De Gruyter | 270 years of academic publishing

Urban vitality reflects the dynamic interaction between residents’ activities and the urban environment, playing a pivotal role in fostering social and economic development. It enhances urban quality, competitiveness, and residents’ well-being. Despite extensive scholarly attention, previous research has often overlooked the integration of big data and the spatial heterogeneity inherent in urban environments. This study addresses these gaps by using Yinchuan as a case study, employing multi-source big data to measure urban vitality, and incorporating 2D/3D variables to comprehensively characterize the built environment. Furthermore, a Geographically Weighted Regression (GWR) model was applied to uncover the spatially heterogeneous relationships between these variables and urban vitality. The findings reveal that built environment factors significantly influence urban vitality, particularly in central districts such as Xingqing, Jinfeng, and Xixia district. These effects demonstrate positive spatial autocorrelation and clustering patterns, with notable spatial heterogeneity across different regions. Based on these insights, the study proposes targeted, multidimensional strategies for the comprehensive enhancement of urban vitality. This research not only provides theoretical support for Yinchuan’s urban development but also offers valuable practical guidance for other cities confronting similar urban challenges.

The presented metamaterial consists of the matrix (magnesium diboride $\mathrm{Mg}{\mathrm{B}}_2$ in a normal state, at room temperature) with randomly (or regularly) embedded spherical nanoparticles of a polaritonic crystal, SiC. The calculations demonstrate explicitly that the metamaterial exhibits negative refraction index behavior with low losses for a scattered wave. The result stands for both random and regular distributions of SiC nanoparticles inside the $\mathrm{Mg}{\mathrm{B}}_2$ matrix. This favorable situation stems from the Drude-like behavior of both the low-energy, $p_2({\omega }_{p_2}\approx 1.9\mathrm{eV})$, and the high-energy, $p_1({\omega }_{p_1}\approx 6.3\mathrm{eV})$, plasmon modes of $\mathrm{Mg}{\mathrm{B}}_2$ with plasmon losses, $\gamma ⩽0.25\mathrm{eV}$. The effective medium parameters were calculated in the framework of the extended theories of Maxwell-Garnett [Philos. Trans. R. Soc. London, Ser. A 203, 385 (1904)] and Lewin [Proc. Inst. Electr. Eng. 94, 65 (1947)], and the obtained results are validated via ab initio finite difference time domain simulations.

The present study examined the usage of deep convolutional neural networks (DCNNs) for the classification, segmentation, and detection of the images of surface defects in heritage buildings. A survey was conducted on the building surface defects in Gulang Island (a UNESCO World Cultural Heritage Site), which were subsequently classified into six categories according to relevant standards. A Swin Transformer- and YOLOv5-based model was built for the automated detection of surface defects. Experimental results suggested that the proposed model was 99.2% accurate at classifying plant penetration and achieved a mean intersection-over-union (mIoU) of over 92% in relation to moss, cracking, alkalization, staining, and deterioration, outperforming CNN-based semantic segmentation networks such as FCN, PSPNet, and DeepLabv3plus. The Swin Transformer-based approach for the segmentation of building surface defect images achieved the highest accuracy regardless of the evaluation metric (with an mIoU of 90.96% and an mAcc of 95.78%), when contrasted to mainstream DCNNs such as SegFormer, PSPNet, and DANet.

This paper deals with an investigation of the dielectric properties of concretes that includes rice husk ash using a planar coaxial probe. The planar coaxial probe has a planar structure with a microstrip and coaxial features. The measurement was performed over the frequency range of 0.5-3.5 GHz, and concrete specimens with different percentages of rice husk ash were tested. The results indicated that the dielectric constant of the concretes was inversely proportional to the frequency, while the conductivity was proportional to the frequency. The dielectric constant decreased with the increasing age of the concrete at the frequency of 1 GHz. The conductivity of the concrete decreased with the increasing age of the concrete at the frequency of 3.2 GHz. In addition, the dielectric constant and the conductivity decreased when the compressive strength increased. It was also shown that the obtained dielectric properties of the concrete could be used to investigate the relationship between the compressive strength and age of the concrete. Moreover, there is an opportunity to apply the proposed probe to determine the dielectric properties of other materials.