Miracle-dz/UniFAR-FTUs huggingface dataset:
Modulation of α1 and β2 Microglobulins, and Albumin-Globulin Ratio in HIV Sero-Positive Subjects on Antiretroviral Drugs at Nnewi, Nigeria - A Case Controlled Study

Human immunodeficiency virus (HIV) is associated with immune system activation and chronic inflammation. This study aimed to evaluate the effect of HIV infection on Albumin and Globulin fractions as a possible marker of inflammation in HIV infected subjects. This is a case controlled study involving one hundred (100) HIV sero-positive subjects made up of male and female aged 20-50years which consist of 50 sero-positive subjects on antiretroviral therapy (ART) for a minimum of 6 months and 50 sero-positive subjects not on ART yet all of which attend HIV clinic at Nnamdi Azikiwe University University Teaching Hospital (NAUTH), and 50 apparently healthy, age sex matched HIV sero-negative as control subjects. CD4 + T- cell count was estimated by Cyflow SLGreen. Alpha1microglobulin (α1M) and Beta2 microglobulin (β2M) were estimated by enzyme linked immunosorbent assay (ELISA). Albumin and total protein were estimated by colometric method, while globulin and albumin/globulin ratio were determined by calculation. The data obtained were subjected to statistical analysis using SPSS software application (version 21.0) and the results expressed as mean ± standard deviation. The serum levels of both αIM and β2M in ART group (29.5±18.22mg/l and 10.84±5.33µg/ml) and Non-ART (32.2±41.6mg/l and 15.45±10.8 µg/ml) were significantly higher compared to the control group (8.0±1.78mg/l and 6.89±2.31 µg/ml) (p<0.05). There was a significant difference (p<0.05) in albumin level in the ART group (39.0±7.43g/l) compared to Non-ART group (35.58±7.62g/l). Furthermore, Albumin was lowest in the Non-ART group (35.58±7.62g/l) and highest in the control (40.4±4.67g/l) while total protein was lowest in the control group (66.38±6.89g/l) and highest in the Non-ART group (82.34±13.63g/l) and when compared between the groups was statistically significant (p<0.05). Globulin was highest in the Non-ART group (46.80±12.60g/l) and lowest in the control group (25.90±3.10g/l), Albumin/globulin ratio was lowest in the Non-ART group (0.70±0.30) and highest in the control group (1.50±0.20) and when compared between the groups was statistically significant (p<0.05). Positive association was observed between α1M (r=0.303 p = 0.033) and Albumin (r = 0.333, p = 0.018) with CD4 + T cell in Non-ART group. In conclusion, the significant changes in the biochemical parameters measured suggest a redistribution in Albumin and globulin fractions in ART and Non-ART group especially the lowest Albumin/globulin ratio observed in the non-ART subjects indicate an active disease progression as against a regressive disease state in ART subjects. Hence, A/G ratio can be used as a prognostic marker for HIV infection".
SLA-aware Approach for IoT Workflow Activities Placement based on Collaboration between Cloud and Edge

In the Internet of Things (IoT) era, various nodes generate vast quantities of records, and data processing solutions consist of a number of activities/tasks that can be executed at the Edge of the network or on the Cloud. Their management at the Edge of the network may limit the time required to complete responses and return the final result/analytic to end users or applications. Also IoT nodes can perform a restricted amount of functionality over the contextual information gathered, owing to their restricted computational and resource capacities. Whether tasks are assigned to an Edge or a Cloud is based on a number of factors such as: tasks’ constraints, the load of nodes, and energy capacity. We propose a greedy heuristic algorithm to allocate tasks between the available resources while minimizing the execution time. The allocation algorithm considers factors such as the deadline associated with each task, location, and budget constraint. We evaluate the proposed work using iFogSim considering two use case studies. The performance analysis shows that the proposed algorithm has minimized cost, execution time, control loop delay, networking, and Cloud energy consumption compared to the Cloud-only approach.
Network intrusion detection model based on multivariate correlation analysis - long short-time memory network

For the purpose of improving the low detection performance of network intrusion detection model caused by high-dimensional data, and from the perspective of time correlation characteristics of intrusion detection datasets, the authors present a network intrusion detection model based on the multivariate correlations analysis - long short-term memory network (MCA-LSTM). Firstly, this model selects the optimal feature subsets through the information gain feature selection method, the MCA module is then used to change the feature subset into the triangle area map (TAM) matrix, and finally inputs the TAM matrix into the LSTM module for the training and testing purpose. To better demonstrate the performance of the proposed model, it is compared with those of convolutional neural networks, recurrent neural network, deep forest, support vector machine, and k-nearest neighbour methods proposed by the previous researchers. Experimental results show that the testing accuracy of the proposed model on 5-classification task using NSL-KDD dataset is up to 82.15%, and that on 10-classification task using UNSW-NB15 dataset is up to 77.74%. Moreover, compared with the traditional machine learning and existing deep learning models, the proposed model has shown to achieve better classification detection performance.
Prenatal exposure to cocaine and other substances: Its effect on newborn behavior and subsequent attachment behavior

PRENATAL EXPOSURE TO COCAINE AND OTHER SUBSTANCES ITS EFFECT ON NEWBORN BEHAVIOR AND SUBSEQUENT ATTACHMENT BEHAVIOR MAY 1997 CYNTHIA L. BOMBARDIER, B.S., WESTFIELD STATE COLLEGE M.Ed., UNIVERSITY OF MASSACHUSETTS AMHERST Ed.D. , UNIVERSITY OF MASSACHUSETTS AMHERST Directed by: Professor J. Kevin Nugent The effects of prenatal substance exposure on newborn behavior and subsequent attachment quality was investigated in a sample of 29 drug-exposed infants. Participants in this study were part of a larger sample of high-risk women and their infants enrolled in a longitudinal study. Data regarding prenatal substance use, 4 weeks prior to delivery, was available for 17 infants. Prenatal substance use data were obtained from substance use histories, obstetrical and neonatal medical chart reviews and case manager notes. Frequency of substance use was reported as either >1 time per day, 1 time per day, 3-6 times per week, 1-2 times per week, 1-3 times per month and unknown. Qualitative substance-use data were converted to quantitative data and were correlated with newborn behavior (NBAS Lester clusters and Als dimensions). At 1 month, maternal report of prenatal substance exposure 4 weeks prior to delivery was significantly associated with the reflex cluster of the NBAS. Increased amounts of drug-exposure was related to an infant displaying an increased number of abnormal reflexes at the 1-month NBAS assessment. NBAS individual items; inanimate auditory stimuli and selfquieting were found to be positively related to attachment security, suggesting that infants with these abilities may appear more attractive to their caregivers, thus helping to create a more positive "goodness-of-fit" between infant and caregiver. Prenatal substance use throughout pregnancy (n=13) was found to be positively associated with security of attachment (p = .03). Other factors undetected within this study (i.e. social support), may have influenced interactions between infant and caregiver and subsequently increased the potential for secure attachment.
XML Data Integration with OWL: Experiences & Challenges 1

XML has evolved to the format of choice for exposing data over the web. Together with mature and maturing standards for querying XML (XSLT, XPath, and XQuery) the basic infrastructure for integrating multiple heterogeneous data sources is there. However, the versatility of XML as a data model and the unrestricted expressive power of XML query languages can lead to rather complex integration architectures, where low level syntactic heterogeneities and semantic heterogeneities are overcome all at once by means of complex query expressions. This paper explores how the Web Ontology Language OWL can be used as a more abstract modelling layer on top of XML data sources, described by an XML Schema, to which extent the semantic relationships provided by OWL can be used for mapping heterogeneous data sources to a common global schema, and how the inference mechanisms of OWL can be used to check the consistency of such mappings. Moreover, it introduces a query language for OWL as a natural extension of XQuery, and describes how these queries against a global schema are translated to XQueries against the original data sources.
Advanced Strength and Power Training for the Elite Athlete

ABSTRACT ADVANCED LEVEL ATHLETES OFTEN DEMONSTRATE PLATEAUS IN STRENGTH AND POWER PERFORMANCE WITH REPEATED EXPOSURE TO TRADITIONAL TRAINING METHODS. MANIPULATION OF THE FORCE-TIME AND POWER-TIME CURVES OF TRADITIONAL EXERCISES MAY OFFER A NOVEL STIMULUS TO BREAK THROUGH SUCH PLATEAUS. THE USE OF TRAINING METHODS, SUCH AS VARIABLE RESISTANCE TRAINING, ECCENTRIC TRAINING, AND OVERSPEED TRAINING, HAS RECEIVED RECENT ATTENTION IN THE LITERATURE. THIS ARTICLE DISCUSSES THE MERITS OF SUCH TRAINING APPROACHES AND PROVIDES PROGRAM DESIGN GUIDELINES FOR THE STRENGTH AND CONDITIONING COACH WORKING WITH ELITE ATHLETES.
Semi-Supervised Single Image Depth Estimation Using Deep Neural Network

There has been tremendous research progress in estimating the depth of a scene from a monocular camera image. Existing methods for single-image depth prediction are exclusively based on deep neural networks, and their training can be unsupervised using stereo image pairs, supervised using LiDAR point clouds, or semi-supervised using both stereo and LiDAR. In general, semisupervised training is preferred as it does not suffer from the weaknesses of either supervised training, resulting from the difference in the camera’s and the LiDAR’s field of view, or unsupervised training, resulting from the poor depth accuracy that can be recovered from a stereo pair. In this thesis, we present our research in single-image depth prediction using semi-supervised training that outperforms the state-of-the-art. We achieve this through a loss function that explicitly exploits left-right consistency in a stereo reconstruction, which has not been adopted in previous semi-supervised training. Furthermore, we showed outputing inverse depth instead of disparity leads to better generalization and it is essential in the training. In addition, we describe the correct use of ground truth depth derived from LiDAR that can significantly reduce prediction error. The performance of our depth prediction model is evaluated on popular KITTI dataset, and the importance of each aspect of our semisupervised training approach is demonstrated through experimental results. Our deep neural network model has been made publicly available. Source code is available at https://github.com/a-jahani/semiDepth ii
Improving Drug-Therapy Decisions through Educational Outreach

Abstract Improving precision and economy in the prescribing of drugs is a goal whose importance has increased with the proliferation of new and potent agents and with growing economic pressures to contain healthcare costs. We implemented an office-based physician education program to reduce the excessive use of three drug groups: cerebral and peripheral vasodilators, an oral cephalosporin, and propoxyphene. A four-state sample of 435 prescribers of these drugs was identified through Medicaid records and randomly assigned to one of three groups. Physicians who were offered personal educational visits by clinical pharmacists along with a series of mailed "unadvertisements" reduced their prescribing of the target drugs by 14 per cent as compared with controls (P = 0.0001). A comparable reduction in the number of dollars reimbursed for these drugs was also seen between the two groups, resulting in substantial cost savings. No such change was seen in physicians who received mailed print materials only. The eff.
State of Charge Estimation of a Composite Lithium-Based Battery Model Based on an Improved Extended Kalman Filter Algorithm

The battery State of Charge (SoC) estimation is one of the basic and significant functions for Battery Management System (BMS) in Electric Vehicles (EVs). The SoC is the key to interoperability of various modules and cannot be measured directly. An improved Extended Kalman Filter (iEKF) algorithm based on a composite battery model is proposed in this paper. The approach of the iEKF combines the open-circuit voltage (OCV) method, coulomb counting (Ah) method and EKF algorithm. The mathematical model of the iEKF is built and four groups of experiments are conducted based on LiFePO4 battery for offline parameter identification of the model. The iEKF is verified by real battery data. The simulation results with the proposed iEKF algorithm under both static and dynamic operation conditions show a considerable accuracy of SoC estimation.
Toward the Future of Computer-Assisted Language Testing

Semi-direct speaking tests have become an increasingly favored method of assessing spoken performance in recent years. Underpinning evidence for their continued development and use has been largely contingent on language testing and assessment researchers' claim of their interchangeability with more traditional, direct face-to-face oral proficiency interviews through theoretical and empirical investigations from multiple perspectives. This chapter initially provides background and research synopses of four significant test facets that have formed the bases for semi-direct and direct speaking test comparison studies. These are followed by the inclusion of a recent case study comparing test taker output from a computer-based Aptis speaking test and a purposively developed identical face-to-face oral proficiency interview that found a slight register shift which may be viewed as advantageous for semi-direct speaking tests. Finally, future research directions are proposed in light of the recent developments in the semi-direct speaking testing research presented throughout this chapter.
Maternal Body Mass Index and Weight Gain in Relation to Low Birth Weight: A Study from Eastern Thailand

Maternal Body Mass Index and Weight Gain in Relation to Low Birth Weight: A Study from Eastern Thailand Introduction: This retrospective study aimed to determine the association of maternal factors and low birth weight newborns at Phrapokklao hospital. Material and Methods: We recruited 1,968 pregnant women delivering babies after 34 weeks of gestation during January 1, 2013 to June 30, 2013. Data collected included fetal weight, maternal age, parity, gestational age, prepregnancy weight, body mass index (BMI), weight gained during pregnancy, hematocrit, place of residence, maternal HIV infection and completion of antenatal care visits. Results: Fifty eight percent of the women had a normal BMI and 65.7% were between 20-34 years old. Pregnancy weight gain categorized by BMI showed that the percent of women in the under, normal, and over-normal ranges were 37.0%, 33.3% and 29.7% respectively. Maternal prepregnancy BMI did not associated with low birth weight newborns (LBWN) or small for gestational (SGA) newborn. Nulliparity and low gestational weight gain was 1.8 and 3.5 times greater risk (adjusted OR 1.814, 95%CI 1.232- 2.671, p-value=0.003 and adjusted OR 3.477, 95%CI 2.132-5.668, p-value=0.001 respectively) of having a LBWN. While low weight gain pregnant women had 6.3 more risks (adjusted OR 6.325, 95%CI 1.437-27.836, p-value = 0.015) of having SGA newborn, it showed protective effect for LGA newborn. The results between using IOM 1990 and 2009 recommendations remained the same irrespective of the different guideline used. Conclusion: Nulliparity and low gestational weight gain were all directly associated with LBWN while low maternal BMI and low gestational weight gain showed protective effect for LGA newborn.
Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Mobile edge caching (MEC) and device-to-device (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users’ preference. In particular, a $Q$-learning algorithm and a deep $Q$-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.
The validity of learning devices with generative learning models to improve mathematical problem-solving ability

This validity aims to determine the validity of mathematics learning tools based on generative learning models to improve students’ mathematical problem-solving abilities. The validity seen is constructed validity and content validity. Construct validity is the suitability of the product that is developed with theories that are the basis of product development, while content validity is the suitability of the product developed with the established curriculum. Validated learning devices are Learning Implementation Plans and Student Worksheets class VII junior high school. This learning tool is validated by 5 Expert Experts consisting of 3 Mathematical Experts, 1 Indonesian Language Expert, and 1 Educational Technology Expert. Validity test data was obtained from the results of validation by experts using the instrument validity test sheet form questionnaire. The results of the experts’ validation indicate that the learning device meets valid criteria.
A Digital Edition between Stylometry and OCR: The Klagenfurter Ausgabe of Robert Musil

Abstract:This article presents the digital edition of Robert Musil’s work (Klagenfurter Ausgabe) and its role in a digital humanities project aimed at reconstructing Musil’s activity in the WWI journal Tiroler Soldaten-Zeitung. First, the article reviews the ways in which the computational methods of stylometry are applied to attribute the anonymous texts published in the Klagenfurter Ausgabe. Second, it explores how optical character recognition (OCR) software is employed to expand the corpus. At the core of this methodology two machine learning algorithms are trained and revised using the transcriptions of the Klagenfurter Ausgabe, to reach an accuracy of about 99.9% in the digitization of the Tiroler Soldaten-Zeitung texts. The work of this project offers not only the possibility of expanding stylometric analysis to the whole journal, but also of improving the transcriptions of the Klagenfurter Ausgabe.
Patient centered care approach to adherence with cardiovascular medications: Self-determination theory integration

PATIENT CENTERED CARE APPROACH TO ADHERENCE WITH CARDIOVASCULAR MEDICATIONS: SELF-DETERMINATION THEORY INTEGRATION David S. Li Old Dominion University, 2010 Director: Dr. Richardean Benjamin Problem statement. Behavioral intervention is used to improve adherence with medication in patients with cardiovascular diseases (CVD), but the effect was not sustainable. Patient-centered care (PCC) as a consumer movement has gained acceptance, but it lacks a theoretical framework. Self-determination theory (SDT) may provide an alternative to improve patients' adherence to chronic cardiovascular medication, as well as a theoretical framework for PCC. Both approaches to CVD management have not been evaluated. Methods. The study was a quasi-experimental pretest posttest comparison design with consecutive sampling of a hospitalized cardiac patient population. Sixty patients each for the usual care (UC) group and the PCC group were recruited. The patient sample consisted of 83% Caucasian, 39% female, 93% insured, an average age of 68 years old, 43% had a NYHF classification of three and above, and an average of 9.7 prescriptions at discharge. A medication teaching intervention was supported by an interactive system designed for PCC. The intervention addressed patients' socioeconomic needs, health literacy adequacy, illness level, timing and amount of teaching, and psychological needs for competence, relatedness, and autonomous motivation. Patients were surveyed once before discharged from the hospital and once three months post discharge. Results. The SDT motivation variables (perceived competence, perceived autonomous support and autonomous motivation) were significantly correlated with the adherence index. Stepwise multivariate regression analyses identified autonomous motivation as an independent predictor of adherence index, along with age, insurance coverage, adverse drug reaction, adequate health literacy, illness level and number of prescription doses taken per day. Patients in the PCC group had significantly higher scores on all three SDT motivation variables, adherence index and higher portion of adherence status than patients in the UC group. Sequential logistic regression analyses reported that PCC intervention, perceived competence and autonomous motivation were independent predictors of adherence status, as well as adverse drug reaction, illness level and number of doses taken per day. Conclusion. Self-determination theory helped to explicate patients' adherence behavior. A patient-centered care environment designed with SDT improved patients autonomous motivation, which improved cardiac patients' adherence to CVD medications.
Quasi Bidirectional Encoder Representations from Transformers for Word Sense Disambiguation

While contextualized embeddings have produced performance breakthroughs in many Natural Language Processing (NLP) tasks, Word Sense Disambiguation (WSD) has not benefited from them yet. In this paper, we introduce QBERT, a Transformer-based architecture for contextualized embeddings which makes use of a co-attentive layer to produce more deeply bidirectional representations, better-fitting for the WSD task. As a result, we are able to train a WSD system that beats the state of the art on the concatenation of all evaluation datasets by over 3 points, also outperforming a comparable model using ELMo.