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Disturbances acting on flexible structures at spatially varying locations instead of fixed points may lead to deteriorated vibration control performance. To tackle this problem, this article presents an optimal sensor/actuator placement method, in which the closed-loop spatial ℋ 2 norm is employed as the optimization criterion. In addition, a new way to calculate the spatial ℋ 2 norm is proposed, which is independent of the modal orthogonality assumption in previous research. An optimization framework is established to optimize sensor/actuator placement by minimizing the closed-loop spatial ℋ 2 norm using the genetic algorithm. Comprehensive numerical simulations are implemented on a fixed-fixed plate to validate the proposed method. Results show that magnitude of vibrations is reduced and decays faster after the optimization, which indicates that the proposed method markedly improves control performance when spatially varying disturbances exist. [ABSTRACT FROM AUTHOR]

By attending to the case of queer men's sexual use of Twitter in China, this article analyzes how mainstream platforms provide underrepresented groups with unique opportunities in the erotic economy. Drawing on in-depth interviews with fourteen queer male "sex influencers"—people who capitalize on their erotic practices on Twitter and other platforms for economic and social perks—and a three-month digital ethnography, we probe how Twitter shapes sex media production in China and opens up multifarious modes of monetization. We suggest that Chinese queer sex influencers' monetization practices not only construct vibrant sexual cultures, but also incentivize heightened cross-platform mobility, which serves as a strategy for coping with China's precarious legal environment. Consequently, there has been a convergence of domestic and transnational platforms, which we argue showcases the vitality and creativity of Chinese non-normative media production and consumption in a globalized and platformized age. [ABSTRACT FROM AUTHOR]

In China, HIV transmission increasingly affected men who have sex with men (MSM) and those in heterosexual marriages. This study addressed a critical gap in understanding HIV and sexual orientation disclosure within conventional heterosexual marriages, framed within a family-oriented cultural context. Heterosexually married MSM living with HIV (HIV+ MMSM) faced complex identity dynamics and interactions between "gay circles" and marital life. Such disclosure was fraught with potential repercussions, including accusations of "marriage fraud", HIV-related stigma, and divorce. This descriptive qualitative study, conducted in 2022, aimed to explore the common factors influencing HIV status disclosure to wives among Chinese HIV+ MMSM. Sixteen semi-structured interviews were conducted to analyze the factors related to the disclosure process model. Four key themes emerged: (a) concern for wives' well-being, (b) perceived marriage strength, (c) the degree of social support they had, and (d) a desire to maintain their status in a heterosexual society. The decision to (non)disclose was shaped by both approach-focused and avoidant-focused goals related to these four themes. The findings underscored the need for culturally sensitive strategies that balance family dynamics, stigma reduction, and HIV care management, emphasizing tailored approaches to effectively support this population. [ABSTRACT FROM AUTHOR]

For a class of nonlinear harmonic gear drive systems with mismatched uncertainties, a novel robust control method is presented on the basis of quadratic integral sliding mode surface, and the closed-loop system has satisfying performance and strong robustness against mismatched uncertainties and nonlinear disturbances. Considering time-varying nonlinear torques and parameters variations which are caused by nonlinear frictions and backlash, a nonlinear harmonic gear drive system mathematic model is established and the effect of nonlinear parts is compensated during control system design. It is proven that the quadratic integral sliding mode surface can be reached in finite time and the closed-loop system is asymptotic stable robustly. The simulation studies are carried out in comparison with traditional linear sliding mode control and integral sliding mode control, verifying the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Iterative learning control (ILC) has been widely used to improve motion performance when reference trajectories and external disturbances are strictly repetitive. However, the occurrence of non‐repetitive trajectories and disturbances would significantly deteriorate the performance of traditional ILC methods. To solve this problem, a time‐iteration‐domain integrated learning control (TIDLC) scheme is proposed for enhancing robustness against non‐repetitive trajectories and disturbances. The TIDLC scheme consists of an ILC term and a time‐domain compensator (TC) term. While the ILC term that performs learning in the iteration‐domain preserves excellent performance under repetitive tasks, the TC term that is updated in the time‐domain can compensate for the variations of trajectories and disturbances. Stability and performance analyses are discussed in both the time‐domain and iteration‐domain. Experimental results on a permanent magnet linear synchronous motor (PMLSM) positioning system verify the validity of the proposed scheme. [ABSTRACT FROM AUTHOR]

Global optimization has been a hot research topic in various engineering applications, where differential evolution (DE) is one of the most popular approaches. Actually, it is inevitable for DE to trap into local optima when dealing with complex optimization problems. Dynamic opposite learning (DOL), which is a new variant of opposition-based learning (OBL), has the potential for enhancing DE, due to its strong exploration capability contributed by the asymmetric and dynamic search space. To balance exploration and exploitation, an adjustable weight parameter of the search space is adopted, yet adjusting the value needs a lot of tests and expert experience. Instead of relying on the additional weight parameter, a mutual learning (ML) strategy, which leads individuals to learn from each other deterministically, is combined with DOL for raising exploitation. The trade-off between exploration and exploitation is guaranteed by randomly switching DOL and ML in the population initialization process and the generation jumping process. A hybrid strategy, named oppositional-mutual learning (OML), is thereby generated, and it is applied for the performance improvement of DE. Benchmarks from CEC 2014, including unimodal, multi-modal, hybrid and composition functions, were adopted to evaluate the performance of the oppositional-mutual learning DE (OMLDE). Numerical results with the comparisons to the state-of-the-art counterparts show that OMLDE has significant advantages of converging to the global optimum on most functions, which also validates the superiority of the novel OML strategy. [ABSTRACT FROM AUTHOR]

Aircraft manufacturers continue to use larger quantities of titanium components to increase strength and reduce weight. While various forming processes can be used, spin forming is particularly well suited for economically producing small and large quantities of axisymmetric parts. However, due to its limited formability at room temperature, titanium is typically warm formed. In the study, a new type of electro-assisted warm spin forming method based on the electroplasticity effect is presented. Experimental results show that electro-assisted forming technology can significantly improve forming quality of titanium parts. Advantages of the method include short forming times, uniform temperature distribution, simple operation, and convenient control. The influence of process parameters, including current intensity, feed rate, spindle speed, and lubrication, on the formability of commercial pure titanium sheets was systematically analyzed using an industrial spinning machine. It was found that deformation is mainly concentrated in the arc phase of the curved generatrix. Finally, components were subjected to uniaxial tensile stress and biaxial compressive stress based on previously defined forming limit curves. The technology is feasible and easy to control and has the potential for application to other rotary sheet forming technologies. [ABSTRACT FROM AUTHOR]

The uncertainties, force ripple, friction and other external disturbances in permanent magnet linear synchronous motors (PMLSMs) pose great challenges to the design of advanced controllers with high speed and high precision. Existing neural network based sliding mode control (NNSMC) methods typically adopt neural network as an online compensator for uncertainties and disturbances, meanwhile stability and robustness are assured owing to sliding mode control. However, PMLSMs usually need to perform repetitive motion tasks within a finite time interval in practical applications, where existing NNSMC methods ignore the previous repetitions' information, and consequently limit the motion performance improvement for trajectory tracking tasks. This paper proposes an iterative learning based neural network sliding mode control (ILNNSMC) scheme, in which the concept of iterative learning is incorporated into NNSMC methods for the first time. In ILNNSMC, the uncertainties and disturbances can be compensated by NN (updating in the time-domain) and ILC (learning in the iteration-domain) in a complementary way, and the sliding variable is driven to converge to zero for arbitrary time within the predesigned time interval with successive iterations. The motion performance for repetitive tasks can be improved substantively. Moreover, a non-linear dynamic learning gain (DLG) strategy is proposed and adopted in iterative learning law, which guarantees both convergence speed and steady-state accuracy in the learning process. Stability of the scheme is discussed in both time and iteration domains, and the control performance is verified by extensive experiments on a PMLSM. • Iterative learning is incorporated into neural network sliding mode control. • Cross-domain compensation is realized by neural network and iterative learning. • Dynamic learning gain trades off convergence speed and steady-state accuracy. • Stability is proved based on Lyapunov direct method in time and iteration domains. • Experimental results on a PMLSM verify the validity for repetitive tasks. [ABSTRACT FROM AUTHOR]