Balancing Human Mobility and Health Care Coverage in Sentinel Surveillance of Brazilian Indigenous Areas: Mathematical Optimization Approach by Juliane Fonseca Oliveira, Adriano O Vasconcelos, Andrêza L Alencar, Maria Célia S L Cunha, Izabel Marcilio, Manoel Barral-Netto, Pablo Ivan P Ramos

“…ObjectiveThis study evaluates the current respiratory pathogen surveillance network in Brazil and proposes an optimized sentinel site distribution that balances Indigenous population coverage and national human mobility patterns. …”
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A deep learning model for predicting systemic lupus erythematosus-associated epitopes by Jiale He, Zixia Liu, Xiaopo Tang

“…Methods The framework comprises six interconnected components: (1) handcrafted feature extraction encoding biochemical and physicochemical attributes; (2) an embedding layer for dense sequence representation; (3) a Convolutional Neural Network (CNN) branch that captures local patterns from handcrafted features; (4) a Long Short-Term Memory branch for learning temporal dependencies in sequence data; (5) a scaled dot-product attention-based fusion module that integrates complementary information from both branches; and (6) a Multi-Layer Perceptron for final classification. …”
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Using Graph-Based Maximum Independent Sets with Large Language Models for Extractive Text Summarization by Cengiz Hark

“…Experiments on the Document Understanding Conference (DUC) and Cable News Network (CNN)/DailyMail datasets are conducted with different summary lengths to evaluate the performance of the framework. …”
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Multi-View Intrusion Detection Framework Using Deep Learning and Knowledge Graphs by Min Li, Yuansong Qiao, Brian Lee

“…The KG represents relational features combined with spatial features extracted by neural networks, enabling a more comprehensive representation of attack patterns through the synergy of both feature types. …”
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Research on Data Repair of Pile-Type Adjustable Wind Turbine Foundation Monitoring Based on FST-ATTNet by WEI Huanwei, ZHAO Jizhang, ZHENG Xiao, TAN Fang, LIU Cong

“…In the time domain, Bidirectional Gated Recurrent Units (BiGRU) capture both forward and backward dependencies within the time series, ensuring a comprehensive understanding of local sequence patterns. The Kolmogorov-Arnold Network (KAN) incorporates a B-spline activation function, further enhancing the model's ability to capture complex nonlinear temporal changes. …”
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Utility of a Large Series of B‐Cell Precursor Acute Lymphoblastic Leukemia Cell Lines as a Model System by Minori Tamai, Chiaki Komatsu, Keiko Kagami, Shin Kasai, Koshi Akahane, Kumiko Goi, Kanji Sugita, Chihiro Tomoyasu, Toshihiko Imamura, Hiroaki Goto, Takeshi Inukai

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Osteoporosis: a problem still faulty addressed by the Romanian healthcare system. Results of a questionnaire survey of people aged 40 years and over by Narcisa Jianu, Narcisa Jianu, Narcisa Jianu, Valentina Oana Buda, Valentina Oana Buda, Denisa Căpățână, Călin Muntean, Teodor Nicolae Onea, Maria Anastasia Jivulescu, Ana Teodor, Ana Teodor, Mirabela Romanescu, Mirabela Romanescu, Lucreția Udrescu, Lucreția Udrescu, Vlad Groza, Mihai Udrescu, Alina Ramona Buzatu, Cristina Adriana Dehelean, Cristina Adriana Dehelean, Minodora Andor, Minodora Andor

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Metaphors in the dictionary of German football language by R. V. Beliutin

“…The status of metaphors in the German football language and their firm position in this communicative space are distinguished through an outstanding network of synonymous constructions and variations, multifarious word-building patterns (mainly compounding) in developing new metaphoric units. …”
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MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching by Yanze Wang, Xuming Han, Shuai Lv, Ting Zhou, Yali Chu

“…Unlike traditional approaches that directly generate waveform outputs, MPFM-VC models the evolutionary trajectory of mel spectrograms with a flow-matching framework and incorporates a multi-dimensional feature perception network to enhance the stability and quality of speech synthesis. …”
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Multi-dimensional water quality indicators forecasting from IoT sensors: A tensor decomposition and multi-head self-attention mechanism. by Li Bo, Lv Junrui, Luo Xuegang

“…To overcome these limitations, we propose TGMHA (Tensor Decomposition and Gated Neural Network with Multi-Head Self-Attention), a novel hybrid model that integrates three key innovations: 1) Tensor-based Feature Extraction: We combine Standard Delay Embedding Transformation (SDET) with Tucker tensor decomposition to reconstruct raw time series into low-rank tensor representations, capturing latent spatio-temporal patterns while suppressing sensor noise. 2) Multi-Head Self-Attention for Inter-Indicator Dependencies: A multi-head self-attention mechanism explicitly models complex inter-dependencies among diverse water quality indicators (e.g., pH, dissolved oxygen, conductivity) via parallel feature subspace learning. 3) Efficient Long-Term Dependency Modeling: An encoder-decoder architecture with gated recurrent units (GRUs), optimized by adaptive rank selection, ensures efficient modeling of long-term dependencies without compromising computational performance. …”
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Object representations drive emotion schemas across a large and diverse set of daily-life scenes by Chuanji Gao, Susan Ajith, Marius V. Peelen

“…To explore this, we collected emotion ratings for 4913 daily-life scenes from 300 participants, and predicted these ratings from representations in deep neural networks and functional magnetic resonance imaging (fMRI) activity patterns in visual cortex. …”
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Research on deep learning model for stock prediction by integrating frequency domain and time series features by Wenjie Sun, Jianhua Mei, Shengrui Liu, Chunhong Yuan, Jiaxuan Zhao

“…By fusing information from both domains, the deep neural network significantly improves prediction accuracy and reliability. …”
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Mitigating Sinkhole Attacks in MANET Routing Protocols using Federated Learning HDBNCNN Algorithm by Sherril Sophie Maria Vincent

“…Then, every node gathers information about the local routing and contributes towards an inclusive model, which captures behaviour of the entire network when conserving its specific privacy. Further, the Hierarchical Deep Belief Network Convolutional Neural Network (HDBNCNN) algorithm has analysed the accumulated data in detecting the anomalies revealing the sinkhole activity centred on learning routing patterns. …”
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Developing a framework for medical student feedback literacy using a triangulated thematic analysis by Mohamad Nabil Mohd Noor, Jessica Grace Cockburn, Chan Choong Foong, Chiann Ni Thiam, Yang Faridah Abdul Aziz, Tiiu Sildva, Galvin Sim Siang Lin, Jamuna Vadivelu

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Revolutionizing spinal interventions: a systematic review of artificial intelligence technology applications in contemporary surgery by Hao Han, Ran Li, Dongming Fu, Hongyou Zhou, Zihao Zhan, Yi’ang Wu, Bin Meng

“…Abstract Leveraging its ability to handle large and complex datasets, artificial intelligence can uncover subtle patterns and correlations that human observation may overlook. …”
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Comparing 2D and 3D Feature Extraction Methods for Lung Adenocarcinoma Prediction Using CT Scans: A Cross-Cohort Study by Margarida Gouveia, Tânia Mendes, Eduardo M. Rodrigues, Hélder P. Oliveira, Tania Pereira

“…Next, a deep learning approach, based on a Residual Neural Network and a Transformer-based architecture, was utilised. …”
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Attention-driven echo cancellation: A novel transformer-based approach for robust acoustic echo and noise cancellation by Soni Ishwarya V, Mohanaprasad K

“…A case study was also included to evaluate the model's applicability.…”
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Hybrid SARIMA+BO-LSTM Framework for Forecasting EV Adoption: A Road to Net-Zero in Ireland by Afaq Khattak, Brian Caulfield

“…To support the Climate Action Plan target of registering 945,000 electric vehicles (EVs) by 2030, this study develops a hybrid time series forecasting framework that combines a Seasonal Autoregressive Integrated Moving Average (SARIMA) model with a Bayesian Optimized Long Short-Term Memory (BO-LSTM) network. SARIMA captures linear and seasonal patterns in monthly EV registration data, while BO-LSTM models the non-linear residual structure. …”
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Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

“…Methods: An autoregressive integrated moving average (ARIMA) model is compared against a long short-term memory (LSTM) neural network. The models are evaluated using both a static train-test split and a more rigorous expanding window forecast scheme. …”
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An Intelligent Contract-Driven Bidding Approach for Electric Vehicle Aggregators to Facilitate Blockchain-Powered Energy Trading by Imran Hussain, Hafiz Ashiq Hussain, Nasim Ullah, Stanislav Misak

“…The performance evaluation of the proposed scheme demonstrates how well the framework synchronizes power supply and demand by coordinating electric vehicles’ charging and discharging through an appropriate aggregator by consumption patterns. …”
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