RESUMEN

In the realm of urban planning, the integration of deep learning technologies has emerged as a transformative force, promising to revolutionize the way cities are designed, managed, and optimized. This research embarks on a multifaceted exploration that combines the power of deep learning with Bayesian regularization techniques to enhance the performance and reliability of neural networks tailored for urban planning applications. Deep learning, characterized by its ability to extract complex patterns from vast urban datasets, has the potential to offer unprecedented insights into urban dynamics, transportation networks, and environmental sustainability. However, the complexity of these models often leads to challenges such as overfitting and limited interpretability. To address these issues, Bayesian regularization methods are employed to imbue neural networks with a principled framework that enhances generalization while quantifying predictive uncertainty. This research unfolds with the practical implementation of Bayesian regularization within neural networks, focusing on applications ranging from traffic prediction, urban infrastructure, data privacy, safety and security. By integrating Bayesian regularization, the aim is to, not only improve model performance in terms of accuracy and reliability but also to provide planners and decision-makers with probabilistic insights into the outcomes of various urban interventions. In tandem with quantitative assessments, graphical analysis is wielded as a crucial tool to visualize the inner workings of deep learning models in the context of urban planning. Through graphical representations, network visualizations, and decision boundary analysis, we uncover how Bayesian regularization influences neural network architecture and enhances interpretability.

RESUMEN

To further advance the application of flexible piezoelectric materials in wearable/implantable devices and robot electronic skin, it is necessary to endow them with a new function of antibacterial properties and with higher piezoelectric performance. Introducing a specially designated nanomaterial based on the nanocomposite effect is a feasible strategy to improve material properties and achieve multifunctionalization of composites. In this paper, carbon dots (CDs) were sensitized onto the surface of ZnO to form ZnO@CDs nanoparticles, which were then incorporated into polyvinylidene fluoride-hexafluoropropylene (PVDF-HFP) to obtain a multifunctional composite. On the one hand, the antibacterial property of ZnO was improved because CDs had good optical absorption of visible light and their surface functional groups were favorable for electrostatic adsorption with bacteria. Therefore, ZnO@CDs endowed the composite with an outstanding antibacterial rate of 69.1% for Staphylococcus aureus. On the other hand, CDs played a bridging role between ZnO and PVDF-HFP, reducing the negative effect of ZnO aggregation and interface incompatibility with PVDF-HFP. As a result, ZnO@CDs induced ß-phase formation of 80.4% in PVDF-HFP with a d33 value of 33.8 pC N-1. The multifunctional device exhibited excellent piezoelectric and antibacterial performance in the application of energy harvesters and self-powered pressure sensors.

Asunto(s)

RESUMEN

We propose the broadband mode-selective coupler (MSC) formed with a side-polished six mode fiber (6MF) and a tapered side-polished small core single-mode fiber (SC-SMF) or an SMF. The MSCs are designed to allow the LP01 mode in the SC-SMF and SMF to completely couple to the LP01, LP11, LP21, LP02, LP31, LP12 modes in the 6MF over a broadband wavelength range. The phase-matching conditions of the MSCs are satisfied by tapering the SC-SMF and SMF to specific diameters. The tapered fibers are side-polished to designed residual fiber thickness using the wheel polishing technique. The effective indices of the side-polished fibers are measured with the prism coupling method. The MSCs provide high coupling ratio and high mode purity. High coupling efficiencies in excess of 81% for all the higher-order modes are obtained in the wavelength range 1530-1600 nm. For the LP01, LP11, LP21, LP02, LP31, LP12 MSCs at 1550 nm, the coupling ratios are 96.2%, 99.8%, 89.5%, 85.0%, 90.9%, 96.1%, respectively, and the mode purity of the MSCs is higher than 88.0%. The loss of the MSCs is lower than 1.8 dB in the wavelength range 1530-1600 nm. This device can be applied in broadband mode-division multiplexing transmission systems.

RESUMEN

A novel fiber structure, coreless side-polished fiber (CSPF) that is wrapped by polydimethylsiloxane (PDMS), is demonstrated to be highly sensitive to temperature because of the high refractive index sensitivity of the CSPF and the large thermal optic coefficient of the PDMS. Our numerical and experimental results show that the several dips in the transmitted spectra of PDMSW-CSPF is originated from the multimode interference (MMI) in the CSPF and will blueshift with the increase of temperature. Furthermore, for such a PDMSW-CSPF, we investigate its temperature sensing characteristics and the influences of residual thickness (RT) and dip wavelength on the sensitivity both numerically and experimentally. In the temperature range of 30~85°C, the PDMSW-CSPF with RT = 43.26 µm exhibits a high temperature sensitivity of -0.4409 nm/°C, the high linearity of 0.9974, and the high stability with low standard deviation of 0.141 nm. Moreover, in the cycle experiments, where the environmental temperature was set to automatically increase and then decrease, the PDMSW-CSPF exhibits a low relative deviation of sensitivity (RSD) of down to ± 0.068%. Here, the RSD is defined as the ratio of sensitivity deviation to the average sensitivity measured in the heating/cooling cycle experiments. The lower RSD indicates that PDMSW-CSPF has better reversibility than other fiber structure. The investigations also show that the sensitivity of the PDMSW-CSPF could be enhanced further by reducing the residual thickness and choosing the dip at a longer wavelength.