Miracle-dz/UniFAR-FTUs huggingface dataset:
Graphene Quantum Dots as promising probes in electrochemical immunoassay for rapid and sensitive detection of pathogenic Staphylococcus aureus

There is an increasing need for the rapid detection of S. aureus owing to the health concerns it raises worldwide. Electrochemical detection methods have proved to be rapid and capable of offering Point-Of-Care diagnosis. Recently, graphene-based materials have emerged as most promising nanomaterials for electrochemical analysis. They have shown improved sensitivity and stability when used as transducer matrices in electrochemical sensing. The latest entrant in this class of materials is Graphene Quantum Dots (GQDs) which, apart from their optical properties possess excellent electrical conductivity and electro-catalytic properties. In this contribution, we explored GQD as electrochemical probe in a sandwich immunoassay designed to detect pathogenic strain of S. aureus. Our results show that GQDs, when used as immuno-labels, improved the sensitivity of the assay by more than 100%. The limit of detection (LOD) was less than 1 CFU/mL. Moreover, it offers rapid detection of pathogens in less than an hour.
Full-Duplex Cooperative Non-Orthogonal Multiple Access System With Feasible Successive Interference Cancellation

Non-orthogonal multiple access (NOMA) is a promising radio access scheme for future mobile networks due to its high spectral efficiency, but the weak user suffers poor performance due to the interference and poor channel quality. Full-duplex cooperative NOMA (FD C-NOMA) is an attractive solution by allowing the strong user to relay the weak user’s signal simultaneously, but most existing works made an impractical assumption that the signals from the base station (BS) and the strong user can be perfectly resolved and combined at the weak user. Considering feasible successive interference cancellation (SIC) operation, we propose a two-phase FD C-NOMA system where the BS only transmits a new signal for the strong user in the second phase, such that the weak user can perform SIC to obtain its own signal. A sum-rate maximization for this proposed system is investigated and a low-complexity closed form solution is derived under the total BS transmission power and proportional rate constraints. Numerical simulation results show that the proposed FD C-NOMA system model attains better sum-rate performance compared to other schemes.
METAZOAN PARASITIC INFECTIONS OF SWORDFISH (XIPHIAS GLADIUS L., 1758) FROM THE MEDITERRANEAN SEA AND ATLANTIC GIBRALTAR WATERS: IMPLICATIONS FOR STOCK ASSESSMENT

,SUMMARY Between 1997-1998 and 2000-2003 a parasitological survey on the swordfish (Xiphias gladius L., 1758) was carried out. A total of 178 specimens were examined, for metazoan parasites. The specimens were caught from four sampling areas in the Mediterranean Sea and from one area of the NE Atlantic Ocean (Gibraltar). Fourteen taxa were identified by morphological and genetic methodologies. Most of the parasites identified have been previously recorded for the host and the studied areas. Exceptions were the digenean Prosorhynchoides sp. , and the nematode H. petteri, new records for the Mediterranean swordfish populations. Prevalence and intensity values of the infection by all the parasite species were estimated for each sampling areas and according to different age classes. Most of the parasites species identified were specialist for X. gladius. Among the ectoparasites, the monogeneas Tristoma coccineum and T. integrum were the dominant species in both the Mediterranean and Atlantic samples, showing very high levels of infection in the host. Significant higher infection levels by the copepod Pennella instructa were observed in the Mediterranean sample. Among the endoparasites, the anisakid nematodes (genus Hysterothylacium) and the cestode Fistulicola plicatus were most dominant in both the Mediterranean and Atlantic populations. A significant negative correlation was found between the infection by the monogenean Tristoma spp. and host age, with the highest values observed in the first age classes. Data so far obtained in the parasite fauna of swordfish from Mediterranean and Gibraltar area suggesting that the last one could be a mixing area. Preliminary observations concerning the possible use of some parasite taxa and their infection levels as biological tags for swordfish stocks characterization in European waters are also given.
Fast Single Tracking Location Shear Wave Elasticity Imaging

Single tracking location shear wave elasticity imaging (STL-SWEI) has been proven to have the capability to eliminate the speckle bias due to its unique push-tracking mode. However, compared to multiple tracking location shear wave elasticity imaging (MTL-SWEI), STL-SWEI requires more acoustic excitation and push-track sequences, and thus has a lower frame rate. In this work, a new approach named fast single tracking location shear wave elasticity imaging (FSTL-SWEI) is presented. FSTL utilizes spatially modulated ultrasound radiation force (SMURF) applying a special acoustic intensity pattern to generate lateral sinusoidal distributed (LSD) shear waves. It infers shear wave speed (SWS) distribution using the instantaneous phase of the shear wave motion signals obtained by the Hilbert-Huang Transform. Able to map the elasticity of the region of interest (ROI) in only one push-track sequence, it can achieve a frame rate comparative to MTL-SWEI. To validate the performance of this method, a finite element model (FEM) was established to simulate breast tissue with an inclusion. The simulation results show that FSTL can well distinguish the inclusion from the background.
Power Efficient and Expenditure Minimized Optimal Link in Wireless Body Area Networks

Wireless body area network (WBAN) includes elements such as medical sensors, LPU, AP and medical servers. Due to mobility of network frequent failure in link establishment causes data dissemination which is not beneficial for healthcare sector in case of critical data. Therefore WBANs send their data to local servers and then it is transferred to respective medical servers, but it increases the network expenditure. Further due to body movement’s re-connectivity among nodes is required more often, which results into reduction in power of nodes. Managing reduced expenditure and power is main objectives of opportunistic WBAN. Node connectivity quality of inter BAN and intra BAN communication is affected by the mobility of the object implanted with sensor nodes. This result into reduced QoS, network throughput, data dissemination loss, reduced PDR etc. In this paper our major contribution is to propose a pricing based data model for dynamic connectivity in distributed network. To achieve this we are implementing these two algorithms.1) Optimal network management expenditure minimization 2) Power-efficient prioritized opportunistic communication algorithm. To implement these algorithm NS2 environments is used and comparison of proposed system protocol and existing protocol is focused for better performance.
Coherent Polarization States Multiplexer and Its Feasibility in Quantum Communication

In this paper, a multiplexing technique of coherent polarization states based on three-dimensional rotation group is proposed. The technique applies two Stokes parameters in one polarized light as signal and local oscillator (LO), and combines them in the single spatial mode. Three-dimensional rotation group SO(3) is used to describe the transformation of Stokes parameters. SO(3) is decomposed into three subgroups, with each subgroup represents a rotation along different axes. A multiplexer is designed with three electro-optical crystals to realize the function of three-dimensional rotation group. The multiplexer achieves an ergodicity of polarization states. Signal and LO propagates in the same spatial mode, multiplexing of polarization states is realized. Finally, the multiplexer is tested in a free space quantum communication system and its feasibility in quantum communication is confirmed.
Tracking of Flexible Brush Tip on Real Canvas: Silhouette-Based and Deep Ensemble Network-Based Approaches

Digital painting is a process of creating a digital artwork using modern human-computer interaction technologies. One of the core enabling technologies is the real-time tracking of user’s strokes, which is generally supplied by a digital tablet with a stylus. While the digital tablet technology provides highly accurate tracking, the drawing should be done with a rigid stylus on a plastic surface. This sometimes destroys the realism of drawing, such as interaction with the digital tablet cannot provide the feedback of subtle texture, friction of the paper/fabric canvas and tension of soft painting brush. This becomes particularly problematic for traditional painting artists who are trained with and prefer real painting brush and paper/fabric canvas. Thus, the aim of this work is to present an alternative solution where the user’s strokes can be tracked even when the actual brush and canvas are used. To this end, we proposed two approaches for digitally tracking the tip of flexible bristles of a soft brush, so that the painting can be created digitally on a computer. The first approach captures the silhouette of deforming bristles using a pair of well-aligned infra-red (IR) cameras, which extracts the tip from the silhouette, and reconstructs the 2D position of the tip. The second approach predicts the brush tip position through a deep ensemble network-based approach where the relationship between the brush tip position and brush handle pose are trained with our novel model comprising of Long-Short Term Memory Autoencoder and 1-D Convolutional Neural Network. The trained model is used to predict the brush tip position in realtime. Both approaches extensively evaluated through multiple tests. Furthermore, our model outperforms the state-of-the-art models.
A High-Voltage Circuit Breaker Condition Assessment Method using the Vibration Fingerprint based on VMD-EM method

The reliability of the operations of the high-voltage circuit breaker is the key to the stable power system, so it’s fault diagnosis and condition assessment it is of great significance. Considering that high-voltage circuit breaker vibration fingerprints contain valuable information about its mechanical integrity and that the vibration measurements are non-invasive, this paper presents the algorithm for the analysis of residual life of a high-voltage circuit breaker. The algorithm is based on the variational mode decomposition (VMD) and Shannon information entropy mean (EM) in order to obtain indices that are used as an indicator of the circuit breaker structural deterioration.
Inactivation of a Thiol-Dependent Enzyme by Urate Hydroperoxide

There are links between high serum urate (hyperuricemia) and many inflammatory diseases, yet the mechanism is obscure. Urate, the product of purine and ATP break down, builds up in plasma because humans lack the enzyme uricase to convert it to allantoin, which is freely excreted. Urate may benefit health by acting as an antioxidant that scavenges reactive oxygen species. However, hyperuricemia is associated with gout, metabolic syndrome and cardiovascular disease. Oxidative stress is also associated with all these inflammatory diseases. During oxidative stress urate is converted to several reactive electrophiles, including urate hydroperoxide. This novel oxidant could contribute to the adverse effects of urate. Urate hydroperoxide is formed when urate is oxidized to a radical that subsequently combines with superoxide. Activated white blood cells called neutrophils, and xanthine oxidase along with myeloperoxidase/lactoperoxidase, can produce urate hydroperoxide. Previous studies characterized the formation of urate hydroperoxide and its oxidation of small biomolecules. In this investigation, I explored oxidation of thiols and the thioldependent enzyme glyceraldehyde-3-phosphate dehydrogenase (GAPDH) by urate hydroperoxide. The effectiveness of urate hydroperoxide as a thiol oxidant was compared with taurine chloramine. Ellman’s assay for reduced thiols was used to measure depletion of cysteine residues on GAPDH by urate hydroperoxide and taurine chloramine. GAPDH was exposed to oxidants in a dose-dependent manner, then assayed by measuring its ability to catalyse the production of NADH. Mass spectrometry was used to identify specific modifications of GAPDH. Urate hydroperoxide oxidized exposed thiols on GAPDH and fully inactivated the enzyme at a ratio of about 5:1. Half of its activity was recovered by reduction with DTT. In comparison, taurine chloramine inactivated GAPDH at approximately 10:1 and DTT reduction recovered all activity. Hence, urate hydroperoxide inactivates GAPDH by reversible and irreversible routes. GAPDH increased in molecular mass by 132 Da with exposure to urate hydroperoxide, indicating the formation of a GAPDH-urate adduct. However, I could not identify which residue was modified with a tryptic digest. Formation of urate hydroperoxide during inflammation and its subsequent oxidative reactions may explain some of the adverse effects of hyperuricemia.
Noninvasive Arterial Blood Pressure Estimation using ABPNet and VITAL-ECG

Arterial Blood Pressure (ABP) is an important physiological parameter that should be properly monitored for the purposes of prevention and detection of cardiovascular diseases, which represent one of the leading causes of death in the world. Currently, the most common adopted noninvasive blood pressure measurement system is sphygmomanometer, which works by inflating and deflating a cuff around the arm. This work presents ABPNet, a new prediction technique, based on a multilayer perceptron (MLP), which uses ECG and PPG to estimate both systolic and diastolic blood pressure. To train the neural network, signals are gathered from the Physionet MIMIC database. The proposed architecture performances are evaluated w.r. t. both the invasive blood pressure signal and the noninvasive sphygmomanometer measurements. The experimental results are quite promising; they are compliant with the ANSI/AAMI/ ISO 81060- 2:2013 for sphygmomanometer certification because the network predicted values are within +/− 5 mmHg w.r. t. real invasive measurements, as imposed by the legislation. Finally, it is shown how ABPNet can be used to improve the VITAL-ECG, a wearable device designed to acquire vital parameters, such as electrocardiographic (ECG) and photoplethysmographic (PLETH/PPG) signals; indeed, by embedding the ABPNet neural network, VITAL-ECG can be upgraded to estimate, also, ABP. As a consequence, the device could be used to fight cardiovascular diseases and prevent their dangerous effects.
Layer division multiplexing for 5G DL transmission within ultra-dense heterogeneous networks

To date, the main focus of the wireless network has been considered human-centric end-use. Fifth-generation (5G) wireless technology is now evolving towards the era, where the focus is on large scale machine-type communication. 5G & beyond wireless networks are focused on enabling their new forms of usage of wireless connectivity, which are expected to deliver a large number of new applications & services into reality. This will result in a massive increment in terms of the number of users and heterogeneous service requirements. While providing such new applications and services, it is expected to ensure optimal utilization of resources. The challenges to be addressed to attain optimal resource utilization includes meeting heterogeneous capacity requirement to serve a massive number of devices with limited spectrum. The work presented in this paper is focused on exploring the possibility of layer division multiplexing (LDM) as a potential tool to resolve these challenges. While analysing the feasibility of LDM for 5G downlink transmission framework, we have considered unicast & multicast transmission scenarios. This paper presents an analytical framework to define the performance bounds of LDM in the context of 5G downlink transmission. The robustness of our analytical model has been validated with simulation results of LDM. We also have considered specific urban & rural use-case scenarios for 5G with LDM downlink transmission. Our analytical results suggest LDM core layer transmission is one of the strongest candidates for massive machine-type communication (mMTC) within 5G networks to attain enhanced coverage range compared to its existing counterpart.
Bio inspired Ensemble Feature Selection (BEFS) Model with Machine Learning and Data Mining Algorithms for Disease Risk Prediction

Use of machine learning (ML) and data mining (DM) algorithms has surfaced more often in the recent years for disease risk prediction problems in the healthcare communities. Several traditional feature selection models are combined with the DM and ML algorithms to improve accuracy of the disease risk prediction. In this study, a new Bio-inspired Ensemble Feature Selection (BEFS) model is introduced which is applied with the DM and ML algorithms. In the BEFS model, the most relevant and highly contributing features in the prediction are determined with a bio-inspired algorithm i.e., genetic algorithm, and an ensemble algorithm i.e., random forest algorithm. These important features obtained from the proposed model are then combined in various combinations and applied with the DM and ML algorithms, here logistic regression (LR) and random forest (RF), and the results obtained are promising. The experiment is executed using the famous ML language R. To accomplish this objective, the Breast Cancer Wisconsin (Diagnostic) dataset of UCI (University of California, Irvine) ML repository is utilized. In the experimental outcomes, the highest accuracy attained with the BEFS model is 96.49%, the AUC (Area Under Curve) achieved is 96%, and the sensitivity is 98.11%. These results, which greatly improve the disease risk prediction, are higher than several other existing works, while utilizing only six most relevant features out of the thirty two features of the dataset.
Electricity Theft Detection Base on Extreme Gradient Boosting in AMI

Electricity theft by cyber attacks to smart meters in advanced metering infrastructure causes financial losses to utilities every year. This paper proposes an electricity theft detector using metering data based on Extreme Gradient Boosting (XGBoost). The metering data are preprocessed, including recover missing or erroneous values and normalization. The classification model based on XGBoost are trained using both benign and malicious samples after data preprocessing. Simulations are done by using the Irish Smart Energy Trails with six attack types. Compared with the support vector machine, propagation neural network, and k-nearest neighbors algorithm, the proposed method can detect electricity theft with either higher accuracy or faster speed.
Iterative Reweighed Approach for Multiuser Detection with Multiple Measurement Vector in MTC Communications

The most promising feature of the fifth generation (5G) wireless networks is to support the Internet of Things (IoT) application, such as massive machine-type communications (mMTC). In mMTC scenario, a large number of users are connected to an access point, but very few of them are active at the same time, which motivates us to exploit Low-Activity Code Division Multiple Access (LA-CDMA) as the multiple access technology for MTC. The optimal maximum a posterior probability (MAP) assumes that the user activity factor is exactly known, which is in fact unknown in the practical detection scenarios. In this paper, we first formulate the LA-CDMA uplink into a multiple measurement vector (MMV) model for several continuous time slots, then we introduce a novel iterative reweighed (IR) algorithm to reconstruct the sparse signal. The new scheme overcomes the difficulty of unknown user activity factor and the simulation results over massive MTC systems demonstrate that the proposed algorithm achieves substantial performance gain over traditional detectors considerably.
A Novel CS-based Measurement Method for Impairments Identification in Wireline Channels

The paper proposes a new measurement method for impairments identification in wireline channels (i.e. wire cables) by exploiting a Compressive Sampling (CS)-based technique. The method consists of two-phases: (i) acquisition and reconstruction of the channel impulse response in the nominal working condition and (ii) analysis of the channel state to detect any physical anomaly/discontinuity like deterioration (e.g. aging due to harsh environment) or unauthorized side channel attacks (e.g. taps). The first results demonstrate that the proposed method is capable of estimating the channel impairments with an accuracy that could allow the classification of the main channel impairments. The proposed method could be used to develop low-cost instrumentation for continuous monitoring of the physical layer of data networks and to improve their hardware security.
Steering Angle Prediction in Autonomous Vehicles Using Deep Learning

The enhancement in the development of the computer vision and deep learning has led to a renaissance in the automation domain. These advancements can be applied to the work of driving a vehicle where drivers from the car can be replaced by such technology. Most important in designing the autonomous vehicle is to predict the steering angle by which the vehicle should take a curve based on curvature of the road. In this paper, the modules are split into image preprocessing and convolutional neural network. For each point along the trajectory, the steering angle at that point is recorded along with the instantaneous speed at which the car is travelling, and a set of 3 images left; centre; right captured by onboard forward-facing cameras in the vehicle and other corresponding parameters like throttle and brake. The first phase considered is image preprocessing which provides a variety of images assisting the training phase. This variation is achieved using the techniques of data augmentation which includes translation, resize, crop, flip of images. The significance of preprocessing is to eliminate any unwanted features of the image and enhance the things relevant to the model. The second phase of convolutional neural network is the deep learning phase where the preprocessed images from the previous phase is the input and steering angle is predicted as an output. For the accurate prediction of the steering angle it uses multilayer convolutional neural network and multilayer perceptron model. The method of supervised deep learning helps to validate the predicted steering angle and its accuracy. The model achieves strong performance as generic features and are even more effective when fine-tuned to large datasets. Finally, this predicted steering angle is fed to the Udacity’s simulator of driverless car to move the vehicle along the track with all its attributes.
Full Coverage with 3GPP technologies On the feasibility of providing full rural cellular coverage

The new 3GPP New Radio standard (NR) has been designed with deployment flexibility in mind to enable high-rate mobile broadband services as well as massive and ultra-low latency machine type communications for verticals. In this paper, we discuss the challenges and potential solutions to provide full surface coverage based on main-stream terrestrial cellular 3GPP technology to enable all use-cases. New 3GPP based solutions for improving coverage e.g. using 5G for self-backhauling, resource sharing, and protocol parameters are discussed. The paper includes results on the impact of selected link-budget improvements on cellular coverage under given capacity assumptions. It is shown that long-range systems with enough capacity are feasible with inter-site distances of 65 km with 5G on mid-band (3.5 GHz) notably reducing total cost of ownership.