Volume 45, Issue 1

Living space is included for discussion. Therefore, the research on living environment in this paper refers to the research on living space in urban high rise residential buildings, and in the context of the Above the Jiangxi puts forward the point of view: the so called design research on the suitability of aging in the urban living environment starts from the subjectivity of the elderly, follows the aging of the elderly, the influence of physiological factors, and the development of modern informatization. With the rapid development of deep learning, in the face of the age appropriateness of urban living spaces, we propose to use a graph neural network mechanism to treat each housing space as a graph node to construct a large scale graph network relationship structure. Thus, qualitative conclusions of the ageing suitability of living space environment are given. The realization shows that proposed method can give a good interpretable conclusion on the design problem of urban living space environment suitability for aging under the background of aging and help alleviate the lack of social resources caused by urban aging. And compared our model with the baseline model, the experimental results show that the prediction effect of the graph neural network model is significantly improved.

Postmodernism has provided significant insights into the cultural importance of shopping and retail environments in developed societies. However, most postmodern research has focused on shopping in public areas like malls and high streets. This paper argues that these analyses should be broadened to encompass home shopping and the wider context of remote shopping. Recommendations for future research are also outlined.

Financial time series data forecasting is difficult since the data typically exhibit complicated characteristics such high non-linearity and non-smoothness and a lot of noise. In order to do this, it is suggested in this paper that a CNN-GRU neural network be built by combining the serial dependencies of financial time series data and the local correlation features of various financial market time series data into a single model. In order to create a forecasting model based on the various frequencies and fluctuations of the financial time series data, the financial time series data are simultaneously decomposed and reconstructed into trend, low-frequency, and high-frequency terms using an integrated empirical modal decomposition and travel judgment method. The empirical findings demonstrate that the CNN-GRU neural network’s prediction accuracy is superior to that of the GRU neural network, which only takes into account serial dependencies, and the CNN, which only takes into account local correlation characteristics. Compared to the deep learning and machine learning methods for the direct prediction of the SSE index, the integrated prediction accuracy of the SSE index is higher.

This study investigates vertical equity in real estate assessment, emphasizing the importance of fair and consistent property valuations. Vertical equity ensures that properties of similar value are assessed comparably, regardless of their location or other attributes. By analyzing current assessment practices and case studies, this research identifies disparities and challenges that undermine fairness in real estate taxation. It explores how market dynamics, appraisal techniques, and regulatory frameworks influence assessment equity. The study highlights the necessity for reforms in assessment methodologies and oversight to promote greater fairness. It concludes with recommendations for policymakers and stakeholders to improve equity and transparency in property assessment systems.

Exposure to moisture can lead to various detrimental effects on structural sealants, resulting in their degradation. The extent of this degradation is influenced by several factors, including the type of sealant and the curing period. Silicone sealants are commonly used in structural glazing due to their excellent resistance to weather conditions. This research investigates the impact of moisture on commercial silicone sealants by tracking changes in their physical properties. Moisture exposure was simulated using a water spray with a pH range of 4 to 10. These pH levels reflect typical conditions from mildly acidic rain (pH 4) to alkaline cleaning solutions (pH 10). The laboratory assessment of degradation focused on alterations in tensile tangential modulus, percentage elongation, and ultimate tensile strength.