A novel explicit design method for complex thin-walled structures based on embedded solid moving morphable components

In this article, a novel explicit approach for designing complex thin-walled structures based on the Moving Morphable Component (MMC) method is proposed, which provides a unified framework to systematically address various design issues, including topology optimization, reinforced-rib layout optimization, and sandwich structure design problems. The complexity of thin-walled structures mainly comes from flexible geometries and the variation of thickness. On the one hand, the geometric complexity of thin-walled structures leads to the difficulty in automatically describing material distribution (e.g., reinforced ribs). On the other hand, thin-walled structures with different thicknesses require various hypotheses (e.g., Kirchhoff-Love shell theory and Reissner-Mindlin shell theory) to ensure the precision of structural responses. Whereas for cases that do not fit the shell hypothesis, the precision loss of response solutions is nonnegligible in the optimization process since the accumulation of errors will cause entirely different designs. Hence, the current article proposes a novel embedded solid component to tackle these challenges. The geometric constraints that make the components fit to the curved thin-walled structure are whereby satisfied. Compared with traditional strategies, the proposed method is free from the limit of shell assumptions of structural analysis and can achieve optimized designs with clear load transmission paths at the cost of few design variables and degrees of freedom for finite element analysis (FEA). Finally, we apply the proposed method to several representative examples to demonstrate its effectiveness, efficiency, versatility, and potential to handle complex industrial structures

Knowledge-driven Meta-learning for CSI Feedback

Accurate and effective channel state information (CSI) feedback is a key technology for massive multiple-input and multiple-output systems. Recently, deep learning (DL) has been introduced for CSI feedback enhancement through massive collected training data and lengthy training time, which is quite costly and impractical for realistic deployment. In this article, a knowledge-driven meta-learning approach is proposed, where the DL model initialized by the meta model obtained from meta training phase is able to achieve rapid convergence when facing a new scenario during target retraining phase. Specifically, instead of training with massive data collected from various scenarios, the meta task environment is constructed based on the intrinsic knowledge of spatial-frequency characteristics of CSI for meta training. Moreover, the target task dataset is also augmented by exploiting the knowledge of statistical characteristics of wireless channel, so that the DL model can achieve higher performance with small actually collected dataset and short training time. In addition, we provide analyses of rationale for the improvement yielded by the knowledge in both phases. Simulation results demonstrate the superiority of the proposed approach from the perspective of feedback performance and convergence speed.Comment: arXiv admin note: text overlap with arXiv:2301.1347

Enhanced Breast Cancer Therapy with nsPEFs and Low Concentrations of Gemcitabine

Chemotherapy either before or after surgery is a common breast cancer treatment. Long-term, high dose treatments with chemotherapeutic drugs often result in undesirable side effects, frequent recurrences and resistances to therapy. The anti-cancer drug, gemcitabine (GEM) was used in combination with pulse power technology with nanosecond pulsed electric fields (nsPEFs) for treatment of human breast cancer cells in vitro. Two strategies include sensitizing mammary tumor cells with GEM before nsPEF treatment or sensitizing cells with nsPEFs before GEM treatment.Breast cancer cell lines MCF-7 and MDA-MB-231 were treated with 250 65 ns-duration pulses and electric fields of 15, 20 or 25 kV/cm before or after treatment with 0.38 μM GEM. Both cell lines exhibited robust synergism for loss of cell viability 24 h and 48 h after treatment; treatment with GEM before nsPEFs was the preferred order. In clonogenic assays, only MDA-MB-231 cells showed synergism; again GEM before nsPEFs was the preferred order. In apoptosis/necrosis assays with Annexin-V-FITC/propidium iodide 2 h after treatment, both cell lines exhibited apoptosis as a major cell death mechanism, but only MDA-MB-231 cells exhibited modest synergism. However, unlike viability assays, nsPEF treatment before GEM was preferred. MDA-MB-231 cells exhibited much greater levels of necrosis then in MCF-7 cells, which were very low. Synergy was robust and greater when nsPEF treatment was before GEM. Combination treatments with low GEM concentrations and modest nsPEFs provide enhanced cytotoxicity in two breast cancer cell lines. The treatment order is flexible, although long-term survival and short-term cell death analyses indicated different treatment order preferences. Based on synergism, apoptosis mechanisms for both agents were more similar in MCF-7 than in MDA-MB-231 cells. In contrast, necrosis mechanisms for the two agents were distinctly different in MDA-MB-231, but too low to reliably evaluate in MCF-7 cells. While disease mechanisms in the two cell lines are different based on the differential synergistic response to treatments, combination treatment with GEM and nsPEFs should provide an advantageous therapy for breast cancer ablation in vivo

A quantum circuit simulator and its applications on Sunway TaihuLight supercomputer

Classical simulation of quantum computation is vital for verifying quantum devices and assessing quantum algorithms. We present a new quantum circuit simulator developed on the Sunway TaihuLight supercomputer. Compared with other simulators, the present one is distinguished in two aspects. First, our simulator is more versatile. The simulator consists of three mutually independent parts to compute the full, partial and single amplitudes of a quantum state with different methods. It has the function of emulating the effect of noise and support more kinds of quantum operations. Second, our simulator is of high efficiency. The simulator is designed in a two-level parallel structure to be implemented efficiently on the distributed many-core Sunway TaihuLight supercomputer. Random quantum circuits can be simulated with 40, 75 and 200 qubits on the full, partial and single amplitude, respectively. As illustrative applications of the simulator, we present a quantum fast Poisson solver and an algorithm for quantum arithmetic of evaluating transcendental functions. Our simulator is expected to have broader applications in developing quantum algorithms in various fields.Comment: 21 pages, 9 figure

Patterns and drivers of tree species diversity in a coniferous forest of northwest China

IntroductionUnderstanding the pattern of species diversity and underlying ecological determinants driving a forest ecosystem is fundamental to conservation biology and forest management. Boreal forests play an irreplaceable role in providing ecosystem services and maintaining the carbon cycle globally, yet research attention remains disproportionately limited and lacking throughout time.MethodsBased on field measurement data from a large (25 ha) fully-mapped coniferous forest plot, the present study quantified patterns of species diversity and their determinants in Kanas of Xinjiang, northwest China. We applied linear regression analysis to test the effects of biotic and soil factors on alpha-diversity and local contribution of beta diversity (LCBD), and then we adopted path analysis to test the determinants that affected the species diversity index.Results and discussionOur results revealed that alpha-diversity indices did not vary greatly across different subplots, and richness value (between 2 and 6) was low in Kanas. Noteworthy is the discerned negative association between the average diameter at breast height (DBH) and species richness, suggesting that areas with smaller DBH values tend to harbor greater species richness. For beta-diversity, a higher value was observed in the substory layer (0.221) compared to both the canopy layer (0.161) and the understory layer (0.158). We also found that the species abundance distance matrix of biological and soil environmental factors were significantly correlated with species geographic distance matrices. More importantly, our results showed that average DBH and soil pH would affect the alpha diversity indices, and average DBH, soil Ph, average height and soil total Phosphorous would affect the beta diversity indices. Soil pH also indirectly affected the LCBDunder, LCBDsub, and LCBDcan (p ≤ 0.001), upon mediation of alpha diversity indices. Overall, our results provide crucial revelations about species diversity patterns in boreal forests, and insights that can support the protection of forest biodiversity in China

Systematic Theoretical Analysis of Dual-Parameters R

This paper systematically studied the simultaneous measurement of two parameters by a LC-type passive sensor from the theoretical perspective. Based on the lumped circuit model of the typical LC-type passive dual-parameter sensor system, the influencing factors of the signal strength of the sensor as well as the influencing factors of signal crosstalk were both analyzed. It is found that the influencing factors of the RF readout signal strength of the sensor are mainly quality factors (Q factors) of the LC tanks, coupling coefficients, and the resonant frequency interval of the two LC tanks. And the influencing factors of the signal crosstalk are mainly coupling coefficient between the sensor inductance coils and the resonant frequency interval of the two LC tanks. The specific influence behavior of corresponding influencing factors on the signal strength and crosstalk is illustrated by a series of curves from numerical results simulated by using MATLAB software. Additionally, a decoupling scheme for solving the crosstalk problem algorithmically was proposed and a corresponding function was derived out. Overall, the theoretical analysis conducted in this work can provide design guidelines for making the dual-parameter LC-type passive sensor useful in practical applications

RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition

Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system