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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Improved whole-brain reconfiguration efficiency reveals mechanisms of speech rehabilitation in cleft lip and palate patients: an fMRI study by Wenjing Zhang, Qian Si, Zhongtian Guan, Lei Cao, Mengyue Wang, Cui Zhao, Liwei Sun, Xu Zhang, Zhixi Zhang, Chunlin Li, Weiqun Song

“…However, the pattern and underlying mechanisms of functional changes in the brain are not yet clear. …”
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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

“…Machine learning algorithms have already shown the potential to recognize patterns in CT scans to classify the cancer subtype. …”
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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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Unraveling the Complexity of Parkinson's Disease: Insights into Pathogenesis and Precision Interventions by Han Yan, Cole Coughlin, Lee Smolin, Jin Wang

“…The basal ganglia network, specifically the subthalamic nucleus‐external globus pallidus (STN‐GPe) circuitry, exhibits abnormal activity associated with motor dysfunction. …”
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Predicting hydrocarbon reservoir quality in deepwater sedimentary systems using sequential deep learning techniques by Xiao Hu, Jun Xie, Xiwei Li, Junzheng Han, Zhengquan Zhao, Hamzeh Ghorbani

“…Three sequential deep learning models—Recurrent Neural Network and Gated Recurrent Unit—were developed and optimized using the Adam algorithm. …”
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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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Hybrid boosted attention-based LightGBM framework for enhanced credit risk assessment in digital finance by Chengwei Ying, Anlu Shi, Xiongyi Li

“…The HBA-LGBM model introduces four key innovations: (1) a multi-stage feature selection mechanism that dynamically filters key borrower attributes; (2) an attention-based feature enhancement layer, which prioritizes critical financial risk factors dynamically based on contextual importance; (3) a hybrid boosting strategy, integrating LightGBM with an adaptive neural network, enabling the model to capture complex borrower behavior and non-linear credit risk patterns; and (4) an advanced imbalanced learning strategy, combining synthetic data augmentation and cost-sensitive learning to mitigate class imbalance and enhance minority class predictions. …”
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Generation Method for HVAC Systems Design Schemes in Office Buildings Based on Deep Graph Generative Models by Hongxin Wang, Ruiying Jin, Peng Xu, Jiefan Gu

“…Accuracy and precision metrics were used to compare the generated designs with automated forward design solutions, assessing the models’ ability to capture both local variations and broader changes in design logic. A graph-neural-network-based evaluation method was employed to measure the models’ capacity to detect changes. …”
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Breast cancer classification based on hybrid CNN with LSTM model by Mourad Kaddes, Yasser M. Ayid, Ahmed M. Elshewey, Yasser Fouad

“…CNNs extract mammographic features, including spatial hierarchies and malignancy patterns, whereas LSTM networks characterize sequential dependencies and temporal interactions. …”
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Advanced Multi-Scale CNN-BiLSTM for Robust Photovoltaic Fault Detection by Xiaojuan Zhang, Bo Jing, Xiaoxuan Jiao, Ruixu Yao

“…The proposed framework employs four parallel CNN branches with kernel sizes of 3, 7, 15, and 31 to capture temporal patterns across various time scales. These features are then integrated by an adaptive feature fusion network that utilizes multi-head attention. …”
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Pharmacovigilance of drug-induced cataract using the FDA Adverse Event Reporting System by Si-Yuan Hong, Wen-Rui Lu, Xiao-Dong Chen, Hui-Ying Rao, Han Chen

“…This study provides a comprehensive pharmacovigilance evaluation, offering insights into high-risk medications, their onset patterns, and demographic trends, contributing to improved clinical decision-making and cataract prevention strategies.…”
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MTHFD1L is a novel prognostic marker and therapeutic target in cutaneous melanoma by Xinlian Xie, Guangfu Shi, Kebin Ning, Zhenjie Wu

“…Methods This investigation employed the TCGA-SKCM dataset along with combined GSE15605 and GSE19234 datasets to analyze MTHFD1L expression patterns. Comprehensive bioinformatics analyses were conducted, including GO and KEGG pathway enrichment analyses, protein-protein interaction network construction, and evaluation of the relationship between MTHFD1L expression and immune infiltration. …”
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TOM40 as a prognostic oncogene for oral squamous cell carcinoma prognosis by Lifei Deng, Hong Ran, Dunhui Yang, Zhen Wang, Peng Zhao, Hengjie Huang, Yongjin Wu, Peng Zhang

“…The immune infiltration pattern in OSCC was analyzed in the TCGA-OSCC cohort using the CIBERSORT algorithm. …”
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Research Trends in the Field of Participatory Governance: Drawing a Map of Co-occurrence and Co-word Analysis by Seyed Mohsen Mirbagheri, Seyed Mostafa Jalili

“…The Co-word Analysis further elucidates that "management", "network", and "policy" are the concepts most intricately intertwined with participatory governance. …”
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Woody legacies of railroad ties from the Southern Atacama Desert used to strengthen Nothofagus obliqua tree-ring chronologies from Northern Patagonia by Isadora Schneider-Valenzuela, Ariel A. Muñoz, Duncan A. Christie, Karin Klock-Barría, María Eugenia Solari, Marcelo Madariaga-Burgos, Rocío Urrutia-Jalabert, Isabella Aguilera-Betti, Santiago Ancapichún, Alejandro Venegas-González, Mauro E. González

“…The large-scale manufacturing of railroad ties distributed across Chile drove part of this exploitation. This study evaluated the use of this cultural material from abandoned tracks preserved in the southern Atacama Desert to strengthen the existing N. obliqua tree-ring network in Patagonia. …”
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Estimation the validity of remote sensed rainfall data over selected regions in Iraq by Zahraa Al-Montaser

“…Existing rain gauge networks lack the temporal and geographical coverage required for adequate monitoring. …”
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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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