Miracle-dz/UniFAR-FTUs huggingface dataset:
The association between health literacy, numeracy and diet quality in an urban population of socioeconomically diverse African American and white adults

Background: Previous research has shown that health literacy may be a stronger predictor for a person’s health than age, employment status, education level, race, and income. Evidence supports a strong link between low health literacy and poor dietary management of chronic diseases. Aim and Hypothesis: The aim of this study was to evaluate the relationship of dietary quality, health numeracy and health literacy levels of an urban population of socioeconomically diverse African American and White adults. It was hypothesized that individuals with lower levels of health literacy and numeracy would have lower quality diets based on nutrient intakes. Subjects: The final sample contained 1,818 White and African American participants from Wave 3 of the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) Study. Methods: Health literacy was measured using the Rapid Estimate of Adult Literacy in Medicine (REALM) and health numeracy was measured using the numeracy subscale of the Test of Functional Health Literacy in Adults (TOFHLA). Nutrient-based diet quality was measured using Mean Adequacy Ratio (MAR) scores based on diet alone (MAR) and nutrients from diet plus nutritional supplements (MAR-S). Results: Results revealed that REALM and TOFHLA were not significant predictors of diet quality measured by the MAR. Age, education, current cigarette smoking status, and energy intake were all significant predictors of diet quality. Age, current
Outcomes of Extended-Infusion Piperacillin/Tazobactam in Pediatric Patients.

 Data in pediatric patients are limited  Courter et al4 simulated extended and continuous infusion PT dosing regimens  PK data from healthy 2 & 12 year olds  337.5 mg/kg/day divided Q8H, infused over 3 hours provides 98% PTA for MICs up to 8 mg/L  Since 2011, standard of care for PT dosing at Riley Hospital for Children is 112.5 mg/kg IV Q8H, infused over 4 hours5  Prior to 2011, standard of care was 84.4 mg/kg IVq6H infused over 30 minutes
Getting published: group support for academic librarians

Purpose - The purpose of this paper is to offer an effective model for increasing professionals' competence, enthusiasm and success in writing and publishing. Design/methodology/approach - Recent articles on writing groups in the field of library and information science are reviewed and a case study of a group at RMIT University Library is presented. The authors were the facilitator and group members who were librarians, most with relative inexperience in research, writing, presenting, and getting published. A self-rating confidence survey was created to establish developmental priorities which were then addressed through input from experts, practical application and reflection, and constructive advice and support from group members. Group effectiveness and outcomes were evaluated at mid- and end-point review meetings, through a post-intervention confidence survey and by tracking publication output. Findings - The group became a cohesive, task-focused and productive team. A post-intervention confidence survey evidenced improvements on all survey items at a team level. Each member affirmed that they had gained substantial knowledge of writing, presentation and research techniques and understanding of the publication process. Publication output increased over the benchmark year of 2010, and in 2011 and 2012 exceeded initial targets. Practical implications - The model presented offers a practical and effective approach to increasing competence and output in writing, presenting, research, and getting published and can be easily adopted by others. Originality/value - Most literature reviews on library professional writing groups relate to professionals required to publish. The Get Published Group comprised Australian librarians writing voluntarily.
A load balancing strategy using dynamic channel assignment and channel borrowing in cellular mobile environment

A simple load balancing technique is proposed. It is basically a combination of both the dynamic channel assignment and channel borrowing techniques. All the cells of the system are divided into six groups, each group under one mobile switching centre (MSC). Initially a fixed number of channels are assigned to each cell under each mobile switching centre. Finally the channels are distributed among the cells as per their requirement. In the case of sudden channel demand under one mobile switching centre, the cell which needs more channel can borrow a channel from one of its compact pattern cells belonging to the same group (to be defined) with it by exchanging a negligible number of messages among the different components of the network. If there is no probable lender cell in the group, the MSC of the borrower cell can lend it from another MSC.
Multi-Biometric Template Protection: Issues and Challenges

The term biometrics refers to “automated recognition of individuals based on their behavioral and biological characteristics” (ISO/IEC JTC1 SC37). Several physiological (static) as well as behavioral (non-static) biometric characteristics have been exploited (Jain, Flynn & Ross, 2008) such as fingerprints, iris, face, hand, voice, gait, keystroke dynamics, etc., depending on distinct types of applications (see Figure 1). Biometric traits are acquired applying adequate sensors and distinctive feature extractors are utilized in order to generate a biometric template (reference data) in the enrollment process. During verification (authentication process) or identification (identification can be handled as a sequence of biometric comparisons against the enrollment records in a reference databse) the system processes another biometric measurement from which an according template is extracted and compared against the stored template(s) yielding acceptance/ rejection or hit/ no-hit, respectively. The presented work is motivated by very recent advances in the fields of multi-biometric recognition (Ross et al., 2006) and biometric template protection (Rathgeb & Uhl, 2011). Automatic recognition systems based on a single biometric indicator often have to contend with unacceptable error rates (Ross & Jain, 2003). Multi-biometric systems have improved the accuracy and reliability of biometric systems (Ross et al., 2006). Biometric vendors are already deploying multi-biometric systems (e.g. fingerprint and finger vein by SAFRAN Morpho1) and multi-biometric recognition is performed on large-scale datasets (e.g. within the Aadhaar project (Unique Identification Authority of India, 2012) by the Unique Identification Authority of India (UIDAI)). However, security of multi-biometric templates is especially crucial as they contain information regarding multiple traits of the same subject (Nagar et al., 2012). The leakage of any kind of template information to unauthorized individuals constitutes serious security and privacy risks, e.g. permanent tracking of subjects without consent (Ratha et al., 2001) or reconstruction of original biometric traits (e.g. fingerprints (Cappelli et al., 2007) or iris textures (Venugopalan & Savvides, 2011)) might become a realistic threat. Therefore, biometric template protection technologies have been developed in order to protect privacy and integrity of stored biometric data. However, so far, template protection schemes which provide provable security/ privacy, and achieve practical recognition rates have remained elusive, even on small datasets. This bookchapter provides a comprehensive overview of
Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models

We study the classic multiperiod joint pricing and inventory control problem in a data-driven setting. In this problem, a retailer makes periodic decisions on the prices and inventory levels of a product that she wishes to sell. The retailer’s objective is to maximize the expected profit over a finite horizon by matching the inventory level with a random demand, which depends on the price in each period. In reality, the demand functions or random noise distributions are usually difficult to know exactly, whereas past demand data are relatively easy to collect. We propose a data-driven approximation algorithm that uses precollected demand data to solve the joint pricing and inventory control problem. We assume that the retailer does not know the noise distributions or the true demand functions; instead, we assume either she has access to demand hypothesis sets and the true demand functions can be represented by nonnegative combinations of candidate functions in the demand hypothesis sets, or the true demand function is generalized linear. We prove the algorithm’s sample complexity bound: the number of data samples needed in each period to guarantee a near-optimal profit is [Formula: see text], where T is the number of periods, and ϵ is the absolute difference between the expected profit of the data-driven policy and the expected optimal profit. In a numerical study, we demonstrate the construction of demand hypothesis sets from data and show that the proposed data-driven algorithm solves the dynamic problem effectively and significantly improves the optimality gaps over the baseline algorithms. This paper was accepted by J. George Shanthikumar, big data analytics.
Mise en place de la Sélection Génomique dans les trois principales races françaises de bovins laitiers

La disponibilite en France de larges populations de taureaux testes sur descendance et genotypes sur la puce « BovineSNP50 ® » d'Illumina dans les trois principales races de bovins laitiers (Holstein, Montbeliarde et Normande) et les travaux de recherche menes dans le cadre du projet AMASGEN ont permis la mise en place d'une selection genomique en juin 2010 sur l'ensemble des caracteres evalues en selection classique. La methodologie retenue en France est une evaluation BLUP-QTL integrant plusieurs centaines de QTL (Quantitative Trait Locus) par caractere, les plus importants d'entre eux (a effets forts) etant detectes a l'aide d'analyses LD-LA (Linkage Desequilibrium-Linkage Analysis) et les autres, les plus nombreux (a effets faibles) etant detectes par une approche statistique appelee EN (Elastic Net). Des travaux de validation menes sur 1310 taureaux Holstein, 343 taureaux Montbeliards et 320 taureaux Normands montrent que la selection genomique est plus efficace que la selection classique. Les coefficients de determination (CD) des index genomiques de jeunes animaux sont compris entre 0,50 et 0,70 permettant une selection efficace des leur plus jeune âge. Si elle est bien utilisee, tant sur la voie mâle que sur la voie femelle, la selection genomique doit permettre d'augmenter le progres genetique, en particulier sur les caracteres faiblement heritables, sans consequences nefastes sur la variabilite genetique.
Anisotropic Residual-Based Mesh Adaptation for Reaction-Diffusion Systems: Applications to Cardiac Electrophysiology

Accurate numerical simulation of reaction-diffusion systems can come with a high cost. A system may be stiff, and solutions may exhibit sharp localized features that require fine grids and small time steps to properly resolve the physical phenomena they represent. The development of efficient methods is crucial to cut down the demands of computational resources. In this thesis we consider the use of adaptive space and time methods driven by a posteriori error estimation. The error estimators for the spatial discretization are built from a variety of sources: the residual of the partial differential equation (PDE) system, gradient recovery operators and interpolation estimates. The interpolation estimates are anisotropic, not relying on classical mesh regularity assumptions. The adapted mesh is therefore allowed to include elements elongated in specified directions, as dictated by the type of solution being approximated. This thesis proposes an element-based adaptation method to be used for a residual estimator. This method avoids the usual conversion of the estimator to a metric, and instead applies the estimator to directly control the local mesh modifications. We derive a new error estimator for the L-norm in the same anisotropic setting and adjust the element-based adaptation algorithm to the new estimator. This thesis considers two new adaptive finite element settings for reaction-diffusion problems. The first is the extension to a PDE setting of an estimator for the time discretization with the backward difference formula of order 2 (BDF2), based on an estimator for ordinary differential equation (ODE) problems. Coupled with the residual estimator, we apply a space-time adaptation method. The second is the derivation of anisotropic error estimates for the monodomain model from cardiac electrophysiology. This model couples a nonlinear parabolic PDE with an ODE and this setting presents challenges theoretically as well as numerically. In addition to theoretical considerations, numerical tests are performed throughout to assess the reliability and efficiency of the proposed error estimators and numerical methods.
Human-animal relationship: stockmanship and housing in organic livestock systems

The theme of the 3rd NAHWOA Workshop was "Human-animal relationship: stockmanship and housing in organic livestock systems". The stockperson's ability to understand livestock and to respond to the needs of the domesticated animals is probably the most important building block of animal health and welfare in any livestock production system. It is clear, from the papers presented in the Workshop, that great demands are put on the herdsperson's, shepherd's or flockmaster's ability to adopt …
Optimizing paediatric HIV care in Kenya: challenges in early infant diagnosis.

PROBLEM In 2003, the goal of the Kenyan Ministry of Health was to avail antiretroviral treatment (ART) to 50% of the estimated 250 000 eligible individuals by the end of 2005. By July 2005, 45 000 adults and more than 2000 children were on treatment. A study was conducted to determine the barriers to identification of HIV-infected children. APPROACH Existing government policies were reviewed and the ART register of the Kenya National AIDS Control Programme was used to identify facilities providing ART. This paper reports the findings around diagnosis and staging of HIV infection in children. LOCAL SETTING At the time of the study, 58 health facilities were providing ART to children. Only one institution had achieved universal HIV testing in the antenatal clinics. Six facilities systematically followed up HIV-exposed children. HIV antibody testing was not readily available to the children. Although four research centres were capable of carrying out diagnostic HIV polymerase chain reaction (PCR), the services were restricted to research purposes. Other constraints were inadequate physical infrastructure, inadequate systems for quality control in the laboratories and shortage of staff. LESSONS LEARNT The policy framework to support identification of HIV-infected children had been established, albeit with narrow focus on sick children. The assessment identified the weaknesses in the structures for systematic diagnosis of HIV through laboratory or clinical-based algorithms. The researchers concluded that health staff training and implementation of a systematic standard approach to identification of HIV-infected children is urgently required.
Delivering continuing education in health education using self-directed computer-mediated instruction: moving from intention to action

Using advanced technologies can help increase the availability of educational offerings; however, the steps taken in this direction must be appropriate for the target population and the specific content taught. As such, understanding factors that lead to health educators' intentions and behavior related to computer-mediated instruction for continuing education is an important step in developing and marketing appropriate computer-mediated instruction programs (Hoffman & Novak, 1994). Using the theory of planned behavior (Ajzen, 1988) this study explored the relationships between health educators' perceived behavioral control, attitudes, and subjective norms related to computer-mediated continuing education programs and their intentions to use, and previous experience with, computer-mediated education. Employing a cross sectional survey design, data were collected from 504 members of the Society for Public Health Education (SOPHE) (40% response rate) using an online survey instrument. Logistic regression was used to investigate the associations between attitudes, subjective norm, perceived behavioral control, and intention related to using computer-mediated continuing education programs and a proxy measure representing their computer-mediated continuing education behavior. Perceived behavioral control and attitudes were found to have significant associations with computer-mediated continuing education behavior, with intention partially mediating the association with perceived behavioral control and fully mediating the association with attitudes. When studying a subset of the group composed of respondents with a positive intention toward computer-mediated continuing education programs, respondent characteristics and barriers identified as distinguishing between individuals with positive and negative behaviors included perceived behavioral control, presence of a license or certification, a lack of programs, a lack of relevant topics for programs, and a lack of technical support for programs. These results suggest that for health education and health promotion professionals to engage in computer-mediated continuing education programs, more programs, especially ones that address topics relevant to their current functioning, need to be created and made readily available. Also, ensuring that appropriate technical support is available to assist participants, and informing potential participants of the availability of this technical assistance, may encourage more health educators and health promotion professionals to follow through on their intentions to participate in computer-mediated programs.
Teachers and Access to Schooling in Ghana

This paper provides insights about the specific impact teachers’ actions and attitudes can have on access to schooling. The paper reports how teachers’ irregular attendance, lateness and absenteeism as well as misuse of instructional hours and indiscriminate use of corporal punishment contribute to pupils’ irregular attendance and dropping out. It also reports how poor supervision by head teachers contribute to irregular teacher attendance, punctuality and absenteeism, and how these in turn impacted negatively on pupils’ access to schooling. The study also identifies gaps in research around teacher contribution to children dropping out of school and suggests how further research could address some of these.
Detection and Classification of Myocardial Delayed Enhancement Patterns on MR Images with Deep Neural Networks: A Feasibility Study.

Purpose To evaluate whether deep neural networks trained on a similar number of images to that required during physician training in the American College of Cardiology Core Cardiovascular Training Statement can acquire the capability to detect and classify myocardial delayed enhancement (MDE) patterns. Materials and Methods The authors retrospectively evaluated 1995 MDE images for training and validation of a deep neural network. Images were from 200 consecutive patients who underwent cardiovascular MRI and were obtained from the institutional database. Experienced cardiac MR image readers classified the images as showing the following MDE patterns: no pattern, epicardial enhancement, subendocardial enhancement, midwall enhancement, focal enhancement, transmural enhancement, and nondiagnostic. Data were divided into training and validation datasets by using a fourfold cross-validation method. Three untrained deep neural network architectures using the convolutional neural network (CNN) technique were trained with the training dataset images. The detection and classification accuracies of the trained CNNs were calculated with validation data. Results The 1995 MDE images were classified by human readers as follows: no pattern, 926; epicardial enhancement, 91; subendocardial enhancement, 458; midwall enhancement, 118; focal enhancement, 141; transmural enhancement, 190; and nondiagnostic, 71. GoogLeNet, AlexNet, and ResNet-152 CNNs demonstrated accuracies of 79.5% (1592 of 1995 images), 78.9% (1574 of 1995 images), and 82.1% (1637 of 1995 images), respectively. Conclusion Deep learning with CNNs using a limited amount of training data, less than that required during physician training, achieved high diagnostic performance in the detection of MDE on MR images. © RSNA, 2019Supplemental material is available for this article.
INVESTIGATING THE OPINIONS OF MoNE STAFF ABOUT INSET PROGRAMS VIA DISTANCE EDUCATION

The aim of this study is to investigate the opinions of the Ministry of National Education (MoNE) staff about in-service training (INSET) programs via distance education. The subjects of this study were the staff (n=15) of the Inservice Training Department of MoNE in 2008. During the study, the qualitative data were collected through semi-structured interviews held with the (MoNE) staff by the researcher. The results of the interviews revealed the importance of needs assessment, the relationship between INSET program course content and participants’ school curriculum, support mechanism in INSET programs via distance education, the application of what is learned and providing various opportunities to them that lead to their active involvement to the application of these programs, the characteristics of learning environments for these programs, INSET instructors’ teaching competencies and skills to fulfill various roles in online learning environments, of measuring and evaluating the performance of teachers during INSET programs via distance education and of the effectiveness of INSET programs via distance education.
Thermometer Encoding: One Hot Way To Resist Adversarial Examples

It is well known that it is possible to construct “adversarial examples” for neu1 ral networks: inputs which are misclassified by the network yet indistinguishable 2 from true data. We propose a simple modification to standard neural network ar3 chitectures, thermometer encoding, which significantly increases the robustness 4 of the network to adversarial examples. We demonstrate this robustness with ex5 periments on the MNIST, CIFAR-10, CIFAR-100, and SVHN datasets, and show 6 that models with thermometer-encoded inputs consistently have higher accuracy 7 on adversarial examples, without decreasing generalization. State-of-the-art accu8 racy under the strongest known white-box attack was increased from 93.20% to 9 94.30% on MNIST and 50.00% to 79.16% on CIFAR-10. We explore the proper10 ties of these networks, providing evidence that thermometer encodings help neural 11 networks to find more-non-linear decision boundaries. 12