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Understanding the spatial distribution of forest properties can help improve our knowledge of carbon storage and the impacts of climate change. Despite the active use of remote sensing and machine learning (ML) methods in forest mapping, the associated uncertainty predictions are relatively uncommon. The objectives of this study were: (1) to evaluate the spatial resolution effect on growing stock volume (GSV) mapping using Sentinel-2A and Landsat 8 satellite images, (2) to identify the most key predictors, and (3) to quantify the uncertainty of GSV predictions. The study was conducted in heterogeneous landscapes, covering anthropogenic areas, logging, young plantings and mature trees. We employed an ML approach and evaluated our models by root mean squared error (RMSE) and coefficient of determination (R2) through a 10-fold cross-validation. Our results indicated that the Sentinel-2A provided the best prediction performances (RMSE = 56.6 m3/ha, R2 = 0.53) in compare with Landsat 8 (RMSE = 71.2 m3/ha, R2 = 0.23), where NDVI, LSWI and B08 band (near-infrared spectrum) were identified as key variables, with the highest contribution to the model. Moreover, the uncertainty of GSV predictions using the Sentinel-2A was much smaller compared with Landsat 8. The combined assessment of accuracy and uncertainty reinforces the suitability of Sentinel-2A for applications in heterogeneous landscapes. The higher accuracy and lower uncertainty observed with the Sentinel-2A underscores its effectiveness in providing more reliable and precise information for decision-makers. This research is important for further digital mapping endeavours with accompanying uncertainty, as uncertainty assessment plays a pivotal role in decision-making processes related to spatial assessment and forest management. [ABSTRACT FROM AUTHOR]

Digital maps structure how we understand and interact with space while simultaneously affording agency and the potential for an alternative use of space. This article investigates how digital maps exercise data and spatial agency. Our empirical object is the environmental mobile mapping application water refill map (WRM, Hong-Te/ Fengcha Action 奉茶行動) in Taiwan. Drawing on the walk-through method and semi-structured interviews with WRM's founders, we examine how the app leverages the power of digital mapping to encourage participation in environmental activities. We argue that by facilitating data and spatial agency, digital maps enable placemaking at the cognitive and hermeneutic levels. They also turn closed spaces into public ones, thus aiding place-shaping. Meanwhile, this digital environmental activism takes place in a field of contestation and negotiation between agency and structure, apps and infrastructural platforms, collective action and individual power, and environmentalism and (adjusted) commercialism. [ABSTRACT FROM AUTHOR]

In digital cartography, Google Maps and Google Street View (GSV) are often used side-by-side at the computer interface. This study consists of an analysis of recordings of Geoguessr, an online game that presents players with GSV imagery and challenges them to guess the location of the images on a digital map. Gameplay requires semiotic moves that put the diagrammatic signs of the GSV images and the map into a dynamic interplay. By specifying these practices, the present analysis offers a processual, practice-oriented perspective on theoretical debates in the study of digital mapping. Rather than existing a priori, the constructed or transparent nature of the map, as well as the kind of cartographical subject involved in manipulations of the map both emerge and change through the practical use of the two representations. Furthermore, the abductive inferences that characterize particular moments of gameplay constitute an intersection of reasoning and play. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Cardiac radioablation (CRA) is a new and promising treatment modality for patients with ventricular tachycardia refractory to standard‐of‐care treatment. Electroanatomic maps are used to define radiation target volumes; however, there is currently no native method to import electroanatomic maps into the treatment planning system (TPS).To develop Edico, a semi‐automated tool to enable electroanatomic map import into a TPS, by converting electroanatomic maps to a Digital Imaging and COmmunications in Medicine (DICOM) standard. The overall aim is to facilitate target volume delineation and improve workflow efficiency in treating patients.Edico imports voltage and spatial data from electroanatomic maps and sorts these into voxels to be exported in a DICOM format, with each voxel containing the average voltage value of the data that falls within it. Three different rectangular electroanatomic maps were created and processed using Edico to ensure that expected features are maintained through processing. A sensitivity analysis of voxel size was completed using 19 different electroanatomic maps processed at five different sets of voxel dimensions, for a total of 95 resulting voxelized datasets. The coefficient of variation in each populated voxel in the datasets was analyzed to determine which voxel sizes are necessary to ensure that data loss is kept to a minimum throughout processing, despite averaging. Five electroanatomic maps were used to re‐contour clinical target volumes and planning target volumes for previously‐treated patients with their electroanatomic maps now directly registered to their planning computed tomography (CT) scans.All three rectangular test electroanatomic maps were processed as expected. All tested voxel sizes resulted in low coefficients of variation overall, with the exception of the largest voxel size of 1.8 × 1.8 × 8 mm. When using Edico, a user should choose voxel dimensions similar to or smaller than those of a planning CT. Of five pairs of clinical and planning target volumes from previously treated patients, adjustments were made to four (80%), retrospectively, using the electroanatomic maps generated using Edico, registered to the patients’ planning CTs.Edico provides a reliable solution for electroanatomic map import into a TPS and facilitates clinical and planning target volume identification in CRA. [ABSTRACT FROM AUTHOR]

Introduction: The overarching goal of the UN Ocean Decade is to "change humanity's relationship with the ocean." While this may be a challenge, it is, at the same time, a once in a generation opportunity. How can 8 billion people, including those who don't live near coastal areas, be inspired to value and care for the ocean? This is the essence of ocean literacy, and the driver of ocean literacy research (OLR). Methods: In 2021, we began a research initiative to co-create a global OLR agenda by the developing OLR community, to better understand existing research themes, gaps, future priorities, actions, and impacts of ocean literacy initiatives. To deliver this, a series of virtual workshops – with the first taking place as part of the UN Ocean Decade Laboratories – was complemented by a participatory methodology using digital survey and mapping tools for crowdsourced collaboration. Results and discussion: Through this process, four initial OLR priorities were identified, including measuring ocean literacy, the role of ocean literacy as a policy mechanism, and alignment of OLR with climate change and the blue economy agendas. Finally, a working definition of OLR was developed to further guide OLR priorities for the UN Ocean Decade and beyond. [ABSTRACT FROM AUTHOR]

Stable isotope ratios of rainwater, 2H/1H and 18O/16O, play a crucial role as natural tracers through the various compartments of the hydrological cycle in hydrological studies. Knowing the isotopic signatures of precipitation helps us to interpret water entry into continental hydrological systems, such as basins and aquifers. Spatial predictions of δ18 O and δ2 H in precipitation across South America were performed using multivariate geostatistics supported by remote sensing covariates. This is the first map dedicated to describing countries’ and biogeographical regions’ spatiotemporal isotopic distributions with their local meteoric water lines (LMWLs). R2 from 0.90 to 0.99 were found considering all LMWLs. Modelling errors for historical, May–September, and October–April periods are 2.83, 2.41, and 2.67% for δ2H, and 0.33, 0.37, and 0.41% for δ18O, respectively. [ABSTRACT FROM AUTHOR]

Objective: The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Methods: Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. Results: With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Conclusion: Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials. [ABSTRACT FROM AUTHOR]

Routable Digital Maps represent a recent advancement driven by the growth of Intelligent Transportation System applications. Efforts have been made to develop and utilize maps within computer systems that can autonomously identify optimal routes. Typically, online map visualizations lack essential information such as traffic patterns, construction zones, or lane counts, which are crucial for designing accurate and realistic routes. This research introduces a route planning method that aggregates and reuses various data points to establish a robust framework for developing new routing plans. The primary goal is to analyze the collected data and create a framework for estimating a geometrically smooth centerline of a road map route. This method involves generating a series of parallel curves, known as offset curves, to facilitate the creation of new routes and alternative lanes. The following examples illustrate some empirical results obtained from this approach. [ABSTRACT FROM AUTHOR]

Multi-scale electronic map tiles are important basic geographic information data, and an approach based on deep learning is being used to generate multi-scale map tiles. Although generative adversarial networks (GANs) have demonstrated great potential in single-scale electronic map tile generation, further research concerning multi-scale electronic map tile generation is needed to meet cartographic requirements. We designed a multi-scale electronic map tile generative adversarial network (MsM-GAN), which consisted of several GANs and could generate map tiles at different map scales sequentially. Road network data and building footprint data from OSM (Open Street Map) were used as auxiliary information to provide the MsM-GAN with cartographic knowledge about spatial shapes and spatial relationships when generating electronic map tiles from remote sensing images. The map objects which should be deleted or retained at the next map scale according to cartographic standards are encoded as auxiliary information in the MsM-GAN when generating electronic map tiles at smaller map scales. In addition, in order to ensure the consistency of the features learned by several GANs, the density maps constructed from specific map objects are used as global conditions in the MsM-GAN. A multi-scale map tile dataset was collected from MapWorld, and experiments on this dataset were conducted using the MsM-GAN. The results showed that compared to other image-to-image translation models (Pix2Pix and CycleGAN), the MsM-GAN shows average increases of 10.47% in PSNR and 9.92% in SSIM and has the minimum MSE values at all four map scales. The MsM-GAN also performs better in visual evaluation. In addition, several comparative experiments were completed to verify the effect of the proposed improvements. [ABSTRACT FROM AUTHOR]

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Models (DEMs) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicles (UAVs), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSMs) obtained through UAV photogrammetry, and DEMs acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future; this is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating the area and slope of the study sites. [ABSTRACT FROM AUTHOR]

Many surfactants, such as higher mole alcohol ethoxylates like C12‐15–7EO and C9‐11–8EO, when diluted in water, will form a gel at specific concentrations and temperatures. Gels can be highly viscous and semi‐solid and should be avoided since they take time and energy to disperse once formed. Historically, the creation of gel diagrams or maps for our technical product brochures primarily has depended on visual observation, leading to variable interpretations and inconsistent results over time. Also, completing a gel map for one surfactant grade requires a minimum of one day, due to testing many samples across various concentrations and temperatures. To improve objectivity, consistency, and speed in gel mapping, oscillatory rheology was utilized to identify gels using viscoelastic properties by testing samples prepared at various concentrations. The digitization of the gel mapping technique provides two significant benefits. It offers a rheological‐based approach giving a non‐subjective, digital gel map and it is faster than our visual‐based method. Furthermore, this digital method is consistent with our visual‐based method giving good discrimination between surfactant grades and reproducibility within batches of the same grade. This work also demonstrates the promising potential of utilizing machine learning algorithms to model the rheological behavior of gel maps effectively. R and Python, programming languages widely used for data analysis, graphing, and machine learning, were employed. Overall, the new digital approach presented yields several benefits for surfactant gel behavior study, including a reduction in subjectivity, faster data generation, and increased efficiency in the gel map analysis process. [ABSTRACT FROM AUTHOR]

Information Architecture (IA) organises digital content ranging from web interfaces to ATM screens and car dashboards. Beyond structure, it encompasses the labelling and organisation of information within each interface. Presented through mind maps, IA is essential in both academic and professional environments. In complex products such as social networks, multiple IA patterns are employed simultaneously. This research introduces IA to Applied Communication Sciences, highlighting its significance for enhancing user experience in social networks. The study identifies standard patterns and new types of mental maps for digital creation. [ABSTRACT FROM AUTHOR]

Procedural content generation (PCG) algorithms have become increasingly vital in video games developed by small studios due to their ability to save time while creating diverse and engaging environments, significantly enhancing replayability by ensuring that each gameplay experience is distinct. Previous research has demonstrated the effectiveness of PCG in generating various game elements, such as levels and weaponry, with unique attributes across different playthroughs. However, these studies often face limitations in processing efficiency and adaptability to real-time applications. The current study introduces an improved spawn algorithm designed for 2D map generation, capable of creating maps with multiple room sizes and a decorative object. Unlike traditional methods that rely solely on agent-based evaluations, this constructive algorithm emphasizes reduced processing power, making it suitable for generating small worlds in real time, particularly during loading screens. Our findings highlight the algorithm's potential to streamline game development processes, especially in resource-constrained environments, while maintaining high-quality content generation. [ABSTRACT FROM AUTHOR]

Land degradation is accelerating in the Himalayan ecosystem, resulting in the loss of soil nutrients due to severe erosion. Soil erosion presents a significant environmental challenge, resulting in both on-site and off-site consequences, such as reduced soil productivity and siltation in reservoirs. Soil erodibility (K factor), an inherent soil property, determines the susceptibility of soils to erosion. Sampling across hilly and mountainous terrain pose challenges due to its complex landscape. Despite these challenges, it is essential to study K factor variations in different land use/land cover types to comprehend the threat of erosion. Digital soil mapping offers an opportunity to overcome this limitation by providing spatial predictions of soil properties. The objective of our study is to map the spatial distribution of soil erodibility using the Random Forest (RF) model, a machine learning method based on sampled in situ soil data and environmental covariates. We collected 556 surface soil samples from the mountainous catchment (Tehri dam catchment) using the stratified random sampling approach. The model performed satisfactorily in both training (r2 = 0.91; RMSE = 0.00185) and testing (r2 = 0.45; RMSE = 0.00318) phases. Subsequently, we generated a digital map with a resolution of 12.5 m to depict the distribution of the K factor. Our analysis revealed that key environmental variables influencing the prediction of the K factor included geology, mean NDVI, and climatic factors. The average K factor value was estimated at 0.0304 and ranging from 0.0251 to 0.0400 t ha h ha−1 MJ−1 mm−1. A higher K factor was observed in the barren land (0.0344) primarily located in the higher and trans-Himalayan region of seasonally snow-covered areas. These areas typically feature young soils with weak soil formation and unstable soil aggregates. Subsequently cropland/cultivated soils (0.0307) exhibited higher K factor values due to the breakdown of soil aggregates by ploughing activities and exposing carbon to decomposition. The average K factor value of evergreen (0.0294) and deciduous (0.0295) forests were the lowest compared to other land use/land cover types indicating the role of forests in resisting soil erosion. By assessing and predicting soil erodibility, land planners and farmers can implement erosion control measures to protect soil health, prevent sedimentation in water bodies, and sustain agricultural productivity in the Himalayas. [ABSTRACT FROM AUTHOR]

The mining activity is fundamental for human development, but it requires changes in the environment, negatively affecting soil organic carbon (SOC) stock. The only way to restore the original SOC stock is via rehabilitation practices. Considering adopting more efficient techniques and technologies for the rehabilitation process, this study aimed to assess SOC stock in rehabilitating iron minelands and to use remote sensing tools to predict it. The study was conducted in two waste piles (WP-W and WP-S4) each presenting a chronosequence (initial, intermediate, and advanced stages) in addition to the forest (reference), all located in the Carajás Mineral Province, southeastern Amazon, Brazil. Results showed a gradual increase in the SOC stock for the WP-S4 from the initial to the advanced stage (average of 26.5 Mg ha−1). There were no differences among the rehabilitation stages for the WP-W. The difference in SOC stocks between the two waste piles is mainly related to the soil fertility status, indicating a higher SOC accumulation potential for the WP-S4. Principal component analysis revealed that the attributes presenting major influence on the SOC stock are K, SOC, cation exchange capacity, B, and total N (positive); and soil bulk density, pH, and sand (negative). The regression model results showed that the remote sensing indices Normalized Difference Vegetation Index (NDVI) and Bare Soil Index (BSI) are useful to derive digital maps of SOC stock and assist in the monitoring of the rehabilitation in the waste piles of the iron mineland. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging techniques have a unique ability to probe the inhomogeneity of material properties whether driven by compositional variation or other forms of phase segregation. In the doped cuprates, iridates, and related materials, scanning tunneling microscopy/spectroscopy (STM/STS) measurements have found the emergence of pseudogap "puddles" from the macroscopically Mott insulating phase with increased doping. However, categorizing this hyperspectral data by electronic order is not trivial and has often been done with ad hoc methods. In this paper, we demonstrate the utility of k-means, a simple and easy-to-use unsupervised clustering method, as a tool for classifying heterogeneous scanning tunneling spectroscopy data by electronic order for Rh-doped Sr2IrO4, a cuprate-like material. Applied to STM data acquired within the Mott phase, k-means was able to identify areas of Mott order and of pseudogap order. The unsupervised nature of k-means limits avenues for bias and provides clustered spectral shapes without a priori knowledge of the physics. Additionally, we demonstrate the use of k-means as a preprocessing tool to constrain phenomenological function fitting. Clustering the data allows us to reduce the fitting parameter space, limiting over-fitting. We suggest k-means as a fast, simple model for processing hyperspectral data on materials of mixed electronic order. [ABSTRACT FROM AUTHOR]

The unparalleled resolving power of electron microscopy is both a blessing and a curse. At 30,000 x magnification, 1 µm corresponds to 3 cm in the image and the field of view is only a few micrometres or less, resulting in an inevitable reduction in the spatial data available in an image. Consequently, the gain in resolution is at the cost of loss of the contextual 'reference space', which is crucial for understanding the embedded structures of interest. This problem is particularly pronounced in immunoelectron microscopy, where the detection of a gold particle is crucial for the localisation of specific molecules. The common solution of presenting high-magnification and overview images side by side often insufficiently represents the cellular environment. To address these limitations, we propose here an interactive visualization strategy inspired by digital maps and GPS modules which enables seamless transitions between different magnifications by dynamically linking virtual low magnification overview images with primary high-resolution data. By enabling dynamic browsing, it offers the potential for a deeper understanding of cellular landscapes leading to more comprehensive analysis of the primary ultrastructural data. [ABSTRACT FROM AUTHOR]

This study aims to assess how the construction patterns within residential communities influence the adolescent myopia using general survey. In a private high school from a megacity in mid-west China, a questionnaire gathered data on the 10th-grade students' level of myopia, home address, and some potential confounding factors. Additionally, satellite digital images were utilized to calculate the proportion of shadow area (PSA) and the proportion of greenness area (PGA) within a 500 m×500 m area centered on each student's home address. Correlations between myopia levels and PSA, along with other variables, were analyzed. The prevalence of mild, moderate, and high myopia were 39.2%, 32.5%, and 8.3%, respectively. A negative correlation was observed between myopia levels and PSA, albeit marginally significant (r=-0.189*, P = 0.05). Upon dividing the sample into higher and lower PSA groups using a cut-off point of 20%, a significant difference in myopia levels was evident (χ2 = 8.361, P = 0.038), while other confounding factors remained comparable. In conclusion, high-rise apartment constructions, which often cast more shadows on digital satellite maps, may not exacerbate myopia progression. Instead, they could potentially serve as a protective factor against adolescent myopia in densely populated megacities, as they allow for more ground space allocation. [ABSTRACT FROM AUTHOR]

Purpose: A densely distributed network radar system compensates for the disadvantages of sparse radars and poses a significant threat to low-altitude penetration by an unmanned combat aerial vehicle (UCAV). Unlike previous studies, this paper aims to consider radar blind areas and proposes a rapid online method for planning low-altitude penetration paths. Design/methodology/approach: First, the optimization problem coupling digital elevation map (DEM), radar detection probability model and nonholonomic UCAV kinematic model is established. Second, an online solution framework of penetration path planning is constructed. An intervisibility method and map scaling are proposed to generate a detection probability map (DPM). Through completeness and consistency analysis, an adaptive hybrid A* algorithm with fast local replanning strategy is proposed to search a path that takes into account time-consuming, detection probability under nonholonomic constraints. Finally, three scenarios of multiple known, pop-up and vanished static radars are simulated using C++. The computational performance is compared and analyzed. Findings: The results showed that the proposed online method can generate low-detection-probability penetration paths within subseconds. Originality/value: This paper provides a new online method to plan UCAV penetration trajectory in military and academic contexts. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to delve into the effectiveness of applying traditional and more advanced digital means to document elements of cultural heritage, in this case large-format cartography. Application of multimethod digitalisation to a school map of the American continent dating to the early part of the 20th century has served to address specific issues, notably its multilayers consisting of paper, inks and a protective varnish on a textile medium. Its large format is likewise an obstacle to its digital capture. Design/methodology/approach: The method applied here resorted to three registration systems: single-shot photography, panoramic photography and photogrammetry. The first two widely serve to capture works of large-format, whereas the third is commonly used to record volumetric assets. A variety of parameters were applied, notably different focal lengths, capture methods and processing software. The images obtained in each case were subjected to qualitative and quantitative comparisons so as to analyse their differences in terms of resolution and accuracy when compared to the map's real measurements, key criteria when duplicating cartographic documents. Findings: Although the final products gleaned from the digital photographs, panoramic photographs and photogrammetry fulfil the basic functions required to record documents housed in archives, libraries, museums and other cultural institutions, this study highlights new advances and complementary functions stemming from certain of these techniques. Originality/value: Digitalisation is a tool that serves to register, preserve, disseminate and analyse cultural heritage. However, some of the available techniques have rarely been applied specifically to graphic and documentary artefacts. It is for this reason that this study intends to demonstrate their utility in the detailed study of this heritage typology. Moreover, optimising the school map into a digital form favours its dissemination and remote consultation while simultaneously minimising direct manipulation, hence improving its long-term preservation. [ABSTRACT FROM AUTHOR]

Detection and mapping of Sosnowsky's hogweed (HS) using remote sensing data have proven effective, yet challenges remain in identifying, localizing, and eliminating HS in urban districts and regions. Reliable data on HS growth areas are essential for monitoring, eradication, and control measures. Satellite data alone are insufficient for mapping the dynamics of HS distribution. Unmanned aerial vehicles (UAVs) with high-resolution spatial data offer a promising solution for HS detection and mapping. This study aimed to develop a method for detecting and mapping HS growth areas using a proposed algorithm for thematic processing of multispectral aerial imagery data. Multispectral data were collected using a DJI Matrice 200 v2 UAV (Dajiang Innovation Technology Co., Shenzhen, China) and a MicaSense Altum multispectral camera (MicaSense Inc., Seattle, WA, USA). Between 2020 and 2022, 146 sites in the Moscow region of the Russian Federation, covering 304,631 hectares, were monitored. Digital maps of all sites were created, including 19 digital maps (orthophoto, 5 spectral maps, and 13 vegetation indices) for four experimental sites. The collected samples included 1080 points categorized into HS, grass cover, and trees. Student's t-test showed significant differences in vegetation indices between HS, grass, and trees. A method was developed to determine and map HS-growing areas using the selected vegetation indices NDVI > 0.3, MCARI > 0.76, user index BS1 > 0.10, and spectral channel green > 0.14. This algorithm detected HS in an area of 146.664 hectares. This method can be used to monitor and map the dynamics of HS distribution in the central region of the Russian Federation and to plan the required volume of pesticides for its eradication. [ABSTRACT FROM AUTHOR]

Currently, the Internet of Things (IoT) is in a premature phase. Although it is growing at a steady pace, there is still a need for further research in the field of security. In this work, the Fujian Province was selected as the study area. The climate, parent material and topographic information of the area were obtained, and the soil-landscape quantitative model was used to quantitatively obtain the relationship between the attributes of coastal sand and gravel soil. On the basis of soil type map, according to the difference of soil type elevation distribution, further predict the soil type distribution and make a map. The results show that the method can achieve more than 80% coincidence with the survey results on the scale of soil digital mapping and can make up for the missing areas of the survey. [ABSTRACT FROM AUTHOR]

Concept maps have been used widely in various subjects of K-12 and higher education for diverse purposes. Digital concept maps (DCM) provide even more benefits, such as higher efficiency, expanded canvas, and multimedia integration. The present paper sought to analyze the research and application trends and issues in DCM research and practices concerning education so that educators and researchers can gain valuable insights into pedagogical improvements, technological choices, and methodological design within diverse educational contexts. After reviewing 43 journal articles regarding DCM using the WIRE model, we found that (1) prestigious journals are likely to publish DCM studies that integrate advanced technology, creative instructional design, and measurement of innovative variables; (2) the most frequently played role of DCM was students’ organization and analysis tool and teachers’ presentation tool; (3) primary school educators often resort to DCM for natural science teaching, while university academics are more inclined to use DCM for social science subjects; (4) qualitative methods were underutilized and cognitive goals were still a primary focus in DCM research; (5) there is a tenuous link to pedagogical theories, coupled with a requirement for transparent and consistent reporting of study details, which may have led to mixed results in learning outcomes; (6) DCM tools should be able to support both students and teachers as well as their interactions with each other. [ABSTRACT FROM AUTHOR]

This study investigates the influence of mineralogical characteristics on the mechanical properties of tight formations from the Montney Play in Canada. A series of instrumented indentation tests were conducted on rock samples cored at depths of 2068–2037 m. The mineralogical characteristics of the indents from the indentation tests were obtained from scanning electron microscopy (SEM) and energy-dispersive spectroscopy (EDS) analysis. By creating digital mineral maps from EDS elemental maps, the localized mineralogical composition and pore distribution around the indents were quantified, and their variations during indentation tests were correlated to the indentation depth/contact area. This new approach can obtain a unique dataset that directly relates rock indentation to their mineralogical characteristics. Pearson correlation analysis was employed to examine the relationships between mineralogical characteristics and mechanical behaviors, such as elastic, plastic and maximum indentation depths, indentation Young's modulus, and hardness. Furthermore, machine learning models were developed using data from instrumented indentation tests and SEM–EDS analysis to predict indentation behavior. The results indicated albite and porosity both have a positive influence on elastic and maximum indentation depths, while orthoclase and pyrite displayed negative correlations. Gaussian process machine learning models were developed to simulate both the loading and unloading stages of an indentation test, which can accurately forecast the indentation force-depth relation on rocks with different mineralogical characteristics. The findings of this study provide valuable insights into the collective effects of mineralogical characteristics on rock mechanical properties. Highlights: Instrumented indentation and SEM–EDS analysis were used on Montney tight formations. Digital mineral map was created to relate their composition to indentation results. Albite/porosity positively relate to elastic and maximum indentation depth. Orthoclase/pyrite negatively correlate to elastic and maximum indentation depth. Gaussian process model predicts indentation behaviors from mineral characteristic. [ABSTRACT FROM AUTHOR]

Soils are natural bodies that exhibit intricate variations across diverse landscape, evolving dynamically over both space and time. When evaluating and modeling spatial variability, multiple variables are commonly sampled alongside the primary point of interest. Moreover, efficient land management can be supported by grouping spatial data to delineate homogeneous zones. As soil heterogeneity is characterized by both continuous variation and discontinuities, the aim of this research was the combination of the classic pedological approach, using only soil data, with a digital mapping approach that uses both soil and ancillary data. The study area, covering an area of 100,000 ha is located in the Bam region, southeast Iran. The major land use in the study area includes pasture; bare soil and farmland. Mean annual precipitation and temperature of the region are 59 mm and 23 °C, respectively. Soil samples were taken up to 0.20 m-depth at 116 locations and some physical and chemical properties, including sand, silt, clay, sodium adsorption ratio (SAR), electrical conductivity (EC), pH, calcium carbonate equivalent (CCE), soil organic matter (SOM) and CaSO4, were determined. First, the variables were interpolated using a combination of block kriging with irregular geographical units and simple kriging with varying local means, determined according to a previous soil stratification, and then submitted to a clustering approach including also terrain attributes from an existing DEM, remote sensing variables and spatial coordinates. Pseudo F statistic, R squared and Cubic Clustering Criterion were used to optimize the number of clusters that was set at four zones which differed significantly with respect to the selected properties. The proposed method described in this research could be efficiently used to delineate spatially homogeneous zones to optimize land-use planning. [ABSTRACT FROM AUTHOR]

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ONE OF EGYPT'S top targets for future agricultural development is the Northern El Bahrya Oases region. The current study aims at digitally mapping the soils of the area under consideration and producing land capability and suitability digital maps. The considered region lies between longitudes 29° 9' 28.433'', 29° 22' 32.785'' East and latitudes 28° 57' 38.309'', 28° 58' 41.324" N. The region is covering roughly 46228 Fed. The Digital Elevation Model (DEM) is utilized along with the thematic maps of soil properties to produce digital land capability and suitability maps. The spatial analysis tool in the Arc-GIS 10.8 software generated thematic layers. Physiographic mapping units were created and showed that the studied area has one landform which is plateau. The land capability was evaluated by the method of Storie index where the studied area was classified into three capability grades, namely; grade 2, 3 and 4. The soils of grade 2 (66.41 % of the whole region) have moderate limitations for agricultural crops and texture is the main limiting factor. The area of grade 3 (25.59%), has soils with texture and salinity limiting factors, while the soils of grade 4 (7.99%), have texture, depth and salinity main limiting factors. Land suitability was done according to Sys and others for some crops and transferred to GIS to produce digital maps. The fertility maps of soil nutrients for N, P, K, Fe, Mn, Zn, and Cu are included. The study established a detailed database to serve decision-makers and help in sustainable development. [ABSTRACT FROM AUTHOR]

By employing the doorway-window (DW) on-the-fly simulation protocol, we performed ab initio simulations of peak evolutions and beating maps of electronic two-dimensional (2D) spectra of a polyatomic molecule in the gas phase. As the system under study, we chose pyrazine, which is a paradigmatic example of photodynamics dominated by conical intersections (CIs). From the technical perspective, we demonstrate that the DW protocol is a numerically efficient methodology suitable for simulations of 2D spectra for a wide range of excitation/detection frequencies and population times. From the information content perspective, we show that peak evolutions and beating maps not only reveal timescales of transitions through CIs but also pinpoint the most relevant coupling and tuning modes active at these CIs. [ABSTRACT FROM AUTHOR]

In the machining process of computer numerical control (CNC) machine tools, both dynamic and static errors significantly impact the machining accuracy. The ability to accurately estimate the machining quality of products in real time is of great significance for the closed-loop control of machining accuracy of CNC machine tools. For this purpose, this paper presents a digital twin synchronous evolution method of CNC machine tools by associating dynamic and static errors. Initially, a digital twin intrinsic model of CNC machine tools with static geometric errors is constructed in the virtual space, and the high-fidelity mapping of digital twin of CNC machine tools oriented to product processing path is realized. Subsequently, an acquisition method of multi-scene perception data of CNC machine tools is described, and the unified representation of multi-source heterogeneous data is achieved based on semantic mapping. Finally, dynamic errors such as spindle thermal deformation and tool wear are analyzed in real time using integrated multi-scene sensing data. These errors, along with the static geometric errors of the tool, are reflected to the workpiece through the tool machining trajectory, which provides a basis for the closed-loop control of digital twin oriented to product quality. The proposed method is integrated into the digital twin platform of CNC machine tools, and the effectiveness of the method is verified. [ABSTRACT FROM AUTHOR]

The charge density wave (CDW), as a hallmark of vanadium-based kagome superconductor AV3Sb5 (A = K, Rb, Cs), has attracted intensive attention. However, the fundamental controversy regarding the underlying mechanism of CDW therein persists. Recently, the vanadium-based bi-layered kagome metal ScV6Sn6, reported to exhibit a long-range charge order below 94 K, has emerged as a promising candidate to further clarify this core issue. Here, employing micro-focusing angle-resolved photoemission spectroscopy (μ-ARPES) and first-principles calculations, we systematically studied the unique CDW order in vanadium-based bi-layered kagome metals by comparing ScV6Sn6 with its isostructural counterpart YV6Sn6, which lacks a CDW ground state. Combining ARPES data and the corresponding joint density of states (DOS), we suggest that the VHS nesting mechanism might be invalid in these materials. Besides, in ScV6Sn6, we identified multiple hybridization energy gaps resulting from CDW-induced band folding, along with an anomalous band dispersion, implying a potential electron-phonon coupling-driven mechanism underlying the formation of the CDW order. Our finding not only comprehensively maps the electronic structure of V-based bi-layer kagome metals but also provides constructive experimental evidence for the unique origin of CDW in this system. We investigated the origins of charge density wave (CDW) mechanisms in the bi-layered kagome metal ScV6Sn6 by comparing its electronic structure with that of its isostructural counterpart YV6Sn6, which does not exhibit a CDW state. Our ARPES measurements reveal that the Van Hove singularity (VHS) nesting mechanism may not be valid in the CDW state. In ScV6Sn6, the electronic structure shows a CDW-induced band gap accompanied by anomalous band dispersion near the M point of the Brillouin zone. These findings provide experimental evidence for the origin of CDW in vanadium-based kagome metals. [ABSTRACT FROM AUTHOR]

The study aims to develop a model for evaluating the technological advancement of teachers engaged in distance education, with variations explained by their individual attributes. These attributes encompass organization skills, adaptation, communication abilities, independence, proficiency, efficiency, and attitude towards distance education. The study involved surveys conducted on 130 geography teachers in Poland during the initial months of the pandemic. The authors hypothesized that the variability in technological advancement could be attributed to the functional capabilities of maps used in geography lessons and the identified personal characteristics. To test these assumptions, 25 original indicators were devised, forming the Technological Advancement Index (TAI). Through statistical analysis, a disparate technological advancement among teachers was observed, with TAI scores ranging from +6.0 to −10.60. At the lower proficiency levels, variability was primarily linked to independence, followed by proficiency and efficiency. As advancement levels increased, so did the functional diversity of maps used, particularly in problem-solving and the creation of new digital maps. This exploration into technological advancement yielded novel conceptual and empirical insights, allowing validation of the Substitution, Augmentation, Modification, Redefinition (SAMR) model used in traditional education. Additionally, it led to development of the Recommendation, Imitation, Gamification, Mobilization, Action (RIGMA) model tailored for distance education. The proposed model holds applicability in enhancing geographic education during distance learning initiatives. [ABSTRACT FROM AUTHOR]

Phthalazine can behave as a diene in Diels–Alder (DA) cycloadditions, typically at the pyridazine ring, however, its application is somewhat limited because these reactions usually require harsh conditions or sophisticated catalysts. As an unconventional example, phthalazine was reported to undergo cycloaddition with the [PCO]− anion without any catalyst. In this computational study, we scrutinise the mechanism of the DA reactions between phthalazine and the so far known [ECX]− (E: P, As; X: O, S, Se) anions as dienophiles. In principle, the attack of an [ECX]− anion may occur at two different sites of phthalazine, either at the benzene or the pyridazine ring, and both of these possible reaction channels were juxtaposed on the basis of energetic aspects. In all of the investigated cases, the analysis of the energy profiles reveals a clear regioselectivity that favours the attack at the pyridazine ring. As a result, so far unprecedented 2‐pnictanaphth‐3‐olate analogues seem achievable as final products. Comparing the characteristics of these pathways allowed us to clarify the source of this regioselectivity: The pyridazine ring of phthalazine exhibits lower aromaticity than the benzene subring; therefore, in the DA step, the former ring shows a higher affinity toward a dienophile than the latter, leading to lower activation barriers. To further map the electronic and structural features of the cycloaddition steps, the local interactions evolving in the transition states were analysed and compared using global and local descriptors. In most aspects, the characteristics of both pathways were found to be rather similar, in contrast to the markedly differing activation barriers on the two routes. [ABSTRACT FROM AUTHOR]

The combination of UAV aerial surveys and LiDAR scanning provides high resolution, accuracy, and data comprehensiveness, which are critical for creating detailed digital models and maps. The aim of the work was to create an orthophotomap and an accurate digital surface model of the research area using a synergy of UAV aerial imagery and airborne LiDAR scanning with laser scanners from two different manufacturers. Aerial surveying with UAVs and aerial LiDAR scanning using two scanners, DJI Zenmuse L2 and CHCNAV Alpha Air 450, was conducted for the territory of the historical and cultural reserve "Ancient Zvenyhorod" in Ukraine, which was chosen as the research object. Based on UAV aerial survey data, an orthophotomap of the study area was created at a scale of 1:2000. Additionally, point clouds of the reserve's surface were generated from the aerial survey data and compared with point clouds obtained from LiDAR scanning. It was determined that, since the object's territory covers 26.8 hectares, the aerial survey was conducted from a height of 145 meters in one flight, while the laser scanning of the territory was carried out at a height of 60 meters to better capture the elements of the area. The scanning operations were automatically divided into two scans with UAV landings for battery replacement. The object's territory located at the junctions of the automatically divided scans lacked point cloud information for both scanners. It was proposed to supplement the LiDAR scanning point cloud with points generated from the aerial survey in the area where they were missing. Before copying the point cloud for the area lacking LiDAR data, an analysis and height alignment of points between the LiDAR data and aerial survey were performed. As a result of the research, an accurate digital surface model of the historical and cultural reserve was constructed, containing all elements of its territory. [ABSTRACT FROM AUTHOR]

This paper explores the numerical intergator of ODE based on combination of Appelroth's quadratization of dynamical systems with polynomial right-hand sides and Kahan's discretization method. Utilizing Appelroth's technique, we reduce any system of ordinary differential equations with a polynomial right-hand side to a quadratic form, enabling the application of Kahan's method. In this way, we get a difference scheme defining the one-to-one correspondence between the initial and final positions of the system (Cremona map). It provides important information about the Kahan method for differential equations with a quadratic right-hand side, because we obtain dynamical systems with a quadratic right-hand side that have movable branch points. We analyze algebraic properties of solutions obtained through this approach, showing that (1) the Kahan scheme describes the branch points as poles, significantly deviating from the behavior of the exact solution of the problem near these points, and (2) it disrupts algebraic invariant variety, in particular integral relations describing the relationship between old and Appelroth's variables. This study advances numerical methods, emphasizing the possibility of designing difference schemes whose algebraic properties differ significantly from those of the initial dynamical system. [ABSTRACT FROM AUTHOR]

Coal mining in the Loess Plateau can very easily generate ground cracks, and these cracks can immediately result in ventilation trouble under the mine shaft, runoff disturbance, and vegetation destruction. Advanced UAV (Unmanned Aerial Vehicle) high-resolution mapping and DL (Deep Learning) are introduced as the key methods to quickly delineate coal mining ground surface cracks for disaster prevention. Firstly, the dataset named the Ground Cracks of Coal Mining Area Unmanned Aerial Vehicle (GCCMA-UAV) is built, with a ground resolution of 3 cm, which is suitable to make a 1:500 thematic map of the ground crack. This GCCMA-UAV dataset includes 6280 images of ground cracks, and the size of the imagery is 256 × 256 pixels. Secondly, the DRA-UNet model is built effectively for coal mining ground surface crack delineation. This DRA-UNet model is an improved UNet DL model, which mainly includes the DAM (Dual Dttention Dechanism) module, the RN (residual network) module, and the ASPP (Atrous Spatial Pyramid Pooling) module. The DRA-UNet model shows the highest recall rate of 77.29% when the DRA-UNet was compared with other similar DL models, such as DeepLabV3+, SegNet, PSPNet, and so on. DRA-UNet also has other relatively reliable indicators; the precision rate is 84.92% and the F1 score is 78.87%. Finally, DRA-UNet is applied to delineate cracks on a DOM (Digital Orthophoto Map) of 3 km2 in the mining workface area, with a ground resolution of 3 cm. There were 4903 cracks that were delineated from the DOM in the Huojitu Coal Mine Shaft. This DRA-UNet model effectively improves the efficiency of crack delineation. [ABSTRACT FROM AUTHOR]

AbstractThe GIS Center at the University of California, Merced provides research services to faculty and is responsible for managing and maintaining a geospatial collection for our students and researchers. We support Geographic Information Systems (GIS) software for over 500 users yearly and provide workshops and consultations. GIS Center technical research staffs have provided consulting services to researchers and direct project support. Through four case studies, we will detail our processes for converting historic maps into GIS products that contribute to research publications and data sets. Specifically, we will discuss the technical demands and research benefits of georeferencing and vectorizing digitized historic maps utilizing various methods including machine learning and heads up digitizing and sharing that data via ArcGIS Online and other cloud-based applications. We conclude with recommendations for others embarking upon this type of work. [ABSTRACT FROM AUTHOR]

Comprehensive path planning can greatly contribute to the single plots and optimal traversal order for multiple plots in the hilly and mountainous regions. It is very necessary to enhance the operational quality and energy efficiency of agricultural machinery. In this study, path planning was designed using an improved whale optimization algorithm (IWOA). Firstly, the digital elevation model (DEM) of the operation area was obtained using electronic maps. ArcMap software was used to perform the vector clipping on the DEM data, in order to extract the latitude and longitude information of the operation area, thereby constructing and mapping the plot models. The entire operation area was divided into 13 subplots, according to the distribution of field roads and farmland. An energy consumption model was then constructed in the comprehensive path planning for single plots. The angle of the operation direction depended directly on the operational quality and energy consumption of the tractor. MATLAB platform was used to simulate the comprehensive path planning for the 13 subplots, in order to quantitatively analyze the impact of parameters. The energy consumption costs of the tractor were gradually compared under different operation direction angles in the simulation. The optimal operation direction angle was identified and determined to realize the energy-optimal comprehensive path planning for single plots, together with the optimal entry and exit coordinates for each plot. Finally, the entry and exit positions of each plot were determined to solve the optimal traversal order for the multiple plots using improved whale optimization. The tendency fell into the local optima and premature convergence. Tent mapping was used for the population initialization. The nonlinear convergence factor was introduced to reduce algorithm oscillation and instability. The improved whale optimization balanced the global and local search. The energy-optimal traversal path was successfully planned for the 13 subplots, thus achieving an orderly connection for each subplot. Taking a certain tractor model as the research object, path planning simulation was conducted with energy and path length optimization as the targets. The results showed that the total operation energy consumption was reduced by 22.26%, compared with the optimal scenario of path length. The energy consumption for traversal was 11.72% lower using the improved whale optimization than that using the whale optimization, demonstrating the significant energy-saving. This finding can provide a feasible way for the comprehensive path planning of plots in the hilly and mountainous regions. A theoretical basis was also offered for the global optimization of operation energy [ABSTRACT FROM AUTHOR]

This article approaches digital activism as an object of discourse and asks: how is the political prowess of digital technologies discursively articulated in news magazine coverage of digital activism? We take an exploratory approach that maps the representations of digital activism in two world-renowned news magazines – Wired and Time, between 2010 and 2021. We find five dominant narratives through which digital technologies gain political significance, namely: as a last resort; as a witness; as a double-edged sword; as sites of creativity; and as enablers of horizontalism. We argue that these narratives contribute to a persistent discourse casting digital technologies as powerful political tools of grassroots empowerment, which enable unprecedented levels of citizen mobilization. This discourse rehearses techno-utopian imaginaries casting digital technologies as democratizing forces while underplaying the difficulties of sustaining mobilization and of working towards political change. [ABSTRACT FROM AUTHOR]

The concept of electronic orbitals has enabled the understanding of a wide range of physical and chemical properties of solids through the definition of, for example, chemical bonding between atoms. In the transmission electron microscope, which is one of the most used and powerful analytical tools for high‐spatial‐resolution analysis of solids, the accessible quantity is the local distribution of electronic states. However, the interpretation of electronic state maps at atomic resolution in terms of electronic orbitals is far from obvious, not always possible, and often remains a major hurdle preventing a better understanding of the properties of the system of interest. In this review, the current state of the art of the experimental aspects for electronic state mapping and its interpretation as electronic orbitals is presented, considering approaches that rely on elastic and inelastic scattering, in real and reciprocal spaces. This work goes beyond resolving spectral variations between adjacent atomic columns, as it aims at providing deeper information about, for example, the spatial or momentum distributions of the states involved. The advantages and disadvantages of existing experimental approaches are discussed, while the challenges to overcome and future perspectives are explored in an effort to establish the current state of knowledge in this field. The aims of this review are also to foster the interest of the scientific community and to trigger a global effort to further enhance the current analytical capabilities of transmission electron microscopy for chemical bonding and electronic structure analysis. [ABSTRACT FROM AUTHOR]

This study focused on applying numerical simulations to assess damaged areas caused by debris flows, employing the LS-RAPID program while emphasizing the importance of terrain information. Terrain information used in the numerical simulation included a 1:5000 digital terrain map and a digital surface model using an unmanned aerial vehicle. Quantification of the amount of soil that collapsed from the road embankment slope, which is the source of the debris flow, facilitated the computation of the debris flow that closely resembled real-world conditions. In particular, incorporating the high-resolution digital surface model (DSM) with 3-cm topographic information resulted in an interpretation of the actual soil flow damage range that is similar to actual observations of the digital elevation model (DEM), which had 1-m grid topographic information. This difference arises from DSM as it reflects the information of low hills downstream. The range of damage changed as the direction of the debris flow changed because of the low hill. Many variables need adjustment for the accuracy of debris flow numerical simulation. However, the direction and range of flow vary greatly depending on topographic information, highlighting the necessity of applying high-resolution terrain information. The results of debris flow simulations with high-resolution terrain information are expected to improve accuracy and help prepare risk or damage maps. [ABSTRACT FROM AUTHOR]

In secondary English classrooms, poetry is often a text that is least liked because it is viewed as being “inaccessible,” reserved for the elite, and/or too abstract. Part of the reason for this also lies in the traditional, colonial structures of introducing poetry such as relying on canonical texts and close reading analysis. Yet, outside of the classroom poetry is used in more accessible and engaging manners. With the advancements of technology, there also includes multimodal ways in which to read and write poetry that could be much more interesting for both educators and youth. This paper opens a discussion to consider multimodal and aesthetic responses to including poetry such as using digital apps like PhoneMe, a free accessible platform that allows users to post their written poems, record themselves reciting poems, and pin their poems directly on to an interactive digital map. The uniqueness of PhoneMe—a layered multimodal approach—can provide a more engaging way to teach and learn poetry. [ABSTRACT FROM AUTHOR]

In this paper, wavelets are applied to develop new models for the self-calibration of aerial metric camera systems. It is well known and mathematically proven that additional parameters (APs) can compensate image distortions and remaining error sources by a rigorous photogrammetric bundle-block adjustment. Thus, kernel functions based on orthogonal wavelets (e.g., asymmetric Daubechies wavelets, least asymmetric Daubechies wavelets, Battle-Lemarié wavelets, Meyer wavelets) are used to build the wavelets-based family of APs for self-calibrating digital frame cameras. These new APs are called wavelet APs. Its applications in rigorous tests are accomplished by using aerial images taken by an airborne digital mapping camera in situ and practical calibrations. The test results demonstrate that these orthogonal wavelet APs are applicable and largely avoid the risk of over-parameterization. Their external accuracy is evaluated using reliable and high precision check points in the calibration field. [ABSTRACT FROM AUTHOR]

Violation of the integrity of the histohematologic barriers (blood-brain, blood-testis, blood-ocular, blood-labyrinth, blood-thyroid) leads to autoimmune damage to these organs. One of the manifestations of the latter is autoimmune thyroiditis, the structural and quantitative changes of which can be more informatively accurately assessed by polarization biomedical optics. The purpose of the study was to substantiate the possibility of using polarization biomedical optics methods in the diagnosis of autoimmune thyroiditis based on the use of pathophysiological analysis of blood-brain barrier integrity disorders. Materials and methods. Two groups of patients were studied: control group 1 — healthy donors (n = 51), study group 2 — people with autoimmune thyroiditis (n = 51) who underwent a puncture biopsy of the thyroid gland for diagnostic purposes. Instrumental laser methods were used: polarization, interference, multifractal. The statistical parameters of polarization ellipticity maps, polarization ellipticity of phase and multifractal spectra of digital microscopic images of native thyroid histological sections in patients with autoimmune thyroiditis were quantified: mean, dispersion, asymmetry, and kurtosis. The probability of differences compared to the controls, taken as 100%, was evaluated using the Student’s parametric test (p < 0.05). Results. A significant increase in the mean and variance at inhibition of the asymmetry and kurtosis of polarization ellipticity, as well as in the mean and variance at reduction of the asymmetry and kurtosis of polarization ellipticity of phase digital microscopic images of thyroid native histological sections was revealed. There were a significant increase in dispersion and a decrease in the asymmetry and kurtosis of multifractal spectra of polarization ellipticity maps of digital microscopic images of native histological sections. Conclusions. A significant increase in the biophysical optical parameters of digital microscopic images of thyroid native histological sections from patients with autoimmune thyroiditis was found due to the growth of connective tissue in the interstitium as a result of an autoimmune inflammation. There was a significant inhibition of the asymmetry and kurtosis of the ellipticity of polarization of phase digital and multifractal spectra of polarization ellipticity maps of microscopic images of native histological sections in patients with autoimmune thyroiditis due to a decrease in the amount of colloids as a crystalline component caused by damage to the blood-thyroid barrier. [ABSTRACT FROM AUTHOR]

Land cover maps are frequently produced via the classification of satellite imagery. There is a need for a practicable and automated approach for the generalization of these land cover classification results into scalable, digital maps while minimizing information loss. We demonstrate a method where a land cover raster map produced using the classification of Sentinel 2 imagery was generalized to obtain a simpler, more readable land cover map. A replicable procedure following a formal generalization framework was applied. The result of the initial land cover classification was separated into binary layers representing each land cover class. Each binary layer was simplified via structural generalization. The resulting images were merged to create a new, simplified land cover map. This map was enriched by adding statistical information from the original land cover classification result, describing the internal land cover distribution inside each polygon. This enrichment preserved the original statistical information from the classified image and provided an environment for more complex cartography and analysis. The overall accuracy of the generalized map was compared to the accuracy of the original, classified land cover. The accuracy of the land cover classification in the two products was not significantly different, showing that the accuracy did not deteriorate because of the generalization. [ABSTRACT FROM AUTHOR]

The current demand for wood products is growing globally. Hybrid poplars are fast growing trees and it is beneficial to expand their cultivation area. Digital mapping techniques based on land suitability assessment can effectively support land use decision-making processes in this perspective. The aim of this study was to develop a model to produce land suitability maps to determine the potential production area of hybrid poplar (Populus × canadensis Moench) in Italy. The evaluation was based on a fuzzy logic procedure to generate a raster map with a pixel resolution of 250 m over the country. The modelling approach is planning-oriented: the objective is to predict, on a national scale, the suitability of the land for poplar cultivation by taking into account environmental factors for which geodatabases with adequate and comparable spatial resolution are available on a national scale in Italy. Georeferenced databases of experimental poplar plantations were used as calibration dataset. The assessment identified around 145,000 hectares of highly suitable land and around 1,926,500 hectares of suitable land for hybrid poplar cultivation in Italy. These areas are mainly located in northern Italy (around 36% of the territory) and, to a lesser extent, in central Italy (around 19%) and in southern Italy and the islands (less than 3%). The modelling approach adopted can be easily replicated in other geographical areas or at finer scales, provided that appropriate data are available. [ABSTRACT FROM AUTHOR]