CFD Analysis and Experiment Study of the Rotary Two-Stage Inverter Compressor with Vapor Injection

The offset angle of the upper and lower part of the crankshaft will affect the resistance of inspiration of high stage cylinder in the rotary two-stage inverter compressor with vapor injection, and then affect the performance. this paper presents the performance of the rotary two-stage inverter compressor with vapor injection in the bias angle of the crankshaft is calculated and compared with the experimental. The simulation results are in agreement with the experimental results. Under the operation of close vapor injection and open vapor injection, the performance of compressor can be improved 1% and 3% separately by optimize the bial angle of crankshaft.

Robust interface between flying and topological qubits

Hybrid architectures, consisting of conventional and topological qubits, have recently attracted much attention due to their capability in consolidating the robustness of topological qubits and the universality of conventional qubits. However, these two kinds of qubits are normally constructed in significantly different energy scales, and thus this energy mismatch is a major obstacle for their coupling that supports the exchange of quantum information between them. Here, we propose a microwave photonic quantum bus for a direct strong coupling between the topological and conventional qubits, in which the energy mismatch is compensated by the external driving field via the fractional ac Josephson effect. In the framework of tight-binding simulation and perturbation theory, we show that the energy splitting of the topological qubits in a finite length nanowire is still robust against local perturbations, which is ensured not only by topology, but also by the particle-hole symmetry. Therefore, the present scheme realizes a robust interface between the flying and topological qubits. Finally, we demonstrate that this quantum bus can also be used to generate multipartitie entangled states with the topological qubits.Comment: Accepted for publication in Scientific Report

Graph Neural Networks for an Accurate and Interpretable Prediction of the Properties of Polycrystalline Materials

Various machine learning models have been used to predict the properties of polycrystalline materials, but none of them directly consider the physical interactions among neighboring grains despite such microscopic interactions critically determining macroscopic material properties. Here, we develop a graph neural network (GNN) model for obtaining an embedding of polycrystalline microstructure which incorporates not only the physical features of individual grains but also their interactions. The embedding is then linked to the target property using a feed-forward neural network. Using the magnetostriction of polycrystalline Tb0.3Dy0.7Fe2 alloys as an example, we show that a single GNN model with fixed network architecture and hyperparameters allows for a low prediction error of ~10% over a group of remarkably different microstructures as well as quantifying the importance of each feature in each grain of a microstructure to its magnetostriction. Such microstructure-graph-based GNN model therefore enables an accurate and interpretable prediction of the properties of polycrystalline materials.Comment: 28 pages, 6 figures

Molecular identification of endophytic fungi from Aquilaria sinensis and artificial agarwood induced by pinholes-infusion technique

Agarwood, the resinous portions of Aquilaria plants, have been used as medicines and incenses. Aquilaria sinensis is the major producer of agarwood in China. Agarwood are generally viewed as pathological products formed as defense symptom against fungal infection. In this study, microbial communities inhabiting the leaves of non-resinous and agarwood-producing wounded A. sinensis tree were investigated by cultivation-independent approaches, such as PCR, restriction fragment length polymorphism (RFLP) analysis and sequencing of rDNA internal transcribed spacer (ITS) library. Molecular phylogenetic analysis demonstrated that Botryosphaeria, Colletotrichum gloeosporioides, Phomopsis and Cylindrocladium species are members of the agarwood-producing wounded tree, while Phoma, Mycosphaerella, Sagenomella, Alternaria and Ramichloridium species is able to colonize the non-resinous tree internally. C. gloeosporioides was the only fungus shared by the two rDNA ITS libraries. C. gloeosporoides, Botryosphaeria, and Cylindrocladium were considered to be related to agarwood formation. A pinholes-infusion method to induce the generation of agarwood by chemically stimulated and/or inoculate combined method was established. One to two years after the artificial inoculation, resinous wood were collected and the inoculating effects were detected by ethanol extraction content, thin layer chromatography (TLC) and gas chromatography-mass spectrometry (GCMS) techniques. The results reveal that chemically stimulated with formic acid and infected by Botryosphaeria dothidea produced high yield and high quality artificial agarwood in a relatively short time.Keywords: Agarwood, endophytic fungi, Aquilaria sinensis, molecular identification, artificial induce of agarwoodAfrican Journal of Biotechnology Vol. 12(21), pp. 3115-313

Optimal Acceleration-Velocity-Bounded Trajectory Planning in Dynamic Crowd Simulation

Creating complex and realistic crowd behaviors, such as pedestrian navigation behavior with dynamic obstacles, is a difficult and time consuming task. In this paper, we study one special type of crowd which is composed of urgent individuals, normal individuals, and normal groups. We use three steps to construct the crowd simulation in dynamic environment. The first one is that the urgent individuals move forward along a given path around dynamic obstacles and other crowd members. An optimal acceleration-velocity-bounded trajectory planning method is utilized to model their behaviors, which ensures that the durations of the generated trajectories are minimal and the urgent individuals are collision-free with dynamic obstacles (e.g., dynamic vehicles). In the second step, a pushing model is adopted to simulate the interactions between urgent members and normal ones, which ensures that the computational cost of the optimal trajectory planning is acceptable. The third step is obligated to imitate the interactions among normal members using collision avoidance behavior and flocking behavior. Various simulation results demonstrate that these three steps give realistic crowd phenomenon just like the real world

Coherent Resonant Coupling between Atoms and a Mechanical Oscillator Mediated by Cavity-Vacuum Fluctuations

We show that an atom can be coupled to a mechanical oscillator via quantum vacuum fluctuations of a cavity field enabling energy transfer processes between them. In a hybrid quantum system consisting of a cavity resonator with a movable mirror and an atom, these processes are dominated by two pair-creation mechanisms: the counter-rotating (atom-cavity system) and dynamical Casimir interaction terms (optomechanical system). Because of these two pair-creation mechanisms, the resonant atom-mirror coupling is the result of high-order virtual processes with different transition paths well described in our theoretical framework. We perform a unitary transformation to the atom-mirror system Hamiltonian, exhibiting two kinds of multiple-order transitions of the pair creation. By tuning the frequency of the atom, we show that photon frequency conversion can be realized within a cavity of multiple modes. Furthermore, when involving two atoms coupled with the same mechanical mode, a single vibrating excitation of the mechanical oscillator can be simultaneously absorbed by the two atoms. Considering recent advances in strong and ultrastrong coupling for cavity optomechanics and other systems, we believe our proposals can be implemented using available technology.Comment: 18 pages, 13 figur

Graph Neural Network for Predicting the Effective Properties of Polycrystalline Materials: A Comprehensive Analysis

We develop a polycrystal graph neural network (PGNN) model for predicting the effective properties of polycrystalline materials, using the Li7La3Zr2O12 ceramic as an example. A large-scale dataset with >5000 different three-dimensional polycrystalline microstructures of finite-width grain boundary is generated by Voronoi tessellation and processing of the electron backscatter diffraction images. The effective ion conductivities and elastic stiffness coefficients of these microstructures are calculated by high-throughput physics-based simulations. The optimized PGNN model achieves a low error of <1.4% in predicting all three diagonal components of the effective Li-ion conductivity matrix, outperforming a linear regression model and two baseline convolutional neural network models. Sequential forward selection method is used to quantify the relative importance of selecting individual grain (boundary) features to improving the property prediction accuracy, through which both the critical and unwanted node (edge) feature can be determined. The extrapolation performance of the trained PGNN model is also investigated. The transfer learning performance is evaluated by using the PGNN model pretrained for predicting conductivities to predict the elastic properties of the same set of microstructures.Comment: 23 pages, 6 figures; added testing results on a new dataset and sequential feature selectio

Effects of blood flow restriction training on bone turnover markers, microstructure, and biomechanics in rats

ObjectiveThe present study aimed to investigate the effects of blood flow restriction training on muscle strength, bone tissue structure material, and biomechanical properties in rats applying various exercise interventions and to analyze the process by identifying the bone turnover markers, it provides a theoretical basis for the application of BFRT in clinical rehabilitation.MethodsA total of 24, 3-month-old male SD (Sprague Dawley) rats were randomly divided into pressurized control group (CON, n=6), low-intensity training group (LIRT, n=6), high-intensity training group (HIRT, n=6), and blood flow restriction training group (LIBFR, n=6) for 8-week ladder-climbing exercises. The pressured control group were given only ischemia treatments and did not undertake any burden. The low-intensity training group was allowed to climb the ladder with 30% of the maximum voluntary carrying capacity (MVCC). The rats in the high-intensity training group were allowed to climb the ladder with 70% MVCC. The blood flow restriction training group climbed the ladder with 30% MVCC while imposing blood flow restriction. Before sampling, the final MVCC was measured using a ladder-climbing protocol with progressively increasing weight loading. The serum, muscle, and bone were removed for sampling. The concentrations of the bone turnover markers PINP, BGP, and CTX in the serum were measured using ELISA. The bone mineral density and microstructure of femur bones were measured using micro-CT. Three-point bending and torsion tests were performed by a universal testing machine to measure the material mechanics and structural mechanics indexes of the femur bone.ResultsThe results of maximum strength test showed that the MVCC in LIRT, HIRT, and LIBFR groups was significantly greater than in the CON group, while the MVCC in the HIRT group was significantly higher than that in the LIRT group (P&lt;0.05). According to the results of the bone turnover marker test, the concentrations of bone formation indexes PINP (amino-terminal extension peptide of type I procollagen) and BGP (bone gla protein) were significantly lower in the CON group than in the HIRT group (P&lt;0.01), while those were significantly higher in the LIRT group compared to the HIRT group (P&lt;0.01). In terms of bone resorption indexes, significant differences were identified only between the HIRT and other groups (P&lt;0.05). The micro-CT examination revealed that the HIRT group had significantly greater bone density index values than the CON and LIRT groups (P&lt;0.05). The results of three-point bending and torsion test by the universal material testing machine showed that the elastic modulus and maximum load indexes of the HIRT group were significantly smaller than those of the LIBFR group (P&lt;0.05). The fracture load indexes in the HIRT group were significantly smaller than in the LIBFR group (P&lt;0.05).Conclusion1. LIRT, HIRT, LIBFR, and CON all have significant differences, and this training helps to improve maximum strength, with HIRT being the most effective. 2. Blood flow restriction training can improve the expression of bone turnover markers, such as PINP and BGP, which promote bone tissue formation. 3. Blood flow restriction training can improve muscle strength and increase the positive development of bone turnover markers, thereby improving bone biomechanical properties such as bone elastic modulus and maximum load

Multi-objective optimization based network control principles for identifying personalized drug targets with cancer

It is a big challenge to develop efficient models for identifying personalized drug targets (PDTs) from high-dimensional personalized genomic profile of individual patients. Recent structural network control principles have introduced a new approach to discover PDTs by selecting an optimal set of driver genes in personalized gene interaction network (PGIN). However, most of current methods only focus on controlling the system through a minimum driver-node set and ignore the existence of multiple candidate driver-node sets for therapeutic drug target identification in PGIN. Therefore, this paper proposed multi-objective optimization-based structural network control principles (MONCP) by considering minimum driver nodes and maximum prior-known drug-target information. To solve MONCP, a discrete multi-objective optimization problem is formulated with many constrained variables, and a novel evolutionary optimization model called LSCV-MCEA was developed by adapting a multi-tasking framework and a rankings-based fitness function method. With genomics data of patients with breast or lung cancer from The Cancer Genome Atlas database, the effectiveness of LSCV-MCEA was validated. The experimental results indicated that compared with other advanced methods, LSCV-MCEA can more effectively identify PDTs with the highest Area Under the Curve score for predicting clinically annotated combinatorial drugs. Meanwhile, LSCV-MCEA can more effectively solve MONCP than other evolutionary optimization methods in terms of algorithm convergence and diversity. Particularly, LSCV-MCEA can efficiently detect disease signals for individual patients with BRCA cancer. The study results show that multi-objective optimization can solve structural network control principles effectively and offer a new perspective for understanding tumor heterogeneity in cancer precision medicine.Comment: 15 pages, 8 figures; This work has been submitted to IEEE Transactions on Evolutionary Computatio