Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PD

Particle mesh simulations of the Lyman-alpha forest and the signature of Baryon Acoustic Oscillations in the intergalactic medium

We present a set of ultra-large particle-mesh simulations of the LyA forest targeted at understanding the imprint of baryon acoustic oscillations (BAO) in the inter-galactic medium. We use 9 dark matter only simulations which can, for the first time, simultaneously resolve the Jeans scale of the intergalactic gas while covering the large volumes required to adequately sample the acoustic feature. Mock absorption spectra are generated using the fluctuating Gunn-Peterson approximation which have approximately correct flux probability density functions (PDFs) and small-scale power spectra. On larger scales there is clear evidence in the redshift space correlation function for an acoustic feature, which matches a linear theory template with constant bias. These spectra, which we make publicly available, can be used to test pipelines, plan future experiments and model various physical effects. As an illustration we discuss the basic properties of the acoustic signal in the forest, the scaling of errors with noise and source number density, modified statistics to treat mean flux evolution and misestimation, and non-gravitational sources such as fluctuations in the photo-ionizing background and temperature fluctuations due to HeII reionization.Comment: 11 pages, 10 figures, minor changes to address referee repor

Development of coatings from zircon sand by oxyacetilene flame spraying for application on refractory bricks

En el presente trabajo se documenta el desarrollo experimental empleado para obtener mediante proyección térmica por llama oxiacetilénica, recubrimientos a partir de arena de circonio (ZrSiO4) de origen mineral y de una mezcla de ZrSiO4 con 50 % en peso de alúmina comercial referencia Oerlikon-Metco 105 SFP. La arena de ZrSiO4 fue molida y tamizada, con el fin de seleccionar las fracciones de tamaño de partícula -37 +25 μm y -25 μm. Diferentes parámetros de proyección térmica fueron simulados con la herramienta computacional Jets et Poudres SPCTS versión 2002-2009 y las condiciones con mayor potencial para obtener recubrimientos fueron verificadas experimentalmente. Los recubrimientos elaborados sobre sustratos refractarios silico-aluminosos fueron caracterizados mediante MEB y DRX, encontrando que aquellos depositados a partir de la fracción de tamaño más fina, utilizando una llama producida con 22 y 70 L/min de acetileno y oxígeno respectivamente, a una distancia de proyección de 10 cm y con una velocidad de desplazamiento de la antorcha de 0,275 cm/s y con una rotación de los sustratos de 34,57 rpm fueron los que presentaron las mejores características estructurales. La porosidad de los recubrimientos fabricados a partir de la arena de circonio y de la mezcla ZrSiO4 con alúmina fueron de 30,5 ± 6,6% y 20,3 ± 9,2% en área y su dureza fue de 3.06±0.70 GPa y 6.0±0.30 GPa, respectivamente. A partir de los resultados obtenidos se concluye que es posible la utilización de este mineral como materia prima en el proceso de proyección térmica oxiacetilénica.In this paper, the experimental development used to flame sprayed coatings from mineral zircon sand (ZrSiO4) and a mixture of ZrSiO4 with 50 wt.% of commercial alumina Oerlikon Metco 105 SFP is presented. The ZrSiO4 sand was milled and sieved to obtain the particle sizes distribution corresponding to -37 +25 m and -25 m. Different parameters of thermal spraying process were simulated with the Jets & Poudres SPCTS software version 2002-2009 and the conditions with the greatest potential to obtain coatings were verified experimentally. The coatings elaborated on silico-aluminous refractory substrates were characterized by SEM and XRD, finding that those deposited from the lower size distribution, using a flame produced with 22 and 70 L / min of acetylene and oxygen respectively, at a spray distance of 10 cm and with a displacement of the gun of 0,275 cm/s and with a rotation of the substrate of 34,57 rpm were those that presented the best structural characteristics. The porosities of coatings sprayed from ZrSiO4 sand and the mixture of ZrSiO4 with Al2O3 were 30,5 ± 6,6% and 20.3 ± 9.2% in area and their Vickers hardness were 3.06 ± 0.70 GPa and 6.0 ± 0.30 GPa, respectively. From the results obtained it was concluded that it is possible to use this mineral as a raw material in the Oxy-acetylene flame thermal spraying process

Internet of things (IOT) based air conditioner monitoring system for intelligent facility maintenance

Office buildings often consume high energy to sustain building operations such as HVAC systems. A lack of proper decision-making approaches and a lack of maintenance planning will cause higher operational costs. This paper proposes data analytics for air conditioner’s performance in laboratory by using Internet of Things (IoT)-based monitoring system to improve efficiency in facility maintenance. It provides a monitoring system, notification system and performance dashboard to enable data analytics. The data analytics methods used here are i) condition-based maintenance which includes thermal analysis and electrical analysis; and ii) Overall Equipment Effectiveness (OEE) approach. The pre-maintenance performance measured for AC-1 is adequate while AC-2 does not meet the requirement. After the reactive maintenance was performed on AC-2; there was a performance increment of 63.15%. Based on sensors data, it seems to correlate between current draw and low refrigerant. It aids facility maintenance for early failure detection, which helps in decision-making. The result from the OEE approach also suggested the same decision-making to schedule maintenance. Performance needs to balance out to leverage power consumption without hefty operational costs for maintenance strategies. In conclusion, the data analytics provide insight for the maintenance management to monitor and schedule preventive maintenance before air conditioner (AC) faults happen. Meanwhile, the modified OEE approach for ACs to measure performance takes into consideration speed to cool down air and cost to run the AC which has not been explored yet elsewhere

Support directional shifting vector: A direction based machine learning classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm

Equation of motion of a classical scalar field with back reaction of produced particles

In the one-loop approximation we derive the equation of motion for a classical scalar field \phi_c (t) with the back reaction of particle production included. Renormalization of mass and couplings of \phi_c is done explicitly. The equation is non-local in time, but can easily be treated perturbatively or numerically. For the weak trilinear coupling of the external field to the produced particles, the new equation gives the same solution as the familiar one with the \Gamma \phi_c term. For a stronger coupling and other types of couplings the results are significantly different. The equation can be applied to the universe heating by the inflaton decay and to spontaneous baryogenesis.Comment: 29 pages, 16 figures, subm to NP

Stochastic Production Of Kink-Antikink Pairs In The Presence Of An Oscillating Background

We numerically investigate the production of kink-antikink pairs in a $(1+1)$ dimensional $\phi^4$ field theory subject to white noise and periodic driving. The twin effects of noise and periodic driving acting in conjunction lead to considerable enhancement in the kink density compared to the thermal equilibrium value, for low dissipation coefficients and for a specific range of frequencies of the oscillating background. The dependence of the kink-density on the temperature of the heat bath, the amplitude of the oscillating background and value of the dissipation coefficient is also investigated. An interesting feature of our result is that kink-antikink production occurs even though the system always remains in the broken symmetry phase.Comment: Revtex, 21 pages including 7 figures; more references adde

Working mothers, injury and embodied care work

In this article, we examine how mothers respond when injury interrupts maternal care, using the lens of embodied care, which we conceptualize as a form of ‘body work’. We draw on findings from a qualitative research project with two organizations in Australia that help people with injuries to return to work, examining the experiences of workers who are also mothers of dependent children. Mothers' inability to care for children during periods of injury was a significant concern for our interviewees; constraints on physical labour and physical affection were particularly troubling, indicating the importance of embodied maternal caregiving to maternal roles. Yet, while these mothers inhabited the spheres of paid work and unpaid care work simultaneously, service providers did not consider embodied care work or its relevance to injured women's ongoing needs for support. While our findings reflect the experiences of injured women, they also suggest the need for a materialist analysis of the ways that both paid work and care activities are deeply enmeshed in and through the bodies of those doing the work. Employers and service organizations still fail to recognize maternal ‘body work’, and this may be typical of social attitudes more widely

Complementarity of Future Dark Energy Probes

In recent years a plethora of future surveys have been suggested to constrain the nature of dark energy. In this paper we adapt a binning approach to the equation of state factor ``w'' and discuss how future weak lensing, galaxy cluster counts, Supernovae and baryon acoustic oscillation surveys constrain the equation of state at different redshifts. We analyse a few representative future surveys, namely DES, PS1, WFMOS, PS4, EUCLID, SNAP and SKA, and perform a principal component analysis for the ``w'' bins. We also employ a prior from Planck cosmic microwave background measurements on the remaining cosmological parameters. We study at which redshifts a particular survey constrains the equation of state best and how many principal components are significantly determined. We then point out which surveys would be sufficiently complementary. We find that weak lensing surveys, like EUCLID, would constrain the equation of state best and would be able to constrain of the order of three significant modes. Baryon acoustic oscillation surveys on the other hand provide a unique opportunity to probe the equation of state at relatively high redshifts.Comment: 22 pages, 20 figure

The Completed SDSS-IV extended Baryon Oscillation Spectroscopic Survey::N-body Mock Challenge for the eBOSS Emission Line Galaxy Sample

21 pages, 7 figures and 9 tables, A summary of all SDSS BAO and RSD measurements with accompanying legacy figures can be found at https://www.sdss.org/science/final-bao-and-rsd-measurements/ . The full cosmological interpretation of these measurements can be found at https://www.sdss.org/science/cosmology-results-from-eboss/ . Comments are welcomeInternational audienceCosmological growth can be measured in the redshift space clustering of galaxies targeted by spectroscopic surveys. Accurate prediction of clustering of galaxies will require understanding galaxy physics which is a very hard and highly non-linear problem. Approximate models of redshift space distortion (RSD) take a perturbative approach to solve the evolution of dark matter and galaxies in the universe. In this paper we focus on eBOSS emission line galaxies (ELGs) which live in intermediate mass haloes. We create a series of mock catalogues using haloes from the Multidark and {\sc Outer Rim} dark matter only N-body simulations. Our mock catalogues include various effects inspired by baryonic physics such as assembly bias and the characteristics of satellite galaxies kinematics, dynamics and statistics deviating from dark matter particles. We analyse these mocks using the TNS RSD model in Fourier space and the CLPT in configuration space. We conclude that these two RSD models provide an unbiased measurement of redshift space distortion within the statistical error of our mocks. We obtain the conservative theoretical systematic uncertainty of $3.3\%$, $1.8\%$ and $1.5\%$ in $f\sigma_8$, $\alpha_{\parallel}$ and $\alpha_{\bot}$ respectively for the TNS and CLPT models. We note that the estimated theoretical systematic error is an order of magnitude smaller than the statistical error of the eBOSS ELG sample and hence are negligible for the purpose of the current eBOSS ELG analysis