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Atomic force microscopy (AFM) is the gold-standard technique to simultaneously map the morphology and viscoelastic properties of living cells. Although existing software tools, both open-source and from AFM manufacturers, can analyze cells individually, there is a growing need for fast and accessible codes to compile data from multiple cells into a single dataset. To address this, we present CellMAP, a user-friendly software tool that streamlines the batch-processing of AFM-derived topography and stiffness maps of living cells. Our analysis pipeline includes but is not limited to: flattening of the underlying substrate surface, filtering of outlier values, measurement of the cell surface and volume, and measurement of height and stiffness distributions. CellMAP can also generate a composite cell that reflects the height and stiffness properties of an entire cell population. [ABSTRACT FROM AUTHOR]

Utilizing remote sensing and deep learning methods can greatly enhance efficiency and accuracy in landslide identification. However, the presence of vegetation cover and human activities pose challenges to identify landslides using optical images alone. In addition, the limited availability of landslide samples affects the generalization and adaptability of identification models. This study introduces a novel feature fusion model called SAMLS, which is adapted from the vision foundation model Segment Anything Model (SAM). The SAMLS is designed with two-branch encoders, namely the RGB branch and the digital elevation model (DEM) branch, which retains the strong feature extraction abilities from the original SAM encoder but utilizes adapter approach to finetune on landslide datasets. The SAMLS utilizes a cross-branch attention mechanism to dynamically fuse terrain features from DEM branch with optical features from RGB branch based on different types of landslides. The SAMLS also follows the prompt learning approach for an automatic landslide identification process. Experiment results on the Bijie dataset demonstrate the effectiveness of SAMLS in identifying visually distinct landslides with an improvement of 0.820 and 0.831 to 0.843 in terms of F1-score compared with Mask R-CNN and Mask2Former. Furthermore, experiment results on the Zhejiang dataset indicate that SAMLS is capable to identify both visually distinct and occluded landslides. The combination of RGB and color relief image map (CRIM) data achieves the best F1-score of 0.592, primarily due to its enhanced visibility of terrain characteristics of landslides in CRIM images. The proposed method holds promise for applications in landslide mapping and risk management in the future. [ABSTRACT FROM AUTHOR]

The demand for large-scale topographic maps in Indonesia has significantly increased due to the implementation of several government initiatives that necessitate the utilization of spatial data in development planning. Currently, the national production capacity for large-scale topographic maps in Indonesia is 13,000 km2/year using stereo-plotting/mono-plotting methods from photogrammetric data, Lidar, high-resolution satellite imagery, or a combination of the three. In order to provide the necessary data to the respective applications in a timely manner, one strategy is to only generate critical layers of the maps. One of the topographic map layers that is often needed is land cover. This research focuses on providing land cover to support the accelerated provision of topographic maps. The data used are very-high-resolution satellite images. The method used is a deep learning approach to classify very-high-resolution satellite images into land cover data. The implementation of the deep learning approach can advance the production of topographic maps, particularly in the provision of land cover data. This significantly enhances the efficiency and effectiveness of producing large-scale topographic maps, hence increasing productivity. The quality assessment of this study demonstrates that the AI-assisted method is capable of accurately classifying land cover data from very-high-resolution images, as indicated by the Kappa values of 0.81 and overall accuracy of 86%, respectively. [ABSTRACT FROM AUTHOR]

Cartographic representations have evolved alongside technological advancements in digital mobility, with the widespread use of mobile devices, prompting a need for improved map usability. This article explores the creation of new symbols for topographic OR reference mapping on mobile devices by developing a three-step methodology for symbol design specifically tailored for Brazilian users. First, a Comprehension Test with 632 individuals assessed their understanding of symbols on mobile devices, considering figure-background distinction suitable for smaller screens. The symbols were adapted using the Semiotics and Gestalt Theory. Next, a Production Test involved nine volunteers creating concept sketches to propose new representations for unvalidated symbols, with components integrated into new designs. Lastly, a Map Reading Task Test with 80 users evaluated the symbols' detection, distinction, and recognition to ensure their suitability for mobile device maps. This user-centric approach reduced noise in the recognition process and accounted for diverse perspectives and cultures. The resulting symbols provided mobile device map usability comparable to desktop computers. The study also found that using a white halo instead of a white square with a black outer line did not negatively impact symbol perception on mobile interfaces. Conducted remotely during the COVID-19 pandemic, this approach to user integration in cartographic symbol creation shows promise for other use contexts. [ABSTRACT FROM AUTHOR]

Historical topographical maps contain valuable, spatially and thematically detailed information about past landscapes. Yet, for analyses of landscape dynamics through geographical information systems, it is necessary to "unlock" this information via map processing. For two study areas in northern and central Jutland, Denmark, we apply object-based image analysis, vector GIS, colour image segmentation, and machine learning processes to produce machine-readable layers for the land use and land cover categories forest, wetland, heath, dune sand, and water bodies from topographic maps from the late nineteenth century. Obtained overall accuracy was 92.3%. A comparison with a contemporary map revealed spatially explicit landscape dynamics dominated by transitions from heath and wetland to agriculture and forest and from heath and dune sand to forest. However, dune sand was also characterised by more complex transitions to heath and dry grassland, which can be related to active prevention of sand drift but that can also be biased by different categorisations of dune sand between the historical and contemporary data. We conclude that automated production of machine-readable layers of land use and land cover categories from historical topographical maps offers a resource-efficient alternative to manual vectorisation and is particularly useful for spatially explicit assessments of long-term landscape dynamics. Our results also underline that an understanding of mapped categories in both historical and contemporary maps is critical to the interpretation of landscape dynamics. [ABSTRACT FROM AUTHOR]

Life on the nanoscale has been made accessible in recent decades by the development of fast and noninvasive techniques. High-speed atomic force microscopy (HS-AFM) is one such technique that shed light on single protein dynamics. Extending HS-AFM to effortlessly incorporate mechanical property mapping while maintaining fast imaging speed allows a look deeper than topography and reveal details of nanoscale mechanisms that govern life. Here, we present high-speed in-line force mapping (HS-iFM) to record mechanical properties and topography maps with high spatiotemporal resolution. Using HS-iFM, a comprehensive study of the nanoscale mechanical properties of living Escherichia coli revealed localized stiffening and details during cell division, formation and diffusion of pores in the membrane, and the impact of depressurization of a cell. The frame time was as low as 15 seconds with a spatial resolution of 5.5 nanometers per pixel in topography and 22 nanometers per pixel in force maps, allowing the capture of transient phenomena on bacterial surfaces in striking detail. [ABSTRACT FROM AUTHOR]

Topography estimation is essential for autonomous off-road navigation. Common methods rely on point cloud data from, e.g., Light Detection and Ranging sensors (LIDARs) and stereo cameras. Stereo cameras produce dense point clouds with larger coverage but lower accuracy. LIDARs, on the other hand, have higher accuracy and longer range but much less coverage. LIDARs are also more expensive. The research question examines whether incorporating LIDARs can significantly improve stereo camera accuracy. Current sensor fusion methods use LIDARs' raw measurements directly; thus, the improvement in estimation accuracy is limited to only LIDAR-scanned locations The main contribution of our new method is to construct a reference ground plane through the interpolation of LIDAR data so that the interpolated maps have similar coverage as the stereo camera's point cloud. The interpolated maps are fused with the stereo camera point cloud via Kalman filters to improve a larger section of the topography map. The method is tested in three environments: controlled indoor, semi-controlled outdoor, and unstructured terrain. Compared to the existing method without LIDAR interpolation, the proposed approach reduces average error by 40% in the controlled environment and 67% in the semi-controlled environment, while maintaining large coverage. The unstructured environment evaluation confirms its corrective impact. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effectiveness of a design thinking teaching and learning model on students' design thinking skills in science class. The study utilised two projects, 'Making a Simple Solar Water Heater' and 'Drawing a 3D Topographic Map of Zhejiang Province,' based on the 5th-grade science curriculum in China, and involved 45 Grade-5 students from a primary school. The quasi-experimental repeated measure method was used to evaluate the students' improvement in design thinking skills, with task-based pre-, mid-, and post-tests. Employing quantitative data analysis, the results showed significant improvement in students' overall performance, with more pronounced improvement between the pre-test and post-test than between the pre-test and mid-test. The analysis of the seven design indexes of students' design work demonstrated significant improvement in five indicators, except for 'material' and 'shape.' The study highlights the importance of incorporating design thinking into elementary school curricula to equip students with the necessary competencies for success in the twenty-first-century workforce. [ABSTRACT FROM AUTHOR]

Over the past decade, the Southwest coast of Sri Lanka has experienced recurrent coastal floods, particularly in the stretch from Akurala to Godagama. This study, spanning from 2010 to 2020, seeks to unravel the factors influencing these floods, filling a void in existing research on the subject. Employing topographic maps, field observations, and bathymetric data from the Lanka Hydraulic Institute and Coastal Conservation Department, the research delves into geomorphological features and elevation variations in the study area. Findings indicate that high elevation in the Ambalangoda to Akurala and Godagama to Hikkaduwa regions contributes to coastal flooding, as these areas are elevated above the mean sea level. The bathymetric analysis exposes closely spaced contours and steep slopes, exacerbating flooding when waves collide with the coastline. The absence of mangroves amplifies the impact, distinguishing the study area from its neighbouring regions. Socioeconomic consequences include road closures, accidents on the Galle-Colombo main road in 2020, and adverse effects on fisheries and tourism. Environmental impacts encompass coastal erosion, degraded water quality, coral bleaching, and saltwater intrusion. While artificial structures like rock barriers mitigate flooding, the study underscores the interplay of land morphology, bathymetry, land use, and climate influencing coastal flooding along the Akurala to Godagama coast. [ABSTRACT FROM AUTHOR]

The objective is to analyse the evolution of the vineyard in the Calatayud region throughout the 20th century. The main sources of information are the National Topographic Map and the Map of Crops and Uses of Spain. The QGIS geographic information system and the Python programming language have been used to collect and process data. The vineyard has become a marginal element in the agricultural land uses, having disappeared from the valley bottoms and plain lands, but it has survived and relocated on slopes not suitable for other crops. [ABSTRACT FROM AUTHOR]

Knowledge of the thickness, volume, and subglacial topography of glaciers is crucial for a range of glaciological, hydrological, and societal issues, including studies on climate-warming-induced glacier retreat and associated sea level rise. This is not in the least true for Svalbard, one of the fastest-warming places in the world. Here, we present new maps of the ice thickness and subglacial topography for every glacier on Svalbard. Using remotely sensed observations of surface height, ice velocity, rate of surface elevation change, and glacier boundaries in combination with a modelled mass balance product, we apply an inverse method that leverages state-of-the-art ice flow models to obtain the shape of the glacier bed. Specifically, we model large glaciers with the Parallel Ice Sheet Model (PISM) at 500 m resolution, while we resolve smaller mountain glaciers at 100 m resolution using the physics-informed deep-learning-based Instructed Glacier Model (IGM). Actively surging glaciers are modelled using a perfect-plasticity model. We find a total glacier volume (excluding the island Kvitøya) of 6800 ± 238 km3, corresponding to 16.3 ± 0.6 mm sea level equivalent. Validation against thickness observations shows high statistical agreement, and the combination of the three methods is found to reduce uncertainties. We discuss the remaining sources of errors, differences from previous ice thickness maps of the region, and future applications of our results. [ABSTRACT FROM AUTHOR]

Mozambique Island, a UNESCO World Heritage property, faces significant challenges due to climate change and extreme weather events. This study proposes a comprehensive framework for assessing morphological vulnerabilities and enhancing urban resilience in this unique historical urban landscape. The research methodology involves a thorough analysis of historical cartography, urban evolution, topography, and vernacular architecture, combined with recent conservation assessments and case studies from other climate-vulnerable regions. This study reveals the island's dual urban structure, comprising the Stone and Lime town and the Macuti town, each with distinct morphological characteristics and vulnerabilities. Historical maps and topographical analysis demonstrate how the island's geography has shaped its urban development, with the Stone and Lime town built on higher ground and the Macuti town situated at or below sea level, increasing its flood risk. The research highlights the importance of integrating traditional knowledge with resilience strategies while respecting the authenticity and integrity of the World Heritage property. Key findings include the need for a GIS-based management tool for continuous conservation assessment, and the crucial role of community engagement in implementing resilience mechanisms. This study contributes to the broader discourse on cultural heritage as a contributor to architectural and urban resilience, offering valuable insights for other World Heritage properties facing similar climate challenges. The proposed framework emphasizes the importance of balancing heritage preservation with adaptive strategies, while enhancing the island's resilience facing climate-related threats. [ABSTRACT FROM AUTHOR]

Tropical riverine floods have escalated their frequency and magnitude causing individual and community-level livelihood vulnerability, especially in the rural areas. Livelihood vulnerability induces social vulnerability in a community in the face of recurrent floods. Thus, while measuring livelihood vulnerability, the non-technocratic factors were emphasized. The livelihood vulnerability index (LVI) devised by the Intergovernmental Panel on Climate Change in 2007 is a widely accepted livelihood vulnerability framework that is applied in the present study to reveal the nature of exposure, sensitivity, and adaptive capacity of rural communities. The study measures 36 parameters based mainly on the primary field survey of 2382 households in the Mayurakshi River Basin (India) along with district census reports, annual flood reports, satellite images and topographical maps. The result depicts that Kandi is the most exposed community development block (score: 0.591) owing to low-lying topography and drainage congestion, with a greater adaptive capacity (score: 0.480) on account of the receipt of higher foreign remittances. Thus, floods could not escalate the livelihood vulnerability due to the rural communities' higher adaptive capacity. However, the nature of the LVI is primarily determined by the flood hazards, as shown by the close clustering of LVI and exposure using principal component analysis. The hierarchical cluster analysis depicts that the northern part of the study area, characterized by the lower flood hazards, is distinctly separated from the southern part in terms of the LVI. The one-way ANOVA also found significant differences (p < 0.05) among the villages based on exposure and LVI. These findings help various stakeholders to prepare flood management plans. [ABSTRACT FROM AUTHOR]

In modern ocean exploration, forward-looking sonar (FLS) provides real-time 2D imaging of the seabed ahead, but its detection range is relatively limited. Conversely, side-scan sonar (SSS) enables large-scale imaging of the seabed during movement but struggles to effectively image areas directly beneath the sensor. Integrating FLS and SSS offers a promising solution by leveraging their complementary strengths to achieve comprehensive seabed mapping. However, no prior research has explored this fusion approach. This paper presents a novel method for FLS and SSS fusion mapping. Firstly, a novel sonar image enhancement method based on equalization is proposed, enabling simultaneous enhancement and grayscale unification of two sonar images. Additionally, an effective area extraction approach for FLS images, grounded on the approximate erosion method, is introduced to produce high-quality FLS mapping. Furthermore, by examining the data distribution in FLS and SSS mappings, the standard deviation of these datasets is utilized to refine the grayscale distribution of FLS mapping, thereby enhancing the grayscale distribution similarity between the two mapping results. Finally, FLS map data are seamlessly integrated into the gaps of the SSS map, resulting in a fused, comprehensive seabed representation. Large-scale experiments demonstrate that the proposed method effectively combines the strengths of FLS and SSS, producing complete and detailed seabed topography maps. Simultaneously, numerous ablation experiments are conducted to evaluate the impact of various parameters on fusion mapping, providing guidelines for selecting the optimal parameters. This fusion approach, thus, holds significant practical value for ocean exploration and seabed mapping applications. [ABSTRACT FROM AUTHOR]

Introduction: Dobšiná Ice Cave (Slovakia) has attracted the attention of many researchers since its discovery more than 150 years ago. Although the cave is located outside the high-mountain area, it hosts one of the largest volumes of underground perennial ice. The topographic mapping of this unique UNESCO Natural Heritage site has led to several historical surveys. In the last decades of rapid climate change, this natural formation has been subject to rapid changes that are dynamically affecting the shape of the ice body. Increased precipitation, the rise in year-round surface temperatures, and the gravity cause significant shape changes in the ice filling. Methods: This paper describes modern technological tools to comprehensively survey and evaluate interannual changes in both the floor and wall of the underground ice body. Technologies such as digital photogrammetry, in combination with precise digital tacheometry and terrestrial laser scanning, make it possible to detect ice accumulation and loss, including the effect of sublimation due to airflow, as well as sliding movements of the ice body to the lower part of the cave. To get a comprehensive model of the ice volume, geophysical methods (microgravimetry and ground penetrating radar) have been added to determine the thickness of the floor ice in the upper parts of the cave in the last 2 years. Results: Between 2018 and 2023, the ice volume in certain sections of the cave decreased by up to 667 m³, with notable reductions in ice thickness ranging from 0.3 to 0.9 m in areas like the Small Hall and Collapsed Dome. The study also detected dynamic changes, such as the widening of the ice tunnel by 20 cm in some sections, and a vertical ice wall in Ruffinyi's Corridor showed localized volume losses up to 9 m3 (between 2018 and 2023). Additional geophysical methods - microgravimetry and ground penetrating radar - revealed an average ice thickness ranging from 10 to 25 m. Discussion: The paper not only highlights the current technological possibilities but also points out the limitations of these technologies and then sets out solutions with a proposal of technological procedures for obtaining accurate geodetic and geophysical data. [ABSTRACT FROM AUTHOR]

Resting-state electroencephalogram (EEG) microstate analysis resolves EEG signals into topographical maps representing discrete, sequential network activations. These maps can be used to identify patterns in EEGs that may be indicative of underlying neurological conditions. One such pattern is observed in EEGs of patients with Alzheimer's disease (AD), where a global microstate disorganization is evident. We initially investigated the classification efficacy of microstate parameters as markers for AD classification. Subsequently, we compared the classification efficacy of EEG conventional features to ascertain the superiority of microstate features. We extracted raw EEG data from a public, independent database, OpenNeuro EEG. The raw EEG was subjected to preprocessing and band-pass filtering to obtain five distinct frequency bands. The SVM classifier was used to input the microstate feature set to determine the one with the best classification effect as the main band. In order to verify the advantage of the microstate features, the AD group and the healthy control group were filtered for the main frequency bands respectively. Then the microstate feature set and the regular feature set were extracted. The two feature sets were input into four different conventional machine learning classifiers, namely SVM, KNN, RF, and LR, in order to avoid the classifiers as the dependent variable. And the comparison of the classification results of simply two feature sets as the dependent variable can be obtained. The results show that in the Alpha (8–13 Hz) sub-band, the microstate feature set as model input to SVM is optimal for the recognition of AD, with a classification accuracy of 99.22%. The Alpha band, as the main frequency band, the microstate feature set as model input to the four classifiers obtains an average classification accuracy of 98.61%, and the average classification accuracy obtained by the conventional EEG feature set as model is 91.19%. Based on four different classifiers, microstate parameters can be served as markers to effectively classify the EEG of AD patients. The microstate feature set outperforms the conventional EEG feature set after excluding the effect of classifiers. [ABSTRACT FROM AUTHOR]

The load Love numbers for different rheological models of Venus are calculated, based on a static approach for the surface load (the planetary relief) and buried anomalous density waves. The planet was modeled as an elastic self-gravitating body with radius-dependent density, compression modulus, and shear modulus. The calculations have been performed for each harmonic up to the degree and order n = 70, based on the accuracy of determining the gravity field at the moment. This article considers three rheological models of Venus. A purely elastic model (model A) was analyzed first. In the second case (model B) we assume the presence of an elastic lithosphere, under which a weakened layer extending to the core was introduced, which partially lost its elastic properties. The weakening in this layer was modeled by a ten times lower shear modulus. The thickness of the elastic lithospheric layer varied from 100 to 500 km. In the third model (model C), a gradient change in the shear modulus was set in the weakened layer under the crust, that is, a ten times decrease in the shear modulus directly under the crust gradually increased to its value in the elastic model at the core boundary. On the basis of the described models, the interpretation of the anomalous external gravitational field has been carried out. It is shown that the load numbers are sensitive to the rheological structure of the planet and this can be used when choosing between the rheological models of Venus. A relief map of the crust–mantle boundary was constructed, as calculated under the assumption of isostatic compensation. The obtained values of the crust thickness may be slightly less than the real ones, since the component of dynamic compensation was not taken into account in the work. [ABSTRACT FROM AUTHOR]

The inverse design methods for aerodynamic shapes face significant challenges, especially in specifying reasonable distributions of target pressure and achieving the comprehensive optimization of aerodynamic characteristics. To overcome the limitations of existing inverse design methods, a nonlinear dimensionality reduction method based on topographic mapping was proposed. In this work, a high-precision bidirectional mapping between aerodynamic shape and its pressure distributions and low-dimensional latent space variables was built using the generative topographic mapping (GTM) model. This method, combined with the genetic algorithm, could provide efficient optimization in the latent space without the computational fluid dynamics iteration, yielding the optimal pressure distribution and the corresponding aerodynamic shape. It did not require the target pressure distribution to have direct physical significant, but instead uses coefficients to integrate the target pressure distribution and different aerodynamic performances into a hybrid objective function, achieving comprehensive aerodynamic optimization. In addition, this method was applied to high-speed natural laminar flow (HSNLF)(1)–0213 airfoil. The results revealed that a local pressure distribution of the optimal airfoil was close to the target distribution, and the total drag was reduced by 15.3%. Moreover, this method was applied to the complex multi-objective optimization design of the XN12 rotor airfoil, considering various performance metrics such as the maximum lift coefficient, cruise drag reduction, and drag divergence Mach number. The results demonstrated that the drag divergence Mach number of the optimized airfoil was significantly improved, achieving a balance between various performance metrics. [ABSTRACT FROM AUTHOR]

The Aras Mountains cover about 50 km of the volcanic relief between the Pamuk and Durak Mountains, controlled by the Iğdır, Doğubayazıt and Balıkgöl fault zones. The orographic roof of the NW-SE axis volcanic mountain belt consists of a series of stratovolcanoes. To the north of the Aras Mountains are the Tuzluca and Iğdır pull-apart basins and to the south is a belt of tectonic depressions including the Ombulak Basin, Balıkgöl Basin, Balıkgöl Creek fluvio-tectonic trough and the Doğubayazıt Basin. All of these depressions are the remnants of the palaeo-Ağrı Basin and the Aras Mountains have evolved geomorphically by volcanism due to opening cracks in this depression system. Within this regional deformation system, parasitic cones, which formed as independent central eruptions and flank eruptions, resulted in the development of typical craters in the Aras Mountains. Fourteen distinctive crater structures have been identified on the mountain range and they are largely the topographical results of acidic lava eruptions or basaltic pyroclastic outflows. In this study, it was aimed to make a morphological description/classification of the crater structures and to reveal the effect of different geologic-geomorphologic processes on crater morphology. For this purpose, crater systems were handled individually, and 3D views, swath profiles and red relief maps of the craters were produced using digital elevation models. While the results show that fluvial processes are effective on the morphology of all craters, palaeowinds affect the geomorphic texture of two craters, glacial processes affect three craters, tectonism affects seven craters and in-crater secondary volcanism affects three craters. Thus, due to these processes all craters lack the closed bowl feature. These parameters show that deformed crater structures are among the substantial geomorphic sources in understanding the changing natural environment conditions. [ABSTRACT FROM AUTHOR]

Using historical topographic maps and aerospace remote sensing data since the 1930s, this study investigates the spatial and temporal evolution of Dongting Lake beach. The evolution characteristics of the beaches in different regions and the related formation mechanism were also analyzed. The results show that Dongting Lake beach expanded from 1 622.17 km2 in 1938 to 1 962.28 km2 in 2018. With the addition of 980.96 km2 of reclaimed high bay beach, the beach area increased by 1 321.07 km2. However, the change process fluctuated somewhat rather than continuously increased. Substantial expansion of the beach area occurred during 1938–1948 and 1958–1998, while slow contraction of the beach area occurred during 1948–1958 and 1998–2018. Dongting Lake beach was dominated by terrigenous debris, the sedimentary types included lacustrine deposits, river alluvial deposits, floodplain and main channel deposits, and river-lake interaction deposits. The rapid expansion occurred in the estuary delta of the east branch of the Ouchi River, which advanced 38.55 km from the estuary toward the lake over the past 90 years. The causes of the changes in the beach included beach reclamation, sediment changes, and lake sand mining. Seventy embankments (covering 2 057.77 km2) have been enclosed in the Dongting Lake area since 1930s, of which the high bay beach covered an area of 980.96 km2. The amount of sediment deposited in Dongting Lake has reached 230 857 × 104 m3 since 1950s, which is equivalent to an average deposition height of 0.85 m on the lake's bottom. The mining of lake sand caused the beach to shrink, and the proportion of the beach area decreased from 77.18% in 1998 to 72.60% in 2018. The results of this study provide objective data for protecting the lakeshore's ecosystem and biodiversity and supporting the ecological restoration and environmental protection of the Yangtze River Basin. [ABSTRACT FROM AUTHOR]

Multi-platform geospatial data synergy is critical for complete and reliable surveys. This study investigates various methods for combining data from terrestrial laser scanning (TLS), orthophotos, databases of topographic objects, utility databases, tacheometry, and GNSS to assess and improve positioning accuracy and consistency of data in spatial databases. The study highlights the challenges and solutions regarding integrating various datasets to yield a complete and reliable geospatial database for building surveys and the construction and keeping of spatial databases. Input from diversified surveying technologies, such as TLS, GNSS, and orthophotos, offers detailed and precise data necessary to create and update accurate base maps and databases of topographic objects. A complete survey of a structure and its surroundings demonstrates how the synergistic application of diverse data sources helps improve the positioning accuracy and consistency of spatial databases. The results indicate the importance of multi-platform collaboration for high data quality standards in surveying, which is critical for effective planning and implementation of surveying projects. [ABSTRACT FROM AUTHOR]

Lianas, or woody vines, are an often-overlooked component of forested ecosystems. Studies exploring how lianas are influenced by historical land use are scarce, particularly in temperate regions. We aimed to 1) characterize the composition, abundance, and diversity of native and non-native lianas in southwest Virginia, and 2) explain variations in the number of lianas per plot based on historical land use categories and edge or interior location. We collected data on 1303 lianas across 102 study plots, within four historical land use categories: mining, logging, agriculture, or rural townsite. We used historical records and legacy U.S. Geological Survey Topographic Maps from the Historical Topographic Map Collection to define the spatial extents of each land use type. We found 42.5% of measured trees hosted at least one liana. Native lianas were more abundant and diverse than non-native lianas within our study plots. Furthermore, historical land use, particularly in formerly logged plots, was associated with lower liana counts compared to other land use classes. Although knowledge about lianas in the mountainous regions of the Southeastern United States remains limited, it is evident that the complex historical land use legacy in our study region significantly impacts liana prevalence. [ABSTRACT FROM AUTHOR]

Enantiospecific effects play an uprising role in chemistry and technical applications. Chiral molecular networks formed by self‐assembly processes at surfaces can be imaged by scanning probe microscopy (SPM). Low contrast and high noise in the topography map often interfere with the automatic image analysis using classical methods. The long SPM image acquisition times restrain Artificial Intelligence‐based methods requiring large training sets, leaving only tedious manual work, inducing human‐dependent errors and biased labeling. By generating realistic looking synthetic images, the acquisition of real datasets is avoided. Two state‐of‐the‐art object detection architectures are trained to localize and classify chiral unit‐cells in a regular molecular chiral network formed by self‐assembly of linear molecular bricks. The comparison of different architectures and datasets demonstrates that the training on purely synthetic data outperforms models trained using augmented datasets. A Faster R‐CNN model trained solely on synthetic data achieved an excellent mean average precision of 99% on real data. Hence this approach and the transfer to real data show high success, also highlighting the high robustness against experimental noise and different zoom levels across the full experimentally reasonable parameter range. The generalizability of this idea is demonstrated by achieving equally high performance on a different structure, too. [ABSTRACT FROM AUTHOR]

We present an extensive dataset of ice thickness measurements from Jostedalsbreen ice cap, mainland Europe's largest glacier. The dataset consists of more than 351 000 point values of ice thickness distributed along ∼ 1100 km profile segments that cover most of the ice cap. Ice thickness was measured during field campaigns in 2018, 2021, 2022 and 2023 using various ground-penetrating radar (GPR) systems with frequencies ranging between 2.5 and 500 MHz. A large majority of the ice thickness observations were collected in spring using either snowmobiles (90 %) or a helicopter-based radar system (8 %), while summer measurements were carried out on foot (2 %). To ensure accessibility and ease of use, metadata were attributed following the GlaThiDa dataset (GlaThiDa Consortium, 2020) and follow the FAIR (findable, accessible, interoperable and reusable) guiding principles. Our findings show that glacier ice of more than 400 m thickness is found in the upper regions of large outlet glaciers, with a maximum ice thickness of ∼ 630 m in the accumulation area of Tunsbergdalsbreen. Thin ice of less than 50 m covers narrow regions joining the central part of Jostedalsbreen with its northern and southern parts, making the ice cap vulnerable to break-up with future climate warming. Using the point values of ice thickness as input to an ice thickness model, we computed 10 m grids of ice thickness and bed topography that cover the entire ice cap. From these distributed datasets, we find that Jostedalsbreen (458 km2 in 2019) has a present (∼ 2020) mean ice thickness of 154 ± 22 m and an ice volume of 70.6 ± 10.2 km3. Locations of depressions in the map of bed topography are used to delineate potential future lakes, consequently providing a glimpse of the landscape if the entire Jostedalsbreen melts away. Together, the comprehensive ice thickness point values and ice-cap-wide grids serve as a baseline for future climate change impact studies at Jostedalsbreen. All data are available for download at 10.58059/yhwr-rx55 (Gillespie et al., 2024). [ABSTRACT FROM AUTHOR]

This study aimed to investigate the neurophysiological effects of obstructive sleep apnea (OSA) using multi-channel sleep electroencephalography (EEG) through machine learning methods encompassing various analysis methodologies including power spectral analysis, network analysis, and microstate analysis. Twenty participants with apnea-hypopnea index (AHI) ≥ 15 and 18 participants with AHI <15 were recruited. Overnight polysomnography was conducted concurrently with 19-channel EEG. Preprocessed EEG data underwent computation of relative spectral power. A weighted network based on graph theory was generated; and indices of strength, path length, eigenvector centrality, and clustering coefficient were calculated. Microstate analysis was conducted to derive four topographic maps. Machine learning techniques were employed to assess EEG features capable of differentiating two groups. Among 71 features that showed significant differences between the two groups, seven exhibited good classification performance, achieving 88.3 % accuracy, 92 % sensitivity, and 84 % specificity. These features were power at C4 theta, P3 theta, P4 theta, and F8 gamma during NREM1 sleep and at Pz gamma during REM sleep from power spectral analysis; eigenvector centrality at F7 gamma during REM sleep from network analysis; and duration of microstate 4 during NREM2 sleep from microstate analysis. These seven EEG features were significantly correlated with polysomnographic parameters reflecting the severity of OSA. The application of machine learning techniques and various EEG analytical methods resulted in a model that showed good performance in classifying moderate to severe OSA and highlights the potential of EEG to serve as a biomarker of functional changes in OSA. • To classify patients with OSA, 19-channel EEG data during sleep were collected. • Machine learning-based algorithm using EEG exhibited good classification performance. • Sleep EEG has the potential to reflect neurophysiological changes caused by OSA. [ABSTRACT FROM AUTHOR]

The pandemic has been the catalyst for many families with young children to leave the urban environment and settle in rural areas, attracted by discourses that are in tune with the imported and classic 'ideal of rural childhood': natural environment, freedom, autonomy, mutual help, peace and quiet. However, we do not know to what extent this ideal construction of childhood in rural areas corresponds nowadays to the everyday reality of children. In this article we analyse the daily experiences and emotions of children in rural areas of Catalonia (Spain). By applying a qualitative methodology that allows children to express themselves, we provide a critical reading of the current rural areas based on children's lived experiences. The use of the relief map as a methodological tool makes possible to graphically capture children's emotions related to places. The research fills a gap on the current rural childhood experiences in non-Anglo-Saxon contexts, places the concept of the rural ideal in the Catalan environment and provides insight into the diversity of rural childhoods based on children's experiences and emotions. [ABSTRACT FROM AUTHOR]

The state of Sabah in Northern Borneo, Malaysia is surrounded by numerous small tropical islands. However, the recent surge in tourist activities has significantly impacted the biological diversity and natural resources of these islands. This study aims to develop a systematic database for the region to prioritize and preserved the conservation and integrity of these tropical islands. The database was compiled using topographic maps and Google Earth data. Variables such as island size, distance, distribution, and isolation from the Sabah coastline were analysed through maps and scatter plots generated in a GIS environment. The significance of the islands was evaluated across various aspects based on the compiled data. The database identifies a total of 510 islands along Sabah's coastline with 82 located on the west coast and the remaining 428 on the east coast. Approximately 80% of these islands are small with an area of less than 1 km2. Only five islands exceed 100 km2, while smaller islands are predominantly located closer to the coast. The finding indicate that the significance of the islands varies based on factors such as tourist attractions, flora and fauna, marine ecosystems, and national security. The methodology adopted in this study is robust and offers potential for examining island distribution in other regions. [ABSTRACT FROM AUTHOR]

Numerous studies highlight the urgent issue of groundwater depletion due to overexploitation. The United Nations' World Water Development Report (2023) warns that if current extraction rates persist, many regions could face severe groundwater shortages in the coming decades. A 2021 study in Nature reports that about one-third of the world's largest groundwater basins are depleting faster than they are replenished, escalating the risk of critical shortages. In Chickballapur District, excessive agricultural activity has led to an imbalance between groundwater loss and recharge. To address this challenge, a weighted overlay analysis of spatial layers in QGIS was employed to identify potential groundwater zones. This methodical approach involved mapping topographic features and integrating six spatial layers: slope, geomorphology, lithology, drainage, lineament and land use/cover. Each subcategory was ranked and scored based on expert opinion and existing literature. The Central Ground Water Board data was used to validate the resulting groundwater prospect map. The study further included an assessment of groundwater prospects across administrative units by superimposing the administrative boundaries on the final map. Calculating the percentage of land within each groundwater prospect category is crucial for effective groundwater management at the administrative level. [ABSTRACT FROM AUTHOR]

The ICESat-2 Land and Vegetation Height (ATL08) product is a new control point dataset for large-scale topographic mapping and geodetic surveying. However, its elevation accuracy is typically affected by multiple factors. The study aims to propose a new approach to classify ATL08 terrain estimates into different accuracy levels and extract reliable ground control points (GCPs) from ICESat-2 ATL08. Specifically, the methodology is divided into three stages. First, the ATL08 terrain estimates are matched with the raw ATL03 photon cloud data, and the ATL08 terrain estimates are used to fit a continuous terrain curve. Then, using the fitted continuous terrain curve and raw ATL03 photon cloud data, a pseudo-waveform is generated for grading the ATL08 terrain estimates. Finally, all the ATL08 terrain estimates are graded based on the peak characteristics of the generated pseudo-waveform. To validate the feasibility of the proposed method, four study areas from the National Ecological Observatory Network (NEON), characterized by various terrain features and forest types were selected. High-accuracy airborne lidar data were used to evaluate the accuracy of graded ICESat-2 terrain estimates. The results demonstrate that the method effectively classified all ATL08 terrain estimates into different accuracy levels and successfully extracted high-accuracy GCPs. The root mean square errors (RMSEs) of the first accuracy level in the four selected study areas were 0.99 m, 0.51 m, 1.88 m, and 0.65 m, representing accuracy improvement of 51.7%, 58.2%, 83.1%, and 68.8%, respectively, compared to the original ATL08 terrain estimates before classifying. Additionally, a comparison with the conventional threshold-based GCP extraction method demonstrated the superior performance of our proposed approach. This study introduces a new approach to extract high-quality elevation control points from ICESat-2 ATL08 data, particularly in forested areas. [ABSTRACT FROM AUTHOR]

This article takes a closer look at a selection of body-oriented art practices by two artists – Mona Hatoum and Amal Kenawy – to unearth how women artists render female corporeality as a topographical map. Interdisciplinary in nature, these artists' earlier practices explore corporeality to underscore how their gender, far from being a static phenomenon, is in fact a series of processes that are co-constituted via a multitude of material, sociopolitical or geographical contexts. Drawing upon Elizabeth Grosz's theory of corporeal feminism, along with her reiterations of Deleuze and Guattari's 'Body without Organs', I argue that these two artists delineate their own and other women's bodies as corporeal topographies that encapsulate the fluid carnality or porous surfaces of the female body. Moving beyond representational paradigms that approach female bodies as fixed constructs, this article foregrounds the ontologically fluid and non-representational nature of corporeal feminism to understand how these women conceptualize the female body as an open-ended composition that always remains in ceaseless flux. [ABSTRACT FROM AUTHOR]

The advent of high‐performance virtual screening techniques nowadays allows drug designers to explore ultra‐large sets of candidate compounds in search of molecules predicted to have desired properties. However, the success of such an endeavor heavily relies on the pertinence (drug‐likeness and, foremost, chemical feasibility) of these candidates, or otherwise, virtual screening will return valueless "hits", by the garbage in/garbage out principle. The huge popularity of the judiciously enumerated Enamine REAL Space is clear proof of the strength of this Big Data trend in drug discovery. Here we describe a new dataset of make‐on‐demand compounds called the Freedom space. It follows the principles of Enamine REAL Space and contains highly feasible molecules (synthesis success rate over 75 percent). However, the scaffold and chemography analysis revealed significant differences to both the REAL and biologically annotated compounds from the ChEMBL database. The Freedom Space is a significant extension of the REAL Space and can be utilized for a more comprehensive exploration of the synthetically feasible chemical space in hit finding and hit‐to‐lead campaigns. [ABSTRACT FROM AUTHOR]

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Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article presents the results of long-term monitoring of land subsidence in the Bielszowice area (Upper Silesian Coal Basin) using archival maps from the late 19th and 20th centuries, as well as contemporary LIDAR models from 2012 and 2022. The research work conducted included an analysis of subsidence caused by mining activities based on four terrain models: a historical terrain model obtained by digitizing Messtischblätter topographic maps, showing the land surface in 1883, a terrain model obtained by vectorizing Polish topographic maps from 1993, and LIDAR digital terrain models from 2012 and 2022. The study shows that over a period of 139 years, the study area subsided by an average of 9.5 m, which translated into an anthropogenic land subsidence rate of 68 mm/year and a subsidence volume of 100.5 million m3. The greatest subsidence occurred in the northern part of the study area, where basins with depths exceeding 30 m (the maximum subsidence amounted to 36 m) emerged. During the 139 years studied, land subsidence affected the entire area that was built up until 2022. Overall, 38.9% of built-up areas subsided by less than 10 m, 54.0% was subject to subsidence ranging between 10 and 20 m, and subsidence of more than 20 m affected 7.1% of the areas. Such large-scale subsidence in an urbanized area resulted in mining damage to houses and other infrastructure (e.g., railroads, roads); in extreme cases, some structures had to be demolished. Bielszowice is a good example of an area where spatial conflicts have emerged that have been related to the activities of industrial plants on the one hand and the development of urban areas on the other. [ABSTRACT FROM AUTHOR]

Digital elevation models (DEM) are widely used in many hydrologic applications, providing key information about the topography, which is a major driver of water flow in a landscape. Several open access DEMs with near-global coverage are currently available, however, they represent the elevation of the earth's surface including all its elements, such as vegetation cover and buildings. These features introduce a positive elevation bias that can skew the water flow paths, impacting the extraction of hydrological features and the accuracy of hydrodynamic models. Many attempts have been made to reduce the effects of this bias over the years, leading to the generation of improved datasets based on the original global DEMs, such as MERIT DEM and, more recently, FABDEM. However, even after these corrections, the remaining bias still affects flow path delineation in a significant way. Aiming to improve on this aspect, a new vegetation bias correction method is proposed in this work. The method consists of subtracting from the Copernicus DEM elevations their respective forest height but adjusted by correction factors to compensate for the partial penetration of the SAR pulses into the vegetation cover during the Copernicus DEM acquisition process. These factors were calculated by a new approach where the slope around the pixels at the borders of each vegetation patch were analyzed. The forest height was obtained from a global dataset developed for the year 2019. Moreover, to avoid temporal vegetation cover mismatch between the DEM and the forest height dataset, we introduced a process where the latter is automatically adjusted to best match the Copernicus acquisition year. The correction method was applied for regions with different forest cover percentages and topographic characteristics, and the result was compared to the original Copernicus DEM and FABDEM, which was used as a benchmark for vegetation bias correction. The comparison method was hydrology-based, using drainage networks obtained from topographic maps as reference. The new corrected DEM showed significant improvements over both the Copernicus DEM and FABDEM in all tested scenarios. Moreover, a qualitative comparison of these DEMs was also performed through exhaustive visual analysis, corroborating these findings. These results suggest that the use of this new vegetation bias correction method has the potential to improve DEM-based hydrological applications worldwide. [ABSTRACT FROM AUTHOR]

• We assessed posture-dependent changes in cortical motor maps in awake marmosets. • The results demonstrated rapid modulation in forelimb motor maps. • Hindlimb posture was particularly effective in inducing these modulations. • Posture is a crucial variable to be controlled in future studies of motor control. Recent neuroimaging and electrophysiological studies have suggested substantial short-term plasticity in the topographic maps of the primary motor cortex (M1). However, previous methods lack the temporal resolution to detect rapid modulation of these maps, particularly in naturalistic conditions. To address this limitation, we previously developed a rapid stimulation mapping procedure with implanted cortical surface electrodes. In this study, employing our previously established procedure, we examined rapid topographical changes in forelimb M1 motor maps in three awake male marmoset monkeys. The results revealed that although the hotspot (the location in M1 that elicited a forelimb muscle twitch with the lowest stimulus intensity) remained constant across postures, the stimulus intensity required to elicit the forelimb muscle twitch in the perihotspot region and the size of motor representations were posture-dependent. Hindlimb posture was particularly effective in inducing these modulations. The angle of the body axis relative to the gravitational vertical line did not alter the motor maps. These results provide a proof of concept that a rapid stimulation mapping system with chronically implanted cortical electrodes can capture the dynamic regulation of forelimb motor maps in natural conditions. Moreover, they suggest that posture is a crucial variable to be controlled in future studies of motor control and cortical plasticity. Further exploration is warranted into the neural mechanisms regulating forelimb muscle representations in M1 by the hindlimb sensorimotor state. [ABSTRACT FROM AUTHOR]

This paper proposes a nonlinear space dimension reduction method named Optimized Generative Topographic Mapping (OGTM). The Generative Topographic Mapping (GTM) method relies on the training sample set to capture the manifold of objective functions, and the generation of the training sample set causes an enormous computational burden. The choice of GTM hyperparameters has a significant influence on the design results. Traditional research has generally adopted the "cut-and-try" method to determine the corresponding hyperparameters and the best design, leading to wasted computational cost. The proposed OGTM overcomes this issue by minimizing the fitting error between the low-dimensional and high-dimensional samples, and the suitable hyperparameters are directly obtained by minimizing the fitting. In addition, the paper adopts a variable-fidelity sample filtration method to extract the promising regions with fewer sample points. To test and verify the effectiveness of the proposed method, it was then compared with the PCA and EGO methods in RAE2822 airfoil and ONERA M6 wing aerodynamic designs. The results demonstrate that the proposed method could capture the effective design space and generally take less computational cost to find the ideal results in all design optimizations. [ABSTRACT FROM AUTHOR]

Neuroimaging is revolutionizing our ability to investigate the brain's structural and functional properties, enabling us to visualize brain activity during diverse mental processes and actions. One of the most widely used neuroimaging techniques is electroencephalography (EEG), which records electrical activity from the brain using electrodes positioned on the scalp. EEG signals capture both spatial (brain region) and temporal (time-based) data. While a high temporal resolution is achievable with EEG, spatial resolution is comparatively limited. Consequently, capturing both spatial and temporal information from EEG data to recognize mental activities remains challenging. In this paper, we represent spatial and temporal information obtained from EEG signals by transforming EEG data into sequential topographic brain maps. We then apply hybrid deep learning models to capture the spatiotemporal features of the EEG topographic images and classify imagined English words. The hybrid framework utilizes a sequential combination of three-dimensional convolutional neural networks (3DCNNs) and recurrent neural networks (RNNs). The experimental results reveal the effectiveness of the proposed approach, achieving an average accuracy of 77.8% in identifying imagined English speech. [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]

Tectonic processes resulting from solid Earth dynamics control uplift and generate sediment accommodation space via subsidence. Unraveling the mechanism of basin subsidence elucidates the link between deep-Earth and surface processes. The NW Zagros fold–thrust belt resulted from the Cenozoic convergence and subsequent collision between the Arabian and Eurasian plates. The associated Neogene foreland basin includes ∼3 –4 km of synorogenic, mostly nonmarine, clastic sediments, suggesting a strongly subsiding basin inconsistent with the adjacent moderate topographic load. To explain such a discrepancy, we assessed the magnitude of the basin's subsidence with respect to the effect of the surface load and dynamic topography. The lower Miocene isopach map of the Fatha Formation displays a longitudinal depocenter aligned with the orogenic trend. In contrast, the middle–upper Miocene maps of the Injana and Mukdadiya formations illustrate a focused depocenter in the southeastern region of the basin. This rapid basin subsidence in the southeast during the middle–late Miocene was coeval with the Afar plume northward flow beyond the Arabia–Eurasia suture zone in the northwestern segment of the Zagros belt. Based on isopach maps, subsidence curves, and reconstructions of flexural profiles, supported by Bouguer anomaly data and maps of dynamic topography and seismic tomography, we argue for a two-stage basin evolution. The Zagros foreland basin subsided due to the combined loads of the surface topography and the subducting slab during the early Miocene and was affected by dynamic topography due to the Neotethys horizontal slab tear propagation during the middle–late Miocene. This tear propagation was associated with a northward mantle flow above the detached slab segment in the NW and a focused pull on the attached portion of the slab in the SE. [ABSTRACT FROM AUTHOR]

This study is based on the coupling of geological field data with geophysical studies in the geological mapping of the Central Domain of the Mbere Division (Adamawa-Cameroon). The first step was to calculate the residual grid associated with the geophysical data that will be used for the mapping of the basement. The second step before mapping consisted in calculating the analytical signal associated with the residual grid, used to interpret the geological formations under cover or in intrusion. Subsequently, a geological mapping approach based on the categorisation of geophysical signatures into ranges using the Encom Discover program was adopted. This approach is based on the analysis of the relief of geophysical anomalies. The process of categorisation which is done iteratively, takes into consideration in the same georeferenced space, the resolution of the input data, the variation of the field considered and the structural model previously interpreted. The result of the categorisation is then compared to the spatial distribution of outcrop data to build the geological model. The application of this approach to the Central Domain of the Mbere Division led to the production of a synthetic geological map at a scale of 1/75,000. The superposition of this map to the topographic model allows to observe a concordance between the Cretaceous basins represented by their edge faults and the topographic depression zones. The various mineral indicators (primary and secondary) collected during the fieldwork, combined with the geological model (1:75,000 scale) as well as the Cretaceous deposits and vein intrusion models were used to create the mineral indicators and targets map for this sector. All these models can thus be used to underpin mineral exploration projects in the study area. [ABSTRACT FROM AUTHOR]

In order to control desertification, it must first be discovered and classified, and then sustainable agriculture must be promoted to avoid it. It is identified using topographic features of desert areas that vary over time. Therefore, it is necessary to classify and identify desert areas with high accuracy using satellite remote sensing images (SRSI). In this paper, a hybridization was made between Exception transfer learning method, which was used to extract features, and state of art machine learning LightGBM method, which was used to classify (SRSI) dataset approved by Kaggle website. Several preprocessing was also performed on the dataset, such as image cropping to get the important features, as well as performing the data augmentation process to increase the amount of data and make it in different positions. After making a comparison with traditional and previous methods, such as Naive Bayes and K-Nearest Neighbors (KNN), the experiment results showed that LightGBM outperformed them and achieved a high accuracy of 99% and AUC of 100%. [ABSTRACT FROM AUTHOR]

Ras Sudr has garnered significant interest owing to various initiatives aimed at expanding and developing this area. It holds considerable strategic importance, serving as a key development hub in Sinai and a focal point for tourism destination. Moreover, it has a remarkable event with flashfloods which can be utilized for groundwater or direct usage. Integrating geophysical and geospatial analyses to study the surface and subsurface characteristics as well as identify groundwater potential areas in Ras Sudr, west-central Sinai is the main objective of this study. Firstly, geophysical data including gravity and magnetic methods have a crucial importance in qualitative and quantitative interpretation of the subsurface elements. Filtering techniques were implemented to distinguish between regional and residual anomalies. Geophysical data were subjected to the radial average power spectrum technique and 3D Euler deconvolution to identify the depth of the subsurface sources. The structure pattern that characterizes the interested region was defined by employing bandpass filter and edge detection appoarches using residual anomaly maps, regional anomaly maps, tilt derivatives and total horizontal gradient maps reflecting four distinct structural trends; NW-SE parallel to the Gulf of Suez, NEE-SWW parallel to the Syrian arc system, N-S parallel to the the Nile Valley, and NNE-SSW parallel to the Gulf of Aqaba. A basement relief map was constructed using 3D magnetic modelling showing that the depth of the basement ranges from 1.6 to 6.3 km. Secondly, the remote sensing data including Sentienl-2 and SRTM datasets were employed to extract the surface analyses in the GIS environment to develop the occurrence of groundwater potentiality utilizing seven factors; LU-LC, soil, geology, slope, drainage network and lineament density and rainfall data of the study area which were ranked from 1 to 5 and weighted according to their effective contribution to the infiltration of groundwater using AHP-GIS based multi criteria method. The resulting Groundwater Potential zone (GWPZ) was categorized into five zones from poor to excellent and validated using 41 observed wells. A significant quantity of wells was identified in the areas of high potential located to the west of the region, while five wells were situated within the moderate potential zone. Thereby, the GWPZ map identifying locations with viable groundwater resources suitable for habitation, development and economic plans in Sinai for decision makers. [ABSTRACT FROM AUTHOR]

Parcellations of resting-state functional magnetic resonance imaging (rs-fMRI) data are widely used to create topographical maps of functional networks in the human brain. While such network maps are highly useful for studying brain organization and function, they usually require large sample sizes to make them, thus creating practical limitations for researchers that would like to carry out parcellations on data collected in their labs. Furthermore, it can be difficult to quantitatively evaluate the results of a parcellation since networks are usually identified using a clustering algorithm, like principal components analysis, on the results of a single group-averaged connectivity map. To address these challenges, we developed the FunMaps method: a parcellation routine that intrinsically incorporates stability and replicability of the parcellation by keeping only network distinctions that agree across halves of the data over multiple random iterations. Here, we demonstrate the efficacy and flexibility of FunMaps, while describing step-by-step instructions for running the program. The FunMaps method is publicly available on GitHub (https://github.com/persichetti-lab/FunMaps). It includes source code for running the parcellation and auxiliary code for preparing data, evaluating the parcellation, and displaying the results. [ABSTRACT FROM AUTHOR]

The Camellia sinensis (L.) Kuntze agroforestry system has been a dominant driver of land transformation for over a century. However, most previous studies have not captured the dynamics of tea plantations since their inception. To address this research gap, this study investigates 150 years (1876 to 2023) of tea area dynamics and two decades (2001–2022) of tree loss in the Bengal-Dooars region (Jalpaiguri and Alipurduar districts). Various data sources were employed, including Sentinel-2A imagery (10 m), early twentieth century topographic maps at a 1:50,000 scale, and historical records from the British colonial period. Results revealed that the tea area expanded from 331 to 95,800 ha (a 70% increase) during the study period. The 1-km grid-wise spatial analysis indicated that approximately 70% of the increase was attributed to the 60–90% and > 90% grid categories, signifying the expansion of large tea estates. Most tea gardens (TG) are sustained with trees, which can significantly contribute to carbon stock. Tree loss within and outside TG for 2001–2022 was analyzed using annual tree loss data at 30 m from the Global Forest Watch (GFW) platform. Between 2001 and 2022, about 52.7% of tree loss was attributed to TG, although tea covers only 15.3% of the geographic area of the study region. Our findings highlight the historical expansion of tea plantations and their impact on natural land cover. We suggest that future studies address the mean age of TG along with tree fractions for improved carbon estimates. [ABSTRACT FROM AUTHOR]

The morphology of the coastal landscape in transitional environments results from the gradual and complex dynamics of natural processes at different scalarity, capable of elaborating and remodeling the relief. Their arrangements and interactions are reflected in the configuration and evolution of the landscape and waterscape governed by allogenic factors (climate, tectonics and, more recently, anthropogenic) generating autogenic responses in environmental systems. In this sense, several studies have demonstrated the importance of sea level oscillations, sedimentary balance and river‐coastline displacements associated with climate fluctuations during the Quaternary. However, there is still a relative lack of recent research that focuses on the last glacial maximum (LGM) and the Holocene for the eastern Brazilian coast. In this way, the intense morphodynamics between estuarine and deltaic systems could become interpretative keys in the general understanding of these environments worldwide because they are located in a particular context within the connectivity of these geomorphological systems. To investigate these processes between the Jequitinhonha, Pardo and Una Rivers, detailed mapping and geomorphic topographic profiles were carried out using pre‐selected digital elevation models, and fieldworks were carried out on land and water to validate the mappings and to collect samples. Then, they were subjected to geochronological analysis using Optically Stimulated Luminescence and grain size distribution to recognize the depositional age and characterize the surficial cover. It was possible to identify five depositional landscape units: fluvial terraces, fluvial‐marine terraces and three staggered levels of marine terraces. Based on these results, a paleogeographic reconstruction of the evolutionary phases of this eastern sector of Brazil's coast was carried out, chronologically covering the Pleistocene/Holocene transition up to the present. The aim is to understand littoral dynamics as a response to both fluvial adjustments and oscillations of the regional relative sea level. [ABSTRACT FROM AUTHOR]