Charge-Density-Wave Transitions of Dirac Fermions Coupled to Phonons

The spontaneous generation of charge-density-wave order in a Dirac fermion system via the natural mechanism of electron-phonon coupling is studied in the framework of the Holstein model on the honeycomb lattice. Using two independent and unbiased quantum Monte Carlo methods, the phase diagram as a function of temperature and coupling strength is determined. It features a quantum critical point as well as a line of thermal critical points. Finite-size scaling appears consistent with fermionic Gross-Neveu-Ising universality for the quantum phase transition, and bosonic Ising universality for the thermal phase transition. The critical temperature has a maximum at intermediate couplings. Our findings motivate experimental efforts to identify or engineer Dirac systems with sufficiently strong and tunable electron-phonon coupling.Comment: 4+3 pages, 4+2 figure

Holographic Schwinger effect in a confining D3-brane background with chemical potential

Using the AdS/CFT correspondence, we investigate the Schwinger effect in a confining D3-brane background with chemical potential. The potential between a test particle pair on the D3-brane in an external electric field is obtained. The critical field $E_c$ in this case is calculated. Also, we apply numerical method to evaluate the production rate for various cases. The results imply that the presence of chemical potential tends to suppress the pair production effect.Comment: 7 pages, 4 figure

Kondo regime of the impurity spectral function and the current noise spectrum in the double impurity Anderson model

The dissipaton equations of motion (DEOM) method is one of the most popular methods for simulating quantum impurity systems. In this article, we use DOEM theory to deal with the Kondo problem of the double quantum dots (DQDs) impurity system. We focus on the impurity spectral function and the total noise spectral function, this two function will be used to describe the Kondo effect of this system. The influence of the interaction, the hooping and the difference of the chemical potential between the two dots on the Kondo effect of the system is studied. We find that the interaction between the two dots can influence the Kondo effect of the system a lot

Cesium Removal from High Level Liquid Waste Utilizing a Macroporous Silica-based Calix[4]arene-R14 Adsorbent Modified with Surfactants

Abstract1,3-[(2,4-diethylheptylethoxy)oxy]-2,4-crown-6-Calix[4]arene(Calix[4]arene-R14) modified with dodecanol and dodecyl benzenesulfonic acid (DBS) was impregnated into the pores of macroporous silica-based polymer support (SiO2-P). The adsorbent was used to uptake Cs(I), Na(I), K(I), Sr(II), Pd(II), Ru(III), Y(III), La(III), Eu(III), Ce(III), Rh(III), Zr(IV), and Mo(VI) from HNO3 solution by batch technique. The leakage of total organic carbon (TOC) and dodecyl benzenesulfonic acid from the adsorbent into aqueous phase were below 60ppm and 0.51 wt% at 298K, 75ppm and 1 wt% at 318K in the range of 0.5 ∼ 5M HNO3, respectively. The adsorbent containing DBS presented a higher selectivity for Cs(I) compared to the DBS-free one. The Kd value of Cs(I) was about 3×103cm3/g at 0.5M HNO3. The adsorbent had almost no uptake for other tested metals in the range of 0.5 ∼ 7M HNO3

Elevated MTSS1 Expression Associated with Metastasis and Poor Prognosis of Residual Hepatitis B-Related Hepatocellular Carcinoma

Background: Hepatectomy generally offers the best chance of long-term survival for patients with hepatocellular carcinoma (HCC). Many studies have shown that hepatectomy accelerates tumor metastasis, but the mechanism remains unclear. Methods: An orthotopic nude mice model with palliative HCC hepatectomy was performed in this study. Metastasis-related genes in tumor following resection were screened; HCC invasion, metastasis, and some molecular alterations were examined in vivo and in vitro. Clinical significance of key gene mRNA expression was also analyzed

Palm tree detection in UAV images: a hybrid approach based on multimodal particle swarm optimisation

In recent years, there has been a surge of interest in palm tree detection using unmanned aerial vehicle (UAV) images, with implications for sustainability, productivity, and profitability. Similar to other object detection problems in the field of computer vision, palm tree detection typically involves classifying palm trees from non-palm tree objects or background and localising every palm tree instance in an image. Palm tree detection in large-scale high-resolution UAV images is challenging due to the large number of pixels that need to be visited by the object detector, which is computationally costly. In this thesis, we design a novel hybrid approach based on multimodal particle swarm optimisation (MPSO) algorithm that can speed up the localisation process whilst maintaining optimal accuracy for palm tree detection in UAV images. The proposed method uses a feature-extraction-based classifier as the MPSO's objective function to seek multiple positions and scales in an image that maximise the detection score. The feature-extraction-based classifier was carefully selected through empirical study and was proven seven times faster than the state-of-the-art convolutional neural network (CNN) with comparable accuracy. The research goes on with the development of a new k-d tree-structured MPSO algorithm, which is called KDT-SPSO that significantly speeds up MPSO's nearest neighbour search by only exploring the subspaces that most likely contain the query point's neighbours. KDT-SPSO was demonstrated effective in solving multimodal benchmark functions and outperformed other competitors when applied on UAV images. Finally, we devise a new approach that utilises a 3D digital surface model (DSM) to generate high confidence proposals for KDT-SPSO and existing region-based CNN (R-CNN) for palm tree detection. The use of DSM as prior information about the number and location of palm trees reduces the search space within images and decreases overall computation time. Our hybrid approach can be executed in non-specialised hardware without long training hours, achieving similar accuracy as the state-of-the-art R-CNN

Symmetry Enforced Self-Learning Monte Carlo Method Applied to the Holstein Model

Self-learning Monte Carlo method (SLMC), using a trained effective model to guide Monte Carlo sampling processes, is a powerful general-purpose numerical method recently introduced to speed up simulations in (quantum) many-body systems. In this work, we further improve the efficiency of SLMC by enforcing physical symmetries on the effective model. We demonstrate its effectiveness in the Holstein Hamiltonian, one of the most fundamental many-body descriptions of electron-phonon coupling. Simulations of the Holstein model are notoriously difficult due to the combination of the typical cubic scaling of fermionic Monte Carlo and the presence of extremely long autocorrelation times. Our method addresses both bottlenecks. This enables simulations on large lattices in the most difficult parameter regions, and evaluation of the critical point for the charge density wave transition at half-filling with high precision. We argue that our work opens a new research area of quantum Monte Carlo (QMC), providing a general procedure to deal with ergodicity in situations involving Hamiltonians with multiple, distinct low energy states.Comment: 4 pages, 3 figures with 2 pages supplemental materia