Miracle-dz/UniFAR-FTUs huggingface dataset:
Is hypovitaminosis D associated with fibromyalgia? A systematic review.

CONTEXT Recent findings have suggested a high prevalence of vitamin D deficiency or insufficiency in fibromyalgia (FM) patients despite the lack of clinical and pathophysiological evidence. OBJECTIVE A systematic review was conducted to examine the association between vitamin D status and FM, including the effect of vitamin D supplementation. DATA SOURCE PubMed, LILACS, Scopus, SciELO, Cochrane, and EMBASE were searched, from January 2000 to July 2018, using the descriptors "Fibromyalgia" and "Vitamin D. " STUDY SELECTION Trials including FM patients in whom vitamin D levels were assessed were eligible for inclusion. DATA EXTRACTION Data comprised age, gender, country, aims, bias, diagnosis criteria, cutoff point, and status of vitamin D, together with FM symptoms and vitamin D supplementation protocol. RESULTS A total of 26 articles were selected. Most of the studies were found to present unreliable control groups and small samples. Experimental data on vitamin D supplementation indicated improvement in certain FM symptoms. CONCLUSION Prevalence of hypovitaminosis D in the FM population and the cause-effect relationship were inconclusive. Nevertheless, vitamin D supplementation may be considered as a co-adjuvant in FM therapy.
Lack of expression of ALK and CD30 in breast carcinoma by immunohistochemistry irrespective of tumor characteristics

Abstract CD30 is a member of the tumor necrosis factor family of cell surface receptors normally expressed in lymphocytes, as well as some lymphomas, but has been described in other malignancies. Anaplastic lymphoma kinase (ALK) is a tyrosine kinase receptor that belongs to the insulin receptor superfamily, and is normally expressed in neural cells, but has been detected in several malignancies. There is conflicting data in the literature that describes the expression of these receptors in breast cancer, and the aim of this study is to test the expression of CD30 and ALK in a cohort of Middle Eastern patients with breast carcinoma. Cases of invasive breast cancer from the archives of AUBMC were reviewed over a period of 9 years, and the blocks that were used for immunohistochemical staining for ER, PR, Her-2/neu were selected. Immunohistochemical staining for CD30 (JCM182) and ALK (5A4 and D5F3) was performed. Two hundred eighty-four cases were identified (2 cases were male), with a mean age of 55 ± 12. CD30 and ALK expression was not seen in any of the cases. Our cohort showed complete negativity to both CD30 and ALK, adding to the conflicting data available in the literature, and more studies are needed to reliably identify a trend of expression of CD30 and ALK in breast carcinoma, especially in the Middle East.
The Prevalence and Case-Fatality Rates of Post-Neonatal Tetanus in a Population of Hospitalized Nigerian Children: An 8-Year Retrospective Review.

BACKGROUND Although tetanus is a vaccine-preventable disease, reports indicate that it remains a significant cause of morbidity and mortality in both neonatal and post-neonatal periods especially in most developing countries. AIM This study aimed to determine the prevalence and case fatality rates of post-neonatal tetanus among children managed at the Federal Medical Centre Asaba, Delta State, Nigeria over 8 years. METHOD The study was a retrospective review of the records of the children above 1 month of age admitted into Federal Medical Centre, Asaba, with a clinical diagnosis of tetanus from January 2008 to May 2016. Data analysis was conducted on descriptive and inferential statistics using Statistical Package for Social Sciences version 22.0. Mean, standard deviation and other relevant parameters were calculated. Level of significance was set at p < 0.05. RESULTS During the study period, 32 out of 3693 admitted Paediatric patients had post-neonatal tetanus: giving a prevalence rate of 0.9%. The male : female ratio was 1.9 : 1 and patients' ages ranged from 2 to 15 years with a mean age 8.9 ± 3.1 years. Twenty-nine percent of them had complete immunization during infancy, but none had booster doses. Also, 60.9% of them presented with lower limb injuries as the portal of entry. Over 17.4% of the patients had very severe tetanus. Of the 32 patients studied, 62.5% resided in the neighbouring rural communities, while 50.0% were admitted for ≤10 days. The calculated case fatality rate was 50%. CONCLUSION The prevalence and case fatality rates of post-neonatal tetanus are still relatively high in this clime. Given the prevalent nature of the disease in children aged 5 years and above, there is a need to include the booster doses of tetanus toxoid in the country's National Programme on Immunization schedule.
Suspended-Core Microstructured Polymer Optical Fibers and Potential Applications in Sensing

The study of the fabrication, material selection, and properties of microstructured polymer optical fibers (MPOFs) has long attracted great interest. This ever-increasing interest is due to their wide range of applications, mainly in sensing, including temperature, pressure, chemical, and biological species. This manuscript reviews the manufacturing of MPOFs, including the most recent single-step process involving extrusion from a modified 3D printer. MPOFs sensing applications are then discussed, with a stress on the benefit of using polymers.
A Graph Based Clustering Approach for Relation Extraction From Crime Data

Application of natural language processing techniques based on crime data can prove to be beneficial in several processes of the criminal justice industry. The availability of massive crime reports helps law enforcement agencies when a criminal investigation is launched. While investigating a crime, questions like what type of crime, who committed the crime, what happened at which place, on what time, and what actions are taken, keep arising. Now, it is not feasible for the law enforcement agencies to get into the detail of these available massive crime reports and get the answers. To tackle these problems associated with criminal justice industry, the proposed work considers a textual corpus containing information of crime against women in India and extracts substantial relations between the named entities present in the corpus by a hierarchical graph-based clustering technique. For extracting the relations, different types of entity pairs have been chosen and similarities among them have been measured based on the intermediate context words. Depending on the similarity score, a weighted graph has been formed and a similarity threshold is set to partition the graph based on the edge weights. With the iterative application of the clustering algorithm, all the named entity pairs are grouped into clusters, each of which signifies different crime aspects. Each cluster is characterized using the most frequent context word present in it. The proposed relation extraction scheme helps in crime pattern analysis that can aid in various criminal investigation requirements. The results with optimal cluster validation indices depict the effectiveness of this method.
ON-OFF current ratio in Organic Ferroelectric Memory Diodes: the role of the Density of States

Solution processed Organic Ferroelectric Memory Diodes (OFMDs) are one of the most promising non-volatile memory elements for large area flexible electronics [1] – [4] . In OFMDs it is possible to improve the ON/OFF current ratio by orders of magnitude by simply increasing the charge injection barrier [5] , [6] , at the expenses of the data retention [7] . In this work we demonstrate that it is possible to tune the ON/OFF current ratio by orders of magnitude even in the case of very low charge injection barriers, paving the way for long-missing OFMDs.
Self-Organized Construction by Population Coding

The automatic generation of robot controllers by machine learning or evolutionary computation is still challenging and even more so for collective robotics. We follow the recently proposed paradigm of 'population coding' to compose robot swarms for collective construction. We define a controller template as finite state machine, enumerate a finite number of specified robot controller types to choose from, and use evolutionary robotics to evolve effective homogeneous and heterogeneous compositions of robot swarms using selections of these controllers. Besides an objective for solving the actual construction task we also add objectives for subtasks, and to minimize the number of different chosen robot types. For three variants of a collective construction task we find effective solutions with both homogeneous and heterogeneous swarms.
ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network

This paper aims at proposing an abnormality detection framework for electrocardiogram (ECG) signals, which owns unbalance distribution among different classes and gaining high accuracy in rhythm/morphology abnormalities classification. The proposed framework is composed of two models: data augmentation model and classification model. In this framework, data augmentation model is designed to recast a class-balanced training dataset by generating artificial data of minor class. The outputs of augmentation model are transferred into classification model. The classification model is designed to identify abnormalities accurately after training using both the experimental and generated datasets. Data augmentation model is supported by auxiliary classifier generative adversarial network (ACGAN). We construct Generator and Discriminator of the ACGAN by stacking multiple 1-dimensional convolutional layers with small kernel size. Dropout function and batch normalization are added to prevent gradients vanish and speed up convergence. In order to evaluate the performance of augmentation model, a set of quantitative indicators are introduced to verify the quality of generated ECG signals. We establish classification model based on stacked residual network parallel connected with long short-term memory (LSTM) network. The experimental study is conducted for single heartbeat detection and consecutive heartbeat detection. The results based on standard benchmark, MIT-BIH, and competition database provided by 2018 China physiological signal challenge (CPSC) have verified the proposed framework can achieve high performance in robustness and accuracy for class-imbalanced dataset.
Specification-based Test Case Generation with Genetic Algorithm

As the current specification-based testing (SBT) face some challenges in test case generation for regression testing, we propose a new method for efficient test case generation that combines formal specifications with genetic algorithm (GA). This method mainly reforms formal specifications though GA to generate inputs that can kill as many as possible mutants of the target program under test. For case study, two classic examples are presented to demonstrate the efficiency of this method. The result shows that this method is able to help efficiently generate useful test cases to uncover all the program mutants, which contributes to further maintenance of software.
EvoDNN - An Evolutionary Deep Neural Network with Heterogeneous Activation Functions

Many problems in Computational Biology and Bioinformatics involve classification, such as the classification of cell samples into malignant (cancer) or benign (normal). For such tasks, we propose EvoDNN, an evolutionary deep neural network that employs an evolutionary algorithm to evolve deep heterogeneous feed-forward neural networks. While the majority of current feed-forward neural networks employ user defined homogeneous activation functions, EvoDNN creates heterogeneous multi-layer networks where each neuron’s activation function is not statically defined by the user, but dynamically optimized during evolution. The main advantage offered by EvoDNN lies in that the activation functions do not need to be differentiable. This feature gives users a great degree of flexibility over which activation functions EvoDNN can utilize. This paper demonstrates how EvoDNN can simultaneously optimize each neuron’s weight, bias, and activation function, and empirically shows a superior performance compared to a backpropagation-trained feed-forward neural network at the cost of additional training time. In addition, advantages of the deep architecture of EvoDNN over our earlier approach, EvoNN, which employed a single hidden layer are discussed.
Descriptive Symbolic Models of Gaits from Parkinson's Disease Patients

Parkinson's disease (PD) is a degenerative disorder of the central nervous system that has many debilitating symptoms which affect the patient's motor system and can cause significant changes in their gait. By using genetic programming, we aim to develop descriptive symbolic nonlinear models of PD patient gait from time series data recorded from pressure sensors under subjects' feet. When compared to popular types of linear regression (OLS and LASSO), the nonlinear models fit their data better and generalize to unseen data significantly better. It was found that models developed for healthy control subjects generalized to other control subjects well, however the models trained on subjects with PD did not generalize well to other PD patients, which complicates the issue of being able to detect the progression of the disease. It is suspected that health care professionals can have difficulty classifying PD due to a lack of accurate data from patient reports; having individually trained models for active monitoring of patients would help in effectively diagnosing PD.
Automated Testing of Energy Hotspots and Defects for Android Applications

Battery efficiency is a significant factor influencing the quality of user experience. Much work pays attention to energy profiling or energy-hotspot/defect detection, but none of them solves the key problem that how to generate test cases automatically which have strong abilities to expose the energy hotspots. In this paper, we present EHDetector, a tool performing automatic test case generation for Android energy-hotspot detection. EHDetector leverages the control flow graph and the window transition graph for the test case generation, while identifying the trigger callback of a specific Energy-consuming spots and improving the precise of WTG by capturing the back-stack related configurations. The evaluation conducted on 5 apps detects 4 energy hotspots, and 9 energy-drain defects are also exposed, which outperforms the detection results by GreenDroid and indicates that the generated test cases have strong ability to expose energy hotspots and defects.
Science-based discussions using imaginative stories as a method of teaching in Urine microbial fuel cells

Improving the quality of education is not possible without the transformation of teaching methods and techniques. Nowadays, the audience is always exposed to a wealth of different information, it is necessary to find a method for learning them in the least amount of time. This is vital, especially in academic environments requiring extensive and complex learning. The edutainment technique, despite its old age, is one of the new concepts introduced today to simplify complex concepts. A branch of this applied technique is the expression of concepts in a simple way by using graphic stories. In this research, a sample of modern scientific issues in a university level thought using science-based discussions, fictional expressions and various images in the statistical society of 60 students of engineering and the results of the project's effectiveness in various aspects of education have been studied. These results show an overall satisfaction of over 72% of the statistical population.
Finite-Time Adaptive Fuzzy Consensus Stabilization for Unknown Nonlinear Leaderless Multi-Agent Systems With Unknown Output Dead-Zone

In this paper, the finite-time adaptive consensus stabilization is investigated for unknown nonlinear leaderless multi-agent systems with unknown output dead-zone. Different from the previous results on the multi-agent systems with unknown dead-zone, the dead-zone nonlinear is researched in the output channel. By using the recursive backstepping design method and the universal approximation ability of fuzzy logic systems, a local controller for each follower is constructed. Meanwhile, a Lyapunov-based logic switching rule is applied to handle the unknown control gain problems aroused by the output dead-zone nonlinearities. It is, thus, shown that the established adaptive control protocol can assure the finite-time state synchronization of each node. Besides, the state synchronization error between any adjacent followers also converges to a small region of zero when time tends to $T_{0}$ . Finally, the two simulations are conducted to further verify our theoretical results.
An Investigation Study on Mode Mixing Separation in Empirical Mode Decomposition

Mode mixing is a limitation of the empirical mode decomposition (EMD) method appropriate for physiological signal analysis. In 2008, boundary condition map presented by Rilling and Flandrin provided the efficiency of separating the two components of a two-tone signal as a function of their amplitude and frequency ratios. Until 2019, their findings were still applied. However, their maps only give an uncertainty-like efficiency of mode mixing separation for two-tone signals. In this paper, we propose a criterion for mode mixing separation in EMD, which provides a binary judgment on mode mixing separation instead of the above-mentioned efficiency. By comparing the slopes of the two components, we found that the phenomenon of mode mixing occurs as the extrema of the high-tone component are suppressed by the low-tone component. Under this condition, the criterion shows the relation among their amplitude ratio, frequency ratio, and relative phase between the two components. Given with the values of the three parameters, one can affirm whether the two components are mixed according to the criterion. Accordingly, we derive a black/white three-dimensional (3D) map that plots the binary result of mode mixing in black or white as a function of the three parameters. Our map agrees with Rilling’s map and the results obtained from our gait analysis. Among the 23 sets of center-of-mass trajectory signals, six sets encountered the mode mixing problem and their coordinates of the three parameters were found in the black region of the map, while the other 17 sets were in the white region.
Realistic Data Synthesis Using Enhanced Generative Adversarial Networks

Real data with privacy and confidentiality concerns are not often available or are too expensive to afford in respect of both time and money. In this situation, it is a good alternative to use synthetic data. The objective of this research is to generate realistic synthetic data so that people can use it freely. We propose a synthetic data generation model based on boundary-seeking generative adversarial networks (BGANs)–designated as medical BGAN or medBGAN and compare its performances with an existing method medical GAN (medGAN). We aim to perform the investigation on several datasets in two different domains: electronic health records (EHRs) in the medical domain and a crime dataset in the City of Los Angeles Police Department. Firstly, we train the models and generate synthetic data by using these trained models. We then analyze and compare the models' performance by applying some statistical methods (dimension-wise average and Kolmogorov-Smirnov test) and two machine learning tasks (association rule mining and prediction). The comprehensive analysis of this study shows that the proposed model is more efficient in generating realistic synthetic data than those generated using medGAN.
Safe Driving at Traffic Lights: An Image Recognition Based Approach

With the increasing number of vehicles, the number of traffic accidents also increases, especially at traffic lights. To enhance the driving safety at traffic lights, in this paper, we propose an intelligent safe driving assistant to provide drivers with driving advice based on traffic light phases, which information has been neglected by existing research. The driving assistant consists of an image recognition system with a single on-board camera, which can ameliorate the difficulties of observing traffic light phases. The recognition system obtains traffic light countdown information using a Convolutional Neural Network, and estimates the countdown time using the results of traffic light information. In addition, we develop a model to calculate the distance between the traffic light and vehicle by using the information of camera and traffic light. Based on the traffic light phase and the distance obtained, the driving assistant can provide a velocity control strategy to improve driver's safety. Finally, extensive experiments are conducted to verify the effectiveness of proposed methods.
All Work and No Play?

Many conversational agents (CAs) are developed to answer users' questions in a specialized domain. In everyday use of CAs, user experience may extend beyond satisfying information needs to the enjoyment of conversations with CAs, some of which represent playful interactions. By studying a field deployment of a Human Resource chatbot, we report on users' interest areas in conversational interactions to inform the development of CAs. Through the lens of statistical modeling, we also highlight rich signals in conversational interactions for inferring user satisfaction with the instrumental usage and playful interactions with the agent. These signals can be utilized to develop agents that adapt functionality and interaction styles. By contrasting these signals, we shed light on the varying functions of conversational interactions. We discuss design implications for CAs, and directions for developing adaptive agents based on users' conversational behaviors.
A Grammatical Evolution Approach for the Automated Generation of Perturbative Heuristics

Perturbative heuristics, or move operators, are domain specific operators generally used with search techniques, for example the 2-opt operator for the travelling salesman problem. These operators are usually derived manually which is an extremely time consuming task. The study presented in this paper investigates automating this process focussing on deriving perturbative heuristics for discrete optimization problems. This research forms part of a larger initiative aimed at automating the design of machine learning and search techniques. While there has been some research on the automated generation of perturbative operators, this has not been a well researched domain and most work has focussed on recombining existing human derived perturbative heuristics or components thereof together with move acceptance criteria rather than producing new perturbative heuristics from scratch. In this research pertubative heuristics are defined in terms of basic actions and solution components. Grammatical evolution is used to combine the basic actions and solution components into perturbative heuristics. The performance of the generated heuristics is compared to human derived heuristics for two problems, namely, examination timetabling (ITC 2007 benchmark instances) and capacitated vehicle routing (Christofides and Golden instances). For both problems the generated perturbative heuristics outperformed the human derived heuristics. Given the potential of the automated generation of perturbative heuristics established in this study, future work will extend the this work by including conditional and iterative constructs in the perturbative heuristics as well as applying the approach to other problems.