Inter-city movement pattern of notifiable infectious diseases in China: a social network analysisResearch in context by Lin-Jie Yu, Peng-Sheng Ji, Xiang Ren, Yan-He Wang, Chen-Long Lv, Meng-Jie Geng, Jin-Jin Chen, Tian Tang, Chun-Xi Shan, Sheng-Hong Lin, Qiang Xu, Guo-Lin Wang, Li-Ping Wang, Simon I. Hay, Wei Liu, Yang Yang, Li-Qun Fang

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The Patterns and Environmental Factors of Diversity, Co-Occurrence Networks, and Assembly Processes of Protistan Communities in Bulk Soils of Forests by Bing Yang, Lin Wu, Zhisong Yang, Zhihe Zhang, Wanju Feng, Weichao Zheng, Chi Xu

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Recognizing Mixing Patterns of Urban Agglomeration Based on Complex Network Assortativity Coefficient: A Case Study in China by Kaiqi Zhang, Lujin Jia, Sheng Xu

“…Based on multi-source data (Baidu index data, investment data of listed companies, high-speed rail operation data, and highway network data) from 2017 to 2019 across seven national-level urban agglomerations, this study introduces complex network assortativity coefficients to analyze the mechanisms of urban relationship formation from two dimensions, structural features and socioeconomic attributes, to evaluate how these features shape urban agglomeration networks and reveal the distribution of network assortativity coefficients across urban agglomerations to classify diverse developmental patterns. …”
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Local Binary Pattern–Cycle Generative Adversarial Network Transfer: Transforming Image Style from Day to Night by Abeer Almohamade, Salma Kammoun, Fawaz Alsolami

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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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Array inspired wideband and high gain antenna with enhanced pattern diversity for 5G mm wave networks by Nawaf A. Alqwaifly, Wahaj Abbas Awan, Nabeel Alsaab, Fahad Nasser Alsunaydih, Khaled Alhassoon

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Network analysis of cognitive function, glycemic–lipid profiles, and hepatic–renal function in individuals with diverse drinking patterns by Shuqi Xu, Shuqi Xu, Shuqi Xu, Shuqi Xu, Ranran Zhao, Ranran Zhao, Ranran Zhao, Jincheng Wang, Jincheng Wang, Jincheng Wang, Xue Yang, Xue Yang, Xue Yang, Lan Wang, Lan Wang, Lan Wang, Cuixia An, Cuixia An, Cuixia An, Xueyi Wang, Xueyi Wang, Xueyi Wang, Ran Wang, Ran Wang, Ran Wang

“…The node DSCT ranked highly in terms of betweenness centrality.ConclusionCorrelations may exist among cognitive function, glycemic and lipid profiles, and hepatic–renal function in individuals with varying alcohol consumption patterns. Lipid and liver function indicators were identified as the most central factors in the network model. …”
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Histopathological Image Analysis Using Machine Learning to Evaluate Cisplatin and Exosome Effects on Ovarian Tissue in Cancer Patients by Tuğba Şentürk, Fatma Latifoğlu, Çiğdem Gülüzar Altıntop, Arzu Yay, Zeynep Burçin Gönen, Gözde Özge Önder, Özge Cengiz Mat, Yusuf Özkul

“…A set of 177 Local Binary Pattern (LBP) features were extracted from histopathological images, followed by feature selection using Lasso regression. …”
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Efficient Classification and Rapid Processing of Big Data in Power Distribution Networks by Luan Ning, Cheng Li, Wang Dingji, Wang Shuaimei

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Performance Evaluation of YOLOv11 and YOLOv12 Deep Learning Architectures for Automated Detection and Classification of Immature Macauba (<i>Acrocomia aculeata</i>) Fruits by David Ribeiro, Dennis Tavares, Eduardo Tiradentes, Fabio Santos, Demostenes Rodriguez

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Supply and demand flow patterns and optimization of food ecosystem services in China’s Yangtze River Delta by Xiaoling Qiu, Xiaoling Qiu, Xinmei Wang, Xinmei Wang, Zeni Wang, Zeni Wang, Wei He, Wei He

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Machine learning technique for the identification of two-phase (oil-water) flow patterns through pipelines by Daniel Yesid Uribe-Tarazona, Carlos Mauricio Ruiz-Diaz, Octavio Andrés González-Estrada

“…This study develops a robust machine learning model based on artificial neural networks to classify six flow patterns in oil-water two-phase flow within horizontal pipelines, a key aspect for ensuring operational efficiency, integrity, and cost-effective design in the oil and gas industry. …”
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Integration of wing pattern morphology and deep learning to support Plusiinae (Lepidoptera: Noctuidae) pest identification by Karina M. Torres, Chenjiao Tan, Kayla A. Mollet, Allan H. Smith-Pardo, Rui Xu, Changying Li, Silvana V. Paula-Moraes

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VA-Creator—A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns by Michael Meiser, Benjamin Duppe, Ingo Zinnikus, Alexander Anisimov

“…These VAs synthesize power consumption patterns (PCPs) based on Neural Networks (NNs) which adapt their architecture to the training data structure to simplify the creation of new VAs. …”
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Diel and Annual Patterns of Vocal Activity of Three Neotropical Wetland Birds Revealed via BirdNET by Cristian Pérez-Granados, Karl-L. Schuchmann

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Analysis of traffic patterns between Karkh and Rusafa by Mahdi Lateef Ishraq, Hussein Abed Alaa, Moutaz Asmael Noor

“…Rapid and radical changes in urban activities lead to changes in spatial travel patterns. Recently, users of the road network in Baghdad have faced difficulty in mobility between Karkh and Rusafa due to the congestion of bridges connecting the east of the city to the west across the Tigris River during peak hours. …”
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Hybrid statistical-algorithmic approach using the frog algorithm to optimize blast patterns for reducing blast vibrations by Abbas Khajouei Sirjani, Farhang Sereshki, Mohammad Ataei, Manoj Khandelwal, Hojatollah Mohammadi Anayi, Seyed Mohammad Mehdi Mousavi Nasab, Mohammad Amiri Hosseini

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Tensor Network Methods for Hyperparameter Optimization and Compression of Convolutional Neural Networks by A. Naumov, A. Melnikov, M. Perelshtein, Ar. Melnikov, V. Abronin, F. Oksanichenko

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Dietary patterns derived by Gaussian graphical models and metabolic profiles among overweight and obese individuals by Reyhaneh Mokhtari Hemami, Mohammad Asghari-Jafarabadi, Mahdieh Abbasalizad Farhangi, Maedeh Alipour

“…Identifying dietary networks provides valuable insights into the complex interactions between food groups within typical dietary patterns. …”
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Unsupervised post-training learning in spiking neural networks by Reyhaneh Naderi, Arash Rezaei, Mahmood Amiri, Herbert Peremans

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