Search Results (166)

Image-assisted digital shade guides are increasingly popular for shade matching; however, research on their accuracy remains limited. This study aimed to compare the accuracy and reliability of color coordination in image-assisted digital shade guides constructed using calibrated images of their shade tabs captured by a mirrorless camera (Canon, Tokyo, Japan) (MC-DSG) and a smartphone camera (Samsung, Seoul, Korea) (SC-DSG), using a spectrophotometer as the reference standard. Twenty-nine VITA Linearguide 3D-Master shade tabs were photographed under controlled settings with both cameras equipped with cross-polarizing filters. Images were calibrated using Adobe Photoshop (Adobe Inc., San Jose, CA, USA). The L* (lightness), a* (red-green chromaticity), and b* (yellow-blue chromaticity) values, which represent the color attributes in the CIELAB color space, were computed at the middle third of each shade tab using Adobe Photoshop. Specifically, L* indicates the brightness of a color (ranging from black [0] to white [100]), a* denotes the position between red (+a*) and green (–a*), and b* represents the position between yellow (+b*) and blue (–b*). These values were used to quantify tooth shade and compare them to reference measurements obtained from a spectrophotometer (VITA Easyshade V, VITA Zahnfabrik, Bad Säckingen, Germany). Mean color differences (∆E₀₀) between MC-DSG and SC-DSG, relative to the spectrophotometer, were compared using a independent t-test. The ∆E₀₀ values were also evaluated against perceptibility (PT = 0.8) and acceptability (AT = 1.8) thresholds. Reliability was evaluated using intraclass correlation coefficients (ICC), and group differences were analyzed via one-way ANOVA and Bonferroni post hoc tests (α = 0.05). SC-DSG showed significantly lower ΔE₀₀ deviations than MC-DSG (p < 0.001), falling within acceptable clinical AT. The L* values from MC-DSG were significantly higher than SC-DSG (p = 0.024). All methods showed excellent reliability (ICC > 0.9). The findings support the potential of smartphone image-assisted digital shade guides for accurate and reliable tooth shade assessment. Full article

Container terminals are facing significant challenges in meeting the increasing demands for volume and throughput, with limited space often presenting as a critical constraint. Key areas of concern at the quayside include the berth allocation problem, the quay crane assignment, and the scheduling problem. Effectively managing these issues is essential for optimizing port operations; failure to do so can lead to substantial operational and economic ramifications, ultimately affecting competitiveness within the global shipping industry. Optimization models, encompassing both mathematical frameworks and metaheuristic approaches, offer promising solutions. Additionally, the application of machine learning and reinforcement learning enables real-time solutions, while robust optimization and stochastic models present effective strategies, particularly in scenarios involving uncertainties. This study expands upon earlier foundational analyses of berth allocation, quay crane assignment, and scheduling issues, which have laid the groundwork for port optimization. Recent developments in uncertainty management, automation, real-time decision-making approaches, and environmentally sustainable objectives have prompted this review of the literature from 2015 to 2024, exploring emerging challenges and opportunities in container terminal operations. Recent research has increasingly shifted toward integrated approaches and the utilization of continuous berthing for better wharf utilization. Additionally, emerging trends, such as sustainability and green infrastructure in port operations, and policy trade-offs are gaining traction. In this review, we critically analyze and discuss various aspects, including spatial and temporal attributes, crane handling, sustainability, model formulation, policy trade-offs, solution approaches, and model performance evaluation, drawing on a review of 94 papers published between 2015 and 2024. Full article

Despite the importance of Urban Green Infrastructure (UGI) as a provider of multiple Ecosystem Services (ESs), some concerns have been raised regarding Ecosystem Disservices (EDs) associated with UGI design and management, namely, the link between Urban Green Spaces’ (UGSs) attributes and invasion spatial patterns. This research takes the UGI of Coimbra, a medium-sized Portuguese city, as a case study to explore the relationships between UGS attributes and the occurrence of invasive plant species. The methodology involved aerial photo-interpretation and full patch survey to collect data about UGSs types, maintenance level and occurrence of invasive plant species, and landscape metrics analysis. Our results showed that the UGI of Coimbra exhibits a large prevalence of small UGSs with regular maintenance and the occurrence of invasive plant species in a low number of patches (17%). Although these patches correspond to 64% of the UGI. The area of recent sprawl (zone 2) registers higher occurrence of invasive plant species across different UGSs types, with higher prevalence in patches with no or low maintenance. Mapping the occurrence of invasive plant species in UGS is of utmost importance to implement appropriate maintenance practices, allowing medium-sized cities like Coimbra to optimize ESs associated with UGI and minimize potential EDs. Full article

Urban park vitality, a key indicator of public space performance, has garnered significant research attention. However, existing studies often neglect the temporal variability in vitality patterns, thus failing to accurately reflect actual park performance and limiting their relevance for strategic urban planning and sustainable resource allocation. This study constructs a “temporal behavior–spatial attributes–park typology” framework using high-precision (50 m) mobile signaling data to capture hourly vitality fluctuations in 59 parks of Hangzhou’s Gongshu District. Using dynamic time-warping-optimized K-means clustering, we identify three vitality types—Morning-Exercise-Dominated, All-Day-Balanced, and Evening-Aggregation-Dominated—revealing distinct weekday/weekend usage rhythms linked to park typology (e.g., community vs. comprehensive parks). Geographical Detector analysis shows that vitality correlates with spatial attributes in time-specific ways; weekend morning vitality is driven by park size and surrounding POI density, while weekday evening vitality depends on interactions between facility density and residential population. These findings highlight how transportation accessibility and commercial amenities shape temporal vitality, informing time-sensitive strategies such as extended evening hours for suburban parks and targeted facility upgrades in residential areas. By bridging vitality patterns with strategic planning demands, the study advances the understanding of how sustainable park management can optimize resource efficiency and enhance public space equity, offering insights for urban green infrastructure planning in other regions. Full article

Vegetation carbon sequestration (CS) is critical for mitigating climate change in urban agglomerations, yet its driving mechanisms remain poorly understood in rapidly urbanizing regions. This study introduces an integrated attribution and influence analysis framework, GWR-RF-SEM, to quantitatively assess the driving forces, mechanisms, and pathways of CS using multi-source remote sensing data at the county scale within the Yangtze River Delta Urban Agglomeration (YRDUA), China, from 2001 to 2020. Our results reveal an overall increase in CS across 70.14% districts in the YRDUA, with municipal districts exhibiting significantly lower CS compared to the outside districts. Photosynthesis and human activities emerged as the dominant drivers, collectively accounting for 73.1% of CS variation, significantly surpassing the influence of climate factors. Although most factors influenced urban vegetation CS either directly or indirectly, photosynthesis, afforestation, and urban green space structure were identified as the primary direct drivers of CS enhancement in both districts. Notably, we found significant spatial heterogeneity in CS drivers between municipal districts and the outside districts, highlighting the need for targeted strategies to enhance CS efficiency. These findings advance our understanding of urban vegetation CS mechanisms, providing essential support for the enhancement of nature-based solutions depending on ecosystem services under urbanization and climate change. Full article

High-resolution remotely sensed data, which are characterised by their advanced spectral and spatial capabilities, provide unprecedented opportunities to monitor and analyse the dynamic structures of urban environments. Platforms like Google Earth Engine (GEE) enhance these capabilities, as they provide access to vast datasets and tools for analysing key urban parameters, including land use, vegetation cover, and surface roughness–all critical components in urban sustainability studies. This study presents a knowledge-based framework for processing high-resolution satellite imagery tailored to address the demands of sustainable urban planning in the Municipality of Kalamaria in Thessaloniki, Greece. The framework emphasises the extraction of essential urban parameters, such as the spatial distribution of built-up and green spaces, alongside the analysis of surface roughness attributes, including displacement height and roughness length. Unlike conventional methods, our framework enables a detailed intra-urban analysis as these surface roughness attributes are calculated within 200 m × 200 m sub-units. Surface roughness indicators offer essential insights into aerodynamic drag and turbulent air mixing, both of which are directly influenced by the structural characteristics of the urban landscape. Using this approach, ‘wake interference flow’ type was identified as the dominant airflow pattern in the study area. This type was observed in 105 out of 150 sub-units, suggesting that these areas likely suffer from poor air circulation and are prone to higher concentrations of air pollutants. The integration of Google Earth Engine offered a scalable and replicable solution for large-scale urban analysis making it easily adaptable to other urban areas, especially where detailed morphological datasets are unavailable. By providing a robust, scalable, and data-driven tool for assessing urban form and airflow characteristics, our study offers a significant advancement in sustainable urban planning and climate resilience strategies, with clear potential for adaptation in other cities facing similar data limitations. Full article

While extensive research has examined the contribution of urban parks to well-being, empirical evidence on the role of cultural attributes in historical urban parks and their impact on visitors’ well-being remains limited. This study explores the impact of physical characteristics of historical urban parks on well-being from the perspective of human settlement environment. Quantitative data were collected from 11 urban parks in Wuhan, China, combining online crowdsourcing for physical characteristic assessments and questionnaire surveys for psychological evaluations. Machine learning techniques, spatial analysis, and statistical methods including multistep regression and Bootstrap sampling were employed to test our hypotheses. Our results demonstrate that objective physical features—including park area, green coverage rate, green space shape index, and the proportion of heritage landmarks—positively influence well-being, whereas road density exhibits a negative association. Cultural perception and place attachment serve as significant mediators between physical characteristics and well-being outcomes, with the proportion of heritage landmarks influencing well-being through a dual mediation path. Additionally, we found interaction effects between physical and psychological factors, with education level moderating the relationship between cultural perception and well-being. These findings advance environmental psychology theory by elucidating how historical elements foster unique pathways to well-being, distinct from those offered by conventional green spaces. Our research provides evidence-based guidance for historical urban park design and renovation in the context of urban renewal, where balancing preservation and modernization presents significant challenges. Full article

Architectural ambiance refers to the mood perceived in a built environment, assessed through human reactions to virtual drawings of prospective spaces. This paper investigates the use of a ready-made artificial intelligence model to automate this task. Based on professional BIM models, videos of virtual tours of typical urban areas were built: a business district, a strip mall, and a residential area. GPT-4V was used to assess the aesthetic quality of the built environment based on keyframes of the videos and characterize these spaces shaped by subjective attributes. The spatial qualities analyzed through subjective human experience include space and scale, enclosure, style, and overall feelings. These factors were assessed with a diverse set of mood attributes, ranging from balance and protection to elegance, simplicity, or nostalgia. Human participants were surveyed with the same questions based on the videos. The answers were compared and analyzed according to these subjective attributes. Our findings indicate that, while GPT-4V demonstrates adequate proficiency in interpreting urban spaces, there are significant differences between the AI and human evaluators. In nine out of twelve cases, the AI’s assessments aligned with the majority of human voters. The business district environment proved more challenging to assess, while the green environment was effectively modeled. Full article

With rapid urbanization, enhancing the quality of public spaces is critical to residents’ well-being and sustainable urban development. However, user perceptions of these spaces remain insufficiently quantified. This study introduces a perception-based evaluation framework encompassing four dimensions: service, spatial, cultural, and aesthetic. A three-dimensional importance-performance analysis (3-D IPA) model is applied to assess two multifunctional public spaces in Wuhan—the Citizens’ Home (CH) and the Creative World Industrial Park (CWIP)—with the aim of identifying user-prioritized attributes that inform sustainable design interventions. The findings reveal the following: (1) At CH, spatial perception (importance = 3.93; performance = 4.02) received the highest ratings, particularly for openness and ecological pavement, highlighting areas for green infrastructure improvement. (2) At CWIP, cultural perception (importance = 3.75; performance = 3.73) dominated, with a need to enhance the signage systems and cultural integration for greater place identity. (3) Optimization priorities included energy-efficient lighting, entrance enhancements, and recreational layout improvements at CH, and thematic diversity and wayfinding systems at CWIP. (4) The 3-D IPA framework effectively identifies user-perceived priorities and supports experience-driven, resource-conscious spatial improvements. This study provides a user-centered, data-informed approach for evaluating and optimizing urban public spaces, offering practical strategies to align spatial quality with long-term sustainability goals. Full article

This research explores the potential of green encapsules uploaded with eucalypt essential oil (EEOs) in enhancing their functionality and application in pest control, focusing on suitable ecotype selection from King Abdulaziz University (KAU) campus, Hada Al-Sham (HAS) village, and Briman district as well as optimizing extraction processes. Eucalypt hybrids’ leaves were collected from three different sites, and the EEOs were extracted using microwave-assisted steam distillation (MASD) and electric steam distillation (ESD) techniques. The physical and chemical properties of the EEO were determined. The identification of volatile chemical ingredients in the resulting EEOs was conducted using GC/MS after saponification and methylation procedures, and the ingredients were compared to those obtained from Eucalyptus globulus Labill, the ideal species containing the 1,8-cineol, the principal compound in its essential oil. The 1,8-cineole was found to be the major chemical constituent of the EEOs all over the two extraction methods, regardless of the ecotypes examined, and was interfered with other minor components such as 3-carene, α-pinene, α-myrcene, D-limonene, and α-terpinene. Eucalypt ecotypes grown at Hada Al-Sham village had the highest cineole content (59.29%) among the other sites studied. Compared to the ESD technique, MASD showed much promise because it is simple, facile, more ecofriendly and cost-effective, it kept oils true to their original form, and it allows to warm larger machines and spaces. The polymeric encapsules of either guar gum crosslinked by borax or sodium alginate crosslinked by calcium chloride were fabricated. Moreover, a bioassay screening of the encapsules uploaded with 1,8-cineole was evaluated against termite infection. The encapsules were found to be versatile tools with a wide range of applications; in particular, the alginate encapsules displayed superior characteristics. Furthermore, regardless of the encapsule type and the exposure duration, the mortality (%) of the insects was exceeded significantly for the high cineol concentrations compared to the lower ones for both alginate-based encapsules (ABEs) and guar gum-based encapsules (GGBEs). The higher the cineol concentrations, the higher the mortality percent of the termites. This finding can be attributed to the rapid toxic effect of the cineol compound at higher concentrations. Full article

Urban greenery and cooling initiatives have become top priorities for municipalities worldwide as they contribute to improved environmental quality and urban resilience. This study leverages advancements in remote sensing (RS) and cloud-based processing to assess and monitor changes in public urban green spaces (PUGS) in Sanandaj, Iran. Using high-resolution Sentinel-2 imagery (10 m) processed in Google Earth Engine (GEE), we calculated and mapped the normalized difference vegetation index (NDVI) across 20 major PUGSs over a five-year period, from 2019 to 2023. A total of 507 Sentinel-2 images were analyzed, offering a comprehensive view of seasonal and annual greenness trends. Our findings reveal that May is the peak month for greenery, while February consistently shows the lowest NDVI values, indicating seasonal greenness variability. Specifically, the mean NDVI of PUGSs decreased significantly between 2019 and 2022, with values recorded at 0.735, 0.737, 0.622, 0.417, and 0.570 in the greenest month of each respective year, highlighting a noticeable decline in vegetation health and extent. This reduction can be attributed to water scarcity and suboptimal management practices, as evidenced by dried or underperforming green spaces in recent years. Our results underscore the potential of integrating NDVI-based assessments within urban development frameworks to more accurately define and sustain PUGSs in Sanandaj. This methodology provides a replicable approach for cities aiming to optimize urban greenery management through RS technology. Full article

Landscape spatial structure serves as the foundational framework for vegetation arrangement and spatial organization, playing a crucial role in assessing landscape morphology. Traditional 2D graph theory methods have provided insights into planar structural characteristics but fail to capture the complexity of three-dimensional spatial attributes and organizational processes inherent in landscape systems. To address these limitations, this study proposes a novel multidimensional framework for the quantitative analysis and evaluation of landscape spatial structure by integrating 3D point cloud technology with spatial network analysis. The methodology consists of three key components: (1) the formulation of multidimensional spatial organization theory, (2) spatial unit extraction and structure analysis through ArcGIS 10.5 and Cytoscape v3.6.1, and (3) the development of an indicator system for evaluating spatial structure organization. The framework was validated through the analysis of 30 urban parks, where the regularity and range of indicators are generalized to establish evaluation criteria and determine weights. The findings indicate that spatial structure indicators are moderation indicators with optimal value ranges. The evaluation system was subsequently applied across the 30 parks for comprehensive evaluation. A total of 6 of 30 parks have comprehensive scores over 0.95. In practical application, the design score of Shuyang Park improved from 0.692 to 0.826 after evaluation and optimization, demonstrating the method’s effectiveness. This study underscores the potential of digital methodologies in advancing landscape spatial structure modeling, enhancing the understanding of spatial organization, and transitioning subjective assessments toward evidence-based objective evaluations. The proposed methodology and findings offer valuable insights for diagnosing, assessing, optimizing, and managing urban green spaces. Full article

Under the context of global climate change, floods are one of the major challenges facing urban development. Based on resilience theory, this study proposed an evaluation method to accurately assess the resilience of urban flooding prevention and control systems (FPCs), integrating both attribute resilience (AR) and functional resilience (FR). First, the method organized FPC attributes from the perspective of the waterlogging generation and elimination processes using foundational data from the study area, and it established a resilience indicator system. The Entropy Weight Method (EWM) was applied to calculate indicator weights, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to calculate indicator values, ultimately deriving the attribute resilience (AR). Subsequently, functional performance during actual operations was evaluated using scenario simulation based on hydrodynamic model results, and the FR was determined. Finally, spatial correlation analysis of the AR and FR was conducted to identify areas with weak resilience. This study developed an evaluation method that considers both system attributes and functional performance using the central urban area of Beijing as a case study to assess flood resilience. The results indicated that the most influential factors affecting the AR of the FPC are the green space percentage (GSP), average slope, and drainage capacity (DC), with their weights calculated as 0.17, 0.137, and 0.205, respectively. Among resistance, absorption, and recovery, absorption had the greatest influence, with a weight of 0.447. The Moran’s I indices for the AR and FR were 0.66 and 0.49, respectively, indicating spatial clustering, although the clustering locations differed. There was spatial correlation between the AR and FR, enabling more precise identification of areas with high and low flood resilience. However, the trends of the AR and FR were not entirely consistent across different types of sub-districts due to differences in evaluation methods and the influence of various indicators. Full article

Understanding the characteristics of urban plant multidimensional diversity and urban green spaces (UGSs) landscape patterns is the central theme of urban ecology, providing theoretical support for UGSs management and biodiversity conservation. Taking Changchun, a provincial city, as an example, a total of 240 plots were surveyed using the stratified random sampling method. We studied the effects of the urban park construction period on plant multidimensional diversities, landscape patterns of green spaces, and their associations in Changchun City, Northeast China. The results indicated that total woody species and tree species diversity attributes were both the highest in the construction period of 2001–2020 and lowest in the construction period before 1940. However, shrub species diversity attributes were completely the opposite. Diameter at the breast height (DBH) diversity index (H_d) was the highest in the construction period before 1940 and lowest in the construction period of 2001–2020. However, the height diversity index (H_h) showed the opposite trend. Phylogenetic structures of total woody species and tree species showed divergent patterns in parks constructed before 1940 and 1940–2000 period, while that in 2001–2020 period could not be determined. In contrast, the phylogenetic structure of the shrub species clustered across all construction periods. Landscape pattern metrics varied significantly among different construction periods. Total Area (TA) was the highest in the construction period of 2001–2020. The structural equation model (SEM) revealed that construction periods exerted significant direct effects on both multidimensional diversities and landscape patterns of green spaces. Specifically, construction periods indirectly affected tree species diversity through structural diversity and influenced shrub species’ phylogenetic diversity through shrub species diversity. What is more, Patch Density (PD), Edge Density (ED), and Aggregation Index (AI) correlated with H_h, which had a direct effect on the Shannon–Wiener diversity index of tree species (H′_t). Overall, the results indicated that species diversity can be enhanced through regulating landscape patterns, rationally selecting tree species, and optimizing plant configuration. These above results can provide scientific references for the configuration of plant communities and selection of tree species in urban parks, and offer important guidance for urban biodiversity conservation and enhancement. Full article

The Central Place Theory (CPT) proposed the basic concepts of central places and their service areas. Urban parks provide a wide variety of ecosystem services to residents. Most studies on central places focus on urban commercial facilities; however, it remains unclear whether parks exhibit patterns of central places, what features their service areas, and hierarchical structures. Based on mobile signaling data, we identified the service areas, dominant influence areas, and hierarchical structures of Beijing and Changsha. We also analyzed the factors influencing the hierarchical structure of park services, as well as the number of visitors and dominant service areas at each level of parks. We found that visits by residents to parks in Beijing and Changsha exhibit a clear hierarchical structure. Parks that occupy the top level attract a large number of residents and demonstrate strong service capacity and extensive coverage. We also found that park area and infrastructure attributes are significantly correlated with the hierarchical outcomes in Changsha but entirely different results in Beijing. Box plot analysis of visitor numbers and service areas at each level reveals that the influencing factors for these two aspects differ. Overall, both cities’ parks exhibit centrality and hierarchical structures in providing services to residents; however, there is a considerable difference in the factors influencing visitor numbers and dominant service areas for the two cities. These conclusions provide important theoretical support for government officials to better understand the characteristics of park services and offer practical guidance for optimizing urban park planning, enhancing service efficiency, and formulating policies that promote equitable access to green spaces. Full article