Simple Modification of Karl-Fischer Titration Method for Determination of Water Content in Colored Samples by Eva Tavčar, Erika Turk, Samo Kreft

“…Titration endpoint is determined from the graph of absorbance plotted against titration volume. …”
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The Effects of Royal Jelly Samples Collected from Sivas Province on the Proliferation of Endothelial Cells by Safa Uğur Koçköprü, Sevgi Durna Daştan

“…In this context, royal jelly samples were obtained from the province of Sivas, where beekeeping is carried out intensively and successfully, in the 2022 harvest period. …”
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Phasing millions of samples achieves near perfect accuracy, enabling parent-of-origin analyses by Cole M. Williams, Jared O’Connell, Ethan Jewett, William A. Freyman, Christopher R. Gignoux, Sohini Ramachandran, Amy L. Williams

“…Remarkably, both methods’ median switch error rate (SER) (after excluding single SNP switches, which we call “blips”) is 0.00% across all tested 23andMe trio children and 0.026% in British samples from UKB. Across UKB samples, switch errors predominantly occur in regions lacking identity-by-descent (IBD) coverage. …”
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Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach by Song Hu, Jiancheng Weng, Quan Liang, Wei Zhou, Peizhao Wang

“…This paper extracts individual PT travel chain information and constructs individual travel knowledge graphs of PT passengers based on the association matching algorithm and the theory of multilayer planning. …”
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MalGEA: A malware analysis framework via matrix factorization based node embedding and graph external attention by Ruisheng Li, Qilong Zhang, Huimin Shen

“…However, these existing malware studies still have two major limitations. (1) The complex topological structures of malware graphs often result in high computational overhead during feature extraction and processing. (2) Most existing approaches rely on conventional graph neural networks that are not specifically designed for malware classification tasks, leading to suboptimal performance, especially when dealing with minority class samples. …”
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Predicting correlation relationships of entities between attack patterns and techniques based on word embedding and graph convolutional network by Weicheng QIU, Xiuzhen CHEN, Yinghua MA, Jin MA, Zhihong ZHOU

“…Threat analysis relies on knowledge bases that contain a large number of security entities.The scope and impact of security threats and risks are evaluated by modeling threat sources, attack capabilities, attack motivations, and threat paths, taking into consideration the vulnerability of assets in the system and the security measures implemented.However, the lack of entity relations between these knowledge bases hinders the security event tracking and attack path generation.To complement entity relations between CAPEC and ATT＆amp;CK techniques and enrich threat paths, an entity correlation prediction method called WGS was proposed, in which entity descriptions were analyzed based on word embedding and a graph convolution network.A Word2Vec model was trained in the proposed method for security domain to extract domain-specific semantic features and a GCN model to capture the co-occurrence between words and sentences in entity descriptions.The relationship between entities was predicted by a Siamese network that combines these two features.The inclusion of external semantic information helped address the few-shot learning problem caused by limited entity relations in the existing knowledge base.Additionally, dynamic negative sampling and regularization was applied in model training.Experiments conducted on CAPEC and ATT＆amp;CK database provided by MITRE demonstrate that WGS effectively separates related entity pairs from irrelevant ones in the sample space and accurately predicts new entity relations.The proposed method achieves higher prediction accuracy in few-shot learning and requires shorter training time and less computing resources compared to the Bert-based text similarity prediction models.It proves that word embedding and graph convolutional network based entity relation prediction method can extract new entity correlation relationships between attack patterns and techniques.This helps to abstract attack techniques and tactics from low-level vulnerabilities and weaknesses in security threat analysis.…”
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Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor by Eric S. Semmel, Vince D. Calhoun, Frank Hillary, Robin Morris, Tricia Z. King

“…Comparison to results of microstructural network analysis from a similar sample suggest functional connectivity graph metrics provide different and complementary information and additional post-hoc analyses are also discussed. …”
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High-Accuracy HLA Type Inference from Whole-Genome Sequencing Data Using Population Reference Graphs. by Alexander T Dilthey, Pierre-Antoine Gourraud, Alexander J Mentzer, Nezih Cereb, Zamin Iqbal, Gil McVean

“…We evaluate HLA*PRG on six classical class I and class II HLA genes (HLA-A, -B, -C, -DQA1, -DQB1, -DRB1) and on a set of 14 samples (3 samples with 2 x 100bp, 11 samples with 2 x 250bp Illumina HiSeq data). …”
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SAR Target Depression Angle Invariant Recognition of Few-Shot Learning Via Dense Graph Prototype Network by Xiangyu Zhou, Yuhui Zhang, Qianru Wei

“…Specifically, by leveraging the information propagation mechanism of a densely connected graph convolutional network (GCN), potential features are iteratively learned while retaining previous features, thereby clustering samples of the same class with different elevation angles and eliminating feature deviations. …”
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Transient Stability Assessment of Power Systems Built upon Attention-Based Spatial–Temporal Graph Convolutional Networks by Yu Nan, Weiping Niu, Yong Chang, Zhenzhen Kong, Huichao Zhao

“…In addition, an adaptive focal loss function is designed to enhance the fitting of unstable samples and increase the weight of misclassified samples, thereby improving global accuracy and reducing the occurrence of missed instability samples. …”
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FastClothGNN: Optimizing message passing in Graph Neural Networks for accelerating real-time cloth simulation by Yang Zhang, Kailuo Yu, Xinyu Zhang

“…We present an efficient message aggregation algorithm FastClothGNN for Graph Neural Networks (GNNs) specifically designed for real-time cloth simulation in virtual try-on systems. …”
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The effects of students’ previous learning on graph-comprehension in the English as second language (ESL) textbooks in a Pakistani university by Irfan A Rind, Sajjad Alhawsawi

“…The textbooks contains, among others, many exercises and activities which require learners to comprehend graphs, charts, diagrams and maps (hereafter ‘graphs’ will be used to refer to all of these terms). …”
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GAPF-DFT: A graph-based alchemical perturbation density functional theory for catalytic high-entropy alloys by Mohamed Hendy, Okan K. Orhan, Homin Shin, Ali Malek, Mauricio Ponga

“…Here, we introduce a novel approach that combines alchemical perturbation density functional theory (APDFT) with a graph-based correction scheme to explore the binding energy landscape of HEAs. …”
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Robust Low-Snapshot DOA Estimation for Sparse Arrays via a Hybrid Convolutional Graph Neural Network by Hongliang Zhu, Hongxi Zhao, Chunshan Bao, Yiran Shi, Wenchao He

“…We propose a hybrid Convolutional Graph Neural Network (C-GNN) for direction-of-arrival (DOA) estimation in sparse sensor arrays under low-snapshot conditions. …”
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Combining graph neural network and Mamba to capture local and global tissue spatial relationships in whole slide images by Ruiwen Ding, Kha-Dinh Luong, Erika Rodriguez, Ana Cristina Araujo Lemos da Silva, William Hsu

“…We introduce a model that combines a message-passing graph neural network (GNN) with a state space model (Mamba) to capture both local and global spatial relationships among the tiles in WSIs. …”
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GT-STAFG: Graph Transformer with Spatiotemporal Attention Fusion Gate for Epileptic Seizure Detection in Imbalanced EEG Data by Mohamed Sami Nafea, Zool Hilmi Ismail

“…The graph transformer exploits dynamic graph data, while STAFG leverages self-attention and gating mechanisms to capture complex interactions by augmenting graph features with both spatial and temporal information. …”
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Interpretable graph Kolmogorov–Arnold networks for multi-cancer classification and biomarker identification using multi-omics data by Fadi Alharbi, Nishant Budhiraja, Aleksandar Vakanski, Boyu Zhang, Murtada K. Elbashir, Harshith Guduru, Mohanad Mohammed

“…This paper introduces Multi-Omics Graph Kolmogorov–Arnold Network (MOGKAN), a deep learning framework that utilizes messenger-RNA, micro-RNA sequences, and DNA methylation samples together with Protein-Protein Interaction (PPI) networks for cancer classification across 31 different cancer types. …”
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A Spatio-Temporal Tensor Graph Neural Network-Based Method for Node-Link Prediction in Port Networks by Zhixin Xia, Zhangqi Zheng, Feiyang Wei, Yongshan Liu, Lu Yu

“…Firstly, the model extracts the corresponding spatial features for the port network node links using the graph compression technique and updates the spatial features based on GraphSAGE by sampling and aggregating the neighbor node features. …”
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Deciphering the prokaryotic community and metabolisms in South African deep-mine biofilms through antibody microarrays and graph theory. by Yolanda Blanco, Luis A Rivas, Antonio García-Moyano, Jacobo Aguirre, Patricia Cruz-Gil, Arantxa Palacín, Esta van Heerden, Víctor Parro

“…The inherent difficulties for sampling these delicate habitats, together with transport and storage conditions may alter the community features and composition. …”
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Vietnamese Sentence Fact Checking Using the Incremental Knowledge Graph, Deep Learning, and Inference Rules on Online Platforms by Huong To Duong, Van Hai Ho, Phuc do

“…ViKGFC integrates a Knowledge Graph (KG), inference rules, and the Knowledge graph - Bidirectional Encoder Representations from Transformers (KG-BERT) deep learning model. …”
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