Advancing Urban Renewal: An Automated Approach to Generating Historical Arcade Facades with Stable Diffusion Models

Urban renewal and transformation processes necessitate the preservation of the historical urban fabric, particularly in districts known for their architectural and historical significance. These regions, with their diverse architectural styles, have traditionally required extensive preliminary research, often leading to subjective results. However, the advent of machine learning models has opened up new avenues for generating building facade images. Despite this, creating high-quality images for historical district renovations remains challenging, due to the complexity and diversity inherent in such districts. In response to these challenges, our study introduces a new methodology for automatically generating images of historical arcade facades, utilizing Stable Diffusion models conditioned on textual descriptions. By classifying and tagging a variety of arcade styles, we have constructed several realistic arcade facade image datasets. We trained multiple low-rank adaptation (LoRA) models to control the stylistic aspects of the generated images, supplemented by ControlNet models for improved precision and authenticity. Our approach has demonstrated high levels of precision, authenticity, and diversity in the generated images, showing promising potential for real-world urban renewal projects. This new methodology offers a more efficient and accurate alternative to conventional design processes in urban renewal, bypassing issues of unconvincing image details, lack of precision, and limited stylistic variety. Future research could focus on integrating this two-dimensional image generation with three-dimensional modeling techniques, providing a more comprehensive solution for renovating architectural facades in historical districts.Comment: HABITS OF THE ANTHROPOCENE - Proceedings of the 43rd ACADIA Conference - Volume II: Proceedings book one, University of Colorado Denver, Denver, Colorado, USA, 26-28 October 2023, pp. 616-625, CUMINCAD, 202

Environmental and Biological Determinants of Brain Mass

According to the expensive brain hypothesis, periodic energy level determines the brain mass. However, various environmental and biological factors directly or indirectly relevant to energy intake have not been well studied. Here, we systematically examined how body mass, hibernation, diurnally, substrate use, diet individually and synergistically determine brain mass in a large dataset of more than 1000 species. We found that body mass and hibernation are the major determinants of brain mass in most species. These findings will shed light on future studies of how evolutionary constraints acting on brain size

X70 STEEL CRITICAL CTOD VALUE ANSYS CALCULATION

With the development of ocean engineering,increase in the number of project exploration of Marine oil and gas,natural gas and oil transportation engineering are increasingly developed. UOE production technology of high strength of X70 steel is widely used in oil and gas transportation project,and then the higher the strength of steel,the fracture toughness is lower. We need to determine the identity of the fracture toughness values. Material fracture toughness size can use critical crack tip opening displacement CTOD（ crack tipopening below） to measure. Critical CTOD is important parameters in elastic-plastic fracture mechanics,the size directly reflects the material’s ability to resist cracking. The bigger the critical value of CTOD show that the cracking resistance of materials,the better. Determining the critical value of CTOD is of great significance. On the basis of BS7448 specification of X70 steel critical fracture toughness CTOD was tested,and then by using ANSYS software of X70 steel in terms of CTOD define critical CTOD was calculated,and implement the test and simulation comparison and analysis,safety asse

Dynamic evaluation on sealing capacity of caprocks of the Meso-Neoproterozoic reservoirs in Ordos Basin, China

The Meso-Neoproterozoic is a new play in the Ordos Basin. A deeper understanding about the dynamic relationship between the caprocks and the source rocks is needed. Based on the comprehensive analysis of hydrocarbon source development characteristics of the Meso-Neoproterozoic and its overlying strata, as well as the formation contact relationships, lithology characteristics and exploratory drilling data, it is recognized that the Meso-Neoproterozoic contains two types of petroleum accumulation assemblage, that is, the “self-sourced indigenous” and “upper source rock-lower reservoir” assemblages. The former is mainly controlled by the development and distribution of source rocks of the Changcheng System, with the Lower Cambrian shale sequence as its caprock. The later is controlled by the superposition between the Meso-Neoproterozoic and its overlying source rocks and this assemblage is mainly distributed in Hangjinqi and Pingliang areas with the Carboniferous-Permian shale sequence as its caprock. The dynamic evaluation on the displacement pressure serves to reconstruct the displacement pressure history of the caprock. The results show that the shale sequence of the Cambrian Maozhuang Formation in well XY 1 in the southern Ordos Basin has possibly acquired the ability of sealing natural gas since the early of Late Triassic. Its displacement pressure increased rapidly up to 20 MPa during the Late Triassic-Jurassic and keeps at 9.2 MPa at present, indicating fair sealing ability. The Carboniferous-Permian caprocks in Hangjinqi area could have acquired the ability to seal natural gas in the Late Jurassic-Early Cretaceous, and the present-day displacement pressure is 9–12 MPa, indicating good sealing ability. The upper Paleozoic caprock in Pingliang area has been able to seal natural gas since the Early Jurassic, with a maximum displacement pressure of 23 MPa during the Cretaceous period and a current value of 17–20 MPa, indicative of strong ability to seal natural gas. The sealing ability of caprocks of both the “self-sourced indigenous” and “upper source rock - lower reservoir” assemblages has come into being earlier than or at least no later than the peak gas generation of the source rocks and therefore the caprocks are dynamically effective in geohistory. The Meso-Neoproterozoic reservoirs in the Ordos Basin are well preserved and probabally of better potential for exploration in terms of the caprock-source rock combination

Image Retrieval for Local Architectural Heritage Recommendation Based on Deep Hashing

Propagating architectural heritage is of great significance to the inheritance and protection of local culture. Recommendations based on user preferences can greatly benefit the promotion of local architectural heritage so as to better protect and inherit historical culture. Thus, a powerful tool is necessary to build such a recommendation system. Recently, deep learning methods have proliferated as a means to analyze data in architectural domains. In this paper, based on a case study of Jiangxi, China, we explore a recommendation system for the architectural heritage of a local area. To organize our experiments, a dataset for traditional Chinese architecture heritage is constructed and a deep hashing retrieval method is proposed for the recommendation task. By utilizing a data fine-tuning strategy, our retrieval method can realize high-accuracy recommendation and break the model training restriction caused by insufficient data on local architectural heritage. Furthermore, we analyze the retrieval answers and map the data into a two-dimensional space to reveal the relationships between different architectural heritage categories. An image-to-location application is also provided for a better user experience

Towards a Fairer Green city: measuring unfairness in daily accessible greenery in Chengdu’s Central city

Urban green spaces exert beneficial effects on both individuals and communities. However, as urban sprawl intensifies, socioeconomic disparities widen, and populations burgeon, the concepts of green fairness and environmental justice confront substantial hurdles. The daily exposure to greenery emerges as a crucial determinant of these factors, yet no comprehensive methodologies currently exist to gauge the levels of daily accessible urban greenery or to probe the distribution of green inequities. In this research, we harness the capabilities of Spatial Design Network Analysis (sDNA) to scrutinize spatial choice and integration, using the metropolis of Chengdu as a case study. Three indicators representing daily accessed urban greenery are utilized, including the Green View Index (GVI) at the street level, the assessment of green spaces based on the Normalized Differential Vegetation Index (NDVI), and the level of greenery within the visible range of buildings. Green Accessibility Index (GAI) was further proposed and calculated for three states of commuting, recreation, and work to synthesize the accessibility and greenness levels. The distribution of green unfairness in the study area are evaluated using bivariate local spatial autocorrelation. Our findings reveal that (1) frequent expressway commuting and existing greenery does not satisfy urban fairness needs. (2) Significant differences in unfair areas of building visible greenery (3) Unfair areas are concentrated in high-income neighborhoods (4) Severe unfairness between greenery and population in large cities, where most people do not enjoy the benefits of adequate greenery. We provide recommendations based on these findings, thereby offering actionable insights to optimize the spatial distribution of green unfairness through enhanced accessibility of urban greenery

Exploring the Impact of Built Environment Attributes on Social Followings Using Social Media Data and Deep Learning

Streets are an important component of urban landscapes and reflect the image, quality of life, and vitality of public spaces. With the help of the Google Cityscapes urban dataset and the DeepLab-v3 deep learning model, we segmented panoramic images to obtain visual statistics, and analyzed the impact of built environment attributes on a restaurant’s popularity. The results show that restaurant reviews are affected by the density of traffic signs, flow of pedestrians, the bicycle slow-moving index, and variations in the terrain, among which the density of traffic signs has a significant negative correlation with the number of reviews. The most critical factor that affects ratings on restaurants’ food, indoor environment and service is pedestrian flow, followed by road walkability and bicycle slow-moving index, and then natural elements (sky openness, greening rate, and terrain), traffic-related factors (road network density and motor vehicle interference index), and artificial environment (such as the building rate), while people’s willingness to stay has a significant negative effect on ratings. The qualities of the built environment that affect per capita consumption include density of traffic signs, pedestrian flow, and degree of non-motorized design, where the density of traffic signs has the most significant effect