Integration of OWL Password-Authenticated Key Exchange Protocol to Enhance IoT Application Protocols by Yair Rivera Julio, Angel Pinto Mangones, Juan Torres Tovio, María Clara Gómez-Álvarez, Dixon Salcedo

“…Its one-round exchange model and resistance to both passive and active attacks make it particularly well-suited for constrained devices and dynamic network topologies. …”
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Risk factors for head and neck squamous cell carcinoma in disease pathogenesis and prognosis: the importance of smoking cessation and the concept of harm reduction by A. I. Stukan, V. N. Bodnya, V. A. Porkhanov, T. Yu. Semiglazova, O. Yu. Chukhray, E. A. Nefedova, O. N. Nefedov, D. V. Xenodokhov, S. D. Maksimenko

“…To analyze the smoking status and the effect of etiopathogenesis on molecular and clinical features in patients with oropharyngeal squamous cell carcinoma (OpSCC).   …”
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An explainable multi-task deep learning framework for crash severity prediction using multi-source data by Yuanyuan Xiao, Zongtao Duan

“…The explicit interpretation mechanism bridges the critical gap between predictive performance and model interpretability in traffic safety analytics, providing engineering-relevant insights. …”
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Predicting clozapine-induced adverse drug reaction biomarkers using machine learning by John-Paul Cooper, Pierre Chue, Arno G. Siraki, Lusine Tonoyan

“…The best-performing model was the Gradient Boosting with Synthetic Minority Over-sampling technique (GB-SMOTE), achieving recall (sensitivity) of 0.85, AUC-PR (area under the precision-recall (PR) curve) of 0.77, PPV (Positive Predictive Value) of 0.40 and a Matthews Correlation Coefficient of 0.56. …”
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Biomechanical Optimization of Lumbar Fusion Cages with a Porous Design: A Finite Element Analysis by Chenkai Zhu, Kan Deng, Zhenzong Shao, Yong Wang

“…A finite element model of the L3–L4 segment, derived from CT scans of a healthy male volunteer, was developed to simulate six motion modes (compression, rotation, flexion, extension, and left/right bending). …”
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EDGE DETECTION TECHNIQUE BASED ON BILATERAL FILTERING AND ITERATIVE THRESHOLD SELECTION ALGORITHM AND TRANSFER LEARNING FOR TRAFFIC SIGN RECOGNITION by Milind PARSE, Dhanya PRAMOD

“…The proposed edge detection algorithm and transfer learning is used to train the Convolutional Neural Network (CNN) models and recognize the traffic signs. The performance of the proposed method is evaluated and compared with existing edge detection methods. …”
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Enhanced People Re-identification in CCTV Surveillance Using Deep Learning: A Framework for Real-World Applications by Mossaab Idrissi Alami, Abderrahmane Ez-zahout, Fouzia Omary

“…Our model's performance is assessed using key metrics, including Cumulative Matching Characteristic (CMC) and mean Average Precision (mAP), demonstrating its robustness in diverse surveillance scenarios. …”
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SMALL OBJECT DETECTION APPROACH BASED ON ENHANCED SINGLE-SHOT DETECTOR FOR DETECTION AND RECOGNITION OF INDONESIAN TRAFFIC SIGNS by Phie Chyan, Norbertus Tri Suswanto Saptadi, Jeremias Mathias Leda

“…Current traffic sign detection and recognition model technology is proficient in identifying and interpreting traffic signs. …”
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Novel design of magnetization and study of magnetic force characteristics of annular Halbach permanent magnet electrodynamic wheel by CHEN Yihao, LIANG Le, LIU Xin, DENG Zigang

“…A three-dimensional finite element model was created to determine the optimal size of each structure through a solving process that incorporates two performance indexes: the levitation-to-weight and levitation-to-drag ratios. …”
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269 Systems Marketing Analysis for Research Translation (SMART) innovation program by Peter S. Hovmand, Bethany Snyder, Callie Ogland-Hand, Braveheart Gillani, Robinson Salazar, Brian J. Biroscak

“…Results/Anticipated Results: Anticipated results from piloting the SMART innovation program with four research teams include (1) design matrices pre and post-workshop for each innovation; (2) system dynamics simulation models and analyses of implementation and scale-up of innovations; (3) analysis of the SMART program for highest impact, with priors for estimating subsequent SMART program performance; and (4) a revised SMART program based on results from the developmental program evaluation. …”
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Multi-modal action recognition via advanced image fusion techniques for cyber-physical systems by Zaiyong Shou, Daoyu Zhu

“…Traditional methods often lack adaptability and perform poorly when integrating diverse information sources, such as spatial and temporal cues from diverse image sources.MethodsTo address these limitations, we propose a novel Multi-Scale Attention-Guided Fusion Network (MSAF-Net), which leverages advanced image fusion techniques to significantly enhance action recognition performance in CPS environments. …”
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Proteomic alterations in ovarian cancer—Predicting residual disease status using artificial intelligence and SHAP-based biomarker interpretation by Seyma Yasar, Rauf Melekoglu

“…From an initial set of 97 differentially expressed proteins, 18 significant proteins were selected using the BORUTA feature selection method. Three machine learning models-Random Forest (RF), Support Vector Machine (SVM), and Bootstrap Aggregation with Classification and Regression Trees (BaggedCART)-were developed and evaluated.ResultsThe Random Forest model achieved the best performance with an AUC of 0.955, accuracy of 0.830, sensitivity of 0.904, specificity of 0.763, and F1-score of 0.839. …”
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A Fast and Low-Impact Embedded Orientation Correction Algorithm for Hand Gesture Recognition Armbands by Andrea Mongardi, Fabio Rossi, Andrea Prestia, Paolo Motto Ros, Danilo Demarchi

“…This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. …”
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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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Poincare guided geometric UNet for left atrial epicardial adipose tissue segmentation in Dixon MRI images by Marjan Firouznia, Erik Ylipää, Markus Henningsson, Carl-Johan Carlhäll

“…In this study, we present and validate PoinUNet, a deep learning model that integrates a Poincaré embedding layer into a 3D UNet to enhance LA wall and fat segmentation from Dixon MRI data. …”
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Innovation in Russian District Heating: Opportunities, Barriers, Mechanisms by Andrey Kovalev, Liliana Proskuryakova

“…Energy infrastructure innovation centres are sparse and feature limited specialization and competition. Large companies tend to follow the ‘closed innovation’ model where R&D activities are concentrated within an organization, and focus on incremental innovations while lagging in radical innovations in cogeneration and trigeneration. …”
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Trustworthy AI and Federated Learning for Intrusion Detection in 6G-Connected Smart Buildings by Rosario G. Garroppo, Pietro Giuseppe Giardina, Giada Landi, Marco Ruta

“…Collaborative training across multiple cooperative smart buildings enables model development without direct data sharing, ensuring privacy by design. …”
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Enhancing precision flood mapping: Pahang's vulnerability unveiled. by Tahmina Afrose Keya, Siventhiran S Balakrishnan, Maheswaran Solayappan, Saravana Selvan Dheena Dhayalan, Sreeramanan Subramaniam, Low Jun An, Anthony Leela, Kevin Fernandez, Prahan Kumar, A Lokeshmaran, Abhijit Vinodrao Boratne, Mohd Tajuddin Abdullah

“…The Random Forest (RF) model was employed in this study to categorize the study area into distinct flood susceptibility classes. …”
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A Deep Learning Algorithm for Multi-Source Data Fusion to Predict Effluent Quality of Wastewater Treatment Plant by Shitao Zhang, Jiafei Cao, Yang Gao, Fangfang Sun, Yong Yang

“…Finally, the RReliefF approach was used to identify the key parameters affecting the water quality behaviour of IETP effluent, and it was found that, by optimising the multi-source feature structure, not only the monitoring and management strategies can be optimised, but also the modelling efficiency of the model can be further improved.…”
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Predicting Wastewater Characteristics Using Artificial Neural Network and Machine Learning Methods for Enhanced Operation of Oxidation Ditch by Igor Gulshin, Nikolay Makisha

“…A total of 20 time lags and the “month” feature were selected as significant predictors. These models employed include Multi-head Attention Gated Recurrent Unit (MAGRU), long short-term memory (LSTM), Autoregressive Integrated Moving Average–Long Short-Term Memory (ARIMA–LSTM), and Prophet and gradient boosting models: CatBoost and XGBoost. …”
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