A coupled model of zebra mussels and chlorine in collective pressurized irrigation networks by J. Burguete, B. Latorre, P. Paniagua, E.T. Medina, J. Fernández-Pato, E. Playán, N. Zapata

“…Simulations predicted similar mussel settlement patterns across all scenarios, suggesting that network morphology and total larval abundance primarily influence settlement distribution. …”
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Enhancing Network Security: A Study on Classification Models for Intrusion Detection Systems by Abeer Abd Alhameed Mahmood, Azhar A. Hadi, Wasan Hashim Al-Masoody

“…The authors utilize three datasets (Knowledge Discovery in Databases 1999 dataset, used for network intrusion detection research), UNSW-NB15 (a dataset capturing contemporary network attack patterns generated at the University of New South Wales), and CICIDS2017 (Canadian Institute for Cybersecurity Intrusion Detection System dataset, containing modern attack scenarios)(KDD99, UNSW NB15, and CICIDS2017) with varying train-test ratios to train the classifiers. …”
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Air Pollution Forecasting Using Artificial and Wavelet Neural Networks with Meteorological Conditions by Qingchun Guo, Zhenfang He, Shanshan Li, Xinzhou Li, Jingjing Meng, Zhanfang Hou, Jiazhen Liu, Yongjin Chen

“…Evaluating twelve algorithms and nineteen network topologies for the ANN and WANN models, we discovered that the optimal input variables for an API forecasting model were the APIs from the 3 preceding days and sixteen selected meteorological factors. …”
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An ingredient co-occurrence network gives insight into e-liquid flavor complexity by Jeroen L. A. Pennings, Ina M. Hellmich, Sanne Boesveldt, Reinskje Talhout

“…Big data analyses on product data can be used to detect such patterns, but expert knowledge and additional data are needed for further interpretation. …”
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Adaptive multi-scale phase-aware fusion network for EEG seizure recognition by Yanting Liang, Jingyuan Liu, Xinzhou Zhang

“…However, traditional methods rely heavily on manual feature extraction, and current deep learning-based approaches still face challenges in frequency adaptability, multi-scale feature integration, and phase alignment.MethodsTo address these limitations, we propose an Adaptive Multi-Scale Phase-Aware Fusion Network (AMS-PAFN). The framework integrates three novel components: (1) a Dynamic Frequency Selection (DFS) module employing Gumbel-SoftMax for adaptive spectral filtering to enhance seizure-related frequency bands; (2) a Multi-Scale Feature Extraction (MCFE) module using hierarchical downsampling and temperature-controlled multi-head attention to capture both macro-rhythmic and micro-transient EEG patterns; and (3) a Multi-Scale Phase-Aware Fusion (MCPA) module that aligns temporal features across scales through phase-sensitive weighting.ResultsThe AMS-PAFN was evaluated on the CHB-MIT dataset and achieved state-of-the-art performance, with 98.97% accuracy, 99.53% sensitivity, and 95.21% specificity (Subset 1). …”
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Neural Networks vs. Regression: A Comparative Analysis in Medical Data Processing by Minodora ANDOR, Gheorghe Ioan MIHALAŞ

“… Background and Aim: The increasing adoption of artificial intelligence (AI) in medical research offered alternative methods for medical data processing. This study evaluated comparatively the predictive performance of feedforward neural networks (FFNN) regression versus classical statistical regression analysis in estimating the risk of post-COVID-19 type 2 diabetes based on metabolic factors. …”
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Soil Fungal Diversity, Community Structure, and Network Stability in the Southwestern Tibetan Plateau by Shiqi Zhang, Zhenjiao Cao, Siyi Liu, Zhipeng Hao, Xin Zhang, Guoxin Sun, Yuan Ge, Limei Zhang, Baodong Chen

“…Despite substantial research on how environmental factors affect fungal diversity, the mechanisms shaping regional-scale diversity patterns remain poorly understood. This study employed ITS high-throughput sequencing to evaluate soil fungal diversity, community composition, and co-occurrence networks across alpine meadows, desert steppes, and alpine shrublands in the southwestern Tibetan Plateau. …”
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Revealing heterogeneity in mild cognitive impairment based on individualized structural covariance network by Xiaotong Wei, Ronglong Xiong, Ping Xu, Tingting Zhang, Junjun Zhang, Zhenlan Jin, Ling Li

“…Significant differences between two subtypes were found in clinical cognition and biomarkers, cerebral atrophy patterns, and enriched genes for metal ion transport and neuron projection development. …”
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DeepQSP: Identification of Quorum Sensing Peptides Through Neural Network Model by Md. Ashikur Rahman, Md. Mamun Ali, Kawsar Ahmed, Imran Mahmud, Francis M. Bui, Li Chen, Santosh Kumar, Mohammad Ali Moni

“…This study introduces DeepQSP, a novel technique for QSP identification, which combines Latent Semantic Analysis (LSA), a word embedding feature extraction method, with classical amino acid-based extraction Pseudo Amino Acid Composition (PAAC), and a convolutional neural network (CNN) classifier. The DeepQSP model was evaluated using a dataset of 440 peptide sequences, achieving impressive performance metrics: 0.9697 accuracy, 0.9655 sensitivity, 0.9730 specificity, and a Matthews correlation coefficient (MCC) of 0.9385. …”
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Exploring the interaction effects of subclinical hypothyroidism and major depressive disorder on brain networks by Shuai Zhao, Jindan Wu, Xiaomei Liu, Yishan Du, Xiaoqin Wang, Yi Xia, Hao Sun, Haowen Zou, Xumiao Wang, Zhilu Chen, Rui Yan, Hao Tang, Qing Lu, Zhijian Yao

“…Each participant received resting-state functional magnetic resonance imaging scans and underwent neuropsychological evaluations. Results We found significantly altered functional connectivity (FC) within the resting-state networks (RSNs) of the ventral and dorsal sensorimotor network (VSMN and DSMN) and occipital pole visual network (PVN) (p < 0.05, FDR corrected). …”
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Construction and Overall Protection of Ecological and Marine Cultural Composite Landscape Network in Quanzhou, Fujian by Dongfang LU, Yaru ZHENG, Tianteng HAN, Shunhe CHEN

“…Based on the evaluation results of the gravity model, these corridors are classified into first-class, second class, and third-class corridors, forming the spatial pattern of the Quanzhou ecological network. …”
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Evaluation of multi-source precipitation products for monitoring drought across China by Yongyi Yuan, Boyi Liao

“…Hotspot analyses of indices such as CDD, PRCPTOT, and R95p further confirmed IMERG Final’s accuracy in identifying drought and wet event patterns, closely reflecting ground measurements, whereas ERA5 and GSMaP MVK occasionally overestimated drought frequencies. …”
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Impact of Geopolitical and International Trade Dynamics on Corporate Vulnerability and Insolvency Risk: A Graph-Based Approach by Yu Zhang, Elena Sánchez Arnau, Enrique A. Sánchez Pérez

“…Through the integration of data from sources such as the World Trade Organization, national customs, and international relations research centers, a quantitative, exploratory, and descriptive approach based on graph theory, random forest, multivariate regression models, and neural networks is developed. This quantitative system makes it possible to identify patterns of risk propagation and to evaluate the degree of vulnerability of each country according to its commercial and financial structure. …”
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Combining Style Transfer in a Frame with Random Rotation Using Convolutional Neural Networks by Mahdi Musa Semnani, Kourosh Kiani

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The spatial correlation network evolution and influencing factors of industrial carbon emission efficiency in China by Zeni Wang, Xinmei Wang, Zhipeng Fan, Ciran Feng, Xiaoling Qiu, Wei He

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Brain Network Alterations in Chronic Spinal Cord Injury: Multilayer Community Detection Approach by Farzad V. Farahani, Lukman E. Ismaila, Cristina L. Sadowsky, Haris I. Sair, Li Min Chen, Visar Belegu, James J. Pekar, Martin A. Lindquist, Ann S. Choe

“…Cortical reorganization, in particular, can be evaluated through network (graph) analysis of interregional functional connectivity. …”
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2D Spatiotemporal Hypergraph Convolution Network for Dynamic OD Traffic Flow Prediction by Cheng Fang, Li Wang

“…Initially, temporal characteristics of traffic flow between OD pairs are captured using a 1D convolution neural network (1D-CNNs). Subsequently, a 2D hypergraph convolutional network is introduced to uncover spatial correlations in OD flow patterns. …”
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The Impact of Microscopic Pore Network Characteristics on Movable Fluid Properties in Tight Oil Reservoir by Jie Gao, Hu Wang, Xiaojun Ding, Qingxiao Yuchi, Qiang Ren, Bo Ning, Junxiang Nan

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Self-Supervised Neural Networks for Precoding in MIMO Rate Splitting Multiple Access Systems by Dheeraj Raja Kumar, Carles Anton-Haro, Xavier Mestre

“…The intention is to explore several alternatives to conventional iterative precoding benchmarks like Weighted Minimum Mean Square Error (WMMSE) which are computationally intensive algorithms. We evaluate the different precoding policies learnt by the neural network architectures by closely studying the respective radiation patterns. …”
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Real-Time Fire Risk Classification Using Sensor Data and Digital-Twin-Enabled Deep Learning by In-Seop Na, Vani Rajasekar, Velliangiri Sarveshwaran

“…Advanced deep learning architectures such as convolutional neural networks (CNNs), deep CNNs (DCNNs), and recurrent neural networks (RNNs) are utilized to identify critical spatial and temporal patterns in the data. …”
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