Based on orbital angular momentum (OAM) properties of Laguerre–Gaussian beams LG($p,\ell$), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on an intensity profile generated by a coherent superposition of two OAM-carrying Laguerre–Gaussian modes and a machine learning detection method. In the encoding process, the intensity profile for data encoding is generated based on the selection of p and ℓ indices, while the decoding process is performed using a support vector machine (SVM) algorithm. Two different decoding models based on an SVM algorithm are tested to verify the robustness of the optical encoding model, finding a BER $={10}^{-9}$ for $10.2$ dB of signal-to-noise ratio in one of the SVM models.

Surface or edge states represent an important class of modes in various photonic crystal systems such as in dielectric topological insulators and in photonic crystal fibers. In the later, strong attenuation peaks in the transmission spectrum are attributed to coupling between surface and core-guided modes. Here, we explore a modified implementation of the spatial and spectral interference method to experimentally characterize surface modes in photonic crystal fibers. Using an external reference and a non-uniform Fourier transform windowing, the obtained spectrogram allows clear observation of anti-crossing behavior at wavelengths in which surface and core modes are strongly coupled. We also detect surface modes with different spatial symmetries, and give insight into mode families couple to the fundamental or high-order core modes, as well as the existence of uncoupled surface modes.

Teacher training in Physics often faces challenges related to engaging students and conveying abstract concepts effectively. Generative AI tools, such as ChatGPT, present transformative opportunities for designing innovative and tailored educational activities. This study investigates the impact of ChatGPT on pre-service Physics teacher training, focusing on its usability, effectiveness, and influence on participant satisfaction. Utilizing a quantitative research approach, two Likert-scale surveys were administered to 24 prospective Physics teachers in Ecuador, both before and after an intervention workshop. The workshop introduced participants to ChatGPT’s features and its applications in designing playful, Physics-focused learning activities. Results indicated a significant increase in familiarity with AI tools, enhanced activity design quality, and high satisfaction rates. Notably, $79\%$ of participants highlighted ChatGPT’s utility in adapting activities to diverse learning levels, and $83\%$ acknowledged its efficiency in reducing preparation time. These findings underscore ChatGPT’s potential to revolutionize Physics education by facilitating the creation of personalized and engaging learning resources. Future research should explore larger sample sizes and longitudinal impacts to fully realize the implications of AI-driven tools in educational contexts.

Based on orbital angular momentum (OAM) properties of Laguerre Gaussian beams LG(p,ℓ), a robust optical encoding model for efficient data transmission applications is designed. This paper presents an optical encoding model based on intensity profile generated by a coherent superposition of two OAM-carrying Laguerre-Gaussian modes and Machine-learning detection method. In the encoding process, the intensity profile for data encoding is generated based on selection of p and ℓ indices, while the decoding process is performed using support vector machine (SVM) algorithm. Two different decoding models based on SVM algorithm are tested to verify the robustness of optical encoding model, finding a BER =10−9 for 10.2dB of signal-to-noise ratio in one of the SVM models.

Project- or problem-based learning (PBL) is a methodology that makes students take an active role in their knowledge construction, improving the learning proces

In the last years, a few experiments in the fields of biological and soft matter physics in colloidal suspensions have reported “normal diffusion” with a Laplacian probability distribution in the particle’s displacements (i.e., Brownian yet non-Gaussian diffusion). To model this behavior, different stochastic and microscopic models have been proposed, with the former introducing new random elements that incorporate our lack of information about the media and the latter describing a limited number of interesting physical scenarios. This incentivizes the search of a more thorough understanding of how the media interacts with itself and with the particle being diffused in Brownian yet non-Gaussian diffusion. For this reason, a comprehensive mathematical model to explain Brownian yet non-Gaussian diffusion that includes weak molecular interactions is proposed in this paper. Based on the theory of interfaces by De Gennes and Langevin dynamics, it is shown that long-range interactions in a weakly interacting fluid at shorter time scales leads to a Laplacian probability distribution in the radial particle’s displacements. Further, it is shown that a phase separation can explain a high diffusivity and causes this Laplacian distribution to evolve towards a Gaussian via a transition probability in the interval of time as it was observed in experiments. To verify these model predictions, the experimental data of the Brownian motion of colloidal beads on phospholipid bilayer by Wang et al. are used and compared with the results of the theory. This comparison suggests that the proposed model is able to explain qualitatively and quantitatively the Brownian yet non-Gaussian diffusion.