Table 4_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx حسب 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. …"

Table 11_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx حسب 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. …"

Table 6_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).xlsx حسب 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. …"

Image 2_Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.).tif حسب 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. …"

Identify different types of urban renewal implementations at streetscape scale حسب 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. …"

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

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

AI Influence in the Educational Environment حسب 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.…"

Electronic supplement to "Computational De Novo Design of Group II Introns Yields Highly Active Ribozymes" حسب Deni Szokoli (21503496)

"…Here we use the RNA inverse folding algorithm aRNAque to design G2Is de novo, yielding three novel self-splicing ribozymes with unusually stable structures. …"

<b>R</b><b>esidual</b> <b>GCB-Net</b>: Residual Graph Convolutional Broad Network on Emotion Recognition حسب 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. …"

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems حسب Xuhui Lin (19505503)

"…</p><h2>Data Structure</h2><p dir="ltr">The dataset is organized into four primary components:</p><ol><li><b>Road Network Data</b>: Topological representations including spatial geometry, functional classification, and connectivity information</li><li><b>Traffic Sensor Data</b>: Sensor metadata, locations, and measurements at both 5-minute and hourly resolutions</li><li><b>Precipitation Data</b>: Hourly meteorological information with spatial grid cell metadata</li><li><b>Derived Analytical Matrices</b>: Pre-computed structures for advanced spatial-temporal modelling and network analyses</li></ol><h2>File Formats</h2><ul><li><b>Tabular Data</b>: Apache Parquet format for optimal compression and fast query performance</li><li><b>Numerical Matrices</b>: NumPy NPZ format for efficient scientific computing</li><li><b>Total Size</b>: Approximately 2 GB uncompressed</li></ul><h2>Applications</h2><p dir="ltr">The IUTF dataset enables diverse analytical applications including:</p><ul><li><b>Traffic Flow Prediction</b>: Developing weather-aware traffic forecasting models</li><li><b>Infrastructure Planning</b>: Identifying vulnerable network components and prioritizing investments</li><li><b>Resilience Assessment</b>: Quantifying system recovery curves, robustness metrics, and adaptive capacity</li><li><b>Climate Adaptation</b>: Supporting evidence-based transportation planning under changing precipitation patterns</li><li><b>Emergency Management</b>: Improving response strategies for weather-related traffic disruptions</li></ul><h2>Methodology</h2><p dir="ltr">The dataset creation involved three main stages:</p><ol><li><b>Data Collection</b>: Sourcing traffic data from UTD19, road networks from OpenStreetMap, and precipitation data from ERA5 reanalysis</li><li><b>Spatio-Temporal Harmonization</b>: Comprehensive integration using novel algorithms for spatial alignment and temporal synchronization</li><li><b>Quality Assurance</b>: Rigorous validation and technical verification across all cities and data components</li></ol><h2>Code Availability</h2><p dir="ltr">Processing code is available at: https://github.com/viviRG2024/IUTDF_processing</p>…"

ImproBR Replication Package حسب 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 حسب 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. …"

<b>A virtual tracer experiment to assess the temporal origin of root water uptake, evaporation, and </b><b>drainage</b> حسب Paolo Nasta (19710883)

"…</p><p dir="ltr"><a href="" target="_blank">Two open-source Matlab scripts are available in the zip-files. The PT.m Matlab code determines the drainage transit time based on the particle tracking algorithm, while the VTE.m Matlab code determines the drainage and RWU transit times and relative rainfall contributions to actual evaporation, actual transpiration, and drainage using isotope transport simulations in HYDRUS-1D</a>. …"

Data Sheet 1_MACRPO: Multi-agent cooperative recurrent policy optimization.pdf حسب Eshagh Kargar (20458304)

"…The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.…"

Code repository حسب Dimitrios Iason Papadopoulos (21556811)

"…A portion of the resulting data is used to train various ML models aimed at both classifying the type of damage and estimating its severity. …"

CIOP database حسب Zeyi Wang (19846350)

"…</p><p dir="ltr">The database is produced from a throughput experiment and a regression algorithm.</p><p dir="ltr">10 common SEI containing elements are included and will be updated based on more experimental data later. …"

Video 2_MACRPO: Multi-agent cooperative recurrent policy optimization.mp4 حسب Eshagh Kargar (20458304)

"…The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.…"

Video 1_MACRPO: Multi-agent cooperative recurrent policy optimization.mp4 حسب Eshagh Kargar (20458304)

"…The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.…"

Video 3_MACRPO: Multi-agent cooperative recurrent policy optimization.mp4 حسب Eshagh Kargar (20458304)

"…The results show superior performance against other algorithms. The code is available online at https://github.com/kargarisaac/macrpo.…"

Survey Data on NSSI, Stress Response, Emotional Regulation Strategies and gender in Chinese Undergraduates حسب Yilin Ma (21154754)

"…</p><p dir="ltr"><b>Behavioral sub-questionnaire (AFASM1-12) :</b> Assesses NSSI with or without visible tissue damage</p><p dir="ltr"><b>Functional sub-questionnaire (BFASM1-19):</b> Measures three function types</p><ul><li><i>Automatic negative reinforcement: r</i>efers to engaging in NSSI to relieve or escape from an unpleasant internal state</li><li><i>Social positive reinforcement: </i>refers to engaging in NSSI to create a favorable state or to fulfill social needs</li><li><i>Emotional expression:</i>refers to engaging in NSSI as a means of expressing one’s emotional experiences</li></ul><p dir="ltr"><b>Scale:</b> 5-point Likert scale</p><h3><b>4.Gender</b></h3><ul><li>In this study, males were coded as “1” and females as “2”.</li></ul><p dir="ltr"><b>Usage Notes</b></p><ul><li></li><li>Each row represents one participant; columns include both item‑level and aggregated subscale scores.…"