Search Results (67,577)

Cultural ecosystem services (CES) represent the non-material relationships between people and nature, yet their intangible nature poses challenges for spatial planning and policy integration. This study examines CES in Gauja National Park, Latvia, focusing on symbolic, sacred, educational, and cultural heritage values—types often underrepresented in CES assessments. Using a Participatory Geographic Information Systems (PGIS) approach, a map-based public survey was conducted via ArcGIS Survey123, enabling respondents to mark and describe places of personal significance. While widely applied internationally, PGIS remains rarely used in Latvia, especially in planning and municipal decision-making. This study explores the use of the PGIS method for the assessment of CES, serving as a pilot application to test its suitability and potential for integration into spatial planning. Points of value were successfully georeferenced and reflect diverse associations. While well-known heritage sites were commonly mentioned, respondents also identified personally meaningful locations, sometimes situated outside the park’s formal boundaries. The findings highlight both the strengths and limitations of digital participatory methods, including issues related to response rates, accessibility, and digital literacy. The study demonstrates that mapping CES with PGIS can offer valuable insights for inclusive landscape governance and supports the incorporation of local perspectives into spatial planning. Full article

Time-Weighted Dynamic Time Warping (TWDTW), adapted from speech recognition, is used in agricultural remote sensing to model crop growth, particularly under limited ground sample conditions. However, most related studies rely on full-season or empirically selected features, overlooking the systematic optimization of features at each observation time to improve TWDTW’s performance. This often introduces a large amount of redundant information that is irrelevant to crop discrimination and increases computational complexity. Therefore, this study focused on maize as the target crop and systematically conducted mapping experiments using Sentinel-1/2 images to evaluate the potential of integrating TWDTW with optimally selected multi-source time series features. The optimal multi-source time series features for distinguishing maize from non-maize were determined using a two-step Jeffries Matusita (JM) distance-based global search strategy (i.e., twelve spectral bands, Normalized Difference Vegetation Index, Enhanced Vegetation Index, and the two microwave backscatter coefficients collected during the maize jointing to tasseling stages). Then, based on the full-season and optimal multi-source time series features, we compared TWDTW with two widely used temporal machine learning models in agricultural remote sensing community. The results showed that TWDTW outperformed traditional supervised temporal machine learning models. In particular, compared with TWDTW driven by the full-season optimal multi-source features, TWDTW using the optimal multi-source time series features improved user accuracy by 0.43% and 2.30%, and producer accuracy by 7.51% and 2.99% for the years 2020 and 2021, respectively. Additionally, it reduced computational costs to only 25% of those driven by the full-season scheme. Finally, maize maps of Yangling District from 2020 to 2023 were produced by optimal multi-source time series features-based TWDTW. Their overall accuracies remained consistently above 90% across the four years, and the average relative error between the maize area extracted from remote sensing images and that reported in the statistical yearbook was only 6.61%. This study provided guidance for improving the performance of TWDTW in large-scale crop mapping tasks, which is particularly important under conditions of limited sample availability. Full article

With the widespread deployment of 5G technology, high-precision positioning in global navigation satellite system (GNSS)-denied environments is a critical yet challenging task for emerging 5G applications, enabling enhanced spatial resolution, real-time data acquisition, and more accurate geolocation services. Traditional methods relying on single-source measurements like received signal strength information (RSSI) or time of arrival (TOA) often fail in complex multipath conditions. To address this, the positional encoding multi-scale residual network (PE-MSRN) is proposed, a novel deep learning framework that enhances positioning accuracy by deeply mining spatial information from 5G channel state information (CSI). By designing spatial sampling with multigranular data and utilizing multi-source information in 5G CSI, a dataset covering a variety of positioning scenarios is proposed. The core of PE-MSRN is a multi-scale residual network (MSRN) augmented by a positional encoding (PE) mechanism. The positional encoding transforms raw angle of arrival (AOA) data into rich spatial features, which are then mapped into a 2D image, allowing the MSRN to effectively capture both fine-grained local patterns and large-scale spatial dependencies. Subsequently, the PE-MSRN algorithm that integrates ResNet residual networks and multi-scale feature extraction mechanisms is designed and compared with the baseline convolutional neural network (CNN) and other comparison methods. Extensive evaluations across various simulated scenarios, including indoor autonomous driving and smart factory tool tracking, demonstrate the superiority of our approach. Notably, PE-MSRN achieves a positioning accuracy of up to 20 cm, significantly outperforming baseline CNNs and other neural network algorithms in both accuracy and convergence speed, particularly under real measurement conditions with higher SNR and fine-grained grid division. Our work provides a robust and effective solution for developing high-fidelity 5G positioning systems. Full article

Mangroves in Sri Lanka provide critical ecosystem services, yet they have undergone significant changes due to anthropogenic and natural drivers. This study presents the first national-scale assessment of mangrove dynamics in Sri Lanka using remote sensing techniques. A total of 4670 Landsat images from Landsat 5, 7, 8, and 9 were selected to detect mangrove distribution, changes in extent, and structure and stability patterns from 1987 to 2022. A Random Forest classification model was applied to elucidate the spatial changes in mangrove distribution in Sri Lanka. Using national-scale data enhanced mapping accuracy by incorporating region-specific spectral and ecological characteristics. The average overall accuracy of the maps was over 96.29%. The total extent of mangroves in 2022 was 16,615 ha, representing 0.25% of the total land of Sri Lanka. The results further indicate that, at the national scale, mangrove extent increased from 1989 to 2022, with a net gain of 1988 ha (13.6%), suggesting a sustained and continuous recovery of mangroves. Provincial-wise assessments reveal that the Eastern and Northern Provinces showed the largest mangrove extents in Sri Lanka. In contrast, the Colombo, Gampaha, and Kalutara districts in the Western Province showed persistent declines. The top mangrove spatial structure and stability districts were Jaffna, Trincomalee, and Gampaha, while the most degraded mangrove districts were Batticaloa, Colombo, and Kalutara. This study offers critical insights into sustainable mangrove management, policy implementation, and climate resilience strategies in Sri Lanka. Full article

Cattle fever ticks, Rhipicephalus microplus and Rhipicephalus annulatus, transmit Babesia pathogens, the causative agents of cattle fever worldwide. Although eradicated from the United States, increasing incursions of cattle fever ticks in Texas have put considerable strain on the Cattle Fever Tick Eradication Program (CFTEP). The movement of ticks between wildlife and cattle along the Texas–Mexico border complicates control efforts. Here, we used habitat suitability models, the literature, and quantitative survey data to project the distributions of native and introduced ungulates in Texas. Specifically, we used habitat suitability models and downscaling to estimate potential overlap between cattle and free-ranging white-tailed deer (Odocoileus virginianus) and nilgai (Boselaphus tragocamelus) that may carry cattle fever ticks and generate maps of estimated tick exposure risk. Our findings suggest that the introduction and spread of exotic ungulates, such as the nilgai antelope, may facilitate the expansion of cattle fever ticks within and beyond the historical quarantine zone established in 1943. The increasing range of nilgai populations could enhance landscape connectivity for cattle fever ticks in sensitive areas along the Texas–Mexico border. By combining these models with cattle inventory data, we provide tools to help the CFTEP better allocate resources, monitor tick populations, prevent incursions, and implement early interventions. Full article

Optical mapping, nanotechnology-based multielectrode arrays and automated patch-clamp allow transmembrane voltage mapping with high spatial resolution, as well as L-type calcium and inward rectifier currents measurements using native mammalian cardiomyocytes. However, these methods are limited to in vitro and ex vivo experiments, while magnetocardiography (MCG) might offer a novel approach for non-invasive preclinical safety assessments of new drugs in intact and even conscious rodents by reconstructing the ventricular action potential waveform (rVAPw) from MCG signals. Objective: This study aims to assess the feasibility of rVAPw reconstruction from MCG signals in Guinea pigs (GPs) and validate the results by comparison with simultaneously recorded epicardial ventricular monophasic action potentials (eVMAP). Methods: Unshielded MCG (uMCG) data of 18 GPs, investigated anaesthetized and awake at ages of 5, 14, and 26 months using a 36-channel DC-SQUID system, were analyzed to calculate rVAPw from MCG’s current arrow map. Results: Successful rVAPw reconstruction from averaged MCG showed good alignment with eVMAP waveforms. However, some rVAPw displayed incomplete or distorted repolarization at sites with lower MCG amplitude. Conclusions: 300-s uMCG averaging allowed rVAPw reconstruction in intact GPs. Occasionally distorted rVAPw suggests the need for dedicated MCG devices development, with higher density of optimized vector sensors, and modelling tailored for small animal hearts. Full article

To promote sustainable development and guide the responsible use of natural ecosystems, the United Nations introduced the concept of ecosystem accounting. Ecosystem services are key components of ecosystem accounting. Water-related ecosystem services (ES) are of primary importance for Armenia due to relatively dry climate, and dependence on irrigation water for agriculture. This study aims to conduct a pilot-level quantitative scoping assessment and mapping of key water-related regulating ES in accordance with the SEEA-EA guidelines, and to offer recommendations to initiate their accounting in Armenia. We used three Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) models—Seasonal Water Yield, Sediment Delivery Ratio, and Urban Flood Risk Mitigation. Input data for these models were sourced from global and national databases, as well as ESRI land cover datasets for 2017 and 2023. Government-reported data on river flow and water consumption were used to assess the ES supply–use balance. The results show that natural ecosystems contribute between 11% and 96% of the modeled ES, with the strongest impact on baseflow supply and erosion prevention. The average current erosion is estimated at 2.3 t/ha/year, and avoided erosion at 46.4 t/ha/year. Ecosystems provide 93% of baseflow, with an average baseflow index of 34%, while on bare ground it is only 3%. Changes in land cover from 2017 to 2023 have resulted in alterations across all assessed ES. Comparison of total water flow and baseflow with water consumption revealed water-deficient provinces. InVEST models show their general operability at the scoping phase of ecosystem accounting planning. Advancing ES accounting in Armenia requires model calibration and validation using local data, along with the integration of InVEST and hydrological and meteorological models to account for the high diversity of natural conditions in Armenia, including terrain, geological structure, soil types, and regional climatic differences. Full article

Background: Adeno-associated viral (AAV) vectors are the leading platform for gene therapy, but common delivery routes show limited spread to distal cortical structures, hence the utility of direct, intrathalamic infusions for broader transgene distribution. In this preliminary study, we recapitulate previous studies targeting the thalamus as a conduit to achieve cortical transgene spread and showcase novel data evaluating biodistribution of a green fluorescent protein (GFP) using cryo-fluorescence tomography (CFT). For the first time in nonhuman primates (NHPs) and coupled with magnetic resonance imaging (MRI)-guidance, we demonstrated the application of CFT as a powerful tool to map out vector distribution in the NHP brain. Methods: Briefly, a single thalamic infusion was performed in African green monkeys using ClearPoint’s navigational platform to deliver an AAV serotype 2 vector containing a GFP payload. Transgene biodistribution was assessed in the left and right hemispheres using CFT and histological analysis, respectively. Results: Infusions were successfully performed with sub-millimetric target accuracy and with minimal error, achieving ~86% thalamic coverage with the largest infusion volume. Histology confirmed the presence of the GFP transgene, with the strongest signal in the cerebral gray/white matter and internal capsule, while CFT allowed for the three-dimensional detection of the transgene starting at the site of infusion and spreading to multiple cortical regions. Conclusions: These findings suggest that by combining MRI-guided technology with CFT imaging, it is feasible to map whole-brain gene biodistribution in NHPs. This proof-of-concept study bridges the gap between cellular microscopy and MRI-guidance to provide a complete picture of disease and treatment with clinical applicability. Full article

To identify SSR markers associated with both quantitative and qualitative traits in Perilla, we analyzed a total of 68 individuals from an F₂ population derived from a cross between WPC06-339 (weedy var. crispa) and WPF17-049 (weedy var. frutescens) using 40 SSR primer sets. The genetic diversity of these markers ranged from 0.464 to 0.676, with a mean value of 0.607. Correlation analysis of 13 morphological traits (4 qualitative, 9 quantitative) revealed significant positive correlations among three leaf-related traits and two inflorescence-related traits. Association analysis involving 40 SSR markers and the 13 morphological traits identified 39 significant marker–trait associations, comprising 18 SSR markers associated with 11 morphological traits. Among these SSR markers, 12 were associated with two to five quantitative or qualitative traits. Additionally, 10 SSR markers were significantly associated with three qualitative traits, while 15 SSR markers were associated with eight quantitative traits. Notably, GBPFM179, KNUPF59, and KNUPF167 were significantly associated with multiple quantitative or qualitative traits. GBPFM179 and KNUPF182 exhibited the highest R² values, of 0.38, for stem color and days to maturity, respectively. These SSR markers demonstrate the potential for use in marker-assisted selection in Perilla breeding programs aimed at enhancing leaf or seed productivity through the selection of both quantitative and qualitative traits. Full article

Long noncoding RNAs (lncRNAs) have emerged as key regulators of gene expression during seed development and physiology. This review examines the diverse roles of lncRNAs in key stages of seed development, including embryogenesis, maturation, dormancy, germination, and aging. It integrates the current understanding of the biogenesis and classification of lncRNAs, emphasizing their functional mechanisms in seeds, particularly those acting in cis and trans. These mechanisms include the scaffolding of polycomb and SWI/SNF chromatin remodeling complexes, the guidance of RNA-directed DNA methylation, the ability to function as molecular decoys, and the modulation of small RNA pathways via competitive endogenous RNA activity. This review highlights the regulatory influence of lncRNAs on abscisic acid (ABA) and gibberellin (GA) signaling pathways, as well as light-responsive circuits that control dormancy and embryonic root formation. Endosperm imprinting processes that link parental origin to seed size and storage are also discussed. Emerging evidence for epitranscriptomic modifications, such as m6A methylation, and the formation of LncRNA–RNA-binding protein condensates that maintain resting states and coordinate reserve biosynthesis are also reviewed. Advances in methodologies, including single-cell and spatial transcriptomics, nascent transcription, direct RNA sequencing, and RNA–chromatin interaction mapping, are expanding the comprehensive lncRNA landscape during seed development and germination. These advances facilitate functional annotation. Finally, possible translational research applications are explored, with a focus on developing lncRNA-based biomarkers for seed vigor and longevity. Full article

Methane emissions from freshwater reservoirs represent a globally relevant greenhouse gas source, which are estimated to range from 3% to 10% of all global anthropogenic methane emissions. However, there is high uncertainty in estimating reservoir emissions on local to global scales due to a combination of data paucity in key regions, particularly in the Southern Hemisphere, and challenges monitoring emission pathways. The key to improved spatially and temporally representative estimation of emission rates is to better understand the primary drivers of emission pathways, in particular, ebullition. We examine ebullition from 15 freshwater storages located in the Southern Hemisphere subtropical (South East Queensland) and temperate (Tasmania) regions using a combination of optical methane detection to develop the high-resolution mapping of ebullition zones and floating chamber incubation within ebullition zones to quantify areal emission rates. We demonstrate the equivalent water level, through air pressure or physical water level change, as a key driver of ebullition and examine the implications for spatially and temporally representative estimation of reservoir emissions. This study represents the largest broadscale ebullition survey undertaken across Australian temperate and subtropical reservoirs. The study findings are of broad relevance to scientists and corporate and government entities navigating the complexities of estimating greenhouse gas emissions from reservoirs and related policy instruments. Full article

The topic of energy security is relevant for EU countries that pay great attention to new renewable energy sources and sustainable environmental development. The purpose of the article is to analyze and group EU countries by their level of energy security. To achieve this goal, general scientific methods and Kohonen maps (Deductor Studio package) were used. This article analyzes the state of energy security in EU countries, energy imports, the development of renewable energy sources, energy consumption, and energy security challenges. As a result of grouping EU countries according to Kohonen maps, three clusters were identified: countries with high, medium, and relatively low levels of energy security. The approach demonstrated the effectiveness of neural network-based clustering in revealing structural differences in national energy systems. The findings indicate that to strengthen energy security across the European Union, it is important to adopt differentiated approaches tailored to the specific needs of each cluster. The practical significance of the article lies in clustering EU countries by their energy security potential, which provides a basis for developing and implementing appropriate policies to enhance energy security. Recommendations for strengthening energy security were proposed for each cluster. Full article

Fish diseases are one of the primary causes of economic losses in aquaculture. Existing deep learning models have progressed in fish disease detection and lesion segmentation. However, many models still have limitations, such as detecting only a single type of fish disease or completing only a single task within fish disease detection. To address these limitations, we propose FDMNet, a multi-task learning network. Built upon the YOLOv8 framework, the network incorporates a semantic segmentation branch with a multi-scale perception mechanism. FDMNet performs detection and segmentation simultaneously. The detection and segmentation branches use the C2DF dynamic feature fusion module to address information loss during local feature fusion across scales. Additionally, we use uncertainty-based loss weighting together with PCGrad to mitigate conflicting gradients between tasks, improving the stability and overall performance of FDMNet. On a self-built image dataset containing three common fish diseases, FDMNet achieved 97.0% mAP50 for the detection task and 85.7% mIoU for the segmentation task. Relative to the multi-task YOLO-FD baseline, FDMNet’s detection mAP50 improved by 2.5% and its segmentation mIoU by 5.4%. On the dataset constructed in this study, FDMNet achieved competitive accuracy in both detection and segmentation. These results suggest potential practical utility. Full article

This paper investigates a new subclass of bi-univalent analytic functions defined on the open unit disk in the complex plane, associated with the subordination to $1+$ sinz. Coefficient bounds are obtained for the initial Taylor–Maclaurin coefficients, with a particular focus on the second- and third-order Hankel determinants. To illustrate the non-emptiness of the proposed class, we consider the function $\sqrt{1+tanhz}$, which maps the unit disk onto a bean-shaped domain. This function satisfies the required subordination condition and hence serves as an explicit member of the class. A graphical depiction of the image domain is provided to highlight its geometric characteristics. The results obtained in this work confirm that the class under study is non-trivial and possesses rich geometric structure, making it suitable for further development in the theory of geometric function classes and coefficient estimation problems. Full article

A lightweight enhanced detection model named YOLO-LDFI is proposed in this study for ship target detection in SAR images, aiming to improve detection accuracy and deployment efficiency under complex maritime environments. Based on YOLOv11n, the model incorporates four architectural improvements in a progressive manner: linear deformable convolution (LDConv), deformable context-aware attention mechanism (DCAM), frequency-adaptive dilated convolution detection head (FAHead), and Inner-EIoU. Experiments conducted on the public SAR ship detection dataset HRSID demonstrate that the proposed model achieves an AP50 of 90.7% and an F1 score of 87.0%, with only 2.63 M parameters and a computational complexity of 6.7 GFLOPs. Ablation experiments validate the contribution of each component to improved feature alignment, reduced background interference, and more accurate target localization. Overall, the results indicate that the proposed model offers a reasonable trade-off between detection performance and computational efficiency in SAR ship detection tasks. Full article