Image 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif by Zhu Yang (756364)

“…Additionally, the least absolute shrinkage and selection operator (LASSO) regression algorithm in machine learning was used to screen transcription factors such as bHLH, WRKY, and MYB that influenced the expression of TaAkPHY genes. …”

Online Bayesian Streaming Structure. by Jiaqi Liu (309587)

“…We first present the BS-CP1 algorithm, which is an efficient implementation using assumed density filtering (ADF). …”

Information of four datasets. by Jiaqi Liu (309587)

“…We first present the BS-CP1 algorithm, which is an efficient implementation using assumed density filtering (ADF). …”

CP decomposition Bayes network. by Jiaqi Liu (309587)

“…We first present the BS-CP1 algorithm, which is an efficient implementation using assumed density filtering (ADF). …”

Table 1_Effectiveness of deep neural networks in hearing aids for improving signal-to-noise ratio, speech recognition, and listener preference in background noise.docx by Matthew B. Fitzgerald (11526808)

“…In this study, we evaluated the efficacy of a novel deep neural network (DNN)-based algorithm, commercially implemented as Edge Mode™, in improving SPIN outcomes for individuals with sensorineural hearing loss beyond that of conventional environmental classification approaches.…”

Identify different types of urban renewal implementations at streetscape scale by Xiaotong Wang (20852492)

“…Existing research primarily focuses on detecting pixel-level or object-level changes in urban physical space, often neglecting the semantic complexity inherent in urban renewal. …”

Sample size determination for multidimensional parameters and the A-optimal subsampling in a big data linear regression model by Sheng Zhang (111330)

“…Our findings revealed that the A-optimal subsampling method significantly outperformed uniform and leverage-score subsampling techniques. Furthermore, the algorithm considerably reduced the computational time required for implementing the full sample LSE. …”

Identification of ferroptosis-related LncRNAs as potential targets for improving immunotherapy in glioblastoma by Zhaochen Wang (12176245)

“…<p>The effect of ferroptosis-related long non-coding RNAs (lncRNAs) in predicting immunotherapy response to glioblastoma (GBM) remains obscure. …”

Design of stiffened panels for stress and buckling via topology optimization: data by Sheng Chu (19655605)

“…A free-form mesh deformation approach is improved to adjust the finite element mesh. Sizing optimization is also included. …”

AI Influence in the Educational Environment by Lev Radman (21381269)

“…The CSV file contains Likert-scale and categorical responses, with a separate README describing each variable and coding scheme.</p><p dir="ltr"><b>Potential reuse</b><br>Researchers can replicate or extend technology-acceptance models in emerging-economy contexts, compare student versus professional cohorts, or conduct secondary analyses on AI self-efficacy and algorithmic trust.…”

Training and test routes for simulated agents navigating using insect inspired strategies by Amany Said Amin (11234025)

“…</p><p dir="ltr">- VBO = view based orientation</p><p dir="ltr">- FCS = cast and surge</p><p dir="ltr">- FBM = familiarity based modulation</p><p dir="ltr">The remaining batches of 3-character sets refer to the route heuristics implemented during the initial training route.</p><p dir="ltr">- _OS = oscillatory (gated)</p><p dir="ltr">- NES = goal loop</p><p dir="ltr">- _BA = beacon aiming</p><p dir="ltr">- RES = restricted field of view</p><p dir="ltr">- __NA = baseline (i.e. path integration and obstacle avoidance alone)</p><p dir="ltr">All experiments are repeated across 75 environment seeds, hence 75 entries for train, test and terrain for each folder representing the route learning heuristic and navigation algorithm being used.…”

QERGD+: Quantum-Enhanced Recurrent Gradient Descent with Shor-Inspired Periodicity Priors for Stabilizing Dynamic Neural Network Training by Lakshit Mathur (20894549)

“…Detailed pseudocode, Qiskit implementation patterns, complexity analysis, and empirical use-case guidance are provided.…”

data and code by PENGCHENG LIU (20680641)

“…</p><p dir="ltr"> <b>Description of development environment: </b>this algorithm model is mainly based on ArcGIS Engine provided by ESRI company in the United States Net framework.…”

Data.rar by MohamadReza Fatemi (20922791)

“…This study employs the Random Forest (RF) machine learning algorithm, integrating remote sensing and in situ data, to develop a robust model for estimating Chl-a concentrations in three distinct areas of Gorgan Bay, Iran. …”

<b>R</b><b>esidual</b> <b>GCB-Net</b>: Residual Graph Convolutional Broad Network on Emotion Recognition by Qilin Li (535447)

“…It can accurately reflect the emotional changes of the human body by applying graphical-based algorithms or models. EEG signals are nonlinear signals. …”

ImproBR Replication Package by Anonymus (18533633)

“…<br><br>**Import Errors:**<br>Make sure you're in the replication package directory:<br>```bash<br>cd ImproBR-Replication<br>python improbr_pipeline.py --help<br>```<br><br>## Research Results & Evaluation Data<br>### RQ1: Bug Report Improvement Evaluation (139 reports)<br>**Manual Evaluation Results:**<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 1 Responses.csv</u>) - First evaluator assessments<br>- [`RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Author 2 Responses.csv</u>) - Second evaluator assessments <br>- [`RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv`](<u>RQ1-RQ2/RQ1/manual_evaluation/Final Results.csv</u>) - Consolidated evaluation results<br><br>**Inter-Rater Agreement (Cohen's Kappa):**<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_s2r_label.png</u>) - Steps to Reproduce κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_ob_label.png</u>) - Observed Behavior κ scores<br>- [`RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png`](<u>RQ1-RQ2/RQ1/cohen's_cappa_coefficient_matrices/confusion_matrix_eb_label.png</u>) - Expected Behavior κ scores<br><br>**Algorithm Results:**<br>- [`RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/improbr_outputs/</u>) - ImproBR improved reports<br>- [`RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/`](<u>RQ1-RQ2/RQ1/algorithm_results/chatbr_outputs/</u>) - ChatBR baseline results<br>- [`RQ1-RQ2/RQ1/algorithm_results/bee_analysis/`](<u>RQ1-RQ2/RQ1/algorithm_results/bee_analysis/</u>) - BEE tool structural analysis<br><br>### RQ2: Comparative Analysis vs ChatBR (37 pairs)<br>**Similarity Score Results:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_tfidf.csv</u>) - TF-IDF similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/overall_word2vec.csv</u>) - Word2Vec similarity scores<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/exact_string_comparisons.json</u>) - Complete TF-IDF comparison with scores for each comparison unit (full debugging)<br>- [`RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json`](<u>RQ1-RQ2/RQ2/algorithm_results/similarity_scores/word2vec_comparisons.json</u>) - Complete Word2Vec comparison with scores for each comparison unit (full debugging)<br><br>**Algorithm Outputs:**<br>- [`RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ImproBR_outputs/</u>) - ImproBR enhanced reports<br>- [`RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/`](<u>RQ1-RQ2/RQ2/algorithm_results/ChatBR_outputs/</u>) - ChatBR baseline outputs<br>- [`RQ1-RQ2/RQ2/dataset/ground_truth/`](<u>RQ1-RQ2/RQ2/dataset/ground_truth/</u>) - High-quality reference reports<br>## Important Notes<br><br>1. …”

Figure 8 from Prostate Cancer Progression Modeling Provides Insight into Dynamic Molecular Changes Associated with Progressive Disease States by Runpu Chen (14942572)

“…Each tumor sample was color-coded by its <i>ERG</i> fusion status inferred by the <i>ERG</i> gene expression level. …”

Manipulating an Instrumental Variable in an Observational Study of Premature Babies: Design, Bounds, and Inference by Zhe Chen (196097)

“…Three elements changed with the strengthened IV: the study cohort, compliance rate and latent complier subgroup. …”

Datasheet_YEH.csv by YOUNESS EL HAMZAOUI (19943346)

“…Experimental Setup:</p><p dir="ltr">- Used a photochemical reactor with 250ml capacity</p><p dir="ltr">- Employed UV lamp (15W, 352nm) and ultrasonic probe (500W, 20kHz)</p><p dir="ltr">- Monitored six key variables:</p><p dir="ltr"> * Reaction time</p><p dir="ltr"> * pH</p><p dir="ltr"> * TiO₂ concentration</p><p dir="ltr"> * UV light intensity</p><p dir="ltr"> * Ultrasound frequency</p><p dir="ltr"> * Herbicide concentration</p><p><br></p><p dir="ltr">2. Neural Network Implementation:</p><p dir="ltr">- Tested five backpropagation algorithms:</p><p dir="ltr"> * Gradient Descent</p><p dir="ltr"> * Conjugate Gradient</p><p dir="ltr"> * Scaled Conjugate Gradient</p><p dir="ltr"> * Quasi-Newton</p><p dir="ltr"> * Levenberg-Marquardt</p><p dir="ltr">- Conducted 30 independent runs for each algorithm</p><p dir="ltr">- Split data: 70% training, 30% testing</p><p><br></p><p dir="ltr">Key Findings:</p><p dir="ltr">1. …”

Supplementary file 1_An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction.docx by Songpeng Zhao (21563714)

“…However, accurately predicting their mechanical behavior remains challenging because of the vast compositional design space and complex multi-element interactions. In this study, we propose a stacking learning-based machine learning framework to improve the accuracy and robustness of HEA mechanical property predictions. …”