Development and Application of Equipment for Anchor Cable with C-Shaped Tube Tension-Shear Test System by Renliang Shan, Chen Liang, Weijun Liu, Yuzhao Jiang, Tianwen Li

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Application of Variational Quantum Eigensolver for Ground State Energies Calculation in Hydrogen and Helium Atomic Sequences by Difa Farhani Hakim, Teguh Budi Prayitno, Yanoar Pribadi Sarwono

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A new application for reading optical form with standard scanner by using image processing techniques by Ayşe Eldem, Fatih Taha Özcan

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Bidirectional reduced-order electrothermal modeling of power MOSFETs for electric vehicle thermal management applications by Chih-Chun Hsu, Yi-Hsuan Hsieh, Hsiao Chi Tang, Yu-Min Meng, Ming-Shi Huang, Po-Hsuan Tseng, Hai-Han Lu, Hua-Yi Hsu

“…Thermal management of power MOSFETs in electric vehicle applications presents critical challenges due to nonlinear electrothermal coupling that invalidates conventional decoupled thermal models. …”
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Application of Fourier Transform Infrared Spectroscopy for the Oxidation and Peroxide Value Evaluation in Virgin Walnut Oil by Pengjuan Liang, Chaoyin Chen, Shenglan Zhao, Feng Ge, Diqiu Liu, Binqiu Liu, Qimeng Fan, Benyong Han, Xianfeng Xiong

“…PLS model correlates the actual and FT-IR estimated value of peroxide value with a correlation coefficient of 0.99, and the root mean square error of the calibration (RMSEC) value is 0.4838. …”
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NOVEL MICROWAVE SENSOR FOR ENHANCED BIOCHEMICAL DETECTION AND PREDICTION THROUGH MACHINE LEARNING FOR INDUSTRIAL APPLICATIONS by G. Challa Ram, M. Venkata Subbarao, K. Padma Satya Sri, Naveen Kumar Maurya, D. Ramesh Varma, M. Prema Kumar

“…Performance analysis indicates that exponential regression outperforms other approaches, showcasing a minimal root mean squared error of 0.0013. Machine learning techniques bring about substantial enhancements in sensor performance, thereby creating pathways for sophisticated applications in biochemical sensing.…”
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Application of Non-Linear System Model Updating Using Feature Extraction and Parameter Effects Analysis by John F. Schultze, François M. Hemez, Scott W. Doebling, Hoon Sohn

“…This approach is an expansion of the `classic' update metrics to include specific phenomena or character of the response that is critical to model application. This is an extension of the familiar linear updating paradigm of utilizing the eigen-parameters or frequency response functions (FRFs) to include such devices as peak acceleration, time of arrival or standard deviation of model error. …”
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A comprehensive study on robust Poisson James–Stein estimator for outlier and multicollinearity: Simulation and applications by Ohud A. Alqasem, Ali T. Hammad, Abdirashid M. Yousuf, E. Lsiddig Idriss Mohamed, Amin M. A. Haleeb, Ahmed M. Gemeay

“…Outliers introduce bias and inefficiency in the estimation, while multicollinearity leads to an inflated variance and thus increases the mean squared error and weakens the coefficient reliability. The Poisson maximum likelihood estimator often performs poorly under these conditions. …”
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Efficient Parallel Carrier Recovery for Ultrahigh Speed Coherent QAM Receivers with Application to Optical Channels by Pablo Gianni, Laura Ferster, Graciela Corral-Briones, Mario R. Hueda

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Optimized design of a permanent magnet brushless DC motor for solar water-pumping applications by Aryadip Sen, Bhim Singh, Kumar Mahtani, Arash Moradzadeh, S.M. Muyeen

“…The control strategy, based on two voltage sensing points, accurately manages electronic commutation and speed control across a wide range, independent of motor parameters, using adaptive error optimization. The motor and solar maximum power are controlled through a single-stage three-phase inverter, making the electronic controller compact and enhancing its suitability for integrated applications. …”
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Filter Learning-Based Partial Least Squares Regression and Its Application in Infrared Spectral Analysis by Yi Mou, Long Zhou, Weizhen Chen, Jianguo Liu, Teng Li

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Application and feasibility analysis of knowledge-based machine learning in predicting fatigue performance of stainless steel by Jia Wang, Dongkui Fan, C.S. Cai

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A novel model reference adaptive control approach investigation for power electronic converter applications by Muhammad Ahmed Qureshi, Salvatore Musumeci, Francesco Torelli, Alberto Reatti, Andrea Mazza, Gianfranco Chicco

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Projection and assessment of future droughts in Iowa: developing a machine learning model and an interactive application by Ingrid Cintura, Antonio Arenas

“…With a Root Mean Squared Error of 0.19 and an R2 of 91%, the model achieved a high level of accuracy, making it effective in guiding conservation practices and enabling timely interventions. …”
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MULTI-MODEL STACK ENSEMBLE DEEP LEARNING APPROACH FOR MULTI-DISEASE PREDICTION IN HEALTHCARE APPLICATION by Bhaskar Adepu, T. Archana

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Data-driven modeling of the Yld2000 yield criterion and its efficient application in numerical simulation by Xiaomin Zhang, Jianzhong Mao, Zhi Cheng

“…Compared with traditional analytical computations, the RF-based model achieves approximately a 50 % improvement in computational efficiency while maintaining comparable accuracy, with a maximum error of <0.5 % across different materials and tolerance conditions. …”
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Application of an improved LSTM model based on FECA and CEEMDAN VMD decomposition in water quality prediction by Jie Long, Chong Lu, Yiming Lei, Zhong Yuan Chen, Yihan Wang

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Development and testing of an open source mobile application for audiometry test result analysis and diagnosis support by Michał Kassjański, Marcin Kulawiak, Tomasz Przewoźny, Dmitry Tretiakow, Andrzej Molisz

“…Its integration in medical facilities presents a substantial opportunity to decrease clinical workload, enhance diagnostic accuracy and reduce the likelihood of human error in hearing loss evaluations, which is particularly important in developing countries.…”
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Application of 3D point cloud and visual-inertial data fusion in Robot dog autonomous navigation. by Hongliang Zou, Chen Zhou, Haibo Li, Xueyan Wang, Yinmei Wang

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Parameter estimation of submarine power cables in offshore applications using machine learning-based methods by Felipe P. de Albuquerque, Rafael Nascimento, Gabriel de Castro Biage, Rooney R.A. Coelho, Ronaldo F. Ribeiro Pereira, Eduardo C. Marques da Costa, Mario L. Pereira Filho, Cassio G. Lopes, José R. Cardoso

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