Quantitative Assessment of Flame Stability Through Image Processing and Spectral Analysis

This paper experimentally investigates two generalized methods, i.e., a simple universal index and oscillation frequency, for the quantitative assessment of flame stability at fossil-fuel-fired furnaces. The index is proposed to assess the stability of flame in terms of its color, geometry, and luminance. It is designed by combining up to seven characteristic parameters extracted from flame images. The oscillation frequency is derived from the spectral analysis of flame radiation signals. The measurements involved in these two methods do not require prior knowledge about fuel property, burner type, and other operation conditions. They can therefore be easily applied to flame stability assessment without costly and complex adaption. Experiments were carried out on a 9-MW heavy-oil-fired combustion test rig over a wide range of combustion conditions including variations in swirl vane position of the tertiary air, swirl vane position of the secondary air, and the ratio of the primary air to the total air. The impact of these burner parameters on the stability of heavy oil flames is investigated by using the index and oscillation frequency proposed. The experimental results obtained demonstrate the effectiveness of the methods and the importance of maintaining a stable flame for reduced NOx emissions. It is envisaged that such methods can be easily transferred to existing flame closed-circuit television systems and flame failure detectors in power stations for flame stability monitoring

Anisotropic magnetoresistance in antiferromagnetic Sr2IrO4

We report point-contact measurements of anisotropic magnetoresistance (AMR) in a single crystal of antiferromagnetic (AFM) Mott insulator Sr2IrO4. The point-contact technique is used here as a local probe of magnetotransport properties on the nanoscale. The measurements at liquid nitrogen temperature revealed negative magnetoresistances (MRs) (up to 28%) for modest magnetic fields (250 mT) applied within the IrO2 a-b plane and electric currents flowing perpendicular to the plane. The angular dependence of MR shows a crossover from four-fold to two-fold symmetry in response to an increasing magnetic field with angular variations in resistance from 1-14%. We tentatively attribute the four-fold symmetry to the crystalline component of AMR and the field-induced transition to the effects of applied field on the canting of AFM-coupled moments in Sr2IrO4. The observed AMR is very large compared to the crystalline AMRs in 3d transition metal alloys/oxides (0.1-0.5%) and can be associated with the large spin-orbit interactions in this 5d oxide while the transition provides evidence of correlations between electronic transport, magnetic order and orbital states. The finding of this work opens an entirely new avenue to not only gain a new insight into physics associated with spin-orbit coupling but also better harness the power of spintronics in a more technically favorable fashion.Comment: 13 pages, 3 figure

Estimation Method of Path-Selecting Proportion for Urban Rail Transit Based on AFC Data

With the successful application of automatic fare collection (AFC) system in urban rail transit (URT), the information of passengers’ travel time is recorded, which provides the possibility to analyze passengers’ path-selecting by AFC data. In this paper, the distribution characteristics of the components of travel time were analyzed, and an estimation method of path-selecting proportion was proposed. This method made use of single path ODs’ travel time data from AFC system to estimate the distribution parameters of the components of travel time, mainly including entry walking time (ewt), exit walking time (exwt), and transfer walking time (twt). Then, for multipath ODs, the distribution of each path’s travel time could be calculated under the condition of its components’ distributions known. After that, each path’s path-selecting proportion can be estimated. Finally, simulation experiments were designed to verify the estimation method, and the results show that the error rate is less than 2%. Compared with the traditional models of flow assignment, the estimation method can reduce the cost of artificial survey significantly and provide a new way to calculate the path-selecting proportion for URT

Gauged Q ball in a piecewise parabolic potential

Q ball solutions are considered within the theory of a complex scalar field with a gauged U(1) symmetry and a parabolic-type potential. In the thin-walled limit, we show explicitly that there is a maximum size for these objects because of the repulsive Coulomb force. The size of Q ball will increase with the decrease of local minimum of the potential. And when the two minima degenerate, the energy stored within the surface of the Q ball becomes significant. Furthermore, we find an analytic expression for gauged Q ball, which is beyond the conventional thin-walled limit.Comment: 1 figure

Exploring QCD matter in extreme conditions with Machine Learning

In recent years, machine learning has emerged as a powerful computational tool and novel problem-solving perspective for physics, offering new avenues for studying strongly interacting QCD matter properties under extreme conditions. This review article aims to provide an overview of the current state of this intersection of fields, focusing on the application of machine learning to theoretical studies in high energy nuclear physics. It covers diverse aspects, including heavy ion collisions, lattice field theory, and neutron stars, and discuss how machine learning can be used to explore and facilitate the physics goals of understanding QCD matter. The review also provides a commonality overview from a methodology perspective, from data-driven perspective to physics-driven perspective. We conclude by discussing the challenges and future prospects of machine learning applications in high energy nuclear physics, also underscoring the importance of incorporating physics priors into the purely data-driven learning toolbox. This review highlights the critical role of machine learning as a valuable computational paradigm for advancing physics exploration in high energy nuclear physics.Comment: 146 pages,53 figure

The Analysis of Peasant Household's Credit Behavior

AbstractPeasant household's credit behavior not only affect the financial ability of the peasant household, but also influence the credit decisions of the rural financial institution, consequently impact on the development of the Chinese rural economic and the peasant household economic. This paper, based on the credit behavior of peasant household in the process of the Chinese rural economic development11Peasant household's credit behavior is the credit behavior under the comprehensive influence of the repayment capacity and willingness, in which repayment capacity is the key element., analysis the credit behavior game in rural credit and loan process between peasant households and rural financial institution, and the game among peasant households in rural joint warrant process. Finally, provides methods to improve the credit behavior of peasant household in credit game

9-[(Furan-2-ylmeth­yl)amino]-5-(3,4,5-trimeth­oxy­phen­yl)-5,5a,8a,9-tetra­hydro­furo[3′,4′:6,7]naphtho­[2,3-d][1,3]dioxol-6(8H)-one

In title compound, C27H27NO8, the dihydrofuran-2(3H)-one ring and the six-membered ring fused to it both display envelope conformations. The dihedral angle between the benzene ring of the benzo[d][1,3]dioxole group and the other benzene ring is 60.59 (2)°. In the crystal, weak inter­molecular C—H⋯O hydrogen bonds link the mol­ecules into a three-dimensional network. The furan ring is disordered over two sets of sites with occupancies of 0.722 (7) and 0.278 (7