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Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

There is no question that South Asians have higher rates of diabetes than the general population and that poor diet and exercise are the major risk factors behind this trend. What was surprising from the study was that the rate of coronary artery disease was reported to be higher in the Caucasian subgroup than in the South Asians. We are unclear how to interpret this information in light of the growing evidence for disproportionately high rates of coronary artery disease among South Asians.2,3 We wonder whether the methods (i.e., self-reported measures) influenced the results of [Maria Chiu MSc] and colleagues' study. Nevertheless, it is clear that we require more definitive measures for determining the rates of coronary artery disease in the South Asian population in Canada. We hope that Chiu and colleagues' article sets the precedent and generates further interest to encourage high-quality epidemiologic studies in Canada. Such work will be fundamental to policy decisions required to reduce the burden of cardiovascular disease in this country.

This paper presents an investigation on micropipe evolution from hexagonal voids in physical vapor transport-grown 4H-SiC single crystals using the cathodoluminescence (CL) imaging technique. Complementary techniques optical microscopy, scanning electron microscopy, and energy-dispersive spectroscopy (EDS) are also used to understand the formation mechanism of hexagonal voids along with the origin of pipes from these voids. The ability of CL to image variations along the depth of the sample provides new insights on how micropipes are attached to hexagonal voids that lie deep within the bulk single crystals. CL imaging confirms that multiple micropipes can originate from a single hexagonal void. EDS mapping shows that the inside of the micropipe walls exhibits higher levels of carbon. Investigation of the seed region by optical imaging shows that improper fixing of the seed to the crucible lid is the root cause for the formation of hexagonal voids that subsequently lead to micropipe formation.

Aims and Objectives: To evaluate the pre-treatment and post-treatment dental arch changes in both upper and lower arches in orthodontic patients treated with extraction of first premolar teeth. Subjects and Methods: Measurements were made on casts of 50 post treated cases in the age group of 12-30 years for individual tooth measurements, width of the arches (inter-incisal, inter-canine, inter-premolar and inter-molar) arch length (both right and left sides, anterior segment, posterior segment and total arch length for both for the maxillary and mandibular dental casts. Statistical Analysis Used: Kolmogorov-Smirnov test for Normality, Regression Analysis was done as variables were tested and proved to follow normality. Statistical software Statistical Package for the Social Sciences version 18 (SPSS Inc.; Chicago, Illinois, USA) was used for data analysis. Descriptive statistics and paired t-tests were used to compare the changes in the Class I and Class II div 1 groups. Results: There was a significant increase in the inter-incisor and inter-canine width post-treatment in the Class I and Class II div 1 subjects in the upper arch but no significant change in inter-incisor width in the lower arch in class I subjects. A significant decline in the inter-molar arch width in both the groups were seen. The inter-premolar arch width declined significantly in Class I cases while it increased significantly in Class II div 1 subjects. There was a significant increase in anterior arch length and a significant decrease in posterior arch length in all subjects. Conclusions: The findings of this original clinical research should significantly help the orthodontists in orthodontic treatment planning in patients requiring extractions of premolars.