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Secondary dentine deposition is responsible for the decrease in the volume of the pulp cavity with age. Therefore, the volume of the pulp cavity can be considered as a predictor for estimating age. The aims of this study were to investigate the relationship strength between canine pulp volumes and chronological age from homogenous (approximately equal numbers of individuals in each age range) age distribution and to assess the effect of sex as predictor in age estimation. This study was performed on 719 subjects of Pakistani origin. Cone beam computed tomography images of 521 left maxillary and 681 left mandibular canines were collected from 368 females and 349 males aged from 15 to 65 years. Planmeca Romexis® software was used to trace the outline of the pulp cavity and to calculate pulp volumes. Regression analysis was performed to assess the correlation between pulp volumes considering with and without sex as a predictor with chronological age. The obtained results showed that mandibular canine pulp volume and sex have the highest predictive power (R2 = 0.33). The relationship between mandibular canine pulp volume and sex with chronological age demonstrates an odd S-shaped non-linear relationship. A statistically significant difference in volumes of pulp was found (p = 0.000) between males and females. The conclusion was that predictions using the pulp volume of the mandibular canine and sex produced the best estimates of chronological age.

The Super Dual Auroral Radar Network (SuperDARN) is a network of high-frequency (HF) radars located in the high- and mid-latitude regions of both hemispheres that is operated under international cooperation. The network was originally designed for monitoring the dynamics of the ionosphere and upper atmosphere in the high-latitude regions. However, over the last approximately 15 years, SuperDARN has expanded into the mid-latitude regions. With radar coverage that now extends continuously from auroral to sub-auroral and mid-latitudes, a wide variety of new scientific findings have been obtained. In this paper, the background of mid-latitude SuperDARN is presented at first. Then, the accomplishments made with mid-latitude SuperDARN radars are reviewed in five specified scientific and technical areas: convection, ionospheric irregularities, HF propagation analysis, ion-neutral interactions, and magnetohydrodynamic (MHD) waves. Finally, the present status of mid-latitude SuperDARN is updated and directions for future research are discussed.

Ranking enables coaches, sporting authorities, and pundits to determine the relative performance of individual athletes and teams in comparison to their peers. While ranking is relatively straightforward in sports that employ traditional leagues, it is more difficult in sports where competition is fragmented (e.g. athletics, boxing, etc.), with not all competitors competing against each other. In such situations, complex points systems are often employed to rank athletes. However, these systems have the inherent weakness that they frequently rely on subjective assessments in order to gauge the calibre of the competitors involved. Here we show how two Internet derived algorithms, the PageRank (PR) and user preference (UP) algorithms, when utilised with a simple 'who beat who' matrix, can be used to accurately rank track athletes, avoiding the need for subjective assessment. We applied the PR and UP algorithms to the 2015 IAAF Diamond League men's 100m competition and compared their performance with the Keener, Colley and Massey ranking algorithms. The top five places computed by the PR and UP algorithms, and the Diamond League '2016' points system were all identical, with the Kendall's tau distance between the PR standings and '2016' points system standings being just 15, indicating that only 5.9% of pairs differed in their order between these two lists. By comparison, the UP and '2016' standings displayed a less strong relationship, with a tau distance of 95, indicating that 37.6% of the pairs differed in their order. When compared with the standings produced using the Keener, Colley and Massey algorithms, the PR standings appeared to be closest to the Keener standings (tau distance = 67, 26.5% pair order disagreement), whereas the UP standings were more similar to the Colley and Massey standings, with the tau distances between these ranking lists being only 48 (19.0% pair order disagreement) and 59 (23.3% pair order disagreement) respectively. In particular, the UP algorithm ranked 'one-off' victors more highly than the PR algorithm, suggesting that the UP algorithm captures alternative characteristics to the PR algorithm, which may more suitable for predicting future performance in say knockout tournaments, rather than for use in competitions such as the Diamond League. As such, these Internet derived algorithms appear to have considerable potential for objectively assessing the relative performance of track athletes, without the need for complicated points equivalence tables. Importantly, because both algorithms utilise a 'who beat who' model, they automatically adjust for the strength of the competition, thus avoiding the need for subjective decision making.

Learning suturing skills is an important area of the undergraduate curriculum and ideally requires realistic and anatomically accurate surgical training models to prepare students for treating patients. Little is currently understood regarding which model might be perceived by students to be the best or which might most effectively facilitate their learning. The aim of this study was to compare four teaching models: a tabletop silicon dental model, a restricted access tabletop model, a traditional phantom head mounted model, and a Thiel cadaver. Student preferences were explored for each of the models. Following lecture and video-based teaching 67 fourth-year students attended a practical suturing teaching session followed by the second session more focused on the experience of cutting and suturing mucoperiosteal flaps. Forty-six students (67%) gave online anonymous feedback on the first session. The majority (95%) felt prepared to place a simple interrupted suture on a patient, and 88% felt confident to do so. Twenty-eight students (40%) provided feedback on the second session with 82% agreeing that they were prepared to cut a mucoperiosteal flap and 48% felt confident to do this for a patient. The cadaver model was rated as the best of the four models for both suturing and mucoperiosteal flap skills. These results support its use for teaching students to suturing and surgical skills. However, despite this teaching student-rated confidence to cut and suture flaps for a patient remains poor.

The duskside and dawnside subauroral polarization streams (SAPS) refer to high‐velocity westward and eastward plasma flows located equatorward of the auroral oval. While extensive research has focused on the duskside SAPS, the simultaneous evolution of both dawnside and duskside SAPS remains unreported. In this study, for the first time, we investigated the simultaneous evolution of duskside and dawnside SAPS using multiple Super Dual Auroral Radar Network radars during an intense storm. Observations indicate that the duskside SAPS exhibits a wider magnetic local time extension (∼7 MLT) and longer duration (∼1 hr) than the dawnside SAPS. Furthermore, the duskside SAPS resides within low‐density mid‐latitude troughs, whereas the dawnside SAPS is not located within the trough. The dawnside SAPS exhibits significantly higher electron density but comparable velocity to the duskside SAPS. These findings highlight the distinct evolution of dawnside and duskside SAPS, providing new insights into the electrodynamic processes of subauroral ionosphere and magnetosphere coupling.

Background: The detrimental impact of the COVID-19 pandemic on dental education prompted the Scottish Government to fund an additional year to the dental course to ensure that the students had the necessary clinical experience. The aim of the study was to better understand the final year student perceptions of this extension on their oral surgery experience at the University of Dundee. Methods: This mixed methods study consisted of an anonymous online questionnaire and a focus group. Results: Forty-one students (69.3%) completed the questionnaire and ten students participated in the focus group. Thirty-six (88.8%) students agreed that the oral surgery teaching provided sufficient knowledge to undertake independent practice. All of the students felt confident to carry out an extraction, and the majority of them (n = 40, 95%) felt confident to remove a retained root, however, their confidence with surgery was lower. Conclusion: The extension gave the students sufficient experience in oral surgery to gain confidence in clinical skills and an appropriate level of knowledge in preparation for the next phase of their career. Most of the students agreed that the extension was necessary and beneficial. This cohort graduated with more oral surgery experience than any of the students did in the previous 4 years from Dundee and with experience that was comparable with the students at other schools in the pre-COVID-19 era.

Intrusion Detection System (IDS) is crucial to protect smartphones from imminent security breaches and ensure user privacy. Android is the most popular mobile Operating System (OS), holding above 85% market share. The traffic generated by smartphones is expected to exceed the one generated by personal computers by 2021. Consequently, this prevalent mobile OS will stay one of the most attractive targets for potential attacks on fifth generation mobile networks (5G). Although Android malware detection has received considerable attention, offered solutions mostly rely on performing resource intensive analysis on a server, assuming a continuous connection between the device and the server, or on employing supervised Machine Learning (ML) algorithms for profiling the malware’s behaviour, which essentially require a training dataset consisting of thousands of examples from both benign and malicious profiles. However, in practice, collecting malicious examples is tedious since it entails infecting the device and collecting thousands of samples in order to characterise the malware’s behaviour and the labelling has to be done manually. In this paper, we propose a novel Host-based IDS (HIDS) incorporating statistical and semi-supervised ML algorithms. The advantage of our proposed IDS is two folds. First, it is wholly autonomous and runs on the mobile device, without needing any connection to a server. Second, it requires only benign examples for tuning, with potentially a few malicious ones. The evaluation results show that the proposed IDS achieves a very promising accuracy of above 0.9983, reaching up to 1.