Search Results (877)

Background/Objectives: Advances in treatment options have barely improved the prognosis of ovarian carcinoma (OC) in recent decades. The inherent heterogeneity of OC underlies challenges in treatment (development) and patient stratification. One hurdle for effective drug development is the lack of patient-representative disease models available for preclinical drug research. Based on quantitative measurement of signal transduction pathway (STP) activity in cell lines, we aimed to identify cell line models that better mirror the different clinical subtypes of OC. Methods: The activity of seven oncogenic STPs (signal transduction pathways) was determined by previously described STP technology using transcriptome data from untreated OC cell lines available in the GEO database. Hierarchal clustering of cell lines was performed based on STP profiles. Associations between cell line histology (original tumor), cluster, and STP profiles were analyzed. Subsequently, STP profiles of clinical OC tissue samples were matched with OC cell lines. Results: Cell line search resulted in 80 cell line transcriptome data from 23 GEO datasets, with 51 unique cell lines. These cell lines were derived from eight different histological OC subtypes (as determined for the primary tumor). Clustering revealed seven clusters with unique STP profiles. When borderline tumors (n = 6), high-grade serous (n = 51) and low-grade (n = 31) OC were matched with cell lines, twelve different cell lines were identified as potentially patient-representative OC cell line models. Conclusions: Based on STP activity, we identified twelve different cell lines that were the most representative of the common subtypes of OC. These findings are important to improve drug development for OC. Full article

The semantic segmentation of laser point clouds is critical for many applications of aerial point clouds. However, most of the existing deep learning networks do not make full use of point cloud data information. PointNet++ was chosen as the baseline network, and a deep-residual enhanced encoding method of multi-feature information is proposed in this work. Firstly, a more efficient network structure to enhance geometric information encoding is constructed, called the GEO–PointNet layer. Then, a novel structure for feature aggregation, named SEP–PointNet, is introduced to encode the auxiliary and geometric features of points separately. Additionally, the segmentation network is deepened in the way of residual structures, which can effectively restrain network degradation. Meanwhile, ‘Dropout’ operations are applied to the fully connected layer to cope with the problem that the model is prone to overfitting due to many network parameters. Finally, a novel segmentation network, named SGDD–PointNet++, is built, and its effectiveness was evaluated by using four airborne benchmark datasets. The experimental results performed on the DALES dataset indicate that the overall accuracy and average intersection-over-union (mIoU) value of the modified PointNet++ networks are better than the original baseline and the other two state-of-the-art segmentation methods. The overall accuracy of the improved SGDD–PointNet++ network reached 87.88%. For the category IoU, it also outperforms other networks, and it has a maximum accuracy increment of 11.43%. Meanwhile, in terms of the generalization capabilities of the trained models, the proposed network can provide better discrimination effects for three public aerial datasets than other methods. Full article

Landslide susceptibility mapping (LSM) is a vital tool for proactive disaster mitigation. Although numerous studies utilize slope units (SUs) for LSM, the limited integration of adjacency information, including spatial autocorrelation, often reduces predictive accuracy. In this study, GRASS GIS was utilized to generate slope units, and a spatial logistic regression (SLR) model was developed to incorporate the adjacency information of the slope units to predict the landslide susceptibility. Then, the spatial stratification heterogeneity patterns of landslide susceptibility were analyzed using GeoDetector. The results showed that the SLR model achieved an area under the curve (AUC) of 0.89, a notable improvement of 0.26 compared to the traditional logistic regression (LR) model that does not incorporate adjacency information. This indicates that incorporating adjacency information effectively enhances LSM accuracy by mitigating spatial autocorrelation. Furthermore, lithology, PGV, and distance to the epicenter were identified as the primary factors contributing to the formation of the spatial stratification heterogeneity of landslide susceptibility. Among these, the interaction between lithology and PGV exhibits the strongest nonlinear enhancement. By integrating both mapping units and their adjacency information, this study provides a novel approach to improving the predictive accuracy of LSM. Moreover, by analyzing the driving factors of spatial stratification heterogeneity in landslide susceptibility maps, the study advances the practical utility of LSM for disaster management and mitigation. Full article

Traditional coastal flood risk prediction often overlooks critical geographic features, underscoring the need for accurate risk prediction in coastal cities to ensure resilience. This study enhances the prediction of coastal flood occurrence by utilizing the Geospatial Artificial Intelligence (GeoAI) approach. This approach employed models—random forest (RF), k-nearest neighbor (kNN), and artificial neural networks (ANN)—and compared them to the IPCC risk framework. This study used El Salvador as a demonstration case. The models incorporated seven input variables: extreme sea level, coastline proximity, elevation, slope, mangrove distance, population, and settlement type. With a recall score of 0.67 and precision of 0.86, the RF model outperformed the other models and the IPCC approach, which could avoid imbalanced datasets and standard scaler issues. The RF model improved the reliability of flood risk assessments by reducing false negatives. Based on the RF model output, scenario analysis predicted a significant increase in flood occurrences by 2100, mainly under RCP8.5 with SSP5. The study also highlights that the continuous mangrove along the coastline will reduce coastal flood occurrences. The GeoAI approach results suggest its potential for coastal flood risk management, emphasizing the need to integrate natural defenses, such as mangroves, for coastal resilience. Full article

The growing variety of transportation options and increasing traffic congestion pose new challenges for road safety. As a result, there is an intensified focus on developing automated driving features and assistance systems aimed at minimizing accidents caused by human errors. The creation of these systems requires a substantial amount of testing kilometers, with estimates suggesting that around 2.1 billion kilometers would be necessary to ensure that each situation pertinent to the driving function is encountered at least once with a probability of 50%. This paper advances the microscopic simulation of traffic scenarios beyond linear patterns, utilizing the open-source environment openPASS. It addresses the research question of whether existing microscopic simulations are able to realistically represent non-linear traffic scenarios. A comprehensive toolchain integrates simulation with video recordings and laser scans. The study compares recorded traffic flow data with simulations at a T-junction, assessing the realism of vehicle models and trajectory representation. Three scenarios are analyzed, considering vehicles and pedestrians. The 3D geometry of the scene was captured with a laser scanner, enabling the mapping of recorded video data onto a geo-referenced environment. Object trajectories were extracted using an ’Regions with Convolutional Neural Networks features’ object detector. While openPASS simulated vehicle and pedestrian behaviors effectively, limitations in trajectory variability and reaction times were observed. These findings highlight the need for more realistic behavior models. This research emphasizes the necessity for improvements to accommodate complex driving behaviors and pedestrian dynamics. Full article

Soil salinization is a critical global environmental issue, exacerbated by climatic and anthropogenic factors, and posing significant threats to agricultural productivity and ecological stability in arid regions. Therefore, remote sensing-based dynamic monitoring of soil salinization is crucial for timely assessment and effective mitigation strategies. This study used Landsat imagery from 2001 to 2021 to evaluate the potential of support vector machine (SVM) and classification and regression tree (CART) models for monitoring soil salinization, enabling the spatiotemporal mapping of soil salinity in the Yutian Oasis. In addition, the land use transfer matrix and spatial overlay analysis were employed to comprehensively analyze the spatiotemporal trends of soil salinization. The geographical detector (Geo Detector) tool was used to explore the driving factors of the spatiotemporal evolution of salinization. The results indicated that the CART model achieved 5.3% higher classification accuracy than the SVM, effectively mapping the distribution of soil salinization and showing a 26.76% decrease in salinized areas from 2001 to 2021. Improvements in secondary salinization and increased vegetation coverage were the primary contributors to this reduction. Geo Detector analysis highlighted vegetation (NDVI) as the dominant factor, and its interaction with soil moisture (NDWI) has a significant impact on the spatial and temporal distribution of soil salinity. This study provides a robust method for monitoring soil salinization, offering critical insights for effective salinization management and sustainable agricultural practices in arid regions. Full article

With the increasing demands of urban populations, achieving a balance between the supply and demand in the spatial allocation of urban green park spaces (UGSs) is essential for effective urban planning and improving residents’ quality of life. The study of UGS supply and demand balance has become a research hotspot. However, existing studies of UGS supply and demand balance rarely simultaneously improve the supply side, demand side, and transportation methods that connect the two, nor do they conduct a comprehensive, multi-dimensional supply and demand evaluation. Therefore, this study evaluates the accessibility of UGS within Hefei’s built-up areas, focusing on age-specific demands for UGS and incorporating various travel modes, including walking, cycling, driving, and public transportation. An improved two-step floating-catchment area (2SFCA) method is applied to evaluate the accessibility of UGS in Hefei’s built-up areas. This evaluation combines assessments using the Gini coefficient, Lorenz curve, location entropy, and local spatial autocorrelation analysis, utilizing the ArcGIS 10.8 and GeoDa 2.1 platforms. Together, these methods enable a supply–demand balance analysis of UGSs to identify areas needing improvement and propose corresponding strategies. The research results indicate the following: (1) from a regional perspective, there are significant disparities in the accessibility of UGS within Hefei’s urban center, with the old city showing more imbalance than the new city. Areas with high demand and low supply are primarily concentrated in the old city, which require future improvement; (2) in terms of travel modes, higher-speed travel (such as driving) offers better and more equitable accessibility compared to slower modes (such as walking), highlighting transportation as a critical factor influencing accessibility; (3) regarding population demand, there is an overall balance in the supply of UGS, with local imbalances observed in the needs of residents across different age groups. Due to the high specific demand for UGS among older people and children, the supply and demand levels in these two age groups are more consistent. This study offers valuable insights for achieving the balanced, efficient, and sustainable development of the social benefits of UGS. Full article

We utilized remote sensing and ground cover data to predict soil organic carbon (SOC) content across a vast geographic region. Employing a combination of machine learning and deep learning techniques, we developed a novel data fusion approach that integrated Digital Elevation Model (DEM) data, MODIS satellite imagery, WOSIS soil profile data, and CHELSA environmental data. This combined dataset, named GeoBlendMDWC, was specifically designed for SOC prediction. The primary aim of this research is to develop and evaluate a novel optimization algorithm for accurate SOC prediction by leveraging multi-source environmental data. Specifically, this study aims to (1) create an integrated dataset combining remote sensing and ground data for comprehensive SOC analysis, (2) develop a new optimization technique that enhances both machine learning and deep learning model performance, and (3) evaluate the algorithm’s efficiency and accuracy against established optimization methods like Jaya and GridSearchCV. This study focused on India, Australia, and South Africa, countries known for their significant agricultural activities. We introduced a novel optimization technique for both machine learning and deep neural networks, comparing its performance to established methods like the Jaya optimization technique and GridSearchCV. The models evaluated included XGBoost Regression, LightGBM, Gradient Boosting Regression (GBR), Random Forest Regression, Decision Tree Regression, and a Multilayer Perceptron (MLP) model. Our research demonstrated that the proposed optimization algorithm consistently outperformed existing methods in terms of execution time and performance. It achieved results comparable to GridSearchCV, reaching an R² of 90.16, which was a significant improvement over the base XGBoost model’s R² of 79.08. In deep learning optimization, it significantly outperformed the Jaya algorithm, achieving an R² of 61.34 compared to Jaya’s 30.04. Moreover, it was 20–30 times faster than GridSearchCV. Given its speed and accuracy, this algorithm can be applied to real-time data processing in remote sensing satellites. This advanced methodology will greatly benefit the agriculture and farming sectors by providing precise SOC predictions. Full article

In this study, GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI) and Lightning NETwork (LINET) data were used for lightning detection. A total of 20 lightning cases from the summer of 2020–2021 were selected, with 14 cases for training and 6 for validation to develop lightning detection algorithms. Since these two datasets have different spatiotemporal resolutions, spatiotemporal matching was performed to use them together. To find the optimal lightning detection algorithm, we designed 25 experiments and selected the best experiment by evaluating the detection level. Although the best experiment had a high POD (>0.9) before post-processing, it also showed over-detection of lightning. To minimize the over-detection problem, statistical and Region-Growing post-processing methods were applied, improving the detection performance (FAR: −19.14~−24.32%; HSS: +76.92~+86.41%; Bias: −59.3~−66.9%). Also, a sensitivity analysis of the collocation criterion between the two datasets showed that the detection level improved when the spatial criterion was relaxed. These results suggest that detecting lightning in mid-latitude regions, including the Korean Peninsula, is possible by using GK2A/AMI data. However, reducing the variability in detection performance and the high FAR associated with anvil clouds and addressing the parallax problem of thunderstorms in mid-latitude regions are necessary to improve the detection performance. Full article

This study aims to develop the marine geo-polymer cement that was produced with seawater, recycled particles from paste, recycled particles from glass, and alkaline activators, including NaOH or Na₂O·3.3SiO₂. The physicochemical properties and strength of MGPC were investigated with a Uniaxial Compression Test, Particle Size Analysis, Energy Dispersive Spectrometer, X-ray Diffraction, and Thermal-field Emission Scanning Electron Microscopy. The results indicated that the main hydration products in MGPC were calcium carbonate (CaCO₃), silica (SiO₂), sodium aluminosilicate hydrate (Na₂O·Al₂O₃·xSiO₂·2H₂O, N-A-S-H), and aluminum calcium silicate hydrate (CaO·Al₂O₃·2SiO₂·4H₂O, C-A-S-H). The calcium carboaluminate (3CaO·Al₂O₃·CaCO₃·32H₂O, CO₃-AFm) in MGPC was converted into CaCO₃ and Friedel’s salt (3CaO·Al₂O₃·CaCl₂·10H₂O), which prompted the carbon sequestration. The microstructure of MGPC prepared using Na₂O·3.3SiO₂ was based on RPG as the matrix, with N-A-S-H, C-A-S-H, and fibrous AFt growing on the periphery. This structure reduces the impact of the alkali–silica reaction on the material and improves its compressive strength. Therefore, the MGPC developed in this study shows the exact benefits of freshwater and natural minerals saving, carbon sequestration, and damage resistance. Full article

Global aerosol models often underestimate the mass concentration of aerosols in the remote troposphere, as evidenced by aircraft measurements. This study leveraged data from the NASA Atmospheric Tomography Mission (ATom), which provides remote aerosol concentrations, to refine algorithms for simulating these concentrations. Using the GEOS-Chem model, we simulate five fine aerosol types and enhance the simulation results using five machine-learning algorithms: Random Forest, XGBoost, SVM, KNN, and LightGBM, and compare the performance of these algorithms. Additionally, we evaluate the refinement effect of algorithms based on decision trees on a validation dataset. The results demonstrate that GEOS-Chem generally underestimated aerosol mass concentration. Among the tested algorithms, algorithms based on decision trees, particularly the Random Forest algorithm and the LightGBM algorithm, exhibited a superior performance, significantly improving prediction accuracy and computational efficiency in both the training and testing phases, as well as on the validation dataset. Full article

Background: Neglected tropical diseases (NTDs) are a diverse group of twenty diseases that occur in tropical and subtropical regions that particularly affect vulnerable and often marginalised populations. Five of these are classified as “preventive chemotherapy” (PC) diseases such as trachoma, onchocerciasis, geo-helminthiasis, lymphatic filariasis, and schistosomiasis. This study aimed to describe the knowledge, attitudes, and practices of healthcare providers in the Forecariah health district with respect to PC-NTDs in Guinea in 2022. Methods: A descriptive cross-sectional study was conducted from 7 to 22 November 2022 among healthcare providers in the health district of Forécariah in Guinea. Data on participants’ socio-demographic characteristics and knowledge of and attitudes and practices regarding PC-NTDs were collected using an electronic (KoboToolbox) semi-structured questionnaire and analysed using descriptive statistics. Results: Among the 86 healthcare providers who participated in this study, nurses (44.2%) and young adults aged between 25 and 49 years (81.4%) were mostly represented. The majority of respondents declared having already heard about onchocerciasis (70.7%) and lymphatic filariasis (60.0%) but only the minority declared having already heard about geo-helminthiasis (30.7%), schistosomiasis (21.3%), and trachoma (9.3%). Only a few respondents knew how to prevent PC-NTDs (onchocerciasis 26.7%, lymphatic filariasis 26.7%, geo-helminthiasis 29.3%, and schistosomiasis 17.3%). Many healthcare providers reported they would refer cases of onchocerciasis (50.6%), lymphatic filariasis (58.7%), and schistosomiasis (46.7%) to a management centre. Conclusions: This study highlights the varying levels of knowledge, attitudes, and practices among healthcare providers in dealing with PC-NTDs, suggesting areas for improvement in training and resource allocation. Full article

Large language models (LLMs) have demonstrated remarkable capabilities in document processing, data analysis, and code generation. However, the generation of spatial information in a structured and unified format remains a challenge, limiting their integration into production environments. In this paper, we introduce a benchmark for generating structured and formatted spatial outputs from LLMs with a focus on enhancing spatial information generation. We present a multi-step workflow designed to improve the accuracy and efficiency of spatial data generation. The steps include generating spatial data (e.g., GeoJSON) and implementing a novel method for indexing R-tree structures. In addition, we explore and compare a series of methods commonly used by developers and researchers to enable LLMs to produce structured outputs, including fine-tuning, prompt engineering, and retrieval-augmented generation (RAG). We propose new metrics and datasets along with a new method for evaluating the quality and consistency of these outputs. Our findings offer valuable insights into the strengths and limitations of each approach, guiding practitioners in selecting the most suitable method for their specific use cases. This work advances the field of LLM-based structured spatial data output generation and supports the seamless integration of LLMs into real-world applications. Full article

Media visual sculpture is a landscape element with high carbon emissions. To reduce carbon emission in the process of creating and displaying visual art and structures (visual communication), geo-polymer concrete (GePC) is considered by designers. It has emerged as an environmentally friendly substitute for traditional concrete, boasting reduced carbon emissions and improved longevity. This research delves into the prediction of the compressive strength of GePC (CSGePC) employing various soft computing techniques, namely SVR, ANNs, ANFISs, and hybrid methodologies combining Genetic Algorithm (GA) or Firefly Algorithm (FFA) with ANFISs. The investigation utilizes empirical datasets encompassing variations in concrete constituents and compressive strength. Evaluative metrics including RMSE, MAE, R², VAF, NS, WI, and SI are employed to assess predictive accuracy. The results illustrate the remarkable precision of all soft computing approaches in predicting CSGePC, with hybrid models demonstrating superior performance. Particularly, the FFA-ANFISs model achieves a MAE of 0.8114, NS of 0.9858, RMSE of 1.0322, VAF of 98.7778%, WI of 0.9236, R² of 0.994, and SI of 0.0358. Additionally, the GA-ANFISs model records a MAE of 1.4143, NS of 0.9671, RMSE of 1.5693, VAF of 96.8278%, WI of 0.8207, R² of 0.987, and SI of 0.0532. These findings underscore the effectiveness of soft computing techniques in predicting CSGePC, with hybrid models showing particularly promising results. The practical application of the model is demonstrated through its reliable prediction of CSGePC, which is crucial for optimizing material properties in sustainable construction. Additionally, the model’s performance was compared with the existing literature, showing significant improvements in predictive accuracy and robustness. These findings contribute to the development of more efficient and environmentally friendly construction materials, offering valuable insights for real-world engineering applications. Full article

Effective forest management necessitates spatially explicit information about tree species composition. This information supports the safeguarding of native species, sustainable timber harvesting practices, precise mapping of wildlife habitats, and identification of invasive species. Tree species identification and geo-location by machine learning classification of UAV aerial imagery offer an alternative to tedious ground surveys. However, the timing (season) of the aerial surveys, input variables considered for classification, and the model type affect the classification accuracy. This work evaluates how the seasons and input variables considered in the species classification model affect the accuracy of species classification in a temperate broadleaf and mixed forest. Among the considered models, a Random Forest (RF) classifier demonstrated the highest performance, attaining an overall accuracy of 83.98% and a kappa coefficient of 0.80. Simultaneously using input data from summer, winter, autumn, and spring seasons improved tree species classification accuracy by 14–18% from classifications made using only single-season input data. Models that included vegetation indices, image texture, and elevation data obtained the highest accuracy. These results strengthen the case for using multi-seasonal data for species classification in temperate broadleaf and mixed forests since seasonal differences in the characteristics of species (e.g., leaf color, canopy structure) improve the ability to discern species. Full article