Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of immunochemistry methods for determination of cardiac troponin i concentration with the two analyzers

Objective. Cardiac troponin I is a biochemical marker that reflects damage to myocardial cells, which has been used for years in Clinical Hospital Center 'Zvezdara', Belgrade. Chemiluminescence immunoassay (Siemens Immulite 1000) was regularly used for determination of cardiac troponin I in sera of the patients. Aiming to improve the turnaround time for troponin I determination, it was considered to introduce a new method, electrochemiluminescence essay, by using Roche Cobas e411 analyzer. Methods. We investigated a novel, candidate method for determination of serum cardiac troponin I before its eventual adoption in clinical routine and it was compared with the one already applied in our laboratory. Comparison of cardiac troponin I concentrations essayed with the use of Roche Cobas e411analyzer (x) and of Siemens Immulite 1000 analyzer (y) was performed on serum samples of 94 adult patients, both genders, with signs and symptoms of acute myocardial infarction. Results. Analysis of results revealed regression equation of y=1.91x + 4.67; correlation coefficient was 0.92. In 15 out of 94 patients (16%) results obtained by two methods in the same sample disagreed. In such subjects, the result of troponin I determined with one method indicated presence of acute myocardial infarction, but with the other it was below the diagnostic cut-off value. Conclusion. Between the concentrations of troponin I determined with compared methods there was no proper agreement, expressed by the correlation coefficient and regression equation. Interpretation of results in about every sixth patient would point to conflicting conclusion in diagnosis of acute coronary event.
Laser-Induced Multi-Functional Biomimetic Surfaces

Nature has provided a plethora of functional surfaces exhibiting unique, complex hierarchical morphologies with dimensions of features ranging from the macroscale to the nanoscale. Such morphologies are behind the superior properties exhibited by the natural surfaces, including extreme wetting [1], antireflection [2], floatation, adhesion, friction and mechanical strength [3]. In principle, femtosecond laser induced surface structuring has been employed to produce numerous biomimetic structures for a range of applications, including microfluidics, tribology, tissue engineering and advanced optics.
Robust Synchronization of Complex-Valued Neural Networks with Probabilistic Delay

This paper focuses on the hybrid effects of parameter uncertainties, stochastic perturbation, leakage delay and probabilistic time-varying delay on the synchronization of complex-valued neural networks. Based on the stochastic analysis and Lyapunov theory, sufficient conditions are derived to ensure the networks under consideration to be globally robustly synchronized in the mean square. In addition, an example is also given to illustrate the effectiveness of the obtained theoretical results.
Low Redundancy Double Error Correction Spotty Codes Combined with Gray Coding for 64 Data Bits Memories of 4-bit Multilevel Cells

Non-volatile emerging Multilevel Cell (MLC) memories (such as magneto electric, magnetic resistive, memristor-based and phase change memories) are attractive to increase density. A key advantage of these memories is that they can store several bits per cell by using different levels. This however reduces the margins against noise and other effects and can lead to larger error rates. Errors in MLC memories are usually limited to magnitude-2 levels, and thus corrupt one or two bits per cell when using a Gray mapping from levels to bits. This enables the use of codes that can correct those error patterns in a memory cell instead of codes that correct all possible patterns in the cell, thus reducing complexity and cost. In this paper, the case of a 64 data bit memory built using memory cells that can store four bits and suffer up to double bit errors per cell is considered. Several (72, 64) Spotty codes that can correct double bit errors in 4-bit cells are designed and evaluated. The new codes require fewer parity bits than existing Spotty codes or symbol-based codes such as Hong-Patel codes. Therefore, they reduce the size of the memory while having encoding and decoding complexity similar to existing alternative codes.
Design and performance investigation of small-scale wind turbine system based on hybrid excited flux switching machine

In this study, a new design of a hybrid excited flux switching machine is developed for small-scale wind turbine power generation in rural and/or urban context. The hybrid excited flux switching machine uses double excitations (permanent magnets and excitation coils) to control the flux linkage and the DC bus voltage. The design and the investigation of the electromagnetic performances have been effectuated on a 3 kW wind turbine equipped with hybrid excited flux switching machine. It is shown that the produced power can be easily controlled using the excitation current which is very interesting for wind turbine applications. According to the particular stator and rotor design, this presented machine is intrinsically safe in case of fault since no flux weakening is required. The integration of unconventional electric machine in a small-scale wind turbine system appears as a good solution and meets additional criteria such as improving reliability.
Q-band Indoor Propagation Modelling and Measurements for 5G

With ESA’s philosophy of transferring possible space-related or satellite technologies into terrestrial applications, we have developed a Q-band measurement system in order to investigate the indoor propagation conditions in the millimetre wave band, capable of transmitting and receiving in the 38GHz mm-wave radio band that the new generation of cellular mobile 5G networks will also utilize. Our paper concentrates on determining a signal level map for a specific indoor measurement and compares it with existing ITU-R model P.1238 which provides an analytical base model for indoor propagation. We describe a simulated indoor environment and created a propagation map for the stages which have been compared with real measurements.
Aircraft Post-stall Maneuver Control Using Attitude Feedback Linearization

The Nonlinear Dynamic Inversion (NDI) flight control law artificially separates the plant dynamics as the fast and slow loops, which impairs the performance of the aircraft post-stall maneuver. To avoid this problem, a nonlinear flight controller based on the attitude feedback linearization is developed and it controls the angle-of-attack and sideslip angle indirectly through changing the aircraft’s attitude. In the controller proposed, the shadow Modified Rodrigues Parameters (MRPs) employed may describe arbitrary maneuver without singularity. The sliding mode control technique is utilized to address the modeling error regarding the aerodynamics and thrust vectoring. The stability of the closed-loop system is analyzed with the input-to-state stability theory. In particular, it is proved that the sliding mode controller, with the boundary layer against the chattering, may asymptotically stabilize the plant in the absence of disturbance, even if the states will not reach the sliding manifold in finite time. Via the Cobra and Herbst maneuver simulations, the proposed controller is compared with the NDI controller and is shown to yield better performance.
Influence of the sensory information ambiguity on the brain state during the decision-making task

The process of visual information classification includes different states of brain activity, such as sensory processing and decision-making. The stage of sensory processing is usually short and occurs in the occipital and parietal areas of the brain, while the decision-making stage is more pronounced over time and includes the parietal and frontal areas. In the current paper, we consider the brain activity during the classification task using ambiguous visual stimuli with varying degrees of ambiguity. We show that with increasing ambiguity of the visual stimulus, the brain engages neuronal populations in the distributed cortical regions in 15-30 Hz frequency band, including the frontal area at the early stages of information processing.
Adaptive Finite-Time Consensus for Multi-Agent Systems with Input Constraints

This paper studies the finite-time consensus problem for leader-following nonlinear multi-agent systems with input constraints. Under the condition that the coupling weights are unknown, finite-time control protocol combined with adaptive feedback controller is proposed. Based on homogeneous theory, it is proved that the states of followers can converge to those of leader in finite time through local interaction under undirected graph. Finally, two numerical examples are presented to verify the effectiveness of theoretical results.
Structuring the Industry 4.0 Landscape

The industry and research efforts to standardize Industry 4.0 related developments have merged into an unmanageable amount of reference models, architectures and specification activities. As these efforts have only been roughly coordinated, am incomprehensible and confusing landscape occurred. These developments contradict the initial need for more clarity and structure, especially as many different aspects are framed under the same terminology.We contribute to this challenge by providing a structured overview of the current state of standardization. We create a graphical landscape of Industry 4.0 specifications and standards based on an integrated knowledge graph. Various views provide different illustrations for several information needs. Explicitly stated relations between Industry 4.0 frameworks and technical standards enable the flexible discovery of related information. In addition, we use machine inference techniques to add new links and to further extend the knowledge graph.
Atrial Fibrillation detection using Discrete Wavelet Transform

Many heart diseases can be identified and cured at very early stage by studying the changes in the features of electrocardiogram (ECG) signal. In this paper, an algorithm to detect Atrial Fibrillation (AF) in the ECG signal is developed. For correct detection of AF, pre-processing and feature extraction of ECG signal shall be performed before it detects AF. After considering the ECG signal from the database, in the preprocessing stage de-noising of the ECG signal is carried out in order to obtain a clean ECG signal. After pre-processing, before feature extraction R peak detection is carried out for the signal. Since R peak is having the highest amplitude, and therefore it is detected in the first round and subsequently location of other peaks of the ECG signals are performed. After completing preprocessing and feature extraction using Discrete Wavelet Transform (DWT) applied based on inverted T wave logic and ST-segment elevation. Our classification algorithm was demonstrated to successfully acquire, analyze and interpret ECGs for the presence of AF indicating its potential to support m-Health diagnosis, monitoring, and management of therapy in AF patients.
Stabilization for Switched Nonlinear Stochastic Systems Under Arbitrary Switchings Via Output Feedback

This paper concerns with the global stabilization problem under the unpredictable switching signal for a class of switched nonlinear stochastic systems with time-varying control gains by utilizing output feedback method. A set of coordinate transformations is introduced to lump together all the unknown control gains and transform the considered system to a new one. Then, for the transformed system a state feedback controller and a robust reduced-order observer are constructed to cope with the uncertainties including the unknown time-varying control gains and the unknown system nonlinear terms. In place of the unmeasured state with the observer state, we obtain an implementable output feedback controller for the considered switched system. By recursively choosing the gain parameters the designed controller make closed-loop system globally asymptotically stable in probability under arbitrary switching. The effectiveness of the provided control method is illustrated with an example.
The First Quantum Interference in the Mid-Infrared is on a Silicon Chip

Quantum computing technology is at a crossroads. The technological route we choose must scale without limit. For quantum optics to play a part, optical loss, miniaturisation, and manufacturability must be simultaneously addressed. In quantum optics, a loss of a single photon is a loss of irreplaceable quantum information. Macroscale optics offers exceptionally low loss, and high performance, but has no hope to scale to thousands or millions of modes. Microscale optics, and silicon photonics in particular, offer exceptional miniaturisation and large-scale manufacturability, but pay a high price in loss [1].
Development of Data Acquisition System and Hardware Simulator for Turbojet Engine Test Bench Using Alternative Fuels

Different types of gas turbine engines are very important in transportation and industrial applications, therefore their environmental impact should be also decreased for sustained development. Among others, one possibility for a reduced footprint is the utilization of alternative fuels. The goal of this paper is to realize an improved data acquisition system for a turbojet engine test bench intended for alternative fuel investigations to understand the processes of different fuels. Another important aspect of the present research is to introduce an advanced hardware simulator, which allows the generation of signals from a gas turbine engine without the need of a real engine after the dynamic behavior of the system has been identified through measurements. The paper provides an evaluation of measurement and simulations as well.
Knowledge and Practices of young people related to Human Papillomavirus Infection: New updated

RESUMEN Introduccion: el virus del papiloma humano (VPH) se constituye en un problema de salud pu-blica dada su magnitud y las implicaciones en el desencadenamiento de varias formas de cancer, como el de cuello uterino. Objetivo: el estudio tuvo como proposito reali-zar una revision sistematica de la literatura sobre sexualidad, conocimientos, practicas preventivas y vulnerabilidad a la infeccion por el VPH entre adolescentes y adultos jovenes. Materiales y metodos: se realizaron busquedas bibliograficas en revistas y periodicos cientificos con publicaciones seriadas indexadas en las siguientes bases de datos: Bireme, PubMed, ScienceDirect, SpringerLink y Scopus, en el periodo de 1995 a 2012. Resultados: fueron seleccionados 60 articulos: 36 revisiones, 17 estudios transversales, 2 estudios de cohorte, 2 casos y controles, 1 ensayo clinico, 1 estudio cualitativo y una serie de casos. Los jovenes, en una proporcion significativa, dimensionan de manera limitada no solo las formas de transmision sino tambien las consecuencias de la infeccion, resaltandose las diferencias por genero. Sin iden-tificar el riesgo personal de contraer la infeccion, los jovenes dejan de recurrir al uso de proteccion como ejercicio del sexo seguro ya sea en los casos de relacionamiento estable o no, como tambien en las relaciones hetero y homosexuales.
Classification Research Based on Residual Network for Hyperspectral Image

Classification is one of the main ways to mining the rich information in hyperspectral image (HSIs). At present, the deep learning methods have been applied to image processing and achieved good classification results. But overfitting is still a problem in the image classification by the deep neural networks. Residual network (ResNet) can help to overcome the overfitting phenomenon. It can optimize parameters and extract characteristic information conveniently by adding identity mapping between input and output. In this paper, ResNet is introduced to the HSI classification. In order to reduce the dependence of network on parameter initialization and to improve the generalization ability of the model, batch normalization method is adopted to optimize the network. Then, some virtual samples are generated to reduce the impact of limited number of training samples on the classification accuracy. Two different HSIs have been classified by the proposed method and the experimental results show that this method has promising prospects in the HSI classification.
Real-time Monitoring of Mobile User using Trajectory Data Mining

This paper examines the mobility patterns of a mobile User through trajectory data mining. The spatio-temporal trajectory of a mobile User is captured with the aid of GPS devices. An efficient data dependent approach is presented to conveniently monitor in real-time, the positions of a mobile User in a specific location. Considering a spatial set of positions, P with a specified weight, w and a rectangle with specified-size, Ri,j. A trajectory monitoring problem is introduced, that is to identify a centroid of P to represent the centre of Ri,j. Such that there exists a maximised weighted-sum of P in Ri,j. As a result, an efficient algorithm which is termed as Trajectory Maximised Weighted-Sum (TMaxWS) has been proposed to identify any evolving P, and to monitor the trajectory of P in order to locate the centroid. The propose approach improves on the limitations of an existing approach including G2 algorithm and Best Region Search that are based on Maximised Range-Sum (MaxRS) without localisation and recognition of a mobile User’s position and location data. In this paper, the propose approach makes use of a grid-based index structure, data dependent multi-object classes and branch and bound formalism. The experimental evaluation with Geolife as real-life massive dataset has proven that TMaxWS can adequately localise and recognise the positions and location data of a mobile User. It performs relatively faster on update of valid and efficient results.
Optimization Algorithms in Reconstructions of Neuron Morphology: An Overview

Neuron morphological reconstruction is the momentousness in the study of brain connectome which can extract the types of neurons and the connection patterns on the micron scale. Due to the vast data volume of neurons, complex neuron structure and the reconstruction of neuron population, the reconstruction of neuron is challenging. This paper reviews the current research status of neuron reconstruction. The state-of-the-art optimization algorithms in neuron reconstruction methods are summarized. We divide the process of neuron reconstruction into three steps based on the function of the optimization algorithm, which is branch tracing, branch merging and branch adjustment. What's more, we propose a framework of the optimization algorithm and decompose the objective functions into internal energy terms and external energy terms.
Photogrammetric Multi-Camera Calibration Using An Industrial Programmable Robotic Arm

Calibration of multiple cameras is a critical step in most vision enhancement systems. Target-based calibration approaches are known to provide accurate and stable results. However, they require manually performed capture procedures. This paper presents a generalization of a widely used single-camera target-based calibration algorithm to the case of n cameras. In order to obtain fully repeatable results, we propose the elimination of the manual capture step using a programmable robotic arm. Furthermore, we investigate the use of the position feedback provided by the robot. This is done specifically for the case of calibrating cameras without assumptions on their positions and overlapping of their fields of view. Results show that automatically captured images provide more accurate calibration results than the classical approach. Additionally, calibration of fully non-overlapping setups is made possible through our approach.
Toward a Hardware Man-in-the-Middle Attack on PCIe Bus for Smart Data Replay

The growing need for speed of recent embedded systems leads to the adoption of the high speed communication PCIe protocol (Peripheral Component Interconnect Express) as an internal data bus. This technology is used in some recent smartphones, and will be probably adopted by the others in the next few years. The communication between the SoC and its memory through the PCIe bus represent an important source of information for criminal investigations. In this paper, we present a new reliable attack vector on PCIe. We chose to perform a hardware Man-in-the-Middle attack, allowing real-time data analysis, data-replay and a copy technique inspired by the shadow-copy principle. Through this attack, we will be able to locate, duplicate and replay sensitive data. The main challenge of this article is to develop an architecture compliant with PCIe protocol constraints such as response time, frequency and throughput, in order to be invisible to the communication parts. We designed a proof of concept of an emulator based on a computer with PCIe 3.0 bus and a Stratix 5 FPGA with an endpoint PCIe port as development target.
Smoke Recognition Based on Deep Transfer Learning and Lightweight Network

Fire is one of the most common catastrophic events, and smoke is one of the most significant characteristics of fire. Accurate and real-time smoke detection is an important means to reduce fire risk. In this paper, we construct AlexNet, VGG16, GoogLeNet Inception V3, ResNet50 and lightweight MobileNet network models to identify smoke, and then add transfer learning methods to make a contrast. We find that pre-training VGG16 network which has a big module and many parameters is most suitable for this identification and the accuracy of it is about 0.01 higher than that of others. Pre-training MobileNet network model whose size and number of model parameters are only about 1/15 of AlexNet network can achieve similar accuracy to AlexNet. This research is of great help to improve the training speed and reduce the cost of updating the model and it provides the possibility for the deployment of the model on the devices with limited computing power.
A low cost data acquisition system based on wireless sensor networks for environmental monitoring in landfills

This paper proposes a data acquisition system based on wireless sensor networks to measure the methane concentration level in a sanitary landfill, and a sensor node prototype for this application. The sensor node was designed to measure temperature, relative humidity and methane concentration. Based on the IEEE 802.15. 4 standard, the proposed system allows a continuous monitoring of the methane emissions and reduces the exposition of workers to the hazardous environmental conditions of the landfill. A sensor node prototype was developed and tested as a demonstration of the feasibility of the system.
Research on the Detection of the UAV Remote Sensing Chili Images Based on Superpixel Segmentation and SVM

The collection of agricultural field sample sets needs generally to be carried out according to the crop growth cycle, and it is not easy to obtain large amounts of data. For specific crop detection problems, it often takes time and effort to perform data labeling. Aiming at the problem of detecting the unmanned aerial remote sensing chili images, this paper proposes a recognition framework of the chili images, which mainly contains three processes. Firstly, the classifier is designed, and the remote sensing image is automatically sliced to make the sample set. Then, the SVM classifier is trained. Secondly, it is the detection process. The input image is segmented into superpixel to generate candidate region, and then the regions are classified using a classifier to complete the detection process. Finally, the results of detection are fine-tuned by the method of template matching, and the sample set is updated with a part of the detection result in chili image patches.