Search Results (17,059)

As urbanization accelerates, a host of negative ecological impacts have become increasingly prominent. Green roofs, as a sustainable solution, can effectively mitigate the urban heat island effect and reduce carbon footprints. However, the lack of datasets on plant species suitable for green roofs in China has hindered the advancement of relevant research and practical applications. Therefore, this study constructed a diversified dataset of plant species for green roofs in China, using data sources from the China National Knowledge Infrastructure (CNKI) and Web of Science (WoS). Generated by integrating the Kimi large language model (Kimi LLM) API with knowledge graph technology, the dataset contains 2248 plant records. It specifically includes a statistical CSV file with detailed plant information, a CSV file of species combinations, a CSV file linking plant combinations to cities, and original plant data extracted from research papers. Technical experiments have validated the accuracy and efficiency of this dataset in acquiring plant species. Suitable for plant selection in green roof projects, this dataset will provide strong support for in-depth research and wider applications in the field of urban sustainability. Full article

Digital transformation (DT) has become a strategic priority for public administrations, particularly due to the need to deliver more efficient and citizen-centered services and respond to societal expectations, ESG (Environmental, Social, and Governance) criteria, and the United Nations Sustainable Development Goals (UN SDGs). In this context, the main objective of this study is to propose an innovative methodology to automatically evaluate the level of digital transformation (DT) in public sector organizations. The proposed approach combines traditional assessment methods with Artificial Intelligence (AI) techniques. The methodology follows a dual approach: on the one hand, surveys are conducted using specialized staff from various public entities; on the other, AI-based models (including neural networks and transformer architectures) are used to estimate the DT level of the organizations automatically. Our approach has been applied to a real-world case study involving local public administrations in the Valencian Community (Spain) and shown effective performance in assessing DT. While the proposed methodology has been validated in a specific local context, its modular structure and dual-source data foundation support its international scalability, acknowledging that administrative, regulatory, and DT maturity factors may condition its broader applicability. The experiments carried out in this work include (i) the creation of a domain-specific corpus derived from the surveys and websites of several organizations, used to train the proposed models; (ii) the use and comparison of diverse AI methods; and (iii) the validation of our approach using real data. Based on the deficiencies identified, the study concludes that the integration of technologies such as the Internet of Things (IoT), sensor networks, and AI-based analytics can significantly support resilient, agile urban environments and the transition towards more effective and sustainable Smart City models. Full article

Given the growing adverse effects of drought on water resources, agriculture, and various sectors, assessing and evaluating drought and producing high-quality drought maps despite the data scarcity to better understand its impacts and develop effective mitigation strategies is essential. Considering the existing gaps related to drought evaluation, especially in scarce data regions, this research aims to evaluate the efficiency of acceptable time period for drought studies (10–20 years), evaluate the performance of ERA5-LAND and IMERG-NASA precipitation data in estimating the Standardized Precipitation Index (SPI) using different statistical metrics and the innovative drought classification matrix (IDCM), and finally produce and compare high-quality and accurate drought characteristics maps resulted from in situ stations, ERA5-LAND, and IMERG-NASA. The Kocaeli province in Türkiye, which has limited data and is a scarce data region, has been selected as an application. The results ensure that an acceptable time period can be sufficient and provide reliable accuracy for assessing drought with RMSE ranging between 0.09 and 0.23 standard deviation and IDCM ranging between 85% and 97%. NASA IMERG data gave more accurate drought results than ERA5-LAND, and the Pearson correlation ranges between 0.57 and 0.89. Also, in situ data showed longer drought duration, while ERA5-LAND and NASA had higher intensity. This article enables policymakers and decision-makers to manage and plan water resources within the city boundary, ensuring sustainable agricultural, economic, and industrial activities and supporting effective climate change adaptation strategies. Full article

The intensification of urban heat in high-density cities has raised growing concerns for public health, infrastructural resilience, and environmental sustainability. As large-scale, multi-functional open spaces, sports stadiums play an underexplored role in shaping urban thermal patterns. This study investigates the spatial and temporal thermal characteristics of eight representative stadiums in central Shanghai and the Pudong New Area from 2018 to 2023. A dual-framework approach is proposed: the Stadium-based Urban Island Regulation (SUIR) model conceptualizes stadiums as active cooling agents across micro to macro spatial scales, while the Multi-source Thermal Cognition System (MTCS) integrates multi-sensor satellite data—Landsat, MODIS, Sentinel-1/2—with anthropogenic and ecological indicators to diagnose surface temperature dynamics. Remote sensing fusion and machine learning analyses reveal clear intra-stadium thermal heterogeneity: track zones consistently recorded the highest land surface temperatures (up to 37.5 °C), while grass fields exhibited strong cooling effects (as low as 29.8 °C). Buffer analysis shows that cooling effects were most pronounced within 300–500 m, varying with local morphology. A spatial diffusion model further demonstrates that stadiums with large, vegetated buffers or proximity to water bodies exert a broader regional cooling influence. Correlation and Random Forest regression analyses identify the building volume (r = 0.81), NDVI (r = −0.53), nighttime light intensity, and traffic density as key thermal drivers. These findings offer new insight into the role of stadiums in urban heat mitigation and provide practical implications for scale-sensitive, climate-adaptive urban planning strategies. Full article

Background/Objectives: Acute liver failure (ALF) is a life-threatening condition with high mortality in nontransplant candidates. Therapeutic plasma exchange (TPE) has emerged as a promising intervention for removing inflammatory mediators and toxic metabolites. In Latin America, data on the efficacy of TPE in ALF patients are limited. This real-world study aimed to compare 30-day survival outcomes between patients receiving standard medical treatment (SMT) and those receiving SMT plus TPE. Methods: We analyzed 25 ALF patients admitted to the tertiary intensive care unit (ICU) of Hospital Juárez of Mexico City, Mexico, from 2018 to 2024. Patients received either standard medical treatment (SMT group, n = 12) or SMT with TPE (TPE group, n = 13), including high-volume TPE (n = 8) and standard-volume TPE (n = 5). Survival analysis was performed via Kaplan–Meier estimates, and binomial regression analysis was run to estimate the mortality probability stratified by the hepatic encephalopathy grade. Results: At 30 days, survival was significantly greater in the TPE group (92%) than in the SMT group (50%) (p = 0.02). The greatest survival benefit was observed in patients with Grade 4 encephalopathy. The ICU stay was longer in the TPE group, reflecting the complexity of ALF management. Conclusions: TPE significantly improves 30-day survival in ALF patients compared with SMT alone, supporting its role as an adjunct therapy. Further studies are needed to refine patient selection and optimize treatment protocols. Full article

The implementation of European Union policies contributed to substantial air pollution reductions in recent years, but atmospheric aerosols remain a key pollutant class with environmental and public health risks. This study develops a novel method for assessing environmental impact and the risk associated with urban atmospheric aerosols. The integrated approach for air quality evaluation and prediction of the effects and risk of certain pollutants is based on Aerosol Optical Depth (AOD) analysis, considering the Aerosol Robotic Network (AERONET) database. To validate the method, it was applied using monitored air quality data for two cities in Romania, with 13 years (from 2011 to 2023) in one case and 12 years (from 2012 to 2023) in the other. The results demonstrated that an AOD risk index can be developed and utilized for air quality evaluation and prediction, enabling estimation of impacts and risks. In this case, aerosols measured by AERONET (Aerosol Robotic Network) over Cluj-Napoca (2011–2023) were dominated (46%) by a mixture of elemental (EC) and organic carbon (OC), while measurements over Iasi (2012–2023) showed 55% of the EC/OC mixture. The impacts and risks, as calculated by the AOD index for EC, show few significant ones, with an AOD range of 0.88 to 1.05 for Iasi and 0.73 to 0.88 for Cluj-Napoca. Full article

Rapid urban growth poses distinct energy and environmental challenges in various regions of the world. This study evaluated the technical and economic feasibility of a grid-connected photovoltaic system in Santo Domingo Tehuantepec, Oaxaca, Mexico, using Homer Pro software, version 3.14.2, to simulate realistic scenarios. The analysis incorporated local climate data, residential load profiles, and updated economic parameters for 2024. System optimization resulted in an installed capacity of 173 kW of solar panels and 113 kW of inverters, yielding a levelized cost of energy (LCOE) of MXN 1.43/kWh, a return on investment (ROI) of 5.3%, an internal rate of return (IRR) of 8%, and a simple payback period of 10 years. The projected annual energy output was 281,175 kWh, covering 36% of the local energy demand. These results highlight the potential for integrating renewable energy into urban contexts, offering significant economic and environmental benefits. The integration of public policy with urban planning can enhance energy resilience and sustainability in intermediate cities. This study also supports the application of tools such as Homer Pro in designing energy solutions tailored to local conditions and contributes to a fair and decentralized energy transition. Full article

In the context of growing climate change and more frequent heat extremes, tourism in Mediterranean cities like Mostar (Bosnia and Herzegovina) is becoming increasingly vulnerable. This study aimed to provide a detailed analysis of the human bioclimatic conditions in Mostar using the physiologically equivalent temperature (PET) index, the modified PET (mPET), and the Climate-Tourism Information Scheme (CTIS), based on hourly meteorological data for the period 2000–2020. By applying the RayMan model, relevant bioclimatic parameters were calculated for three key times of day (07:00, 14:00, and 21:00 CET), and the results were analyzed in terms of seasonal and daily patterns of thermal stress. The most intense thermal stress was observed during summer afternoon hours, while the transitional seasons (spring and autumn) offer significantly more favorable conditions for tourist activities. A major contribution of this study is the creation of the first integrated bioclimatic information sheet for Mostar, which brings together PET, mPET, and CTIS outputs in accessible format tailored to local tourism needs. It serves as a scientifically based and practical tool for informing visitors and improving the planning of tourism activities in accordance with local climatic characteristics. Due to its visual clarity and ease of interpretation, the information sheet has strong potential for strategic adaptation in climate-sensitive tourism management. Full article

The search for renewable energy sources has driven the desire for knowledge about the energy source of photovoltaic systems and the factors that can influence it. This study applies powerful machine learning techniques to identify the best model for predicting photovoltaic energy generation, using meteorological variables as key inputs. The energy generated data were collected in a photovoltaic plant installed in the city of Pontes e Lacerda, while the meteorological variables were collected from nearby INMET stations. Four different techniques were employed, including SVR (Support Vector Machine), Random Forest, LSTM Neural Network and SARIMAX. The results showed that the Random Forest technique presented the best performance, with calculated values for the coefficient of determination (R²) and Willmott index of 0.909 and 0.972, respectively, standing out for accuracy and efficiency in scenarios where data is available. On the other hand, it was revealed that the model generated by the SARIMAX technique had great potential for applications where there is little data availability, presenting satisfactory estimates. This study highlights the practical applications of machine learning in optimizing photovoltaic power generation plant design and management, including improving energy prediction accuracy, enabling better decision-making, and supporting the expansion of renewable energy sources, especially in areas with scarce data. The findings also reinforce the critical role of meteorological variables in influencing the performance of photovoltaic systems, offering valuable insights for future applications in energy systems planning and operation. Full article

This study focuses on buildings in the Chinese–Baroque Historic Shopping District in Harbin. In view of global climate change and high carbon emissions from the construction industry, this study aims to quantify carbon emissions during the decoration process and explore low-carbon decoration strategies that suit the local characteristics. This research adopts a four-stage framework of “data collection–quantitative analysis–strategy design–verification and optimization” and integrates Life Cycle Assessment (LCA) and multi-objective optimization theory. Data are collected through questionnaires and field investigations, and simulations and analyses are carried out using Grasshopper and Honeybee. The results show that there are differences in carbon emissions between different decoration schemes. The chosen scheme of raw concrete and paint results in relatively low carbon emissions over the 10.12-year usage cycle. Based on this, design strategies such as extending the service life of decorations, rationally renovating windows, and preferentially selecting local low-carbon materials are proposed and applied to practical projects. This study not only fills a gap in the research on the low-carbon renovation of historical commercial blocks from the perspective of LCA but also provides practical solutions for the sustainable development of historical shopping blocks in Harbin and similar regions, promoting the low-carbon transformation of cities. Full article

Cities have become the largest consumers of resources and contributors to pollution due to urbanization. Therefore, measuring quality and maintaining standards have become crucial, as the boundaries of measurements for a city’s environmental quality are vague. This research study followed a qualitative approach to verify the factors affecting city environmental quality and to identify the boundaries of measurements using Sri Lankan cities as a case study. Data analysis was conducted using a thematic analysis approach, which adhered to the qualitative nature of the research. Findings revealed that seven main factors—energy consumption, water consumption, material and resource consumption, land utilization, disaster resilience, education, and governance—play a significant role in maintaining a city’s environmental quality. It was revealed that measuring boundaries can vary according to individual units (such as household, industrial, or commercial buildings) or city boundaries, in order to maintain quality standards. The findings revealed significant considerations for environmental quality performance, highlighting the influence of urban planning, governance, and public awareness on environmental sustainability outcomes in cities. Notably, this study contributes to a deeper understanding of how environmental quality intersects with social well-being in urban planning, affecting the quality of life and equitable access to urban resources. Full article

With the advancement of global climate governance, public attention—an emerging form of social capital—has played an increasingly important role in the carbon emission effects of green technological innovation. Based on panel data from 267 prefecture-level cities in China from 2012 to 2022, this study employed a two-way fixed-effects model to identify the nonlinear relationship between green innovation and carbon emissions, incorporated interaction terms to examine the moderating effect of public attention, and applied a spatial Durbin model to analyze the spatial spillover effects of green innovation. The results reveal an inverted U-shaped relationship between green innovation and carbon emissions, with the inflection point corresponding to 8.58 authorized green patents per 10,000 people—a threshold that most cities have yet to reach. Public attention significantly altered the shape of the carbon effect curve by making it steeper; in cities with a higher share of secondary industry, it delayed the inflection point, whereas in cities dominated by the tertiary industry, the turning point appeared earlier. In addition, green innovation had significant spatial spillover effects, and its impact on carbon emissions in neighboring cities displayed a U-shaped pattern. This paper proposes an analytical framework of “socially empowered innovation” to reveal the nonlinear moderating mechanism through which public attention influences the carbon effects of green innovation. The findings offer important policy implications: efforts should focus on long-term innovation, promote regional coordination, guide rational public participation, and avoid short-sighted and unsustainable mitigation practices. Full article

This study develops an integrated analytical framework to examine the interplay of urbanization, energy consumption, and CO₂ emissions at the city level in China. Utilizing the Entropy-TOPSIS method for multidimensional urbanization measurement, the GM_Combo model for spatial spillover analysis, and Random Forest for identifying emission drivers, we analyze data from 282 Chinese cities from 2006 to 2020. Results reveal significant hierarchical differences in urbanization, with K-means clustering identifying high, medium, and low urbanization groups reflecting diverse regional development pathways. Energy consumption increasingly drives emissions, while urbanization’s influence declines, indicating partial decoupling. Strong spatial spillovers highlight the need for regional coordination. Ecological assets provide moderate mitigation effects. These findings contribute to the literature by introducing a multidimensional urbanization index, uncovering nonlinear energy–emissions dynamics, and quantifying intercity spillovers, offering empirical support for tailored low-carbon policies and sustainable urban governance. Full article

Attackers can use Ethernet or WiFi sniffers to capture smart home device traffic and identify device events based on packet length and timing characteristics, thereby inferring users’ home behaviors. To address this issue, traffic obfuscation techniques have been extensively studied, with common methods including packet padding, packet segmentation, and fake traffic injection. However, existing research predominantly utilizes non-real-time traffic to verify whether traffic obfuscation techniques can effectively reduce the recognition rate of traffic analysis attacks on smart home devices. It often overlooks the potential impact of obfuscation operations on device connectivity and functional integrity in real network environments. To address this limitation, an online experimental framework for three fundamental traffic obfuscation techniques is proposed: packet padding, packet segmentation, and fake traffic injection. Experimental results demonstrate that the proposed framework maintains the continuous connectivity and functional integrity of smart home devices with a low system overhead, achieving an average CPU usage rate of less than 0.4% and an average memory occupancy rate of less than 2%. Evaluation results based on the random forest classification method show that the device event recognition accuracy for injected fake traffic exceeds 89%. In this context, a higher recognition accuracy indicates that attackers are more effectively deceived by the injected fake traffic. Conversely, the recognition accuracy for packet padding and packet segmentation methods is nearly zero, and a lower recognition accuracy in these cases implies a more effective implementation of those obfuscation techniques. Further evaluation results based on the deep learning classification method reveal that the packet segmentation approach significantly reduces device recognition accuracy for certain devices to below 5%, while simultaneously increasing the false recognition rate for other devices to over 95%. In contrast, fake traffic injection achieves a device recognition accuracy exceeding 90%. Moreover, the obfuscation effect of the packet padding method is found to be suboptimal, a finding consistent with existing literature suggesting that no single obfuscation technique can effectively withstand all types of traffic analysis attacks. Full article

Immigration is being used as a policy lever to sustain the demography of Canadian Francophone minority communities (FMCs). As FMCs become increasingly diverse, concerns have been raised regarding their capacity to develop and sustain a sense of community cohesion. This study draws on the mobilities paradigm to examine how community members within three different FMCs engaged within and beyond formal and informal Francophone spaces within the cities of Metro Vancouver, Winnipeg and Moncton. Using an occupational mapping method to elicit spatial and dialogic data, we analyze the descriptions of maps from 62 French-speaking participants who were born in, or who immigrated to, Canada in order to obtain diverse perspectives on community cohesion. Our findings are presented according to three themes. The first addresses socio-geographically shaped mobilities within the three FMCs, the second examines participants’ engagement in a range of (in)formal Francophone spaces, and the third explores their convergent and divergent mobilities as shaped by local dynamics. We contribute insights into the relationship between forms of spatial and social mobility that shape experiences of community cohesion within FMCs. Full article