The Mae-Kok River in northern Thailand is a transboundary river system influenced by multiple upstream and downstream anthropogenic pressures. This study presents a multi-matrix assessment of metal contamination by integrating river water, soils, and sediments with enrichment factor (EF) analysis, multivariate statistical approaches, and arsenic speciation. Concentrations of arsenic, lead, and nickel in river water frequently exceeded international guideline values, while EF results indicate substantial anthropogenic enrichment, particularly in the upstream reach of the river. Hierarchical cluster analysis, principal component analysis, and positive matrix factorization identify coherent groupings of elements (As, U, Co, Ni, and Cu) characterized by polymetallic geochemical associations. These patterns are compatible with upstream anthropogenic inputs involving sulfide-rich materials but do not uniquely identify specific point sources. In contrast, zinc and cadmium exhibit enrichment patterns and statistical character distinct from the polymetallic element group, consistent with diffuse anthropogenic influences such as agricultural activities, although these associations are indirect. Differences in clustering between river water and soil-sediment matrices highlight contrasting metal behavior between short-term aqueous transport and longer-term depositional accumulation. Synchrotron-based X-ray absorption spectroscopy shows that arsenic occurs predominantly as arsenate [As(V)], with localized enrichment of arsenite [As(III)] indicating spatial variability in redox conditions. Chromium is present mainly as Cr(III), suggesting limited mobility under prevailing environmental conditions. Overall, the results indicate a multi-source contamination regime shaped by overlapping upstream and local anthropogenic influences. This integrated approach improves understanding of metal behavior in transboundary river systems and provides a scientific basis for future monitoring and management efforts.

The Alocasia–Colocasia group (Araceae) has an intricate taxonomic history, with traditional generic circumscriptions of the two major genera, Alocasia and Colocasia, being challenged by phylogenetic evidence. Current phylogenetic and morphological evidence together confirm that the two lineages—Alocasia evrardii-A. vietnamensis and Colocasia affinis-C. fallax—are distinct and coherent groups whose current generic placements in the tribe Colocasieae stand in contrast to the established classification. We here formally describe two new genera to accommodate this situation: Vandua and Cryptocasia. Vandua is characterised by its tuberous, epiphytic or lithophytic habit; production of bulbiliferous stolons; a pistillate zone flanked by two clusters of pistillodes, with the basal cluster specialised into a basal sterile zone; absence of interspersed staminodes; ovaries with 4–5 ovules in parietal placentation; and single-seeded berries. Cryptocasia differs from Colocasia sensu stricto by its small plant size, the presence of a basal sterile zone, and a pistillate zone lacking interspersed staminodes. Recognition of these genera resolves long-standing inconsistencies between phylogenetic and morphological evidence, providing a preliminary circumscription of Alocasia and Colocasia and a firmer basis for establishing a natural systematic framework for the tribe.

Plant diversity loss driven by human activities and climate change underscores the urgent need for effective ex situ conservation through botanical gardens. Yet many national botanical garden (NBG) systems remain fragmented due to the lack of objective spatial planning. Here we present a data-driven framework to optimise the size and configuration of NBG systems by integrating species coverage, distinctiveness, and complementarity. As a case study, we combine 49,308 living collection records from 29 botanical gardens with 1.56 million native plant distribution records across China. Similarity-based clustering analysis reveals six geographically coherent groups, and a complementarity ranking maximises taxonomic coverage while minimising avoidable duplication across institutional collections. This analysis prioritises 16 existing gardens and identifies 5 additional regions for new NBGs, together potentially safeguarding 73.2% of China's vascular flora. Our framework improves the efficiency, representativeness, and resilience of ex situ conservation, providing a transferable model for strengthening botanical garden systems in biodiversity-rich countries. • A data-driven framework for optimising national ex situ conservation networks • A prioritisation scheme integrating distinctiveness and complementarity to reduce redundancy • An optimised national botanic garden network for China safeguarding 73.2% of the vascular flora • A transferable model for strengthening botanic garden systems in biodiversity-rich countries

Sour cherry (Prunus cerasus L.) exhibits remarkable phenotypic and genetic diversity, historically classified into morello and amarelle groups based on fruit pigmentation. However, the genetic foundations of these categories remain unclear. Here, we combine 10 SSR loci with S-RNase genotyping to evaluate genetic diversity, phylogenetic relationships, and population structure across 27 Hungarian and internationally relevant sour cherry cultivars. The marker panel proved highly informative, yielding 78 SSR alleles and 17 S-alleles, with a multilocus probability of identity of 3.97 × 10-7. Phylogenetic reconstruction, minimum spanning networks, Bayesian clustering, and PCoA consistently resolved five genetically coherent groups that largely reflect known breeding histories and regional selection rather than fruit color classes. High- and low-anthocyanin cultivars frequently co-occurred within clades, demonstrating that pigmentation does not track genome-wide relatedness. To investigate proximate molecular mechanisms, we profiled flavonoid-pathway gene expression in contrasting accessions (VN-1 and 'Pipacs 1'). VN-1 exhibited strong late-ripening induction of structural genes and MYB10, whereas 'Pipacs 1' showed attenuated late activation and higher early expression of ANR, LAR, and UFGT, suggesting divergent transcriptional regulation and pathway flux between the two genotypes. Together, these results indicate that fruit color variation is largely independent of the multilocus relatedness patterns captured by our marker set, and is likely influenced by lineage-specific regulatory differences.

Quantifying the health of civil infrastructure using sensor data remains challenging, as degradation-related signals are typically weak and obscured by dominant environmental and operational effects. In structural health monitoring (SHM), this often results in sensor measurements that are highly periodic or intermittent, while long-term degradation manifests only as subtle drift. This study addresses the problem of extracting meaningful proxies for structural health from such data. We propose monotonicity as a guiding principle, operationalized through absolute Spearman's rank correlation between sensor values and time. Two complementary methods are introduced. First, subgroup discovery is employed to identify structurally coherent groups of sensors that exhibit significantly elevated monotonicity, enabling the construction of robust health proxies through aggregation. Second, we present Latent Monotonic Feature Discovery (LMFD), a data-driven method inspired by equation discovery, which searches for arithmetic combinations of sensors that yield monotonic behaviour even when individual sensors are predominantly non-monotonic. The methods are evaluated on a two-year monitoring dataset from a Dutch concrete highway bridge comprising strain gauges, geophones, and temperature sensors. Results show that meaningful monotonic proxies can be derived both from naturally monotonic sensor subgroups and from composite features constructed from periodic signals. The proposed approach provides indirect yet interpretable indicators of structural health and offers a principled way to uncover latent degradation trends in long-term SHM data.

The rise of atmospheric oxygen fundamentally transformed Earth's surface environment and enabled the evolution of complex life. However, the processes driving long-term oxygen fluctuations remain poorly resolved, partly from limited proxy resolution and temporal coverage. Trace element (TE) concentrations in sedimentary pyrite offer a robust archive of redox conditions in ancient oceans and their linkage to atmospheric oxygen levels. Here we integrate high-resolution geochemical data from pyrite grains spanning 3.5 billion years with machine learning to reconstruct atmospheric oxygen evolution. We identify two coherent TE groups representing redox-sensitive and hydrothermal influences. Our results reveal that the long-term, secular trend of atmospheric oxygen is tightly coupled with biosphere expansion, whereas superimposed short-term fluctuations are influenced by tectonic events, including supercontinent assembly and breakup. Specifically, we show that primary oxygenation events (GOE and NOE) correlate strongly with biological expansion. Episodes of prolonged oxygenation broadly overlap with continental assembly, reflecting enhanced weathering, nutrient fluxes, and organic carbon burial, whereas supercontinent breakup phases are commonly associated with more reducing conditions, likely linked to increased volcanic emissions and diminished net biospheric oxygen. This reconstruction not only refines the temporal dynamics of Earth's redox evolution but also highlights the interconnected roles of biological productivity, tectonics, ocean chemistry, and Earth-system processes in shaping planetary habitability. These findings provide a comprehensive framework for understanding Earth's atmospheric evolution and inform models of environmental change on early Earth and other habitable planets.

Atlantic Forest alluvial ecosystems experience strong environmental filtering driven by seasonal flooding and hydrological variability, shaping their structure and functioning. This study investigated how leaf and demographic traits organize functional groups and structure functional diversity in an 11.5-ha urban fragment in Guarapuava, Paraná, southern Brazil. We surveyed 103 permanent plots (1.03 ha), assessing 34 species based on six traits (specific leaf area – SLA, leaf length – LL, leaf dry matter content – LDMC, leaf thickness – LT, mortality rate (M), and relative growth rate (RGR)). Functional groups were identified using Ward’s clustering and PCoA, group comparisons were tested using Generalized Linear Models (GLMs), and functional diversity was assessed with FRic, FEve, FDiv, FDis, and RaoQ indices. The community was dominated by Gymnanthes klotzschiana, Ligustrum lucidum, and Matayba elaeagnoides. Clustering revealed four coherent functional groups along the acquisition–conservation axis. The PCoA explained 64.64% of trait variation, highlighting the coexistence of acquisitive species (high SLA, elevated mortality) and conservative species (thicker leaves, reduced mortality), with partial overlap among groups and complementary niches. Overall, the community maintained moderate to high functional diversity (FRic = 4.62; FEve = 0.67; FDiv = 0.79; FDis = 1.33; RaoQ = 2.71). We conclude that both functional redundancy and differentiation underpin the resilience of this system under hydrological variability and anthropogenic disturbance. We propose prioritizing acquisitive species in early restoration phases and conservative ones in advanced stages to optimize resource-use efficiency and ensure long-term ecosystem stability.

Research on age-related changes in trunk extensor force control is currently limited, and the underlying neuromuscular mechanisms remain largely unexplored. To address this, we examined the relationship between oscillations in lumbar erector spinae (LES) activity and torque fluctuations in 20 young and 20 older adults during isometric and isokinetic (concentric) trunk extension contractions at 25% and 50% of maximal voluntary contraction (MVC). High-density surface electromyography (HDsEMG) signals were recorded bilaterally from the LES using 64-electrode grids. Torque steadiness was quantified using the coefficient of variation (CoV) of torque. Coherence analysis in the δ band (0–5 Hz) was applied between filtered interference HDsEMG and torque signals. Topographical maps were also generated to assess regional differences in HDsEMG-torque coherence. Older individuals exhibited greater torque CoV than young adults during both isometric (+ 23.03%, p < 0.001) and isokinetic (+ 72.62%, p < 0.001) contractions, with a larger between-group difference at 25% MVC for isokinetic contractions (Group × Torque interaction; p = 0.007). At this intensity, the older group also showed reduced HDsEMG-torque coherence (Group × Torque interaction; p = 0.004). During isometric contractions, coherence magnitude was similar across groups (p > 0.05), but older adults exhibited higher coherence in more cranial and medial LES regions (p = 0.005 and p = 0.001, respectively). Older individuals exhibited the greatest impairment in force steadiness during low-intensity isokinetic contractions. Distinct neuromuscular patterns, possibly influencing force control, emerged depending on contraction type.

Epigeic bryophytes represent an important but often overlooked component of forest biodiversity, closely linked to fine-scale habitat heterogeneity. The conducted research revealed clear differences in both species richness and composition between phytocoenoses and substrates. Mineral soil was the most species-rich substrate, hosting 90 taxa, whereas litter supported the lowest diversity, with only 33 species, emphasising the role of substrate stability and microhabitat availability in shaping bryophyte assemblages. Although forest ecosystems and forest roads exhibited comparable overall species richness, their bryophyte floras differed markedly in species composition, indicating that non-forest habitats provide distinct ecological niches and function as refugia for rare and restricted taxa. Analyses demonstrated that substrates with similar properties often formed coherent species groups across different phytocoenoses and may play a more important role than forest type in structuring epigeic bryophyte communities. On the other hand, species with broad ecological amplitudes were widespread and structurally dominant, whereas taxa restricted to single phytocoenoses showed high bioindicative value. These restricted species proved particularly useful for differentiating phytocoenoses despite their limited spatial extent. Overall, the results highlight bryophytes’ sensitivity to microhabitat variation and underscore their value as effective indicators of habitat differentiation in forest ecosystems.

With the high penetration of renewable energy in modern power systems, the deployment of grid-forming converters (GFMCs) has increased significantly, owing to their inherent capabilities of voltage and frequency support. However, the high-order nonlinearity and mode-switching dynamics of GFMCs with current limiters pose enormous challenges to the modelling of multi-GFMC systems under faults. To address this problem, this paper investigates the aggregated modelling of multi-GFMC systems under current saturation caused by grid faults. By analyzing the key factors that affect the similarities of power angle curves among individuals, a coherency criterion is proposed to divide the converter group into distinct coherent groups. Under the premise of ensuring similar power dynamics among converters, the equivalent parameters of the coherent groups are calculated, and an equivalent aggregated model for the Multi-GFMC system under current saturation is established. Finally, a simulation model of 12 GFMCs integrated into the IEEE 39-bus power grid is developed in the MATLAB/Simulink platform to validate the accuracy of the proposed equivalent aggregated model.

Correlations between onsite and global networks of random number generators during group healing meditations.

Tourist satisfaction in wellness destinations is increasingly shaped by user-generated content on social media, yet limited evidence exists on how specific aspects of wellness experiences drive sentiment in particular locations. This study uses aspect-based sentiment analysis to evaluate tourist satisfaction and the perceived importance of wellness treatments, cultural experiences, and service-related attributes in the wellness destinations of Granada (Spain) and Algarve (Portugal). A dataset of 2613 X posts, 7712 Instagram posts, and 1850 TripAdvisor reviews collected between 2018 and 2024 was pre-processed for grammar-based extraction of aspect–opinion pairs, which were then analysed using the VADER sentiment model and grouped with k-means clustering in the aspect embedding space. Model performance was quantified through accuracy, macro-precision, macro-recall, and macro-F1 and compared on this dataset with alternative sentiment analysis approaches based on TextBlob, a bidirectional LSTM classifier, and a transformer architecture. The VADER–k-means configuration achieved an accuracy of 96.02%, with macro-precision of 0.9847, macro-recall of 0.9149, and macro-F1 of 0.9434, outperforming the baseline models in classifying sentiments related to wellness tourism. Clustered aspects revealed coherent groupings of spa and wellness services, natural landscapes, cultural heritage, and hospitality, with spa quality, staff responsiveness, and environment-related features most frequently associated with positive sentiment. These results demonstrate the value of social media–based sentiment analysis for complementing traditional survey approaches and provide data-driven guidance for destination managers seeking to enhance wellness tourism offerings in Granada and Algarve.

Neurodegenerative diseases (NDDs) exhibit heterogeneous patterns of anatomical involvement and cognitive impairment, yet a unified, brain-wide comparison across disorders is lacking. In this study, we synthesized published neuroimaging and neuropathological findings to construct a standardized, atlas-based mapping of 29 NDDs. Rather than analyzing primary MRI or PET datasets, we systematically extracted anatomical localization data from the literature and harmonized all reported regions using the 38-region Yale Brain Atlas. For each disease, we quantified affected regions, identified shared neuroanatomical signatures, and linked these patterns to 18 key cognitive and behavioral functions. Using this integrated dataset, we generated spatial heat maps, disease-region matrices, and a comprehensive disease-function matrix that captured the extent of functional disruption. Principal component and clustering analyses showed that neurodegenerative diseases form coherent groupings based on shared functional impact profiles, revealing robust latent structure in how anatomical degeneration translates into cognitive impairment. Finally, an interactive companion website has been developed to visualize disease-specific and cross-disease anatomical patterns. This synthesis-based mapping approach provides a unified landscape of neurodegeneration across molecular, anatomical, and functional dimensions, offering a transparent and accessible resource for understanding how diverse NDDs converge and diverge across the human brain.

The rising incidence of dengue hemorrhagic fever (DHF) in Indonesian urban areas highlights the urgent need for analytical frameworks capable of capturing spatial heterogeneity in vulnerability while supporting targeted public health interventions. However, most existing dengue vulnerability studies rely on clustering or indicator-based scoring in isolation, limiting interpretability and reducing their operational relevance for policy-driven decision making. This study explicitly addresses this gap by proposing an integrated spatial clustering and epidemiologically weighted composite risk scoring framework for urban dengue vulnerability mapping. Using Semarang Municipality as a case study, K Means based spatial clustering was combined with composite risk scoring to analyze dengue vulnerability across administrative subdistricts. Seven key indicators consisting of population density, area size, total population, morbidity, mortality, incidence rate, and health facility availability were processed through systematic imputation, normalization, and attribute selection to ensure data consistency and analytical robustness. The optimal number of clusters was determined using the Elbow Method and Silhouette Score, after which K-Means clustering was applied to generate spatially coherent vulnerability groupings. A composite risk scoring mechanism was subsequently employed to classify regions into five operational risk categories: Low-Risk, Moderate-Risk, High-Risk, Very High-Risk, and Emergency-Priority. The results reveal clear structural differentiation in dengue vulnerability patterns, where Emergency-Priority and Very High-Risk clusters are not only characterized by elevated epidemiological indicators but also by constrained health service availability, amplifying outbreak susceptibility. Specifically, 13 subdistricts (7.5%) were identified as Emergency-Priority and 22 subdistricts (12.4%) as Very High-Risk, together accounting for approximately 20% of the study area. Beyond numerical classification, the integration of spatial clustering and composite risk scoring enhances interpretability by linking cluster structure with epidemiological severity and service capacity, thereby improving policy relevance compared to conventional clustering-only approaches. Validation through heatmap visualization, risk category distribution, and cluster ranking confirms the stability and interpretive clarity of the proposed framework. By moving beyond descriptive clustering toward an integrated analytical model, this study contributes a scalable and adaptive decision-support framework for dengue risk mapping. The findings provide actionable insights for policymakers, enabling evidence-based prioritization, optimized resource allocation, and the development of responsive intervention strategies to mitigate dengue burden in complex urban environments.

To develop a self-supervised learning (SSL) model that classifies optic disc phenotypes in primary open angle glaucoma (POAG) and explores novel phenotypic patterns with optic disc photographs (ODPs). We collected 850 ODPs from patients with POAG and applied data augmentation to address class imbalances, yielding 10,493 images. Using the DINO Vision Transformer as the backbone model, we trained an SSL model to extract 2048-dimensional latent features. These features were used for both supervised classification of six known phenotypes and unsupervised clustering. Classification performance was evaluated with Random Forest and XGBoost models. UMAP (Uniform Manifold Approximation and Projection) was used for dimensionality reduction and feature visualization, and attention maps were generated for model interpretability. The DINO-EYE model features enabled phenotype classification with 91% accuracy with Random Forest and 92.1% after merging clinically similar phenotypes. Unsupervised clustering revealed coherent groupings, particularly for concentric thinning and extensive Peripapillary Atrophy (PPA), though no new phenotypes were unanimously confirmed by clinicians. The proposed model outperformed the RETFound SSL model in phenotype classification and demonstrated interpretable attention regions consistent with expert criteria. Our DINO-EYE effectively extracts clinically meaningful features from fundus images and enables accurate classification of optic disc phenotypes in POAG. It surpasses existing SSL models in performance and interpretability, offering promise for real-world glaucoma decision support and individualized care planning.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-33140-1.

To address the critical challenge of efficiently and ethically managing surgical waiting lists in digital health systems by developing a decision support framework based on biopsychosocial prioritization. The authors integrate the Analytic Hierarchy Process (AHP) with K-Means clustering to create a hybrid decision support model that prioritizes patients using multidimensional biopsychosocial variables. The model was applied in the otolaryngology (ENT) unit of a high-complexity public hospital in Chile. Expert-informed weightings guided the AHP process, while K-Means clustering enabled data-driven segmentation into clinically coherent patient groups. The proposed methodology significantly outperformed traditional chronological scheduling approaches. Specifically, it achieved a 27 % reduction in mean clinical risk, a 41 % decrease in urgent hospitalizations, a 32 % reduction in urgent bed days, and more than 12-days of acceleration in access for high-priority patients. While the AHP-clustering hybrid is established in prior literature, our contribution lies in operationalizing it with ethical safeguards and real-world validation within a high complexity ENT unit. Our hybrid AHP and K-Means approach offers a transparent, scalable, and interpretable decision support tool for surgical prioritization. It aligns with the goals of digital health transformation by improving the fairness, efficiency, and responsiveness of healthcare delivery.

In the digital age, readers value quantitative journalism that is clear, concise, analytical, and human-centred. To understand complex topics, they often piece together scattered facts from multiple articles. Visual storytelling can transform fragmented information into clear, engaging narratives, yet its use with unstructured online articles remains largely unexplored. To fill this gap, we present Compendia, an automated system that analyzes online articles in response to a user's query and generates a coherent data story tailored to the user's informational needs. through two modules covering addresses key challenges of storytelling from unstructured text through two modules covering: Online Article Retrieval, which gathers relevant articles; Data Fact Extraction, which identifies, validates, and refines quantitative facts; Fact Organization, which clusters and merges related facts into coherent thematic groups; and Visual Storytelling, which transforms the organized facts into narratives with visualizations in an interactive scrollytelling interface. We evaluated Compendia through a quantitative analysis, confirming the accuracy in fact extraction and organization, and through two user studies with 16 participants, demonstrating its usability, effectiveness, and ability to produce engaging visual stories for open-ended queries.

ABSTRACT The high complexity and long computation time in simulating dual‐active‐bridge (DAB)‐based parallel DC microgrid systems pose significant challenges. To address this, coherency identification and aggregation algorithms (CIAA) for multi‐DAB parallel DC microgrid systems (MDPDMS) are proposed. First, a correlation model between common DC bus voltage disturbances and measurable DAB output quantities is established. Using output current fluctuation similarity as the coherency criterion, a dynamic similarity index is derived. Coherent DAB groups (CDGs) are then adaptively identified using the K‐Medoids algorithm, with the clustering results refined by a defined cost function. Second, under the core constraints of power conservation and dynamic response equivalence, a mapping is established between the equivalent model (EM) and the internal topology and control parameters of the CDG to accurately aggregate parameter‐heterogeneous DABs. The proposed method maintains high fidelity to the dynamic characteristics of the detailed system (DS) while significantly improving simulation efficiency. The simulation time of the reduced‐order system (ROS) is only 56% of that required by the detailed system.

Idiopathic generalized epilepsy is characterized by marked brain network alterations as assessed using electrophysiology. Logistical challenges and the need for a volumetric MRI often hinder the clinical application of high-density EEG or magnetoencephalography. This study investigates the influence of EEG channel density and the head model on brain metrics derived from 256-channel EEG and 19-channel routine EEG in two samples balanced for age and sex. First, we evaluated resting-state data from 35 individuals with idiopathic generalized epilepsy and 54 healthy controls collected using the 256-channel setup. Data were analysed at full density and then iteratively downsampled to lower densities. Source reconstruction was performed either using individual MRI data or a standard brain template and dynamic imaging of coherent sources. We assessed EEG power and connectivity (imaginary part of coherency) group differences at all channel compositions, head model types and parcellations (cortical vertices, anatomical and network parcellations). Second, a routine sample recorded with 19 channels was analysed to validate findings in a real epilepsy monitoring scenario (71 patients, 43 controls). We found that lower-density arrays reliably identified global group differences for both power and connectivity and in frequency bands for which the strongest effects were observed. The spatial similarity of the results for the 256 channels set and those with fewer channels were good to moderate for power (r spin ∼0.97 to 0.33), but dropped for connectivity with fewer than 64 channels (r spin ∼0.78 to -0.12). Comparing individual and canonical head models revealed consistent effects (r spin ∼0.77 to 0.5), with coarser brain parcellations increasing stability for low-density maps. In sum, low-density EEG arrays suffice for detecting global alterations in idiopathic generalized epilepsy, particularly in signal power. Our findings advocate for leveraging clinical EEG for brain-wide analyses in idiopathic generalized epilepsy while emphasizing the need for high-density coverage if spatial precision is needed. Canonical head models are a viable alternative if no individual MRI is available, especially for regional- or network-level assessments.

Aims: The Niger Delta experiences high levels of routine gas flaring. This leads to wasted associated gas, economic losses, and contributes to environmental degradation. The fragmented spatial distribution of flare sites further complicates gas capture and infrastructure planning. This study aims to apply a geospatial cluster-based optimization approach to group twenty-four (24) onshore gas-flaring sites in the Niger Delta. The objective is to improve flare-gas recovery potential and guide the design of centralized gas-gathering infrastructure. Study Design: It follows a quantitative geospatial clustering using Python-based K-means analysis, supported by internal cluster-validation metrics. Place and Duration of Study: The study was carried out using flare-volume records and GPS data from 24 onshore flowstations across the Niger Delta, covering approximately 100 days of operational reporting between July to December 2024. Methodology: Daily flare-volume datasets were pre-processed to compute average active-day flare rates for each flowstation. Latitude and longitude coordinates were compiled using Google Earth Pro and field entries. The Elbow Method was used to determine the optimal number of clusters (K) based on inertia values. K-means clustering was then applied to group the flowstations into distinct geospatial clusters. Internal cluster quality was evaluated using the silhouette coefficient. Aggregated flare volumes were computed for each cluster to assess recovery potential. Results: The Elbow Method identified four clusters as the optimal configuration. K-means clustering produced coherent spatial groupings reflecting natural geographic alignments within the region. Cluster 0 recorded the highest aggregated average flare volume (≈ 45.99 mmscf/day), followed by Cluster 3 (≈ 30.22 mmscf/day), Cluster 1 (≈ 24.56 mmscf/day), and Cluster 2 (≈ 22.55 mmscf/day). Silhouette analysis confirmed strong internal cohesion and clear separation between clusters, with no misclassified points. Together, Clusters 0 and 3 accounted for approximately 63% of total aggregated flare volume; this indicates priority zones for possible centralized gas-gathering development. Conclusion: Geospatial clustering provides a robust foundation for designing shared-infrastructure flare-gas recovery systems in the Niger Delta. The four-cluster model presented two priority hubs suitable for centralized infrastructure development. This can reduce total pipeline distance. The cluster model forms a baseline for subsequent techno-economic feasibility studies.