Skyrmion defects and competing singlet orders in a half-filled antiferromagnetic Kondo-Heisenberg model on the honeycomb lattice

Due to the interaction between topological defects of an order parameter and underlying fermions, the defects can possess induced fermion numbers, leading to several exotic phenomena of fundamental importance to both condensed matter and high energy physics. One of the intriguing outcome of induced fermion number is the presence of fluctuating competing orders inside the core of topological defect. In this regard, the interaction between fermions and skyrmion excitations of antiferromagnetic phase can have important consequence for understanding the global phase diagrams of many condensed matter systems where antiferromagnetism and several singlet orders compete. We critically investigate the relation between fluctuating competing orders and skyrmion excitations of the antiferromagnetic insulating phase of a half-filled Kondo-Heisenberg model on honeycomb lattice. By combining analytical and numerical methods we obtain exact eigenstates of underlying Dirac fermions in the presence of a single skyrmion configuration, which are used for computing induced chiral charge. Additionally, by employing this nonperturbative eigenbasis we calculate the susceptibilities of different translational symmetry breaking charge, bond and current density wave orders and translational symmetry preserving Kondo singlet formation. Based on the computed susceptibilities we establish spin Peierls and Kondo singlets as dominant competing orders of antiferromagnetism. We show favorable agreement between our findings and field theoretic predictions based on perturbative gradient expansion scheme which crucially relies on adiabatic principle and plane wave eigenstates for Dirac fermions. The methodology developed here can be applied to many other correlated systems supporting competition between spin-triplet and spin-singlet orders in both lower and higher spatial dimensions.Comment: 15 pages, 11 figure

Numerical calculation and finite element calculation on impeller of stainless steel multistage centrifugal pump

In order to save energy and materials, some mechanical structures are very thin. Aiming to study the influence of impeller blade thickness on the performance of stainless steel multistage centrifugal pump, the whole flow field of stainless steel multistage centrifugal pump with different blade thickness were calculated based on ANSYS Fluent. The relationship between the impeller blade thickness and the overall performance of the pump was analyzed. To further study the reliability of the impeller structure for stainless steel multistage centrifugal pump, based on the ANSYS Workbench, the final stage impeller of the pump with different blade thickness were calculated by using the finite element method. Results indicate that with the increase of blade thickness, the maximum stress and deformation of the impeller gradually decreased, while the stability of the impeller structure increased

DescFold: A web server for protein fold recognition

<p>Abstract</p> <p>Background</p> <p>Machine learning-based methods have been proven to be powerful in developing new fold recognition tools. In our previous work [Zhang, Kochhar and Grigorov (2005) <it>Protein Science</it>, <b>14</b>: 431-444], a machine learning-based method called DescFold was established by using Support Vector Machines (SVMs) to combine the following four descriptors: a profile-sequence-alignment-based descriptor using Psi-blast <it>e</it>-values and bit scores, a sequence-profile-alignment-based descriptor using Rps-blast <it>e</it>-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. In this work, we focus on the improvement of DescFold by incorporating more powerful descriptors and setting up a user-friendly web server.</p> <p>Results</p> <p>In seeking more powerful descriptors, the profile-profile alignment score generated from the COMPASS algorithm was first considered as a new descriptor (i.e., PPA). When considering a profile-profile alignment between two proteins in the context of fold recognition, one protein is regarded as a template (i.e., its 3D structure is known). Instead of a sequence profile derived from a Psi-blast search, a structure-seeded profile for the template protein was generated by searching its structural neighbors with the assistance of the TM-align structural alignment algorithm. Moreover, the COMPASS algorithm was used again to derive a profile-structural-profile-alignment-based descriptor (i.e., PSPA). We trained and tested the new DescFold in a total of 1,835 highly diverse proteins extracted from the SCOP 1.73 version. When the PPA and PSPA descriptors were introduced, the new DescFold boosts the performance of fold recognition substantially. Using the SCOP_1.73_40% dataset as the fold library, the DescFold web server based on the trained SVM models was further constructed. To provide a large-scale test for the new DescFold, a stringent test set of 1,866 proteins were selected from the SCOP 1.75 version. At a less than 5% false positive rate control, the new DescFold is able to correctly recognize structural homologs at the fold level for nearly 46% test proteins. Additionally, we also benchmarked the DescFold method against several well-established fold recognition algorithms through the LiveBench targets and Lindahl dataset.</p> <p>Conclusions</p> <p>The new DescFold method was intensively benchmarked to have very competitive performance compared with some well-established fold recognition methods, suggesting that it can serve as a useful tool to assist in template-based protein structure prediction. The DescFold server is freely accessible at <url>http://202.112.170.199/DescFold/index.html</url>.</p

Improved PSO algorithm based on chaos theory and its application to design flood hydrograph

AbstractThe deficiencies of basic particle swarm optimization (bPSO) are its ubiquitous prematurity and its inability to seek the global optimal solution when optimizing complex high-dimensional functions. To overcome such deficiencies, the chaos-PSO (COSPSO) algorithm was established by introducing the chaos optimization mechanism and a global particle stagnation-disturbance strategy into bPSO. In the improved algorithm, chaotic movement was adopted for the particles' initial movement trajectories to replace the former stochastic movement, and the chaos factor was used to guide the particles' path. When the global particles were stagnant, the disturbance strategy was used to keep the particles in motion. Five benchmark optimizations were introduced to test COSPSO, and they proved that COSPSO can remarkably improve efficiency in optimizing complex functions. Finally, a case study of COSPSO in calculating design flood hydrographs demonstrated the applicability of the improved algorithm

TIM-Finder: A new method for identifying TIM-barrel proteins

<p>Abstract</p> <p>Background</p> <p>The triosephosphate isomerase (TIM)-barrel fold occurs frequently in the proteomes of different organisms, and the known TIM-barrel proteins have been found to play diverse functional roles. To accelerate the exploration of the sequence-structure protein landscape in the TIM-barrel fold, a computational tool that allows sensitive detection of TIM-barrel proteins is required.</p> <p>Results</p> <p>To develop a new TIM-barrel protein identification method in this work, we consider three descriptors: a sequence-alignment-based descriptor using PSI-BLAST e-values and bit scores, a descriptor based on secondary structure element alignment (SSEA), and a descriptor based on the occurrence of PROSITE functional motifs. With the assistance of Support Vector Machine (SVM), the three descriptors were combined to obtain a new method with improved performance, which we call TIM-Finder. When tested on the whole proteome of <it>Bacillus subtilis</it>, TIM-Finder is able to detect 194 TIM-barrel proteins at a 99% confidence level, outperforming the PSI-BLAST search as well as one existing fold recognition method.</p> <p>Conclusions</p> <p>TIM-Finder can serve as a competitive tool for proteome-wide TIM-barrel protein identification. The TIM-Finder web server is freely accessible at <url>http://202.112.170.199/TIM-Finder/</url>.</p