Miracle-dz/UniFAR-FTUs huggingface dataset:
The dynamics of settling particles in vertical channel flows: gravity, lift and particle clusters

Abstract The dynamics of settling finite-size particles in vertical channel flows of Newtonian and viscoelastic carrier fluids is examined using particle resolved simulations. Comparison with neutrally buoyant particles in the same configuration highlights the effect of settling. The particle volume fraction is $5\,\%$, and a gravity field acts counter to the flow direction. Despite a modest density ratio ($\rho _r = 1.15$), qualitative changes arise due to the relative velocity between the particle and fluid phases. While dense particles are homogeneously distributed in the core of the channel, the mean concentration profile peaks at approximately two particle diameters from the wall due to a competition between shear- and rotation-induced lift forces. These forces act in the cross-stream directions, and are analysed by evaluating conditional averages along individual particle trajectories. The correlation between the angular and translational velocities of the particles highlights the significance of the Magnus lift force in both the spanwise and wall-normal directions. The collective behaviour of the particles is also intriguing. Using a Voronoï analysis, strong clustering is identified in dense particles near the wall, which is shown to alter their streamwise velocities. This clustering is attributed to the preferential transport of aggregated particles towards the wall. The practical implication of the non-uniformity of particle distribution is a significant increase in drag. When the carrier fluid is viscoelastic, the particle migration is enhanced which leads to larger stresses, thus negating the capacity of viscoelasticity to reduce turbulent drag.
Investigation of supersonic twin-jet coupling using spatial linear stability analysis

Abstract The present work focuses on the study of the resonance and coupling of an underexpanded circular twin-jet system operating at a nozzle pressure ratio of $5.0$. Particle image velocimetry data from previous work were revisited, and a symmetry-imposed proper orthogonal decomposition (POD) was performed. It is shown that the system is dominated by a single POD mode pair symmetric about the internozzle plane, and the resonance loop is modulated by a third POD mode related to shear thickness modulation. A spatial Fourier transform of the leading POD mode pair leads to the identification of the peak wavenumbers and radial shapes of the different waves at play in the screech phenomenon. Locally parallel linear stability analysis around the experimental mean flow is also performed, in order to provide clarification of the mode ‘locking’ mechanism, i.e. the selection of the global mode associated with screech. It is shown that the characteristics of the Kelvin–Helmholtz wavepackets alone are not sufficient to explain the coupling observed in the experimental data. A consideration of the upstream-travelling guided jet mode offers an explanation; only specific symmetries of upstream modes can be supported in the frequency range at which resonance occurs. Results from stability analysis point to structures at frequencies and wavenumbers close to those found experimentally, and their spatial structures show excellent agreement with the POD modes. The present results suggest that the resonance loop is closed by an upstream-travelling guided jet mode for the twin-jet system at high nozzle pressure ratio.
Telomere Length Differently Associated to Obesity and Hyperandrogenism in Women With Polycystic Ovary Syndrome

Background Polycystic Ovary Syndrome (PCOS) often present metabolic disorders and hyperandrogenism (HA), facts that may influence the telomere length (TL). Aims To compare the absolute TL (aTL) between women with PCOS and control women, and their association with the presence of obesity and HA parameters. Materials and methods The PCOS group included 170 unrelated women outpatients and the control group, 64 unrelated donor women. Anthropometric, biochemical-clinical parameters and androgen profile were determined. The PCOS patients were divided accordingly to the presence of obesity and androgenic condition. The aTL was determined from peripheral blood leukocytes by Real Time quantitative PCR. Results Women with PCOS exhibited a significantly longer aTL than controls after age adjustment (p=0.001). A stepwise multivariate linear regression in PCOS women, showed that WC (waist circumference) contributed negatively (b=-0.17) while testosterone levels contributed positively (b=7.24) to aTL. The non-Obese PCOS (noOB-PCOS) presented the longest aTL when compared to controls (p=0.001). Meanwhile, the aTL was significantly higher in the hyperandrogenic PCOS phenotype (HA-PCOS) than in the controls (p=0.001) and non hyperandrogenic PCOS phenotype (NHA-PCOS) (p=0.04). Interestingly, when considering obesity and HA parameters in PCOS, HA exerts the major effect over the aTL as non-obese HA exhibited the lengthiest aTL (23.9 ± 13.13 Kbp). Conversely, the obese NHA patients showed the shortest aTL (16.5 ± 10.59 Kbp). Conclusions Whilst a shorter aTL could be related to the presence of obesity, a longer aTL would be associated with HA phenotype. These findings suggest a balance between the effect produced by the different metabolic and hormonal components, in PCOS women.
A Comprehensive RNA Study to Identify circRNA and miRNA Biomarkers for Docetaxel Resistance in Breast Cancer

To investigate the relationship between non-coding RNAs [especially circular RNAs (circRNAs)] and docetaxel resistance in breast cancer, and to find potential predictive biomarkers for taxane-containing therapies, we have performed transcriptome and microRNA (miRNA) sequencing for two established docetaxel-resistant breast cancer (DRBC) cell lines and their docetaxel-sensitive parental cell lines. Our analyses revealed differences between circRNA signatures in the docetaxel-resistant and -sensitive breast cancer cells, and discovered circRNAs generated by multidrug-resistance genes in taxane-resistant cancer cells. In DRBC cells, circABCB1 was identified and validated as a circRNA that is strongly up-regulated, whereas circEPHA3.1 and circEPHA3.2 are strongly down-regulated. Furthermore, we investigated the potential functions of these circRNAs by bioinformatics analysis, and miRNA analysis was performed to uncover potential interactions between circRNAs and miRNAs. Our data showed that circABCB1, circEPHA3.1 and circEPHA3.2 may sponge up eight significantly differentially expressed miRNAs that are associated with chemotherapy and contribute to docetaxel resistance via the PI3K-Akt and AGE-RAGE signaling pathways. We also integrated differential expression data of mRNA, long non-coding RNA, circRNA, and miRNA to gain a global profile of multi-level RNA changes in DRBC cells, and compared them with changes in DNA copy numbers in the same cell lines. We found that Chromosome 7 q21.12-q21.2 was a common region dominated by multi-level RNA overexpression and DNA amplification, indicating that overexpression of the RNA molecules transcribed from this region may result from DNA amplification during stepwise exposure to docetaxel. These findings may help to further our understanding of the mechanisms underlying docetaxel resistance in breast cancer.
Acalabrutinib: A Selective Bruton Tyrosine Kinase Inhibitor for the Treatment of B-Cell Malignancies

Bruton tyrosine kinase (BTK) is a validated target for treatment of B-cell malignancies, and oral inhibitors of BTK have emerged as a standard of care for these diseases. Acalabrutinib is a second generation, highly selective, potent, covalent BTK inhibitor that exhibits minimal off-target activity in in vitro assays, providing the potential to improve tolerability over the first-in-class BTK inhibitor, ibrutinib. Acalabrutinib was approved for the treatment of relapsed/refractory mantle cell lymphoma (MCL) and chronic lymphocytic leukemia (CLL) in the US in 2017 and 2019, respectively. Acalabrutinib is also undergoing trials for other B-cell malignancies, both as monotherapy and in combinations. In this review, we discuss results from clinical trials evaluating the efficacy and safety of acalabrutinib in patients with CLL, MCL, and Waldenstrom’s macroglobulinemia. Recent phase 3 data showed that acalabrutinib improved progression-free survival (PFS) compared with rituximab plus idelalisib or rituximab plus bendamustine in patients with relapsed/refractory CLL, and acalabrutinib with or without obinutuzumab improved PFS compared with chlorambucil plus obinutuzumab in patients with treatment-naïve CLL. Overall, acalabrutinib had a tolerable safety profile, with most adverse events being grade 1/2 severity (most commonly headache and diarrhea) and a low rate of discontinuation due to adverse events.
Adsorption behavior of B-doped/N-doped graphene sheets toward NO2, NO and NH3 molecules: A first-principles study

Based on the first-principles of density-functional theory (DFT), the effects of NO2, NO and NH3 adsorption on the change in geometric stability, adsorption properties and electronic structures of B-doped or N-doped graphene are investigated. For NO/NO2-B/N-doped graphene systems, NO/NO2 have more stability on B-doped graphenes than them on N-doped graphenes. The introduction of B atom can make the adsorption of NO/NO2 on graphene much easier. The stable configuration of B-doped graphene is more likely to be gas sensor for detecting toxic gases such as NO2 and NO. This makes it possible to use B-doped graphene as sensing materials for designing novel gas sensors. The interaction between NH3 and B/N-doped graphene is rather weak. The results will provide a new direction for the adsorbed NO2, NO and NH3 on graphene by introducing B/N doping atoms into graphene. (© 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)
Simulation Analysis of Trajectory Control of Tire Bursting Vehicles Based on MPC

In view of the yaw problem of a flat tire vehicle, a front wheel steering controller based on MPC is proposed to correct the vehicle's yaw and ensure that the vehicle runs on the safe path. Based on the model predictive control, the simplified double track vehicle model is selected, and the vehicle motion state is described by differential equation. The prediction equation is derived by linear discretization. Considering the vehicle stability factor, it is added as a constraint condition and transformed into the standard quadratic form to calculate the optimal solution. The closed-loop system is constructed for simulation. The results show that: the front wheel active steering controller based on MPC can keep the vehicle running stably and effectively control the running track of the flat tire vehicle. The sideslip angle of the center of mass, the sideslip angle of the tire and the yaw rate are all within the stability requirements, so that the vehicle can drive in the safe path and achieve the trajectory control effect of the tire burst vehicle.
Ensuring Secured Iris Authentication for Mobile Devices

The secured authentication in mobile devices and individual consumer products based on iris patterns is a vital challenge. An adversary may deceive the IR system through artifacts such as printed iris, synthetic eyeballs, textured contact lenses, etc. This paper introduces an approach to discriminate between the real and forged iris, through the pooling of multi-scale BSIF and Fine-tuned DenseNet features extracted from raw and normalized iris. The paper also validates the generalizability of the proposed approach across several attack variants, iris sensors, and datasets. Besides, the experiments performed on the publicly available LivDet-2017 datasets endorse the efficacy of the proposed method.
A Review on UAV Wireless Charging: Fundamentals, Applications, Charging Techniques and Standards

Unmanned Aerial Vehicles (UAVs) are becoming increasingly popular for applications such as inspections, delivery, agriculture, surveillance, and many more. It is estimated that, by 2040, UAVs/drones will become a mainstream delivery channel to satisfy the growing demand for parcel delivery. Though the UAVs are gaining interest in civil applications, the future of UAV charging is facing a set of vital concerns and open research challenges. Considering the case of parcel delivery, handling countless drones and their charging will become complex and laborious. The need for non-contact based multi-device charging techniques will be crucial in saving time and human resources. To efficiently address this issue, Wireless Power Transmission (WPT) for UAVs is a promising technology for multi-drone charging and autonomous handling of multiple devices. In the literature of the past five years, limited surveys were conducted for wireless UAV charging. Moreover, vital problems such as coil weight constraints, comparison between existing charging techniques, shielding methods and many other key issues are not addressed. This motivates the author in conducting this review for addressing the crucial aspects of wireless UAV charging. Furthermore, this review provides a comprehensive comparative study on wireless charging’s technical aspects conducted by prominent research laboratories, universities, and industries. The paper also discusses UAVs’ history, UAVs structure, categories of UAVs, mathematical formulation of coil and WPT standards for safer operation.
Learning Accurate Head Pose for Consumer Technology From 3D Synthetic Data

Accurate 3D head pose estimation from a 2D image frame is an essential component of modern consumer technology (CT). It enables a better determination of user attentiveness and engagement and can support immersive audio and AR experiences. While deep learning methods have improved the accuracy of head pose estimation models, these depend on the accurate annotation of training data. The acquisition of real-world head pose data with a large variation of yaw, pitch and roll is a very challenging task. Available head-pose datasets often have limitations in terms of the number of data samples, image resolution, annotation accuracy and sample diversity (gender, race, age). In this work, a rendering pipeline is proposed to generate pixel-perfect synthetic 2D headshot images from high-quality 3D facial models with accurate pose angle annotations. A diverse range of variations in age, race, and gender are provided. The resulting dataset includes more than 300k pairs of RGB images with the corresponding head pose annotations. For every hundred 3D models there are multiple variations in pose, illumination and background. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation dataset BIWI. The results show training with purely synthetic data produced by the proposed methodology can achieve close to state-of-the-art results on the head pose estimation task and is better generalized for age, gender and racial diversity than solutions trained on ‘real-World’ datasets.
Automatic and Intelligent Stressor Identification Based on Photoplethysmography Analysis

A stressor is an external stimulus that an individual might consider demanding or threatening regarding his/her safety. Stressors cause the release of stress hormones and can be categorized into two categories: physiological and psychological. Wearable technology has been on the rise in the past decade, and the advancement of IoT will only further the benefits that wearable technology will bring. This work leverages the output of wearable technology to provide automatic stress and stressor identification model. In particular, this study proposes a novel algorithm that first detects instances of stress and then classifies the stressor type using photoplethysmography (PPG) data from wearable smartwatches. The time-series PPG data is transformed into 2D spatial images by first extracting the interbeat interval (IBI), and the blood volume pulse (BVP) features from the PPG, then transforming these data to 2D image data. The developed model successfully identified stress instances using IBI-BVP spatial domain images with an average accuracy of 98.10% with a convolutional neural network (CNN) and 99.18% using the average pixel intensity of these images with the extra trees classifier. It also successfully categorized stressor types into physical, cognitive, and social with an accuracy of 98.5% using the CNN and 96.45% using the extra trees classifier, outperforming state-of-the-art.
Cosserat Rod-Based Dynamic Modeling of Tendon-Driven Continuum Robots: A Tutorial

This article provides a tutorial on the dynamic modeling of continuum robots. Continuum robots (CRs) have gained popularity in recent years due to their flexible backbone structure. Modeling and control of CRs motivate accurate and efficient dynamic models. Such models will enable simulation of dynamic behavior, improved structural design, and the development of dynamics-based control systems for CRs. As a unified underlying approach, the Cosserat rod-based modeling of tendon-driven CRs is used as the basis of the modeling techniques discussed in this paper. In addition to conventional continuum robot assemblies, new and emerging assemblies such as tendon-bent concentric tube and co-operative robots are also considered. The governing equations of motion for conventional CRs are first summarized and then extended for other tendon-driven CRs. This tutorial also contributes to developing a MATLAB code package for simulation of the dynamic response of these robots. The presented method and codes provide a useful and compact resource for readers and can be used for further development of tendon-driven continuum robots.
Wireless versus Wired Network-on-Chip to Enable the Multi- Tenant Multi-FPGAs in Cloud

The new era of computing is not CPU-centric but enriched with all the heterogeneous computing resources including the reconfigurable fabric. In multi-FPGA architecture, either deployed within a data center or as a standalone model, inter-FPGA communication is crucial. Network-on-chip exhibits a promising performance for the integration of one FPGA. A sustainable communication architecture requires stable performance as the number of applications or users grows. Wireless network-on-chip has the potential to be that communication architecture, as it boasts the same performance capability as wired solutions in addition to its multicast capacities. We conducted an exploratory study to investigate different performance parameters using the Noxim simulator. The preliminary results showed better average delay and network throughput for wireless network-on-chip as compared to the wired counterpart.
Research on Bottle Defect Detection Based on Improved FCOS

In order to solve the problem of slow speed and low efficiency of product defect detection, this paper proposes an improved Fully Convolutional One-Stage Object Detection (FCOS) algorithm for detection, and compares it with several currently widely used target detection algorithms. Through experimental verification, the improved Fully Convolutional One-Stage Object Detection (FCOS) has improved the detection effect by 6.9% compared with the original Fully Convolutional One-Stage Object Detection (FCOS) algorithm, and the applicability of the improved Fully Convolutional One-Stage Object Detection algorithm is verified through experiments.
Tiny defects identification of mechanical components in die-cast aluminum using artificial neural networks for micro-controllers

This work was targeting the identification of tiny visual defects which were present on mechanical components in die-cast aluminum, just produced by a foundry. From those, an image data-set has been created composed by 500 colored pictures, 2048x850 pixel resolution each. The tools to perform the analysis were based on Artificial Neural Network (ANN) to achieve accurate defects detection. Moreover we were interested to map resulting ANNs topologies on micro-controllers MCUs as well as investigating architectures for efficient mapping. At first we have designed an ANN without tight implementation constraints, memory and computational, achieving 86% accuracy. Careful analysis of memory and computational needs, by using X-CUBE-AI tool, concluded it required an embedded RAM memory of 786 KB combining 64x64 input and activation buffers. To lower that number we explored an ANN implementation by removing the residual connections between convolutional layers featuring stride 2. That version achieved an accuracy of 75% in 100KB of embedded RAM.
Malware Detection and Security Analysis Capabilities in a Continuous Integration / Delivery Context Using Assemblyline

Risk management is an essential part of software security. Assemblyline is a software security tool developed by the Canadian Centre for Cyber Security (CCCS) for malware detection and analysis. In this paper, we examined the performance of Assemblyline for assessing the risk of executable files. We developed and examined use-cases where Assemblyline is included as part of a security safety net assessing vulnerabilities that would lead to risk. Finally, we considered Assemblyline’s utility in a continuous integration / delivery context using our test results.
Content Targeted Ad Insertion in Temporal Structured Video Documents

The present paper introduces a novel commercial insertion framework dedicated to video documents broadcasted over the Internet, which aims at ensuring the ads contextual relevance, with respect to the video content, while reducing the level of intrusiveness. Unlike most commercial systems that insert the ads at fixed locations, the proposed system is able to establish, completely automatic, the optimal insertion points determined at the level of the stories boundaries. The core of the proposed methodology concerns a video scene segmentation algorithm based on visual and semantic features extracted using various CNNs architectures.
Filtered Multicarrier Waveforms Classification: A Deep Learning-Based Approach

Automatic signal recognition (ASR) plays an important role in various applications such as dynamic spectrum access and cognitive radio, hence it will be a key enabler for beyond 5G communications. Recently, many research works have been exploring deep learning (DL) based ASR, where it has been shown that simple convolutional neural networks (CNN) can outperform expert features based techniques. However, such works have been primarily focusing on single-carrier signals. With the advent of spectrally efficient filtered multicarrier waveforms, we propose in this paper, to revisit the DL based ASR to account for the variety and complexity of these new transmission schemes. Specifically, we design two types of classification algorithms. The first one relies on the cyclostationarity characteristics of the investigated waveforms combined with a support vector machine (SVM) classifier; while the second one explores the use of a four-layer CNN which performs both features extraction and classification. The proposed approaches do not require any a priori knowledge of the received signal parameters, and their performance is evaluated in a multipath channel through simulations for a signal-to-noise ratio (SNR) ranging from −8 to 20 dB. The simulation results show that, despite cyclostationary characteristics being highly discriminative, the CNN outperforms the cyclostationary based classification especially for short time received signals, and low SNR levels.