The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force. The constructions and documentation of the testing platform through the instrumentation of a resistance welding will assess the variability of the input parameters and their impact on the output in surface and thermographic imaging, welding nugget diameter and it's mechanical strength. Additionally, it documents characteristics of the material used as thickness and material type and its output as the mechanical resistance and nugget diameter, along with its corresponding classification. Thus, the database not only captures the details of the welding process, but it also provides a valuable resource for analyzing and evaluating the performance of the welding operation.
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http://dx.doi.org/10.1016/j.dib.2025.111373 | DOI Listing |
Pediatr Infect Dis J
March 2025
From the Department of Pediatrics.
Background: Critically ill children are at risk for subtherapeutic antibiotic concentrations. The frequency of target attainment and risk factors for subtherapeutic concentrations of cefepime in children have not been extensively studied.
Methods: We performed an observational study in critically ill children receiving a new prescription of standard dosing of cefepime for suspected sepsis (≥2 systemic inflammatory response syndrome criteria within 48 hours of cefepime start).
Sci Adv
March 2025
Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark.
The human brain has a remarkable ability to learn and update its beliefs about the world. Here, we investigate how thermosensory learning shapes our subjective experience of temperature and the misperception of pain in response to harmless thermal stimuli. Through computational modeling, we demonstrate that the brain uses a probabilistic predictive coding scheme to update beliefs about temperature changes based on their uncertainty.
View Article and Find Full Text PDFInt J Womens Health
March 2025
Center of Excellence for Pharmaceutical Care Innovation, Universitas Padjadjaran, Bandung, Indonesia.
Introduction: Multiple micronutrient deficiencies might increase the adverse outcome during pregnancy and after birth. Considering the WHO recommendations since 2016 and scientific evidence from previous studies that multiple-micronutrient supplementation (MMS) is more effective than iron folic acid (IFA) in improving pregnant women's health, it is imperative to conduct an economic evaluation to assess the cost-effectiveness of MMS compared with IFA.
Methods: We conducted a systematic review from PubMed and Scopus to identify the cost-effectiveness analyses of MMS compared to IFA for pregnant women up to January 2024.
Data Brief
April 2025
Universidad Autónoma de Querétaro, Mexico.
The evaluation of the Resistance Spot Welding (RSW) that guarantees satisfactory performance of mechanical characteristics without altering physical properties can be reached by modeling the input parameters such as current, welding time, and applied force from which each unit has been built and correlating with digital images of the surface and infrared images that allows to identify variations on the parameters that modify the quality of the welding spot [1]. With this, mechanical and surface characteristics can be detected without the need for a mechanical test that modifies the structure of the unit. The database serves as a comprehensive record of the welding spot process, including the monitor of crucial input parameters such as current and force.
View Article and Find Full Text PDFSci Rep
March 2025
Department of Petroleum and Geo-energy Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
Given the application of cycloalkanes in surrogate blends for aviation fuels, their determination of critical characteristics pertinent to fuel transportation and combustion becomes imperative. In this study, we aim to construct intelligent models based on machine learning methods of random forest (RF), adaptive boosting, decision tree (DT), ensemble learning, K-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP) artificial neural network and convolutional neural network (CNN) to predict the density of binary blends of ethylcyclohexane or methylcyclohexane with n-hexadecane/n-dodecane/n-tetradecane in terms of operational conditions (pressure and temperature) and cycloalkane mole fractions in n-alkanes, utilizing laboratory data extracted from existing scholarly publications. The reliability of the data used is affirmed using an outlier detection algorithm, and the relevancy factor concept is utilized to find the relative effects of the input parameters on the output parameter.
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