ABSTRACT
We describe a search for gravitational waves from compact binaries with at least one component with mass $0.2$–$1.0 \, \mathrm{M}_\odot$ and mass ratio q ≥ 0.1 in Advanced Laser ...Interferometer Gravitational-Wave Observatory (LIGO) and Advanced Virgo data collected between 2019 November 1, 15:00 utc and 2020 March 27, 17:00 utc. No signals were detected. The most significant candidate has a false alarm rate of $0.2 \, \rm {yr}^{-1}$. We estimate the sensitivity of our search over the entirety of Advanced LIGO’s and Advanced Virgo’s third observing run, and present the most stringent limits to date on the merger rate of binary black holes with at least one subsolar-mass component. We use the upper limits to constrain two fiducial scenarios that could produce subsolar-mass black holes: primordial black holes (PBH) and a model of dissipative dark matter. The PBH model uses recent prescriptions for the merger rate of PBH binaries that include a rate suppression factor to effectively account for PBH early binary disruptions. If the PBHs are monochromatically distributed, we can exclude a dark matter fraction in PBHs $f_\mathrm{PBH} \gtrsim \, 0.6$ (at 90 per cent confidence) in the probed subsolar-mass range. However, if we allow for broad PBH mass distributions, we are unable to rule out fPBH = 1. For the dissipative model, where the dark matter has chemistry that allows a small fraction to cool and collapse into black holes, we find an upper bound fDBH < 10−5 on the fraction of atomic dark matter collapsed into black holes.
The gravitational-wave signal GW190521 is consistent with a binary black hole (BBH) merger source at redshift 0.8 with unusually high component masses, M ⊙ and M ⊙, compared to previously reported ...events, and shows mild evidence for spin-induced orbital precession. The primary falls in the mass gap predicted by (pulsational) pair-instability supernova theory, in the approximate range 65–120 M ⊙. The probability that at least one of the black holes in GW190521 is in that range is 99.0%. The final mass of the merger ( M ⊙) classifies it as an intermediate-mass black hole. Under the assumption of a quasi-circular BBH coalescence, we detail the physical properties of GW190521’s source binary and its post-merger remnant, including component masses and spin vectors. Three different waveform models, as well as direct comparison to numerical solutions of general relativity, yield consistent estimates of these properties. Tests of strong-field general relativity targeting the merger-ringdown stages of the coalescence indicate consistency of the observed signal with theoretical predictions. We estimate the merger rate of similar systems to be . We discuss the astrophysical implications of GW190521 for stellar collapse and for the possible formation of black holes in the pair-instability mass gap through various channels: via (multiple) stellar coalescences, or via hierarchical mergers of lower-mass black holes in star clusters or in active galactic nuclei. We find it to be unlikely that GW190521 is a strongly lensed signal of a lower-mass black hole binary merger. We also discuss more exotic possible sources for GW190521, including a highly eccentric black hole binary, or a primordial black hole binary.
We search for signatures of gravitational lensing in the gravitational-wave signals from compact binary coalescences detected by Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) ...and Advanced Virgo during O3a, the first half of their third observing run. We study: (1) the expected rate of lensing at current detector sensitivity and the implications of a non-observation of strong lensing or a stochastic gravitational-wave background on the merger-rate density at high redshift; (2) how the interpretation of individual high-mass events would change if they were found to be lensed; (3) the possibility of multiple images due to strong lensing by galaxies or galaxy clusters; and (4) possible wave-optics effects due to point-mass microlenses. Several pairs of signals in the multiple-image analysis show similar parameters and, in this sense, are nominally consistent with the strong lensing hypothesis. However, taking into account population priors, selection effects, and the prior odds against lensing, these events do not provide sufficient evidence for lensing. Overall, we find no compelling evidence for lensing in the observed gravitational-wave signals from any of these analyses.
The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The ...histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.
The reliability of assembled structures is significantly influenced by the applied thermomechanical stresses and the robustness degree of the simulation numerical methods. The utilization of ...classical numerical methods such as the finite element method (FEM), extended finite element method XFEM, and mean weighted residuals method are computational costs due to the complexity of the materials behaviour laws, physicals mathematical model and laboratory apparatus cost. To ensure accurate investigation techniques, it should be performed a numerical model used for resolving welding physical equations governed. The main objective of this study is to architect and optimize an intelligent model based on an artificial neural network to resolve a complex model of the calculation effect of spot welding on the behaviour of HLE steel. The ANN model gives a strong correlation between the dataset as numeric input and the target. The artificial neuron network gives a proxy model approach to exploit input data and results extracted by simulation of weld spot using finite element method FEM. The performance evaluation of the ANN model was carried out using mean square error and regression analysis. As a result, the present model ANN gives with minimum computational cost a good match of temperature estimating, equivalent stress and strain along the contact area of two thin plates of steel studied assembled by weld spot with a comparison between classical models using FEM.
➢Fast tool for assessment of materials behaviour➢Minimum competition time➢Give forward and backward investigation on the physical model by estimation or prediction parameters➢Forecast behaviour of materials➢Carry out the solution of complex problems with minimum computational cost.➢Give fast decisions to make predictive control and monitor of material behaviour with the variation of the operators parameters.
The wadis are environments of great ecological and economic importance. They are the seat of several hydraulic developments. The latter disrupts the functioning of the wadi in different ways. They ...modify their hydrological regime, disrupt the ecological conditions upstream and downstream of the reservoir, reduce the self-purification capacities, and modify the processes of erosion and solid transport. It is in this perspective that we have carried out a study of the impact of hydraulic installations on the quality of the waters of the Mafragh watershed. The hydrographic network of the watershed receives the wastewater discharged by the localities and by the industries located along these rivers. This wastewater contributes to the degradation of the water quality of the wadis. The spatio-temporal variation of the water quality index showed a good quality at the level of the dams, while at the level of the sites, which are located downstream, the quality generally varies between bad and very bad during the study period.
COVID-19 has spread rapidly worldwide, despite the availability of vaccines, the fear of the World Health Organization continues due to the mutation of the Coronavirus. This is what prompted us to ...propose this work of social distance and wearing a face mask to fight against this pandemic to save lives. In this work, we propose a real-time four-stage model with monocular camera and deep learning based framework for automating the task of monitoring social distancing and face mask detection using video sequences. This work based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model, Simple Online and Real-time Tracking with a deep association metric approach to tracking people. The perspective transformation is used to approximate the three-dimensional coordinates with Euclidean metric to compute distance between boxes. The Dual Shot Face Detector (DSFD) and MobileNetv2 face mask model used to detect faces of people who violate or cross the social distance. Accuracy of 56.2% and real-time performance of 32 frames per second are achieved by the Social-Scaled-YOLOv4 (Social-YOLOv4-P6) model trained on the MS COCO dataset and Google-Open-Image dataset. The results are compared with other popular state-of-the-art models in terms of Mean-Average-Precision, frame rate and loss of values. The DSFD&MobileNetv2 facemask detectors trained on Wider Face and Real Face mask dataset achieves an accuracy of 99.3%. The proposed approach is validated on indoor/outdoor public images and video sequences such as wider face dataset, Oxford Town Center dataset and open access sequences.
Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection ...and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.
This work focuses on the development of optimized algorithms applied in the field of Face Recognition (FR). The strategy adopted represents the contribution of hybrid face descriptors followed by the ...selection of optimal characteristics for significantly improving systems performance. The hybrid descriptors use the combination of several pieces of information and their optimization. Indeed, these are two hybrid structures developed and implemented. The first supports the combination of several classic descriptors Gabor filter with Histogram Oriented Gradient (HOG), Local Phase Quantization (LPQ) or Principal Component Analysis (PCA) for the facial features extraction. The second structure relies on Deep Learning (Transfer Learning (TL)) by relying on the Convolutional Neural Networks
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CNN) named AlexNet without its last layers and this in order to extract the most relevant face characteristics. The Particle Swarm Optimization (PSO) algorithm optimizes these characteristics extracted from these two algorithms. The first structure is followed by a data reduction step based on Linear Discriminant Analysis (LDA). The classification is carried out by the cosine distance measurement with the data normalization. A two-part data division algorithm, one part for training and one part for testing, will follow the second structure and a “Softmax” single-layer classifier is added to its output. The experimentation is conducted on existing dataset: Labeled Faces in the Wild (LFW), as well as on databases (ORL, AR and Thermal Tufts Face) and good performance is obtained.
The Job Shop Scheduling Problem (JSSP) has been widely studied in recent decades. Various approaches have been proposed to support scheduling decisions according to the evolving production ...environment. The emergence of technological advancements in the context of Industry 4.0 has brought many changes and made production scheduling more and more efficient. Today’s Industry 5.0 environment pays much attention to human considerations, sustainability, and resilience. These modern production environments can be accurately represented by the flexible shop floor scheduling problem in which various coordinating machines (with many alternative routing possibilities) and different operators are challenging. Recent literature on JSSP, which considers the human in the loop, has shown that the well-being and skills of workers significantly affect scheduling performance. In addition, knowing that industries are responsible for a significant part of the world’s energy consumption and greenhouse gas (GHG) emissions, new studies in scheduling focus on environmental factors. This paper introduces the Sustainable Flexible Scheduling Problem (SFJSSP) as a human and energy-efficiency-centered scheduling problem. First, we review the last decade’s literature on Flexible Job Shop Scheduling Problems (FJSSP) with human and/or environmental considerations. Next, we analyze the development trends in manufacturing scheduling problems. Finally, we discuss future research challenges to move towards scheduling 5.0 and suggest a mathematical model that considers human and environmental factors (in addition to the factors considered by the Classical Flexible Job Shop Scheduling Problem (CFJSSP)).
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•Human Centric Manufacturing.•Environmental consideration.•Resilience.
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