Search Results (6,341)

This study explores the intricate relationship between urban amenities and the transformation of urban green spaces (UGS) in Guangzhou, China, over the decade from 2013 to 2022. Amid rapid urbanization, maintaining and expanding green spaces has become increasingly challenging, especially in densely populated urban centers. This research utilizes remote sensing data and Point of Interest (POI) data to assess how different types of urban amenities influence UGS dynamics based on geospatial analytics. The study focuses on the central districts of Guangzhou, a city facing significant urban development pressures, to provide a nuanced understanding of these interactions. Employing both Ordinary Least Squares (OLS) regression and Random Forest (RF) models, the analysis examines the impact of 23 categories of POIs on the spatial and temporal changes in UGS. Key findings reveal that amenities such as auto repair shops, shopping services, and transit facilities are negatively correlated with UGS, indicating that their presence may contribute to the reduction in green space. Conversely, amenities like scenic spots and life services show a positive correlation, suggesting they might support the preservation or expansion of green spaces. The results underscore the dual role of urban amenities in both supporting and constraining green space development, highlighting the need for carefully balanced urban planning strategies. This study provides valuable insights for policymakers and urban planners aiming to promote sustainable urban growth while preserving essential green spaces, ensuring that urban environments remain livable and ecologically resilient. Full article

The endeavor of spatial position reasoning effectively simulates the sensory and comprehension faculties of artificial intelligence, especially within the purview of multimodal modeling that fuses imagery with linguistic data. Recent progress in visual image–language models has marked significant advancements in multimodal reasoning tasks. Notably, contrastive learning models based on the Contrastive Language-Image pre-training (CLIP) framework have attracted substantial interest. Predominantly, current contrastive learning models focus on nominal and verbal elements within image descriptions, while spatial locatives receive comparatively less attention. However, prepositional spatial indicators are pivotal for encapsulating the critical positional data between entities within images, which is essential for the reasoning capabilities of image–language models. This paper introduces a spatial location reasoning model that is founded on spatial locative terms. The model concentrates on spatial prepositions within image descriptions, models the locational interrelations between entities in images through these prepositions, evaluates and corroborates the spatial interconnections of entities within images, and harmonizes the precision with image–textual descriptions. This model represents an enhancement of the CLIP model, delving deeply into the semantic characteristics of spatial prepositions and highlighting their directive role in visual language models. Empirical evidence suggests that the proposed model adeptly captures the correlation of spatial indicators in both image and textual representations across open datasets. The incorporation of spatial position terms into the model was observed to elevate the average predictive accuracy by approximately three percentage points. Full article

With global warming and rapid urban development, the urban surface temperature in summer has been increasing, seriously affecting people’s work and life. The formation and changes in surface temperature are directly related to material surroundings and spatial functions. Urban construction has led to an increase in POIs (points of interest), and the POI represents the functional activity within the space to a certain extent. Therefore, this paper attempts to reproduce the process of the urban internal function development of Chengdu according to the distribution characteristics of different types of points of interest. It also delves into the influence of internal spatial functions on surface temperature in Chengdu. The results show that the surface heat values for all types of functions show a significant increase from 2009 to 2022. The rate of increase is particularly pronounced for public transportation, with temperatures increasing by an average of 0.317 °C per year. In addition, there are differences in the thermal contribution values of different functions. The residential and commercial functions have the most significant impact on surface temperature, with both accounting for more than 0.45 of all functional contribution values. Public transportation has a small thermal contribution value but shows a trend of doubling growth. The findings will provide some insights into the design of cooling in future urban planning. Full article

Lateral flow assays are widely used in point-of-care diagnostics but face challenges in sensitivity and accuracy when detecting low analyte concentrations, such as thyroid-stimulating hormone biomarkers. This study aims to enhance assay performance by leveraging textural features and hybrid artificial intelligence models. A modified Gray-Level Co-occurrence Matrix, termed the Averaged Horizontal Multiple Offsets Gray-Level Co-occurrence Matrix, was utilised to compute the textural features of the biosensor assay images. Significant textural features were selected for further analysis. A deep learning Convolutional Neural Network model was employed to extract features from these textural features. Both traditional machine learning models and hybrid artificial intelligence models, which combine Convolutional Neural Network features with traditional algorithms, were used to categorise these textural features based on the thyroid-stimulating hormone concentration levels. The proposed method achieved accuracy levels exceeding 95%. This pioneering study highlights the utility of textural aspects of assay images for accurate predictive disease modelling, offering promising advancements in diagnostics and management within biomedical research. Full article

The present work analyzes the behavior of an activated carbon fabricated from almond shells for the removal of cationic dyes (methylene blue, MB, and malachite green, MG) by adsorption from aqueous solutions. The carbonized precursor was activated with KOH at a 1:2 (w/w) ratio with the objective of increasing both the surface area and the pore volume. Both non-activated and activated carbon were characterized in different aspects of interest in dye adsorption studies (surface structure, point of zero charge, specific surface area, and pore size distribution). The effect of the dye’s initial concentration and adsorbent dosage on dye removal efficiency and carbon adsorption capacity was studied. Adsorption kinetics were analyzed under different experimental conditions, and different models were assayed to determine the adsorption mechanism. Dye adsorption in the adsorbent surface could be considered the rate-limiting step. Different adsorption equilibrium models were evaluated to fit the experimental data. This adsorbent allowed us to reach high Langmuir adsorption capacity for both dyes (MB: 341 mg·g⁻¹, MG: 364 mg·g⁻¹ at 25 °C and 0.5 g·L⁻¹). Moreover, kinetic and equilibrium adsorption data have been used to simulate breakthrough curves in a packed-bed column using different conditions (bed length, liquid flowrate, and dye initial concentration). The simulation results showed that almond shell activated carbon is a suitable adsorbent for methylene blue and malachite green removal from wastewater. Full article

Background. Urinary tract infections (UTIs) and asymptomatic bacteriurias (ABU) represent a large field of interest for antimicrobial stewardship programmes especially after 2020 EUCAST update in antimicrobial susceptibility testing interpretation and the possible related increase in carbapenems’ prescription rate. The aim of this study was to evaluate the impact of the 2020 EUCAST update on antibiotic prescription in UTI due to Pseudomonas aeruginosa organism and their characteristics. Methods. A retrospective observational study. We enrolled all the patients with P. aeruginosa isolation from urine, admitted to our hospital from 2018 to 2021. We compared demographic, clinical, and microbiological characteristics and treatment between cases before 2020 EUCAST update (period A, 2018–2019) and after it (period B, 2020–2021). Results. A total of 643 cases was analysed, 278 in period A and 365 in period B; 65% were ABU. Carbapenems’ prescription rate significantly increased in period B when considering ABU alone (21.4% vs. 41%, p = 0.016) and all the treated cases (treated ABU and UTI; 27.8% vs. 41.4%, p = 0.013); anti-Pseudomonas cephalosporins prescription significantly decreased in period B when considering ABU alone (15.7% vs. 3.6%, p = 0.021), UTI alone (20.7% vs. 5.9%, p = 0.009) and all the treated cases (18.5% vs. 5.9%, p = 0.001). Conclusions. The 2020 EUCAST update could have contributed to an increase in carbapenem prescriptions. UTI and ABU represent a large field of interest for stewardship interventions both from a diagnostic and therapeutic point of view. Full article

Detailed spatial planning serves as statutory guidance for regulating specific spatial functions, including public services, living conditions, and production spaces. It emphasizes meeting the comprehensive needs of the local population, making it crucial to understand the relationship between population distribution and the mix of various city functions, particularly in the era of urban regeneration. Therefore, this study utilized point-of-interest (POI) data representing land functions and population data to investigate these relationships via spatial analysis and Multiscale Geographically Weighted Regression (MGWR). Applied to the central urban area of Shanghai, the study reveals that the level of mixed land use and various functionalities affect population distribution at different adaptive scales. We also found a higher degree of functional mix does not always meet population needs. Although generally there is a positive correlation between functional mix and population distribution, they are not always closely bonded. The proposed method provides an efficient workflow for identifying the applicable scale of various functions to increase functional mix and attract the population, which can provide real-time evidence supporting detailed planning. Test results also reveal the less-considered space along the boundaries of administrative districts. We also found developing tools for detailed planning is an urgent need to facilitate cross-boundary cooperation and development, especially in the context of urban regeneration where they always are overlooked at the detailed planning level. By using open-sourced POI and population data, our proposed workflow can be easily applied to other cities or regions, enhancing their practical value for similar research contexts. Full article

The present research aimed to evaluate whether two sensors, optical and laser, could highlight the change in olive trees’ canopy structure due to pruning. Therefore, two proximal sensors were mounted on a ground vehicle (Kubota B2420 tractor): a multispectral sensor (OptRx ACS 430 AgLeader) and a 2D LiDAR sensor (Sick TIM 561). The multispectral sensor was used to evaluate the potential effect of biomass variability before pruning on sensor response. The 2D LiDAR was used to assess its ability to discriminate volume before and after pruning. Data were collected in a traditional olive grove located in Tenute di Cesa Farm, in the east of Tuscany, Italy, characterized by a 4x6 m planting layout and by developed plants. LiDAR data were used to measure canopy volumes, height, and diameter, and the generated point cloud was studied to assess the difference in density between treatments. Ten plants were selected for the study. To validate the LiDAR results, manual measurements of the canopy height and diameter dimensions of the plants were taken. The pruning weights of the monitored plants were obtained to assess the correlation with the canopy characterization data. The results obtained showed that pruning did not affect the results of the multispectral sensor, and the potential variation in canopy density and porosity did not lead to different results with this instrument. Plant volumes, height, and diameters calculated with the LiDAR sensor correlated well with the values of manual measurements, while volume differences between before and after pruning obtained good correlations with pruning weights (Pearson correlation coefficient: 0.66–0.83). The study of point cloud density in canopy thickness and height showed different shapes before and after pruning, especially in the former case. Correlations between point cloud density obtained from LiDAR and multispectral sensor results were not statistically significant. Even if more studies are necessary, the results obtained can be of interest in pruning management. Full article

With the development of robotic welding automation, there is a strong interest in welding seam identification and localization methods with high accuracy, real-time performance, and robustness. This paper proposed a 3D workpiece weld identification and localization method based on DBSCAN (density-based spatial clustering of applications with noise) to realize stable feature extraction for multiple joint types. Firstly, this method employs combinatorial filtering to effectively eliminate non-target point clouds, including outliers and installation platform point clouds, which can minimize the computational load. Secondly, DBSCAN is used to classify workpiece point clouds into different clusters, which can be used for point cloud segmentation of flat workpieces and curved workpieces. Thirdly, the edge detection and feature extraction methods are used to obtain joint gap and weld feature points while combining the information of point clouds for different types of welds. Finally, based on the identification and localization of the welds, welding path planning and attitude planning are implemented. Experimentation results indicated that the proposed method exhibits robustness across various types of welded joints, including butt joints with straight seams, butt joints with curved seams, butt joints with curved workpieces, and lap joints. Meanwhile, the average error of joint gap detection was 0.11 mm and the processing time of a 90 mm straight-seam butt joint is 701.12 ms. Full article

This article presents STEAM Architecture, a STEAM project for all educational levels, from pre-school to high school, a project that links the learning of subjects with architectural concepts, thus trying to generate meaningful learning in students. The project is the result of an ERASMUS+ project (DART4City (2020-1-ES01-KA227-SCH-095545) Empowering Arts and creativity for the cities of tomorrow) in which a methodology was developed to extract STEAM projects from European curricula. This methodology has two variants: “forward” and “backward”. The “forward” variant analyzes the curriculum and found the areas of opportunity with more connections among the contents while the “backward” methodology proposes a specific theme to look for the connections. The “backward” variant allows finding a topic that may be of social interest. This is the variant we use in this article. We explore the “backward” methodology in order to find an area of opportunity in society, in particular related to architecture. A questionnaire is distributed to different sectors of people in society to find out whether the learning of different architectural concepts at pre-university levels is interesting. The results of these tests show the potential of a STEAM project related to architecture. The design of the STEAM architecture project shows how the subdivision is carried out from an educational point of view, and also from an architectural point of view. Both worlds agree on dividing space into micro-, meso- and macro-space depending on the scale of what is being treated. For this reason, the STEAM architecture project is subdivided into Room, House, Neighbourhood and City for each educational level: pre-school, primary school and high school (which is 4 years of secondary school (ESO) and the last 2 years of high school). At the end of the article, we show the different workshops that were held in order to analyze the goodness of the proposal. Full article

Wireless Sensor Networks are used in an ever-increasing range of applications, thanks to their ability to monitor and transmit data related to ambient conditions in almost any area of interest. The optimization of coverage and the assurance of connectivity are fundamental for the efficiency and consistency of Wireless Sensor Networks. Optimal coverage guarantees that all points in the field of interest are monitored, while the assurance of the connectivity of the network nodes assures that the gathered data are reliably transferred among the nodes and the base station. In this research article, a novel algorithm based on Particle Swarm Optimization is proposed to ensure coverage and connectivity in Wireless Sensor Networks. The objective function is derived from energy function minimization methodologies commonly applied in bounded space circle packing problems. The performance of the novel algorithm is not only evaluated through both simulation and statistical tests that demonstrate the efficacy of the proposed methodology but also compared against that of relative algorithms. Finally, concluding remarks are drawn on the potential extensibility and actual use of the algorithm in real-world scenarios. Full article

Ecosystem services, encompassing the provision of food, water, air purification, climate regulation, and disease mitigation, form the bedrock upon which human well-being and socio-economic development are anchored. The preservation of these services is instrumental in safeguarding human survival and fostering progress. Qinghai Province, located on the Qinghai–Tibetan Plateau, holds a pivotal role as a crucial ecological barrier within China. The optimization of its ecosystem services is vital for the effective management of ecologically fragile areas. This study focuses on Qinghai Province. By integrating natural geographic, social, and Points of Interest (POI) big data, we utilized tools like InVEST, CASA, and advanced algorithmic optimizations to analyze ecosystem services and their trade-off synergies in Qinghai from 2000 to 2020. Based on these synergies, we developed a novel optimization algorithm to generate spatial bundles that amplify synergistic interactions while minimizing costs. Our findings indicate that the following: (1) Between 2000 and 2020, recreational services, water yield, and habitat quality in Qinghai Province generally exhibited an upward trend, whereas carbon sequestration showed a decline; (2) Notable synergies were evident between carbon sequestration and habitat quality and between recreation services and habitat quality. Conversely, significant trade-offs were observed between water yield and habitat quality and between water yield and cultural services, with these trade-off synergy effects varying markedly across different regions; (3) We constructed ecosystem service bundles characterized by “strong synergy–weak trade-off” based on these relationships. Following optimization, regions demonstrating significant synergies expanded, while those showing significant trade-offs contracted, thereby adapting to the ecological heterogeneity of high-altitude areas. This study advances the optimization of ecosystem service bundles in ecologically sensitive zones through a lens of trade-off synergies. The results offer a scientific foundation for formulating effective ecological protection and restoration strategies, providing valuable insights for ecosystem service research in other high-altitude regions globally. Full article

This study assessed environmental metal and metalloids (TE) levels using hair of Apodemus sylvaticus as a non-lethal biomonitor. TE decreased as follows: Zn > Al > Fe > Cu > Pb > Cr > Ni > Mn > Cd > Se > As > Hg; TE widely distributed in soils as Zn, Al, Fe, and Cu, are more abundant than those of ecotoxicological interest, such as Cd, Se, As and Hg. Cd, Pb, Cu, and Cr concentrations are highly variable, while Zn, Fe, and Mn are less variable. TE in hair are below the threshold levels in soil and decrease the same way in both sexes. Concentrations in soil and hair are significantly related, and their level can be modulated both by homeostatic control of essential metals and absorbance from the soil by keratin. Slight differences in Ni and Cr can be related to the differing behaviour of males and females during reproduction. A scarce tendency toward mercury bioaccumulation has been observed in both sex and age classes; from an ecological point of view, these data suggest that the species is a primary consumer, feeding more on the leaves and seeds than on small invertebrates. Full article

This study investigates the intricate relationship between CSR, green innovation, and environmental performance within the context of China’s manufacturing industries. Given the pressing environmental challenges faced by this sector, understanding how CSR practices correlate with sustainable innovations is critical for stakeholders aiming to enhance environmental outcomes. This was a survey-based study using a questionnaire and the five-point Likert scale; items were adopted from previous studies. Sampling was drawn through random sampling. Utilizing a sample of 327 respondents, this research employs SPSS and Structural Equation Modeling with Partial Least Squares (SEM-PLS) as analytical tools. The findings reveal a robust positive correlation between CSR practices and green innovation, as evidenced by a path coefficient of 0.704. These data support the stakeholder theory, which posits that organizations attentive to stakeholder expectations are more inclined to adopt sustainable practices. Furthermore, this study underscores the mediating role of green innovation in the relationship between CSR and environmental performance, highlighting its importance in aligning organizational strategies with sustainability-oriented stakeholder interests. This conclusion aligns with the existing literature emphasizing CSR’s significance in improving environmental performance through innovative approaches. However, an unexpected finding emerged: there exists a weak negative relationship between green innovation and organizational agility (−0.080). This suggests that, while firms strive for sustainable innovations, they may inadvertently compromise their flexibility in responding to evolving market demands. By addressing these dynamics, this research contributes valuable insights into how CSR can effectively spur green innovation and promote sustainable practices within China’s manufacturing sector. This study fills a gap in the existing literature by elucidating the mechanisms that connect CSR with enhanced environmental performance while also recognizing the potential trade-offs associated with innovation strategies. Also, the exploration of agility, which is least investigated, can also open various doors towards sustainability and the adaptation of new changes. Future research is encouraged to further explore these relationships across different industries and delve deeper into the mechanisms linking CSR to improved environmental outcomes, ultimately guiding organizations in balancing sustainability efforts with market responsiveness. Full article

The selection of construction sites for Cultural and Museum Public Buildings (CMPBs) has a profound impact on their future operations and development. To enhance site selection and planning efficiency, we developed a predictive model integrating Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs). Taking Shanghai as our case study, we utilized over 1.5 million points of interest data from Amap Visiting Vitality Values (VVVs) from Dianping and Shanghai’s administrative area map. We analyzed and compiled data for 344 sites, each containing 39 infrastructure data sets and one visit vitality data set for the ANN model input. The model was then tested with untrained data to predict VVVs based on the 39 input data sets. We conducted a multi-precision analysis to simulate various scenarios, assessing the model’s applicability at different scales. Combining GA with our approach, we predicted vitality improvements. This method and model can significantly contribute to the early planning, design, development, and operational management of CMPBs in the future. Full article