Miracle-dz/UniFAR-FTUs huggingface dataset:
Using Meta-Heuristic Algorithm in Spiking Neural Network for Pattern Recognition Tasks

The Bat Algorithm (BA) is a meta-heuristic algorithm based on echolocation behavior of microbats. The authors propose BA based Spiking Neural Network (SNN) model, where the advantages of BA and efficiency of SNN are exploited for classification tasks using some benchmark datasets. The advantages of the BA have been well exploited in the Artificial Neural Networks (ANN) domain particularly with the adjustment of weights. We therefore, leveraged on the BA as a learning strategy to train an SNN using the Leaky Integrate and Fire (LIF) and Izhikevich models to solve non-linear pattern classification tasks. In order to successfully discriminate between the various classes, the models are trained to fire at the same or similar firing rate for inputs from the same class, and inputs patterns from different classes to also spike or fire at different rate. To justify how efficient and how powerful the proposed model is, only one neuron is used. Finally, the model is tested on different non-linear pattern recognition tasks and comparison is made between our model and other similar existing models and our proposed model outperformed some of the state-of-the-art-models. To the best of our knowledge, this is the first work to implement BA in SNN.
A Low SAR In-Body Antenna for Wireless Monitoring Purpose of Pacemaker System

This research paper deals with a low SAR patch antenna that resonates at 2.415 GHz. This antenna can be employed to monitor the pacemaker system wirelessly, considering its performances. The main aim was to operate the antenna at ISM (Industrial, Scientific and Medical) band (2.4–2.48 GHz) in the pacemaker system, where body granted materials were used to construct both the pacemaker and the antenna to ensure the biocompatibility. To investigate the antenna parameters' changes between free space and in-side body condition, the designed antenna was examined and compared for both conditions. The key speciality of this design is that the SAR (Specific Absorption Rate), which is the most crucial parameter for any in-body antenna, was found in a considerable-safe region. Moreover, Computer Simulation Technology (CST)-based desired findings of this antenna for return loss, VSWR, and far-field radiation characteristics were found with compared to other recent body-implantable antenna related published works.
Social Media Reputation and Political Popularity: Study of a Moroccan Case

Popularity is one of the key reasons for the electoral success of a politician and his party. Discovering the reasons behind a political notoriety leads inevitably to the constitution of a stable and perennial political class. The present work seeks to measure, using a mixed method, the Facebook trust ratings of a political figure in Morocco, taking advantage of the huge amounts of information that big data are overflowing with, especially data arisen from social networking services, which have recently become a promising opportunity and an ingenious tool for extracting and exploring public opinion. This research has collected and examined 6 million of posts and comments about the head of Moroccan government in 2016. This study sought to identify the key reasons behind the Facebook popularity of this politician and his party. This contribution is intended to be multidisciplinary, both useful for policymakers, data mining experts and opinion analysts to explore public trends and inclinations.
Fault Classification in VSC-HVDC Transmission System using Machine Learning Approach

Transmission system nowadays a complex network due to interconnection with the nearby system for fulfilling high load demand. Therefore, protection of transmission system is also much important. As, protection of transmission system is much complex problem due to less known approaches. HVDC transmission is also at present time important part of transmission line. Therefore, the protection of HVDC system several protection scheme specifically for fault classification are used. This paper presents the use of support vector machine (SVM) based classification approach for Fault estimation in HVDC transmission system. SVM based method is comparatively a novel method that is based on computational as well as theory of statistical learning. Along with the input space vector is made into a dot product of high dimension that is called a feature space in SVM theory. Therefore, the optimal hyper-plane has to be consider greater ability of classification in the feature space. Thus the optimal hyper-plane is to be determined by exploring the theory of optimization, and the critical information providing via the theory of statistical learning. Therefore, SVM approach has got the ability to counter a large featured space vector. So that the proposed approach has ability to assist in the area of fault classification as there is no restriction on the number of features.
Alternative FCS-MPC concepts for cascade free motor speed control

This paper provides alternative FCS-MPC algorithms for cascade-free motor speed control and compares with conventional FCS-MPC. The proposed algorithms uses the concept of virtual vectors combined with a modulation to reduce the ripple caused by conventional FCS-MPC. The difference in the proposed algorithms is related with the search of the virtual vectors in order to reduce computational cost, memory use or processing. Simulation results are provide showing the benefits of the proposed algorithms.
Time Course of Exercise Training–Induced Muscle Strength Changes in Type 2 Diabetes

Purpose:To investigate longitudinal muscle strength changes in response to exercise training in people with type 2 diabetes. Methods:Twenty-one people with type 2 diabetes were randomly allocated to either a moderate or high-intensity exercise training group with the only difference between the groups being the prescribed exercise training intensity. Muscle strength (8 repetition maximum in kilograms) was measured weekly for 12 weeks using the chest press, leg press, row, and knee extension exercises. Muscle strength was expressed as the sum of 8 repetition maximum scores for each exercise. Results:Linear mixed models indicated significant gains in muscle strength (P < .001) in both groups and no significant difference in gains in muscle strength (P = .47) between the groups. Pooled data from both groups indicated significant improvement in muscle strength (P < .05) week-to-week beginning after week 1 and continued until week 9. Conclusion:Participation in either a moderate or high-intensity exercise training program can result in similar gains in muscle strength for people with type 2 diabetes. Muscle strength improvements can occur on a weekly basis for the first 8 weeks of exercise training, with a plateau in gains occurring at week 9 through week 12.
Systematics of the neotropical woody bamboo genus Rhipidocladum (Poaceae: Bambusoideae)

CHAPTER 1. GENERAL INTRODUCTION Background and Significance Research Objectives Thesis Organization Literature Cited CHAPTER 2. PHYLOGENY OF THE BAMBOO SUBTRIBE ARTHROSTYLIDIINAE WITH EMPHASIS ON RHIPIDOCLADUM Abstract Introduction Methods and Materials Results Discussion Taxonomic Treatment Literature Cited CHAPTER 3. REVISION OF RHIPIDOCLADUM Abstract Introduction Methods and Materials Taxonomic Treatment Unidentified Specimens Acknowledgements Literature Cited CHAPTER 4. GENERAL CONCLUSIONS Summary Recommendations Future Research
Targeting the purinome to control hyperactivity of the human urinary bladder

Purpose: Deregulation of purinergic bladder signaling may contribute to persistent detrusor overactivity in patients with bladder outlet obstruction. Activation of uridine diphosphate sensitive P2Y6 receptors increases voiding frequency in rats indirectly by releasing adenosine triphosphate from the urothelium. To our knowledge this mechanism has never been tested in the human bladder. Materials and Methods: We examined the role of the uridine diphosphate sensitive P2Y6 receptor on tetrodotoxin insensitive nonneuronal adenosine triphosphate and [H]acetylcholine release from the human urothelium with the lamina propria of control organ donors and patients with benign prostatic hyperplasia. Results: The adenosine triphosphate-to-[H]acetylcholine ratio was fivefold higher in mucosal urothelium/lamina propria strips from benign prostatic hyperplasia patients than control men. The selective P2Y6 receptor agonist PSB0474 (100 nM) augmented by a similar amount adenosine triphosphate and [H]acetylcholine release from mucosal urothelium/lamina propria strips from both groups of ___________________________________________________ORIGINAL RESEARCH ARTICLES PhD Thesis: Isabel Sofia Dias da Silva________________________________________64 individuals. The facilitatory effect of PSB0474 was prevented by MRS2578 (50 nM) and by carbenoxolone (10 μM), which block P2Y6 receptor and pannexin-1 hemichannels, respectively. Blockade of P2X3 (and/or P2X2/3) receptors with A317491 (100 nM) also attenuated release facilitation by PSB0474 in control men but not in patients with benign prostatic hyperplasia. Immunolocalization studies showed that P2Y6, P2X2 and P2X3 receptors were present in choline acetyltransferase positive urothelial cells. In contrast to P2Y6 staining, choline acetyltransferase, P2X2 and P2X3 immunoreactivity decreased in the urothelium of benign prostatic hyperplasia patients. Conclusions: Activation of P2Y6 receptor amplifies mucosal adenosine triphosphate release underlying bladder overactivity in patients with benign prostatic hyperplasia. Therefore, we propose selective P2Y6 receptor blockade as a novel therapeutic strategy to control persistent storage symptoms in obstructed patients.
An Efficient Technique of Hemoglobin Level Screening Using Machine Learning Algorithms

Hemoglobin (Hb), a very significant parameter for the human body and deficiency of it causes anemia. During pregnancy, menstruation and ICU deficiency of it can be very risky and even caused death. So, it is important to diagnose it continuously. Usually, physicians examine it by conducting a blood test to confirm it is painful, time-consuming and costly. The major concept of this study is to screen Hb levels within a short period of time. In this study, the data of clinical blood Hb levels of a total of 104 people (54 males and 50 females) are collected along with an eye conjunctiva image. The images are taken with a Smartphone camera of constant resolution and lighting. Using MATLAB, image processing method, the percentages of the red, green and blue pixels are extracted. Taking those features, the Hb level is plotted. The 104 data have been split into two sets where the first 81 data for training purposes, the remaining 23 data have been considered for testing. To train the model of 81 data, Multivariate Linear Regression (MLR), Decision Tree (Medium), Linear Support Vector Regression (SVR) are taken and the lowest percentage of error of 11.01% has been found in the Decision Tree (Medium) while testing the 23-test data.
Bandwidth-Aware Dynamic Prefetch Configuration for IBM POWER8

Advanced hardware prefetch engines are being integrated in current high-performance processors. Prefetching can boost the performance of most applications, however, the induced bandwidth consumption can lead the system to a high contention for main memory bandwidth, which is a scarce resource in current multicores. In such a case, the system performance can be severely damaged. This article characterizes the applications’ behavior in an IBM POWER8 machine, which presents many prefetch settings, varying the bandwidth contention. The study reveals that the best prefetch setting for each application depends on the main memory bandwidth availability, that is, it depends on the co-running applications. Based on this study, we propose Bandwidth-Aware Prefetch Configuration (BAPC) a scalable adaptive prefetching algorithm that improves the performance of multi-program workloads. BAPC increases the performance of the applications in a 12, 15, and 16 percent of 6-, 8-, and 10-application workloads over the IBM POWER8 default configuration. In addition, BAPC reduces bandwidth consumption in 39, 42, and 45 percent, respectively.
Cervical cancer in patients without abnormal cytology. Youth island, study by four years

Study the epithelial cervical lesions is a priority in the prevention of this cancer that it shows an evident increase of its morbidity and mortality. A retrospective descriptive study showed the behavior of 78 patients with cervical cancer without abnormal pap smear that were attended in the Young Island during June/2013 to May/2017. The 58% of the patients were younger than 42 years old, the average of first sexual relation was after 16 years old and the average of delivery and miscarriage was minor of two gestations; the 51% was attended for cervical lesions and 68% had a lesion area between 100-299mm2. To 40% of the patient needed conization to diagnostic, 65% had earlier stages and 62% received surgery as primary treatment. The results of this study can be to design local strategies oh health to decrease the morbidity and mortality by cancer. *Correspondence to: Heenry Luis Dávila Gómez, Department of Obstetrics and Gynecology, Héroes del Baire General Teaching Hospital, Nueva Gerona, Juventud, Cuba. E-mail: heenry@infomed.sld. cu
Physical Limitation of Range-Domain Secrecy Using Frequency Diverse Arrays

In this paper, the concept and recent development of exploiting frequency diverse array (FDA) and its variants for the physical-layer wireless security have been revisited and carefully examined. Following rigorous analytical derivation and illustrative simulations, the authors realize that the investigations performed in some recent works overlooked one critical issue facing the real-world applications, and system models established and used before were based on a limited assumption, i.e. that the legitimate and eavesdropping users at different ranges sample the signal waveforms at the same time instant. The limitation of this assumption results in their studies inconclusive. The authors aim to take the first step to divert research efforts and rectify the previous incomplete analyses. The authors argue that in the current technology base the FDA cannot secure a free-space wireless transmission in range domain, because the previously claimed ‘secure reception region’ propagates in range domain as time elapses.
Space-Borne GNSS-R Signal Over a Complex Topography: Modeling and Validation

A significant quantity of space-borne Global Navigation Satellite Systems-Reflectometry (GNSS-R) data over land was made available in the last decade, leading to an increasing interest in the assessment of the potentialities of this new remote sensing technique for land monitoring. In this frame, an electromagnetic simulator, such as the Soil And VEgetation Reflection Simulator (SAVERS), has the key role to support the understanding of the physical mechanism involved in the bistatic scattering and to identify the surface features mainly contributing to the observed signal. Originally developed for ground and airborne GNSS-R observations over homogeneous areas, in this study, SAVERS was upgraded to account for space-borne systems. The new version of SAVERS takes into account the inhomogeneity characterizing the large area observed from space altitudes, due to a variable surface elevation and land cover. Coherent and incoherent scattering and polarization rotation are computed taking into account the local slope and elevation of the surface. The simulator was validated against TechDemoSat-1 observations over a bare surface with a complex topography and over a forested surface with a gentle topography. The validation results show the capability of SAVERS to correctly estimate the effect of the topography, enhancing the understanding of the observations. Moreover, it was found that the sensitivity to soil moisture is independent of the topography (about 1.5 dB for a 10% variation of soil moisture). Whereas a saturation of the GNSS-R reflectivity over a variable topography is reached for lower values of biomass, earlier than in the flat case.
Epileptic Seizure Detection With a Reduced Montage: A Way Forward for Ambulatory EEG Devices

Electroencephalogram (EEG) is one of the fundamental tools for analyzing the behavior of brain and particularly helpful for treatment of epilepsy and detection of associated seizures. For long-term recording of EEG signals, current research is heading towards simple, unobtrusive and ambulatory devices with a small number of channels. The primary contribution of this paper is to assess the performance difference between the seizure detection results using features from all channels versus only the channels in/around the temporal region. For this purpose, we develop a supervised seizure detection algorithm that uses time domain features extracted sequentially for every 1-second epoch. By using this algorithm, we obtained sensitivity values of 0.95 and 0.92, specificity values of 0.99 and 0.99 and false positive per hour values as 0.16 and 0.21 for all 23 channels and 10 temporal region channels, respectively. These results show that restricting the EEG analysis to temporal region results only in a graceful and gradual degradation of classifier performance. We conclude that EEG ambulatory devices with a montage local to the temporal region could demonstrate satisfactory performance. This presents a promising way forward for the use of ambulatory devices with compact wearable design.
Enhanced Space-State Reduced-Order Microgrid Model in Common DQ-Reference Frame

Inverter-based droop-controlled microgrids systems are becoming attractive solutions to meet future energy demand in a sustainable way. The development of accurate models is crucial for control loops design and performance assessment of the system. However, extracting essential information about the overall microgrid behavior from complete models can be a hard task, as well as can involve a high computational burden depending on the microgrid size. Therefore, the development of a reduced-order model that properly represents the dynamic performance of the microgrid in a computationally efficient way is mandatory. In this paper, an enhanced reduced-order model able to represent the influence of network dynamics on the performance of the droop-controlled microgrid is presented. By means of simulation results the fidelity improvement of the proposed microgrid model is confirmed.
Short-term Wind and PV Generation Forecasting of time-series using ANN

The main sources of energy from renewable energy sources (RES) that have penetrated into the generation mix depend on the climate and cannot be shipped. The data of Independent Ontario Electricity System Operator (IESO), Canada and meteorological parameters available near the Oak Ridge Laboratory, USA are considered for the short-term forecast of generation from wind and photovoltaic (PV) sources, according to some hypotheses. Hourly data for two years 2017 and 2018 is taken and training validation and testing of the artificial neural network (ANN) is performed. Three models for the short-term prognosis of time-series are taken for the study, namely, non-linear automatic regression with exogenous input (NARX) Automatic nonlinear regression (NAR) and InputOutput model. The Bayesian regularization method (BR) is used for training. NARX shows the minimum mean square error (MSE) and regression during training, validation and testing. When implementing the prediction by this ANN, it is expected that the generation from these RES can be more dependable to meet the load contributing a further reduction in the cost of the conventional generation.
Machine Learning Classifiers for Asthma Disease Prediction: A Practical Illustration

Asthma is an inflammatory and airway-induced lung disease that causes breathlessness, wheezing and often life-threatening attacks. It is necessary for patients with asthma to predict severe exacerbation caused by uncontrolled asthma. So, there is a need for a framework that can predict the likelihood of asthma in a patient with maximum accuracy. In this paper, an effective mechanism for asthma disease prediction has been used by mining the data containing patients’ previous health records. Four machine learning classification algorithms named as Naïve Bayes, J48, Random Forest and Random tree are used in these experiments to predict asthma disease at an early stage. The performance of all four algorithms is evaluated on various measures. Accuracy is measured over instances that are classified correctly and incorrectly. After experiments, results show that Naïve Bayes approaches 98% with the highest accuracy.
Stacking based approach for prediction of faulty modules

Determination of a software module, prone to fault is important before the defects are discovered; because it can be used for better prioritization of resources. Software fault prediction is one of such tasks that predicts the fault proneness of the developed modules by applying machine learning techniques on software defect data. State-of-art software defect prediction techniques suffer from achieving good accuracy due to the imbalanced nature of software defect datasets. To address this issue, here we present an approach for software defect prediction by combining imbalance removal and ensemble-model. As ensemble approach is very effective and provides better prediction results as compared to the individual techniques. The stacking-based framework is developed by considering the outperforming ensemble classifiers in order to predict the faulty software modules. All the experiments are performed over twelve benchmark NASA MDP datasets. The paper presents an improved ensemble-based stacking approach to classify the fault prediction for the software system in an effective way.
Noise-Robust Pupil Center Detection Through CNN-Based Segmentation With Shape-Prior Loss

Detecting the pupil center plays a key role in human-computer interaction, especially for gaze tracking. The conventional deep learning-based method for this problem is to train a convolutional neural network (CNN), which takes the eye image as the input and gives the pupil center as a regression result. In this paper, we propose an indirect use of the CNN for the task, which first segments the pupil region by a CNN as a classification problem, and then finds the center of the segmented region. This is based on the observation that CNN works more robustly for the pupil segmentation than for the pupil center-point regression when the inputs are noisy IR images. Specifically, we use the UNet model for the segmentation of pupil regions in IR images and then find the pupil center as the center of mass of the segment. In designing the loss function for the segmentation, we propose a new loss term that encodes the convex shape-prior for enhancing the robustness to noise. Precisely, we penalize not only the deviation of each predicted pixel from the ground truth label but also the non-convex shape of pupils caused by the noise and reflection. For the training, we make a new dataset of 111,581 images with hand-labeled pupil regions from 29 IR eye video sequences. We also label commonly used datasets (ExCuSe and ElSe dataset) that are considered real-world noisy ones to validate our method. Experiments show that the proposed method performs better than the conventional methods that directly find the pupil center as a regression result.