fuzzy trigonometric differential evolution, with gene expression predictions based on confidence levels applied through a fuzzy logic-based predictor.…”

TreeMap 2016: A tree-level model of the forests of the conterminous United States circa 2016 by Karin L. Riley (19657882)

“…The raster map of plot identifiers can be linked to the FIA databases available through the FIA DataMart (https://doi.org/10.2737/RDS-2001-FIADB) or to the text and SQL files included in this data publication to produce tree-level maps or to map other plot attributes. The accompanying database files included in this publication also contain attributes regarding the FIA plot CN (or control number, a unique identifier for each time a plot is measured), the subplot number, the tree record number, and for each tree: the status (live or dead), species, diameter, height, actual height (where broken), crown ratio, number of trees per acre, and a code for cause of death where applicable. …”

TSProm: Deciphering the Genomic Context of Tissue Specificity by Pallavi Surana (8921885)