Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Dataset on impacts of land cover and climate change on Indian birds by Vishesh L. Diengdoh (10738896)

“…Then, an ensemble of machine learning algorithms - random forest, k-nearest neighbour, artificial neural network, support vector machine, and gradient boosting model - were used to project habitat suitability of each species under present conditions (2015) and future scenarios (2100) under two Shared Socio-economic Pathways (SSP3–7.0 and SSP5–8.5). …”

Peer Review Fundamentals: Enhancing Quality and Integrity in Scholarly Publishing by Thaer Al-Jadir (12237572)

“…</li><li>Checklist: clarity of research question, reproducibility of methods, ethical compliance, and data/code availability.</li></ul><h3>6. <b>Use of AI in Peer Review</b></h3><ul><li>AI tools support plagiarism screening, reference checks, and image/data anomaly detection.…”

Processed Dataset for “Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism” by Lingling Hu (22555691)

“…<p dir="ltr">This dataset contains the anonymized and processed production data used in the study titled “Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism,” submitted to <i>Applied Sciences</i>.…”

All the Places I Have Lived v3.0 by Christian Berg (18034411)

“…<br><br>To take this further, I developed a small Python-based tool I call Memory Infuser. …”

The ROC curve for the experiment. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

System architecture of this study. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

Description of the train test split dataset. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

The dataset’s summarized description. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

Feature selection procedure. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

Histogram of attributes. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

Illustration of all features correlation. by Mahade Hasan (20536430)

“…In response to this need, we applied nine machine learning algorithms XGBoost, logistic regression, decision tree, random forest, k-nearest neighbors (KNN), support vector machine (SVM), gaussian naïve bayes (NB gaussian), adaptive boosting, and linear regression to predict heart disease based on a range of physiological indicators. …”

Table 1_Predicting the potential distribution of major marine mammals in the Cosmonaut Sea.docx by Yufei Dai (621612)

“…The Cosmonaut Sea, notable for its dynamic sea ice and rich biological activity, remains one of the least explored regions in the Southern Ocean, with limited data on its marine mammal populations. This study conducted during the 38th Chinese National Antarctic Research Expedition (CHINARE) from January to March 2022, collected systematic data on marine mammal occurrences. …”

Unsupervised machine learning with different sampling strategies and topographic factors for distinguishing between landslide source and runout areas to improve landslide inventory... by Jhe-Syuan Lai (19733974)

“…Because of practical challenges associated with the inability to determine whether remote sensing results correspond to landslide sources or runout areas, the first sampling strategy was used to test the feasibility of the adopted algorithms. …”

Table 1_The quintenary influence of industrial wastes on the compressive strength of high-strength geopolymer concrete under different curing regimes for sustainable structures; a... by Cesar Garcia (17935273)

“…However, repeated experimental trials costs a huge budget, hence the prediction and validation of the strength behavior of the GPC mixed with some selected industrial wastes. Data gathering and analysis of a total 249 globally representative datasets of a high-strength geopolymer concrete (HSGPC) collected from experimental mix entries has been used in this research work. …”

Data Sheet 1_Application and research progress on artificial intelligence in the quality of Traditional Chinese Medicine.pdf by Mei-Yu Li (8505678)

“…It also identifies key challenges and technical bottlenecks, and outlines priorities for building scalable, regulation-aware, data-driven quality-control systems that support the sustainable, high-quality development of the TCM industry.</p>…”

Estilometría TIP: enhanced text analysis tool with customisable metrics for Spanish texts by Francisco J. Carreras-Riudavets (20562834)

“…However, most tools are developed for English, limiting their effectiveness for Spanish texts, which involve complex inflections. …”

Processed Dataset for “Optimization of Mixed-Model Multi-Manned Assembly Lines for Fuel–Electric Vehicle Co-Production under Workstation Sharing” by Lingling Hu (22555691)

“…<p dir="ltr">This dataset contains the anonymized and processed production data used in the study titled “Optimization of Mixed-Model Multi-Manned Assembly Lines for Fuel–Electric Vehicle Co-Production under Workstation Sharing,” submitted to <i>World Electric Vehicle Journal (WEVJ)</i>.…”

Coupling machine learning and group contribution method to predict density and viscosity of ionic liquid-inorganic solvent-water ternary mixtures by Sulei Ma (18629684)

“…This study explores the use of a hybrid approach that combines the group contribution (GC) method with advanced machine learning (ML) algorithms to predict the density and viscosity of ternary mixtures comprising ionic liquids (ILs), inorganic solvents (ICs), and water. …”