Revealing the nebular properties and Wolf-Rayet population of IC10 with Gemini/GMOS

We present a deep imaging and spectroscopic survey of the Local Group irregular galaxy IC10 using Gemini North and GMOS to unveil its global Wolf-Rayet (WR) population. We obtain a star formation rate (SFR) of 0.045 ± 0.023 M⊙yr−1, for IC10 from the nebular Hα luminosity, which is comparable to the SMC. We also present a revised nebular oxygen abundance of log(O/H) + 12 = 8.40 ± 0.04, comparable to the LMC. It has previously been suggested that for IC10 to follow the WR subtype-metallicity dependance seen in other Local Group galaxies, a large WN population awaits discovery. Our search revealed 3 new WN stars, and 6 candidates awaiting confirmation, providing little evidence to support this claim. The new global WR star total of 29 stars is consistent with the LMC population when scaled to the reduced SFR of IC10. For spectroscopically confirmed WR stars, the WC/WN ratio is lowered to 1.0, however including all potential candidates, and assuming those unconfirmed to be WN stars, would reduce the ratio to ∼0.7. We attribute the high WC/WN ratio to the high star formation surface density of IC10 relative to the Magellanic Clouds, which enhances the frequency of high mass stars capable of producing WC stars

Infusing known operators in convolutional neural networks for lateral strain imaging in ultrasound elastography

Convolutional Neural Networks (CNN) have been employed for displacement estimation in ultrasound elastography (USE). High-quality axial strains (derivative of the axial displacement in the axial direction) can be estimated by the proposed networks. In contrast to axial strain, lateral strain, which is highly required in Poisson's ratio imaging and elasticity reconstruction, has a poor quality. The main causes include low sampling frequency, limited motion, and lack of phase information in the lateral direction. Recently, physically inspired constraint in unsupervised regularized elastography (PICTURE) has been proposed. This method took into account the range of the feasible lateral strain defined by the rules of physics of motion and employed a regularization strategy to improve the lateral strains. Despite the substantial improvement, the regularization was only applied during the training; hence it did not guarantee during the test that the lateral strain is within the feasible range. Furthermore, only the feasible range was employed, other constraints such as incompressibility were not investigated. In this paper, we address these two issues and propose kPICTURE in which two iterative algorithms were infused into the network architecture in the form of known operators to ensure the lateral strain is within the feasible range and impose incompressibility during the test phase.Comment: Accepted in MICCAI 202

Exploiting Mechanics-Based Priors for Lateral Displacement Estimation in Ultrasound Elastography

Tracking the displacement between the pre- and post-deformed radio-frequency (RF) frames is a pivotal step of ultrasound elastography, which depicts tissue mechanical properties to identify pathologies. Due to ultrasound's poor ability to capture information pertaining to the lateral direction, the existing displacement estimation techniques fail to generate an accurate lateral displacement or strain map. The attempts made in the literature to mitigate this well-known issue suffer from one of the following limitations: 1) Sampling size is substantially increased, rendering the method computationally and memory expensive. 2) The lateral displacement estimation entirely depends on the axial one, ignoring data fidelity and creating large errors. This paper proposes exploiting the effective Poisson's ratio (EPR)-based mechanical correspondence between the axial and lateral strains along with the RF data fidelity and displacement continuity to improve the lateral displacement and strain estimation accuracies. We call our techniques MechSOUL (Mechanically-constrained Second-Order Ultrasound eLastography) and L1-MechSOUL (L1-norm-based MechSOUL), which optimize L2- and L1-norm-based penalty functions, respectively. Extensive validation experiments with simulated, phantom, and in vivo datasets demonstrate that MechSOUL and L1-MechSOUL's lateral strain and EPR estimation abilities are substantially superior to those of the recently-published elastography techniques. We have published the MATLAB codes of MechSOUL and L1-MechSOUL at http://code.sonography.ai.Comment: Link to the Supplemental Video: https://drive.google.com/file/d/1uOmt-T4i9MwR98jUoMsu-eOhQ2mgjrBd/view?usp=sharin

High-gain millimeter-wave antenna design and fabrication using multilayer inkjet printing processes

The research provided in this thesis focuses on the development of high-gain multilayer millimeter-wave (mm-Wave) antenna structures through additive inkjet printing fabrication processes. This work outlines the printing processes of thick dielectric films for use as printed radio frequency (RF) substrates and provides a proof-of-concept demonstration of the first fully-printed RF structures. Using the outlined processes, demonstrations of high-gain mm-Wave proximity-coupled patch array and Yagi-Uda array antennas are presented, achieving the highest realized gain within the 24.5 GHz ISM band for inkjet-printed antennas in literature.M.S

Impacts of EMC effects on the D meson modification factor in equilibrating QGP

In this article we employ the nuclear EMC effect to extract the parton distribution functions (PDFs) inside the Lead (Pb) and Gold (Au) nuclei. Extracted PDFs are utilized to obtain the transverse momentum dependent (TMD) ones, using the computing codes like Pythia 8 or MCFM-10. Through this procedure TMDPDFs for charm and bottom quarks in Au at $\sqrt{s_{NN}}=200\;GeV$, Pb at $\sqrt{s_{NN}}=2.76\;TeV$ and $\sqrt{s_{NN}}=5.02\;TeV$ are calculated. To evaluate the validity of results and investigate the influence of nuclear EMC effect, the numerated TMDs are used as input to estimate heavy quark modification factor $R_{AA}$ at transverse plane $P_T$. This observable is calculated through numerical solution of the Fokker-Planck equation. For this purpose we need to extract the drag and diffusion coefficients, using the hard thermal loop correction. It is done in the frame work of the relativistic hydrodynamics up to the third order approximation of gradient expansion. The results are compared with same solutions when the input PFDs are considered inside the unbounded protons where the nuclear effect is not included. The comparison indicates a significant improvement of computed $R_{AA}$ with available experimental data when the EMC effect is considered.Comment: 16 pages 6 figures 1 table

C and O stable isotopes and rare earth elements in the Devonian carbonate host rock of the Pivehzhan iron deposit, NE Iran

The Pivehzhan iron deposit is located at about 80km southwest of Mashhad, NE Iran. It occur within the Devonian carbonates as lenticular and massive bodies, as well as veinlets of magnetite and iron sulphides, transformed to goethite and haematite by weathering process. The hydrothermal calcite is the most important gangue mineral, which is observed in the form of veins/veinlets and open-space filling. The iron ores are accompanied by some minor elements such as Mn, Ti, Cr, and V and negligible amounts of Co and Ni. The distribution pattern of Rare Earth Elements (REEs) normalized to Post Archean Australian Shale (PAAS), which is characterized by the upward convex, as well as the positive Eu anomalies indicate the activity of reduced and acidic hydrothermal fluids. The negative Ce anomalies of host carbonates, although slight, point to the dominance of anoxic conditions during interaction with hydrothermal fluids. The hydrothermal calcite and quartz coexisting with the iron minerals contain principally fluid, which were homogenized into a liquid phase. The Homogenization Temperature (TH) and the salinity of the analysed fluid inclusions range from 129°C to 270°C and from 0.4wt.% to 9.41wt.% NaCl eq., respectively. The δ13CPDB and δ18OSMOW values range from -2.15‰ to -5.77‰ (PeeDee Belemnite PDB standard) and from +19.87‰ to +21.64‰ (Standard Mean Ocean Water SMOW standard) in hydrothermal calcite veinlets occurring with iron minerals, and from -0.66‰ to -4.37‰ (PDB) and from +15.55‰ to +20.14‰ (SMOW) within the host carbonates, respectively. The field relations and petrographic examination along with geochemical and isotopic considerations indicate that the Pivehzhan iron deposit was formed through replacement processes by reducing and acid fluids containing light carbon and oxygen isotopes. Variations in the physico-chemical conditions of hydrothermal fluids and their interaction with carbonates were the most effective mechanisms in the formation of this iron deposit. The potential source of iron was probably the basement magmatic rocks from which iron was leached by hydrothermal solutions

C and O stable isotopes and rare earth elements in the Devonian carbonate host rock of the Pivehzhan iron deposit, NE Iran

The Pivehzhan iron deposit is located at about 80km southwest of Mashhad, NE Iran. They occur within the Devonian carbonates as lenticular and massive bodies, as well as veinlets of magnetite and iron sulphides, transformed to goethite and haematite by weathering process. The hydrothermal calcite is the most important gangue mineral, which is observed in the form of veins/veinlets and open-space filling. The iron ores are accompanied by some minor elements such as Mn, Ti, Cr, and V and negligible amounts of Co and Ni. The distribution pattern of Rare Earth Elements (REEs) normalized to Post Archean Australian Shale (PAAS), which is characterized by the upward convex, as well as the positive Eu anomalies indicate the activity of reduced and acidic hydrothermal fluids. The negative Ce anomalies of host carbonates, although slight, point to the dominance of anoxic conditions during interaction with hydrothermal fluids.The hydrothermal calcite and quartz coexisting with the iron minerals contain principally fluid, which were homogenized into liquid phase. The homogenization temperature (TH(L-V)) and the salinity of the analysed fluid inclusions range from 129°C to 270°C and from 0.4wt.% to 9.41wt.% NaCl eq., respectively. The δ13CPDB and  δ18OSMOW values ranges from -2.15‰ to -5.77‰ (PeeDee Belemnite standard, PDB) and from +19.87‰ to +21.64‰ (Standard Mean Ocean Water standard, SMOW) in hydrothermal calcite veinlets occurring with iron minerals and -0.66‰ to -4.37‰ (PDB) and +15.55‰ to +20.14‰ (SMOW) within the host carbonates, respectively.The field relations and petrographic examination along with geochemical and isotopic considerations indicate that the Pivehzhan iron deposit was formed through replacement processes by reducing and acid fluids containing light carbon and oxygen isotopes. Variations in the physico-chemical conditions of hydrothermal fluids and their interaction with carbonates were the most effective mechanisms in the formation of this iron deposit. The potential source of iron was probably the basement magmatic rocks from which iron was leached by hydrothermal solutions

Sanction or financial crisis? An artificial neural network-based approach to model the impact of oil price volatility on stock and industry indices - Under review

In this paper, we model the impact of oil price volatility on Tehran stock and industry indices in two periods of international sanctions and post-sanction. To analyse the purpose of study, we use Feed-forward neural net-works. The period of study is from 2008 to 2018 that is split in two periods during international energy sanction and post-sanction. The results show that Feed-forward neural networks perform well in predicting stock market and industry, which means oil price volatility has a significant impact on stock and industry market indices. During post-sanction and global financial crisis, the model performs better in predicting industry index. Additionally, oil price-stock market index prediction performs better in the period of international sanctions. Herein, these results are, up to some extent, important for financial market analysts and policy makers to understand which factors and when influence the financial market, especially in an oil-dependent country such asIran with uncertainty in the international politics.Keywords: Feed-forward neural networks·Industry index·International energy sanction·Oil price volatility·Tehran stock inde

Optimal Customer Targeting for Sustainable Demand Response in Smart Grids1

AbstractDemand Response (DR) is a widely used technique to minimize the peak to average consumption ratio during high demand periods. We consider the DR problem of achieving a given curtailment target for a set of consumers equipped with a set of discrete curtailment strategies over a given duration. An effective DR scheduling algorithm should minimize the curtailment error - the difference between the targeted and achieved curtailment values - to minimize costs to the utility provider and maintain system reliability. The availability of smart meters with fine-grained customer control capability can be leveraged to offer customers a dynamic range of curtailment strategies that are feasible for small durations within the overall DR event. Both the availability and achievable curtailment values of these strategies can vary dynamically through the DR event and thus the problem of achieving a target curtailment over the entire DR interval can be modeled as a dynamic strategy selection problem over multiple discrete sub-intervals. We argue that DR curtailment error minimizing algorithms should not be oblivious to customer curtailment behavior during sub-intervals as (expensive) demand peaks can be concentrated in a few sub-intervals while consumption is heavily curtailed during others in order to achieve the given target, which makes such solutions expensive for the utility. Thus in this paper, we formally develop the notion of Sustainable DR (SDR) as a solution that attempts to distribute the curtailment evenly across sub-intervals in the DR event. We formulate the SDR problem as an Integer Linear Program and provide a very fast -factor approximation algorithm. We then propose a Polynomial Time Approximation Scheme (PTAS) for approximating the SDR curtailment error to within an arbitrarily small factor of the optimal. We then develop a novel ILP formulation that solves the SDR problem while explicitly accounting for customer strategy switching overhead as a constraint. We perform experiments using real data acquired from the University of Southern Californias smart grid and show that our sustainable DR model achieves results with a very low absolute error of 0.001-0.05 kWh range