Vibration Control of Double-Beam System with Multiple Smart Damping Members by Dominik Pisarski, Tomasz Szmidt, Czesław I. Bajer, Bartłomiej Dyniewicz, Jacek M. Bajkowski

“…By employing the finite element model, the performance of the controller is validated for strong wind blow load and concentrated impact excitement of the particular point of one of the beams. …”
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Exploration of ductility for refractory high entropy alloys via interpretive machine learning by Shaolong Zheng, Lingwei Yang, Liyang Fang, Chenran Xu, Guanglong Xu, Yifang Ouyang, Xiaoma Tao

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JAXA Level 2 cloud and precipitation microphysics retrievals based on EarthCARE radar, lidar, and imager: the CPR_CLP, AC_CLP, and ACM_CLP products by K. Sato, H. Okamoto, T. Nishizawa, Y. Jin, T. Y. Nakajima, M. Wang, M. Satoh, W. Roh, W. Roh, H. Ishimoto, R. Kudo

“…Simulated EarthCARE Level 1 data mimicking actual global observations were used to assess the performance of the JAXA Level 2 cloud microphysics product. …”
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ML-Driven Transistor Self-Heating Analysis From TCAD to Large IP Circuits by Simon Thomann, Nico Mayr, Albi Mema, Hussam Amrouch

“…We propose a technology-agnostic methodology for extracting localized temperature data on the circuit level using the trained machine learning model (with a mean error of 0.1 K). We explore our method to study self-heating in complex large circuits featuring over 3700 transistors. …”
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Optimized wavelength selection for eggplant seed vitality classification using information acquisition techniques by Bing Yang, Xuyang Liu, Dongfang Zhang, Dongfang Zhang, Xiaofei Fan, Xiaofei Fan, Bo Peng, Jun Zhang, Jun Zhang

“…The MSC-EIAO-RF model demonstrated the best performance, achieving an accuracy of 91.45%, which is 9.04% higher than the MSC-IAO model (82.41%).Validation on four UCI datasets further confirmed the EIAO algorithm's superiority over conventional feature selection methods. …”
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Hadamard defense mechanisms for rice grain image privacy by D. Suma, V. G. Narendra, M. Raviraja Holla, M. Darshan Holla

“…The proposed model achieves 97.01% reconstruction accuracy, 45 ms processing time, 60 KB memory usage, and demonstrates strong robustness (score: 0.89), scalability (60 images/s), and energy efficiency (0.035 J/image), outperforming existing approaches in security and performance.…”
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DeepSpoofNet: a framework for securing UAVs against GPS spoofing attacks by Aziz Ur Rehman Badar, Danish Mahmood, Adeel Iqbal, Sung Won Kim, Sedat Akleylek, Korhan Cengiz, Ali Nauman

“…The Analysis of Variance (ANOVA) + CNN-BiLSTM hybrid model demonstrates superior performance, producing exceptional results with a precision of 98.84%, accuracy of 99.25%, F1 score of 99.26%, and recall of 99.69%. …”
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Super-resolution reconstruction of mine image based on generative adversarial network by Fan ZHANG, Ying LIU, Hui SONG, Jiarong ZHANG, Haixing CHENG

“…First, two 5×5 convolutional layers are used in the low-level feature extraction layer and reconstruction layer of the generator, and non-linearity is added after each convolutional layer of the low-level feature extraction layer, and the high-level feature extraction layer adopts the residual structure, and the sub-pixel convolutional layer is cascaded to achieve super-resolution reconstruction of different multiples. …”
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Machine Learning-Based Prediction of Postoperative Deep Vein Thrombosis Following Tibial Fracture Surgery by Humam Baki, İsmail Bülent Özçelik

“…The top-performing model (random forest with RFE) attained an AUC of ~0.99, while several others (including LightGBM and SVM-based models) also reached AUC values in the 0.97–0.99 range. …”
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Ecosystem Sustainability Assessment Using IUCN (Case Study: Dorook Basin) by Atefeh Teimoori, Seyed Mortza Seyedian, Hamed Rouhani, Reza Ahmadi

“…Weather and climate criteria in Aghcheshme sub-basins is in acceptable condition and in Ghaleghafi sub-basins is in the middle. 4-Conclusion Sustainable development indicators are expressing such interaction model between society and the environment. In general, Dorook basin ecosystems in Ghaleghafi sub-basins socio-economic performance and water criteria is in stable condition and in Aghcheshme sub-basins conservation and soil conservation are in unstable condition. …”
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Õhtumaine kirikulaul ja vaimne heaolu Anno Domini 2024. Juhtumiuurimus Pühalaulu Kooli kogukonnas / Occidental Ecclesiastical Chant and Mental Well-Being Anno Domini 2024: A Case S... by Eerik Jõks

“…The practice of pühalaul was seen not as musical performance but as communal spiritual action, where boundaries between performer and audience dissolve. …”
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Gear Fault Diagnosis Based on Empirical Mode Decomposition and 1.5 Dimension Spectrum by Jianhua Cai, Xiaoqin Li

“…The result shows that this method can greatly inhibit the interference of Gauss noise to raise the SNR and recognize the secondary phase coupling feature of the signal. The proposed method has a good real-time performance and provides an effective method to determine the early crack fault of gear root.…”
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A case study of optimal design and techno-economic analysis of an islanded AC microgrid by M. S. Elborlsy, R. M. Mostafa, Mohamed A. Ghalib, Shimaa Barakat, H. E. Keshta

“…The results reveal the contribution of each energy component to meeting the electricity demand, yielding an LCOE of $0.153/kWh and a TNPC of $1,775,300.00. The dynamic performance of the islanded microgrid is examined, introducing a Model Reference Adaptive Control based PI controller (MRAC-PI) to enhance transient response across all operational conditions. …”
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Boosting skin cancer diagnosis accuracy with ensemble approach by Priya Natha, Sivarama Prasad Tera, Ravikumar Chinthaginjala, Safia Obaidur Rab, C. Venkata Narasimhulu, Tae Hoon Kim

“…Moreover, feature vectors that were optimally produced from image data by a Genetic Algorithm (GA) were given to the ML models. …”
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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

“…Among the evaluated models—Bayes Net, Naive Bayes, SMO, K-Nearest Neighbors (KNN), Logistic Regression, AdaBoost, Multilayer Perceptron (MLP), J48, Logistic Model Tree (LMT), and Random Forest (RF)—the best-performing classifiers (MLP, LMT, J48 and Logistic Regression) achieved F1-score values exceeding 0.83, indicating strong predictive capability. …”
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Prompt-based fine-tuning with multilingual transformers for language-independent sentiment analysis by Faizad Ullah, Safiullah Faizullah, Imdad Ullah Khan, Turki Alghamdi, Toqeer Ali Syed, Ahmad B. Alkhodre, Muhammad Sohaib Ayub, Asim Karim

“…Baseline models are established using traditional machine learning approaches such as Support Vector Machines (SVM) and Logistic Regression, with feature extraction methods like TF-IDF. …”
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Deciphering microbial and metabolic influences in gastrointestinal diseases-unveiling their roles in gastric cancer, colorectal cancer, and inflammatory bowel disease by Daryll Philip, Rebecca Hodgkiss, Swarnima Kollampallath Radhakrishnan, Akshat Sinha, Animesh Acharjee

“…These models were then employed for cross-disease analysis, revealing that models trained on GC data successfully predicted IBD biomarkers, while CRC models predicted GC biomarkers with optimal performance scores. …”
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Integrated GBR–NSGA-II Optimization Framework for Sustainable Utilization of Steel Slag in Road Base Layers by Merve Akbas

“…Subsequently, the Gradient Boosted Regressor model was integrated into a Non-Dominated Sorting Genetic Algorithm II (NSGA-II) framework to explore optimal trade-offs between cost and emissions. …”
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Global road extraction using a pseudo-label guided framework: from benchmark dataset to cross-region semi-supervised learning by Xiaoyan Lu, Yanfei Zhong, Zhuo Zheng, JunJue Wang, Dingyuan Chen, Yu Su

“…To enhance the quality of the pseudo-labels, strong data augmentation perturbation and auxiliary feature perturbation techniques are employed to ensure model prediction consistency. …”
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AFT-SAM: Adaptive Fusion Transformer with a Sparse Attention Mechanism for Audio–Visual Speech Recognition by Na Che, Yiming Zhu, Haiyan Wang, Xianwei Zeng, Qinsheng Du

“…The algorithm adopts the sparse attention mechanism in the feature-encoding process to reduce excessive attention to non-important regions and dynamically adjusts the attention weights through adaptive fusion to capture and integrate the multimodal information more effectively and reduce the impact of redundant information on the model performance. …”
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