Miracle-dz/UniFAR-FTUs huggingface dataset:
X-Ray Images Analytics Algorithm based on Machine Learning

The rapid development of information technology has led to a huge amount of data generated by large or complex systems and devices. Applications in information technology, medicine, and many other fields generate large volumes of data that challenge analysts. Data mining analysis finds application in areas where statistical and analytical methods and the models built through them are not sufficient. The paper discusses sources of medical data, use cases, and data analysis in medicine, as well as methods and algorithms for data analysis. The purpose and objectives of the study, presented in the paper are to propose an algorithm for processing X-Ray images based on tools and techniques from the field of machine learning. The preprocessing phase is concerned with image transformation, feature extraction, and the selection of training and testing datasets. Preprocessing data enables the processing of data that would not otherwise be appropriate by adjusting the data to the specifications established by each data retrieval procedure. Each feature is examined in the second stage to identify and classify any potential patterns. In the final stage, the most effective model to capture the pattern or behaviour of the data is chosen using a machine learning algorithm. The proposed algorithm is verified using publicly available X-Ray image datasets consisting of four classes: Normal, Lung Opacity, Pneumonia, and COVID-19. A medical image classification workflow was designed for verification. In the experimental workflow, five algorithms in the field of machine learning are determined and implemented: Logistic Regression, Naive Bayes, Random Forest, SVM, and Neural Network. In comparison to the outcomes of Random Forest, Logistic Regression, Naive Bayes, and SVM, the findings of the experimental analysis and results demonstrate that Neural Networks produce the greatest results, and these results can be taken to be the most dependable.
Performance Improvement of Different Resonant Inductive Couplers Used for Wireless Power Transfer

This article experimentally investigates the performance of spiral coil configurations commonly used in magnetic resonant wireless power transfer (WPT) inductive couplers. Three different designs are illustrated, namely, single-core, twin-core “bifilar,” and single-core coaxial cable spiral coils. The major concern is the improvement of both the voltage gain and transmitted power efficiency at different distances between the transmitter and receiver coils. Moreover, the performance analysis of the experimental setup is compared with the results of three-dimensional frequency-domain extensive finite element (FE) simulations of the COMSOL Multiphysics package, through radio frequency modeling of coupled electromagnetic waves and electric circuits. Such FE simulations help in realizing the relative effect of inherent parameters that are hard to be investigated through experimental measurement without disturbing the WPT system. Additionally, PSPICE is used to facilitate the performance investigation of such magnetic resonant WPT couplers. Results reveal that adding the ferrite sheets to single-core spiral coils helps in improving the WPT efficiency. Shorted bifilar wound flat spiral coils show higher WPT efficiency at short coil separation due to their intrinsic self-capacitance and high self-inductance. Self-resonating “capacitorless,” single-core coaxial cable wound flat spiral coils show an enhanced performance improvement when compared with the other two designs.
Spectrograms of overlapping speech based upon instantaneous frequency

A method is presented for the production of a novel type of spectrogram which is intended to emphasise the harmonic nature of speech. A bank of gammatone filters separates the speech into frequency bands whereupon the frequency of the most dominant speech component in each band is obtained by exploiting the FM capture effect. A validation process rejects the frequency estimates from any band where immediately previous estimates show large fluctuations due to noise and cross-interference. The remaining current estimates are then plotted on a spectrogram type display. Even with two simultaneous voices, clear harmonic patterns can be seen from which it is hoped that two pitch tracks may be determined. <<ETX>>
Performance Efficient Jet Noise Reduction for Supersonic Nozzles

The variable area, round, axisymmetric, supersonic exhaust nozzle for high performance military aircraft represents a remarkable technological achievement. These variable exhaust nozzles (VEN) utilize conic contraction and expansion sections and provide a simple mechanical means to control the throat-to-exhaust area ratio as a function of throttle input while maintaining structural and thermal integrity. Despite this admirable design, the result is a jet that unavoidably includes shockwaves both inside the nozzle and in the exhaust. The sharp throat radius provides the potential for flow separation which is exacerbated by shock-boundary-layer interactions within the nozzle resulting in reduced pressure recovery on the wall which translates into reduced thrust. A novel design, patented by Dr. John M. Seiner, is shown to eliminate shocks by altering the contour of the VEN seals. This results in the elimination of shock noise in the far-field spectra. The shape of the contoured seals provides the added benefit of generating streamwise vorticity which is evident in the reduction in mixing noise in the far-field spectra. Particle image velocimetry data is presented to demonstrate the enhanced mixing that is generated. Experimental evidence obtained at both model scale and full-scale demonstrates the ability to obtain 2–5 dB noise reduction with an optimized contoured seal design.
Electrospinning Novel Sodium Alginate/MXene Nanofiber Membranes for Effective Adsorption of Methylene Blue

Understanding how to develop highly efficient and robust adsorbents for the removal of organic dyes in wastewater is crucial in the face of the rapid development of industrialization. Herein, d-Ti3C2Tx nanosheets (MXene) were combined with sodium alginate (SA), followed by electrospinning and successive Ca2+-mediated crosslinking, giving rise to a series of SA/MXene nanofiber membranes (NMs). The effects of the MXene content of the NMs on the adsorption performance for methylene blue (MB) were investigated systemically. Under the optimum MXene content of 0.74 wt. %, SA/MXene NMs possessed an MB adsorption capacity of 440 mg/g, which is much higher than SA/MXene beads with the same MXene content, pristine MXene, or electrospinning SA NMs. Furthermore, the optimum SA/MXene NMs showed excellent reusability. After the adsorbent was reused ten times, both the MB adsorption capacity and removal rate could remain at 95% of the levels found in the fresh samples, which indicates that the electrospinning technique has great potential for developing biomass-based adsorbents with high efficiency.
Dose- and dosage-dependent spectrum of respiratory toxicity of cypermethrin in rabbits

Abstract Pyrethroids are popular insecticides. Shortness of breath and dyspnea are key signs of exposure; however, their respiratory effects are rarely studied. This study investigated the combination and ratios of specific blood tests and lung lesions as respiratory effects of cypermethrin (CY) in rabbits. The effects of CY at various doses and times are the novelty of this study. Doses (50, 100, and 150 mg.kg-1 body weight) of CY were injected intraperitoneally every week for up to 9 weeks in rabbits assigned into four equal groups. Data analysis revealed various respiratory signs, significantly (p ≤ 0.05) lower fibrinogen, higher neutrophils-lymphocytes (NL), LDH-lymphocytes (LL), and De-Ritis ratios (DR), and lesions in the lungs. The frequency and incidence of these effects were dose and dosage dependent. The CY leads to pulmonary signs and allergic effects. Lung injury increases cell-free heme in plasma, causing pulmonary edema with hemolysis. Emphysema and fibrosis followed the migration of basophils and mononuclear cells to the lungs. This study inferred that CY exposure caused lower fibrinogen, higher NL, LL, and DR ratios, and pulmonary lesions, which forecast poor immunity, especially increased risk for cardiac and lung diseases.
A Novel Missense Mutation in the TGF-β-binding Protein-Like Domain 3 of FBN1 Causes Weill–Marchesani Syndrome with Intellectual Disability

Background: Weill–Marchesani syndrome (WMS) is a rare connective tissue disorder characterized by locus heterogeneity and variable expressivity. Patients suffering from WMS are described by short stature, brachydactyly, joint stiffness, congenital heart defects, and eye abnormalities. This disorder is inherited in two different modes; the autosomal dominant form of the disease occurs due to a mutation in FBN1, and the recessive form results from mutations in ADAMTS10, ADAMTS17, or LTP2 genes. Materials and Methods: The family recruited in this study was a consanguineous Iranian family with an intellectually disabled girl referred to the Sadra Genetics laboratory, Shahrekord, Iran. The clinical history of family members was investigated. Whole-Exome Sequencing (WES) for the proband was performed. Sanger sequencing was used to assess the segregation of candidate variants in the other family members. Results: Whole-exome sequencing analysis revealed a novel heterozygote mutation in the proband located at the third TGF-β-binding protein-like (TB) domain of the FBN1 gene (NM000138: c.2066A>G: (p. Glu689Gly), NP_000129. 3, in exon 17 of the gene). Co-segregation analysis with Sanger sequencing confirmed this mutation in the affected members of the pedigree. Conclusion: Our findings represent an autosomal dominant form of specific WMS resulting from a substitution mutation in the FBN1 gene. In addition to the typical manifestations of the disorder, mild intellectual disability (ID) was identified in the 8-year-old proband. Given the fact that ID is primarily reported in ADAMTS10 mutated cases, this family was clinically and genetically a novel case.
Fighting for Routes: Resource Allocation among Competing Planners in Transportation Networks

In transportation networks, incomplete information is ubiquitous, and users often delegate their route choice to distributed route planners. To model and study these systems, we introduce network control games, consisting of multiple actors seeking to optimise the social welfare of their assigned subpopulations through resource allocation in an underlying nonatomic congestion game. We first analyse the inefficiency of the routing equilibria by calculating the Price of Anarchy for polynomial cost functions, and then, using an Asynchronous Advantage Actor–Critic algorithm implementation, we show that reinforcement learning agents are vulnerable to choosing suboptimal routing as predicted by the theory. Finally, we extend the analysis to allow vehicles to choose their route planner and study the associated equilibria. Our results can be applied to mitigate inefficiency issues arising in large transport networks with route controlled autonomous vehicles.
Investigating Bug Report Changes in Bugzilla

: Bug report change behavior in bug tracking systems may help pinpoint negligence or misunderstanding when submitters fill in bug report information. This study investigates bug report changes in several projects within Mozilla’s Bugzilla to identify which fields in a report change the most, which bug profiles receive more changes and the relationship between these changes. We found that the most changed fields are flagtypes.name and cc . Reports are often modified when they indicate a valid bug, with medium to high priority and severity. Moreover, there are moderate to high correlations between changes in the following field pairs: product-component , priority-severity , and platform-op sys . We believe these results are relevant to indicate which submitter’s skills must be enhanced to improve the bug-tracking process.
X-ray variability of 104 active galactic nuclei

Abstract We have performed a uniform analysis of the power spectrum densities (PSDs) of 104 nearby (z<0.4) active galactic nuclei (AGN) using 209 XMM-Newton/pn observations, including several AGN classes. These PSDs span ≃ 3 decades in temporal frequencies, ranging from minutes to days. We have fitted each PSD to two models: (1) a single power-law model and (2) a bending power-law model. A fraction of 72% show significant variability. The PSD of the majority of the variable AGN was well described by a simple power-law with a mean index of α = 2.01±0.01. In 15 sources we found that the bending power law model was preferred with a mean slope of α = 3.08±0.04 and a mean bend frequency of 〈νb〉 ≃ 2 × 10−4 Hz. Only KUG 1031+398 (RE J1034+396) shows evidence for quasi-periodic oscillations. The ‘fundamental plane’ relating variability timescale, black hole mass, and luminosity is demonstrated using the new X-ray timing results presented here together with a compilation of the previously detected timescales from the literature.
Theory of double-chirped mirrors

A theory of double-chirped mirrors (DCMs) for dispersion compensation in ultrashort pulse laser sources is presented. We describe the multilayer interference coating by exact coupled-mode equations. They show that the analysis and synthesis of a coating with a slowly varying chirp in the layer thicknesses can be mapped onto a weakly inhomogeneous transmission line problem. Solutions of the transmission line equations are given using the WKB-method. Analytic expressions for reflectivity and group delay are derived. The solutions show that the main problem in chirped mirror design is the avoidance of spurious reflections, that lead to Gires-Tournois-like interference effects responsible for the oscillations in the group delay. These oscillations are due to an impedance matching problem of the equivalent transmission line. The impedance matching can be achieved by simultaneously chirping the strength of the coupling coefficient and the Bragg wavenumber of the mirror. An adiabatic increase in the coupling coefficient removes the typical oscillations in the group delay and results in broad-band mirrors with a controlled dispersion. Finally, the mirror is matched to air with a broadband antireflection coating. We discuss a complete design of a laser mirror with a reflectivity larger than 99.8% and a controlled dispersion over 300-nm bandwidth. Using such mirrors in a Ti:sapphire laser, we have demonstrated /spl ap/30-fs pulses, tunable over 300 nm, as well as 8-fs pulses from the same setup. A different design resulted in 6.5-fs pulses.
A Fast Hybrid Cascade Network for Voxel-based 3D Object Classification

Voxel-based 3D object classification has been thoroughly studied in recent years. Most previous methods convert the classic 2D convolution into a 3D form that will be further applied to objects with binary voxel representation for classification. However, the binary voxel representation is not very effective for 3D convolution in many cases. In this paper, we propose a hybrid cascade architecture for voxel-based 3D object classification. It consists of three stages composed of fully connected and convolutional layers, dealing with easy, moderate, and hard 3D models respectively. Both accuracy and speed can be balanced in our proposed method. By giving each voxel a signed distance value, an obvious gain regarding the accuracy can be observed. Besides, the mean inference time can be speeded up hugely compared with the state-of-the-art point cloud and voxel based methods.
Interplay of electron-magnon scattering and spin-orbit induced electronic spin-flip fcattering in a two-band Stoner model

Magnons are one of the carriers of angular momentum that are involved in the ultrafast magnetization dynamics in ferromagnets, but their contribution to the electronic dynamics and their interplay with other scattering process that occur during ultrafast demagnetization has not yet been studied in the framework of a microscopic dynamical model. The present paper presents such an investigation of electronic scattering dynamics in itinerant ferromagnets at the level of Boltzmann scattering integrals for the magnon distributions and spin-dependent electron distributions. In addition to electron-magnon scattering, we include spin-conserving and effective Elliott-Yafet like spin-flip electron-electron scattering processes and the influence of phonons. In our model system, the creation or annihilation of magnons leads to transitions between two spin-split electronic bands with energy and momentum conservation. Due to the presence of spin-orbit coupling, Coulomb scattering transitions between these bands are also possible, and we describe them on an equal footing in terms of Boltzmann scattering integrals. For an instantaneous carrier excitation process we analyze the influence of both interaction processes on the magnon and spin-dependent electron dynamics, and show that their interplay gives rise to an efficient creation of magnons at higher energies and wave vectors accompanied by only a small increase of the electronic spin polarization. These results present a microscopic dynamical scenario that shows how non-equilibrium magnons may dominate the magnetic response of a ferromagnet on ultrafast timescales.
Relative cubulation of relative strict hyperbolization

We prove that many relatively hyperbolic groups obtained by relative strict hyperbolization admit a cocompact action on a CAT(0) cubical complex. Under suitable assumptions on the peripheral subgroups, these groups are residually finite and even virtually special. We include some applications to the theory of manifolds, such as the construction of new non-positively curved Riemannian manifolds with residually finite fundamental group, and the existence of non-triangulable aspherical manifolds with virtually special fundamental group.
HandCT: hands-on computational dataset for X-Ray Computed Tomography and Machine-Learning

Machine-learning methods rely on sufficiently large dataset to learn data distributions. They are widely used in research in X-Ray Computed Tomography, from low-dose scan denoising to optimisation of the reconstruction process. The lack of datasets prevents the scalability of these methods to realistic 3D problems. We develop a 3D procedural dataset in order to produce samples for data-driven algorithms. It is made of a meshed model of a left hand and a script to randomly change its anatomic properties and pose whilst conserving realistic features. This open-source solution relies on the freeware Blender and its Python core. Blender handles the modelling, the mesh and the generation of the hand's pose, whilst Python processes file format conversion from obj file to matrix and functions to scale and center the volume for further processing. Dataset availability and quality drives research in machine-learning. We design a dataset that weighs few megabytes, provides truthful samples and proposes continuous enhancements using version control. We anticipate this work to be a starting point for anatomically accurate procedural datasets. For instance, by adding more internal features and fine tuning their X-Ray attenuation properties.
A noise-robust acoustic method for recognition of foraging activities of grazing cattle

To stay competitive in the growing dairy market, farmers must continuously improve their livestock production systems. Precision livestock farming technologies provide individualised monitoring of animals on commercial farms, optimising livestock production. Continuous acoustic monitoring is a widely accepted sensing technique used to estimate the daily rumination and grazing time budget of free-ranging cattle. However, typical environmental and nat-ural noises on pasture noticeably aﬀect the performance and generalisation of current acoustic methods. In this study, we present an acoustic method called Noise-Robust Foraging Activity Recognizer (NRFAR). The proposed method determines foraging activity bouts by analysing ﬁxed-length segments of identi-ﬁed jaw movement events associated with grazing and rumination. The additive noise robustness of NRFAR was evaluated for several signal-to-noise ratios, us-ing stationary Gaussian white noise and four diﬀerent non-stationary natural noise sources. In noiseless conditions, NRFAR reaches an average balanced accuracy of 89%
High-resolution three-dimensional imaging of topological textures in single-diamond networks

Highly periodic structures are often said to convey the beauty of nature. However, most material properties are strongly influenced by the defects they contain. On the mesoscopic scale, molecular self-assembly exemplifies this interplay; thermodynamic principles determine short-range order, but long-range order is mainly impeded by the kinetic history of the material and by thermal fluctuations. For the development of self-assembly technologies, it is imperative to characterise and understand the interplay between self-assembled order and defect-induced disorder. Here we used synchrotron-based hard X-ray nanotomography to reveal a pair of extended topological defects within a self-assembled single-diamond network morphology. These defects are morphologically similar to the comet and trefoil patterns of equal and opposite half-integer topological charges observed in liquid crystals and appear to maintain a constant separation across the thickness of the sample, resembling pairs of full vortices in superconductors and other hard condensed matter systems. These results are expected to open new windows to study defect formation in soft condensed matter, particularly in biological systems where most structures are formed by self-assembly.
Ensuring Reliable Robot Task Performance through Probabilistic Rare-Event Verification and Synthesis

Providing guarantees on the safe operation of robots against edge cases is challenging as testing methods such as traditional Monte-Carlo require too many samples to provide reasonable statistics. Built upon recent advancements in rare-event sampling, we present a model-based method to verify if a robotic system satisfies a Signal Temporal Logic (STL) specification in the face of environment variations and sensor/actuator noises. Our method is efficient and applicable to both linear and nonlinear and even black-box systems with arbitrary, but known, uncertainty distributions. For linear systems with Gaussian uncertainties, we exploit a feature to find optimal parameters that minimize the probability of failure. We demonstrate illustrative examples on applying our approach to real-world autonomous robotic systems.