Search Results (10,154)

In recent years, the sustainable use of marine space has become increasingly important due to the growing number of competing activities. To minimize conflicts and environmental impacts, the co-location of these activities in multi-use marine areas is essential. Several approaches have been proposed to evaluate synergies and incompatibilities among marine uses, but most of them are either complex, case-specific, or lack full automation, which can limit their broader applicability. In this context, the paper presents an enhanced version of a Decision Support Tool for identifying optimal combinations of co-located activities. The tool is based on a multi-criteria analysis integrating technological, environmental, social, and economic factors, and it automatically provides an optimal configuration through a guided, user-friendly procedure. Experts select options for each activity and criterion from drop-down menus, and the tool automatically assigns scores and combines them to rank the different activity combinations. Implemented in an Excel sheet with a wizard interface, it can be easily completed by experts from different fields, who can assign weights to each criterion through discussion. The tool’s general structure also allows its use by policy-makers and consultants, supporting informed decision-making and facilitating science–policy interaction. Full article

Solar flares are among the most powerful and dynamic events in the solar system, resulting from the sudden release of magnetic energy stored in the Sun’s atmosphere. These energetic bursts of electromagnetic radiation can release up to ${10}^{32}$ erg of energy, impacting space weather and posing risks to technological infrastructure and therefore require accurate forecasting of solar flare occurrences and intensities. This study evaluates the predictive performance of three machine learning algorithms—Random Forest (RF), k-Nearest Neighbors (kNN), and Extreme Gradient Boosting (XGBoost)—for classifying solar flares into four categories (B, C, M, X). Using 13 parameters of the SHARP dataset, the effectiveness of the models was evaluated in binary and multiclass classification tasks. The analysis utilized 8 principal components (PCs), capturing 95% of data variance, and 100 PCs, capturing 97.5% of variance. Our approach uniquely combines binary and multiclass classification with different levels of dimensionality reduction, an innovative methodology not previously explored in the context of solar flare prediction. Employing a 10-fold stratified cross-validation and grid search for hyperparameter tuning ensured robust model evaluation. Our findings indicate that RF and XGBoost consistently demonstrate strong performance across all metrics, benefiting significantly from increased dimensionality. The insights of this study enhance future research by optimizing dimensionality reduction techniques and informing model selection for astrophysical tasks. By integrating this newly acquired knowledge into future research, more accurate space weather forecasting systems can be developed, along with a deeper understanding of solar physics. Full article

Currently, an active debate is underway among the academic community, urban planners, and policymakers regarding optimal models of urban development, given that the majority of the population now resides in cities. One concept under discussion is the 15 min city, which posits that all urban residents should be able to reach key, frequently used services within a 15 min walk or cycle. Although the literature suggests numerous potential benefits, debate persists about whether such cities would be optimal from the standpoint of sustainable development objectives and residents’ quality of life. The ongoing discussion also concerns the extent to which existing cities are capable of aligning with this concept. This is directly linked to the actual spatial distribution of individual services within the city. The literature indicates a research gap arising from a shortage of robust case studies that would enable a credible assessment of the practical implementation of this idea across diverse cities, countries, and regions. This issue pertains to Poland as well as to other countries. A desirable future scenario would involve comprehensive mapping of all cities, with respect to both the spatial distribution of specific services and related domains such as the quality and coherence of linear infrastructure. This article presents an analysis of the spatial accessibility of basic urban services in the context of implementing the 15 min city concept, using the city of Koszalin (Poland) as a case study. This city was selected due to its representative character as a medium-sized urban centre, both in terms of population and area, as well as its subregional functions within Poland’s settlement structure. Koszalin also exhibits a typical spatial and functional layout characteristic of many Polish cities. In light of growing challenges related to urbanisation, climate change, and the need to promote sustainable mobility, this study focuses on evaluating access to services such as education, healthcare, retail, public transport, and green spaces. The use of Geographic Information System (GIS) tools enabled the identification of spatial variations in service accessibility across the city. The results indicate that only 11% of Koszalin’s area fully meets the assumptions of the 15 min city concept, providing pedestrians with convenient access to all key services. At the same time, 92% of the city’s area offers access to at least one essential service within a 15 min walk. Excluding forested areas not intended for development increases these values to 14% and 100%, respectively. This highlights the extent to which methodological choices in assessing pedestrian accessibility can shape analytical outcomes and the interpretations drawn from them. Moreover, given this article’s objective and the adopted analytical procedure, the assumed pedestrian walking speed is the key parameter. Accordingly, a sensitivity analysis was conducted, comparing the reference scenario (4 km/h) with alternative variants (3 and 5 km/h). This approach demonstrates the extent to which a change in a single parameter affects estimates of urban-area coverage by access to individual services reachable on foot within 15 min. The analysis reveals limited integration of urban functions at the local scale, highlighting areas in need of planning intervention. This article proposes directions for action to improve pedestrian accessibility within the city. Full article

Rainfall stations in small and medium-sized river basins in China are sparsely distributed and unevenly spaced, resulting in insufficient spatial representativeness of precipitation data and posing challenges to the accuracy of flood forecasting. Spatial interpolation methods for rainfall data are a key tool for bridging the gap between discrete rainfall station data and continuous surface rainfall data; however, their applicability in flood forecasting for small and medium-sized river basins with sparse rainfall stations requires further investigation. Taking the Hezikou basin as the study area and focusing on the Liuxihe model, this study analyzes the distribution characteristics of the seven rainfall stations in the basin and the interpolation effectiveness of the original Thiessen Polygon Interpolation (THI) method in the model. It compares and discusses the applicability of the THI, the Inverse Distance Weighting (IDW) method, and the Trend Surface Interpolation (TSI) method in flood forecasting for this basin. Different rainfall station distribution scenarios (full coverage, upstream only, downstream only, single rainfall station) were set up to study the performance differences in each method under extremely sparse conditions. The results indicate that, under the sparse condition of only 0.0068 rainfall stations per square kilometer in the Hezikou basin, IDW interpolation yields the best flood forecasting results, with model Nash–Sutcliffe Efficiency (NSE) values all above 0.85, Kling–Gupta Efficiency (KGE) values exceeded 0.78, and the Peak Relative Error (PRE) was controlled within 0.09, significantly outperforming THI and TSI. Additionally, as rainfall station sparsity increased, IDW exhibited the smallest decline in performance, showing a weak negative correlation (p ≤ 0.05) between prediction performance and rainfall station sparsity, demonstrating stronger adaptability to sparse scenarios. When station information is extremely limited, IDW performs more stably than THI and TSI in terms of certainty coefficients (NSE, KGE) and flood peak error control. The Inverse Distance Weighting method (IDW) can provide reliable rainfall spatial interpolation results for flood forecasting in small and medium-sized basins with sparse rainfall stations. Full article

This study presents an adaptive conservation framework for the Fujia Residence, a vernacular house located in the tropical region of Hainan, China. The primary aim of this study is to develop a symbiotic design approach that integrates GIS spatial analysis, modular design, and community participation to ensure the long-term sustainability, cultural preservation, and resilience of vernacular housing in tropical regions. The framework leverages GIS data, including elevation, temperature distribution, ecological features, and water systems, to inform the design, ensuring it is both disaster-resilient and environmentally adaptive. The modular design components, such as prefabricated structures and flexible spaces, offer a sustainable and adaptable solution to meet residents’ needs while preserving cultural heritage. The community participation model, incorporating a revenue-sharing mechanism and government subsidies, encourages the long-term involvement of local residents in the maintenance and protection of the residence. The outcome of this study demonstrates that the proposed framework provides a replicable model for cultural heritage preservation in tropical and economically underdeveloped regions, offering a scalable and adaptable solution to address the challenges of vernacular housing conservation in similar contexts. Full article

Very-Low Earth Orbit Synthetic Aperture Radar (VLEO SAR) satellites, defined as SAR satellites operating at orbital altitudes 350 km or below, offer distinct technical advantages compared to conventional SAR satellites. Equipped with a high-resolution SAR payload, the Haishao-1 (HS-1) satellite was successfully launched on 4 December 2024. According to publicly available information, the HS-1 satellite represents the world’s first VLEO SAR satellite and has successfully demonstrated 1-m resolution Stripmap mode imaging with continuous azimuth coverage. Through an analysis of the HS-1 satellite’s system parameters and imaging results, this paper comprehensively explores the advantages of VLEO SAR satellites over traditional orbit SAR satellites, particularly in terms of enhanced resolution, reduced payload costs, and improved constellation deployment capabilities. VLEO SAR satellites possess significant advantages, including the potential for higher-resolution imagery and lower-cost payload designs, positioning them for extensive application prospects in fields such as space-based military reconnaissance, natural resource surveying, and natural disaster monitoring. Full article

This article argues that sustainability governance in small-scale regenerative aquaculture arises less from formal regulation than from the relational, ethical, and temporal labour of practitioners. Based on an ethnographic study of Câr-y-Môr, Wales’s first community-owned regenerative ocean farm, the research combines over 250 h of participant observation, 25 interviews, and document analysis with transdisciplinary humanities-informed sustainability science (THiSS). The study shows how technocratic environmentalism, reliant on auditing, reporting, and standardised procedures, often clashes with the shifting rhythms of tides, weather, and the embodied work of marine labour. Ethnography uniquely reveals the embodied knowledge, improvisation, and moral commitment through which practitioners continually remake governance, translating bureaucratic rules into ecologically and socially meaningful practice. The findings demonstrate that adaptive governance requires recognition of local and experiential expertise, proportionate regulatory frameworks, and protected spaces for experimentation and learning. Seen in this way, sustainability shifts from a fixed goal to a relational process. When governance learns from practice and care is recognised as a form of knowledge, it becomes more adaptive, situated, and responsive, revealing both the constraints of technocratic control and the possibilities of care-based policy and practice. Full article

Alpha helical transmembrane proteins are a specific type of membrane proteins that consist of helices spanning the entire cell membrane. They make up almost a third of all transmembrane (TM) proteins and play a significant role in various cellular activities. The structural prediction of these proteins is crucial in understanding how they behave inside the cell and thus in identifying their functions. Despite their importance, only a small portion of TM proteins have had their structure determined experimentally. Inter-helical residue contact is one of the most successful computational approaches for reducing the TM protein fold search space and generating an acceptable 3D structure. Most current TM protein residue contact predictors use features extracted only from protein sequences to predict residue contacts. However, these features alone deliver a low-accuracy contact map and, as a result, a poor 3D structure. Although there are models that explore leveraging features extracted from protein 3D structures in order to produce a better representative contact model, such an approach remains theoretical, assuming the structure features are available, whereas in reality they are only available in the training data, but not in the test data, whose structure is what needs to be predicted. This presents a brand new transfer learning paradigm: training examples contain two sets of features, but test examples contain only one set of the less informative features. In this work, we propose a novel approach that can train a model with training examples that contain both sequence features and atomic features and apply the model on the test data that contain only sequence features but not atomic features, while still improving contact prediction rather than using sequence features alone. Specifically, our method, AT-TSVM, employs Active Transfer for Transductive Support Vector Machines, which is augmented with transfer, active learning and conventional transductive learning to enhance contact prediction accuracy. Results from a benchmark dataset show that our method can boost contact prediction accuracy by an average of 5 to 6% over the inductive classifier and 2.5 to 4% over the transductive classifier. Full article

To facilitate easy accessibility to the Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) results for the geoscientific community, multiple institutions have successively developed mass anomaly grid products including mass concentration (mascon) grids; these were provided at the Gravity Information Service (GravIS) portal. However, an assessment of their consistency for studying large-scale mass redistribution and transport in Earth’s system is still not available. Here, we compare three major mascon solutions separately from the Center for Space Research (CSR), the Jet Propulsion Laboratory (JPL), the Goddard Space Flight Center (GSFC) and GravIS products based on the Combination Service for Time-variable Gravity fields (COST-G) by analyzing the Antarctic Ice Sheet (AIS) mass changes in four aspects. Our results demonstrate that: (1) the four datasets exhibit strong consistency on the entire AIS mass change time series, with the largest difference occurring in the Antarctic Peninsula; (2) mass trend estimates show better agreement over longer periods and larger regions, but differences with a percentage of 20–40 exist during the late stage of GRACE and the whole GRACE-FO timespan; (3) notable discrepancies arise in the annual statistics of the Eastern AIS in 2016, leading to inconsistency on the sign of annual AIS mass change; (4) good agreement can be seen among these interannual mass variations over the AIS and its three subregions during 2003–2023, excluding the period from mid-2016 to mid-2018. These findings may provide key insights into improving algorithms for mascon solutions and grid products towards refining their applications in ice mass balance studies. Full article

The safety of animal-related agricultural products has been a hot issue. To obtain a multi-feature representation of porcine bodies for detecting their health, visible and infrared imaging is valuable for exploiting multiple images of a porcine body from different modalities. However, the direct registration of visible and infrared porcine body images can easily cause the dislocation of structural information and spatial position, due to different resolutions and spectrums of multi-source images. To overcome the problem, a novel multi-source image feature representation method based on contour angle orientation is proposed and named Gabor-Ordinal-based Contour Angle Orientation (GOCAO). Moreover, a visible and infrared porcine body image registration method is described and named GOCAO-Rough to Fine (GOCAO-R2F). First, contour and texture features of the porcine body are acquired using a Gabor filter with variable scales and an ordinal operation. Second, feature points in contours are obtained by curvature scale space (CSS), and the main orientation of each feature point is determined by GOCAO. Third, modified scale-invariant feature transform (MSIFT) features are received on the main orientation and registered with bilateral matching. Finally, accurate registrations are extracted by R2F. Experimental results show that the proposed registration algorithm accurately matches multi-source images for porcine body multi-feature detection and is capable of achieving lower average root-mean-square error than current registration algorithms. Full article

We propose an information-theoretic encryption scheme consisting of a four-dimensional chaotic map driver in combination with a prediction model using an LSTM neural net to generate a keystream, which was limited only after passing a test based on the largest Lyapunov exponent (LLE). Our security analysis used a permutation phase to remove spatial redundancy, which was followed by an invertible DNA cross-diffusion procedure based on RGB channels. The removal of uncertainty and redundancy was measured using Shannon’s entropy (7.99–8.00 bits per channel), pixel intercorrelation, and differential analysis (NPCR ≈ 99.6%, UACI ≈ 33.3%). In key space analysis (order ≈ 2³⁸⁴), self-right veneering with complete encryption validity was demonstrated in perfect decryptability. We explain how chaos verification enhances the statistical goodness of keystreams and provide ablations that separate each element’s influence on entropy and decorrelation. Full article

Multi-exposure image fusion (MEF) aims to generate a well-exposed image by combining multiple photographs captured at different exposure levels. However, deep learning-based approaches are often highly dependent on the quality of the training data, which can lead to inconsistent color reproduction and loss of fine details. To address this issue, this study proposes a variance-driven hybrid MEF framework based on a U-Net architecture, which adaptively balances structural and chromatic information. In the proposed method, the variance of randomly cropped patches is used as a training weight, allowing the model to emphasize structurally informative regions and thereby preserve local details during the fusion process. Furthermore, a fusion strategy based on the geometric color distance, referred to as the Chroma scale, in the LAB color space is applied to preserve the original chroma characteristics of the input images and improve color fidelity. Visual gamma compensation is also employed to maintain perceptual luminance consistency and synthesize a natural fine image with balanced tone and smooth contrast transitions. Experiments conducted on 86 exposure pairs demonstrate that the proposed model achieves superior fusion quality compared with conventional and deep-learning-based methods, obtaining high JNBM (17.91) and HyperIQA (70.37) scores. Overall, the proposed variance-driven U-Net effectively mitigates dataset dependency and color distortion, providing a reliable and computationally efficient solution for robust MEF applications. Full article

With the expansion of unmanned aerial vehicles (UAVs) into complex three-dimensional (3D) terrains for reconnaissance, rescue, and related missions, traditional path planning methods struggle to meet multi-constraint and multi-objective requirements. Existing swarm intelligence algorithms, limited by the “no free lunch” theorem, also face challenges when the standard Information Acquisition Optimizer (IAO) is applied to such tasks, including low exploration efficiency in high-dimensional search spaces, rapid loss of population diversity, and improper boundary handling. To address these issues, this study proposes a Multi-Strategy Enhanced Information Acquisition Optimizer (MEIAO). First, a Levy Flight-based information collection strategy is introduced to leverage its combination of short-range local searches and long-distance jumps, thereby broadening global exploration. Second, an adaptive differential evolution operator is designed to dynamically balance exploration and exploitation via a variable mutation factor, while crossover and greedy selection mechanisms help maintain population diversity. Third, a globally guided boundary handling strategy adjusts out-of-bound dimensions to feasible regions, preventing the generation of low-quality paths. Performance was evaluated on the CEC2017 (dim = 30/50/100) and CEC2022 (dim = 10/20) benchmark suites by comparing MEIAO with eight algorithms, including VPPSO and IAO. Based on the mean, standard deviation, Friedman mean rank, and Wilcoxon rank-sum tests, MEIAO demonstrated superior performance in local exploitation of unimodal functions, global exploration of multimodal functions, and complex adaptation on composite functions while exhibiting stronger robustness. Finally, MEIAO was applied to 3D mountainous UAV path planning, where a cost model considering path length, altitude standard deviation, and turning smoothness was established. The experimental results show that MEIAO achieved an average path cost of 253.9190, a 25.7% reduction compared to IAO (341.9324), with the lowest standard deviation (60.6960) among all algorithms. The generated paths were smoother, collision-free, and achieved faster convergence, offering an efficient and reliable solution for UAV operations in complex environments. Full article

Humanity is opening up to cosmos in all its dimensions and areas of knowledge. In this context, Paraguay, due to its multicultural uniqueness and two official languages (Spanish and Guaraní), represents an emblematic example of how legends, traditions and its rich mythology are important in their sociocultural translation to space. They coexist as a link between the past and the future. Guarani traditions, mythology, their relationship with nature and their translation into cosmos are amazing and complex aspects of indigenous cultural heritage, which are still present in many Paraguayan initiatives. This article compiles and integrates the cultural information about this topic, which is dispersed in different sources, and frames it in its corresponding context. Likewise, it unequivocally confirms how this intangible heritage is crucial as a living roadmap and a contemporary challenge that should be preserved as it guides individuals, communities and initiatives to implement earth and space science and education. Guaraní cultural heritage offers valuable insights into how indigenous worldviews continue to shape contemporary ecological and cultural practices in our modern intersection pathway to the cosmos. Full article

A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities. Full article