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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]

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 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]

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]

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]

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]

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]

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]

Droughts may exhibit spatiotemporal heterogeneity at a regional scale. Effective drought assessment and management necessitates identifying homogeneous areas. However, previous studies often simplified clustering analysis by focusing only on a single variable. In this study, we present a novel drought clustering map for the southern plains (SP) region of the United States by integrating the wavelet-entropy approach with k-means clustering algorithm to capture spatiotemporal patterns of drought-related variables across various resolutions while eliminating redundant information. We considered multiple drought indicators and indices including gridded precipitation (P), potential evapotranspiration (PET), normalized difference vegetation index (NDVI), and standardized precipitation evapotranspiration index (SPEI) as well as geographical coordinates and topography map. Through evaluating five different combinations of input datasets, we selected the one demonstrating optimal results based on the Davies–Bouldin and Calinski–Harabasz criteria. In addition to P, PET, and NDVI, including the coordinates and elevation as secondary variables significantly enhanced the clustering performance. Using these variables, the region was subdivided into 21 clusters. Pearson's correlation coefficients for the SPEI between centroid members and corresponding cells within clusters averaged between 0.84 and 0.94. Comparison with an existing cluster map [Drought Risk Atlas (DRA)] for the region revealed that our proposed cluster map showed higher variability between clusters for P, PET, and NDVI, confirming the robustness of the clustering results for drought conditions in the SP. The new clustering framework is expected to provide valuable insights for understanding and addressing drought dynamics in the SP region. [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]

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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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper presents a Compact Integrated Water–Land Survey System (CIWS), which combines a remote sensing camera and a LiDAR module, and proposes an innovative underwater topography retrieval technique based on this system. This technique utilizes high-precision water depth points obtained from LiDAR measurements as control points, and integrating them with the grayscale values from aerial photogrammetry images to construct a bathymetry retrieval model. This model can achieve large-scale bathymetric retrieval in shallow waters. Calibration of the UAV-mounted LiDAR system was conducted using laboratory and Dongjiang Bay marine calibration fields, with the results showing a laser depth measurement accuracy of up to 10 cm. Experimental tests near Miaowan Island demonstrated the generation of high-precision 3D seabed topographic maps for the South China Sea area using LiDAR depth data and remote sensing images. The study validates the feasibility and accuracy of this integrated scanning method for producing detailed 3D seabed topography models. [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 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]

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]

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]

Simulating floods and creating vulnerability maps are essential in urban watershed management systems. The present research aimed to simulate floods and assess floodplain risk in the Khalkai watershed located in Gilan Province, Iran. This river is characterized by extensive agricultural areas and urban areas that become flooded during most rainy seasons. Hence, an accurate assessment of flood extent and behavior is necessary. This study employs HEC-HMS, HEC-RAS, and MIKE 21 for advanced hydrodynamic modeling, focusing on two-dimensional (2D) capabilities essential for urban areas and flood is simulated with probability return periods of 50, 100, and 500 years. The research objectives include comparing HEC-RAS and MIKE 21 to identify the most effective flood simulation model, evaluating the impact of hydrological and hydraulic factors, the influence of GPS topographic mapping compared with DEMs extracted from satellite images and assessing land-use changes on flood behavior. Model accuracy is validated against historical flood data, revealing that MIKE 21 effectively addresses challenges related to grid resolution and flood delineation. Results indicate that while HEC-RAS provides a broader extent of flooding, both models offer critical insights into flood depth and flow characteristics. This research contributes to enhanced flood risk management by illustrating the implications of land-use changes on flood events, offering valuable insights for policymakers in mitigating future flood impacts and developing sustainable urban strategies. Highlights: Manning's Roughness variations highlight the coefficient's critical role in model calibration and accuracy. The MIKE 21 model simulate lower flood extent and high peak discharge versus Hec Ras with the same depths ranging simulation. As dem accuracy is important in determining river cross section the MIKE21 software favor HEC-RAS due to flexibility mesh. [ABSTRACT FROM AUTHOR]

Across multiple and diverse populations, exigent inquiries, and in and out of the classroom, an ethic for listening as a way of being has been central to the methodological and pedagogical practices that undergird narrative mapping. Additionally, the author suggests that in situating the practice of listening as crucial to both communication and healing, we are equally bound to an ethic of creating space. As listeners for the other, we commit to holding space for those silences where participants meet themselves in stillness, a place where they might listen deeply to the self, first. Where narrative mapping creates space for respite in contemplation, participants speak of gaining greater clarity and keener insights about health, healing, and being. The process of listening promotes healing across diverse entities, underscoring the multifaceted nature of narrative mapping, which serves as a research practice, pedagogy, and intervention. [ABSTRACT FROM AUTHOR]

Flash flood hazard assessment is a critical component of disaster risk management, particularly in regions vulnerable to extreme rainfall and climatic events. This study focuses on evaluating the flash flood susceptibility of the Rabigh area, located along the Red Sea coast in Makkah province, Saudi Arabia. Using advanced GIS tools and a spatial multi-criteria analysis approach, the research integrates a variety of datasets, including remotely sensed satellite data, the SRTM Digital Elevation Model (DEM), and topographic indices. The main goal was to produce detailed flood susceptibility maps based on the morphometric characteristics of the region's drainage basins. These basins were delineated and assessed for their flood vulnerability using three distinct modeling techniques, each highlighting different aspects of flood behavior. The results show that the northern basin (Dulaidila) and the central basins (Rabigh, Algud, and Al Nuaibeaa) exhibit the highest flood risk, with significant susceptibility also observed in the southern basins (Ofoq and Saabar). Other basins in the region display moderate susceptibility levels. A key aspect of this analysis was the overlay of the integrated flood susceptibility map with the Topographic Position Index (TPI), a crucial topographic indicator, which helped refine the understanding of flood-prone areas by linking basin morphometry with in-situ topographic features. This study's comprehensive approach offers valuable insights that can be applied to other coastal regions where hydrological and climatic data are scarce, contributing to more effective flood risk mitigation and strategic planning. [ABSTRACT FROM AUTHOR]

Gold nanoparticles (AuNPs) have emerged as promising agents in biomedical applications due to their unique physicochemical properties. This study investigates the cellular interactions of AuNPs with A549 (non-small cell lung adenocarcinoma) and BEAS-2B (normal bronchial epithelial) cell lines. AuNPs were synthesized via the citrate reduction method, resulting in 20, 50, and 70 nm particles. Cells were incubated with AuNPs for increasing durations (30 minutes, 4 hours, and 24 hours). Post-incubation, cells were washed with PBS, air-fixed, and subsequently analyzed using Atomic Force Microscopy (AFM) to obtain detailed topographical maps. AFM imaging revealed distinct interactions between AuNPs and the two cell lines. A549 cells displayed darker regions on the cell surface, indicative of topographical depressions likely resulting from nanoparticle-induced membrane collapse. In contrast, BEAS-2B cells did not exhibit such depressions, which is consistent with the literature that suggests cancer cells are mechanically softer than normal cells. The surface roughness analysis results indicated that the preservation of surface integrity post-fixation validates the air-fixation methodology for obtaining reliable mechanical data from AFM analyses. [ABSTRACT FROM AUTHOR]

Backgrounds: We aim to evaluate changes in pressure pain sensitivity before and after cardiac surgery using topographical sensitivity maps utilizing a pressure algometer. Methods: Pressure pain thresholds over 17 thoracic points and 4 distant pain-free points were assessed in 70 patients (women: 29, age: 67.5 years), before and at 1, 3, and 7 postoperative days. Thoracic topographical pressure pain sensitivity maps were calculated at all follow-ups. Postoperative pain was recorded at each follow-up on a numerical pain rate scale. Results: Postoperative pain intensity decreased from 6.4 (SD 1.0) on the first postoperative day to 5.5 (SD 1.9) on the third and to 4.5 (SD 1.7) on the seventh day (p < 0.001). The mixed-model ANOVA revealed that the lowest pressure pain thresholds were observed one day after surgery, increased slightly during follow-up, and were lower at the xiphoid process. Significant negative correlations between postoperative pain intensity and pressure pain thresholds were observed at each time point in thoracic measures (all, p < 0.01), but not with pressure pain thresholds from distant pain-free areas. Conclusions: Postoperative pain after cardiac surgery can be objectively quantified using algometry. Pressure pain hyperalgesia was associated with the intensity of postoperative pain. [ABSTRACT FROM AUTHOR]

This essay expands concerns of rhetorical narrative theory and econarratology by attending to rhetorical agencies of nonhuman beings within emergent conditions of landscape. Through analysis of two site-specific art pieces--Alan Sonfist's Time Landscape and Agnes Denes's Wheatfield: A Confrontation--it considers ways that interactions between humans, animals, plants, and other living beings produce situated networks of rhetorical engagement. In both sites, artists tell specific stories through affordances of landscape. These human narratives take advantage of and exist alongside various nonhuman rhetorical agencies, which are also shaped by affordances of landscape, though directed to different audiences for different purposes. From this ecological perspective, humans and nonhumans exist within rhetorical, multispecies communities that are shaped by and shape the affordances of topographic form. Rhetoricity and topographic form emerge simultaneously and cooperatively through interactive conditions of interspecies being. This understanding of landscape rhetoricity remains sensitive to radical openness in the rhetorical occasion, noting ways that ecological conditions shape and are shaped by rhetorical interaction. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) are widely used for image analysis tasks, including object detection, segmentation, and recognition. Given the advanced capability, this study evaluates the effectiveness and performance of CNN architecture for analysing Historical Topographic Hardcopy Maps (HTHM) by assessing variations in training and validation accuracy. The lack of research specifically dedicated to CNN's application in analysing topographic hardcopy maps presents an opportunity to explore and address the unique challenges associated with this domain. While existing studies have predominantly focused on satellite imagery, this study aims to uncover valuable insights, patterns, and characteristics inherent to HTHM through customised CNN approaches. This study utilises a standard CNN architecture and tests the model's performance with different epoch settings (20, 40, and 60) using varying dataset sizes (288, 636, 1144, and 1716 images). The results indicate that the optimal operation point for training and validation accuracy is achieved at epoch 40. Beyond epoch 40, the widening gap between training and validation accuracy suggests overfitting. Hence, adding more epochs does not significantly improve accuracy beyond the optimum phase. The experiment also shows that the CNN model obtains a training accuracy of 98%, validation accuracy of 67%, and F1-score overall performance of 77%. The analysis demonstrates that the CNN model performs reasonably well in classifying instances from the HTHM dataset. These findings contribute to a better understanding of the strengths and limitations of the model, providing valuable insights for future research and refinement of classification approaches in the context of topographic hardcopy map analysis. [ABSTRACT FROM AUTHOR]

Flash floods are catastrophic phenomena that pose a serious risk to coastal infrastructures, towns, villages, and cities. This study assesses the risk of flash floods in the ungauged Mekkah province region based on specific and effective morphometric and topographic features characterizing the study region. Shuttle Radar Topography Mission (SRTM) data were employed to construct a digital elevation model (DEM) for a detailed analysis, and the geographical information systems software 10.4 (GIS) was utilized to assess the linear, area, and relief aspects of the morphometric parameters. The ArcHydro tool was used to prepare the primary parameters, including the watershed border, flow accumulation, flow direction, flow length, and stream ordering. The study region's flash flood hazard degrees were assessed using several morphometric characteristics that were measured, computed, and connected. Two different and effective methods were used to independently develop two models of flood vulnerability behaviors. The integrated method analysis revealed that most of the eastern and western parts of the studied province provide high levels of flood vulnerability. Due to it being one of the most helpful topographic indices, the integrated flood vulnerability final map was overlayed with the topographic position index (TPI). The integrated results aided in understanding the link between the general basins' morphometric characteristics and their topographical features for mapping the different flood susceptibility locations over the entire studied province. Thus, this can be applied to investigate a surface-specific reduction plan against the impacts of flood hazards in the studied landscape. [ABSTRACT FROM AUTHOR]

The Digital Elevation Model (DEM) is a typical type of topographic data. Data on elevation are often used as a major information source for analysis and modeling by geographic information systems. Modern continuous changes across space are represented by DEM. Particularly, the European Union Digital Elevation Model (EU-DEM), a new dataset derived from the EU's Copernicus Land Monitoring Service, which features contours developed for all DEMs which are compared with the contours of a topographic map. One of the most important sources of topographical information is the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM). Compared to data from the Ground Control Points (GCPs), the vertical differences in ASTER products are utilized to calculate the Root Mean Squared Error (RMSE). The vertical accuracy of this DEM is assessed to be 13.13 m, which is the RMSE that was determined based on the data collected in the field by GNSS. In addition, the Standard Deviation (SDT) error came in at 2.60 m. After considering the effect of correcting bias, the error findings may range from a minimum of -6.47 m to a maximum of 1.998 m. Variations in RMSE and SDT are less than 0.991 m and 0.960 m, respectively. [ABSTRACT FROM AUTHOR]

Recent advances in auditory attention detection from multichannel electroencephalography (EEG) signals encounter the challenges of the scarcity of available online EEG data and the detection of auditory attention with low latency. To this end, we propose a complete deep auditory generative adversarial network auxiliary, named auditory-GAN, designed to handle these challenges while generating EEG data and executing auditory spatial detection. The proposed auditory-GAN system consists of a spectro-spatial feature extraction (SSF) module and an auditory generative adversarial network auxiliary (AD-GAN) classifier. The SSF module extracts the spatial feature maps by learning the topographic specificity of alpha power from EEG signals. The designed AD-GAN network addresses the need for extensive training data by synthesizing augmented versions of original EEG data. We validated the proposed method on the widely used KUL dataset. The model assesses the quality of generated EEG images and the accuracy of auditory spatial attention detection. Results show that the proposed auditory-GAN can produce convincing EEG data and achieves a significant i.e., 98.5% spatial attention detection accuracy for a 10-s decision window of 64-channel EEG data. Comparative analysis reveals that the proposed neural approach outperforms existing state-of-the-art models across EEG data ranging from 64 to 32 channels. The Auditory-GAN model is available at https://github.com/tasleem-hello/Auditory-GAN-/tree/Auditory-GAN. [ABSTRACT FROM AUTHOR]

This research introduces a new multibeam survey line model that optimizes geometric relationships to improve the efficiency and accuracy of surveys over complex seabed topographies. Since existing multibeam echosounder systems have limitations in handling complex terrains, this study presents an advanced model to enhance data quality and operational efficiency. By strategically designing survey lines and optimizing coverage strategies, this paper achieves the optimal configuration of survey lines for secondary measurements in marine areas, ensuring high precision and reliability of measurement data. Experimental results show that the new model significantly outperforms existing technologies in terms of effective coverage area and measurement accuracy, with an average coverage rate of over 95%, higher than existing models, and a weighted average overlap rate of 3.18%, greatly improving the economic efficiency of measurements by reducing redundant coverage and minimizing operational costs. These findings confirm the advantages of the new model in practical applications and offer valuable technical support for advancing seabed mapping technology. [ABSTRACT FROM AUTHOR]

The European Standard EN 16039:2011 provides guidelines for assessing lake hydromorphology within the Water Framework Directive and includes methods for estimating volume, mean depth, and maximum depth based on statistical models applied to data from topographic maps. We tested the predictive accuracy of these models using independently collected bathymetric data from 35 Swedish lakes. The models had no predictive power, and the maximum depth predictions were inversely correlated with the observed values. The mean absolute percent error was 46% for volume and mean depth, and 85% for maximum depth. The models are flawed and should not be used. [ABSTRACT FROM AUTHOR]

Satellite‐derived digital elevation models (DEMs) and geographic information systems (GIS) offer effective means for both qualitative and quantitative analysis of drainage networks within watersheds. An extensive review of various stream order systems reveals their outdated nature, given advancements in digital technology and the availability of fine to coarse resolutions data. Analysis of open‐source DEMs with resolutions ranging from 12.5 to 225 m, along with topographical maps at scales of 1:50 000 and 1:250 000, was conducted across four physiographically distinct micro‐watersheds of approximately equal size (~300 km2). The steepest descent algorithm (D8) was utilized to derive raster stream networks, applying a channel initiation threshold (CIT) of 900 m2 as a criterion. It was observed that stream order numbers are influenced by map scale, leading to variability in mainstream order extraction across different DEM datasets and topographic maps. To address this issue, an innovative stream order system was proposed to ensure consistent mainstream order regardless of the spatial resolution of DEM data or map scale. Correlation analysis highlighted the importance of considering both spatial resolution and topographic variability in stream order analysis, underscoring the significance of accurate DEM data and landscape characteristics in understanding stream network dynamics. This method of classifying stream orders is recognized by scientists in geography, geology, hydrology and geomorphology for providing crucial information about the size and strength of waterways within stream networks, contributing to effective water management strategies. [ABSTRACT FROM AUTHOR]

The present work was carried out on River Yamuna passing through Delhi-National Capital Region (Delhi-NCR) of India for a stretch of about 125 km to assess the fluvial characteristics in the highly human-dominated region for a period of 200 years with the help of historical maps, topographical maps and satellite data with integration of geographic information system (GIS) environment. Erosion, deposition and channel stability data were analyzed for 1977–1986, 1986–1996, 1996–2006, 2006–2016, and 2016–2022 period. Digital Shoreline Analysis System (DSAS) application was utilized to quantify river channel movement with average channel migration of 22.8 m/year. Westward migration is 4149.2 m maximum in length and eastward migration is about 4083.8 m. The river has migrated a total of 32.26 km2 of area during 1955-2022. The findings indicate that various human activities, such as engineering structures, sand mining, embankments, urbanization, land use/land cover changes, and canal networks, have significantly impacted the river. The DSAS application was also used to predict the position of river channel centerline in future for 2032 and 2042 with channel length of 132.5 and 141.6 km respectively. [ABSTRACT FROM AUTHOR]

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The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a "match" by using comparative microscopy and tactile pattern analysis. The material intrinsic properties and microstructures, as well as the exposure history of external forces on a fragment of forensic evidence have the premise of uniqueness at a relevant microscopic length scale (about 2–3 grains for cleavage fracture), wherein the statistics of the fracture surface become non-self-affine. We utilize these unique features to quantitatively describe the microscopic aspects of fracture surfaces for forensic comparisons, employing spectral analysis of the topography mapped by three-dimensional microscopy. Multivariate statistical learning tools are used to classify articles and result in near-perfect identification of a "match" and "non-match" among candidate forensic specimens. The framework has the potential for forensic application across a broad range of fractured materials and toolmarks, of diverse texture and mechanical properties. The complex jagged trajectory of fractured surfaces of two pieces of forensic evidence is used to recognize a match by using comparative microscopy. Here the authors leveraged statistical analysis of the surface topography to provide a quantitative description of the fracture surface, enabling forensic article comparisons with quantified probabilities. [ABSTRACT FROM AUTHOR]