Abstract

Abstract
Many physical and chemical processes of interest evolve on timescales that push the limits of conventional spec troscopic instrumentation. Indeed, the temporal resolution of standard spectrometers is often insufficient to track these dynamics, which is connected to the fact that most systems rely on frame-based sensors, imposing funda mental constraints on acquisition speed, sensitivity, and data efficiency, frequently limiting practical operation to the kilohertz regime. In this work, we present an approach to circumvent this limitation by developing an event-based spectrometer to enable spectral reconstruction with microsecond temporal resolution by leveraging a Czerny-Turner configuration combined with asynchronous and event-driven sensing. A dedicated signal process ing pipeline converts the resulting stream of binary events into calibrated spectra through temporal accumulation, geometric correction, and vertical spatial integration of the spectral line, covering a 234 nm bandwidth in the visible range with an estimated spectral dispersion of approximately 0.18 nm per pixel. Performance characteri zation under temporally modulated illumination demonstrates that the event-based spectrometer can reconstruct spectra at probing rates of up to tens of kilohertz, far exceeding the practical limits of a conventional frame-based spectrometer operated in parallel, while accurately preserving spectral peak positions and relative spectral fea tures. Finally, to further illustrate its potential applications, the system is validated in a microfluidic experiment integrated into an inverted microscope, where spectral changes induced by an absorbing dye are tracked with higher temporal fidelity and resolution compared with the frame-based approach. These results establish event-based spectroscopy as a promising paradigm for real-time, high-temporal-resolution spectral measurements in dynamic and low-light applications.

Abstract

Abstract
Musculoskeletal disorders (MSDs) are a significant concern in occupational health, affecting workers across various industries and work environments. Effective recognition and management of MSDs are essential to improving workplace ergonomics and safeguarding workers' health. This study conducts a comprehensive review of existing research to compare and evaluate different machine learning (ML) algorithms for human posture identification, focusing on key performance metrics such as precision, speed, and relevance. It examines Convolutional Neural Networks (CNNs), Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and hybrid approaches that integrate multiple models. In addition to assessing their classification performance, the study highlights key challenges, including data dependency and the risk of overfitting. The findings indicate that both CNNs and ANNs achieve high classification accuracy for posture recognition, with hybrid models demonstrating even greater reliability. Furthermore, the evaluation considers the impact of various sensor technologies on systemperformance,emphasizing the necessity of incorporating high-precision sensors for improved accuracy. This review provides an analysis of the current state of ML-based posture detection models and their potential to enhance MSD prevention. By advancing posture detection technology, these insights contribute to the development of more effective solutions for mitigating MSD risks and promoting healthier workplace environments.

Abstract

Abstract
A superfluid flows without friction below a critical velocity, exhibiting zero drag force on impurities. Above this threshold, superfluidity breaks down, and the internal energy is redistributed into incoherent excitations such as vortices. We demonstrate that a mobile, finite-mass impurity immersed in a flowing two-dimensional paraxial superfluid of light can swim against the superfluid current when the critical velocity is exceeded. This self-propulsion is achieved by the periodic emission of vortex-antivortex pairs downstream, which impart an upstream recoil momentum that results in a net propulsive force. Analogous to biological systems that minimize effort by exploiting wake turbulence, the impurity harnesses this vortex backreaction as a passive mechanism of locomotion. Based on a simple theoretical model, we quantitatively describe how this mechanism depends on the impurity geometry and the surrounding flow velocity. Our findings establish a fundamental link between internal-energy dissipation in quantum fluids and concepts of self-propulsion in active-matter systems and open new possibilities for exploiting quantum vortices for controlled transport at the microscale.