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High-temperature strain gauges are widely used in the strain monitoring of the hot-end components of aero-engines. In the application of strain gauges, the calibration of the gauge factor (GF) is the most critical link. Evaluating the uncertainty of GF is of great significance to the accuracy analysis of measurement results. Firstly, the calibration test of the GF of the Pt-W high-temperature strain gauge was carried out in the range of 25 °C to 900 °C. The real test data required for the uncertainty evaluation were obtained. Secondly, the guide to the expression of uncertainty in measurement (GUM) and the Monte Carlo method (MCM) were used to evaluate the uncertainty of GF calibration test. The evaluation results of GUM and MCM were compared. Finally, the concept of the weight coefficient W was proposed to quantitatively analyze the influence of each input on the uncertainty of the output GF. The main uncertainty source was found, which had important engineering practical significance. The results show that the mean value of GF decreases with the increase in temperature nonlinearly. At 25 °C, GF is 3.29, and at 900 °C, GF decreases to 1.6. Through comparison and verification, the uncertainty interval given by MCM is closer to the real situation. MCM is superior to GUM, which only uses prior information for uncertainty assessment. MCM is more suitable for evaluating GF uncertainty. Among multiple uncertain sources, the weight coefficient W can effectively analyze Δε as the main uncertain source.
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http://dx.doi.org/10.3390/s25051633 | DOI Listing |
Cureus
August 2025
Department of Orthopaedic Surgery, Teikyo University School of Medicine, Tokyo, JPN.
Osteoporosis is a common condition, and treatment can reduce the risk of fracture and extend healthy life expectancy, but most cases go undiagnosed and untreated. Dual-energy X-ray absorptiometry (DXA), the gold standard for diagnosing osteoporosis, is costly, time-consuming, and labor-intensive, with limited availability in low-resource settings and small clinics, so it is not suitable for screening for potential osteoporosis. To address this problem, in recent years, some studies have attempted to screen for osteoporosis by estimating DXA bone mineral density (BMD) from chest radiographs (CR), which are frequently used in daily clinical practice, by applying deep learning technology.
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September 2025
Department of Plastic and Reconstructive Surgery, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Medical Sciences, Guangxi Academy of Medical Sciences, Nanning, Guangxi, China.
Background: Obesity is a prevalent and clinically significant complication among individuals with diabetes mellitus (DM), contributing to increased cardiovascular risk, metabolic burden, and reduced quality of life. Despite its high prevalence, the risk factors for obesity within this population remain incompletely understood. With the growing availability of large-scale health datasets and advancements in machine learning, there is an opportunity to improve risk stratification.
View Article and Find Full Text PDFFront Endocrinol (Lausanne)
September 2025
Department of Neurology, The First Affiliated Hospital of Anhui University of Chinese Medicine, Hefei, China.
Background: Wilson disease (WD), an inherited copper metabolism disorder, is linked to hepatic injury from copper accumulation-induced dyslipidemia. Children with WD have a high incidence of dyslipidemia, yet personalized risk assessment tools are lacking. This study established a predictive nomogram to provide foundational evidence for early detection in this population.
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September 2025
Center of Reproductive Medicine, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Objective: To establish and validate a nomogram model for the quality of sleep in patients with recurrent implantation failure (RIF) and to evaluate its performance.
Methods: From January 2023 to June 2023, 484 RIF patients who underwent ART fertilization treatment at the Reproductive Medicine Center of Tongji University-affiliated Obstetrics and Gynecology Hospital were selected as the modeling set and internal validation. Additionally, from July to September 2023, 223 RIF patients who underwent ART fertilization treatment at the Reproductive Medicine Center of Tongji University-affiliated Obstetrics and Gynecology Hospital were chosen as the external validation set.
Crit Rev Anal Chem
September 2025
Department of Pharmaceutical Chemistry, JSS College of Pharmacy, JSS Academy of Higher Education & Research, Mysore, India.
The miniaturization of separation platforms marks a transformative shift in analytical science, merging microfabrication, automation, and intelligent data integration to meet rising demands for portability, sustainability, and precision. This review critically synthesizes recent technological advances reshaping the field-from microinjection and preconcentration modules to compact, high-sensitivity detection systems including ultraviolet-visible (UV/Vis), fluorescence (FL), electrochemical detection (ECD), and mass spectrometry (MS). The integration of microcontrollers, AI-enhanced calibration routines, and IoT-enabled feedback loops has led to the rise of self-regulating analytical devices capable of real-time decision-making and autonomous operation.
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