Detailed Mapping Research Articles

Enhancing radioactive environment exploration with bio-inspired swarm robotics: A comparative analysis of Lévy flight and stigmergy methods

Utilizing swarm robotics techniques can significantly enhance the efficiency of exploring and mapping hazardous environments, such as nuclear sites. Instead of relying on a single robot for exploration, employing multiple robots working in coordination allows for fast coverage and more comprehensive data collection. In this study, bio-inspired algorithms, specifically Lévy flight and stigmergy, are utilized to guide the robots' movements. The Lévy flight algorithm mimics the movement patterns observed in animals like sharks and honeybees during their search for food, while stigmergy involves indirect communication between agents through environmental traces. By integrating these algorithms with swarm robotics, the robots effectively explore radioactive environments, gather data, and generate detailed maps of the area. Our research delves into various aspects of exploration, including the influence of the number of deployed robots and their exposure to radiation. Comparative analysis reveals the efficacy of stigmergy as a superior approach for guiding swarm robot movements in radioactive environments. This study underscores the significant potential of employing collective robotics for exploration tasks in nuclear scenarios, highlighting the promising applications of swarm intelligence in enhancing safety and efficiency in hazardous environments.

Mapping the Time-Series of Essential Urban Land Use Categories in China: A Multi-Source Data Integration Approach

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

Generating high-resolution land use and land cover maps for the greater Mariño watershed in 2019 with machine learning

Land Use and Land Cover (LULC) maps are important tools for environmental planning and social-ecological modeling, as they provide critical information for evaluating risks, managing natural resources, and facilitating effective decision-making. This study aimed to generate a very high spatial resolution (0.5 m) and detailed (21 classes) LULC map for the greater Mariño watershed (Peru) in 2019, using the MORINGA processing chain. This new method for LULC mapping consisted in a supervised object-based LULC classification, using the random forest algorithm along with multi-sensor satellite imagery from which spectral and textural predictors were derived (a very high spatial resolution Pléiades image and a time serie of high spatial resolution Sentinel-2 images). The random forest classifier showed a very good performance and the LULC map was further improved through additional post-treatment steps that included cross-checking with external GIS data sources and manual correction using photointerpretation, resulting in a more accurate and reliable map. The final LULC provides new information for environmental management and monitoring in the greater Mariño watershed. With this study we contribute to the efforts to develop standardized and replicable methodologies for high-resolution and high-accuracy LULC mapping, which is crucial for informed decision-making and conservation strategies.

Turn-table micro-CT scanner for dynamic perfusion imaging in mice: design, implementation, and evaluation

Objective.This study introduces a novel desktop micro-CT scanner designed for dynamic perfusion imaging in mice, aimed at enhancing preclinical imaging capabilities with high resolution and low radiation doses.Approach.The micro-CT system features a custom-built rotating table capable of both circular and helical scans, enabled by a small-bore slip ring for continuous rotation. Images were reconstructed with a temporal resolution of 3.125 s and an isotropic voxel size of 65µm, with potential for higher resolution scanning. The system's static performance was validated using standard quality assurance phantoms. Dynamic performance was assessed with a custom 3D-bioprinted tissue-mimetic phantom simulating single-compartment vascular flow. Flow measurements ranged from 1.51to 9 ml min-1, with perfusion metrics such as time-to-peak, mean transit time, and blood flow index calculated.In vivoexperiments involved mice with different genetic risk factors for Alzheimer's and cardiovascular diseases to showcase the system's capabilities for perfusion imaging.Main Results.The static performance validation confirmed that the system meets standard quality metrics, such as spatial resolution and uniformity. The dynamic evaluation with the 3D-bioprinted phantom demonstrated linearity in hemodynamic flow measurements and effective quantification of perfusion metrics.In vivoexperiments highlighted the system's potential to capture detailed perfusion maps of the brain, lungs, and kidneys. The observed differences in perfusion characteristics between genotypic mice illustrated the system's capability to detect physiological variations, though the small sample size precludes definitive conclusions.Significance.The turn-table micro-CT system represents a significant advancement in preclinical imaging, providing high-resolution, low-dose dynamic imaging for a range of biological and medical research applications. Future work will focus on improving temporal resolution, expanding spectral capabilities, and integrating deep learning techniques for enhanced image reconstruction and analysis.

From data to harvest: Leveraging ensemble machine learning for enhanced crop yield predictions across Canada amidst climate change

Accurate crop yield predictions are crucial for farmers and policymakers. Despite the widespread use of ensemble machine learning (ML) models in computer science, their application in crop yield prediction remains relatively underexplored. This study, conducted in Canada, aims to assess the potential of five distinct ensemble ML models—Adaptive Boosting (AdaBoost), Gradient Boosting Machine (GBM), XGBoost, LightGBM, and Random Forest (RF)—in predicting crop yields chosen for their ability to manage complex datasets and their strong performance potential. The study integrated various factors, including climate variables, satellite-derived vegetation indices, soil characteristics, and honeybee census data. Data preparation comprised two main steps: first, climate variables were interpolated and averaged for croplands in ArcGIS Pro, along with averaging vegetation indices and soil characteristics. Honeybee census data was also incorporated. Second, the data was organized in Python to create a structured format for models' input. The models' accuracy was assessed using Root Mean Squared Error (RMSE), R-squared, and Mean Absolute Error (MAE). XGBoost emerged as the most accurate model, with the lowest MAE (68.70 for canola and 39.47 for soybeans), lowest RMSE (119.48 for canola and 102.39 for soybeans), and highest R-squared values (0.95 for canola and 0.96 for soybeans) on the test dataset. The study also assessed crop yields under various climate change scenarios, finding minimal variations across the scenarios, but significant negative impacts on canola and soybean yields across Canada. Honeybee colonies were identified as the most influential factor on crop yields, contributing 52.34 % to canola and 57.18 % to soybean yields. This research provides detailed crop yield maps of canola and soybeans at the Census Consolidated Subdivisions (CCS) level across Canada's agricultural landscape, offering valuable forecasts for localized decision-making. Additionally, it offers a proactive strategy for climate change preparedness, assisting farmers and stakeholders optimise resource allocation and manage risks effectively.

Dyke swarms of Onenhste and adjacent coronae in Parga Chasmata, SE of Atla Regio, Venus: Detailed mapping, swarm interactions and geological history

Parga Chasmata is a 10,000 km long rift system on Venus with abundant coronae (enigmatic circular tectonomagmatic features). In order to further investigate the rift-corona relationship, detailed 1:500,000 geological mapping was focused on Onenhste Corona and the surrounding region (16°-24° S, 145°-131° W). More than 46,000 extensional lineaments were mapped and grouped into 50 sets, comprising 17 radiating, 28 circumferential and 5 linear sets (interpreted to overlie mafic dyke swarms). Radiating and circumferential swarms are linked with Onenhste Corona (OC), Momu Coronae (MC), Ulgen-ekhe Coronae (UEC), Rzhanitsa Corona (RzC, centred just outside the study area) and 5 Unnamed Coronae (UC1–5), as well as Malibran Patera (MP) and Fedchenko Patera (FP). Linear swarms are provisionally linked with magmatic centres outside the study area. Crosscutting relationships between the graben sets (dyke swarms) were used to identify the relative ages of magmatic centres. We applied an additional approach to recognize coeval centres, the swinging or deflection of radiating and circumferential dyke swarms to reveal the stress interaction between different coeval centres. The relative ages (oldest to youngest) of the magmatic centres are UC2 > RzC > MC ≥ OC = UC1 = UEC ≥ MP > UC5, and FP > UC3. These new detailed data provide significant information on coronae timing, evolution and relation to rift zones that will serve as a basis for future quantitative studies of lithospheric and mantle evolution and the combined cause and effect of rifting and mantle diapiric upwelling.Several centres, RzC, UC2, MC, UC3 and FP, ordered from NW to SE, are aligned along a trend parallel to Parga Chasmata but offset about 900 km to the south from the main zone of rifting. It is inferred that this alignment is related to a zone of weakness associated with the rift extension. Along this trend the centres do not show an age progression.Coronae MC, OC, UC1 and UEC are aligned along the NNE trending P13 Linea, orthogonal to the main Parga Chasmata rift. The main centre, Onenhste, is coeval with UC1, which in turn is coeval with UEC. Additional age relationships indicate that the late stage of OC was active at the same time as MP, MC and the UC2–9 centre of UC2. Linea P13 is older than Chondi Chasma. This study, and continuing, detailed geological mapping and analysis are increasing our understanding of the relationships between the formation of the main WNW trending Parga Chasmata rift zone, the orthogonal trends of rifting and the numerous magmatic centres (mainly coronae) distributed along both trends.

Chemical Study of Iraqi Kassub Plant Eryngium creticum Lam and its Geographical Distribution in Diyala Province

General Background: The therapeutic potential of medicinal plants has garnered increasing interest, particularly in relation to their chemical compositions and geographical distributions. Specific Background: This study focuses on Eryngium creticum Lam, an Apiaceae family plant known as Iraqi Kassub, prevalent in the Diyala province of Iraq. Knowledge Gap: Despite its traditional use in treating various ailments, there is a lack of comprehensive chemical analysis and geographical distribution data for this plant. Aims: The objective of this research was to identify the chemical components of Eryngium creticum Lam through GC-MS analysis and to map its geographical distribution within the Diyala province. Results: The chemical study revealed 101 distinct compounds in the alcoholic extract of the plant, highlighting a significant presence of sitosterols and sugars. These compounds are associated with antioxidant properties and therapeutic applications, including the treatment of envenomations, leishmaniasis, and anemia. Novelty: This study is novel in its detailed chemical profiling and geographical mapping of Eryngium creticum Lam, providing new insights into its medicinal value and distribution. Implications: The findings underscore the plant's potential as a therapeutic resource and emphasize the need for further research. Future studies should aim to isolate and characterize bioactive compounds such as phenols and flavonoids and assess their efficacy and safety through in vivo models to validate their therapeutic potential. Highlights: GC-MS identified 101 compounds. Bioactive compounds with therapeutic properties. Mapped geographical distribution in Diyala. Keywords: Eryngium creticum, GC-MS, therapeutic potential, chemical profile, geographical distribution

Multi-contamination groundwater risk assessment based on integrated OSPRC framework considering receptor and consequence components

This study evaluates the risks associated with geogenic and anthropogenic contaminants in the Azarshahr aquifer situated in the Lake Urmia watershed in NW Iran, an area critically affected by both natural mineral deposits and intensive agricultural activities. This region, characterized by its significant geological diversity and extensive use of pesticides and fertilizers, presents a unique opportunity to study the interaction between natural and human-induced groundwater contamination. Employing the Origin-Source-Pathway-Receptors-Consequence (OSPRC) framework, the research focuses on the linkage between these contaminants and waterborne diseases, particularly examining the less-studied “Receptor” and “Consequence” components using the Groundwater Quality Index (GQI) and health risk indices. Our approach integrates vulnerability assessments through the DRASTIC and SPECTR methods, enhanced by Sugeno fuzzy logic, to produce a detailed risk map highlighting two critical zones: Risk Cell 1, impacted predominantly by agricultural contaminants including nitrate and chlorpyrifos, and Risk Cell 2, affected by geogenic contaminants such as arsenic, lead, nickel, and chromium. This comprehensive analysis not only maps out the source and migration pathways of these contaminants but also evaluates their impact on human health. The findings underscore a strong correlation between identified risks and health impacts, emphasizing the pressing need for targeted health interventions and improved management of water resources in the region. By advancing the application of the OSPRC framework, this research fills a vital gap in our understanding of aquifer contamination dynamics and sets a new standard for future groundwater risk assessments.

Pouzolzia friisii (Urticaceae, Boehmerieae), a new species from north‐east India

Based on a collection housed at LWG (Herbarium of CSIR–National Botanical Research Institute, Lucknow, India), collected by Singh et al. on 25 June 2017 from Nagaland state of India, Pouzolzia friisii A.Gupta & V.Wagh sp. nov. (Urticaceae) is described and illustrated. The species resembles Pouzolzia sanguinea var. fulgens, P. niveotomentossa and P. australis in its serrated leaf margins and the indumentum on the abaxial leaf surface however, P. friisii is quite distinct in its grayish or pale yellowish indumentum, 4‐merous sessile male flowers and densely pubescent fruiting perianths without wings and ribs. A detailed description, illustration, distribution map, and information on ecology, as well as phenology are presented.

Deciphering the spatial organization of fibrotic microenvironment in silica particles-induced pulmonary fibrosis

Silicosis represents a form of interstitial lung disease induced by the inhalation of silica particles in production environments. A key pathological characteristic of silica-induced pulmonary fibrosis is its localized tissue heterogeneity, which presents significant challenges in analyzing transcriptomic data due to the loss of important spatial context. To address this, we integrate spatial gene expression data with single-cell analyses and achieve a detailed mapping of cell types within and surrounding fibrotic regions, revealing significant shifts in cell populations in normal and diseased states. Additionally, we explore cell interactions within fibrotic zones using ligand-receptor mapping, deepening our understanding of cellular dynamics in these areas. We identify a subset of fibroblasts, termed Inmt fibroblasts, that play a suppressive role in the fibrotic microenvironment. Validating our findings through a comprehensive suite of bioinformatics, histological, and cell culture studies highlights the role of monocyte-derived macrophages in shifting Inmt fibroblast populations into profibrotic Grem1 fibroblast, potentially disrupting lung homeostasis in response to external challenges. Hence, the spatially detailed deconvolution offered by our research markedly advances the comprehension of cell dynamics and environmental interactions pivotal in the development of pulmonary fibrosis.

Revolutionizing Urban Pest Management with Sensor Fusion and Precision Fumigation Robotics

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot’s navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue.

Egocentric neural representation of geometric vertex in the retrosplenial cortex

Egocentric neural representations of environmental features, such as edges and vertices, are important for constructing a geometrically detailed egocentric cognitive map for goal-directed navigation and episodic memory. While egocentric neural representations of edges like egocentric boundary/border cells exist, those that selectively represent vertices egocentrically are yet unknown. Here we report that granular retrosplenial cortex (RSC) neurons in male mice generate spatial receptive fields exclusively near the vertices of environmental geometries during free exploration, termed vertex cells. Their spatial receptive fields occurred at a specific orientation and distance relative to the heading direction of mice, indicating egocentric vector coding of vertex. Removing physical boundaries defining the environmental geometry abolished the egocentric vector coding of vertex, and goal-directed navigation strengthened the egocentric vector coding at the goal-located vertex. Our findings suggest that egocentric vector coding of vertex by granular RSC neurons helps construct an egocentric cognitive map that guides goal-directed navigation.

Wild salmon migration routes influence sea lice infestations: An agent-based model predicting farm-related infestations on juvenile salmon.

This study presents an Agent-Based Model (ABM) simulation to assess the impact of varying migration routes on sea lice (Caligus clemensi) infestation levels in juvenile wild sockeye salmon (Oncorhynchus nerka) in the Discovery Islands, British Columbia, Canada. This research highlights the importance of migratory routes in determining the extent of exposure to sea lice originating from nearby salmon farms. Three northward out-migration routes were modelled, each exposing the fish to different levels of infestation pressure based on proximity to salmon farms. The ABM incorporates spatially explicit migration patterns of juvenile sockeye salmon using a detailed raster map of the Discovery Islands. Key variables such as swimming speed, progression rate, and infestation levels were integrated into the model, offering a comprehensive analysis of migration and infestation dynamics. The study revealed that infestation rate is highly variable, depending on migration routes. Specifically, salmon traveling longer migration routes with lower infestation pressure may experience higher sea lice loads compared to those on shorter routes with higher infestation pressure. This underscores the role of low infestation pressures and the critical influence of swimming speed, which affects exposure time, and thus infestation rates. Additionally, the study conducted a sensitivity analysis to understand the influence of various parameters on infestation rates. This analysis highlighted the importance of swimming speed and progression rate, particularly in routes closer to the farms. The findings suggest that slower swimming speeds and meandering routes increase exposure to lice, thereby elevating infestation levels. The research contributes to understanding the dynamics of sea lice transmission and its relationship with salmon migration patterns. It underscores the necessity of considering migratory routes and farm proximity in managing and mitigating the impact of sea lice infestation on wild salmon populations. This study's insights are crucial for developing strategies to balance aquaculture practices with the conservation of wild salmon.

A Spectacular Devonian Trilobite Specimen, Dalmanites pratteni, From Illinois Helps Shed Light on How Trilobite Eyes Worked

In 1912, the Field Museum purchased Henry Pratten's collection of fossils. One of the specimens was a dalmanitid trilobite with large eyes (Fig. 1) that Pratten collected from the Devil’s Backbone. In 1933, this specimen was described as a new species of trilobite, Dalmanites pratteni (P 16704) (Roy 1933). Delo (1935) described a new genus of trilobite, Dalmanitoides and assigned it to a new family, Synphorinae. He noted Dalmanites pratteni has similar ornamentation to this genus. Delo (1940) later places D. pratteni in the genus Odontochile. Other authors refer to it as Dalmanites pratteni (Clarkson and Levi-Setti 1975;Levi-Setti 1975; Frankie et al. 2008). Holloway and De Carvalho (2009) refer to it as ‘Dalmanites’ pratteni and state that this species “probably belongs to the Synphoriinae”. The stratigraphic position of this specimen was not recorded. Seid et al. (2009) published a detailed geologic map of the Devil’s Backbone. Most of the ridge is Middle Devonian Grand Tower and St. Laurent formations. However, the southern end of the ridge is faulted, and the Lower Devonian Backbone Limestone and Crystal Creek Chert (Emsian) is exposed. Frankie et al. (2008) and Devera et al. (2020) report Dalmanites pratteni occurring from the Crystal Creek Chert, indicating that the original specimen may have been collected here. One of the most remarkable features of this fossil is the record number of lenses for a Phacopida. Roy (1933) reports 42 rows of eyes, however: “The exact number of lenses cannot be determined, as not all of them are preserved, nor are the areas which they occupied distinctly outlined. It is, however, certain that there are not less than 770 lenses on each eye.” (Roy 1933). I used images of the eyes and illustration software to estimate the number of lenses by filling in the geometric pattern they exhibit. I counted 730 lenses on the right eye and 712 lenses on left eye, which is missing a sizable piece (Fig. 2). The structure of some individual lenses in this specimen can be seen due to differential weathering. Clarkson and Levi-Setti (1975) reported the large round lenses were composed of a doublet structure that followed the principles established by Descartes (1637) and Huygens (1690) to eliminate spherical distortion. This doublet structure allowed the lens to gather more light and focus it on a single point, and strongly indicates that these trilobites had better vision then previously established (Clarkson and Levi-Setti 1975).

The spatial distribution of surface and groundwater abstractions in Slovakia in the period 2013–2022 vs 2022

Abstract. In recent years, the impacts of climate change have become increasingly apparent; Slovakia has witnessed a rise in extreme weather conditions caused by climate change. To address these challenges, we analyze and prepare documents focused on monitoring and issuing drought warnings. These documents, including Slovakia's water resource balance, serve as fundamental materials for water management planning. The water resource balance assesses the relationship between water demands and available resources in the past year, identifying areas and times where water demands exceed supply. Additionally, our analysis provides a detailed map of surface and groundwater abstractions across Slovakia, categorizing total and individual abstractions by sector (e.g., public water supply systems, agriculture, and industry). We present surface and groundwater abstraction averages for the period of the last decade 2013–2022, along with its spatial distribution in Slovak districts. This comprehensive approach enables us to better understand and manage our water resources effectively.

Evaluation of a Structured Light Scanner for 3D Facial Imaging: A Comparative Study with Direct Anthropometry.

This study evaluates the accuracy and repeatability of craniofacial measurements with a 3D light scanner, specifically the EINSTAR scanner, in comparison to traditional caliper measurements for facial anthropometry. Eleven volunteers were assessed by two examiners, one experienced and one inexperienced, who performed direct caliper measurements and indirect measurements using the scanner. Results indicated minimal differences between caliper and scanner results, with overall high accuracy and reliability demonstrated by correlation coefficients. Despite the slightly longer scanning time, the benefits of 3D imaging, including detailed surface mapping and virtual modeling, justify its integration into clinical practice, particularly in maxillofacial surgery and craniofacial assessment. Craniofacial measurements obtained with the EINSTAR scanner showed excellent reliability and accuracy, which qualifies this method for clinical and scientific use.

Decabromodiphenyl ether and thyroid hormone disruption: A detailed mapping of human biomarker studies to uncover heterogeneity

Decabromodiphenyl ether and thyroid hormone disruption: A detailed mapping of human biomarker studies to uncover heterogeneity

Ultrahigh-Resolution Visualization of Vascular Heterogeneity in Brain Tumors via Magnetic Nanoparticles-Enhanced Susceptibility-Weighted Imaging.

The precise assessment of vascular heterogeneity in brain tumors is vital for diagnosing, grading, predicting progression, and guiding treatment decisions. However, currently, there is a significant shortage of high-resolution imaging approaches. Herein, we propose a contrast-enhanced susceptibility-weighted imaging (CE-SWI) utilizing the minimalist dextran-modified Fe3O4 nanoparticles (Dextran@Fe3O4 NPs) for ultrahigh-resolution mapping of vasculature in brain tumors. The Dextran@Fe3O4 NPs are prepared via a facile coprecipitation method under room temperature, and exhibit small hydrodynamic size (28 nm), good solubility, excellent biocompatibility, and high transverse relaxivity (r2*, 159.7 mM-1 s-1) under 9.4 T magnetic field. The Dextran@Fe3O4 NPs-enhanced SWI can increase the contrast-to-noise ratio (CNR) of cerebral vessels to 2.5 times that before injection and achieves ultrahigh-spatial-resolution visualization of microvessels as small as 0.1 mm in diameter. This advanced imaging capability not only allows for the detailed mapping of both enlarged peritumoral drainage vessels and the intratumoral microvessels, but also facilitates the sensitive imaging detection of vascular permeability deterioration in a C6 cells-bearing rat glioblastoma model. Our proposed Dextran@Fe3O4 NPs-enhanced SWI provides a powerful imaging technique with great clinical translation potential for the precise theranostics of brain tumors.

Precision assessment of a hypertension prevalence survey

BackgroundPopulation surveys are crucial for public policy planning and provide valuable representative data. In the health sector studies to identify and assess the prevalence of Arterial Hypertension (AH), a chronic non-communicable disease (NCD), along with its associated risk factors have been conducted.ObjectivesThis study aims to assess the effectiveness of a population health survey in estimating the prevalence of arterial hypertension (AH) in the Sorocaba municipality between August 2021 and June 2023. Methods: The analyzed performance indicator is the precision (design effect - deff) of AH prevalence in adults (≥ 18 years) and their exposure to primary risk factors. The total sample included 1,080 individuals from the urban area, deemed sufficient to estimate a deff of 1.5. This cluster-based study utilized census sectors as clusters, with data collected through household interviews, standardized questionnaires, and measurements of blood pressure and biometric parameters. The deff calculation formula used was weighted variance / raw variance. The Research Ethics Committee approved this study, with registration CAAE 30538520-1-0000-5373.ResultsThe deff values ranged from 0.44 for chronic obstructive pulmonary disease to 1.63 for asthma, with a deff of 1.00 for AH prevalence. Conclusion: The study demonstrated good precision in its results, with high receptivity and cooperation from participants. The cost-effectiveness of the research deemed appropriate. The technique of selecting households within clusters (census sectors) based on detailed mapping and demographic data from the Instituto Brasileiro de Geografia e Estatística (IBGE) proved to be practical and efficient, suitable for replication in other municipalities and for studying other NCDs.

Assessing the potential of multi-source remote sensing data for cropland soil organic matter mapping in hilly and mountainous areas

Cropland soil organic matter (SOM) is recognized as a significant carbon reservoir in terrestrial ecosystems. Digital mapping of SOM in croplands is essential for comprehending the global carbon cycle. Accurately mapping cropland SOM using multi-source remote sensing data has been effectively incorporated into prediction models across various scales. However, the impact of multi-source remote sensing data on cropland SOM mapping outcomes in hilly and mountainous regions remains insufficiently understood. In this study, Jiangyou City, located in Sichuan Province, China, was chosen as a representative example of hilly and mountainous regions. Fifteen distinct feature combinations were devised using three remote sensing variables (Sentinel-1, Sentinel-2, and Landsat-8) along with DEM data. Feature selection was conducted using the Boruta algorithm. Subsequently, the RF, SVR, Cubist, and INLA-SPDE models were adopted to create spatially detailed distribution maps of cropland SOM for the region. Additionally, an uncertainty analysis was performed on the cropland SOM mapping results. The results indicate the following: (1) The INLA-SPDE model, which integrates both data information and spatial structure, achieves the highest accuracy and the less uncertainty in cropland SOM mapping, with an R2 of 0.647 and an RMSE of 4.227 g/kg. (2) Optical imagery is more important than SAR images, but their combination enhances model accuracy. Specifically, Sentinel-2 data has a significant impact cropland SOM prediction in hilly and mountainous areas, followed by Landsat-8 data. (3) The predicted spatial distribution patterns of cropland SOM by the four models show consistency, indicating lower SOM content in the southwest and higher SOM content in the central and northeast regions. This study provides valuable references for future large-scale and high-spatial cropland SOM prediction, highlighting the importance of spatial resolution for precise SOM prediction accuracy in hilly and mountainous regions.