Miracle-dz/UniFAR-FTUs huggingface dataset:
Text mining in radiology reports (Methodologies and algorithms), and how it affects on workflow and supports decision making in clinical practice (Systematic review)

The purpose of this review was to summarize the algorithms and methodologies of text-mining and demonstrate the main objective of text-mining on radiology reports in health care facilities which consider as a common source of medical information. And how it affects radiologist performance and plays a big role in clinical practice workflow and decisions making in critical situations and time-consuming. In case the radiologist widely used a narrative- text box in their reporting and sometimes there is a big need to know very specific and critical information about the patients’ current status and to provide with accurate diagnosis then take the appropriate action as soon as possible. However, here it becomes the need to utilize information technology and the effort was directed to find ways to merge data science with the health care field to solve such a problem. We follow the systematic review methodology conducted by Ahmad Alaiad . et al study completed after 29 quantitative and systematic related articles were searched using relevant database then extract and discuss the text-mining processes and provide overall picture about such new innovation and how the IT now days play a valuable role in health problem solving and make the clinical practice more effective and efficient and improve quality of care by improving clinical decision making process. We develop a research taxonomy that summarizes the most of algorithms and methodologies of existing research, we identify the major future questions, limitations and gaps.
Design of a Universal Partial Discharge Simulator

The processes of inception, growth, and propagation of Partial Discharge (PD) along the dielectric are complicated. Their analysis requires a thorough understanding of underlying physics. A software simulation tool has been developed to study typical PD processes. The simulator generates pulses of varying amplitudes, shapes and time of occurrence of pulses observed in practice using National Instruments LabVIEW software as a base platform. The rise time of PD pulses plays an important role in the characterization of PD. The rise time formulation has been attempted based on the approach using the dipole moment development and law of conservation of energy. PD phenomenon is usually studied with the assumption of an air-filled void being energized by parallel plate/ coaxial cylinder electrode configuration. An SF6-filled spherical void inside a dielectric between two coaxial cylinder electrode plates is used as another configuration. Phase-resolved partial discharge (PRPD) pattern and the characteristics of PD pulses simulated are in agreement with results reported in literature. The contribution of this paper is versatility of the simulator for detailed study and comprehensive analysis of PD without the use of high voltage (HV) setup.
Seed dispersal distance is more strongly correlated with plant height than with seed mass

1. It is often assumed that there is a trade‐off between maternal provisioning and dispersal capacity, leading small‐seeded species to disperse further than large‐seeded species. However, this relationship between dispersal distance and seed mass has only been quantified for species from particular sites or with particular dispersal syndromes.
Low Power and Area efficient Forward Error correction code for SRAM in FPGAs

As submicron technology scales, SRAM bitcell density increases on the chip. This results in an increase of soft errors due to radiation induced multiple-bits upsets (MBUs). SRAM uses SEC-DED [Single Error Correct – Double Error Detect] code along with word interleaving or column muxing to mitigate these soft errors. The number of SRAMs in FPGA is of the order of 1000. Each SRAM uses SEC-DED code to mitigate soft errors due to MBUs. Optimizing the ECC will save considerable amount of area and power for larger FPGAs. We propose a technique where area and power of the ECC can be reduced by 50%.
Design of a Miniaturized Circular Microstrip Patch Antenna for 5G Applications

In this paper, a novel proximity-coupled fed microstrip Circular Patch Antenna (CPA) is proposed for 5G applications. The proposed CPA resonates at a frequency of 3.5 GHz. The simulation of the proposed CPA is done using High Frequency Structure Simulator (HFSS) software. The parametric optimization feature available in HFSS Software is used for determining the optimized dimensions of the proposed CPA. The optimized compact CPA is having a substrate of length 30 mm, and width 45 mm. The radius of the radiating patch is 13.005 mm. The proposed antenna is having an excellent S11 characteristic and impedance matching. The S11 is found to be -40.2827 dB. The CPA also has a substantial gain of 5.8 dB. The VSWR is nearly ideal (1.02) and the CPA has an efficiency of 88.40% and serves a bandwidth of 200 MHz. With such alluring features, the proposed CPA can be a suitable candidate for a variety of 5G applications such as the Internet of Things (IoT), Machine to Machine (M2M) Communication, etc.
Low-Power Resistive Switching Characteristics in TiN/TaON/SiO2/Pt RRAM devices for Neuromorphic Applications

In this work, the TiN/TaON/SiO2/Pt and TiN/TaON/Pt RRAM devices are fabricated and investigated. Compared with the TiN/TaON/Pt control device, TiN/TaON/SiO2/Pt RRAM devices with inserted SiO2 thin film show larger (x10) resistive window at the set compliance current of 1mA. TiN/TaON/SiO2/Pt RRAM devices show low-power resistive switching characteristic with set power of nearly 28. 5µW (5.7V 5µA) and reset power of nearly 5. 4µW (5.4V 1µA). Endurance characteristic with low-power resistive switching over 106 is demonstrated. The inserted SiO2 thin film may play the role of current limiting layer to achieve the low-power resistive switching characteristics in TiN/TaON/SiO2/Pt RRAM devices.
Mushroom tyrosinase activity of novel and known phenolic compounds isolated from Greyia Flanaganii (Bolus)

Hyper-pigmentation of the skin is a common problem that is prevalent in middle aged and elderly people. It is caused by over production of melanin, the pigment that is responsible for the colour of hair and skin in humans. Tyrosinase is known to be the key enzyme in melanin production. Forty plants belonging to different families were tested for their activity on tyrosinase using L-tyrosine as a substrate. Ethanolic leaf extract of Greyia flanaganii (Bolus) showed significant (p<0.05) inhibitory activity exhibiting the 50% inhibitory concentration (IC50) of 32.62 μg/ml. It was further investigated for its toxicity and effect on melanin production by melanocytes cells. G. flanaganii showed significant (P<0.05) 20% reduction of melanin at 6.25 μg/ml while 80% of cells were found to be viable, thus indicating low levels of cytotoxicity (IC50 < 400 μg/ml). The 1.2-diphenyl-2-picrylhydrazyl (DPPH) method was used to quantify the total radical scavenging capacity (RSC) of the extract. The amount of antioxidants necessary to decrease the initial DPPH absorbance by 50% (EC50) by the ethanol extract of the leaves of G. flanaganii was found to be 22.01 μg/ml. ‘Acne vulgaris’ is a chronic inflammatory follicular disorder of the skin that affects areas containing the largest oil glands including face, neck, back and chest. It affects up to 80%
A Circular Slotted Planar Monopole UWB Antenna for RF Energy Harvesting Applications

Ultra Wide Band (UWB) is a buzzing word in recent times. This technology attracted researchers due to its features like less power consumption, low cost, high speed data transfer, multipath immunity, reliability, security, simultaneous ranging and communications. This paper discusses a planar UWB design which is printed on FR4 substrate of dimensions $49mm\times 35mm\times 1.6mm$. It achieves less than 10dB S11 in the frequencies starting from 1.51GHz to 12.5GHz, which covers the RF bands like GSM1800, DCS1800, GSM1900, PCS1900, UMTS2100, LTE2300, Wi-Fi, Bluetooth, ISM bands, LTE2600, FCC unlicensed UWB, WLAN, Wi- MAX3500, C-band, HIPERLAN and X-band. The antenna attains maximum efficiency of 97% at 1.78GHZ, highest peak gain of 4.2dBi at 7.78GHz with an impedance bandwidth of 10.99GHz. The printed prototype is tested by VNA, the measured S11 has good agreement with simulated return loss. This antenna is a good design for UWB applications like RF energy harvesting and spectrum sensing.
Experimental Validation of an Electromechanical Device to Convert Vehicles Mechanical Energy into Electrical Energy

In recent years, research into energy harvesting technologies has increased considerably due to the growing need for alternative energy sources. The particular case of energy harvesting on road pavements is a recent area of research, with different technologies having been developed. However, none of them have presented high energy conversion efficiencies, making that none of them has entered the market. This paper deals with the experimental validation of a new electro-mechanical system to implement on a road pavement energy harvesting electromechanical device, previously developed by the authors. The main goal is to quantify the energy harvesting and conversion efficiency of the new system and compare it with its theoretical results from simulations. Conclusions about the system's efficiency are presented.
Stochastic Deep Learning for Compressive-sensing Radar

Machine deep learning (DL) is very good in fitting nonlinear functions and learning data representations. It is still unclear why DL works well because the statistical properties have not been fully understood yet. Stochastic deep learning (SDL) is proposed which bridges the strong numerical capability of DL with the probabilistic inferences. Since signal models in radar are intrinsically nonlinear with respect to unknown target parameters (range, Doppler or angles), and moreover, radar processing is intrinsically stochastic, SDL can enhance radar, especially, radar with compressive sensing (CS) whose data are more complicated due to the signal compression. The potential of SDL applicable to CS radar is presented.
ZigBee Transceiver Design for Smart Grid Home Area Network using MATLAB Simulink

Smart Grid is necessary to provide extended and energy efficient services with advanced control and communication infrastructure. Home Area Network (HAN) is one of the layers of smart grid [1]. In HAN, a smart meter is connected with all electric home appliances. ZigBee is a wireless mesh networking standard meant for smart grid HAN. It is a short range communication technology and it consumes low energy and it is low expensive. ZigBee is preferable communication protocol for smart grid applications. This paper focuses on Matlab Simulink model of ZigBee transceiver design for communication between smart meter and home appliances. The operating frequency of this model is 2.4 GHz. Channel type assumed for data transmission is Additive White Gaussian Noise (AWGN). The Bit Error Rate (BER) performance was plotted against Signal to Noise Ratio (SNR).
Geometric-Based Localization for Wireless Sensor Networks

In Wireless Sensor Networks, most applications require the absolute location information of the sensors nodes, whereas location-based routing protocols require that each sensor node must know the location information of its own, its one-hop neighbor nodes and the base station. Existing localization techniques require either a relatively large number of anchor nodes to identify the locations of the sensor nodes or manual placement of anchor nodes at specific locations, which are costly options. This paper proposes a novel geometric-based localization algorithm that accurately localizes the sensor nodes using only three anchor nodes that are randomly deployed without having to be neighbors of each other. The performance evaluation results demonstrated that the proposed algorithm could localize over 95% of the randomly deployed sensor nodes with an average localization accuracy of 0.25 meter when the inter-node distance measurements are noiseless and 0.95 meter when the distance measurements suffer from a 2% normally distributed random noise.
Simple Class F Power Amplifier Design with Fundamental Frequency Tuning

Simple class F with medium output power at 433 MHz is designed and simulated in this paper. The design process uses fundamental frequency tuning in find the matching network. At fundamental frequency, the reactance part of the networks needs to be conjugate values of that of the output impedance of the transistor circuit while the resistance part of the network is found by running simulation sweep. The second and third harmonics are also considered in the matching network design. Using the designed matching networks, the amplifiers yield almost optimum efficiency values for input power of 13 and 18 dBm. The proposed technique can save computational costs comparing to load pull technique. In future work it can lead to implementation of a class F power amplifier without using load pull.
Graph Convolutional Networks for Android Malware Detection with System Call Graphs

Nowadays, Android malwares have risen precipitously causing critical security threats. Malware authors now employ a variety of obfuscation techniques to evade their detection. Among various features, system calls are one of the major features used for detecting malwares. Although obfuscated malwares use diverse methods to conceal their malicious nature, the dependencies between the system calls can reveal their malicious nature. The existing malware detection models do not take into account of these structural dependencies and have large feature dimensions. Modelling the system calls as graphs can help in capturing the structural dependencies between the system calls. Recently, there has been an increasing interest in extending deep learning models such as Graph Convolutional Nets (GCN) for graph data. Motivated by this, we propose a novel Android malware detection mechanism using GCN which uses centrality measures of the graph as input features. To the best of our knowledge this is the first application of GCN for dynamic Android malware detection. We achieved a four dimensional feature representation for Android applications and a detection accuracy of 92.30 % on datasets with obfuscated malwares.
Statistical Analysis of Tuberculosis and HIV Cases in West Africa Using Panel Poisson and Negative Binomial Regression Models

Tuberculosis is a leading cause of death worldwide and the leading cause from a single infectious agent, ranking above Human immunodeficiency virus (HIV) and Acquired Immune Deficiency Syndrome (AIDS). The aim of this study is to ascertain the trend of tuberculosis prevalence and the effect of HIV prevalence on Tuberculosis case in some West African countries from 2000 to 2016 using count panel data regression models. The data used annual HIV and Tuberculosis cases spanning from 2000 to 2016 extracted from online publication of World health Organization (WHO). Panel Poisson regression model and Negative binomial regression model for fixed and random effects were used to analyzed the count data, the result revealed a positive trend in TB cases while increased in HIV cases leads to increase in TB cases in West African countries. Among the competing models used in this study, Panel Negative Binomial Regression Model with fixed effect emerged the best model with log likelihood value of -1336.554. This study recommended that Government and NGOs need more strategies to fight against HIV menace in West Africa as this will in turn reduced TB cases in West Africa.
Analysis of the Cellular Proteins, TIA-1 and TIAR, and their Interaction with the West Nile Virus (WNV) 3' SL Minus-Strand RNA

The 75 nt 3’ terminal stem loop (SL) of the West Nile virus minus-strand (WNV3’(-)SL) RNA was previously shown to bind specifically to T-cell intracellular antigen-1 (TIA-1) and the related protein TIAR. TIA-1 and TIAR were also reported to bind specifically to relatively long AU-rich sequences in the 3’UTRs of some cellular mRNAs. Three single-stranded loops (L1, L2, and L3) in the predicted WNV3’(-)SL RNA secondary structure each contain a single short AU stretch. Both TIA-1 and TIAR recombinant proteins bound less efficiently in in vitro gel mobility shift assays to WNV3’(-)SL RNA with the L1 or L2 UAAUU sequences deleted or substituted with Cs. The minimal binding site for both proteins in either L1 or L2 was UAA. In contrast, neither deletion of UAA in L3 nor substitution of L3 with Cs affected the binding of either protein. Deletion or C substitution of the entire AU-rich sequence in either L1 or L2 within the 3’(-) SL RNA in a WNV infectious clone was lethal. Analysis of mutants with partial deletions or substitutions in these sequences showed that AU sequences were needed in both L1 and L2. Immunofluorescence imaging of viral RNA transfected cells 3 hr post transfection showed that mutant viral RNAs with either small plaque or lethal phenotypes expressed similar amounts of viral proteins as wildtype RNA. In contrast, the
A Disaster Response Framework Based on IoT and D2D Communication under 5G Network Technology

This paper provides a conceptual framework for designing a disaster response system based on Internet of Things (IoT) and Device to Device (D2D) under the new generation 5G mobile networks. The next generation of mobile network and technologies such as Fifth Generation cellular network (5G), Device to Device (D2D) communication, Internet of Things (IoT) and big data technology can play a significant role to overcome the drawbacks of the current disaster management system. A conceptual model is simulated to study the performance of the system. Preliminary results show the possible improvements in terms of enhancing responsiveness, increasing resilience and reliability and improving scalability.
Application and Comparison of Different Control Strategies of Induction Generator in Wind Energy Conversion System

This article presents an application of different control strategies to highlight the performances of an induction generator in a wind system. First, a direct torque control (DTC) method was applied and then, to have a constant frequency and a low THD in the network current, a modulated hysteresis torque control (MHDTC) was used. To verify the high performances of this method, a comparison with two other control strategies based on field oriented control theory (FOC) has been made. The first one used a conventional PI controller and the second one used a Fuzzy Logic controller (FOC+FLC). Simulation results obtained using Matlab/Simulink, under a variable wind profile using the four control strategies, are presented and compared taking into account the electromagnetic torque ripple, the power factor, the current distortion, the frequency, losses and the efficiency.
Type Identification of Short Circuit Faults Based on Alexnet

Correct identification of fault types after grid short-circuit faults is the basis for fault handling, and it is also the basis and difficulty of grid fault analysis. In view of the traditional method, it is difficult to quickly and correctly guide the action of the circuit breaker relying on the electrical quantity judgment ,so based on the deep learning, a method of using Alexnet to judge the fault type is proposed. Firstly, this paper transforms the fault time series data into Hankel twodimensional matrix, next, the three-phase Hankel matrix is superimposed into jpg type image data, after that, converts the simulated voltage data into image pixel data between 0-255 through linear transformation. Then divides the graphic data into test set and verification set and puts them into the Alexnet network where the fault recognition model is trained. Finally, the proposed algorithm is verified to have higher accuracy through batch simulation data and it provides a new solution for fault type discrimination.
Model Predictive Control of PMSM Considering Online Parameter Identification

As a high performance control method, model predictive control has many advantages, such as fast dynamic response, simple structure and ability to deal with multivariables, but its control effect depends on the motor model. Aiming at the problem of the parameter sensitivity of model predictive control, in this paper, a model predictive control considering online parameter identification algorithm is proposed. By establishing model reference adaptive system, and designing the parameter adaptive law base on Popov's Superstability theory, the inductance and flux parameters online identified are updated to the model predictive control. Thus, it solves the problem of model predictive control in parameter mismatch, and realizes high performance control under the motor runs for a long time. The simulation results verify the effectiveness of the proposed method.
Hardware Implementation of Softmax Function Based on Piecewise LUT

Deep neural networks (DNN) achieve great results in many fields. While softmax function is widely used in DNN, how to implement it on hardware considering the accuracy, speed, area, and power, is a critical issue. This paper proposes a piecewise exponential lookup table (LUT) method, which reduces the LUT size of the exponential function in DNN's softmax layer. The experiment results show that the hardware using this method consumes less area and power resources than the previous work. The experiment input has a wide range and high accuracy, the absolute error of the calculation result is up to 4.5×10−6. The experiment results prove the proposed design is suitable for the softmax layer in most hardware implementation of DNN.
Glands Of Leaf Beetle Larvae – Protective Structures Against Attacking Predators And Pathogens

Th e exocrine glandular secretions of leaf beetle larvae of the taxon Chryso- melinae are well-known defensive devices us ed against generalistic predators. Re- cently it was shown that larvae of Phratora vitellinae produce fl and volatile exocrine secretions containing antimicrobial compounds, which were used to dis- infect their microenvironment. Th ey are emitted by specialized openings above the larval glandular reservoirs and inhibit the growth of bacteria as well as the germina- tion of fungi. In the present study we investigated in four further species from dif- ferent genera of the taxon Chrysomelinae the morphological structures underlying this novel principle of fumigation in antimicrobial defense. We found in all genera furrow-shaped openings above the glandular reservoirs, allowing the larvae the per- manent emission of volatile components, which build clouds of antimicrobial fumi- gants. Further, we could identify the antimicrobial active iridoid (epi)chrysomelidial as main component of the emitted fumigant of Phaedon cochleariae.