Anatomy of the tthhtthh Physics at HL-LHC

The $tthh$ production at colliders contain rich information on the nature of Higgs boson. In this article, we systematically studied its physics at High-Luminosity Large Hadron Collider (HL-LHC), using exclusive channels with multiple ($\geq 5$) $b$-jets and one lepton ($5b1\ell$), multiple ($\geq 5$) $b$-jets and opposite-sign di-lepton ($5b2\ell$), same-sign di-lepton (SS2$\ell$), multiple leptons (multi-$\ell$), and di-tau resonance ($\tau\tau$). The scenarios analyzed include: (1) the $tthh$ production in Standard Model; (2) the $tthh$ production mediated by anomalous cubic Higgs self-coupling and $tthh$ contact interaction; (3) heavy Higgs ($H$) production with $tt H \to tthh$; and (4) pair production of fermionic top partners ($T$) with $T T \to tthh$. To address the complication of event topologies and the mess of combinatorial backgrounds, a tool of Boosted-Decision-Tree was applied in the analyses. The $5b1\ell$ and SS2$\ell$ analyses define the two most promising channels, resulting in slightly different sensitivities. For non-resonant $tthh$ production, a combination of these exclusive analyses allows for its measurment in the SM with a statistical significance $\sim 0.9\sigma$ (with $S/B > 1 \%$), and may assist partially breaking the sensitivity degeneracy w.r.t. the cubic Higgs self-coupling, a difficulty usually thought to exist in gluon fusion di-Higgs analysis at HL-LHC. These sensitivities were also projected to future hadron colliders at 27 TeV and 100 TeV. For resonant $tthh$ productions, the heavy Higgs boson in type II Two-Higgs-Doublet-Model could be efficiently searched for between the mass thresholds $2 m_h < m_H < 2 m_t$ and even beyond that, for relatively small $\tan\beta$, while the fermionic top partners in composite Higgs models could be probed for up to $\sim 1.5$ TeV and $\sim 1.7$ TeV, for Br$(T\to th)=25\%$ and $50\%$, respectively.Comment: 30 pages, 12 figure

An investigation into minimising total energy consumption and total completion time in a flexible job shop for recycling carbon fiber reinforced polymer

The increased use of carbon fiber reinforced polymer (CFRP) in industry coupled with European Union restrictions on landfill disposal has resulted in a need to develop relevant recycling technologies. Several methods, such as mechanical grinding, thermolysis and solvolysis, have been tried to recover the carbon fibers. Optimisation techniques for reducing energy consumed by above processes have also been developed. However, the energy efficiency of recycling CFRP at the workshop level has never been considered before. An approach to incorporate energy reduction into consideration while making the scheduling plans for a CFRP recycling workshop is presented in this paper. This research sets in a flexible job shop circumstance, model for the bi-objective problem that minimise total processing energy consumption and makespan is developed. A modified Genetic Algorithm for solving the raw material lot splitting problem is developed. A case study of the lot sizing problem in the flexible job shop for recycling CFRP is presented to show how scheduling plans affect energy consumption, and to prove the feasibility of the model and the developed algorithm

Additive Sweeping Preconditioner for the Helmholtz Equation

We introduce a new additive sweeping preconditioner for the Helmholtz equation based on the perfect matched layer (PML). This method divides the domain of interest into thin layers and proposes a new transmission condition between the subdomains where the emphasis is on the boundary values of the intermediate waves. This approach can be viewed as an effective approximation of an additive decomposition of the solution operator. When combined with the standard GMRES solver, the iteration number is essentially independent of the frequency. Several numerical examples are tested to show the efficiency of this new approach.Comment: 27 page

Learning Gaussian Graphical Models with Observed or Latent FVSs

Gaussian Graphical Models (GGMs) or Gauss Markov random fields are widely used in many applications, and the trade-off between the modeling capacity and the efficiency of learning and inference has been an important research problem. In this paper, we study the family of GGMs with small feedback vertex sets (FVSs), where an FVS is a set of nodes whose removal breaks all the cycles. Exact inference such as computing the marginal distributions and the partition function has complexity $O(k^{2}n)$ using message-passing algorithms, where k is the size of the FVS, and n is the total number of nodes. We propose efficient structure learning algorithms for two cases: 1) All nodes are observed, which is useful in modeling social or flight networks where the FVS nodes often correspond to a small number of high-degree nodes, or hubs, while the rest of the networks is modeled by a tree. Regardless of the maximum degree, without knowing the full graph structure, we can exactly compute the maximum likelihood estimate in $O(kn^2+n^2\log n)$ if the FVS is known or in polynomial time if the FVS is unknown but has bounded size. 2) The FVS nodes are latent variables, where structure learning is equivalent to decomposing a inverse covariance matrix (exactly or approximately) into the sum of a tree-structured matrix and a low-rank matrix. By incorporating efficient inference into the learning steps, we can obtain a learning algorithm using alternating low-rank correction with complexity $O(kn^{2}+n^{2}\log n)$ per iteration. We also perform experiments using both synthetic data as well as real data of flight delays to demonstrate the modeling capacity with FVSs of various sizes

Glucocorticoids Inhibit Sodium Depletion-induced Salt Appetite in Rat

Glucocorticoids, produced in adrenal cortex, exert potent natriuretic and diuretic actions in the kidney. Recently, it has been found that glucocorticoids could upregulate the expression of natriuretic peptide receptor A (NPR-A), the primary receptor of atrial natriuretic peptide, in the hypothalamus of the rat. Consequently, systemic administration of glucocorticoid could block dehydration-induced water intake by activation hypothalamic NPR-A. We describe here glucocorticoids could inhibit sodium intake when administrated systemically in conscious, salt-depleted rats; an effect which was strong and long-lasting. The study provided further evidence for the actions of glucocorticoids on central nervous system, which together with their established renal actions coordinated to normalize extracellular fluid volume