Introduction: Young people working for better lives in West and Central Africa

After more than a decade of emphasizing African children’s and youth’s agencies, possibilities and creativities in more or less challenging social, political and economic environments (see Bordonaro & Carvalho, 2010; Christiansen, Utas, & Vigh, 2006; Honwana & de Boeck, 2005; Martin, Ungruhe, & Häberlein, 2016; Spittler & Bourdillon, 2012), other recent studies increasingly highlight the young people’s powerlessness, bleak presents and uncertain futures. Doing so, the image of an enduring soc..

Fitting Time Series Models to Fisheries Data to Ascertain Age

The ability of government agencies to assign accurate ages of fish is important to fisheries management. Accurate ageing allows for most reliable age-based models to be used to support sustainability and maximize economic benefit. Assigning age relies on validating putative annual marks by evaluating accretional material laid down in patterns in fish ear bones, typically by marginal increment analysis. These patterns often take the shape of a sawtooth wave with an abrupt drop in accretion yearly to form an annual band and are typically validated qualitatively. Researchers have shown key interest in modeling marginal increments to verify the marks do, in fact, occur yearly. However, it has been challenging in finding the best model to predict this sawtooth wave pattern. We propose three new applications of time series models to validate the existence of the yearly sawtooth wave patterned data: autoregressive integrated moving average (ARIMA), unobserved component, and copula. These methods are expected to enable the identification of yearly patterns in accretion. ARIMA and unobserved components account for the dependence of observations and error, while copula incorporates a variety of marginal distributions and dependence structures. The unobserved component model produced the best results (AIC: -123.7, MSE 0.00626), followed by the time series model (AIC: -117.292, MSE: 0.0081), and then the copula model (AIC: -96.62, Kendall\u27s tau: -0.5503). The unobserved component model performed best due to the completeness of the dataset. In conclusion, all three models are effective tools to validate yearly accretional patterns in fish ear bones despite their differences in constraints and assumptions

Use of SARIMA Models to Assess Data-Poor Fisheries: A Case Study With A Sciaenid Fishery Off Portugal

Research on assessment and monitoring methods has primarily focused on fisheries with long multivariate data sets. Less research exists on methods applicable to data-poor fisheries with univariate data sets with a small sample size. In this study, we examine the capabilities of seasonal autoregressive integrated moving average (SARIMA) models to fit, forecast, and monitor the landings of such data-poor fisheries. We use a European fishery on meagre (Sciaenidae: Argyrosomus regius), where only a short time series of landings was available to model (n=60 months), as our case-study. We show that despite the limited sample size, a SARIMA model could be found that adequately fitted and forecasted the time series of meagre landings (12-month forecasts; mean error: 3.5 tons (t); annual absolute percentage error: 15.4%). We derive model-based prediction intervals and show how they can be used to detect problematic situations in the fishery. Our results indicate that over the course of one year the meagre landings remained within the prediction limits of the model and therefore indicated no need for urgent management intervention. We discuss the information that SARIMA model structure conveys on the meagre life-cycle and fishery, the methodological requirements of SARIMA forecasting of data-poor fisheries landings, and the capabilities SARIMA models present within current efforts to monitor the world\u27s data-poorest resources

Radiation Safety Practices of Dental Hygienists in the United States

Purpose: The As Low As Reasonably Achievable (ALARA) principle was developed to promote awareness and minimization of radiation exposure and is supported by radiation control and professional organizations. The purpose of this study was to determine licensed dental hygienists\u27 current radiation safety practices. Methods: Data were collected with a 22 item, online survey administered to a convenience sample of 1,500 dental hygienists in the United States. Questions focused on respondents\u27 use of the American Dental Association (ADA) radiographic examination selection guidelines, their individual dental practice policies, and hand-held portable x-ray device use and training. Cross tabulations were obtained using logistic regression and general linear models for significance at a 0.05 level. Results: A response rate of 38% (n = 566) was obtained. A majority of respondents had an associate\u27s degree (62%), were over the age of 55 (41%), and had over 30 years of experience. Respondents with more years of experience were more likely to follow the ADA selection criteria for radiographic need (p=0.0340; SE=0.1093) and respondents with a bachelor\u27s degree or higher were more likely to use techniques to reduce radiation exposure than those with an associate\u27s degree (p=0.0080; SE=0.0169). Respondents who had recently taken dental radiation safety continuing education courses were significantly more likely to wear a clinician lead apron when using a hand-held x-ray device (p=0.0093; M=1.571; SD=1.222). Conclusion: Dental hygienists with more years of experience, a higher level of education, and recent CE course work were more likely to follow ADA radiographic examination selection guidelines and use appropriate techniques to reduce exposure to ionizing radiation

Facial Landmark Feature Fusion in Transfer Learning of Child Facial Expressions

Automatic classification of child facial expressions is challenging due to the scarcity of image samples with annotations. Transfer learning of deep convolutional neural networks (CNNs), pretrained on adult facial expressions, can be effectively finetuned for child facial expression classification using limited facial images of children. Recent work inspired by facial age estimation and age-invariant face recognition proposes a fusion of facial landmark features with deep representation learning to augment facial expression classification performance. We hypothesize that deep transfer learning of child facial expressions may also benefit from fusing facial landmark features. Our proposed model architecture integrates two input branches: a CNN branch for image feature extraction and a fully connected branch for processing landmark-based features. The model-derived features of these two branches are concatenated into a latent feature vector for downstream expression classification. The architecture is trained on an adult facial expression classification task. Then, the trained model is finetuned to perform child facial expression classification. The combined feature fusion and transfer learning approach is compared against multiple models: training on adult expressions only (adult baseline), child expression only (child baseline), and transfer learning from adult to child data. We also evaluate the classification performance of feature fusion without transfer learning on model performance. Training on child data, we find that feature fusion improves the 10-fold cross validation mean accuracy from 80.32% to 83.72% with similar variance. Proposed fine-tuning with landmark feature fusion of child expressions yields the best mean accuracy of 85.14%, a more than 30% improvement over the adult baseline and nearly 5% improvement over the child baseline

Performance of Dental Hygiene Students in Mass Fatality Training and Radiographic Imaging of Dental Remains

Purpose: Mass fatality incidents can overwhelm local, state and national resources quickly. Dental hygienists are widely distributed and have the potential to increase response teams\u27 capacity. However, appropriate training is required. The literature is void of addressing this type of training for dental hygienists and scant in dentistry. Hence, the purpose of this study was to assess one facet of such training: Whether the use of multimedia is likely to enhance educational outcomes related to mass fatality training. Methods: A randomized, double-blind, pre- and post-test design was used to evaluate the effectiveness of comparable educational modules for 2 groups: a control group (n=19) that received low media training and a treatment group (n=20) that received multimedia training. Participants were second-year, baccalaureate dental hygiene students. Study instruments included a multiple-choice examination, a clinical competency-based radiology lab scored via a standardized rubric, and an assessment of interest in mass fatality education as a specialty. ANOVA was used to analyze results. Results: Participants\u27 pre- and post-test scores and clinical competency-based radiology lab scores increased following both educational approaches. Interest in mass fatality training also increased significantly for all participants (p=0.45). There was no significant difference in pre- and post-test multiple choice scores (p=0.6455), interest (p=0.9133) or overall competency-based radiology lab scores (p=0.997) between groups. Conclusion: Various educational technique may be effective for mass fatality training. However, mass fatality training that incorporates multimedia is an appropriate avenue for training instruction. Continued research about multimedia\u27s role in this specialty area is encouraged

Modalites de consommation et valeur nutritionnelle des legumineuses alimentaires au Burkina Faso

Au Burkina Faso les légumineuses constituent une source importante de nutriments pour les populations. La présente étude a eu pour objectif de déterminer la valeur nutritionnelle et la place des légumineuses dans le régime alimentaire des ménages à Ouagadougou, Kaya, Lebda et Nobéré. La méthodologie a consisté en une enquête de consommation alimentaire auprès de 325 ménages et à déterminer les teneurs en macronutriments et en minéraux fer, zinc et calcium de six (6) variétés de légumineuses. Il en résulte que les principales légumineuses consommées dans les ménages sont l’arachide (Arachis hypogea L.), le niébé (Vigna unguiculata L.), le voandzou (Vigna subterranea L.), le soja (Glycine max L.) et le zamnè (Acacia macrostachya R.). L’arachide, le niébé et le voandzou sont consommées plus 2 à 4 fois dans le mois par l’ensemble des ménages sous forme de mets au sein des ménages tandis que le soja et le zamnè sont consommés hors ménage. A l’exception du voandzou qui est consommé au déjeuner par 38,30% des ménages à Ouagadougou, 60% à Kaya, 66,67% à Lebda et 43,18% à Nobéré, les autres légumineuses sont consommées à tout moment de la journée par plus de 80 % des ménages enquêtés. Les légumineuses niébé, voandzou et zamnè sont consommées sous forme de ragout et de plats associés à des céréales respectivement par 99%, 93% et 76%. L’arachide et le soja sont utilisés comme des ingrédients ou comme des collations. Les légumineuses ont des teneurs importantes en protéines, 35,76%, 31,04%, 27,29%, 22,55% et 20,38% respectivement pour le zamnè, le soja, l’arachide, le niébé et le voandzou. Les teneurs en lipides sont faibles et contribuent à moins de 7% à la valeur énergétique. Le niébé et le voandzou ont des teneurs en carbohydrates élevées, près de 60%. Les teneurs en fer des légumineuses varient de 1,77 mg/100g (voandzou) à 6,50 mg/100g (soja). Celles en zinc varient de 5,34 mg/100 g (zamnè) à 4,33 mg/100g (soja). Le soja et le zamnè ont d’importantes teneurs en calcium, respectivement 57,42 mg/100g et 68,40 mg/100g. Du fait de leur valeur nutritionnelle intéressante en micronutriments ces deux légumineuses nécessitent une attention particulière pour l’enrichissement des produits locaux. La diversification des produits issus de ces légumineuses ainsi que celle des méthodes de consommation au sein des ménages permettront un meilleur profilage de leur bénéfice nutritionnel auprès des populations.Mots clés: Légumineuses, importance, consommation, ménages, nutriments, urbain, rural, Burkina Fas

Testing a Novel 3D Printed Radiographic Imaging Device for Use in Forensic Odontology

There are specific challenges related to forensic dental radiology and difficulties in aligning X-ray equipment to teeth of interest. Researchers used 3D printing to create a new device, the combined holding and aiming device (CHAD), to address the positioning limitations of current dental X-ray devices. Participants (N = 24) used the CHAD, soft dental wax, and a modified external aiming device (MEAD) to determine device preference, radiographer\u27s efficiency, and technique errors. Each participant exposed six X-rays per device for a total of 432 X-rays scored. A significant difference was found at the 0.05 level between the three devices (p = 0.0015), with the MEAD having the least amount of total errors and soft dental wax taking the least amount of time. Total errors were highest when participants used soft dental wax-both the MEAD and the CHAD performed best overall. Further research in forensic dental radiology and use of holding devices is needed

A LASSO Chart for Monitoring the Covariance Matrix

Multivariate control charts are essential tools in multivariate statistical process control. In real applications, when a multivariate process shifts, it occurs in either location or scale. Several methods have been proposed recently to monitor the covariance matrix. Most of these methods use rational subgroups and are used to detect large shifts. In this paper, we propose a new accumulative method, based on penalized likelihood estimators, that uses individual observations and is useful to detect small and persistent shifts in a process when sparsity is present