Miracle-dz/UniFAR-FTUs huggingface dataset:
Temperature structure of the intergalactic medium within seven nearby and bright clusters of galaxies observed with XMM-Newton

Aims. Using a newly developed algorithm, we map, to the highest angular resolution allowed by the data, the temperature structure of the intra-cluster medium (ICM) within a nearly complete X-ray flux limited sample of galaxy clusters in the redshift range between z = 0.045 and z = 0.096. Our sample contains seven bright clusters of galaxies observed with XMM-Newton: Abell 399, Abell 401, Abell 478, Abell 1795, Abell 2029, Abell 2065, Abell 2256. Methods. We use a multi-scale spectral mapping algorithm especially designed to map spectroscopic observables from X-ray extended emission of the ICM. By means of a wavelet analysis, this algorithm couples spatially resolved spectroscopy with a structure detection approach. Derived from a former algorithm using Haar wavelets, our algorithm is now implemented with B-spline wavelets in order to perform a more regular analysis of the signal. Compared to other adaptive algorithms, our method has the advantage of analysing spatially the gas temperature structure itself, instead of being primarily driven by the geometry of gas brightness. Results. For the four clusters in our sample that are major mergers, we find a rather complex thermal structure with strong thermal variations consistent with their dynamics. For two of them, A2065 and A2256, we perform a 3-d analysis of cold front-like features evidenced from the gas temperature and brightness maps. Furthermore, we detect a significant non-radial thermal structure outside the cool core region of the other 3 more “regular” clusters, with relative amplitudes of about about 10% and typical sizes ranging between 2 and 3 arcmin. We investigate possible implications of this thermal structure on the mass estimates, by extracting the surface brightness and temperature profiles from complementary sectors in the “regular” clusters A1795 and A2029, corresponding to hottest and coldest regions in the maps. For A2029, the temperature and surface brightness gradients seem to compensate each other, leading to a consistent mass profile. For A1795, however, the temperature structure leads to a significant mass discrepancy in the innermost cluster region. The third “regular” cluster, A478, is located in a particular sky region characterised by strong variations of neutral hydrogen column density, Nh, even on angular scales smaller than the cluster itself. For this cluster, we derive a spectroscopic Nh map and investigate the origin of Nh structure by discussing its correlation with galactic emission of dust in the infrared.
Noncontact machinery operation status monitoring system with gated recurrent unit model

: In manufacturing industry, assembly line monitoring provides statistical information about overall performance and reliability of the legacy machines, ensuring that the machines give maximum yield output. However, most legacy machines lack internet connectivity and advanced functionality, increasing the diﬀiculty for tracking task. Therefore, this work seeks to introduce a noncontact acoustic method to track machines rather than the mainstream vibrational approach. In order to provide accurate tracking of the daily machine operation for our machine tracking system, we consider scenario of background noises such as environmental sounds from multiple sources as well as neighbouring machine’s sound. Thus, several neural networks are employed to recognize the machine status accurately. The objective of our work is to investigate the effect of machine types and states on recognition performance of neural network models under extremely noisy environments as well as to demonstrate the possibility of recognizing the sound on edge device. The main contribution of this article is the proposal of lightweight recurrent and convolutional-based models for machine sound recognition. The experimental results of our extensive testing included with multiple types of machines and background noises show that the proposed system with gated recurrent unit model has the best recognition accuracy of F1 score 0.913 with standard uncertainty of 0.026 with decent inference speed on edge device.
A Study of Fire on Boeing 747 Overhead Cargo Compartment: Experiments and Fire Dynamics Simulator Validation

Hidden fire in aircraft overhead cargo compartments could lead to catastrophic disaster. Early fire detection requires an improved understanding of heat and mass transfer in areas with curved fuselage sections. In this effort, an experimental campaign was conducted at the FAA Hughes Technical Center on different fire scenarios for the Boeing747-SP overhead cargo compartment to advance knowledge on this phenomenon and provide validation data for the Fire Dynamics Simulator (FDS). Extensive work has been done recently to enable computer simulation of fire on complex geometries within this tool. We use the experimental data obtained to perform validation of said capability. Model validation results are defined in terms of thermocouple readings measured and computed with satisfactory overall agreement.
CONSTRUCTIVE DEVIANCE – THE INFLUENCE OF ORGANIZATIONAL SUBCULTURES ON THE ADOPTION OF A NEW INFORMATION TECHNOLOGY SYSTEM

Industrial companies can be characterized as culturally plural organizations, as different occupational groups within the organization are likely to follow specific cultural preferences. This cultural diversity challenges the enterprise information system (EIS) implementation since subcultures may be more or less consistent with the values embedded in the EIS. This paper relies on a longitudinal study of a German industrial company’s subsidiary in China. Data has been gathered through participant observation, interviews and focus groups over a two-year period. We identified and detail the cultural divide between a reference group and three groups. In order to continue performing and fulfilling their professional obligations, the contending groups engage in constructive deviance behaviors, thus accommodating the prescriptions associated with both the dominant culture and their subculture. We identified three types of constructive deviance that result from the cultural contradictions: creative performance, issue selling, and taking charge. We discuss the effects of constructive deviance in the organization and conclude with theoretical and practical contributions.
Comparative Analysis of Deep Learning Models for Network Intrusion Detection Systems

Detecting network intrusions is an imperative part of the modern cybersecurity landscape. Over the years, researchers have leveraged the ability of Machine Learning to identify and prevent network attacks. Recently there has been an increased interest in the applicability of Deep Learning in the network intrusion detection domain. However, Network Intrusion Detection Systems developed using Deep Learning approaches are being evaluated using the outdated KDD Cup 99 and NSLKDD datasets which are not representative of real-world network traffic. Recent comparisons of these approaches on the more modern CSE-CIC-IDS2018 dataset, fail to address the severe class imbalance in the dataset which leads to significantly biased results. By addressing this class imbalance and performing an experimental evaluation of a Deep Neural Network, Convolutional Neural Network and Long Short-Term Memory Network on the balanced dataset, this research provides deeper insights into the performance of these models in classifying modern network traffic data. The Deep Neural Network demonstrated the best classification performance with the highest accuracy (84.312%) and Fl-Score (83.799%) as well as the lowest False Alarm Rate (2.615%).
Spatial Architecture Associated With Organ-Specific Gene Regulation.

Packaging of the genome in the nucleus is a non-random process that is thought to directly contribute to cell type-speciﬁc transcriptomes, although this hypothesis remains untested. Epigenome architecture, as assayed by chromatin conformation capture techniques, such as Hi-C, has recently been described in the mammalian cardiac myocyte and found to be remodeled in the setting of heart failure. In the present study, we sought to determine whether the structural features of the epigenome are conserved between different cell types by investigating Hi-C and RNA-seq data from heart and liver. Investigation of genes with enriched expression in heart or liver revealed nuanced interaction paradigms between compartment A genes preferentially toward the center of the nucleus and liver exhibiting preferential arrangement toward the periphery. Taken together, our data suggest that intra- and interchromosomal chromatin architecture plays a role in orchestrating tissue-speciﬁc gene expression.
Detect and Prevent the Cyber bullying Conversation’s on Social Media Using Machine Learning Algorithm

Now a day’s social media have attracted a lot of web user’s attention towards it for knowing the recent updates, share the new thoughts and exchange the latest updates throughout the world from one location to other location. As this is becoming more popular still it faces some common limitations in terms of controlling the abused or vulgar conversations from individual user account. In general cyber bullying is defined as the process of threatening a small kid or preteen by another child using the internet with some bad conversations and tries to make them feel nervous with these conversations. This is becoming a very severe problem in the current social media by afflicting the children, young adults with these rude messages. Hence we try to construct a machine learning approach for detecting the set of abused words in the conversations and try to block such conversations not to be spread to others. By using this proposed approach we can able to create a positive and safe communication in social media. Here we constructed a filter using Support Vector Machine (SVM) to filter out the abused words during communication.
Perceived Educational Quality of a Municipal School Sports Programme

This study analysed the educational quality of an extracurricular municipal school sport programme based on the perception of the people involved in its development and its participating schoolchildren. For this purpose, qualitative research was carried out in which in-depth interviews were conducted after the question guides had been approved by experts in the field of school sport. Interviews were conducted with 83 informants, including schoolchildren and their family members, instructors and programme coordinators. The information obtained was used to carry out a content analysis based on three dimensions of analysis: 1) Participation, social relations and attitudes of the participants, 2) Sport learning and promotion of healthy habits and active lifestyles, and 3) Transfer to other contexts, by means of the software Atlas.ti. The results demonstrated that participation and equality of participants was promoted; education in values was developed; schoolchildren were integrated and social interactions were positive; habits of regular physical activity were promoted; learning was developed; and what was acquired in the programme was transferable to other contexts. It was concluded that the educational quality of the programme is underpinned by the satisfaction of its participants in line with the achievement of its aims and its coherence with school physical education.
Advances in Applied Mathematics On triangle-free 3-connected matroids

3-Connected Triangle-free We present a method to construct any triangle-free 3-connected matroid starting from a matroid belonging to one of four inﬁnite families and subsequently performing a sequence of small opera- tions on it. This result extends to matroids a theorem proved by Kriesell for graphs.
A numerical study of the subgrid-scale fluid motion in turbulent flows

The numerical solution of the Navier–Stokes equations for different turbulence regimes has become more prevalent for many years. This thesis has investigated the large eddy simulation method for solving the Navier–Stokes equations. A primary goal is to design a subgrid-scale model for small-scale coherent vortices while statistically maintaining an accurate dissipation rate. Past investigations of turbulence indicate that the vortex stretching mechanism can transport the turbulence kinetic energy from large to small scales. Thus, a turbulence model can learn the energy dissipation rate from the statistics of the velocity gradient tensor. Following such a hypothesis, this thesis validates how vortex stretching can directly account for the subgrid-scale dissipation rate while solving the Navier–Stokes equations on a relatively coarse mesh. Current findings suggest a potential subgrid-scale model based on invariants of the square of the velocity gradient tensor. The turbulence statistics obtained from the proposed model agree well with the three commonly used dynamically adaptive large eddy simulation techniques. The results also suggest that statistics of the velocity gradient tensor dynamically adapt the dissipation rate to the local variation of turbulence. Furthermore, considering the square of the deformation tensor, this thesis suggests that the singular values of the snapshots of the velocity gradient may improve the model in future studies of more challenging turbulent flows.
AN EXTREME HIGH-VELOCITY BIPOLAR OUTFLOW IN THE PRE-PLANETARY NEBULA IRAS 08005-2356

We report interferometric mapping of the bipolar pre-planetary nebula IRAS 08005-2356 (I 08005) with an angular resolution of ∼1″–5″, using the Submillimeter Array, in the 12CO J = 2–1, 3–2, 13CO J = 2–1, and SiO J = 5–4 (v = 0) lines. Single-dish observations, using the SMT 10 m, were made in these lines as well as in the CO J = 4–3 and SiO J = 6–5 (v = 0) lines. The line profiles are very broad, showing the presence of a massive (>0.1 M⊙), extreme high velocity outflow (V ∼ 200 km s−1) directed along the nebular symmetry axis derived from the Hubble Space Telescope imaging of this object. The outflow's scalar momentum far exceeds that available from radiation pressure of the central post-AGB star, and it may be launched from an accretion disk around a main-sequence companion. We provide indirect evidence for such a disk from its previously published, broad Hα emission profile, which we propose results from Lyβ emission generated in the disk followed by Raman-scattering in the innermost regions of a fast, neutral wind.
THE METALLICITY DEPENDENCE OF THE MINIMUM MASS FOR CORE-COLLAPSE SUPERNOVAE

Understanding the progenitors of core-collapse supernovae (SNe) and their population statistics is a key ingredient for many current studies in astronomy, but as yet this remains elusive. Using the MESA stellar evolution code, we study the dependence of the lower mass limit for making core-collapse SNe a function of initial stellar metallicity. We find that this mass limit is smallest at [Z] ≈ −2 with a value of ∼8.3 M☉. At [Z] = 0 the limit is ∼9.5 M☉ and continues to rise with higher metallicity. As a consequence, for a fixed initial mass function the SN rate may be 20%–25% higher at [Z] = −2 than at [Z] = 0. This affects the association of observed SN rates as a probe for the cosmological star formation rate, rate predictions for SN surveys, and population synthesis studies.
Intelligent Heart Attack Prediction System Using Big Data

: In today’s world heart attack is the very common. So diagnosing patients correctly on timely basis is the most challenging task. Treatment of heart disease is quite high and not affordable by most of the patients. The main objective is to develop a prototype Intelligent Heart Attack Prediction System using Big data and data mining modelling techniques. This system can discover and extract hidden knowledge (patterns and relationships) associated with heart disease from a historical heart disease database. It can answer complex queries for diagnosing heart disease and thus assist healthcare practitioners to make intelligent clinical decisions which traditional decision support systems cannot. By providing effective treatments, it also helps to reduce treatment costs. The healthcare industry collects huge amounts of big data which, unfortunately, are not “mined”. Remedies can be provided by using advanced data mining techniques. Medical diagnosis is regarded as an important yet complicated task that needs to be executed accurately and efficiently. The automation of this system would be extremely advantageous. Therefore, a medical diagnosis system like heart attack prediction system would probably be exceedingly beneficial.
Relativistic stars in Starobinsky gravity with the matched asymptotic expansions method

We study the structure of relativistic stars in R + αR theory using the method of matched asymptotic expansion to handle the higher order derivatives in field equations arising from the higher order curvature term. We find solutions, parametrized by α, for uniform density stars. We obtain the mass-radius relations and study the dependence of maximum mass on α. We find that Mmax is almost linearly proportional to α. For each α the maximum mass configuration has the biggest compactness parameter (η = GM/Rc), and we argue that the general relativistic stellar configuration corresponding to α = 0 is the least compact among these.
Numerical Simulation of Excitation and Propagation of Helioseismic MHD Waves in Magnetostatic Models of Sunspots

We present comparison of numerical simulations of propagation of MHD waves,excited by subphotospheric perturbations, in two different ("deep" and "shallow") magnetostatic models of the sunspots. The "deep" sunspot model distorts both the shape of the wavefront and its amplitude stronger than the "shallow" model. For both sunspot models, the surface gravity waves (f-mode) are affected by the sunspots stronger than the acoustic p-modes. The wave amplitude inside the sunspot depends on the photospheric strength of the magnetic field and the distance of the source from the sunspot axis. For the source located at 9 Mm from the center of the sunspot, the wave amplitude increases when the wavefront passes through the central part of the sunspot. For the source distance of 12 Mm, the wave amplitude inside the sunspot is always smaller than outside. For the same source distance from the sunspot center but for the models with different strength of the magnetic field, the wave amplitude inside the sunspot increases with the strength of the magnetic field. The simulations show that unlike the case of the uniform inclined background magnetic field, the p- and f-mode waves are not spatially separated inside the sunspot where the magnetic field is strongly non-uniform. These properties have to be taken into account for interpretation of observations of MHD waves traveling through sunspot regions.
ASSEMBLY OF THE OUTER GALACTIC STELLAR HALO IN THE HIERARCHICAL MODEL

We provide a set of numerical N-body simulations for studying the formation of the outer Milky Ways’ stellar halo through accretion events. After simulating minor mergers of prograde and retrograde orbiting satellite halos with a dark matter main halo, we analyze the signal left by satellite stars in the rotation velocity distribution. The aim is to explore the orbital conditions where a retrograde signal in the outer part of the halo can be obtained, in order to give a possible explanation of the observed rotational properties of the Milky Way stellar halo. Our results show that, for satellites more massive than ∼1/40 of the main halo, the dynamical friction has a fundamental role in assembling the final velocity distributions resulting from different orbits and that retrograde satellites moving on low-inclination orbits deposit more stars in the outer halo regions and therefore can produce the counter-rotating behavior observed in the outer Milky Way halo.
Factors Affecting Social Science Students’ Career Choices: A Web-Based Cross-Sectional Study in Bangladesh

This web-based cross-sectional study aimed to investigate university students’ career choices and their determinants. Data were collected from four disciplines within the Social Science School at the Khulna University of Bangladesh. The findings from Pearson’s Chi-square revealed a significant association between career choice and age, sex, discipline, level of education, and socioeconomic status. Exploratory factor analysis indicated a three-factor solution, explaining the variance of over 50% and the overall reliability of α = 0.748. The findings from a multinomial logistic regression showed that older and male students had a lower likelihood of becoming teachers, while Sociology students were more interested in teaching. Furthermore, career choices were substantially influenced by students’ level of education, job quality, job prospect, and job motive. Considering the global demand for specific skills and knowledge, universities should revise their curricula, integrating the cognitive domain of students with practical knowledge-based education in order to widen the horizon of employment options for university graduates.
Effects of the imaginary inflaton component in supergravity new inflation

When models of new inflation are implemented in supergravity, the inflaton is a complex and not a real scalar field. As a complex scalar field has two independent components, supergravity models of new inflation are naturally two-field models. In this paper, we use the δN formalism to analyse how the two-field behaviour modifies the usual single-field predictions. We find that the model reduces to the single-field limit if the inflaton mass term is sufficiently small. Otherwise, the imaginary inflaton component reduces the amplitude As and the spectral index ns of the scalar curvature perturbations. However, the perturbations remain nearly Gaussian, and the reduced bispectrum fNL is too small to be observed.
Source-Finding for the Australian Square Kilometre Array Pathfinder

Abstract The Australian Square Kilometre Array Pathfinder (ASKAP) presents a number of challenges in the area of source finding and cataloguing. The data rates and image sizes are very large, and require automated processing in a high-performance computing environment. This requires development of new tools, that are able to operate in such an environment and can reliably handle large datasets. These tools must also be able to accommodate the different types of observations ASKAP will make: continuum imaging, spectral-line imaging, transient imaging. The ASKAP project has developed a source-finder known as selavy, built upon the duchamp source-finder. selavy incorporates a number of new features, which we describe here. Since distributed processing of large images and cubes will be essential, we describe the algorithms used to distribute the data, find an appropriate threshold and search to that threshold and form the final source catalogue. We describe the algorithm used to define a varying threshold that responds to the local, rather than global, noise conditions, and provide examples of its use. And we discuss the approach used to apply two-dimensional fits to detected sources, enabling more accurate parameterisation. These new features are compared for timing performance, where we show that their impact on the pipeline processing will be small, providing room for enhanced algorithms. We also discuss the development process for ASKAP source finding software. By the time of ASKAP operations, the ASKAP science community, through the Survey Science Projects, will have contributed important elements of the source finding pipeline, and the mechanisms in which this will be done are presented.