Singlino-dominated dark matter in general NMSSM

The general Next-to-Minimal Supersymmetric Standard Model (NMSSM) describes the singlino-dominated dark-matter (DM) property by four independent parameters: singlet-doublet Higgs coupling coefficient $\lambda$, Higgsino mass $\mu_{tot}$, DM mass $m_{\tilde{\chi}_1^0}$, and singlet Higgs self-coupling coefficient $\kappa$. The first three parameters strongly influence the DM-nucleon scattering rate, while $\kappa$ usually affects the scattering only slightly. This characteristic implies that singlet-dominated particles may form a secluded DM sector. Under such a theoretical structure, the DM achieves the correct abundance by annihilating into a pair of singlet-dominated Higgs bosons by adjusting $\kappa$'s value. Its scattering with nucleons is suppressed when $\lambda v/\mu_{tot}$ is small. This speculation is verified by sophisticated scanning of the theory's parameter space with various experiment constraints considered. In addition, the Bayesian evidence of the general NMSSM and that of $Z_3$-NMSSM is computed. It is found that, at the cost of introducing one additional parameter, the former is approximately $3.3 \times 10^3$ times the latter. This result corresponds to Jeffrey's scale of 8.05 and implies that the considered experiments strongly prefer the general NMSSM to the $Z_3$-NMSSM.Comment: 29 pages, 9 figure

Gossip Consensus Algorithm Based on Time-Varying Influence Factors and Weakly Connected Graph for Opinion Evolution in Social Networks

We provide a new gossip algorithm to investigate the problem of opinion consensus with the time-varying influence factors and weakly connected graph among multiple agents. What is more, we discuss not only the effect of the time-varying factors and the randomized topological structure but also the spread of misinformation and communication constrains described by probabilistic quantized communication in the social network. Under the underlying weakly connected graph, we first denote that all opinion states converge to a stochastic consensus almost surely; that is, our algorithm indeed achieves the consensus with probability one. Furthermore, our results show that the mean of all the opinion states converges to the average of the initial states when time-varying influence factors satisfy some conditions. Finally, we give a result about the square mean error between the dynamic opinion states and the benchmark without quantized communication

Proteomics analysis of differentially expressed proteins in chicken trachea and kidney after infection with the highly virulent and attenuated coronavirus infectious bronchitis virus in vivo

<p>Abstract</p> <p>Background</p> <p>Infectious bronchitis virus (IBV) is first to be discovered coronavirus which is probably endemic in all regions with intensive impact on poultry production. In this study, we used two-dimensional gel electrophoresis (2-DE) and two-dimensional fluorescence difference gel electrophoresis (2-DIGE), coupled with matrix-assisted laser desorption/ionization time-of-flight tandem mass spectrometry (MALDI-TOF/TOF-MS), to explore the global proteome profiles of trachea and kidney tissues from chicken at different stages infected <it>in vivo </it>with the highly virulent ck/CH/LDL/97I P₅strain of infectious bronchitis virus (IBV) and the embryo-passaged, attenuated ck/CH/LDL/97I P₁₁₅strain.</p> <p>Results</p> <p>Fifty-eight differentially expressed proteins were identified. Results demonstrated that some proteins which had functions in cytoskeleton organization, anti-oxidative stress, and stress response, showed different change patterns in abundance from chicken infected with the highly virulent ck/CH/LDL/97I P₅strain and those given the embryo-passaged, attenuated P₁₁₅stain. In addition, the dynamic transcriptional alterations of 12 selected proteins were analyzed by the real-time RT-PCR, and western blot analysis confirmed the change in abundance of heat shock proteins (HSP) beta-1, annexin A2, and annexin A5.</p> <p>Conclusions</p> <p>The proteomic alterations described here may suggest that these changes to protein expression correlate with IBV virus' virulence in chicken, hence provides valuable insights into the interactions of IBV with its host and may also assist with investigations of the pathogenesis of IBV and other coronavirus infections.</p

Contraction and expansion dynamics: deciphering genomic underpinnings of growth rate and pathogenicity in Mycobacterium

BackgroundMycobacterium bacteria, encompassing both slow growth (SGM) and rapid growth mycobacteria (RGM), along with true pathogenic (TP), opportunistic pathogenic (OP), and non-pathogenic (NP) types, exhibit diverse phenotypes. Yet, the genetic underpinnings of these variations remain elusive.MethodsHere, We conducted a comprehensive comparative genomics study involving 53 Mycobacterium species to unveil the genomic drivers behind growth rate and pathogenicity disparities.ResultsOur core/pan-genome analysis highlighted 1,307 shared gene families, revealing an open pan-genome structure. A phylogenetic tree highlighted clear boundaries between SGM and RGM, as well as TP and other species. Gene family contraction emerged as the primary alteration associated with growth and pathogenicity transitions. Specifically, ABC transporters for amino acids and inorganic ions, along with quorum sensing genes, exhibited significant contractions in SGM species, potentially influencing their distinct traits. Conversely, TP strains displayed contraction in lipid and secondary metabolite biosynthesis and metabolism-related genes. Across the 53 species, we identified 26 core and 64 accessory virulence factors. Remarkably, TP and OP strains stood out for their expanded mycobactin biosynthesis and type VII secretion system gene families, pivotal for their pathogenicity.ConclusionOur findings underscore the importance of gene family contraction in nucleic acids, ions, and substance metabolism for host adaptation, while emphasizing the significance of virulence gene family expansion, including type VII secretion systems and mycobactin biosynthesis, in driving mycobacterial pathogenicity

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

Optimal Energy Management for Microgrids with Combined Heat and Power (CHP) Generation, Energy Storages, and Renewable Energy Sources

This paper studies an energy management problem for a typical grid-connected microgrid system that consists of renewable energy sources, Combined Heat and Power (CHP) co-generation, and energy storages to satisfy electricity and heat demand simultaneously. We formulate this problem into a stochastic non-convex optimization programming to achieve the minimum microgrid’s operating cost, which is difficult to solve due to its non-convexity and coupling feature of constraints. Existing approaches such as dynamic programming (DP) assume that all the system dynamics are known, which results in a high computational complexity and thus are not feasible in practice. The focus of this paper is on the design of a real-time energy management strategy for the optimal operation of microgrids with low computational complexity. Specifically, derived from a modified Lyapunov optimization technique, an online algorithm with random inputs (e.g., the charging/discharging of energy storage devices, power from the CHP system, the electricity from external power grid, and the renewables generation, etc.), which requires no statistic system information, is proposed. We provide an implementation of the proposed energy management algorithm and prove its optimality theoretically. Based on real-world data traces, extensive empirical evaluations are presented to verify the performance of our algorithm

MCDM-ECP: Multi Criteria Decision Making Method for Emergency Communication Protocol in Disaster Area Wireless Network

Disaster Area Wireless Networks (DAWNs) are widely deployed in natural or man-made disaster scenes, since the communication infrastructure may be completely destroyed by the disaster. This paper proposes a hybrid network architecture for DAWNs due to the mobility of first responders and refugees. Based on the link characterization of DAWNs, we choose four essential criteria and propose a multi-criteria decision-making method for emergency communication protocol (MCDM-ECP), which utilizes the analytic hierarchy process (AHP) method and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method to find the optimal next-hop node in DAWNs. Routing discovery and routing maintenance processes are included in the novel protocol. The simulation results show that MCDM-ECP performs better than other classical protocols both in energy consumption and packet received rate (PRR) for long-term emergency communications

The formation and viscoelasticity of pore-throat scale emulsion in porous media

The emulsion process in reservoirs was simulated by core displacement experiment, and the formation mechanisms of emulsion were studied by visualization core displacement experiment. The particle size distribution of formed emulsion at different transport distances, emulsifier concentrations or injection rates was measured, and the condition of forming stable emulsion of pore-throat scale were analyzed. By measuring the viscosity, the storage modulus and the loss modulus of the formed emulsion, viscoelasticity of emulsion was studied. The study shows that the formation mechanisms are mainly the snapping action of residual oil and the shearing action of emulsifier solution. When the migration distance is greater than 1/3 times the injector-producer spacing, the emulsifier concentration is between 0.4% and 0.5% and the injection rate is between 0.3 mL/min and 0.4 mL/min, the pore-throat scale emulsion with favorably stability can be formed. The viscosity is between 48.6 mP·s and 70.3 mP·s when the shear rate is 7.34 s−1 and the emulsifier concentration is between 0.4% and 0.5%. The storage modulus and loss modulus of emulsion increase with the emulsifier concentration increasing, and the viscoelasticity of emulsion is more favorably. Key words: enhanced oil recovery, emulsifier, emulsion, forming condition, particle size, viscoelasticit