Search Results (1,380)

The paper addresses the limitations of fragmented and delayed hydrological information systems in supporting timely disaster risk mitigation. The paper introduces the RoWaterAPI, a framework that integrates near real-time hydrological measurements with geospatial analytics to improve awareness during flood-related events. The methodology utilizes open-source technologies, including Django, Kafka, and PostGIS, to support scalable data ingestion and hazard mapping. Initial baseline evaluation under a simulated bursty workload indicates an end-to-end latency of ≈1–3 s and a peak throughput of ≈6000–8500 messages/s. This performance supports real-time alerts for data variations, bridging advanced geoprocessing with user-centered design for public and institutional stakeholders. Ultimately, RoWaterAPI provides a transferable implementation model that can be adapted to any national context facing similar constraints in data fragmentation and operational accessibility. Full article

Traditional underground space evaluation systems often employ 2D GIS methods to represent 3D information, leading to issues such as the loss of 3D spatial data and insufficient resolution in depth. To address the practical needs and methodological steps of 3D geological suitability evaluation for underground space (3D UGEE) development, this study adopts an integrated secondary development approach to design and implement a software system capable of conducting quantitative geological suitability evaluation in three dimensions using multivariate data. The system incorporates the latest methods and achievements in 3D UGEE, featuring functional modules such as multidimensional data conversion, 3D statistical analysis, 3D spatial distance analysis, and 3D comprehensive evaluation, which enable the integration and analytical assessment of multivariate geoscientific data. In comparison with existing 3D-UGEE systems, the proposed 3D-UGEE system integrates a broader range of functional modules, conducts in-depth integration and mining of multi-source geological data, boasts robust 3D graphical display and interactive capabilities, and achieves more efficient operational performance. This study elaborates on the system’s overall architecture, development approach, and the design and implementation processes of its functional modules. Application results from a case study in Qingdao demonstrate that the system not only provides a suite of 3D spatial analysis and comprehensive evaluation tools for integrating multivariate geoscientific data but also offers robust support for enhancing 3D UGEE practices. Full article

Urban sky gardens—elevated green spaces on buildings, encompassing rooftop gardens and podium gardens—are critical to the improvement of urban ecosystem services and functions. Understanding the spatial patterns and the influencing factors of sky gardens is essential for the precise allocation of elevated spaces in urban development. Taking the four central urban districts of Lanzhou in China as the study region, a GIS database of 508 sky gardens was established by identifying high-definition image maps and on-site investigations. The spatial patterns and influencing factors, such as building height, ground-level green area, and population density, were analyzed. The development of sky gardens was also compared in Lanzhou and Guangzhou, China. The distribution of sky gardens in Lanzhou exhibited spatial heterogeneity. Most sky gardens were distributed along the Yellow River. Chengguan District had more sky gardens than Xigu District. In terms of structural characteristics, 82% of sky gardens were rooftop gardens, 73% were located in residential buildings, and 63% were attached to mid- and low-rise buildings. Most sky gardens were one floor, characterized by no public accessibility, a location in high-density plots, and low vegetation coverage. Sky garden area was negatively correlated with building height, ground-level green area, and green plot ratio in sky gardens. There were positive associations between sky garden area and higher plot ratio, building density, and population density based on Multiscale Geographically Weighted Regression. Due to the proper climate conditions and economy, Guangzhou had more sky gardens than Lanzhou. Our study suggests that the utilization of rooftops and podiums is relatively low, and the development of sky gardens exhibits spatial clustering. A suite of optimizing strategies should be implemented to enhance the accessibility and usability of sky gardens. Full article

Enhancing carbon sink capacity and optimizing urban blue-green infrastructure (UBGI) are crucial for urban planning and sustainable development. Based on the ArcGIS 10.8 platform and the InVEST model, this study comprehensively evaluates the spatiotemporal evolution characteristics of three ecosystem services (carbon storage, habitat quality, and water retention) in Guilin. By applying the coupling coordination degree model, bivariate spatial autocorrelation, and K-means clustering methods, it systematically reveals the synergistic and trade-off relationships among multiple ecosystem services in karst cities, identifies the spatial differentiation pattern of ecological spaces, and proposes UBGI optimization strategies. The results show that the three types of ecosystem services in Guilin exhibited a spatiotemporal differentiation pattern of stable high values in mountainous areas and continuous expansion of low values around urban areas from 1993 to 2023, with their changes mainly driven by the significant negative impact of human activity intensity (nighttime light, population density). Guilin’s ecological space can be divided into four functional zones: Ecological Core Cluster (77.50%), Degraded Carbon-Poor Cluster (1.47%), Habitat Protection Cluster (0.46%), and Buffer Balance Cluster (20.58%). Carbon storage, habitat quality, and water retention showed significant spatial gradient differences (Kruskal–Wallis nonparametric test, p < 0.001) and local decoupling characteristics. Furthermore, the study proposed key ecological management thresholds, such as impervious surface ratio < 15% and forestland ratio > 30%, and constructed a differentiated “zoning-classification-grading” UBGI optimization strategy system based on the four functional zones, including ecological corridor construction, promotion of vertical greening and sponge facilities, supplementary planting of native vegetation, and integration of ecological agriculture. These strategies aim to enhance the synergistic efficiency of ecosystem services, improve regional carbon sink capacity, and provide a scientific basis for Guilin’s ecological planning, the implementation of “dual carbon” goals, and the construction of the National Innovation Demonstration Zone for Sustainable Development Agenda. Full article

This study investigated land degradation sensitivity in Southern Tunisia’s Jeffara region and examined the effectiveness of water harvesting techniques (WHTs) as countermeasures. Land Degradation Sensitivity Index was calculated using a modified MEDALUS framework, in which thematic quality indices were derived from normalized indicators (climate, soil, vegetation, and management) and combined through a geometric mean within a GIS environment. The model is validated with field observations. The research found that almost the entire study area (≈99%) was classified as critically sensitive under the baseline scenario. Contributing factors include extreme aridity, limited vegetation cover, significant soil erosion, and human pressures. The most severely degraded areas are found in mountainous zones, desert plains, and mining areas, whereas regions dominated by olive orchards showed moderate sensitivity levels. This lower sensitivity is associated with the drought tolerance and deep root systems of olive trees, which enhance resistance to prolonged dry periods. This study modeled the impact of implementing traditional WHTs, notably Jessour and Tabias. Under this scenario, a clear qualitative improvement was observed, with the proportion of land classified as critical decreasing from 99% to 77.3%, indicating a measurable reduction in land degradation sensitivity associated with the implementation of WHTs. Despite their environmental benefits, such as enhancing soil moisture and stabilizing agricultural yields, the spatial expansion of WHTs remains limited. Full article

Nature-based solutions, including green infrastructure (GI), are considered sustainable tools for stormwater treatment. GI elements (rain gardens, green roofs, etc.) are increasingly applied as integrated approaches for climate change mitigation and environmental pollution reduction. This study focused on investigations of rain gardens for reducing stormwater polluted by residual chlorine after the disinfection of outdoor spaces. Laboratory (column test) and field tests were carried out to evaluate the infiltration capacities of an experimental rain garden model, as well as its efficiency for retaining residual chlorine. The experiments were conducted using simulated rain garden layers composed of waste materials that remained after different production processes. The average infiltration coefficient values obtained were 2.55 × 10⁻⁵ m/s, 2.45 × 10⁻⁵ m/s, 2.24 × 10⁻⁵ m/s, 3.4 × 10⁻⁵ m/s, 1.28 × 10⁻⁵ m/s, 1.84 × 10⁻⁵ m/s (laboratory test), and 1.39 × 10⁻⁵ m/s (field test). These values correspond to the characteristics of sand–gravel substrates. A chlorine retention efficiency of 82.5–87% was obtained. Granulometric analysis confirmed fraction size suitability for rain garden filtration. This research indicates the potential of rain gardens for reducing stormwater pollution, providing a basis for future research and practical implementation. Full article

Climate change education requires innovative, action-oriented methodologies to foster student engagement and reflection on sustainable behaviors. This study explores an integrated educational program implemented within the Pathways for Transversal Skills and Orientation (PCTO) framework in three Italian upper secondary schools. The program combined immersive virtual reality experiences, GIS-based image analysis, traditional instruction, and two behavior-oriented web applications. A total of 181 students completed a post-activity questionnaire assessing satisfaction, perceived learning, prior knowledge, and self-reported intentions toward behavioral change. Results show that technology-enhanced and interactive modules were associated with higher levels of perceived engagement and perceived learning (with over 80% of students reporting at least moderate learning in immersive, GIS-based, and carbon footprint activities) compared to theory-only sessions. Modules explicitly linked to everyday behaviors, such as carbon footprint estimation and fast fashion consumption, were more frequently associated with self-reported intentions to adopt more sustainable practices (approximately 70% of positive responses). Given the post-only, perception-based design, findings should be interpreted as exploratory evidence of students’ perceived educational value rather than objective measures of learning outcomes. Overall, the study highlights the potential of experiential and digitally enhanced approaches in climate change education, while underscoring the need for future research incorporating objective and longitudinal assessments. Full article

Green Infrastructure (GI) is crucial for urban climate adaptation, providing ecosystem services like mitigating the urban heat island effect and enhancing stormwater management, alongside benefits for public health and biodiversity. Effective GI implementation remains challenging, particularly in dense, rapidly urbanized mid-Adriatic coastal cities, classified as climate hotspots like other Mediterranean contexts. This paper presents a replicable applied trans-scalar methodology for detailed GI design scenarios, developed through the EU-funded LIFE+ A_GreeNet project to bridge the theory–practice gap and enable pilot implementations in multiple Italian mid-Adriatic coastal municipalities. The research details a comprehensive, multi-disciplinary, five-phase process applied to the Sant’Antonio district of San Benedetto del Tronto—a dense, trafficked urban area projected to face “extremely strong heat stress” by 2050. Design interventions included spatial optimization, strategic species replacement, the creation of vegetated bioretention basins, and systematic pavement de-sealing. The application of the model demonstrated significant improvements: a substantial increase in permeable surface area (+194%), a measurable reduction in the UTCI index (average ENVI-MET simulated reduction of 1.17 °C by 2030), and a series of benefits resulting from increased green space and enhanced meteorological water management. This research offers local authorities a tangible model to accelerate climate-adaptive solutions, showing how precise GI design creates resilient, comfortable, and human-centered urban spaces. Full article

The transition to green hydrogen is critical for achieving sustainable energy systems and climate goals. This study presents MODERHydrogen-H₂, a comprehensive framework for assessing solar- and wind-based green hydrogen production, fossil fuel substitution, and greenhouse gas (GHG) reduction. The method integrates Geographic Information Systems (GIS) to optimize renewable energy resource allocation while adhering to sustainability criteria. Applied to four solar sites (2000 MW) in Colombia’s Magdalena–Cauca Basin and three wind projects (1700 MW) in the Caribbean Basin, the model estimates an annual production of 211,074 tons of green hydrogen by 2030. This output could displace 37,221 terajoules of fossil fuels, contributing 2.5% to the national energy matrix and reducing CO₂ emissions by 10.09 million tons. MODERHydrogen-H₂ demonstrates scalability and adaptability, offering a decision-support tool for global energy transition strategies. Its implementation supports affordable, reliable, and low-carbon energy systems, aligning with Sustainable Development Goals (SDGs) targets. The model offers a single platform from which to simulate renewable energy potential in a sustainable manner within a given geographical area, develop scenarios for modifying the energy matrix of a country or region, simulate rational and efficient water supply and demand for energy uses, including aspects of climate change, calculate green hydrogen production in a sustainable manner, and finally calculate greenhouse gas emissions. Full article

Renewable energy sources play a critical role in modern energy production and transmission systems. This paper presents a GIS-enhanced deep learning framework for spatially informed renewable energy potential assessment, integrating environmental variables with Geographic Information Systems (GIS) to support sustainable energy planning aligned with the United Nations Sustainable Development Goals (SDGs). A synthetic dataset comprising 100 distinct geographical regions was constructed using key environmental parameters, including solar irradiance, wind speed, temperature, relative humidity, and altitude. The dataset was further enriched with GIS-based spatial attributes (latitude and longitude) and aggregated historical energy production records used as reference values for supervised learning, without explicit temporal modeling. The standardized dataset was divided into training and testing subsets using an 80:20 split and employed to train a neural network implemented using TensorFlow’s Sequential API. The architecture incorporated dense layers and dropout regularization to prevent overfitting, and was trained for 50 epochs with a batch size of 16 using the Adam optimizer and mean squared error (MSE) loss. The model achieved stable convergence, with training loss reducing from 98,273.70 to 16,651.12 and consistent validation performance, indicating strong generalization. Model outputs were integrated with GIS tools to generate spatial visualizations of energy potential, revealing distinct geographical patterns and clusters relevant for grid planning and resource allocation. By explicitly embedding spatial features into the learning process, the proposed approach provides accurate and interpretable energy potential estimates, supporting informed decision-making for renewable energy deployment and contributing to SDG 7 (clean energy), SDG 9 (resilient infrastructure), and SDG 13 (climate action). Full article

Gastrointestinal (GI) cancers constitute an umbrella term for a wide variety of malignancies that are located in the digestive tract (esophageal, gastric, small and large intestine, anus, liver, gallbladder, and pancreas), with 25% of total cancers and 35% of cancer-related deaths being attributed to them. An alarming trend of rising GI malignancy diagnoses, especially in younger age groups, underscores the need for discoveries in liquid-based biomarkers that facilitate both early detection and optimal disease management. Extracellular vesicles (EVs) not only constitute promising nano-sized biomarkers, but also, via bioengineering, have shown a great therapeutic potential, with artificial intelligence (AI) revolutionizing their research via the selection of the best biomarkers from omics, the recognition of pathophysiological patterns, and facilitating a faster drug-development via AI-driven EV engineering, drug delivery modeling, and target identification. In this review, we will provide a clear insight into the implementation of AI methodologies in EV-based biomarker discovery and therapeutics for pancreatic and hepatobiliary cancer. Full article

This paper proposes a collaborative mechanism of ecological governance based on digital twin; i.e., the intelligence and precision of environmental governance can be implemented by inducing key indicators from the environment in the physical world into the digital twin system of the digital twin mid-platform; by employing advanced sensors for real-time environmental data collection and leveraging GIS model-based integration of the digital twin platform, the real-time monitoring, in-depth analysis, and predictive forecasting of current environmental status can be performed, thereby enabling timely unified decision-making and management responses to emerging situations. The test results have demonstrated that this methodology can significantly enhance both the accuracy and timeliness of environmental monitoring works. Full article

Among the options to improve the establishment of jalapeno pepper (Capsicum annuum L.), physical biostimulants such as far-infrared bioceramics (FIR) and static magnetic fields (MF) have emerged as non-chemical alternatives. This study evaluated, under in vitro conditions, the individual and combined effects of FIR and positive or negative MF on seed germination dynamics, early seedling morphology, water status, and photosynthetic pigments. A completely randomized design with eight treatments was implemented, including FIR applied continuously throughout the entire experimental period, positive or negative MF applied for 24 h (MF⁺₂₄, MF⁻₂₄), and FIR + MF combinations under continuous or 24 h exposure regimes (n = 7). Germination percentage, mean germination time (MGT), mean germination rate (MGR), germination index (GI), morphological variables, water content (WC), and photosynthetic pigments were measured; ANOVA/alternative tests (a = 0.05), Principal Components Analysis (PCA) and exploratory Spearman’s correlations were used to assess relationships among the evaluated variables. Germination percentage did not change (97.64%), but kinetics did: FIR + MF⁻₂₄ reduced MGT to 4.32 d, FIR increased MGR to 5.83 seeds day⁻¹ (+11.69%), and FIR₂₄ + MF⁺₂₄ showed the highest GI (4.57). For morphological, MF⁺₂₄ increased hypocotyl length (+16.29%), FIR increased collar diameter (+27.27%), and FIR + MF⁻₂₄ increased cotyledon area (25%), and FIR increased chlorophyll a (+139%), chlorophyll b (+141%), and carotenoids (+114%). PCA explained 66.9% of the variance, grouping FIR with growth variables and FIR + MF combinations with WC and pigments. Inferences are limited to one cultivar and controlled in vitro conditions. This study provides novel quantitative evidence that continuous and short-term applications of FIR and MF modulate germination dynamics and early physiological traits without altering final germination, related to structure and pigments, without changing final germination percentage. Full article

Multiphoton endomicroscopy (MPEM) has recently become a key development in optical biomedical diagnostics, providing histologically relevant in vivo images that are eliminating both the need for tissue damage during biopsy sampling and the need for dye injections. Due to its ability to visualize structures at the epithelial, extracellular matrix, and subcellular levels, MPEM offers a promising diagnostic method for precancerous conditions and early forms of gastrointestinal (GI) cancer. The high specificity of multiphoton signals—the two-photon fluorescence response of endogenous fluorophores (NADH, FAD), the second-harmonic generation signal from collagen, and others—makes this method a promising alternative to both traditional histology and confocal endoscopy, enabling real-time assessment of metabolic status, intestinal epithelial cell status, and stromal remodeling. Despite the promising prospects of multiphoton microscopy, its practical implementation is progressing extremely slowly. The main factors here include the difficulty of delivering ultrashort pulses with high peak power, which is necessary for multiphoton excitation (MPE), and obtaining these pulses at the required wavelengths to activate the autofluorescence mechanism. One of the most promising solutions is the use of specialized multimode optical fibers that can both induce beam self-cleaning (BSC), which allows for the formation of a stable beam profile close to the fundamental mode, and significantly broaden the optical spectrum, which can ultimately cover the entire region of interest. This review presents the biophysical foundations of multiphoton microscopy of GI tissue, existing endoscopic architectures for MPE, and an analysis of the potential for using novel nonlinear effects in multimode optical fibers, such as the BSC effect and supercontinuum generation. It is concluded that the use of optical fibers in which the listed effects are realized in the tracts of multiphoton endomicroscopes can become a key step in the creation of a new generation of high-resolution instruments for the early detection of malignant neoplasms of the GI tract. Full article

Heritage buildings pose persistent challenges for documentation and conservation due to their geometric complexity, material heterogeneity, and the fragmentation of spatial and semantic datasets. To address these limitations, this study proposes a geospatially enabled HBIM–GIS framework that integrates hybrid photogrammetric survey data with semantic modeling and spatial analysis to support evidence-based conservation planning. A multi-source acquisition strategy combining terrestrial digital photogrammetry (TDP), Unmanned aerial vehicle digital photogrammetry (UAVDP), and spherical photogrammetry (SP) was employed to capture accurate geometric and semantic information across multiple spatial scales. Staged point-cloud fusion (UAVDP → TDP via ICP; SP → UAV–TDP via SICP) generated a high-density, georeferenced composite, achieving RMS residuals below 0.013 m and resulting in an integrated dataset exceeding 360 million points. From this composite, authoritative 2D drawings and a reality-based 3D HBIM model were developed, while GIS thematic mapping translated heterogeneous observations into structured, queryable layers representing materials, cracks, detachments, deformations, and construction phases. The proposed framework enabled the spatial diagnosis of deterioration mechanisms, revealing moisture-driven decay from plinth to mid-wall and concentrated cracking at openings and architectural transitions; side-to-side cracks accounted for approximately 55% and 65% of mapped fissures on the most affected façades. By embedding these diagnostics as element-level attributes within the HBIM environment, the framework supports precise localization, quantification, and prioritization of conservation interventions, ensuring material-compatible and location-specific decision making. The applicability of the framework is demonstrated through its implementation on a complex historic mosque in Yemen, validating its robustness under constrained access and resource-limited conditions. Overall, the study demonstrates that geospatially integrated HBIM–GIS workflows provide a reproducible, scalable, and transferable solution for the sustainable documentation and conservation of heritage buildings, supporting long-term monitoring and informed management of cultural heritage assets worldwide. Full article