Video-Assisted Thoracoscopic Lung Biopsy as a Possible Cause of Acute Interstitial Pneumonia in a Patient with Nonspecific Interstitial Pneumonia by D Jeffrey Moore, Colm P McParland, Martin J Bullock, Yannick Cartier, Paul Hernandez

“…High-resolution computed tomography showed features of usual interstitial pneumonia, but the lung biopsy obtained by video-assisted thoracoscopy was consistent with a histological pattern of nonspecific interstitial pneumonia. …”
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Preparation of Porous F-WO3/TiO2 Films with Visible-Light Photocatalytic Activity by Microarc Oxidation by Chung-Wei Yeh, Kee-Rong Wu, Chung-Hsuang Hung, Hao-Cheng Chang, Chuan-Jen Hsu

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Modeling the partial equilibrium in the milk and dairy market in Ukraine by S. Kvasha, N. Davydenko, A. Ivanko, Z. Titenko

“…Specifics of establishing the state of partial equilibrium in that market were characterized relative to the main groups of its participants as producers and consumers. Development of methodological and practical aspects of features of establishing the state of market equilibrium in the dairy market, unlike existing, is based on a model basis is still actual. …”
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An Explanation Interface for Healthy Food Recommendations in a Real-Life Workplace Deployment: User-Centered Design Study by Robin De Croon, Daniela Segovia-Lizano, Paul Finglas, Vero Vanden Abeele, Katrien Verbert

“…MethodsAn iterative, user-centered design process guided the development and refinement of the system across 4 phases: (Phase 0) an exploratory qualitative study (N=26) to understand stakeholder needs and initial system impressions, (Phases 1 and 2) rapid prototyping in real-life deployments (N=45 and N=16, respectively) to iteratively improve usability and features, and (Phase 3) a proof-of-principle study with employees (N=136) to evaluate a set of design goals. …”
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Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery by Gabija Veličkaitė, Ignas Daugėla, Ivan Suzdalev

“…Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. …”
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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

“…Feature analysis revealed RHOB, DT, NPHI, and Vp as the most significant inputs for predicting HRQI. …”
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Biomedical Data Analysis Systems for the Diagnosis of Skin Neoplasms by O. O. Myakinin

“…Algorithms for a dermatoscopic module are based on the Haar transform, Local Binary Patterns and color features. Algorithms for OCT are based on the texture features of Haralick, Tamura, fractal dimension, complex directional field and Markov random field. …”
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Optimization of CNN Activation Functions using Xception for South Sulawesi Batik Classification by Aswan Aswan, Eva Yulia Puspaningrum, Billy Eden William Asrul

“…The results demonstrate that selecting an optimal activation function substantially enhances convolutional neural network classification of complex batik patterns. The findings offer practical guidance for development of resource aware batik identification systems in support of cultural digitization and education initiatives.…”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

“…These datasets serve two functions: improving illness prediction and examining large data reservoirs to identify previously unknown disease patterns. Leveraging deep learning models, it becomes feasible to detect and forecast the early stages of numerous diseases based on individual health conditions. …”
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TableBorderNet: A Table Border Extraction Network Considering Topological Regularity by Jing Yang, Shengqiang Zhou, Xialing Li, Yuchun Huang, Honglin Jiang

“…Additionally, a generative self-supervised strategy simulates common degradation patterns, allowing the model to achieve strong performance with minimal reliance on manually annotated data. …”
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A Comprehensive Review of EEG-Based Seizure Detection Techniques by Goldwyn Sudhakar Jebaraj, Konguvel Elango

“…We examine crucial preprocessing techniques, feature extraction strategies, and the application of both established statistical models and contemporary deep learning architectures, including innovative approaches such as image-based representations of EEG data and transfer learning paradigms. …”
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Interpretable Deep Learning for Diabetic Retinopathy: A Comparative Study of CNN, ViT, and Hybrid Architectures by Weijie Zhang, Veronika Belcheva, Tatiana Ermakova

“…The models are assessed based on classification accuracy and interpretability, applying Grad-CAM and Attention-Rollout to analyze decision-making patterns. Results indicate that hybrid models outperform both standalone CNNs and ViTs, achieving a better balance between local feature extraction and global context awareness. …”
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Time-series and deep learning approaches for renewable energy forecasting in Dhaka: a comparative study of ARIMA, SARIMA, and LSTM models by Mohammad Liton Hossain, S. M. Nasif Shams, Saeed Mahmud Ullah

“…The ARIMA(2,1,2) and SARIMA(2,1,2)(1,1,1,24) models captured basic temporal patterns but struggled with non-linear dynamics, achieving R2 scores of − 0.0008 and − 0.1104, respectively. …”
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Synchronized Survey Scan Approach Allows for Efficient Discrimination of Isomeric and Isobaric Compounds during LC-MS/MS Analyses by Keabetswe Masike, Ntakadzeni Madala

“…Thus, the method was shown to distinguish (by differences in fragmentation patterns) between diCQA and their isobars, caffeoylquinic acid (CQA) glycosides. …”
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A Graph Representation Learning-Based Method for Event Prediction by Xi Zeng, Guangchun Luo, Ke Qin, Pengyi Zheng

“…Predicting events allows for the exploration of the developmental trajectories and summarization of patterns associated with these events. …”
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Deep Learning‐Based 3D Microseismic Event Direct Location Using Simultaneous Surface and Borehole Data by Yuanyuan Yang, Omar M. Saad, Tariq Alkhalifah

“…Abstract Microseismic monitoring is crucial for characterizing and assessing fracture systems developed during subsurface operations like geothermal reservoir development, CO2 sequestration, and hydraulic fracturing. …”
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CircDock6 drives metabolic dysfunction–associated steatotic liver disease progression in mice and mouse hepatocytes via mmu-let-7g-5p/insulin-like growth factor 1 receptor regulati... by Hongpeng Lu, Xiaoyun Ding, Peifei Li

“…Objective Circular RNAs belong to a category of noncoding RNAs that feature a unique continuous, covalently bonded ring configuration. …”
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A Smartphone-Based Non-Destructive Multimodal Deep Learning Approach Using pH-Sensitive Pitaya Peel Films for Real-Time Fish Freshness Detection by Yixuan Pan, Yujie Wang, Yuzhe Zhou, Jiacheng Zhou, Manxi Chen, Dongling Liu, Feier Li, Can Liu, Mingwan Zeng, Dongjing Jiang, Xiangyang Yuan, Hejun Wu

“…Based on the lightweight MobileNetV2 network, a Multi-scale Dilated Fusion Attention module (MDFA) was designed to enhance the robustness of color feature extraction. A Temporal Convolutional Network (TCN) was then used to model dynamic patterns in chemical indicators across spoilage stages, combined with a Context-Aware Gated Fusion (CAG-Fusion) mechanism to adaptively integrate image and chemical temporal features. …”
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Data quality and uncertainty issues in flood prediction: a systematic review by Jinhui Yu, Yichen Li, Xiao Huang, Xinyue Ye

“…These datasets often suffer from issues such as incompleteness, inconsistency, and accuracy deficits, further complicated by uncertainties arising from complex spatial features and environmental changes. The literature proposes a range of solutions, including the development of innovative methodologies, model construction, and comparative analysis, to address these challenges. …”
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Profiling antivaccination channels in Telegram: early efforts in detecting misinformation by Aelita Skarzauskiene, Monika Maciuliene, Aiste Dirzyte, Gintare Guleviciute

“…This study addresses the methodological gap in capturing and analysing misinformation on Telegram, with a particular focus on the anti-vaccination community.MethodsThe research was conducted in three phases: (1) a structured review of literature on misinformation dissemination via Telegram, (2) development of a conceptual framework incorporating features of message creators, message content, intended targets and broader social context, and (3) application of this framework to anti-vaccination Telegram channels using latent profile analysis (LPA). …”
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