Analysis on the Green Space System Planning of Small Cities in Mountainous Areas

With the urban green space system planning of small cities in mountainous areas as the study object,the planning practices are analyzed and studied to summarize the common characteristics of urban green space system planning in those small cities.Being combined with relevant national standards and regulations,the problems that those small cities are faced with in their urban green space system planning are analyzed,then corresponding countermeasures and suggestions are put forward to resolve problems in the practical planning.

An Index System of Creative Cities:An Empirical Study with SEM Perspective

Fuelled by the influential work in City Space Re-engineering and Administration Mechanism Regulating,Creative City is considered as a new model in future.In this article,we investigated the characteristic of creative cities in China and constructed an evaluation system for creative cities with Structural Equation Model perspective.

Toward Carbon-Neutral Water Systems:Insights from Global Cities

Many cities have pledged to achieve carbon neutrality.The urban water industry can also contribute its share to a carbon-neutral future.Using a multi-city time-series analysis approach,this study aims to assess the progress and lessons learned from the greenhouse gas(GHG)emissions management of urban water systems in four global cities:Amsterdam,Melbourne,New York City,and Tokyo.These cities are advanced in setting GHG emissions reduction targets and reporting GHG emissions in their water industries.All four cities have reduced the GHG emissions in their water industries,compared with those from more than a decade ago(i.e.,the latest three-year moving averages are 13%–32%lower),although the emissions have“rebounded”multiple times over the years.The emissions reductions were mainly due to various engineering opportunities such as solar and mini-hydro power generation,biogas valorization,sludge digestion and incineration optimization,and aeration system optimization.These cities have recognized the many challenges in reaching carbon-neutrality goals,which include fluctuating water demand and rainfall,more carbon-intensive flood-prevention and water-supply strategies,meeting new air and water quality standards,and revising GHG emissions accounting methods.This study has also shown that it is difficult for the water industry to achieve carbon neutrality on its own.A collaborative approach with other sectors is needed when aiming toward the city’s carbon-neutrality goal.Such an approach involves expanding the usual system boundary of the water industry to externally tap into both engineering and non-engineering opportunities.

Dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in smart cities

The integration of the Internet of Vehicles(IoV)in future smart cities could help solve many traffic-related challenges,such as reducing traffic congestion and traffic accidents.Various congestion pricing and electric vehicle charging policies have been introduced in recent years.Nonetheless,the majority of these schemes emphasize penalizing the vehicles that opt to take the congested roads or charge in the crowded charging station and do not reward the vehicles that cooperate with the traffic management system.In this paper,we propose a novel dynamic traffic congestion pricing and electric vehicle charging management system for the internet of vehicles in an urban smart city environment.The proposed system rewards the drivers that opt to take alternative congested-free ways and congested-free charging stations.We propose a token management system that serves as a virtual currency,where the vehicles earn these tokens if they take alternative non-congested ways and charging stations and use the tokens to pay for the charging fees.The proposed system is designed for Vehicular Ad-hoc Networks(VANETs)in the context of a smart city environment without the need to set up any expensive toll collection stations.Through large-scale traffic simulation in different smart city scenarios,it is proved that the system can reduce the traffic congestion and the total charging time at the charging stations.

Smart Cities as Cyber-Physical Social Systems

The emerging prototype for a Smart City is one of an urban environment with a new generation of inno- vative services for transportation, energy distribution, healthcare, environmental monitoring, business, commerce, emergency response, and social activities. Enabling the technology for such a setting re- quires a viewpoint of Smart Cities as cyber-physical systems （CPSs） that include new software platforms and strict requirements for mobility, security, safety, privacy, and the processing of massive amounts of information. This paper identifies some key defining characteristics of a Smart City, discusses some lessons learned from viewing them as CPSs, and outlines some fundamental research issues that remain largely open.

Understanding the Relationship Between Shrinking Cities and Land Prices:Spatial Pattern,Effectiveness,and Policy Implications

Urban shrinkage has emerged as a widespread phenomenon globally and has a significant impact on land,particularly in terms of land use and price.This study focuses on 2851 county-level cities in China in 2005–2018(excluding Hong Kong,Macao,Taiwan,and‘no data’areas in Qinhai-Tibet Plateau)as the fundamental units of analysis.By employing nighttime light(NTL)data to identify shrinking cities,the propensity score matching(PSM)model was used to quantitatively examine the impact of shrinking cities on land prices,and evaluate the magnitude of this influence.The findings demonstrate the following:1)there were 613 shrinking cities in China,with moderate shrinkage being the most prevalent and severe shrinkage being the least.2)Regional disparities are evident in the spatial distribution of shrinking cities,especially in areas with diverse terrain.3)The spatial pattern of land price exhibits a significant correlated to the economic and administrative levels.4)Shrinking cities significantly negatively impact on the overall land price(ATT=–0.1241,P<0.05).However,the extent of the effect varies significantly among different spatial regions.This study contributes novel insights into the investigation of land prices and shrinking cities,ultimately serving as a foundation for government efforts to promote the sustainable development of urban areas.

Deep Learning Enabled Intelligent Healthcare Management System in Smart Cities Environment

In recent times,cities are getting smart and can be managed effectively through diverse architectures and services.Smart cities have the ability to support smart medical systems that can infiltrate distinct events(i.e.,smart hospitals,smart homes,and community health centres)and scenarios(e.g.,rehabilitation,abnormal behavior monitoring,clinical decision-making,disease prevention and diagnosis postmarking surveillance and prescription recommendation).The integration of Artificial Intelligence(AI)with recent technologies,for instance medical screening gadgets,are significant enough to deliver maximum performance and improved management services to handle chronic diseases.With latest developments in digital data collection,AI techniques can be employed for clinical decision making process.On the other hand,Cardiovascular Disease(CVD)is one of the major illnesses that increase the mortality rate across the globe.Generally,wearables can be employed in healthcare systems that instigate the development of CVD detection and classification.With this motivation,the current study develops an Artificial Intelligence Enabled Decision Support System for CVD Disease Detection and Classification in e-healthcare environment,abbreviated as AIDSS-CDDC technique.The proposed AIDSS-CDDC model enables the Internet of Things(IoT)devices for healthcare data collection.Then,the collected data is saved in cloud server for examination.Followed by,training 4484 CMC,2023,vol.74,no.2 and testing processes are executed to determine the patient’s health condition.To accomplish this,the presented AIDSS-CDDC model employs data preprocessing and Improved Sine Cosine Optimization based Feature Selection(ISCO-FS)technique.In addition,Adam optimizer with Autoencoder Gated RecurrentUnit(AE-GRU)model is employed for detection and classification of CVD.The experimental results highlight that the proposed AIDSS-CDDC model is a promising performer compared to other existing models.

Subsurface multi-physical characterization of mountain excavation and city construction in loess plateau with a fiber-optic sensing system

Mountain excavation and city construction(MECC)projects being launched in the Loess Plateau in China involve the creation of large-scale artificial land.Understanding the subsurface evolution characteristics of the artificial land is essential,yet challenging.Here,we use an improved fiber-optic monitoring system for its subsurface multi-physical characterization.The system enables us to gather spatiotemporal distribution of various parameters,including strata deformation,temperature,and moisture.Yan’an New District was selected as a case study to conduct refined in-situ monitoring through a 77 m-deep borehole and a 30 m-long trench.Findings reveal that the ground settlement involves both the deformation of the filling loess and the underlying intact loess.Notably,the filling loess exhibits a stronger creep capability compared to underlying intact loess.The deformation along the profile is unevenly distributed,with a positive correlation with soil moisture.Water accumulation has been observed at the interface between the filling loess and the underlying intact loess,leading to a significant deformation.Moreover,the temperature and moisture in the filling loess have reached a new equilibrium state,with their depths influenced by atmospheric conditions measuring at 31 m and 26 m,respectively.The refined investigation allows us to identify critical layers that matter the sustainable development of newly created urban areas,and provide improved insights into the evolution mechanisms of land creation.

On Index System and Quantitative Assessment of Eco-cities:A Case Study on Urban Agglomeration of the Yangtze Delta

Urban agglomeration of the Yangtze Delta （UAYD）, one of the most developed regions of China, has witnessed an increasing prevalence in building ecological cities when the ecological cities are pursued by many modem cities, and great achievements have been made in this regard. It is inevitable, however, that certain problems exist during the construction of ecological city, which include but not limited to non-harmonious development of urban complex ecosystem, and the difficulty in quantifying eco-city construction or incomplete quantification in assessing the construction of present and future eco-city. Based on the analysis on social-economic conditions and regional conditions of the UAYD, this paper attempts to set up an index system of eco-cities combining with local characteristics, and to adopt the indices of eco-city, urban harmony, and eco-city colligate to evaluate the ecological level, urban harmonious development and eco-city construction of cities within the UAYD. Results indicate that among 15 cities in UAYD, Suzhou City ranks the highest in terms of eco-city construction, whereas Nantong ranks relatively lower; sustainable eco-city construction is possible only when cities are developed in every respect of harmony.

Smart Object Detection and Home Appliances Control System in Smart Cities

During the last decade the emergence of Internet of Things(IoT)based applications inspired the world by providing state of the art solutions to many common problems.From traffic management systems to urban cities planning and development,IoT based home monitoring systems,and many other smart applications.Regardless of these facilities,most of these IoT based solutions are data driven and results in small accuracy values for smaller datasets.In order to address this problem,this paper presents deep learning based hybrid approach for the development of an IoT-based intelligent home security and appliance control system in the smart cities.This hybrid model consists of;convolution neural network and binary long short term model for the object detection to ensure safety of the homes while IoT based hardware components like;Raspberry Pi,Amazon Web services cloud,and GSM modems for remotely accessing and controlling of the home appliances.An android application is developed and deployed on Amazon Web Services(AWS)cloud for the remote monitoring of home appliances.A GSM device and Message queuing telemetry transport(MQTT)are integrated for communicating with the connected IoT devices to ensure the online and offline communication.For object detection purposes a camera is connected to Raspberry Pi using the proposed hybrid neural network model.The applicability of the proposed model is tested by calculating results for the object at varying distance from the camera and for different intensity levels of the light.Besides many applications the proposed model promises for providing optimum results for the small amount of data and results in high recognition rates of 95.34%compared to the conventional recognition model(k nearest neighbours)recognition rate of 76%.

Study on Landscape Character Assessment System in Small Cities and Towns

The paper elaborated landscape character assessment system of small cities and towns, defi ned the assessment objects, application range of assessment fruits, specifi c evaluation methods and procedures. Taking Gexianshan Town in Pengzhou City for example, technical means for assessing landscape characters were adopted for the landscape assessment, which provided professional support for the future planning.

Evaluation of harmonious development for economy-environment system in coastal cities: a case study of Tianjin Municipality

With the aim of the harmonious development of economy-environment system in coastal cities in China. an index ,system used to evaluate the economy-environment system is built up in this paper, which includes four aspects： economy, environment, resources, and ocean industry. Based on the analysis on present condition and future trends of economic development in Tianjin and the quantification of various evaluation indices, the aathor applies integrated index valuation model to valuate the harmonious development af economy-environment of Tianjin. The results show that the coordinated degree of economy-environment would drop down in the future, from 0.95（superior level of harmonious development） in 2000 to 0.59（inferior level of harmonious development） in 2015. under the circumstance of the current economic development mode. The level of comprehensive development of Tianjin also presents to descend. Based on the analyzing of status and future trends of environment-economy coordinated development, the paper puts forward the countermeasures such as industry, structure adjustment, increasing the level of environmental protection investment, strengthening the enforcement of en vironmental policies to improve the coordinated development of environment-economy in Tianjin municipality.

Enhanced Cooling Efficiency of Urban Trees on Hotter Summer Days in 70 Cities of China

Increasing the urban tree cover percentage(TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees' cooling efficiency to rising urban temperature remains poorly quantified for China's cities. In this study, we quantify the response of urban trees' cooling efficiency to rising urban temperature at noontime [~1330 LT(local time), LT=UTC+8] in 17summers(June, July, and August) from 2003–19 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The enhanced cooling efficiency values of urban trees range from 0.002 to 0.055℃ %-1 per 1℃ increase in temperature across the selected cities, with larger values for the lowTCP-level cities. The response is also regulated by background temperature and precipitation, as the additional cooling benefit tends to be larger in warmer and wetter cities at the same TCP level. The positive response of urban trees' cooling efficiency to rising urban temperature is explained mainly by the stronger evapotranspiration of urban trees on hotter days.These results have important implications for alleviating urban heat risk by utilizing urban trees, particularly considering that extreme hot days are becoming more frequent in cities under global warming.

Factors Influencing the Spatial Variability of Air Temperature Urban Heat Island Intensity in Chinese Cities

Few studies have investigated the spatial patterns of the air temperature urban heat island(AUHI)and its controlling factors.In this study,the data generated by an urban climate model were used to investigate the spatial variations of the AUHI across China and the underlying climate and ecological drivers.A total of 355 urban clusters were used.We performed an attribution analysis of the AUHI to elucidate the mechanisms underlying its formation.The results show that the midday AUHI is negatively correlated with climate wetness(humid:0.34 K;semi-humid:0.50 K;semi-arid:0.73 K).The annual mean midnight AUHI does not show discernible spatial patterns,but is generally stronger than the midday AUHI.The urban–rural difference in convection efficiency is the largest contributor to the midday AUHI in the humid(0.32±0.09 K)and the semi-arid(0.36±0.11 K)climate zones.The release of anthropogenic heat from urban land is the dominant contributor to the midnight AUHI in all three climate zones.The rural vegetation density is the most important driver of the daytime and nighttime AUHI spatial variations.A spatial covariance analysis revealed that this vegetation influence is manifested mainly through its regulation of heat storage in rural land.

Cyber Security within Smart Cities:A Comprehensive Study and a Novel Intrusion Detection-Based Approach

The expansion of smart cities,facilitated by digital communications,has resulted in an enhancement of the quality of life and satisfaction among residents.The Internet of Things(IoT)continually generates vast amounts of data,which is subsequently analyzed to offer services to residents.The growth and development of IoT have given rise to a new paradigm.A smart city possesses the ability to consistently monitor and utilize the physical environment,providing intelligent services such as energy,transportation,healthcare,and entertainment for both residents and visitors.Research on the security and privacy of smart cities is increasingly prevalent.These studies highlight the cybersecurity risks and the challenges faced by smart city infrastructure in handling and managing personal data.To effectively uphold individuals’security and privacy,developers of smart cities must earn the trust of the public.In this article,we delve into the realms of privacy and security within smart city applications.Our comprehensive study commences by introducing architecture and various applications tailored to smart cities.Then,concerns surrounding security and privacy within these applications are thoroughly explored subsequently.Following that,we delve into several research endeavors dedicated to addressing security and privacy issues within smart city applications.Finally,we emphasize our methodology and present a case study illustrating privacy and security in smart city contexts.Our proposal consists of defining an Artificial Intelligence(AI)based framework that allows:Thoroughly documenting penetration attempts and cyberattacks;promptly detecting any deviations from security standards;monitoring malicious behaviors and accurately tracing their sources;and establishing strong controls to effectively repel and prevent such threats.Experimental results using the Edge-IIoTset(Edge Industrial Internet of Things Security Evaluation Test)dataset demonstrated good accuracy.They were compared to related state-of-theart works,which highlight the relevance of our proposal.

Smart Energy Management System Using Machine Learning

Energy management is an inspiring domain in developing of renewable energy sources.However,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and demand.The major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental effects.Modern energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user comfort.To address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve it.An inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy costs.Machine Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)network.The IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource management.In this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management problem.The proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

Do companies influenced by smart cities pay more attention to green governance?Mechanism and optimal spatial correlation

Smart cities are a way for China to construct an innovative and environmentally conscious nation.The paper examines the impact of smart cities on corporate green governance and provides a theoretical foundation for formulating and executing smart city policy in China.Based on panel data from Chinese A-share listed companies in Shanghai and Shenzhen from 2008 to 2020,this study constructs a multiperiod double-difference model to examine the influence of smart cities on corporate green governance.Additionally,it uses a spatial double-difference model to investigate the spatial spillover effect of smart cities on neighboring areas.The findings indicate that smart cities effectively enhance corporate green governance.Analyzing the influencing mechanisms reveals that resource allocation efficiency,technological innovation,management environmental awareness,and regional environmental enforcement efforts act as mediators.Furthermore,the study reveals that the impact of smart cities on promoting corporate green governance is more pronounced in regions with lower levels of marketization and resource-based cities.Moreover,the research explores the spatial spillover effects of smart cities,with an effective radius of approximately 350 km.The optimal spatial correlation zone for green governance of businesses in neighboring areas in relation to smart cities is within a range of 250-350 km.This is manifested by the significant promotion of green governance in neighboring area businesses facilitated by smart cities.

Ensemble Deep Learning Based Air Pollution Prediction for Sustainable Smart Cities

Big data and information and communication technologies can be important to the effectiveness of smart cities.Based on the maximal attention on smart city sustainability,developing data-driven smart cities is newly obtained attention as a vital technology for addressing sustainability problems.Real-time monitoring of pollution allows local authorities to analyze the present traffic condition of cities and make decisions.Relating to air pollution occurs a main environmental problem in smart city environments.The effect of the deep learning(DL)approach quickly increased and penetrated almost every domain,comprising air pollution forecast.Therefore,this article develops a new Coot Optimization Algorithm with an Ensemble Deep Learning based Air Pollution Prediction(COAEDL-APP)system for Sustainable Smart Cities.The projected COAEDL-APP algorithm accurately forecasts the presence of air quality in the sustainable smart city environment.To achieve this,the COAEDL-APP technique initially performs a linear scaling normalization(LSN)approach to pre-process the input data.For air quality prediction,an ensemble of three DL models has been involved,namely autoencoder(AE),long short-term memory(LSTM),and deep belief network(DBN).Furthermore,the COA-based hyperparameter tuning procedure can be designed to adjust the hyperparameter values of the DL models.The simulation outcome of the COAEDL-APP algorithm was tested on the air quality database,and the outcomes stated the improved performance of the COAEDL-APP algorithm over other existing systems with maximum accuracy of 98.34%.

Carrying Capacity and Coupling Coordination of Water and Land Resources Systems in Arid and Semi-arid Areas: A Case Study of Yulin City, China

Quantitatively assessing the carrying capacity of water and land resources systems in arid and semi-arid areas is crucial for achieving the 2030 Sustainable Development Goals.In this work,taking Yulin City in China as a case study and employing the Criteria Importance Through Intercriteria Correlation(CRITIC)method,a modified model of coupling degree was developed to evaluate the car-rying capacity of water and land resources systems endowment and utilization,as well as their coupling coordination degree from 2013 to 2020.Our findings indicate that the water and land resources of Yulin are diminishing due to declines in agriculture,higher industrial water use,and wetland shrinkage.However,reallocating domestic water for ecological sustainability and reducing sloping farmland can mitigate this trend of decline.Temporally,as the coupling coordination between water and land resources system endowment in Yulin continuously improved,the coupling coordination between water and land resources system utilization first decreased and then in-creased with 2016 as the turning point.Spatially,the carrying capacity of water and land resources systems,the coupling coordination degree between water and land resources system endowment,and the coupling coordination degree between water and land resources system utilization in Yulin exhibited the same pattern of being higher in the six northern counties than in the six southern counties.Improving the water resources endowment is vital for the highly efficient use of water and land resources.

Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.