Search Results (469)

Protected areas increasingly require functional zoning approaches that integrate biodiversity conservation, ecosystem service provision, and human use. This study developed a data-driven functional zoning framework for Jirisan National Park, South Korea, by combining ecosystem service assessment with Self-Organizing Map (SOM)-based spatial typology. Five ecosystem services—water yield, sediment retention, carbon storage, net ecosystem productivity, and habitat quality—were assessed using InVEST, RUSLE, and locally derived carbon-related coefficients. These indicators were integrated with topographic and anthropogenic disturbance variables, including distances to roads and trails. The SOM analysis classified the park into seven functional spatial types with distinct environmental and ecosystem service characteristics. High-altitude areas near major trails were characterized by strong visitor pressure and mismatches among regulating services, whereas interior forest areas showed high multifunctionality and evenness, indicating stable ecosystem service provision. Low-altitude facility-dense and disturbance-adjacent zones showed relatively low habitat quality or service imbalance, highlighting the need for restoration-oriented management. These results suggest that ecosystem service bundles, multifunctionality, and evenness can provide a useful basis for functional zoning and evidence-based management of mountainous national parks. Full article

Population aging has become a major demographic process in modern societies, with its course varying considerably across space. This study examined the scale and dynamics of population aging across Poland’s voivodeships in 2015–2024 and identified its regional patterns. The analysis used data from Statistics Poland’s Local Data Bank for 16 voivodeships and included indicators capturing age composition, demographic dependency, and fertility. The analysis was conducted for 16 Polish voivodeships using data from Statistics Poland’s Local Data Bank for 2015–2024 and indicators describing age structure, demographic dependency, and fertility. An analysis of changes in indicator values over time and Kohonen self-organizing maps (SOM) were applied in two model variants, differing in the measure of population aging adopted. To ensure a consistent direction of interpretation, the variables were appropriately transformed and then standardized. The results indicate spatial variation in the level of population aging and differing dynamics of change during the study period. Four regional profiles were identified, reflecting different patterns of indicators describing age structure, demographic burden, and fertility. Kohonen self-organizing maps were used as an exploratory tool to organize voivodeships according to the similarity of their demographic profiles and to describe changes in their profile assignment over time. From the perspective of sustainability, the identified profiles make it possible to capture territorially differentiated demographic conditions that may be relevant to healthcare, long-term care, regional labor markets, social services, and family policy. The results may support sustainable regional development by providing a basis for designing public policy tailored to the specific characteristics of individual voivodeships. Thus, the study links a multidimensional typology of demographic aging with the need for socially sustainable regional policy. The results suggest that SOM can serve as a useful exploratory tool for visualizing and classifying regional demographic aging profiles. Full article

Pre-Cenomanian Nubia sandstone is recognized one of the most prolific reservoirs in the Gulf of Suez, Egypt. Accurately determining its reservoir rock type (RRT) is crucial for reservoir characterization and modeling, especially when the reservoir is extremely heterogeneous. This study addresses the critical challenge of characterization in extremely heterogeneous reservoirs by introducing a novel integrated workflow that bridges the gap between traditional sedimentological geology, traditional x-y approaches, and advanced machine learning methods. To achieve this, this study utilizes sedimentological core description, routine core analysis, and conventional well log data from two wells (well A and well B) located in the southern Gulf of Suez, Egypt. The results demonstrate that the complete Nubia interval in the southern Gulf of Suez can be separated into seven distinct lithofacies (LF1–LF7). The first six lithofacies comprise various types of sandstone, while the seventh is composed of shale. The traditional techniques used to predict the RRTs show that the normalized reservoir quality index (NRQI) was the most effective method for predicting the Nubia rock types. The machine learning K–means clustering and self-organizing map (SOM) techniques utilizing raw log data and principal component analysis (PCA) can properly predict the Nubia reservoir rock types. The reservoir quality ranges from poor to very good; well A is dominated by moderate reservoir quality, while well B exhibits predominantly very good reservoir quality. This discernible difference in reservoir quality between the two wells is probably attributed to post-depositional diagenetic processes and variations in sandstone texture. Full article

In this study, we propose a biologically inspired Self-Organizing Map-based Artificial Visual System (SOM-AVS) for unsupervised orientation detection in static images. By combining a biologically motivated front-end visual processing module with an unsupervised SOM layer, the proposed system captures key characteristics of early-stage visual processing, including localized orientation-sensitive responses and structured feature organization. The model enables the structure of distinct orientation-related representations without requiring labeled data, forming organized response patterns across the neural map. Experimental results demonstrate robustness under various conditions, including noise corruption, restricted perceptual experience, and limited training samples. Furthermore, the model shows adaptive behavior when exposed to new stimuli after initial training, indicating its potential to reflect experience-dependent adjustments in representation. These findings suggest that SOM-AVS provides a useful framework for exploring self-organization mechanisms in artificial visual systems and for developing biologically inspired perception models. Full article

The formation of direction-selective visual circuits is thought to involve the progressive refinement of synaptic connections during development. In biological visual systems, patterned spontaneous activity, such as retinal waves, has been proposed to provide structured spatiotemporal activity that contributes to the refinement of visual pathways before mature sensory experience is fully established. Motivated by this view of activity-dependent circuit organization, this study develops a Self-Organizing Map-Based Artificial Visual System, termed SOM-AVS, to examine how organized connectivity may emerge in a motion direction-detecting circuit. In the proposed model, local motion-detecting units extract elementary direction-related responses from visual input and project them to a global motion direction layer represented by a self-organizing map. Connections are progressively reshaped by winner selection and local cooperative updating, allowing initially unstructured connections to gradually acquire organized direction preference. After repeated exposure to generated retinal-wave-like activity data, the SOM layer develops topographically arranged regions corresponding to distinct motion directions. This organization suggests that direction-related response domains can emerge from activity-dependent self-organization without externally imposed labels. The proposed model should be regarded as a biologically motivated computational abstraction rather than a direct physiological reproduction of retinal-wave-driven circuit development. Within this scope, the model provides a computational framework for examining how retinal-wave-like activity and self-organizing plasticity may contribute to the formation of motion direction-related connectivity, offering a possible developmental interpretation for bio-inspired visual motion processing. Full article

Rurbanization and peri-urbanization are among the most dynamic territorial processes affecting metropolitan regions in Morocco, particularly within the Casablanca–Settat region. These transformations, driven by rapid urban growth, demographic pressure, and socio-economic change, generate complex transitional spaces between rural and urban environments. In this context, the present study proposes a hybrid methodology for detecting, classifying, and analyzing the rural–urban continuum by using remote sensing data and artificial intelligence techniques. The approach integrates Sentinel-2 satellite imagery, spectral indices, Global Human Settlement Layer datasets, and socio-demographic indicators derived from the Moroccan census. Two models, Self-Organizing Maps (SOM) and Graph Neural Networks (GNN), were applied to classify territories into four categories: urban, peri-urban, rurban, and rural. Model outputs were combined with expert-based decision rules to improve classification robustness and interpretability. The SOM model achieved up to $89.3\%$ agreement with expert classifications and a Cohen’s Kappa coefficient of $0.842$, demonstrating strong interpretability and consistency, while the GNN model reached $53\%$ agreement and effectively modeled spatial dependencies and neighborhood interactions. Diachronic analysis between 2014 and 2024 revealed a $54\%$ increase in peri-urban municipalities, a $24\%$ decrease in rurban territories, and a decline in rural municipalities, highlighting intensified urban sprawl and fragmentation of agricultural landscapes. Beyond its scientific contribution, this study provides a valuable decision-support framework for urban planners, environmental agencies, and policy makers involved in territorial governance and sustainable development. It can support land-use planning, monitoring of urban sprawl, protection of agricultural lands, and the implementation of adaptive territorial policies aimed at improving the resilience and sustainability of rurban environments. Full article

The authentication of Algerian honey represents a critical challenge for the valuation of national biological patrimony. The present review provides a comprehensive synthesis of existing literature regarding Algerian honeys, emphasizing their diverse botanical origins and complex chemical profiles across seven distinct biogeographical regions, while proposing an innovative Foodomics and AI-driven roadmap to secure geographic authenticity and sustainable rural development. Such evidence underscores the necessity of transitioning from this classical analytical framework toward the emerging ‘Foodomics’ paradigm. By integrating advanced technologies like DNA metabarcoding and molecular fingerprinting, the establishment of a proposed ‘digital passport’ is proposed as a strategic solution to secure Protected Geographical Indications (PGI). Beyond technical innovation, this evolution is presented as a vital socio-economic necessity to ensure the sustainability of rural beekeeping and the international competitiveness of the industry. Ultimately, bridging established data with a molecular roadmap ensures that the biological prestige of this natural heritage is preserved for future generations. Beyond chemical and botanical analyses, this roadmap also incorporates Chemometric Modeling as a cognitive system. By applying techniques such as self-organizing maps (SOMs) and principal component analysis (PCA). This combination ensures highly accurate classification and supports the implementation of a sustainable digital passport system for the local honey industry. Full article

Electroencephalography (EEG) offers a noninvasive, high-temporal-resolution modality for estimating mental workload. However, session-to-session variability limits the generalizability of workload classifiers, and few systematic cross-session evaluations are reported in the literature. This study systematically evaluates domain adaptation methods for cross-session mental workload classification using the publicly available COG-BCI dataset within an evaluation framework that may guide future studies on EEG-based classification models. We make four contributions: (i) integration of Optimal Transport (OT) with Graph Neural Networks (GNNs) to model spatial relationships and align feature distributions under strict session-wise separation; (ii) a data-centric evaluation pipeline incorporating Self-Organizing Map (SOM) visualizations for data exploration and a heuristic loss function for model selection; (iii) a strict cross-session protocol examining the effects of graph construction, feature selection, and data splits; and (iv) comparison of OT with CORrelation ALignment (CORAL) and GNN with EEGNet. Incorporating OT improved test accuracies across all experimental configurations. SOM visualizations confirmed enhanced feature alignment after OT. Our results highlight the potential of OT for mitigating session-to-session variability and underscore the importance of a data-centric approach and rigorous cross-session evaluation when developing classifiers for complex cognitive state estimation. Future work should explore semi-supervised OT strategies and scalable implementations for real-time applications. Full article

Shallow groundwater in the Dongting Lake area is an important resource for domestic, agricultural, and industrial use, and its quality is essential for regional sustainable development and public health. Therefore, effective protection of this resource is urgently needed. In this paper, we integrate Positive Matrix Factorization (PMF) and Self-Organizing Map (SOM) machine-learning algorithms to conduct an in-depth analysis of the distribution, sources, and risks of toxic elements in shallow groundwater along the southern shore of Dongting Lake. The results indicate that Fe and Mn in the groundwater of the study area are at a severe pollution level, while As is at a light pollution level. The model analysis identified four pollution sources: natural sources (Fe, Mn) accounting for 31.33%, agricultural production (Zn) for 18.96%, traffic-mining mixed source (Pb, Cu, Cd) for 32.67%, and mineral dissolution-redox driven (As) for 17.04%. The average concentrations of Fe and Mn exceeded the standard limits. Although the carcinogenic metal Cd did not pose a health risk, the health risk value of As exceeded the maximum acceptable level, which requires serious attention. The PMF model quantified four potential sources of toxic elements, while SOM was used as a complementary nonlinear clustering tool to examine the consistency of the PMF-derived source contribution patterns. The integrated PMF–SOM framework, together with spatial distribution and geochemical evidence, improved the interpretability and robustness of source identification. Full article

Aiming to address the limitations in accuracy and reliability of land use classification models when encountering complex and variable land use features, a novel method for land use classification (4D-SOMKS) is developed, which constructs a low redundancy four-dimensional (4D) spectral feature system with biophysical meaning of distinct land surface properties and organizes pixel-level features in a topology-preserving space using the Self-Organizing Map (SOM), further groups the SOM neurons into spectrally coherent clusters through K-means, and uses a small number of labeled samples only for semantic assignment of the resulting clusters rather than for pixel-level supervised model training. Empirical research in the Bayannur and Hong Lake Basins (HLB) have revealed the driving role of key spectral indices in classification. High overall classification accuracies were achieved, reaching 98.45% and 98.55% respectively, with robust performance across evaluation metrics including precision, recall, F1-score, and the Kappa coefficient. The results show that 4D-SOMKS achieves high accuracy and robustness while significantly reducing reliance on large-scale labeled data, providing an effective avenue to improve the accuracy and reliability of land use classification under spatiotemporal dynamic changes. Full article

Owing to their reliance on detailed mathematical modeling, traditional control methods encounter challenges such as high control complexity and low precision when applied to high-speed flight vehicle control under strongly disturbed atmospheric conditions. To address this limitation, this study introduces a data-driven neural network mapping approach into the field of flight vehicle control. By excavating the underlying patterns in operational data and leveraging the nonlinear mapping capability of neural networks, accurate prediction and generation of control commands are achieved, thereby eliminating the dependence on precise mathematical models and offering a novel solution for complex control problems. Building on this foundation, a self-organizing map (SOM) radial basis function (RBF) neural network is proposed. Leveraging the competitive learning mechanism of SOM, it performs adaptive clustering on input samples, dynamically optimizes the number of clusters to determine the number of hidden-layer nodes in RBF, and adopts the SOM cluster centers as the centers of RBF basis functions. This design enables the one-click data-driven determination of both the number of nodes and their corresponding center vectors, significantly simplifying the network structure design process. Meanwhile, to address inherent limitations of this network, such as suboptimal output weights, unoptimized width functions, and the inherent drawbacks of the traditional Moth-Flame Optimization (MFO) algorithm, an Adaptive Enhanced Moth-Flame Optimization (AEMFO) algorithm is developed, drawing inspiration from biological swarm intelligence. By integrating strategies such as adaptive spiral update and elite opposition-based learning, it balances the global exploration and local exploitation capabilities, and performs targeted optimization of the RBF width parameters and output-layer weights. This optimization significantly enhances the accuracy of the network in mapping attitude-control commands in strongly disturbed environments, providing robust support for the stable attitude control of high-speed flight vehicles. Finally, simulation results demonstrate that the proposed method achieves high control accuracy for flight vehicle attitude control under strongly disturbed environments. Full article

Receptor modeling of ambient pollutant concentrations plays a central role in urban air quality assessments. This study proposes an integrated framework combining Self-Organizing Maps (SOM), Positive Matrix Factorization (PMF), and Time-Series Analysis (TSA) for a comprehensive evaluation of urban air pollution patterns and source dynamics. The methodology was applied to multi-annual air quality and meteorological datasets (2009–2018) from two major Bulgarian cities, Plovdiv and Varna. The SOM was used for assessing the overall parameter patterns of the cities, leading to a clear clustering of the site samples on the map. Thus, PMF was run separately for the two sites, identifying a different number of sources (three and four, respectively). Traffic-related and sulfur-rich combustion sources were identified in both cities, while a crustal/resuspended dust factor was observed only in Varna. TSA revealed distinct temporal behaviors among source types. Traffic-related aerosol contributions decreased in both cities (−5.14% yr⁻¹ in Plovdiv; −9.30% yr⁻¹ in Varna), whereas sulfur-rich combustion factors showed increasing trends (+4.64% yr⁻¹ and +2.97% yr⁻¹, respectively). Traffic fresh exhaust factors exhibited pronounced seasonal variability and significant weekday–weekend differences in both cities. The integrated SOM–PMF–TSA framework enhanced source interpretability and temporal characterization, providing a robust approach for urban air quality assessment and supporting targeted air pollution management strategies. Full article

Multi-temporal orbital satellite imagery is an alternative for measuring behavioral patterns or trends in different physiognomies through vegetation indices (VIs) and Spectral Linear Mixture Models (SLMMs). In this study, time series of Landsat 7/8/9 and Sentinel-2 have been used to classify a considerable quantity of areas spread over the São Paulo state from 2021 to 2024. Because the large amount of samples considered in our analysis, self-organizing maps (SOMs) have been applied as a convenient method to group similar satellite image time series samples with respect to a certain vegetation index or green vegetation fraction (VEG). Since every dataset area belongs to different types of physiognomies, each cluster has been labeled according to the plurality technique. Additionally, we obtained the mean spectral behavior of the VIs and VEG in the 2021–2024 seasonal cycle of all samples. The results showed similar variations from the rainy to the dry season for most of the physiognomies. On the other hand, this research indicates that the proposed method for classification the Brazilian areas spread over the São Paulo state is consistently good, obtaining the best performance (quantization error) associated with Normalized Difference Vegetation Index (NDVI) time series samples. Full article

Rapid urbanization has profoundly reshaped land use patterns and intensified pressures on ecosystem structures, thereby exacerbating trade-offs and synergies among ecosystem services (ESs). Understanding ecosystem service trade-offs, synergies, and their attribution mechanisms is critical for balancing ecological conservation and regional sustainable development in rapidly developing regions. This study quantified provisioning, regulating, supporting, and cultural ecosystem services in Shandong Province from 2000 to 2020 using the InVEST model and spatial analysis. An integrated framework combining Pearson correlation and bagplot analysis was developed to identify linear and nonlinear ES trade-offs and synergies, while the XGBoost–SHAP model was applied to quantify the relative contributions of natural and socioeconomic drivers. Ecosystem service bundles were further identified using a self-organizing map to delineate spatially functional zones. The results showed that: (1) Provisioning and cultural services increased markedly, whereas regulating and supporting services generally declined. Spatially, provisioning services were concentrated in the western plains, regulating and supporting services in the central mountains and eastern hills, and cultural services in urban areas. (2) Strong trade-offs emerged between provisioning services and most regulating/supporting services, while regulating and supporting services exhibited pronounced synergies. Cultural services reflected a generally compatible relationship with other ESs. (3) Regulating and supporting services were primarily shaped by natural conditions and land use patterns, whereas provisioning and cultural services were more strongly driven by socioeconomic factors. (4) SOM clustering identified four major functional zones, the ecological core zone, the ecological degraded zone, the food production zone, and the urban composite zone, each corresponding to differentiated ecosystem functions and development trajectories. The integrated framework provides a scientific basis for ecosystem-service-oriented spatial zoning and targeted management strategies to reconcile ecological protection and urbanization in rapidly developing regions. Full article

Rheumatoid arthritis (RA) is a chronic autoimmune disease characterised by persistent joint inflammation and systemic immune dysregulation. While bone marrow activation has been linked to RA pathogenesis, direct access to bone marrow tissue for progenitor analysis remains limited by ethical and technical constraints. Analysis of progenitor cells in peripheral blood can serve as a surrogate reflecting bone marrow activation. In this study, we analysed peripheral blood cells from 12 RA patients and 9 healthy controls using high-dimensional spectral flow cytometry with a nine-marker panel (CD45, CD31, CD235, CD133, CD34, CD105, CD271, CD90, PDPN). Flow Self-Organizing Map (FlowSOM) clustering identified 20 distinct cell populations. Additionally, a complementary flow cytometry panel was used to assess CD31 expression on immune subsets in peripheral mononuclear cells (PBMCs) from 9 RA and 9 healthy donors of this cohort. RA patients showed increased CD45⁺CD31⁻ immune cells, but not their putative progenitors. Conversely, putative CD45⁺CD31^int progenitors and CD45⁺CD31^int mature cells were reduced, along with CD31 expression on T cells. Levels of CD235a⁺ putative erythroid precursors and CD45⁺CD31⁺ progenitors were significantly increased in RA patients. Three putative stromal cell populations were detected in circulation. Together, these findings reveal expanded erythroid precursor populations and reduced CD31 expression on T cells in RA. Our data underscore broad systemic alterations in cellular homeostasis in RA patients. In conclusion, our results suggest that the loss of CD31 expression on immune cell precursors plays a role in age-associated immune remodelling and immune activation in RA and provides the rationale for further studies on erythroblast differentiation and the functional role of erythroblasts in chronic inflammation. Full article