Ethical implications of preclinical studies by I. N. Tyurenkov, E. M. Lomkina, D. V. Kurkin, D. A. Bakulin, E. V. Volotova

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Research and application of micro-scale fracture prediction technology for deep coalbed methane based on five-dimensional seismic data by Lei ZHANG, Fengyin XU, Jiangnan TONG, Feng ZHANG, Feng WANG, Wei HOU, Yihang CHANG, Songyi HOU, Xiangbing PEI, Zhenyi CAO, Tong CHEN, Xuefeng LI, Ke ZHOU, Yongzhou LI, Huaibin ZHEN, Wenting ZENG

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New Cure Rate Survival Models Generated by Poisson Distribution and Different Regression Structures with Applications to Cancer Data Set by Mahdy Esmailian, Reza Azimi, Diego I. Gallardo, Parviz Nasiri

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A Review on High-Speed Electromagnetic Flow Control and Applications Considering Numerical Methods, Simulations, and Experimental Research Data by Yang Du, Jun Liu

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DREAMS: A python framework for training deep learning models on EEG data with model card reporting for medical applications by Rabindra Khadka, Pedro G. Lind, Anis Yazidi, Asma Belhadi

“…In this paper, we introduce DREAMS (Deep REport for AI ModelS), a Python-based framework designed to generate automated model cards for deep learning models applied to EEG data. Unlike generic model reporting tools, DREAMS is specifically tailored for EEG-based deep learning applications, incorporating domain-specific metadata, preprocessing details, performance metrics, and uncertainty quantification. …”
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Optimizing data collection requirements for machine learning models in wild blueberry automation through the application of DALL-E 2 by Connor C. Mullins, Travis J. Esau, Qamar U. Zaman, Patrick J. Hennessy

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Application of X-bar R Control Charts for Process Efficiency Monitoring: A Data-Driven Approach in Quality Management by Aleksy Kwilinski, Maciej Kardas, Nataliia Trushkina

“…A key contribution of the study lies in its demonstration of the practical applicability of control charts in quality management and the integration of data-driven techniques for process control. …”
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A Rapid DMeyer-CWT Method Application to the Spectrophotometric Data for the Quantification of Losartan Potassium and Hydrochlorothiazide in a Binary Mixture by Erdal Dinç, Özgür Üstündağ

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Application of Three-Phase Linearized Power Flow and Line Loss Analysis of Distribution Network Driven by Data and Physics Fusion by Huaitian MU, Hongliang LIAN, Juan LIU, Yanqiong LI

“…Moreover, because the grid-connected distributed generation changes the power flow direction of the system, the traditional theoretical line loss calculation methods such as the equivalent resistance method and the pressure drop method are no longer applicable. In order to solve the above problems, this paper proposed a smoothing model considering on-load tap changer regulation and distributed generation droop control and constructed a fast calculation model of linearized theoretical line loss of a three-phase distribution network driven by data and physics fusion. …”
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Estimation of parameters for the modified Weibull distribution under step-stress accelerated life testing with application to aircraft windshields data by Mohammed M. A. Almazah, Mohammed A. Alshahrani, Qasim Ramzan, Irfan Ali Raza, Muhammad Tahir, Zahoor Ahmad

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Multiple myeloma as a challenging multidimensional random process: a data-driven web-based application for treatment selection by Alexander S. Luchinin, Tigran G. Gevorkyan, Anastasia A. Semenova

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Research on the application of network security defence in database security services based on deep learning integrated with big data analytics by Feilu Hang, Linjiang Xie, Zhenhong Zhang, Wei Guo, Hanruo Li

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Non-parametric estimation of survival probabilities with a time-dependent exposure switch: application to (simulated) heart transplant data by Davide Paolo Bernasconi, Maria Grazia Valsecchi, Laura Antolini

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Prediction of Innovation Values of Countries Using Data Mining Decision Trees and a Comparative Application with Linear Regression Model by Merve Doğruel, Seniye Ümit Fırat

“…Linear regression analysis was performed with the same data set, and the regression tree obtained by the CART algorithm was compared with the linear regression model.…”
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Application of chemometric analysis using physicochemical and chromatographic data to differentiate the origin of plant protection products containing trinexapac-ethyl by Patrycja Marczewska, Joanna Rolnik, Tomasz Stobiecki

“…This study explored the application of chemometric methods based on physical, chemical, and technical parameters, as well as data obtained by high-performance liquid chromatography with a diode array detector (HPLC-DAD) and headspace gas chromatography coupled with mass spectrometry (HS-GC/MS), to verify the authenticity of PPPs containing trinexapac-ethyl. …”
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Detection of Illicit Conservation Treatments in Sea Bass (<i>Dicentrarchus labrax</i>): Application and Data Integration of NIR Spectrometers by Giovanna Esposito, Alessandro Benedetto, Elisa Robotti, Masho Hilawie Belay, Eleonora Goggi, Simone Cerruti, Nunzia Giaccio, Davide Mugetti, Emilio Marengo, Laura Piscopo, Marzia Pezzolato, Elena Bozzetta, Maria Cesarina Abete, Paola Brizio

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Artificial Polytropic Behavior of Plasmas Determined from the Application of Chi-squared Minimization Analysis to Data with Significant Statistical Uncertainty by Georgios Nicolaou, George Livadiotis, Charalambos Ioannou

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Modified tree-based selection in hierarchical mixed-effect models with trees: A simulation study and real-data application by Asrirawan, Khairil Anwar Notodiputro, Budi Susetyo, Sachnaz Desta Oktarina

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Can linear regression modeling help clinicians in the interpretation of genotypic resistance data? An application to derive a lopinavir-score. by Alessandro Cozzi-Lepri, Mattia C F Prosperi, Jesper Kjær, David Dunn, Roger Paredes, Caroline A Sabin, Jens D Lundgren, Andrew N Phillips, Deenan Pillay, EuroSIDA Study, United Kingdom CHIC/United Kingdom HDRD Study

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Data-Driven Incipient Fault Prediction for Non-Stationary and Non-Linear Rotating Systems: Methodology, Model Construction and Application by Qingfeng Wang, Bingkun Wei, Jiahe Liu, Wensheng Ma

“…Based on machine learning technology, this paper studies an incipient fault prediction model applying with wavelet packet decomposition and dynamic kernel principal component analysis (WPD-DKPCA) to meet the needs of engineering applications. The incipient fault prediction WPD-DKPCA model, which does not require knowledge on equipment structure and failure mechanisms, only requires normal state data of the machine, and incipient fault prediction can be achieved through self-learning. …”
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