Impacts of Gravitational-Wave Background from Supermassive Black Hole Binaries on the Detection of Compact Binaries by LISA

In the frequency band of Laser Interferometer Space Antenna (LISA), extensive research has been conducted on the impact of foreground confusion noise generated by galactic binaries within the Milky Way galaxy. Additionally, the recent evidence for a stochastic signal, announced by the NANOGrav, EPTA, PPTA, CPTA and InPTA, indicates that the stochastic gravitational-wave background generated by supermassive black hole binaries (SMBHBs) can contribute a strong background noise within in LISA band. Given the presence of such strong noise, it is expected to have a considerable impacts on LISA's scientific missions. In this work, we investigate the impacts of the SGWB generated by SMBHBs on the detection of massive black hole binaries (MBHBs), verified galactic binaries (VGBs) and extreme mass ratio inspirals (EMRIs) in the context of LISA, and find it crucial to resolve and eliminate the exceed noise from the SGWB to ensure the success of LISA's missions.Comment: 6 pages, 3 figure

Search for stochastic gravitational-wave background from string cosmology with Advanced LIGO and Virgo's O1∼\simO3 data

String cosmology models predict a relic background of gravitational-wave (GW) radiation in the early universe. The GW energy spectrum of radiated power increases rapidly with the frequency, and therefore it becomes a potential and meaningful observation object for high-frequency GW detector. We focus on the stochastic background generated by superinflation in string theory and search for such signal in the observing data of Advanced LIGO and Virgo O1$\sim$O3 runs in a Bayesian framework. We do not find the existence of the signal, and thus put constraints on the GW energy density. Our results indicate that at $f=100\,\text{Hz}$, the fractional energy density of GW background is less than $1.7\times10^{-8}$ and $2.1\times10^{-8}$ for dilaton-string and dilaton only cases respectively, and further rule out the parameter space restricted by the model itself due to the non-decreasing dilaton and stable cosmology background ($\beta$ bound).Comment: Accepted by Journal of Cosmology and Astroparticle Physic

Research methods on the role of financial inclusion, energy efficiency and energy R&D: Evidence from G7 economies

Countries around the globe are rapidly targeting energy efficiency goal achievement due to the unproductive and inefficient use of traditional energy sources. Several factors are discovered that are critical for energy efficiency in the region. Still, there are many economic, financial, energy, and research and development factors that could influence energy efficiency and remained ignored in the scholarly research, which is important from economic growth as well as environmental sustainability perspective. This research contributes to the existing literature by providing novel factors affecting energy efficiency in the developed nations. Specifically, the current study investigates the influence of financial inclusion, energy R&D, political- economic-financial risk index, and the energy-related inflation on the energy efficiency of G7 economies covering the period from 2004 to 2020. This study employed the slope heterogeneity and cross-section dependence test, which led to using the second-generation unit root test. For empirical estimations, the current study utilizes the panel Quantile regression, and the outcomes reveal that all the considered variables positively influence the energy efficiency in the region. However, the influence of these variables increases except for the energy-related inflation when moving from lower quantile Q0.25 to medium Q0.50 to higher quantile Q0.75, respectively. The estimated results are found robust, confirmed by the FMOLS estimator. Based on the empirical findings, it is recommended that financial inclusion and energy-related research and development be enhanced to achieve the region’s energy efficiency

EDDA: An Efficient Distributed Data Replication Algorithm in VANETs

Efficient data dissemination in vehicular ad hoc networks (VANETs) is a challenging issue due to the dynamic nature of the network. To improve the performance of data dissemination, we study distributed data replication algorithms in VANETs for exchanging information and computing in an arbitrarily-connected network of vehicle nodes. To achieve low dissemination delay and improve the network performance, we control the number of message copies that can be disseminated in the network and then propose an efficient distributed data replication algorithm (EDDA). The key idea is to let the data carrier distribute the data dissemination tasks to multiple nodes to speed up the dissemination process. We calculate the number of communication stages for the network to enter into a balanced status and show that the proposed distributed algorithm can converge to a consensus in a small number of communication stages. Most of the theoretical results described in this paper are to study the complexity of network convergence. The lower bound and upper bound are also provided in the analysis of the algorithm. Simulation results show that the proposed EDDA can efficiently disseminate messages to vehicles in a specific area with low dissemination delay and system overhead

IDET: Iterative Difference-Enhanced Transformers for High-Quality Change Detection

Change detection (CD) aims to detect change regions within an image pair captured at different times, playing a significant role for diverse real-world applications. Nevertheless, most of existing works focus on designing advanced network architectures to map the feature difference to the final change map while ignoring the influence of the quality of the feature difference. In this paper, we study the CD from a new perspective, i.e., how to optimize the feature difference to highlight changes and suppress unchanged regions, and propose a novel module denoted as iterative difference-enhanced transformers (IDET). IDET contains three transformers: two transformers for extracting the long-range information of the two images and one transformer for enhancing the feature difference. In contrast to the previous transformers, the third transformer takes the outputs of the first two transformers to guide the enhancement of the feature difference iteratively. To achieve more effective refinement, we further propose the multi-scale IDET-based change detection that uses multi-scale representations of the images for multiple feature difference refinements and proposes a coarse-to-fine fusion strategy to combine all refinements. Our final CD method outperforms seven state-of-the-art methods on six large-scale datasets under diverse application scenarios, which demonstrates the importance of feature difference enhancements and the effectiveness of IDET.Comment: conferenc

Benzyl (E)-3-(2-bromo-5-meth­oxy­benzyl­idene)dithio­carbazate

The title compound, C16H15BrN2OS2, was obtained from the condensation reaction of benzyl dithio­carbazate and 2-bromo-5-meth­oxy­lbenzaldehyde. In the mol­ecule, the bromo­meth­oxy­phenyl ring and dithio­carbazate fragment are located on the opposite sides of the C=N double bond, showing the E conformation. The dithio­carbazate fragment is approximately planar (r.m.s deviation 0.0187 Å); its mean plane is oriented with respect to the bromo­meth­oxy­phenyl and phenyl rings at 7.60 (12) and 60.08 (9)°, respectively. In the crystal, inversion dimers linked by pairs of N—H⋯S hydrogen bonds occur. A short Br⋯Br contact of 3.5526 (12) Å is observed in the crystal structure