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Research on artificial intelligence-assisted green space layout in environmental design for smart cities
Abstract
The application of green ecological concept in the design of built environment and space layout has become an important direction of modern architectural design. This paper utilizes the Pix2Pix algorithm to read the target plan area map in the design of smart city environment, and carries the algorithm to construct the green space layout design auxiliary model in Unity3D engine and Python language environment. The model is used to realize the simulation display of green space layout design scheme and assist the designer to determine the optimal green layout scheme. The results of the case validation show that the layout optimization scheme obtained with the aid of the green space layout design model adopts the zoned lighting layout design strategy for the public areas, and after the scheme simulation, it is found that the cooling and heating energy consumption in the total environment of the district building decreases from 75.64kWh/m² and 45.26kWh/m² to 65.32kWh/m² and 42.31kWh/m², respectively. In addition, the residents of the neighborhood were highly satisfied with the simulated retrofit effect of the green layout design of the buildings in the neighborhood. This study promotes the consideration of environmental protection and sustainable development in urban design, which can reduce the negative impact of smart city construction on the environment and create a healthier and livable spatial environment.
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This guide presents a detailed overview of the most used machine learning (ML) techniques for psychologists who may not be familiar with advanced statistical methods, algorithms, or programming. Recognizing the growing interest in using data-driven approaches within psychological research, this guide describes applying ML techniques to investigate complex psychological phenomena. The paper covers the spectrum of algorithms, including decision trees, random forests, gradient boosting, stochastic gradient boosting, and XGBoost, highlighting their concepts and practical applications in psychology. Aiming to bridge the gap between theoretical understanding and practical performance, this paper offers step-by-step instructions on data preprocessing, correlation exploration, feature selection, and model evaluation within the Python programming environment. Readers are offered the necessary tools to apply ML in their research through explanations, examples, and visualization.
In the context of the green economy and sustainable urban development, the rapid expansion of urban construction has given rise to pressing public health concerns, notably environmental pollution and the increased prevalence of chronic illnesses linked to swift urbanization. These urban health issues are escalating, prompting significant attention to the concept of creating “healthy cities”. Meanwhile, the planning and design of urban street space have a far-reaching impact on urban residents’ quality of life and health. Urban planners are facing challenges and need to follow the principle of a green economy while meeting the needs of residents for public activities and adapting to motor vehicle traffic. This study explores the optimization of urban street space to promote the harmonious coexistence between people and cars. This study actively explores the relationship between health, urban environment, and social background, focusing on promoting the harmonious coexistence between people and vehicles, especially the optimization goal of sharing urban streets. The study’s main goal is to design a road that can meet the needs of citizens’ public activities and accommodate motor vehicles, which conforms to the principle of a green economy. To achieve this, geographic information system (GIS) technology and a genetic algorithm (GA) are employed to optimize shared urban street spaces. Among them, GIS tools are used for spatial simulation to evaluate the effect of different shared street space configurations. The urban shared street space is gradually optimized through GA’s selection, crossover, and mutation operations. Simulation experiments are conducted to determine the relationship between street space utilization and the elements of a healthy city, ultimately striving to identify the optimal design parameters for shared street spaces. The research results reveal that the urban street space is optimized from the three aspects of shared allocation of facilities resources, replacement of land use functions, and mixed layout of facilities, and the utilization rate of urban streets is finally ensured to reach 53.43%, fully assuming the essential functions of urban streets. This innovative approach bridges the gap between urban development and public health, offering valuable insights for sustainable urban space planning and enhanced living environments within the framework of the green economy.
Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain.
The development direction of future architectural design should be in line with environmental protection, green and energy conservation, so as to meet people’s sustainable development needs. The promotion of green building technology needs to involve multi-level and multi-disciplinary knowledge and social factors. Through a large number of green building projects, we can know that its ecological landscape not only has artistic value, but also can reflect obvious ecological value. An important concept in the concept of sustainable development is the green building landscape. Based on this background, this paper introduces artificial intelligence algorithm and designs a visual system around the concept of landscape space environment design. This system can be operated in two different forms, in offline application, image training is mainly carried out on the server side; in the online stage, the collected images are uploaded to the server using the Android platform, and the server side performs a series of operations such as image target detection, and then uses the Android mobile terminal to display the virtual reality fusion scene. The system test shows that the green building environment visualization system can not only make full use of the idle time of the cable drainage reader, but also will not affect the search operation in the next stage. The average delay is reduced by 16%, which proves the practicability of the software. In this paper, artificial intelligence algorithm is introduced into the field of green landscape space environment design, and an effective visualization system is constructed.
Generative Adversarial Networks (GANs) possess a significant ability to generate novel images that adhere to specific guidelines across multiple domains. GAN-assisted generative design is a design method that can automatically generate design schemes without the constraints of human conditions. However, more research on complex objects with weak regularity, such as parks, is required. In this study, parks were selected as the research object, and we conducted our experiment as follows: (1) data preparation and collection; (2) pre-train the two neural network, then create the design layout generation system and the design plan generation system; (3) realize the data augmentation and enhanced hundred level dataset to thousand level dataset; (4) optimized training; (5) test the optimized training model. Experimental results show that (1) the machine learning model can acquire specific park layout patterns, quickly generate well-laid-out plan layout plans, and create innovative designs that differ from the human designer’s style within reasonable limits; (2) GAN-driven data augmentation methods can significantly improve the generative ability of algorithms, reduce generative pressure, and achieve better generative results; (3) pix2pix is prone to mode collapse, and CycleGAN has fixed rule errors in expressing certain design elements; and (4) GAN has the ability to mine design rules in the same way as humans.
The fast urban development leads to many complex problems. A smart city aims to solve these problems using scientific approaches. Urban Green Space (UGS) is a fundamental component of urban infrastructure. It needs to be upgraded simultaneously with the concept of the smart city. Methods of smart UGS planning and sustainable landscape design help integrate ecological performance and other functions of UGS. Smart UGS planning is a technical management tool and a human-centered smart application. A novel UGS classification method based on function combinations is proposed in the present work. Five types of UGS (Scenic UGS, Residential UGS, Commercial UGS, Office UGS, and Road UGS) in Baqiao District of Xi’an, Shaanxi Province, are selected as sample sites. Interviewees’ socio-economic attributes, visiting frequencies, and differences in demands are investigated. Eventually, the UGSs researched in this work are optimized based on public preferences and the current conditions of the sites. Results demonstrate the following. (1) The public’s preference for different types of UGS is different considerably. (2) Based on the public’s preference, Scenic UGS is improved in terms of quantity, function, and management; Residential UGS is upgraded regarding quantity and accessibility; and Commercial UGS, Office UGS, and Road UGS are improved in terms of quality. The results could provide methodological references for planning different types of UGS and innovative insights for smart UGS planning and sustainable landscape design.
Cities play important roles in the conservation of global biodiversity, particularly through the planning and management of urban green spaces (UGS). However, UGS management is subject to a complex assortment of interacting social, cultural, and economic factors, including governance, economics, social networks, multiple stakeholders, individual preferences, and social constraints. To help deliver more effective conservation outcomes in cities, we identify major challenges to managing biodiversity in UGS and important topics warranting further investigation. Biodiversity within UGS must be managed at multiple scales while accounting for various socioeconomic and cultural influences. Although the environmental consequences of management activities to enhance urban biodiversity are now beginning to be addressed, additional research and practical management strategies must be developed to balance human needs and perceptions while maintaining ecological processes.
The current economic crisis, combined with growing citizen expectations, is placing increasing pressure on European cities to provide better and more efficient infrastructures and services, often for less cost. This trend has contributed to the growing popularity and use of the term 'Smart City' [1]. The Smart City, represent a new way of thinking about urban space by shaping a model that integrates Green Energy Sources and Systems (GESSs), energy efficiency, sustainable mobility, protection of the environment and economic sustainability, that represent the goals for future developments. Smart cities are made by a high level of Information and Communication Technology-ICT- structures able to transmit energy, information flows multidirectional and connect a different sector that include mobility, energy, social, economy. Into Smart Cities transport systems are sustainable, smart grids are enhanced to ensure greater integration capabilities of production plants from renewable sources, public lighting is efficient, the buildings are equipped with sensors and devices aimed at rationalizing consumption energy and create greater awareness on the part of citizens, with the aim of improving the quality of life of people through a new governance of public administration capable of managing this innovation and cultural change. However, while wishing the transformation of cities in smart systems, have not defined models infrastructure, that allow different subsets to communicate and interact, in order to make the concrete realization of a smart city. The objective of this paper is to discuss a model of Smart City with a multi-agent systems and Internet of things, that provides intelligence to a city, as basic infrastructure for a definition of a model repeatable and exportable, so as advocated by the European Community, that is allocating considerable funds (Horizon 2020) for the creation of Smart City.
The thermo-mechanical-chemical coupling effect presents significant challenges in accurately predicting the fatigue life of 316 austenitic stainless steel in high-temperature and high-pressure water environments (referred to hereafter as environmental fatigue). The complexity of environmental fatigue experiments results in limited and dispersed data, further making the life prediction difficult. Traditional fatigue life prediction models are often constrained by specific loading conditions and do not adequately account for the complex environmental influences. To address these issues, this paper proposes a novel environmental fatigue life prediction model of 316 stainless steel utilizing conditional Generative Adversarial Networks. The proposed model incorporates critical environmental factors, loading conditions and stacking fault energy, allowing direct prediction of environmental fatigue life. A comparative analysis on the predicted and experimental results reveals that the cGAN-based model significantly improves the prediction accuracy, reducing the fatigue life prediction error from a factor of 5 to within 3. To quantify the uncertainty in fatigue life prediction, the Monte Carlo Dropout method is employed to enable a probabilistic assessment of fatigue life. Furthermore, four environmental and loading conditions are established to evaluate the model’s extrapolation capability. The results demonstrate that the probabilistic fatigue assessment effectively captures data distribution and achieves high prediction accuracy.
With the growing prevalence of virtual reality technology across industries like cinema, video animation, and gaming, game developers must cater to a wider range of players. Game engines serve as middleware, reducing the time spent on coding and improving productivity for creating video games. While mainstream game engines offer a plethora of customization options, certain developers may find themselves limited when it comes to controlling specific aspects of the game. Due to the lack of built‐in functionality in current game engines like Unity and Unreal, the author developed TRPGFramework, a lightweight framework based on Unity3D, to address some of these problems. The game framework consists of two main parts: ZGamework (overall framework) and BGamework (game‐specific framework). To enhance performance, scalability, and code reusability in the game development process, the entity‐component‐system structure is implemented. This approach increases maintainability and makes the code clearer, easier to expand, and debuggable. By creating a simple 2D RPG game for performance testing, we confirmed that the framework significantly improves code reusability and modularity, resulting in faster development time and increased efficiency for interactive applications and games. This framework is particularly useful for creating real‐time combat role‐playing games, which are some of the most complex games available today. Using entity‐component‐system principles, we developed a buff system and unit controller optimized to allow hundreds of intelligent units to fight on the same screen or thousands of non‐intelligent units to fight simultaneously.
Urban green spaces are really vital for the well-being of human in urban areas. In urban planning for green space site selection, the study of the bond among the usage of green spaces and their categories that really influence their use can provide useful references. A spatial and temporal research on the allocation of visitors in 157 green areas was carried out in Shanghai to know which green spaces are denser or crowdsourced by utilizing social media big data. We evaluated the association with statistical testing and Kernel Density Estimation among the spatial pattern of the visitor spread in urban green areas. We used check-in data from social media to test this study comparing the number of humans who visit various green parks. We have classified green areas into various categories and our main findings are focused on their characteristics: (1) famous category of green parks according to visitors’ preferences, (2) Differences in the number of visitors by daytime, and (3) crowdsourced area based upon number of check-ins. The main aim of this article is to remind policy makers of the value of providing local people access to green areas and to empower cities with a framework for contacting green parks with the purpose of increasing the comfort of urban people with the architecture of smart city.
The challenges that urbanisation pose is multi-faceted. Land allocation priorities to urban green cover are usually neglected or readily negotiated in the countries that are in transition. Urbanisation devoid of urban green can cause many social and physical impacts on its residents. Hence, locally suitable Green Index should be devised and incorporated in the urban planning of cities, in spite of the size of the city. This analysis is to showcase the green index for the planning of smart cities. The devised green index is verified in the real-world context of Gulbarga city, India, thereby understanding the practicality of this index. The results show scope for alternative green cover, where ever the green index is low. This technique presents intra-city green cover pattern analysis. Such analysis emphasises to reserve the space for green in urban planning for well-being in totality. The aspect of Land Surface Temperature (LST) analysis and correlating the same with the amount of green cover further enhances the process of promoting green cities. The assessment of per capita green space with the standards of World Health Organisation (WHO) identified the scope of greenery initiatives in various parts of the city.
In 2015, the United Nations adopted an ambitious development agenda composed of 17 sustainable development goals (SDGs), which are to be reached by 2030. Beyond SDG 11 concerning the development of sustainable cities, many of the SDGs target activities falling within the responsibility of local governments. Thus, cities will play a leading role in the achievement of these goals, and we argue that the information systems (IS) community must be an active partner in these efforts. This paper aims to contribute to the achievement of the SDGs by developing a conceptual model to explain the role of IS in building smart sustainable cities and providing a framework of action for IS researchers and city managers. To this end, we conduct grounded theory studies of two green IS used by an internationally recognized smart city to manage water quality and green space. Based on these findings, we articulate a model explaining how an integrated information ecosystem enables the interactions between three interrelated spheres – administrative, political and sustainability – to support the development of smart sustainable cities. Moving from theory to practice, we use two real-world scenarios to demonstrate the applicability of the model. Finally, we define an action framework outlining key actions for cities and suggest corresponding questions for future research. Beyond a simple call-to-action, this work provides a much-needed foundation for future research and practice leading to a sustainable future for all.
The quest for a smart' city has exposed the damning evidence of energy and environmental dangers by using Big Data. Analysing around architecture, the behaviours of the public and other aspects shows what needs to be done in order to improve the environment we live in. A rapidly growing global population means that we will need to exploit a space's potential, whether it is taller sustainable buildings, innovative waste management, or better renewable processes. One of the biggest priorities when creating a smart city is to keep it eco-friendly and for us to work towards a cleaner, greener future. We need to look at sustainable buildings - according to interactive platform Urban Hub, about 25amillion new housing units will be required before 2030 in the 12 most populated countries, which account for 61 per cent of the global population.
The present paper analyzes and compares the current urban green space planning and management experiences of 23 European cities in order to understand the critical factors behind their success and/or failure and to obtain strategic and policy relevant information on the key features of urban green provisions and maintenance. The results of our comparative analysis show that - despite a variety in urban green space policies in cities in Europe - the critical success conditions in planning and management of urban green spaces have emerged in four factors/attributes: (1) the share of green space in urban land use; (2) the changes in these shares over time; (3) the intensity of involvement of the city administration; and (4) the degree of citizen participation. The comparative evaluation of our analysis also shows some interesting results in terms of geographical and regional characteristics in the success level. We can say that three geographical regions, northern Europe, southern Europe, and eastern Europe, with different success levels and characteristics, have emerged in the green picture of Europe.
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