Search Results (105)

Vigor-reducing rootstocks are now commonly used in sweet cherry cultivation. However, their application in Hungary presents challenges due to the drier summer climate and limited availability of irrigation water. The aim of this study was to determine the water transport characteristics and potential drought tolerance of three vigor-reducing rootstocks that may be suitable for cherry production in Hungary. The stomatal conductance (gs), midday stem water potential (MSWP), and sap flow velocity were measured in four-year-old Carmen and Regina cherry trees grafted onto MaxMa 14, WeiGi 2, and GiSelA 6 rootstocks. Measurements were taken after harvest during a period of severe drought. Among the rootstocks studied, MaxMa 14 trees exhibited the lowest MSWP values, even after irrigation and during periods with a relatively adequate water supply. No significant or consistent differences in the gs values were observed between the rootstocks. However, the variation in the gs and MSWP values before and after irrigation was the greatest in MaxMa 14 trees and the smallest in GiSelA 6 trees. Furthermore, the sap flow velocity in MaxMa 14 trees showed no significant difference between the pre- and post-irrigation measurements, indicating stable water transport. In contrast, trees on GiSelA 6 and WeiGi 2 rootstocks exhibited significant differences between dry and irrigated conditions. Although MaxMa 14 showed lower MSWP values, its gs responded more dynamically to changes in the water availability, and it maintained consistent water transport parameters across both dry and wet conditions. Based on the evaluated parameters, GiSelA 6 and WeiGi 2 showed similar behavior. However, in regard to some traits—such as the dynamic change in stomatal conductance—WeiGi 2 appeared to be more similar to MaxMa 14. Our results suggest that MaxMa 14 may be the most adaptable to drought among the tested rootstocks. Full article

Urban sprawl and suburbanization are reshaping peri-urban areas, challenging urban planning and community well-being. Our study investigates questions regarding the perception of Cultural Ecosystem Services (CES) and development preferences (DP) related to Green Infrastructure (GI) in Vác, Hungary, including how CES and DP indicators related to GI vary spatially; how they align with municipal DI designations; how they relate to sociodemographic factors; and how they are applicable to urban planning practices. We used PPGIS and structured interviews with 375 residents to collect over 4900 spatial data points in order to analyze how perceived values, development preferences, officially designated GI elements and sociodemographic characteristics, relate to each other. The results show that CES are strongly associated with GI elements, especially along the riverfront and in downtown areas. However, development preferences, especially congestion and safety concerns, were more dispersed, often located in outer residential areas and along transportation routes. Statistical analyses showed significant differences across age, marital status, and co-residence with children, influencing both CES perception and development preferences. Our study highlights the gap between official GI designations and community-valued spaces, emphasizing the importance of participatory planning and the integration of sociodemographic dimensions into planning practices in rapidly transforming suburban environments. Full article

The clean energy transition has elevated renewable hydrogen as a key energy vector, yet challenges in cost-competitiveness and infrastructure planning persist. This study conducts a PRISMA-based systematic review of recent geospatial applications across the hydrogen value chain—production, storage, transport, and end-use. Bibliometric analysis reveals a strong focus on production (48%), with less attention to storage (12%) and end-uses (18%). Geographic Information Systems (GIS) dominate (80%), primarily for siting, potential assessment, and infrastructure planning, while other techniques such as geophysics and real-time monitoring are emerging. Identified research gaps include fragmented and low-resolution data, lack of harmonization, and high computational demands, which are independent from the phase in the hydrogen value chain. Promising areas for future research include hydrological resource mapping for electrolysis, offshore infrastructure clustering, and spatialized levelized cost modeling. The review concludes with a call for high-resolution, AI-enabled geospatial frameworks to support automated, location-specific decision-making and scalable renewable hydrogen deployment. Full article

Previous reviews have examined specific facets of Geographic Information Systems (GIS) in transportation planning, such as transit-focused applications and open source geospatial tools. However, this study offers the first systematic, PRISMA-guided longitudinal evaluation of GIS integration in transportation planning, spanning thematic domains, data models, methodologies, and outcomes from 2004 to 2024. This study addresses this gap through a longitudinal analysis of GIS-based transportation research from 2004 to 2024, adhering to PRISMA guidelines. By conducting a mixed-methods analysis of 241 peer-reviewed articles, this study delineates major trends, such as increased emphasis on sustainability, equity, stakeholder involvement, and the incorporation of advanced technologies. Prominent domains include land use–transportation coordination, accessibility, artificial intelligence, real-time monitoring, and policy evaluation. Expanded data sources, such as real-time sensor feeds and 3D models, alongside sophisticated modeling techniques, enable evidence-based, multifaceted decision-making. However, challenges like data limitations, ethical concerns, and the need for specialized expertise persist, particularly in developing regions. Future geospatial innovations should prioritize the responsible adoption of emerging technologies, inclusive capacity building, and environmental justice to foster equitable and efficient transportation systems. This review highlights GIS’s evolution from a supplementary tool to a cornerstone of data-driven, sustainable urban mobility planning, offering insights for researchers, practitioners, and policymakers to advance transportation strategies that align with equity and sustainability goals. Full article

Ensuring the structural safety of steel trusses in escalators is critical for the reliable operation of vertical transportation systems. While manual inspection remains widely used, its dependence on human judgment leads to extended cycle times and variable defect-recognition rates, making it less reliable for identifying subtle surface imperfections. To address these limitations, a novel context-aware, multi-scale deep learning framework based on the YOLOv5 architecture is proposed, which is specifically designed for automated structural defect detection in escalator steel trusses. Firstly, a method called GIES is proposed to synthesize pseudo-multi-channel representations from single-channel grayscale images, which enhances the network’s channel-wise representation and mitigates issues arising from image noise and defocused blur. To further improve detection performance, a context enhancement pipeline is developed, consisting of a local feature module (LFM) for capturing fine-grained surface details and a global context module (GCM) for modeling large-scale structural deformations. In addition, a multi-scale feature fusion module (MSFM) is employed to effectively integrate spatial features across various resolutions, enabling the detection of defects with diverse sizes and complexities. Comprehensive testing on the NEU-DET and GC10-DET datasets reveals that the proposed method achieves 79.8% mAP on NEU-DET and 68.1% mAP on GC10-DET, outperforming the baseline YOLOv5s by 8.0% and 2.7%, respectively. Although challenges remain in identifying extremely fine defects such as crazing, the proposed approach offers improved accuracy while maintaining real-time inference speed. These results indicate the potential of the method for intelligent visual inspection in structural health monitoring and industrial safety applications. Full article

Unbalanced accelerations occurring during tram travel have a significant impact on passenger comfort and safety, as well as on the rate of wear and tear on infrastructure and rolling stock. Ideally, these dynamic forces should be monitored continuously in real-time; however, traditional systems require high-precision accelerometers and proprietary software—investments often beyond the reach of municipally funded tram operators. To this end, as part of the research project “Accelerometer Measurements in Rail Passenger Transport Vehicles”, pilot measurement campaigns were conducted in Poland on tram lines in Gdańsk, Toruń, Bydgoszcz, and Olsztyn. Off-the-shelf smartphones equipped with MEMS accelerometers and GPS modules, running the Physics Toolbox Sensor Suite Pro app, were used. Although the research employs widely known methods, this paper addresses part of the gap in affordable real-time monitoring by demonstrating that, in the future, equipment equipped solely with consumer-grade MEMS accelerometers can deliver sufficiently accurate data in applications where high precision is not critical. This paper presents an analysis of a subset of results from the Gdańsk tram network. Lateral (x) and vertical (z) accelerations were recorded at three fixed points inside two tram models (Pesa 128NG Jazz Duo and Düwag N8C), while longitudinal accelerations were deliberately omitted at this stage due to their strong dependence on driver behavior. Raw data were exported as CSV files, processed and analyzed in R version 4.2.2, and then mapped spatially using ArcGIS cartograms. Vehicle speed was calculated both via the haversine formula—accounting for Earth’s curvature—and via a Cartesian approximation. Over the ~7 km route, both methods yielded virtually identical results, validating the simpler approach for short distances. Acceleration histograms approximated Gaussian distributions, with most values between 0.05 and 0.15 m/s², and extreme values approaching 1 m/s². The results demonstrate that low-cost mobile devices, after future calibration against certified accelerometers, can provide sufficiently rich data for ride-comfort assessment and show promise for cost-effective condition monitoring of both track and rolling stock. Future work will focus on optimizing the app’s data collection pipeline, refining standard-based analysis algorithms, and validating smartphone measurements against benchmark sensors. Full article

Urban rail transit is a key enabler for optimizing urban spatial structures, and its interactive relationship with land use has long been a focus of attention. However, existing studies suffer from scattered methodologies, a lack of systematic analysis, and insufficient dynamic insights into global trends. This study comprehensively employs CiteSpace, VOSviewer, and Scimago Graphica to conduct bibliometric and knowledge map analysis on 1894 articles from the Web of Science database between 2004 and 2024, focusing on global research trends, collaboration networks, thematic evolution, and methodological advancements. Key findings include the following: (1) research on rail transit and land use has been steadily increasing, with a significant “US-China dual-core” distribution, where most studies are concentrated in the United States and China, with higher research density in Asia; (2) domestic and international research has primarily focused on themes such as the built environment, value capture, and public transportation, with a recent shift toward artificial intelligence and smart city technology applications; (3) research methods have evolved from foundational 3S technologies (GIS, GPS, RS) to spatial modeling tools (e.g., LUTI model, node-place model), and the current emergence of AI-driven analysis (e.g., machine learning, deep learning, digital twins). The study identifies three future research directions—technology integration, data governance, and institutional innovation—which provide guidance for the coordinated planning of transportation and land use in future smart city development. Full article

Green Infrastructure (GI) has increasingly been recognised as a crucial strategy for enhancing urban resilience, particularly in urban transportation systems facing the challenges of climate change. Although several conceptual frameworks for GI planning have been proposed, empirical studies examining their application in urban transport planning contexts remain limited. This study aims to validate a recently developed GI conceptual framework by evaluating its applicability in urban transportation systems. A structured questionnaire was administered to 94 participants in Aotearoa New Zealand comprising urban planners, engineers, architects, policymakers, and academics involved in transportation and sustainability planning with special focus on GI. The framework was assessed across key dimensions including the perceived benefits of GI in transportation, stakeholder and collaborative practices barriers to implementation, and indicators of perceived effectiveness. The results confirm that the stakeholders’ perceptions of GI are significantly aligned with the dimensions of the conceptual framework, reinforcing its validity in assessing GI effectiveness. Key findings highlight a disconnect between stakeholders’ general familiarity with GI and their understanding of its multifunctional benefits beyond stormwater management. Also, the prevalence of multidisciplinary collaboration suggests that additional interdisciplinary and transdisciplinary approaches are required for more holistic GI planning. This study recommends that the conceptual framework be considered for city adaptation to GI integration, and to do so effectively, these knowledge and cooperation gaps must be addressed Full article

The multifunctionality of green infrastructure (GI) can be enhanced through intentional planning that promotes synergies among various functions while minimizing trade-offs. Despite its significance, methodologies for implementing this approach remain underexplored. This paper presents an application-oriented framework for GI planning that emphasizes the relationship between GI functional performance and the provision of ecosystem services. By reframing the issues of multifunctional synergies and trade-offs as quantifiable and spatially explicit problems associated with ecosystem services, the framework offers both a conceptual foundation and technical protocols for practical application. This framework was implemented in the Qinling–Daba Mountain Area (QDMB) in China to evaluate its practicality and identify potential challenges. The planned GI system aims to fulfill multiple functions, including biodiversity maintenance, water and soil conservation, eco-farming, and ecotourism development. Additionally, 73 wildlife corridors were established to connect GI elements, thereby enhancing habitat services for biodiversity. Furthermore, the analysis identified 245 townships and 273 sites as strategic areas and points requiring targeted intervention to mitigate potential multifunctional trade-offs. These locations are characterized by their location within protected areas, protected buffer zones, or wildlife corridors, or at the intersection of wildlife corridors with existing transportation infrastructure. The findings validate the framework’s practicality and highlight the necessity for additional research into the capacity of GI to support diverse human activities and the approaches to enhance GI elements’ connectivity for multifunctionality. Full article

The present study examines the application of four machine learning models—Multi-Layer Perceptron, Naive Bayes, Credal Decision Trees, and Random Forests—to assess landslide susceptibility using Mei County, China, as a case study. Aerial photographs and field survey data were integrated into a GIS system to develop a landslide inventory map. Additionally, 16 landslide conditioning factors were collected and processed, including elevation, Normalized Difference Vegetation Index, precipitation, terrain, land use, lithology, slope, aspect, stream power index, topographic wetness index, sediment transport index, plan curvature, profile curvature, and distance to roads. From the landslide inventory, 87 landslides were identified, along with an equal number of randomly selected non-landslide locations. These data points, combined with the conditioning factors, formed a spatial dataset for our landslide analysis. To implement the proposed methodological approach, the dataset was divided into two subsets: 70% formed the training subset and 30% formed the testing subset. A correlation analysis was conducted to examine the relationship between the conditioning factors and landslide occurrence, and the certainty factor method was applied to assess their influence. Beyond model comparison, the central focus of this research is the optimization of machine learning parameters to enhance prediction reliability and spatial accuracy. The results show that the Random Forests and Multi-Layer Perceptron models provided superior predictive capability, offering detailed and actionable landslide susceptibility maps. Specifically, the area under the receiver operating characteristic curve and other statistical indicators were calculated to assess the models’ predictive accuracy. By producing high-resolution susceptibility maps tailored to local geomorphological conditions, this work supports more informed land-use planning, infrastructure development, and early warning systems in landslide-prone areas. The findings also contribute to the growing body of research on artificial intelligence-driven natural hazard assessment, offering a replicable framework for integrating machine learning in geospatial risk analysis and environmental decision-making. Full article

Urban sprawl and excessive reliance on motorization have led to many urban problems. The balance of supply and demand in the real estate market, as well as price fluctuations, also face many challenges. Urban rail transit not only alleviates traffic congestion and air pollution, but also significantly reduces residents’ commuting time, broadens urban accessibility, and reshapes the decision-making basis for residents when choosing residential locations. This study takes the 1st, 2nd, 3rd, 4th, 8th, 11th, and 13th metro lines that have been opened in Qingdao City as examples. It selects 12,924 residential samples within a 2 km radius along the rail transit lines. By using GIS spatial analysis tools and the multi-scale geographically weighted regression (MGWR) model, it analyzes the spatial differentiation characteristics of housing prices along the rail transit lines and the reasons and mechanisms behind them. The empirical results show that housing prices decrease to varying degrees with the increase in the distance from the rail transit. For every additional 1 km from the rail transit station, the housing price increases by 0.246%. Through model comparison, it was found that MGWR has a better fitting degree than the traditional ordinary least squares method (OLS) and the previous geographically weighted regression model (GWR), and reveals the spatial heterogeneity of the influence of urban rail transit on housing prices. Different indicator elements have different effects on housing prices along these lines. The urban rail transit factor in the location characteristics has a positive impact on housing prices, and has a significant negative correlation in some areas. The significant influence range of the distance to the nearest metro station on housing prices is concentrated within a radius of 373 m, and the effect decays beyond this range. The total floors, building area, green coverage rate, property management fee, and the distance to hospitals and parks in the neighborhood and structural characteristics have spatial heterogeneity. Analyzing the areas affected by the urban rail transit factor, it was found that the double location superposition effect, the networked transportation system, and the agglomeration of urban functional axes are important reasons for the significant phenomena in some local areas. This research provides a scientific basis for optimizing the sustainable development of rail transit in Qingdao and formulating differentiated housing policies. Meanwhile, it expands the application of the MGWR model in sustainable urban spatial governance and has practical significance for other cities to achieve sustainable urban development. Full article

Diffuse agricultural pollution is a leading contributor to surface water degradation, particularly in regions undergoing rapid land use change and agricultural intensification. In many developing countries, conventional assessment approaches fall short of capturing the spatial complexity and cumulative nature of multiple environmental drivers that influence surface water vulnerability. This study addresses this gap by introducing the Integral Index of Vulnerability to Diffuse Contamination (IIVDC), a spatially explicit, multi-criteria framework that combines the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS). The IIVDC integrates six key indicators—slope, soil erodibility, land use, runoff potential, hydrological connectivity, and observed water quality—weighted through expert elicitation and mapped at high spatial resolution. The methodology was applied to the Guachal River watershed in Valle del Cauca, Colombia, where agricultural pressures are pronounced. Results indicate that 33.0% of the watershed exhibits high vulnerability and 4.3% very high vulnerability, with critical zones aligned with steep slopes, limited vegetation cover, and strong hydrological connectivity to cultivated areas. By accounting for both biophysical attributes and pollutant transport pathways, the IIVDC offers a replicable tool for prioritizing land management interventions. Beyond its technical application, the IIVDC contributes to sustainability by enabling evidence-based decision-making for water resource protection and land use planning. It supports integrated, spatially targeted actions that can reduce long-term contamination risks, guide sustainable agricultural practices, and improve institutional capacity for watershed governance. The approach is particularly suited for contexts where data are limited but spatial planning is essential. Future refinement should consider dynamic water quality monitoring and validation across contrasting hydro-climatic regions to enhance transferability. Full article

This study investigates bio-acidification as a method to decrease the pH of animal manure in dairy farms through the application of coffee silverskin (i.e., a coffee torrefaction waste) and acetic acid. The aim was to focus on the preliminary analysis needed to assess the suitability of using this mitigation strategy. This analysis was carried out by developing a three-step methodology. The first step included the identification of the appropriate proportions of coffee silverskin and acetic acid at the laboratory scale; in the second step, the best treated proportions were analysed in field conditions to compare the statistical differences among the pH of the control and treated samples. In the third step, territorial evaluation was carried out to verify the availability of the coffee waste in the territory based on the use of a Geographic Information System (GIS). Based on the results, a reduction of 38% and 31% in pH was observed in samples treated with acetic acid and coffee silverskin at the laboratory scale and in field conditions, respectively. The territorial analysis showed that it is possible to valorise this agro-industrial waste while minimising environmental impacts due to transportation if the coffee industry is located within a 75 km distance. Full article

This study introduces an innovative and scalable approach for automated road surface assessment by integrating Mobile Mapping System (MMS)-based LiDAR data analysis with an open-source WebGIS platform. In a U.S.-based case study, over 20 datasets were collected along Interstate I-65 in West Lafayette, Indiana, using the Purdue Wheel-based Mobile Mapping System—Ultra High Accuracy (PWMMS-UHA), following Indiana Department of Transportation (INDOT) guidelines. Preprocessing included noise removal, resolution reduction to 2 cm, and ground/non-ground separation using the Cloth Simulation Filter (CSF), resulting in Bare Earth (BE), Digital Terrain Model (DTM), and Above Ground (AG) point clouds. The optimized BE layer, enriched with intensity and color information, enabled crack detection through Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Random Forest (RF) classification, with and without intensity normalization. DBSCAN parameter tuning was guided by silhouette scores, while model performance was evaluated using precision, recall, F1-score, and the Jaccard Index, benchmarked against reference data. Results demonstrate that RF consistently outperformed DBSCAN, particularly under intensity normalization, achieving Jaccard Index values of 94% for longitudinal and 88% for transverse cracks. A key contribution of this work is the integration of geospatial analytics into an interactive, open-source WebGIS environment—developed using Blender, QGIS, and Lizmap—to support predictive maintenance planning. Moreover, intervention thresholds were defined based on crack surface area, aligned with the Pavement Condition Index (PCI) and FHWA standards, offering a data-driven framework for infrastructure monitoring. This study emphasizes the practical advantages of comparing clustering and machine learning techniques on 3D LiDAR point clouds, both with and without intensity normalization, and proposes a replicable, computationally efficient alternative to deep learning methods, which often require extensive training datasets and high computational resources. Full article

The present study aimed to investigate the effects of exogenous carnitine treatments on maize seed germination by stimulating lipid metabolism and regulating the mitochondrial respiratory pathway. Maize seeds were grown as control, 5, 7.5, and 10 μM carnitine treatment groups in a germination chamber at 25 °C under dark conditions for 5 d. It was determined that carnitine treatments increased the germination rate (GR), germination index (GI), germination potential (GP), vigor index (VI), root and hypocotyl length, fresh weight (FW), and content of total soluble protein but decreased the total carbohydrate content. It was also found that it increased the activities of α-amylase, isocitrate lyase (ICL), and malate synthase (MS) enzymes, which are critical in the germination process, and upregulated the expression of ICL and MS genes. To clarify the potential of carnitine treatments to promote the participation of lipids in respiration in roots and hypocotyls, lipase, carnitine acyltransferases (CATI and CATII), and citrate synthase (CS) enzyme activities were examined, and significant increases in these activities were detected. It was also found that gene levels of respiratory enzymes cytochrome oxidase (COX), pyruvate dehydrogenase (PDH), and Atp synthase, lipase, and CS proteins were upregulated by carnitine treatment. In support of the enzyme and gene change findings, significant changes were determined in fatty acid contents, free carnitine, and long-chain acylcarnitine levels in seeds, roots, and hypocotyls depending on carnitine application. In roots and hypocotyls, carnitine treatments significantly increased glutamine synthase (GS) and glutamate dehydrogenase (NADH-GDH) activities and gene expression levels, which are closely related to the tricarboxylic acid cycle (TCA). It was also noted that all proteins analyzed at the gene expression level were upregulated by carnitine applications in seeds. In addition, significant increases were recorded in antioxidant enzyme ascorbate peroxidase (APX) and superoxide dismutase (SOD) activities and total ascorbate (AsA) and glutathione (GSH) contents in roots and hypocotyls, while decreases were determined in guaiacol peroxidase (GPX) and catalase activities. Significant changes were recorded in all parameters examined, especially with 7.5 µM carnitine application. The findings suggest that carnitine may promote the transport of fatty acids to mitochondrial respiration by accelerating lipid catabolism in five-day-old maize and contribute to seed germination and growth and development processes by activating other metabolic pathways associated with respiration in this process. Full article