Search

The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R2 > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products. [ABSTRACT FROM AUTHOR]

A multi-port energy router (MER) is an important infrastructure for power management and energy storage after an unexpected power outage. In addition, MERs can relate to various emergency electric power sources (EEPSs) and power grids at the same time. Moreover, by putting an MER in mobile energy storage, an MER allows for more flexible energy management and regulation in urban cities. This paper proposes a new topology of MERs composed of a bidirectional AC/DC converter and a partial power processing-based triple active bridge (PPP-based TAB) converter to supply emergency power for an unexpected power outage. This design of an MER improves the power efficiency significantly to realize peer-to-peer (P2P) trading which realizes the bidirectional power transmission among the main power grid, EEPSs such as the battery of electric vehicles (EV), and clients' loads. The control methods of the power transmission of two power electronic converters in MER are also illustrated in this paper to realize the power transmission among the main power grid, clients' loads, and EEPSs. This allows any power source connected to the MER to be used as an input or output to receive or send out power, which greatly improves the utilization of power. In conclusion, the MATLAB/Simulink and experiment test are executed and meticulously presented to validate the practicality of the proposed configuration. [ABSTRACT FROM AUTHOR]

Buildings are significant components of digital cities, and their precise extraction is essential for the three-dimensional modeling of cities. However, it is difficult to accurately extract building features effectively in complex scenes, especially where trees and buildings are tightly adhered. This paper proposes a highly accurate building point cloud extraction method based solely on the geometric information of points in two stages. The coarsely extracted building point cloud in the first stage is iteratively refined with the help of mask polygons and the region growing algorithm in the second stage. To enhance accuracy, this paper combines the Alpha Shape algorithm with the neighborhood expansion method to generate mask polygons, which help fill in missing boundary points caused by the region growing algorithm. In addition, this paper performs mask extraction on the original points rather than non-ground points to solve the problem of incorrect identification of facade points near the ground using the cloth simulation filtering algorithm. The proposed method has shown excellent extraction accuracy on the Urban-LiDAR and Vaihingen datasets. Specifically, the proposed method outperforms the PointNet network by 20.73% in precision for roof extraction of the Vaihingen dataset and achieves comparable performance with the state-of-the-art HDL-JME-GGO network. Additionally, the proposed method demonstrated high accuracy in extracting building points, even in scenes where buildings were closely adjacent to trees. [ABSTRACT FROM AUTHOR]

China is currently focusing on the cooperative control of air pollution and CO2 emissions, as well as the mitigation of economic policy uncertainty (EPU). By using panel data from 282 cities spanning from 2003 to 2017 and a newly constructed city-level EPU index, a spatial Durbin, two-way fixed-effects model is employed, with the aim of estimating the impact of EPU on the synergistic emissions intensity (SEI) of air pollutants and CO2. Additionally, this paper investigates the potential channels through which EPU influences SEI. It also explores how pressures related to environmental protection and economic development affect the impact of EPU on SEI. The results indicate that a unit increase in EPU will result in a rise in the SEI of local cities, adjacent cities, and total cities by 930.9%, 69,162.7%, and 70,093.6%, respectively. Moreover, the channel analysis suggests that EPU exacerbates SEI by undermining the upgrading of the industrial structure, augmenting industrial structure distortion, and escalating labor market distortion. Furthermore, the effect of EPU on SEI may be lessened by an increase in environmental protection pressure, while an increase in economic development pressure may exert a positive influence. Finally, this paper concludes by recommending that policymakers should prioritize the maintenance and stability of economic policies, facilitate the advancement of the industrial structure, enhance the efficiency of labor resource allocation, and underscore the significance of managing urban air pollution and CO2 emissions. [ABSTRACT FROM AUTHOR]

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China's urban industrial green TFPs as derived from the STE-SRM, employing Dagum's Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China's urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China's urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China's urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. [ABSTRACT FROM AUTHOR]

Digital financial inclusion and common prosperity are pivotal elements in promoting the sustainable socioeconomic development of China. This study introduces a novel Multi-Criteria Decision Analysis (MCDA) method to evaluate the Common Prosperity Index (CPI). Using this index, alongside the Digital Financial Inclusion Index (DFII) released by Peking University, it examines the evolution of the coupling coordination relationship between digital financial inclusion and common prosperity within the Yangtze River Delta (YRD) urban agglomeration from 2011 to 2021. By integrating gravity models and social network analysis, in this paper, we thoroughly investigate the spatiotemporal evolution characteristics of the spatial network of this coupling coordination relationship. The results indicate that both the DFII and CPI generally exhibit an upward trend, but the decline in the coupling degree reflects a weakened interaction strength between them. Specifically, Anhui significantly lags behind Jiangsu, Zhejiang, and Shanghai in the development of digital financial inclusion and common prosperity, indicating regional development imbalances. Furthermore, the strength of spatial connections in city coupling coordination has significantly increased, with Nanjing's siphon effect on cities in Anhui becoming markedly stronger, and the number of core cities in the network increasing, which demonstrates a geographical proximity feature in network development. Additionally, the overall network characteristics are transitioning towards higher density and "small-world" properties, suggesting a trend toward network stabilization. The disparity in centrality among cities has decreased, with an overall enhancement in centrality, where the spatial spillover effects from core areas such as Hangzhou-Ningbo, Nanjing-Changzhou, and Shanghai-Suzhou-Wuxi significantly promote the development of peripheral cities. Based on these findings, this paper proposes policy recommendations for the sustainable development of digital financial inclusion and common prosperity in the YRD region. [ABSTRACT FROM AUTHOR]

One-way carsharing is recognized as one of the most popular transportation services in urban areas, being an alternative option to private cars. Over the last decades, a vast amount of literature on the design of specific aspects of this service (fleet size, stations' locations, fare, balancing operations) has formed. However, a holistic approach for designing carsharing services seems not to be developed. This paper proposes a new approach for designing one-way station-based carsharing services, presenting a five-step method, entirely developed in a GIS environment. The first three steps (suitability analysis, site selection analysis, and walkability analysis) allow finding the candidate locations for carsharing stations. After the assessment of the capacity of the potential stations, a location-allocation analysis allows for assessing the fleet size, the number of stations that maximize the coverage of carsharing demand, and their optimal locations. This paper presents a case study: a new one-way carsharing service was designed in Palermo (Italy) and compared to the existing carsharing service operating in the city. The results highlight that the current carsharing supply is undersized, having about 45% fewer stations and about half the cars compared to those resulting from the model, leaving some POIs unserved. [ABSTRACT FROM AUTHOR]

Cities play a pivotal role in achieving worldwide carbon neutrality due to their significant contribution to global energy consumption and carbon emissions. Therefore, planning effective strategies and guiding evidence-based policymaking at the city scale becomes even more crucial. Composite indices serve as a valuable tool for monitoring urban energy transition trends. This paper aims to present a novel approach, robust and flexible even under conditions of data scarcity, for tracking the energy transition trend of a city by means of a composite index (UETI). The Turin case study is introduced to test the applicability of the proposed approach. Additionally, to demonstrate the robustness of the composite index framework, the paper includes the findings of correlation and sensitivity analyses. This study reveals a significant improvement in Turin's environmental and energy domains, while the socio-economic domain shows more modest improvement. Furthermore, the study highlights the need to address the shortage of urban data to enhance the accuracy and reliability of metric-based frameworks and to extend the assessment to a larger sample of cities. [ABSTRACT FROM AUTHOR]

Thedistribution of products stands out as one of the pivotal activities for logistics companies in recent years, particularly in the aftermath of the COVID-19 pandemic and other geopolitical events. Intense competition compels companies to efficiently execute their logistical processes, with cross-docking emerging as a frequently applied solution. However, the location of cross-dock terminals in urban areas remains a problem insufficiently addressed in the literature, with a dearth of studies and models tackling this issue. This paper introduces a novel and innovative model for locating cross-dock terminals based on the CI-DEA–IDOCRIW–MABAC (Composite Indicators–Data Envelopment Analysis-Integrated Determination of Objective Criteria Weights–Multi-Attributive Border Approximation Area Comparison) methods. In the process of defining input indicators, the following three sources were utilized: relevant literature, practical insights from logistics experts, and the knowledge and experience of the authors. Eight inputs and three outputs were considered (the number of users in the observed channel; the area served by the channel; the average distance a vehicle travels in one delivery; the required number of vehicles; labor availability; competition; construction, and expansion possibilities; proximity to the main infrastructure and traffic facilities; the average number of deliveries; average delivered quantity; and service level). The model underwent testing in a case study analyzing nine distribution channels (areas within the observed urban zone). The results indicated that alternative A4 (in the southwest area) ranked the highest since it was the best-ranked in accordance with the most important criteria, suggesting that the terminal is best located in the southwest zone. The accuracy of the results was confirmed by company management. By developing a completely new model and addressing the identified gap in the literature, this paper provides unequivocal scientific contributions. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to present an innovative approach to addressing the challenges of smart mobility exploiting digital twins within the METACITIES initiative. We have worked on this issue due to the increasing complexity of urban transportation systems, coupled with the urgent need to improve efficiency, safety, and sustainability in cities. The work presented in this paper is part of the project METACITIES, an Excellence Hub that spans a large geographical area, that of Southeastern Europe. The approach of the Greek innovation ecosystem of METACITIES involves leveraging digital twin technology to create intelligent replicas of urban mobility environments, enabling real-time monitoring, analysis, and decision making. Through use cases such as "Smart Parking", "Environmental Behavior Analysis on Traffic Incidents", and "Emergency Management", we demonstrate how digital twins can optimize traffic flow, mitigate environmental impact, and enhance emergency response; these use cases will be tested on a small scale, before deciding on implementation at a larger and more expensive scale. The final outcome is the METACITIES Architecture for smart mobility, which will be part of an Open Digital Twin Framework capable of evolving a smart city into a metacity. [ABSTRACT FROM AUTHOR]

This paper presents the Positive Energy District Database (PED DB), a pivotal web tool developed collaboratively by the COST Action 'PED-EU-NET', in alignment with international initiatives such as JPI Urban Europe and IEA EBC Annex 83. The PED DB represents a crucial step towards sharing knowledge, promoting collaboration, reinforcing decision-making, and advancing the understanding of Positive Energy Districts (PEDs) in the pursuit of sustainable urban environments. The PED DB aims to comprehensively map and disseminate information on PEDs across Europe, serving as a dynamic resource for sustainable urban development according to the objective of making the EU climate-neutral by 2050. Indeed, PEDs imply an integrated approach for designing urban areas—the districts—where a cluster of interconnected buildings and energy communities produce net zero greenhouse gas emissions, managing an annual local/regional overflow production of renewable energy. The paper describes the collaborative step-by-step process leading to the PED DB implementation, the current results and potentials of the online platform, and introduces its future developments towards a more user-friendly and stakeholders-tailored tool. The interactive web map offers a customizable visualizations and filters on multiple information related to PED case studies, PED-relevant cases, and PED Labs. Users can access detailed information through a table view, facilitating comparisons across different PED projects and their implementation phase. The paper offers insights and detailed analysis from the initial dataset that includes 23 PED cases and 7 PED-related projects from 13 European countries, highlighting the key characteristics of surveyed PEDs. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

To fully comprehend the patterns of land and ecological damage caused by coal mining subsidence, and to scientifically carry out ecological mine restoration and management, it is urgent to accurately grasp the information of coal mining, particularly in complex coaling areas, such as North Anhui, China. In this paper, a space–air–ground collaborative monitoring system was constructed for coal mining areas based on multi-source remote sensing data and subsidence characteristics of coaling areas were investigated in North Anhui. It was found that from 2019 to 2022, 16 new coal mining subsidence areas were found in northern Anhui, with the total area increasing by 8.1%. In terms of land use, water areas were increased by 101.9 km2 from 2012 to 2022, cultivated land was decreased by 99.3 km2, and residence land was decreased by 11.8 km2. The depth of land subsidence in the subsidence areas is divided into 307.9 km2 of light subsidence areas with a subsidence depth of less than 500 mm; 161.8 km2 of medium subsidence areas with a subsidence depth between 500 mm and 1500 mm; and 281.2 km2 of heavy subsidence areas with a subsidence depth greater than 1500 mm. The total subsidence governance area is 191.2 km2, accounting for 26.5% of the total subsidence area. From the perspective of prefecture-level cities, the governance rate reaches 51.3% in Huaibei, 10.1% in Huainan, and 13.6% in Fuyang. The total reclamation area is 68.8 km2, accounting for 34.5% of the subsidence governance area. At present, 276.1 km2 within the subsidence area has reached stable subsidence conditions, mainly distributed in the Huaibei mining area, which accounts for about 60% of the total stable subsidence area. [ABSTRACT FROM AUTHOR]

The auto-generation of urban roads can greatly improve efficiency and productivity in urban planning and designing. However, it has also raised concerns amongst researchers over the past decade. In this paper, we present an image-based urban road network generation framework using conditional diffusion models. We first trained a diffusion model capable of generating road images with similar characteristics to the ground truth using four context factors. Then, we used the trained model as the generator to synthesize road images conditioned in a geospatial context. Finally, we converted the generated road images into road networks with several post-processes. The experiments conducted in five cities of the United States showed that our model can generate reasonable road networks, maintaining the layouts and styles of real examples. Moreover, our model has the ability to show the obstructive effect of geographic barriers on urban roads. By comparing models with different context factors as input, we find that the model that considers all four factors generally performs the best. The most important factor in guiding the shape of road networks is intersections, implying that the development of urban roads is not only restricted by the natural environment but is more strongly influenced by human design. [ABSTRACT FROM AUTHOR]

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan's new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran's index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. [ABSTRACT FROM AUTHOR]

As global warming intensifies, reducing carbon emissions has become a global common mission. Tourism transportation is one of the important sources of carbon emissions, and reducing its carbon emissions is a key part of achieving China's carbon reduction goals. Based on the panel data of various provinces and cities in North China from 2000 to 2022, this paper calculates the carbon emissions of tourism transportation by using the carbon emission coefficients of different transportation modes in different segments. Moreover, the temporal and spatial evolution of the tourism economy is systematically analyzed. The Tapio decoupling model and LMDI addition decomposition model are used to analyze the relationship between carbon emissions and tourism economic growth and the effects of 11 influencing factors on carbon emissions. The results show that: (1) The carbon emission of tourism transportation in North China has experienced four stages: a steady growth period, a transitional adaptation period, a stable equilibrium period, and a drastic decline period. The overall carbon emission level of tourism transportation is as follows: Hebei Province > Shanxi Province > Inner Mongolia Autonomous Region > Beijing City > Tianjin City. (2) The decoupling coefficient between tourism traffic carbon emissions and economic development fluctuates but mainly shows a weak decoupling state. (3) In terms of influencing factors, passenger size and passenger density have the greatest impact on the carbon emissions of tourism transportation. [ABSTRACT FROM AUTHOR]

The increased seismic activity observed in the Himalayas, coupled with the expanding urbanization of the surrounding areas in northern India, poses significant risks to both human lives and property. Developing an earthquake early warning system in the region could help in alleviating these risks, especially benefiting cities and towns in mountainous and foothill regions close to potential earthquake epicenters. To address this concern, the government and the science and engineering community collaborated to establish the Uttarakhand State Earthquake Early Warning System (UEEWS). The government of Uttarakhand successfully launched this full-fledged operational system to the public on 4 August 2021. The UEEWS includes an array of 170 accelerometers installed in the seismogenic areas of the Uttarakhand. Ground motion data from these sensors are transmitted to the central server through the dedicated private telecommunication network 24 hours a day, seven days a week. This system is designed to issue warnings for moderate to high-magnitude earthquakes via a mobile app freely available for smartphone users and by blowing sirens units installed in the buildings earmarked by the government. The UEEWS has successfully issued alerts for light earthquakes that have occurred in the instrumented region and warnings for moderate earthquakes that have triggered in the vicinity of the instrumented area. This paper provides an overview of the design of the UEEWS, details of instrumentation, adaptation of attributes and their relation to earthquake parameters, operational flow of the system, and information about dissemination of warnings to the public. [ABSTRACT FROM AUTHOR]

Electric vehicles are becoming a mainstay of road transport. However, the uneven distribution of the electric vehicle charging piles in cities has led to the problem of "mileage anxiety". This has become a significant concern of consumers in purchasing electric vehicles. At the same time, it also hinders the development of the electric vehicle industry. For this reason, this paper designs an electric vehicle energy mutual aid device. In the event that the power battery of an electric vehicle is low on energy and there are no suitable charging piles around, the device can also seek energy supplementation from surrounding electric vehicles with sufficient energy. This device should satisfy the characteristics of wide gain, high power, small size, and light weight. Firstly, the quadratic boost converter and its state space model are constructed. It uses only one electrical switching element to realize the light weight of the device, and it also provides wide voltage gain to fulfill the needs of electric vehicle charging. Secondly, an improved voltage mode controller is proposed to address the shortcomings of the conventional voltage mode controller of the quadratic boost converter. It avoids the tradeoff between the transient and steady-state performance due to the integration action of the conventional voltage mode controller. Also, this controller uses less feedback to make the controlled system output the required DC power, reducing the weight of the device. Finally, the effectiveness of the proposed energy mutual aid device is verified by simulations and experiments. [ABSTRACT FROM AUTHOR]

Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. [ABSTRACT FROM AUTHOR]

Ground deformation is one of the crucial issues threatening many cities in both societal and economic aspects. Interferometric synthetic aperture radar (InSAR) has been widely used for deformation monitoring. Recently, there has been an increasing availability of massive archives of SAR images from various satellites or sensors. This paper introduces Multi-Satellite SBAS that exploits complementary information from different SAR data to generate integrated long-term ground displacement time series. The proposed method is employed to create the vertical displacement maps of Almokattam City in Egypt from 2000 to 2020. The experimental results are promising using ERS, ENVISAT ASAR, and Sentinel-1A displacement integration. There is a remarkable deformation in the vertical direction along the west area while the mean deformation velocity is −2.32 mm/year. Cross-validation confirms that the root mean square error (RMSE) did not exceed 2.8 mm/year. In addition, the research findings are comparable to those of the previous research in the study area. Consequently, the proposed integration method has great potential to generate displacement time series based on multi-satellite SAR data; however, it still requires further evaluation using field measurements. [ABSTRACT FROM AUTHOR]

Human movements in urban areas are essential to understand human–environment interactions. However, activities and associated movements are full of uncertainties due to the complexity of a city. In this paper, we propose a novel sensor-based approach for spatiotemporal event detection based on the Discrete Empirical Interpolation Method. Specifically, we first identify the key locations, defined as "sensors", which have the strongest correlation with the whole dataset. We then simulate a regular uneventful scenario with the observation data points from those key locations. By comparing the simulated and observation scenarios, events are extracted both spatially and temporally. We apply this method in New York City with taxi trip record data. Results show that this method is effective in detecting when and where events occur. [ABSTRACT FROM AUTHOR]

Urban logistics encompass transportation and delivery operations within densely populated urban areas. It faces significant challenges from the evolving dynamic and stochastic nature of on-demand and conventional logistics services. Further challenges arise with application doctrines shifting towards crowd-sourced platforms. As a result, "traditional" deterministic approaches do not adequately fulfil constantly evolving customer expectations. To maintain competitiveness, logistic service providers must adopt proactive and anticipatory systems that dynamically model and evaluate probable (future) events, i.e., stochastic information. These events manifest in problem characteristics such as customer requests, demands, travel times, parking availability, etc. The Stochastic Dynamic Vehicle Routing Problem (SDVRP) addresses the dynamic and stochastic information inherent in urban logistics. This paper aims to analyse the key concepts, challenges, and recent advancements and opportunities in the evolving urban logistics landscape and assess the evolution from classical VRPs, via DVRPs, to state-of-art SDVRPs. Further, coupled with non-reactive techniques, this paper provides an in-depth overview of cutting-edge model-based and model-free reactive solution approaches. Although potent, these approaches become restrictive due to the "curse of dimensionality". Sacrificing granularity for scalability, researchers have opted for aggregation and decomposition techniques to overcome this problem and recent approaches explore solutions using deep learning. In the scope of this research, we observed that addressing real-world SDVRPs with a comprehensive resolution encounters a set of challenges, emphasising a substantial gap in the research field that warrants further exploration. [ABSTRACT FROM AUTHOR]

The focus of this paper is the revitalization of a heritage city center and the significance of retail trade and cruise ship tourism within this context. Fundamental concepts are presented in the literature review and the factors contributing to the decline of old city centers explored. The empirical section of the paper comprises a case study that highlights a specific municipality's efforts to revive its heritage city center. The main attraction in Koper is its well-preserved medieval city center with five-centuries-old Venetian architecture. An analysis of the municipality's initiatives is performed and a qualitative study that investigates the perceptions and experiences of stakeholders regarding the situation and the municipality's actions to preserve commercial activity in the city center. The results of the semi-structured interviews conducted with three distinct stakeholder groups are analyzed using the qualitative content analysis method. The findings reveal that the chosen municipality has achieved relative success in revitalizing its heritage city center. Concurrently, stakeholders' perspectives are utilized to identify potential drawbacks and explore opportunities for mitigating them. This paper concludes by presenting its theoretical and methodological contributions to the field of heritage city center revitalization, not only in the selected municipality but also beyond. These insights can inform the appropriate sustainable policy development and marketing strategy. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to describe the process of building a coherent frame for the identification of local environmental urban vulnerabilities, coping with heat waves' increasing threats, and adopting specific adaptation policies in the Emilia-Romagna region. A microclimate model (ENVI-met) was used to simulate temperature regimes in five areas of Bologna, providing ex ante maps enabling us to locate the most vulnerable areas. Adaptation measures were suggested with the support of WMO Guidance 1234 and included recommendations about the introduction of high-albedo building materials and nature-based solutions. The step-by-step methodology developed, coupling local vulnerabilities with adaptation recommendation, integrates a scientific methodology into a political decision. The results, allowing us to widely represent this city's vulnerability, are considered outstanding with respect to supporting the city's adaptation ambitions and are now part of the running Bologna General Urban Plan (PUG), which regulates building interventions, introducing the obligation for enterprises to respect or ameliorate the microclimate in all the areas in which they intervene. This tool can be replicated and adapted to any municipal area, as it integrates authoritative WMO solutions with accurate microclimate assessment, thus providing locally tailored adaptation interventions. This paper aims to support the vision, shared by both science and policy makers, of transforming cities into widely resilient systems. [ABSTRACT FROM AUTHOR]

With the sustainable and coordinated development of cities, the formulation of urban street policies requires multiangle analysis. In regard to the existing street research, a large number of studies have focused on specific landscapes or accessibility of streets, and there is a lack of research on the multiple functions of streets. Recent advances in sensor technology and digitization have produced a wealth of data and methods. Thus, we may comprehensively understand streets in a less labor-intensive way, not just single street functions. This paper defines an index system of the multiple functions of urban streets and proposes a framework for multifunctional street measurement. Via the application of deep learning to Baidu Street View (BSV) imagery, we generate three functions, namely, landscape, traffic, and economic functions. The results indicate that street facilities and features are suitably identified. According to the multifunctional perspective, this paper further classifies urban streets into multifunctional categories and provides targeted policy recommendations for urban street planning. There exist correlations among the various street functions, and the correlation between the street landscape and economic functions is highly significant. This framework can be widely applied in other countries and cities to better understand street differences in various cities. [ABSTRACT FROM AUTHOR]

Amid growing academic discourse on teenagers' political rights, this paper argues that the inclusion of teenagers in the decision-making process at the municipal level has positive effects. Based on qualitative and quantitative research on three municipalities in Greece and Sweden, this paper concludes that a level of awareness of the critical issue of climate change leads to a greater propensity for action from municipal councils aimed at restoring the environment. The findings demonstrate that including teenagers in the decision-making process at the local and regional level could lead to a greater focus on forward-thinking policies, particularly in areas concerning young people, such as environmental preservation efforts and democratic rights. [ABSTRACT FROM AUTHOR]

This paper examines the challenges of and facilitating strategies for linking disaster risk reduction (DRR) and climate change adaptation (CCA) in addressing urban floods, drawing from Indonesia's experience. The fragmentation between efforts to implement DRR and CCA leading to unnecessary duplication could increase confusion at both the community and wider governance levels and reduce the effectiveness of urban flood management. Through the lens of collaborative governance, this paper analyzes the barriers for integrating DRR and CCA and options to better align their practices in the context of a megapolitan city, Jakarta, Indonesia. The key findings of this study confirmed that institutional fragmentation, in concert with inconsistent facilitation and collaboration mechanisms, were the strongest barriers to aligning DRR and CCA action. The absence of accountable leadership was a key impediment for successful partnership-building processes to support political and technical collaboration. Leadership in these contexts plays an important role in (1) developing sustainable relationships, (2) convincing potential stakeholders to collaborate, (3) persuading partners to commit to sharing resources, and (4) agreeing/sharing a common vision of the partnership actions needed to mitigate harm and reduce urban vulnerability. These factors are critically important for reducing the direct and indirect impacts of flooding in Jakarta. Such lessons from Indonesia on linking DRR and CCA offer valuable insights to inform the development of policies and strategies to deal with urban floods for global cities faced with similar challenges. [ABSTRACT FROM AUTHOR]

Urban areas generate more than 70% of the world's climate change emissions, mainly CO2, produced by the combustion of fossil fuels. Climate change is increasing cities' exposure to climate hazards, such as heat waves or floods. Therefore, there is a need to improve risk management with the inclusion of climate resilience in urban policy design. Despite improved urban climate monitoring, there are still relatively few scientific publications on climate change adaptation in urban areas. Adaptation to climate change is not achieved through specific action, but rather through the adoption and continuous implementation of adaptation actions such as housing rehabilitation, green space management and protection measures for vulnerable groups. This variety of actions makes it difficult not only to identify different indicators, but also to use common benchmarks. Considering the role of municipalities in adapting to climate change, it is crucial to identify adaptation indicators that serve as a basis for decision making, as well as evaluation methods that allow the effectiveness of planned and implemented measures in municipalities. It can be used to determine which measures increase the level of adaptation or lead to poor adaptation. Therefore, monitoring indicators makes it possible to evaluate the effectiveness of the measures, in addition to formulating new ones. This paper includes a literature review of existing index designed to address climate hazards and mitigate their impacts in urban areas. [ABSTRACT FROM AUTHOR]

Urban carbon emissions are an important area for addressing climate change, and it is necessary to establish scientific and effective carbon emission prediction models to formulate reasonable emission reduction policies and measures. In this paper, a novel model based on Lasso regression, an ARIMA model, and a BPNN is proposed. Lasso regression is used to screen the key factors affecting carbon emissions, and the ARIMA model is used to extract the linear components of the carbon emission sequences, while the BPNN is used to predict the residuals of the ARIMA model. The final result is the sum of that from the ARIMA model and the BPNN. The carbon peak, carbon neutralization time, and emissions were analyzed under different scenarios. Taking Suzhou City as an example, the results show that the electricity consumption of the whole population is one of the key drivers of carbon emissions; the carbon emission prediction accuracy and stability of the ARIMA-BPNN combined model are better than those of the single model, which improves the reliability as well as the accuracy of the model's prediction. However, under the constraints of the current policies, the goal of achieving carbon peaking by 2030 in Suzhou City may not be realized as scheduled. This novel carbon emission prediction model built was validated to provide a scientific basis for low-carbon urban development. This study presents an important reference value for predicting carbon emissions and formulating emission reduction measures in other cities. [ABSTRACT FROM AUTHOR]

Fire chemical sensing for indoor detection of fire plays an essential role because it can detect chemical volatiles before smoke particles, providing a faster and more reliable method for early fire detection. A thermal imaging camera and seven distinct fire-detecting sensors were used simultaneously to acquire the multimodal fire data that is the subject of this paper. The low-cost sensors typically have lower sensitivity and reliability, making it impossible for them to detect fire at greater distances. To go beyond the limitation of using solely sensors for identifying fire, the multimodal dataset is collected using a thermal camera that can detect temperature changes. The proposed pipeline uses image data from thermal cameras to train convolutional neural networks (CNNs) and their many versions. The training of sensors data (from fire sensors) uses bidirectional long-short memory (BiLSTM-Dense) and dense and long-short memory (LSTM-DenseDenseNet201), and the merging of both datasets demonstrates the performance of multimodal data. Researchers and system developers can use the dataset to create and hone cutting-edge artificial intelligence models and systems. Initial evaluation of the image dataset has shown densenet201 as the best approach with the highest validation parameters (0.99, 0.99, 0.99, and 0.08), i.e., Accuracy, Precision, Recall, and Loss, respectively. However, the sensors dataset has also shown the highest parameters with the BILSTM-Dense approach (0.95, 0.95, 0.95, 0.14). In a multimodal data approach, image and sensors deployed with a multimodal algorithm (densenet201 for image data and Bi LSTM- Dense for Sensors Data) has shown other parameters (1.0, 1.0, 1.0, 0.06). This work demonstrates that, in comparison to the conventional deep learning approach, the federated learning (FL) approach performs privacy-protected fire leakage classification without significantly sacrificing accuracy and other validation parameters. [ABSTRACT FROM AUTHOR]

Building extraction plays an important role in urban information analysis, which is helpful for urban planning and disaster monitoring. With the improvement of SAR resolution, rich detailed information in urban areas is revealed, but the discretized features also pose challenges for object detection. This paper addresses the problem of individual high-rise building extraction based on single high-resolution SAR image. Different from previous methods that require building facades to be presented in specific appearances, the proposed method is suitable for extraction of various types of high-rise buildings. After analyzing the SAR images of many types of high-rise buildings, we establish a unified high-rise building part model, on the basis of a scattering mechanism of building structures, to describe the facade characteristics of high-rise buildings, including a facade regularity part, facade bright line part, double bounce part, and their spatial topological relationships. A three-level high-rise building extraction framework, named HRBE-PM, is proposed based on the high-rise building part model. At the pixel level, a modified spot filter is used to extract bright spots and bright lines of different scales simultaneously to obtain salient features. At the structure level, building parts are generated based on the salient features according to semantic information. At the object level, spatial topological information between parts is introduced to generate building hypotheses. We define two indicators, completeness and compactness, to comprehensively evaluate each building hypothesis and select the optimal ones. After postprocessing, the final high-rise building extraction results are obtained. Experiments on two TerraSAR-X images show that the high-rise building extraction precision rate of the HRBE-PM method is above 85.29%, the recall rate is above 82.95%, and the F1-score is above 0.87. The results indicate that the HRBE-PM method can accurately extract individual high-rise buildings higher than 24 m in most dense scenes, regardless of building types. [ABSTRACT FROM AUTHOR]

Many methods exist for solving the problem of evaluating efficiency in different processes. They are divided into two basic groups, parametric and non-parametric methods, which can have significant differences in the results. In this study, the authors consider the process of assessing the business climate depending on realized foreign investments. Due to the expected difference in efficiency assessment using different approaches, the goal of this paper is to create an optimization model of an ensemble for efficiency assessment that uses both types of methods with the aim of creating a symmetrical approach that achieves better results than each type of method individually. The proposed solution simultaneously analyzes the impact of different factors on foreign investments in order to determine the most important factors and thus enable each local government to ensure the best possible efficiency in this process. The innovative idea of this study is in the inclusion of classification and feature selection methods of machine learning to fulfill the set goal. Our research, focused on a specific case study in various cities across the Republic of Serbia, evaluated the effectiveness of that process. This study extends previous research and confirms the published results, highlighting the advantages of the newly proposed model. [ABSTRACT FROM AUTHOR]

The call for papers for this Special Issue Iidentifies contemporary humanity as experiencing a global "biogeochemical ... political, economic, technological, ethical, and therefore, biocultural" crisis and asks scholars to consider how "religion may function as an adaptive or maladaptive presence" in response. Unasked is the adaptive capacity of scholarship as a crisis response. When buildings fall in earthquakes, or cities burn in wildfires, or second stories flood, few people just keep on doing what they were doing, "with a change of focus". This is "studying Rome while it burns". It's time to put out the fire if we can and survive it if we cannot. We scholar/teachers can't go on doing the same things and expecting different results. Unprecedented circumstances call for unprecedented actions in response. What would actual crisis responses on our part look like? What steps do we need to take as human beings in response to this crisis? How will that affect us as professionals? Seeking an ecology, rather than unanimity, of action and thought, and guided by Brian Walker's resilience theory and a number of Indigenous scholars, I suggest a process of reintegration, analogous to regenerative agriculture, which is at once both socio-cultural and ecological. This process, necessarily rooted in place, progressively situates us experientially in a dynamic, creative, and relational world characterized by connection, collaboration, and relation. As scholars, we will find forms of discovery, discussion, and dissemination that share these qualities. As teachers, we will model this world to our students and embody it in our classrooms and curricula. [ABSTRACT FROM AUTHOR]

Rapid global urbanization has led to a growing urban population, posing challenges in transportation management. Persistent issues such as traffic congestion, environmental pollution, and safety risks persist despite attempts to mitigate them, hindering urban progress. This paper focuses on the critical need for accurate traffic flow forecasting, considered one of the main effective solutions for containing traffic congestion in urban scenarios. The challenge of predicting traffic flow is addressed by proposing a two-level machine learning approach. The first level uses an unsupervised clustering model to extract patterns from sensor-generated data, while the second level employs supervised machine learning models. Although the proposed approach requires the availability of data from traffic sensors to realize the training of the machine learning models, it allows traffic flow prediction in urban areas without sensors. In order to verify the prediction capability of the proposed approach, a real urban scenario is considered. [ABSTRACT FROM AUTHOR]

Due to increasing urbanization, nowadays, cities are facing challenges spanning multiple domains such as mobility, energy, environment, etc. For example, to reduce traffic congestion, energy consumption, and excessive pollution, big data gathered from legacy systems (e.g., sensors not conformant with modern standards), geographic information systems, gateways of public administrations, and Internet of Things technologies can be exploited to provide insights to assess the current status of a city. Moreover, the possibility to perform what-if analyses is fundamental to analyzing the impact of possible changes in the urban environment. The few available solutions for scenario definitions and analyses are limited to addressing a single domain and providing proprietary formats and tools, with scarce flexibility. Therefore, in this paper, we present a novel scenario model and editor integrated into the open-source Snap4City.org platform to enable several processing and what-if analyses in multiple domains. Different from state-of-the-art software, the proposed solution responds to a series of identified requirements, implements NGSIv2-compliant data models with formal descriptions of the urban context, and a scenario versioning method. Moreover, it allows us to carry out analyses on different domains, as shown with some examples. As a case study, a traffic congestion analysis is provided, confirming the validity and usefulness of the proposed solution. This work was developed in the context of CN MOST, the National Center on Sustainable Mobility in Italy, and for the Tourismo EC project. [ABSTRACT FROM AUTHOR]

Progressive climate changes, drought resulting from them and the prospect of problems with access to water for people in cities mean that actions are being taken to minimize water use in buildings and to implement a circular economy in the water and wastewater sector. Within the water circular economy model, there is also a stage of "water consumption". Minimizing water use in buildings undoubtedly has a number of advantages. However, it should be borne in mind that it may also have weaknesses, and if implemented on a large scale, it may be associated with certain threats. For these reasons, the aim of this paper is to critically analyze the possible directions of water management in buildings in order to reduce water consumption and increase the efficiency of its use. As part of the introduction, the model "towards a water circular economy for households" is presented and the possibilities of minimizing water consumption in buildings are discussed. The prospects for reducing the consumption of tap water are discussed in terms of existing opportunities, but also threats, barriers and limitations. A SWOT analysis of the implementation of drinking-water consumption reduction in cities is presented. The challenges faced by engineers, constructors, policy makers and consumers, and the potentialities for the development of this stage of the water life cycle, are considered. The conclusions summarize the current state and perspectives of water management in buildings. Based on the conducted analysis, suggested directions of activities for cities of the future in the technical, technological as well as socio-economic fields are indicated. There should be broad-based education, and efforts should be made to change the approach to designing and developing new guidelines. The implementation of minimizing water consumption should be accompanied by the control of possible negative effects and actions to mitigate them. In the transformation towards clean and available energy, future success should be seen in minimizing the consumption of drinking water in buildings. [ABSTRACT FROM AUTHOR]

Index spaces serve as valuable metric models for studying properties relevant to various applications, such as social science or economics. These properties are represented by real Lipschitz functions that describe the degree of association with each element within the underlying metric space. After determining the index value within a given sample subset, the classic McShane and Whitney formulas allow a Lipschitz regression procedure to be performed to extend the index values over the entire metric space. To improve the adaptability of the metric model to specific scenarios, this paper introduces the concept of a composition metric, which involves composing a metric with an increasing, positive and subadditive function ϕ. The results presented here extend well-established results for Lipschitz indices on metric spaces to composition metrics. In addition, we establish the corresponding approximation properties that facilitate the use of this functional structure. To illustrate the power and simplicity of this mathematical framework, we provide a concrete application involving the modeling of livability indices in North American cities. [ABSTRACT FROM AUTHOR]

Ubiquitous street lighting is essential for urban areas. While nowadays, LED-based "smart lamps" are commercially available, municipalities can only switch to them in the long run due to financial constraints. Especially, older types of lamps require frequent bulb replacements to maintain the lighting infrastructure's function. To speed up the detection of defects and enable better planning, a non-invasively retrofittable IoT sensor solution is proposed that monitors lamps for defects via visible light sensors, communicates measurement data wirelessly to a central location via LoRaWAN, and processes and visualizes the resulting information centrally. The sensor nodes are capable of automatically adjusting to shifting day- and nighttimes thanks to a second sensor monitoring ambient light. The work specifically addresses aspects of energy efficiency essential to the battery-powered operation of the sensor nodes. Besides design considerations and implementation details, the paper also summarizes the experimental validation of the system by way of an extensive field trial and expounds upon further experiences from it. [ABSTRACT FROM AUTHOR]

Early monastic communities in Egypt were veritable laboratories for the practice of Christian virtue; perhaps surprisingly, they were also large-scale coordinated communities of labor. That manual labor should have been part of anchoritic life is not obvious; given that hermits were leaving the cities and the usual occupations of life in the world, there might be a question as to why they would seemingly return to such occupations having sought the purity of living alone in the desert. Combining Platonic thought with radical Christianity, the monks found a way to make the maximally spiritual life also a worker's life. The architects of this form of life saw manual labor as a means for achieving self-sustenance, an effective weapon against temptation, a resource for the support of the needy, and a vital component in the monks' ascetic program. The argument of this paper is that this powerful cultural consensus on the centrality of work to monastic life endured for almost a thousand years before it came to be qualified, by Thomas Aquinas among others. When Thomas Aquinas writes on the purposes of manual labor he is entirely traditional. However, Aquinas ends up diminishing the extent to which the pursuit of the traditional goods gained by the practice of manual labor is obligatory for monastics. Aquinas's discussion of manual labor as an element of monastic life is a definite departure from the tradition. In the typically polite fashion of a scholastic theologian, Aquinas shifts away from Augustine and re-interprets St. Paul in unprecedented fashion. His argument is influenced by his own commitment to a new form of monastic life, which was changing not just theologically but as a result of the evolving backdrop of the social and economic realities with which religious life necessarily interacted. [ABSTRACT FROM AUTHOR]

Seismic-resistant design incorporates measures to ensure that structures perform adequately under specific limit states, focusing on seismic forces derived from both the equivalent static and spectral modal methods. This study examined buildings on slopes in densely built urban areas, a common scenario in Latin American cities with high seismic risks. The adjustment of high-rise buildings to sloping terrains induces structural asymmetry, leading to plan and elevation irregularities that significantly impact their seismic response. This paper explores the asymmetry in medium-height reinforced concrete frame buildings on variable inclines (0°, 15°, 30°, and 45°) and its effect on their nonlinear response, assessed via displacements, rotations, and damage. Synthetic accelerograms matched with Chile's high seismic hazard design spectrum, scaled for different performance states and seismic records from the Chilean subduction zone, were applied. The findings highlight structural asymmetry's role in influencing nonlinear response parameters such as ductility, transient interstory drifts, and roof rotations, and uncover element demand distributions surpassing conventional analysis and in earthquake-resistant design expectations. [ABSTRACT FROM AUTHOR]

The electrification of transport is expected to progressively replace significant shares of light duty mobility, especially in large cities. The European Alternative Fuel Infrastructure Regulation (AFIR) aims to drive the adoption of electric mobility by establishing specific targets for charging point deployment. Innovative charging concepts may complement and accelerate the uptake of this fundamental part of the urban mobility transition. In this paper, one such innovative concept is described and its potential impact is assessed. The core idea involves integrating charging points into existing city tramway infrastructures. Turin's tramway network is taken as a representative case study. The proposed technical solution encompasses a charging hub powered by four isolated DC/DC converters of 50 kW, directly connected to the DC tramway distribution line. Three of these constitute the heart of a 150 kW charger, while the fourth acts as voltage regulator. This native DC installation greatly simplifies the architecture of the DC chargers. Using a conservative approach, it was estimated that a single recharging station could charge more than 60 vehicles daily. This highly scalable and replicable solution, with the potential for over 100 conversion substations across Italy, would enable the installation of numerous high-power chargers in urban settings. Furthermore, additional benefits could be realized through enhanced recovery of kinetic energy from trams, which is currently dissipated on-board. [ABSTRACT FROM AUTHOR]

Roads are the fundamental elements of transportation, connecting cities and rural areas, as well as people's lives and work. They play a significant role in various areas such as map updates, economic development, tourism, and disaster management. The automatic extraction of road features from high-resolution remote sensing images has always been a hot and challenging topic in the field of remote sensing, and deep learning network models are widely used to extract roads from remote sensing images in recent years. In light of this, this paper systematically reviews and summarizes the deep-learning-based techniques for automatic road extraction from high-resolution remote sensing images. It reviews the application of deep learning network models in road extraction tasks and classifies these models into fully supervised learning, semi-supervised learning, and weakly supervised learning based on their use of labels. Finally, a summary and outlook of the current development of deep learning techniques in road extraction are provided. [ABSTRACT FROM AUTHOR]

Contemporary societies are increasingly multireligious, multiethnic, and multicultural, but to what extent are they ready for coexistence? This paper evaluates the competencies for intercultural and interreligious dialogue in two very different contexts: Barcelona and Melilla, two cities with great ethnic and cultural diversity, in which it is easy to have contact with people of different cultures and religions. To this end, we worked with a total of 1353 adolescents and young people, and four scales were used to evaluate intercultural and interreligious sensitivity, conflict management skills, and prejudices towards unaccompanied migrant minors. The findings suggest that in neither of these two contexts do adolescents and young people form relationships with these minors, despite accepting religious diversity and being educated at school in topics relating to coexistence and spirituality. In fact, participants showed a high level of prejudice towards this population. Implementation of more effective intervention programs in both cities is therefore recommended. [ABSTRACT FROM AUTHOR]

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the 'Google Earth Engine' database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city's land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations. [ABSTRACT FROM AUTHOR]

Reducing the heat demand of existing buildings is an essential prerequisite for achieving a greenhouse gas-neutral energy supply. Numerous studies and open-source tools deal with heat demand mapping. It is not uncommon that estimated heat demands deviate from real heat consumption, so existing approaches should be improved by including in-depth building information. Some tools have recognised this problem and offer built-in functions for factoring various parameters into their assessments. Nevertheless, the necessary information is usually missing and should be obtained first. In this paper, we analyse the impact of thermal refurbishment and climate on building heat demand; hence, generate public datasets with corresponding key figures for each building type in different efficiency states and years. Accounting for already performed refurbishments in methodologies for assessing the actual state heat demand for cities will result in a reduction of at least 8% up to more than 21%, depending on whether conventional or passive house components were installed. As a result of climatic differences within Germany, a building's heat demand can be up to 39% higher or up to 21% lower than the heat demand of an identical building in the reference climate of Germany. By further developing the approaches of the tools Hotmaps and Heat Cadastre Hamburg, we could improve the estimated heat demand of Hamburg to a value approximating the real consumption. [ABSTRACT FROM AUTHOR]

Two-wheeled non-motorized vehicles (TNVs) have become the primary mode of transportation for short-distance travel among residents in many underdeveloped cities in China due to their convenience and low cost. However, this trend also brings corresponding risks of traffic accidents. Therefore, it is necessary to analyze the driving behavior characteristics of TNVs through their trajectory data in order to provide guidance for traffic safety. Nevertheless, the compact size, agile steering, and high maneuverability of these TNVs pose substantial challenges in acquiring high-precision trajectories. These characteristics complicate the tracking and analysis processes essential for understanding their movement patterns. To tackle this challenge, we propose an enhanced You Only Look Once Version X (YOLOx) model, which incorporates a median pooling-Convolutional Block Attention Mechanism (M-CBAM). This model is specifically designed for the detection of TNVs, and aims to improve accuracy and efficiency in trajectory tracking. Furthermore, based on this enhanced YOLOx model, we have developed a micro-trajectory data mining framework specifically for TNVs. Initially, the paper establishes an aerial dataset dedicated to the detection of TNVs, which then serves as a foundational resource for training the detection model. Subsequently, an augmentation of the Convolutional Block Attention Mechanism (CBAM) is introduced, integrating median pooling to amplify the model's feature extraction capabilities. Subsequently, additional detection heads are integrated into the YOLOx model to elevate the detection rate of small-scale targets, particularly focusing on TNVs. Concurrently, the Deep Sort algorithm is utilized for the precise tracking of vehicle targets. The process culminates with the reconstruction of trajectories, which is achieved through a combination of video stabilization, coordinate mapping, and filtering denoising techniques. The experimental results derived from our self-constructed dataset reveal that the enhanced YOLOx model demonstrates superior detection performance in comparison to other analogous methods. The comprehensive framework accomplishes an average trajectory recall rate of 85% across three test videos. This significant achievement provides a reliable method for data acquisition, which is essential for investigating the micro-level operational mechanisms of TNVs. The results of this study can further contribute to the understanding and improvement of traffic safety on mixed-use roads. [ABSTRACT FROM AUTHOR]

With the increasing demand for sustainable urban development, smart cities have emerged as a promising solution for optimizing energy usage, reducing emissions, and enhancing the quality of life for citizens. In this context, the combined use of key performance indicators (KPIs) and data analytics has gained significant attention as a powerful tool for promoting energy efficiency and emissions reduction in urban areas. This paper presents a comprehensive conceptual framework in which a series of KPIs are proposed to serve as essential metrics for guiding, monitoring, and assessing energy efficiency and emissions reduction levels in smart cities. Some of the included KPIs in the analysis are 'annual energy consumption per person', 'reduction in greenhouse gas emissions', 'public transport use', and 'adoption of renewable energy'. By incorporating these KPIs, city planners and policymakers can gain valuable insights into the effectiveness of sustainability initiatives. Furthermore, the paper explores how the integration of KPIs with data analytics can be used for monitoring and assessing the overall performance of the city in terms of energy efficiency, emissions reduction, and the enhancement of urban living conditions. Visualization tools, such as radar plots, and time series analysis forecasting methods allow data to be processed and patterns to be identified, enabling informed decision-making and efficient resource allocation. Real-life case studies of ongoing smart city projects are presented in the paper, which also provides a KPI comparison among different European cities, as well as models to forecast the evolution of KPIs related to energy usage and emissions reduction in different European cities. [ABSTRACT FROM AUTHOR]

In several Western European countries, a significant share of social rental housing stock has been sold since the 1980s as part of government policies aimed at promoting homeownership societies. Research has shown that tenure conversion has contributed to increasing socio-spatial segregation of lower-income groups, with diverging spatial patterns of homeownership among immigrants. This paper examines the impact of recent public housing privatization schemes in Milan in relation to micro-segregation and peripheralization processes of foreign populations, which represent distinctive features of immigrant residential distribution in this city. By employing name analysis, an unconventional approach in segregation studies, I inferred the geographical origins of homebuyers and mapped their distribution across the city. The findings reveal divergent purchasing behaviors, whereby Italians predominantly acquire properties in semi-central areas currently undergoing urban regeneration. In contrast, immigrants tend to concentrate their acquisitions in peripheral post-war public housing neighborhoods or in areas predominantly inhabited by residents with similar geographical origins. This paper contributes to the existing literature on ethnic residential segregation in Southern European cities by shedding light on the underexplored role of public housing privatization policies in shaping specific residential patterns and housing outcomes among different groups. [ABSTRACT FROM AUTHOR]

Higher education has been facing many challenges due to factors including increased diversification, internationalization, massification, and the expansion of different forms of mobility, which are transforming the landscape of higher education towards "superdiversity". These challenges are addressed within the framework of "inclusive education", aiming to increase participation and foster a culture of welcome at higher education institutions. However, scholarly discussions on the ways these initiatives impact the lived experience of students who may face divergent and intersectional forms of exclusion, inequalities, tensions, and discrimination are limited. To address this gap, based on the experience of three participating universities in the European University of Post-Industrial Cities (UNIC) alliance, this paper aims to examine and reflect on the diversity and inclusion practices of the institutions, particularly those targeting concerns for international students with different profiles. Drawing on both existing and emergent data through an extensive case study analysis, the paper focuses on the scope and effectiveness of existing support mechanisms. It concludes that improving the experience of international students and promoting their inclusion at universities requires a combination of top-down and bottom-up mechanisms, as well as centralised and decentralised services. Systematic data collection using a range of engaged research tools also ensures that policies respond to real needs. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) power generation is booming in rural areas, not only to meet the energy needs of local farmers but also to provide additional power to urban areas. Existing methods for estimating the spatial distribution of PV power generation potential either have low accuracy and rely on manual experience or are too costly to be applied in rural areas. In this paper, we discuss three aspects, namely, geographic potential, physical potential, and technical potential, and propose a large-scale and efficient PV potential estimation system applicable to rural rooftops in China. Combined with high-definition map images, we proposed an improved SegNeXt deep learning network to extract roof images. Using the national standard Design Code for Photovoltaic Power Plants (GB50797-2012) and the Bass model, computational results were derived. The average pixel accuracy of the improved SegNeXt was about 96%, which well solved the original problems of insufficient finely extracted edges, poor adhesion, and poor generalization ability and can cope with different types of buildings. Leizhou City has a geographic potential of 1500 kWh/m2, a physical potential of 25,186,181.7 m2, and a technological potential of 442.4 MW. For this paper, we innovatively used the Bass Demand Diffusion Model to estimate the installed capacity over the next 35 years and combined the Commodity Diffusion Model with the installed capacity, which achieved a good result and conformed to the dual-carbon "3060" plan for the future of China. [ABSTRACT FROM AUTHOR]