The accuracies of the original Demirjian, modified Demirjian and Willems dental age estimation methods were compared for a Black Southern African population to determine their usefulness for forensic and anthropological purposes. Data were collected using a community-based prospective study design. Panoramic radiographs of seven left mandibular teeth from 540 children aged 5-15.99 years were scored using the three methods. Obtained estimates were compared to the chronological ages and mean absolute errors were calculated. The original Demirjian method significantly overestimated ages (males 0.85 years, female 1.0 years; mean absolute errors of 1.1 years for both sexes), as did the modified Demirjian method (males 0.90 years, females 1.21 years; mean absolute errors of males 1.1 years, females 1.4 years). The Willems method was the most accurate for Black Southern Africans, with the lowest significant mean difference (males 0.2 years, females 0.3 years) between dental and chronological age, with the least mean absolute errors (males 0.70 years, females 0.68 years).
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http://dx.doi.org/10.1016/j.legalmed.2018.01.004 | DOI Listing |
PLoS One
January 2025
School of Civil and Architectural Engineering, Harbin University, Harbin, China.
This work explores an intelligent field irrigation warning system based on the Enhanced Genetic Algorithm-Backpropagation Neural Network (EGA-BPNN) model in the context of smart agriculture. To achieve this, irrigation flow prediction in agricultural fields is chosen as the research topic. Firstly, the BPNN principles are studied, revealing issues such as sensitivity to initial values, susceptibility to local optima, and sample dependency.
View Article and Find Full Text PDFJ Biomed Opt
January 2025
University of Ljubljana, Faculty of Mathematics and Physics, Ljubljana, Slovenia.
Significance: Machine learning models for the direct extraction of tissue parameters from hyperspectral images have been extensively researched recently, as they represent a faster alternative to the well-known iterative methods such as inverse Monte Carlo and inverse adding-doubling (IAD).
Aim: We aim to develop a Bayesian neural network model for robust prediction of physiological parameters from hyperspectral images.
Approach: We propose a two-component system for extracting physiological parameters from hyperspectral images.
Am J Transl Res
December 2024
Department of Gynecology, Suzhou Ninth People's Hospital Suzhou 215200, Jiangsu, China.
Objective: To investigate the factors influencing recurrence following laparoscopic conservative surgery in patients with ovarian endometriosis (OEM) and to develop a predictive model.
Methods: In this retrospective study, the clinical data from 212 OEM patients who underwent laparoscopic conservative surgery at Suzhou Ninth People's Hospital from May 2013 to December 2021 were meticulously reviewed. According to disease recurrence over a 2-year follow-up period, the patients were divided into a recurrence group and a non-recurrence group.
Physiol Meas
January 2025
Faculty of Sciences, University of Coimbra, Palacio de las Escuelas 3004-531, Coimbra, 3004-504, PORTUGAL.
Objective: The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction.
Approach: A generative adversarial network with fully connected layers (FC-GAN) is proposed for the reconstruction of distorted PPG signals.
Sci Rep
January 2025
Hangzhou Xiangce Electronic Technology Co.Ltd, Hangzhou, 310018, China.
Accurately predicting the State of Health (SOH) of new energy vehicle batteries is critical for ensuring their reliable operation and extending battery's service life. To address the issue of low SOH prediction accuracy across different prediction lengths, this paper proposes a prediction method based on long-short-term battery degradation feature extraction and FEA-TimeMixer model. First, a novel automatic SOH extraction algorithm for offline charging data is introduced to label the battery SOH degradation data.
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