Towards representation learning of radar altimeter waveforms for sea ice surface classification by L. Happ, L. Happ, S. Patil, S. Hendricks, R. Fellegara, L. Kaleschke, A. Gerndt, A. Gerndt

“…Traditional waveform representations are limited to a small set of parameters, leading to information loss. Moreover, machine learning models for sea ice classification often depend on supervised training, which is vulnerable to uncertainties in labeled data, especially in polar regions. …”
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Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers. by Kenta Ninomiya, Hidetaka Arimura, Wai Yee Chan, Kentaro Tanaka, Shinichi Mizuno, Nadia Fareeda Muhammad Gowdh, Nur Adura Yaakup, Chong-Kin Liam, Chee-Shee Chai, Kwan Hoong Ng

“…The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.…”
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Design and performance investigation for substrate filler in protected horticulture by WEI Yuyong, LU Jun, SHENG Kuichuan, QIAN Xiangqun, SHEN Junfeng

“…Development of substrate filler machine and seedling production line can promote the seedling production in protected horticulture. …”
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The impact of integrated genomic surveillance on non-typhoidal Salmonella infection in Australia: an ecological studyResearch in context by Son Nghiem, Nhung Mai, My Tran, Danielle M. Cribb, Liliana Bulfone, Patiyan Andersson, Alireza Zahedi, Tuyet Hoang, Tehzeeb Zulfiqar, Angeline Ferdinand, Katie Glass, Martyn D. Kirk, Vitali Sintchenko, Amy V. Jennison, Benjamin P. Howden, Emily Lancsar

“…This method estimated the parameters of state and period fixed-effects and then adjusted the observed outcomes from those fixed-effects in the first stage. …”
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Analysis dynamic characteristics brushless motor of the mechatronic system in conditions of parametric uncertaintyby computer simulation methods by N. A. Malev, O. V. Pogoditsky, O. V. Kozelkov, A. M. Dyuryagin

“…Currently, brushless motors – electric machines with permanent magnets on the rotor and a rotor position sensor controlled by a sinusoidal voltage from frequency converters, are widely used in mechatronic and robotic systems. …”
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Predicting lymphovascular space invasion in early-stage cervical squamous cell carcinoma using heart rate variability by Junlong Fang, Ming Liu, Zhijing Song, Yifang Zhang, Bo Shi, Jian Liu, Sai Zhang

“…Preoperative 5-minute electrocardiogram (ECG) data were collected from all patients, and their HRV parameters were analysed, including 7 time-domain parameters, 5 frequency-domain parameters, and 2 nonlinear parameters. …”
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Usability testing a web application to support evidence-based commissioning decisions for implementing mobile stroke units by Lisa Moseley, Anna Laws, Michael Allen, Gary A. Ford, Martin James, Stephen McCarthy, Graham McClelland, Laura J. Park, Kerry Pearn, Daniel Phillips, Christopher Price, Lisa Shaw, Phil White, David Wilson, Peter McMeekin, Jason Scott

“…Sixteen stakeholders involved in developing consensus on model parameters and pathways participated with data thematically analysed. …”
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Accurately Models the Relationship Between Physical Response and Structure Using Kolmogorov–Arnold Network by Yang Wang, Changliang Zhu, Shuzhe Zhang, Changsheng Xiang, Zhibin Gao, Guimei Zhu, Jun Sun, Xiangdong Ding, Baowen Li, Xiangying Shen

“…However, many current machine learning methods lack interpretability, making it difficult to grasp the physical mechanisms behind various phenomena, which hampers progress in related fields. …”
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Analysis of the Resistance to Teeth During the Picking Process Based on DEM-MBD Coupling Simulation by Weiquan Fang, Xinzhong Wang, Dianlei Han, Israel Enema Ohiemi

“…To improve the film-picking performance of toothed chain tillage residual film recycling machines, the working parameters of a film-picking device were optimized using a Box–Behnken design, with the film-picking rate as the response parameter. …”
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Detection of Dairy Herd Management Issues Using Fatty Acid Profiles Predicted by Mid-Infrared Spectrometry by Sébastien Franceschini, Claire Fastré, Charles Nickmilder, Débora E. Santschi, Daniel Warner, Mazen Bahadi, Carlo Bertozzi, Didier Veselko, Frédéric Dehareng, Nicolas Gengler, Hélène Soyeurt

“…Milk samples were analyzed through Fourier Transform mid-infrared spectrometry every 1 to 3 days, and their compositions were predicted using machine learning models. Among the predicted parameters, fatty acid profiles appear to be effective indicators of animal status and management practices. …”
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Deep Learning Unravels Differences Between Kinematic and Kinetic Gait Cycle Time Series from Two Control Samples of Healthy Children Assessed in Two Different Gait Laboratories by Alfonso de Gorostegui, Damien Kiernan, Juan-Andrés Martín-Gonzalo, Javier López-López, Irene Pulido-Valdeolivas, Estrella Rausell, Massimiliano Zanin, David Gómez-Andrés

“…Our study emphasizes the importance of standardized protocols and robust data pre-processing to enhance the transferability of machine learning models across clinical settings, particularly for deep learning approaches.…”
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Kinematic calculation of micro mirror elements in micro electro-mechanical systems (MEMS) by D. I. Chernyavsky, D. D. Chernyavsky

“…Currently, the development and application of micro machines is an important direction in the development of microelectromechanical systems (MEMS) technologies. …”
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Software Package for Optimization of Burner Devices on Dispersed Working Fluids by Ruslan V. Fedorov, Igor I. Shepelev, Mariia A. Malyoshina, Dmitry A. Generalov, Vyacheslav V. Sherkunov, Valeriy V. Sapunov

“…Particular attention is paid to the control of fuel composition and geometric parameters of a burner device. Optimal settings of these parameters can significantly impact the reduction in harmful emissions into the atmosphere, though finding such parameters is a labor-intensive process and requires the use of modern automation and data processing tools. …”
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Evaluation of Shelf Life Prediction for Broccoli Based on Multispectral Imaging and Multi-Feature Data Fusion by Xiaoshuo Cui, Xiaoxue Sun, Shuxin Xuan, Jinyu Liu, Dongfang Zhang, Jun Zhang, Xiaofei Fan, Xuesong Suo

“…Multi-feature data fusion of spectral image information and physical and chemical parameters were combined with different machine learning methods to predict and evaluate the shelf life of broccoli.…”
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Research on Fracture Energy Prediction and Size Effect of Concrete Based on Deep Learning with SHAP Interpretability Method by Huiming Wang, Weiqi Zhang, Jie Lin, Shengpin Guo

“…The model effectively captures the intricate nonlinear relationship among characteristic parameters, exhibiting superior accuracy and generalization capabilities compared to empirical formulas. …”
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Effect of warm-rolling and annealing on the microstructure and mechanical properties of dual-phase Al0.45CoCrFeNiTi0.25 high-entropy alloy by Enhao Wang, Jiaqi Li, Fuwei Kang, Xiaolei Liu, Fengchun Jiang, Hongtao Chen, Wei Jiang, Yang Cao

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The Influence of Pulsed Electron Beam Processing on the Quality of Working Surfaces of Titanium Alloy Products by Undrakh Mishigdorzhiyn, Aleksey Pyatykh, Andrey Savilov, Nikolay Ulakhanov, Ivan Galetsky, Kirill Demin, Alexander Tikhonov, Maxim Vorobyov, Elizaveta Petrikova, Shunqi Mei

“…A friction machine with a counterbody was used to study the wear resistance of the specimen surface. …”
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Multi‐Wound Classification: Exploring Image Enhancement and Deep Learning Techniques by Prince Odame, Maxwell Mawube Ahiamadzor, Nana Kwaku Baah Derkyi, Kofi Agyekum Boateng, Kelvin Sarfo‐Acheampong, Eric Tutu Tchao, Andrew Selasi Agbemenu, Henry Nunoo‐Mensah, Dorothy Araba Yakoba Agyapong, Jerry John Kponyo

“…FixCaps had trainable parameters of about 8.28 MB compared with the 195.64 MB of the Gated Wavelet CNN Model.…”
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Integrating multidimensional data analytics for precision diagnosis of chronic low back pain by Sam Vickery, Frederick Junker, Rebekka Döding, Daniel L. Belavy, Maia Angelova, Chandan Karmakar, Luis Becker, Nima Taheri, Matthias Pumberger, Sandra Reitmaier, Hendrik Schmidt

“…We leveraged a comprehensive multi-dimensional data-set and machine learning-based variable importance selection to identify the most effective modalities for differentiating whether a person has cLBP. …”
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Adaptive Management of Multi-Scenario Projects in Cybersecurity: Models and Algorithms for Decision-Making by Vadim Tynchenko, Alexander Lomazov, Vadim Lomazov, Dmitry Evsyukov, Vladimir Nelyub, Aleksei Borodulin, Andrei Gantimurov, Ivan Malashin

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