Toward a high-precision mass–energy test of the equivalence principle with atom interferometers

The equivalence principle (EP) is a basic assumption of the general relativity. The quantum test of the equivalence principle with atoms is an important way to examine the applicable scope of the current physical framework so as to discover new physics. Recently, we extended the traditional pure mass or energy tests of the equivalence principle to the joint test of mass–energy by atom interferometry (Zhou et al.,Phys.Rev.A 104,022822). The violation parameter of mass is constrained to η0 = (−0.8 ± 1.4) × 10–10 and that of internal energy to ηE = (0.0 ± 0.4) × 10–10 per reduced energy ratio. Here, we first briefly outline the joint test idea and experimental results, and then, we analyze and discuss how to improve the test accuracy. Finally, we report the latest experimental progress toward a high-precision mass–energy test of the equivalence principle. We realize atom interference fringes of 2T = 2.6 s in the 10-m long-baseline atom interferometer. This free evolution time T, to the best of our knowledge, is the longest duration realized in the laboratory, and the corresponding resolution of gravity measurement is 4.5 × 10−11 g per shot

Management of granulomatous lobular mastitis: an international multidisciplinary consensus (2021 edition)

Granulomatous lobular mastitis (GLM) is a rare and chronic benign inflammatory disease of the breast. Difficulties exist in the management of GLM for many front-line surgeons and medical specialists who care for patients with inflammatory disorders of the breast. This consensus is summarized to establish evidence-based recommendations for the management of GLM. Literature was reviewed using PubMed from January 1, 1971 to July 31, 2020. Sixty-six international experienced multidisciplinary experts from 11 countries or regions were invited to review the evidence. Levels of evidence were determined using the American College of Physicians grading system, and recommendations were discussed until consensus. Experts discussed and concluded 30 recommendations on historical definitions, etiology and predisposing factors, diagnosis criteria, treatment, clinical stages, relapse and recurrence of GLM. GLM was recommended as a widely accepted definition. In addition, this consensus introduced a new clinical stages and management algorithm for GLM to provide individual treatment strategies. In conclusion, diagnosis of GLM depends on a combination of history, clinical manifestations, imaging examinations, laboratory examinations and pathology. The approach to treatment of GLM should be applied according to the different clinical stage of GLM. This evidence-based consensus would be valuable to assist front-line surgeons and medical specialists in the optimal management of GLM.Improving the Ability of Diagnosis and Treatment of Difficult Disease

Self-Organizing Adaptive Wavelet Backstepping Control Research for AC Servo System

A novel self-organizing adaptive wavelet cerebellar model articulation controller backstepping (SOWCB) control is proposed, aiming at some nonlinear and uncertain factors that caused difficulties in controlling the AC servo system. This controller consists of self-organizing wavelet cerebellar model articulation controller (CMAC) and robust compensator. It absorbs fast learning and precise approaching advantage of self-organizing wavelet CMAC to mimic a backstepping controller, and then robust compensator is added to inhibit influence of the uncertainties on system performance effectively and realize high accuracy position tracking for AC servo system. Moreover, the stability of the control system can be guaranteed by using Lyapunov method. The results of the simulation and the prototype test prove that the proposed approach can improve the steady state performance and control accuracy and possess a strong robustness to both parameter perturbation and load disturbance

A Hybrid Wavelet Fuzzy Neural Network and Switching Particle Swarm Optimization Algorithm for AC Servo System

A hybrid computational intelligent approach which combines wavelet fuzzy neural network (WFNN) with switching particle swarm optimization (SPSO) algorithm is proposed to control the nonlinearity, wide variation in loads, time variation, and uncertain disturbance of the high-power AC servo system. The WFNN method integrated wavelet transforms with fuzzy rules and is proposed to achieve precise positioning control of the AC servo system. As the WFNN controller, the back-propagation method is used for the online learning algorithm. Moreover, the SPSO is proposed to adapt the learning rates of the WFNN online, where the velocity updating equation is according to a Markov chain, which makes it easy to jump the local minimum, and acceleration coefficients are dependent on mode switching. Furthermore, the stability of the closed loop system is guaranteed by using the Lyapunov method. The results of the simulation and the prototype test prove that the proposed approach can improve the steady-state performance and possess strong robustness to both parameter perturbation and load disturbance

Improved Finite Control Set Optimal Control for PMSM in Rocket Launcher Servo System

Permanent magnet synchronous motors (PMSMs) have been widely applied in the shipborne rocket launcher systems due to their high torque density and high efficiency. However, since there are many external disturbances from the shipborne rocket launcher, the tracking performance under random noises needs to be improved. In this paper, an improved finite control set optimal control (IFCSOC) based on a super-twisting extended state observer (SESO) is investigated for position tracking control of PMSMs. The SESO can improve the anti-interference ability of the proposed controller. Moreover, in order to improve tracking accuracy, Taylor’s formula is used to solve the phase-lag problem of nonlinear tracking differentiator in IFCSOC. Simulation shows that compared with conventional FCSOC, IFCSOC exhibits better robustness under random disturbances. Furthermore, the semiphysical experiment is conducted to verify the proposed IFCSOC strategy

Application of a Self-recurrent Wavelet Neural Network in the Modeling and Control of an AC Servo System

To control the nonlinearity, widespread variations in loads and time varying characteristic of the high power ac servo system, the modeling and control techniques are studied here. A self-recurrent wavelet neural network (SRWNN) modeling scheme is proposed, which successfully addresses the issue of the traditional wavelet neural network easily falling into local optimum, and significantly improves the network approximation capability and convergence rate. The control scheme of a SRWNN based on fuzzy compensation is expected. Gradient information is provided in real time for the controller by using a SRWNN identifier, so as to ensure that the learning and adjusting function of the controller of the SRWNN operate well, and fuzzy compensation control is applied to improve rapidity and accuracy of the entire system. Then the Lyapunov function is utilized to judge the stability of the system. The experimental analysis and comparisons with other modeling and control methods, it is clearly shown that the validities of the proposed modeling scheme and control scheme are effective