Miracle-dz/UniFAR-FTUs huggingface dataset:
TLR9-mediated activation of dendritic cells by CD32 targeting for the generation of highly immunostimulatory vaccines

ABSTRACT The rational for designing dendritic cell (DC)-targeted immunotherapies is their central role in orchestrating immunity. Most studies addressing antigen-targeting to DCs for eliciting T cell responses have employed ex-vivo matured DCs derived from monocytes or myeloid DCs isolated from peripheral blood. More recently, also plasmacytoid DCs (pDCs) emerged as attractive targets that can be readily isolated and activated ex vivo. pDCs are known as key effectors of innate and adaptive immunity due to their exquisite ability to produce large amounts of type-1 interferons upon signaling via TLR7 or TLR9 intracellular receptor for viral RNA or bacterial DNA, respectively. In this study, we describe and characterize the immune modulating and targeting module of a composite human specific vaccine platform for active immunotherapy. This module, called warhead (WH), is composed of a single-chain variable fragment (scFv) and CpG-C type oligonucleotides (ODNs) that are covalently coupled. The scFv mediates specific binding to FcγRII/CD32 on APCs and internalization of the ODNs which stimulate TLR9-expressing B cells and pDCs. Furthermore, the scFv in the WH is extended with a five-time heptad repeat (EVSALEK) alpha helix which allows for a coiled-coil complex formation with any immunogen also extended with another five-time heptad (KVSALKE) repeat. WH elicits fast and robust pDC activation as evidenced by the release of interferon-α, TNF-α and IL-6. The WH thus takes advantage of the key features of human pDCs for immunostimulation and can be a versatile tool for antigen-specific vaccination with a variety of proteins or peptides.
Cluster-Based WSA for Decision Support Bayesian Systems: Case of Prognostic in Maintenance Management

A knowledge representation and reasoning from data have produced many models. Probabilistic graphical models, specifically the Bayesian Network (BN) have proved its worth. It is considered to be a very useful tool for representing uncertain knowledge and decision-making support. This presupposes availability of knowledge problem in the conditional probabilities form. However, one is often in a critical situation because of data are insufficient, partially unavailable or heterogeneous. Developing methods and techniques to reconstruct the corpus of data needed for decision making, especially via BN is called the ”knowledge elicitation”. Several elicitation methods exist but they are not always applicable, too demanding in expert knowledge or presenting limits. The most generic and useful is the Weighted Sum Algorithm (WSA) but it presents two major issues concerning the compatible parental configuration. In the present paper, we discuss what the literature proposes for the first one, then we develop the solution for the second and validate it via a case of pump failure prognostic tool based on Bayesian support decision.
Optimized Common Parameter Set Extraction by Benchmarking Applications on a Big Data Platform

This research proposes the methodology to extract common configuration parameter set by applying multiple benchmark applications including TeraSort. , TestDFSIO, and MrBench on the Hadoop Distributed File System. In the process of determining parameter set for each stage, one parameter and its associated values selected which is reduced system performance in terms of overall execution time difference are measured by multiple applications on a Hadoop cluster. The experimental results demonstrate the proposed extended greedy manner provide a feasible benchmark model for the multiple tasks. In this way, we have found several parameter value sets that can reduce the execution time by 27% of the values provided by Hadoop default.
Classification of Different Breath Samples Utilizing E-Nose System

In this work, by using an Electronic Nose, human breath samples were analyzed. Breaths samples were collected from three different groups. These are lung cancer patients, healthy people and healthy smokers. In structure of developed E-Nose, Quartz Crystal Microbalance and Metal Oxide Semiconductor gas sensors were used. Data acquired from system were preprocessed and dimension of these data were reduced with Linear Discriminant Analysis algorithm. Classification of data was performed with k-Nearest Neighbors, Support Vector Machines algorithms. For both algorithms, maximum classification success rates were obtained as 94.1% with 5 Fold Cross Validation.
Can power Doppler ultrasound of the entheses help in classifying recent axial spondyloarthritis? Data from the DESIR cohort

Early diagnosis of axial spondyloarthritis (axSpA) remains a challenge due to the lack of specificity of clinical symptoms and variable prevalence of axial imaging findings permitting a definite diagnosis. Power Doppler ultrasonography (PDUS) of the entheses has demonstrated to be a potential useful tool for the classification and diagnostic management of early SpA independently of the phenotype. Objectives To assess the classification value (sensitivity and specificity) of PDUS-defined enthesitis for identifying patients fulfilling Assessment of SpondyloArthritis international Society (ASAS) classification criteria for axSpA (ASAS+) in patients with recent inflammatory back pain (IBP) (the DESIR (DEvenir des Spondylarthropathies Indifférenciées Récentes) cohort). Methods Baseline PDUS was performed at eight entheseal sites, and PDUS enthesitis was defined by the presence of vascularisation at entheseal insertion. Results 402 patients from the DESIR cohort underwent a PDUS evaluation. PDUS enthesitis was detected in 58 (14.4%) patients of whom 40 (14.2%) belonged to the ASAS+ patients and 18 (17%) to the ASAS- patients. The sensitivity of PDUS enthesitis was 13.9% and the specificity was 83.5%, with a positive predictive value of 69% and 26.8% of negative predictive value for meeting ASAS criteria for axSpA. Of the 18 ASAS- patients with positive PDUS, 59% fulfilled Amor’s criteria, 88% European Spondyloarthropathy Study Group criteria and 59% both. Conclusions In a cohort of patients with recent IBP, the prevalence of PDUS enthesitis was low (14.4%); however, its specificity for classifying patients as axSpA according to ASAS criteria was high (83.5%). PDUS enthesitis might be of additional value for classifying as patients with axSpA IBP who do not fulfil ASAS criteria.
Improvement of Row Hammering Using Metal Nanoparticles in DRAM—A Simulation Study

In this letter, we investigate row hammering (RH) in recessed channel access transistor (RCAT)-DRAM cells. A novel technique is proposed to minimize the row hammer fail in DRAM cells by selective/local introduction of metal nanoparticles (MNPs) at the gate metal–oxide interface. Using TCAD simulations, we show that the introduction of MNPs with an optimized work function (WF) significantly reduces the storage node leakage of unaccessed DRAM cells (victim cells) due to the hammered access of neighboring DRAM cells (aggressor cells) sharing a common bit line. The proposed technique potentially eliminates row hammer fail in RCAT-DRAM cells at 2X and 1X nodes. The improvement in row hammer fail is due to the dual-energy valley induced between the neighboring DRAM cells by the introduction of MNPs. These valleys prevent diffusion of electrons from the aggressor cell to the victim cell, which improves row hammer fail. This letter shows that the introduction of MNPs is a promising technique to eliminate row hammer fail in DRAM cells.
A Robust DS Combination Method Based on Evidence Correction and Conflict Redistribution

To eliminate potential evidence conflicts, an effective and accurate DS combination method is addressed in this paper. DS evidence theory is an outstanding information fusion approach with valid uncertainty treatment. Nevertheless, there are some limitations of the usage of the DS evidence theory. On the one hand, due to the complexity of a combat measurement environment and the inconsistency of sensor capabilities, sensor sources have enormous uncertainty, which would inevitably cause conflicts for evidence combination. On the other hand, DS combination rule realizes the unity property of fusing results with a compulsive normalization, which unavoidably leads to conflicting situations. To solve the possible evidence conflicts in a multisensor fusion system, we raise a robust DS combination method based on evidence correction and conflict redistribution. Firstly, two corrected indexes—the reliability index and consistency index—are separately addressed with the introduction of the Matusita distance function and closeness degree function. After the evidence modification based on two correction indexes, the conflicts caused by unreliable sensor sources are solved. Then, based on the corrected evidences, we put forward a weighted assignment of conflicting mass where the weight index lies on the evidence credibility. As the normalization step is abolished, the conflict redistribution strategy avoids the conflicts caused by straightforward normalization. Through comprehensive conflict management, the proposed DS combination method can not only guarantee the rationality and availability of fusing results, but also enhance the reliability and robustness of a multisensor system. Finally, three combination experiments with different conflicting degrees illustrate the advantage and superiority of the novel combination method for conflict management. Consequently, the innovation of the novel algorithm is verified.
Natural Language Processing of Social Media as Screening for Suicide Risk

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social media networks—offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have “opted in” for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention—we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one?
Beamspace Channel Estimation for Millimeter Wave Massive MIMO System With Hybrid Precoding and Combining

In this paper, a framework of beamspace channel estimation in millimeter wave massive MIMO system is proposed. The framework includes the design of hybrid precoding and combining matrix as well as the search method for the largest entry of over-sampled beamspace receiving matrix. Then based on the framework, three channel estimation schemes including identity matrix approximation (IA) based scheme, scattered zero off-diagonal (SZO) based scheme and concentrated zero off-diagonal (CZO) based scheme are proposed. These schemes together with the existing channel estimation schemes are compared in terms of computational complexity, estimation error and total time slots for channel training. Simulation results show that the proposed schemes outperform the existing schemes and can approach the performance of the ideal case. In particular, total time slots for channel training can be substantially reduced.
Compressed Sensing Based Channel Estimation in FDD Multi-User Massive MIMO Using Angle Domain Sparsity and Transmit Antenna Correlation

We study channel estimation in downlink Frequency Division Duplex multi-user massive MIMO systems. Due to the large number of antennas in massive MIMO, both channel estimation at the users, and channel state information (CSI) feedback to the base station (BS) is a challenging task. Here we use sparsity in the angle domain and the transmit antenna correlation for compressed sensing based channel estimation. We use an approach where the measurements from a group of users with similar transmit covariance matrices are fed back to BS, and BS does joint channel estimation using the multiple measurement vector (MMV) approach. We start with the physical channel model with scatterer clusters and then reduce it to the virtual channel model, where a sparse channel matrix is used, together with the angular domain unitary matrices. The users that share common clusters have common sparsity and use joint (MMV) estimation. Due to the channel structure with scatterer clusters, resulting in groups of channel coefficients, we also use group sparsity and joint group sparsity to improve estimation error. We use antenna selection to transmit training symbols at selected antennas only, which, together with the virtual channel model, results in partial DFT measurement matrix.
Challenges and Research Issues of Data Management in IoT for Large-Scale Petrochemical Plants

The Internet of Things (IoT), which seamlessly interconnects heterogeneous devices with diverse functionalities, is an attractive choice for the large-scale petrochemical industry to develop an integrated system. With the industrial revolution, efforts have mainly been focused on factory automation, transportation security, and surveillance. The IoT plays a major role in factories with its new methods of data management and data collection. However, the IoT is still at an early stage in large-scale petrochemical plants due to the multiple coexisting heterogeneous networks in harsh and complex large-scale industrial networks. This paper presents a comprehensive survey on the IoT in large-scale petrochemical plants as well as recent activities in communication standards for the IoT in industries. This paper addresses the key enabling middleware approaches, e.g., an industrial intelligent sensing ecosystem (IISE), which allows rapid deployment and integration of heterogeneous wireless sensor networks with advancements in crowdsensing based services. In addition, this survey highlights the research issues of data management in the IoT for large-scale petrochemical plants.
Numeric and measurement analysis of earthing resistance in layered soil including GEM material

This article provides a comparison results of calculations the expected earth electrodes resistance in layered soils on the basis of measurements soil resistivity with measured in real conditions its values. The modification of calculations, relies on including soil resistivity enhencement material technology (like GEM), one of the existing methods has also been proposed and described. For verification purposes, actual measurements were made and the results obtained in both tests were compared. Four configurations were taken into considerations. Based on results, conclusions and observations were formulated.
Energy Efficient On-Sensor Processing for Online Activity Recognition

In sensor-based online activity recognition, the communication of sensor samples at high data rates has a great impact on the energy consumptions of wearables. In our work we investigate the idea of calculating data reducing stages of activity recognition systems on wireless sensor nodes in order to reduce the amount of transmitted data and thus the overall energy consumption. In our experiments, this approach could reduce the energy consumption of a wireless sensor node by up to 27%. Since the benefit of this approach highly depends on design parameters of the activity recognition, we introduce an energy trade-off model for wireless sensor nodes to estimate energy-savings of application specific configurations at design time. By calibrating this model for our wireless sensor node, we could achieve an accuracy of more than 99% in our experiments.
Two-Stage Racetrack Segmentation Method Using Color Feature Filtering and Superpixel-Based Convolutional Neural Network

The Széchenyi István University race car team is an active and successful participant of the Shell Eco-marathon for long time ago. The Shell introduces the autonomous vehicle category on the Eco-marathon for 2018. Our long-term goal is to make the Szenergy racing team's vehicle suitable for the autonomous category. The first milestone is to make a reliable computer vision based intelligent detection system that understands the environment of the racing car. In this paper we will present a solution for racetrack detection i.e. a fusion of image processing and neural network systems. The two-stage recognition system is at the first phase an image processing algorithm which finds the red-white and blue-white striped edge of the road, and at the second phase, a pre-trained superpixel-based neural network which recognize the road on the filtered image.
Saliency Detection by Multilevel Deep Pyramid Model

Traditional salient object detection models are divided into several classes based on low-level features and contrast between pixels. In this paper, we propose a model based on a multilevel deep pyramid (MLDP), which involves fusing multiple features on different levels. Firstly, the MLDP uses the original image as the input for a VGG16 model to extract high-level features and form an initial saliency map. Next, the MLDP further extracts high-level features to form a saliency map based on a deep pyramid. Then, the MLDP obtains the salient map fused with superpixels by extracting low-level features. After that, the MLDP applies background noise filtering to the saliency map fused with superpixels in order to filter out the interference of background noise and form a saliency map based on the foreground. Lastly, the MLDP combines the saliency map fused with the superpixels with the saliency map based on the foreground, which results in the final saliency map. The MLDP is not limited to low-level features while it fuses multiple features and achieves good results when extracting salient targets. As can be seen in our experiment section, the MLDP is better than the other 7 state-of-the-art models across three different public saliency datasets. Therefore, the MLDP has superiority and wide applicability in extraction of salient targets.
Impact of ultrasonic pre-treatment on domestic sludge digestion performance and microbial community dynamics

ABSTRACT Ultrasonication-assisted sludge digestion technology is a lately used alternative sludge treatment method in wastewater treatment plants (WWTPs). This study focused on determining the influence of ultrasonication on aerobic and anaerobic sludge digestion, two most commonly used sludge handling processes, as well as on the investigation of microbial community structure after digestion. The effect of ultrasonication as a pre-treatment technique prior to sludge digestion on microbial population dynamics was not yet investigated comprehensively. Sludge sample taken from the primary and secondary settling tanks of a domestic wastewater treatment plant was used during the experiments. Based on the relevant data, while applied ultrasonication did not improve the anaerobic digestion efficiency, progress was achieved in the sludge dewaterability characteristics at the end of aerobic digestion. According to the results of both denaturant gradient gel electrophoresis and pyrosequencing data, ultrasonic pre-treatment decreased the richness of the microbial population in aerobic digestion, whereas increased the biocomplexity of the population in anaerobic digestion. We revealed that sludge pre-treatment with ultrasonication does not always improve the digestion performance. Composition of the sludge was the main factor defining the digestion performance. GRAPHICAL ABSTRACT
18O Isotope Concentration Control

The paper presents a solution to control the 18O isotope concentration at the output of a separation column. The proposed mathematical model which describes the work of the separation column is a distributed parameter one and it approximates with high accuracy the work of the real plant. The isotope separation process is included in a complex control structure which generates high control performances. In order to improve significantly the separation column productivity, an original solution for the efficient rejection of the disturbances effects, is proposed. Also, a solution to determine the instantaneous value of the separation column length constant is proposed, solution which opens the possibility to implement new control
Visual Target Detection and Tracking Framework Using Deep Convolutional Neural Networks for Micro Aerial Vehicles

This paper presents a visual detection and tracking framework which estimates smooth target position for various applications on micro aerial vehicles (MAVs). The proposed framework consists of two major components: a deep learning-based detector and a correlation filter-based tracker. The detector running at a low frequency first detects a target and initializes the tracker. The estimated target position from the tracker will be updated by the detector when the detection confidence is high or the tracking is considered fail. Due to the limited computational power on most MAV platforms, algorithms are implemented at two separated processing units. The detector runs at a ground control station (GCS) equipped with NVIDIA GTX 1060 while the tracker runs at a MAV onboard low-cost CPU. The transmission of image and target pose information is bridged via a high-speed Wi-Fi network to minimize the latency. In our experiment, the proposed framework is able to realize real-time detection and tracking with 30 frames per second (FPS) on our system.
A Comparative Study of PID-PSO and Fuzzy Controller for Path Tracking Control of Autonomous Ground Vehicles

The work presented in this paper focuses on platonning navigation control (train of vehicles) according to different trajectories. As a first step we based our study on two vehicles. an kinematic model of the two vehicles is described followed by a PID multi-controller control approach based on conventional PID, PID optimized by Particle Swarm Optimization (PSO) technique and fuzzy controller applied to the longitudinal and lateral control of each vehicle. Controller parameters optimization is based on a fitness function time weight square error (ITSE). The communication between the two vehicles is ensured with the exchange of information, the speed and orientation angle, respecting the safety distance between the vehicles. To approve our approach we have use different reference trajectory in different simulations in matlab-simulink environment and v-rep 3D simulation. The simulation obtained results illustrate the efficiency of our control design and open the perspectives for future work.
Enhancement of Doppler Unambiguity for Chirp-Sequence Modulated TDM-MIMO Radars

Current automotive radar sensors enhance the angular resolution using a multiple-input multiple-output approach. The often applied time-division multiplexing scheme has the drawback of a reduced unambiguous Doppler velocity proportional to the number of transmitters. In this paper, a signal processing scheme is proposed to regain the same unambiguous Doppler as in the single-input multiple-output case with only one transmit antenna. Simulation and measurement results are shown to prove that the signal processing leads to an enhanced unambiguous Doppler velocity estimation.