Search Results (797)

The gut microbiota plays a vital role in host health. In response to the scarcity of comparative studies examining wild and domesticated South American camelids under identical captive conditions, this study was conducted to compare the gut microbiota of 16 captive guanacos (Lama guanicoe) and 8 alpacas (Vicugna pacos) housed in the same zoo and fed identical diets, using 16S rRNA gene sequencing and multiple ecological metrics for analysis. Alpha diversity indices (Shannon, observed richness, and Shannoneven) did not differ between the two species, but beta diversity (principal component analysis) indicated significant separation (p < 0.05), and the guanacos exhibited significantly lower within-group Bray–Curtis dissimilarity, indicating more consistent microbial communities. Guanacos exhibited a lower average variation degree (AVD), indicating greater community stability, a broader niche, and a co-occurrence network with 81.1% positive edges and high modularity (0.691). In contrast, the alpacas showed a higher AVD (lower stability), a narrower niche, and a network with only 62.2% positive edges and lower modularity (0.534). Linear discriminant analysis effect size analysis revealed that Monoglobus and Bacteroides are enriched in guanacos, while Rikenellaceae_RC9_gut_group is enriched in alpacas. Functional predictions revealed that alpacas had higher predicted abundances of potentially pathogenic taxa and Kyoto Encyclopedia of Genes and Genomes pathways related to Staphylococcus aureus infection (p < 0.05). These findings demonstrate that, despite sharing environments, guanacos have a more stable, generalist-dominated gut microbiota with a higher proportion of positive co-occurrences, whereas alpacas exhibit a less stable, specialist-oriented community with a higher proportion of negative co-occurrences and greater predicted pathogenic potential. These results suggest that domestication may have contributed to the observed divergence in gut microbial ecology between the two species. Full article

Background: Robot-assisted total knee arthroplasty (raTKA) improves the precision of component positioning and coronal alignment restoration, but it remains uncertain whether that technical accuracy modifies the clinical effect of alignment strategy in different varus phenotypes. The present report evaluates alignment strategy and correction magnitude, explicitly as a post hoc exploratory deformity-subgroup analysis within a randomized raTKA cohort. Methods: This single-center, open-label, randomized study enrolled 296 patients with varus knee osteoarthritis who underwent raTKA between 2023 and 2025 using either mechanical alignment (MA; n = 149) or limited/restricted kinematic alignment (lim.-KA; n = 147). The parent randomized comparison was conducted at the whole-cohort level; the deformity-based subgroups reported here were defined after the whole-cohort analysis and are therefore post hoc and exploratory. Patients were stratified according to preoperative varus severity into a mild-deformity subgroup (≤10°; lim.-KA-I n = 99, MA-I n = 102) and a moderate-deformity subgroup (11–20°; lim.-KA-II n = 48, MA-II n = 47). Outcomes included hip–knee–ankle angle (HKA), correction angle, range of motion (ROM), visual analog scale (VAS; 0–10 points), Knee Society Score (KSS; knee and function), Oxford Knee Score (OKS), and Forgotten Joint Score-12 (FJS-12) over 12 months. Estimates are presented with 95% confidence intervals where applicable. Because multiple post hoc subgroup comparisons were performed without formal multiplicity adjustment, p-values are interpreted descriptively and in conjunction with effect sizes and 95% confidence intervals. Results: The primary whole-cohort randomized comparison did not demonstrate an overall between-group advantage of either alignment strategy. The post hoc moderate-varus subgroup showed favorable unadjusted 12-month differences for lim.-KA versus MA in KSS-knee (+6.8 points; 95% CI 5.3 to 8.3; nominal p < 0.001), KSS-function (+4.0 points; 95% CI 2.7 to 5.2; nominal p < 0.001), OKS (+6.4 points; 95% CI 4.5 to 8.3; nominal p < 0.001), and FJS-12 (+11.3 points; 95% CI 9.4 to 13.1; nominal p < 0.001). In contrast, ROM favored MA rather than lim.-KA in the moderate-varus subgroup (−11.8°; 95% CI −16.6 to −7.0; nominal p < 0.001), indicating greater 12-month ROM after MA, and VAS pain, reported on a 0–10 scale, did not support a lim.-KA pain advantage (+0.26 points; 95% CI 0.05 to 0.48; higher scores indicate worse pain; nominal p = 0.018). Exploratory, unadjusted, post hoc 12-month alignment-by-deformity interaction terms were significant for ROM, KSS-knee, KSS-function, OKS, and FJS-12, but not for VAS. Because multiple post hoc comparisons were performed without formal multiplicity adjustment, the results are interpreted descriptively, along with effect sizes and confidence intervals. Conclusions: The primary randomized comparison did not demonstrate a clinical advantage of lim.-KA over MA in the whole cohort. In post hoc exploratory analyses, mild varus deformity was associated with outcomes broadly similar to those after both alignment strategies. In the moderate-varus subgroup, patient-level analyses suggested a possible phenotype-dependent signal for KSS-knee, KSS-function, OKS, and FJS-12 after lim.-KA, whereas ROM favored MA, and VAS pain did not support a lim.-KA pain advantage. These subgroup findings should be interpreted separately from the primary randomized result, considered hypothesis-generating only, and not used in isolation to change clinical practice without prospective confirmation. Full article

Background: The rapid expansion of functional ready-to-drink (RTD) beverages—formulated with prebiotic fibers, botanical extracts, and reduced sugar—has outpaced systematic characterization of their small-molecule composition. Methods: We applied dual-mode untargeted high-resolution liquid chromatography–tandem mass spectrometry (LC–MS/MS), integrating hydrophilic interaction (HILIC) and reversed-phase C18 separations, to profile five commercial RTD beverages spanning distinct formulation categories: Coca-Cola^®, Poppi^® Orange, OLIPOP^® Cream Soda, Pure Leaf^® Unsweetened Black Tea, and BeePop™ Peach + Orange Blossom Honey. Results: Across all products, 478 compounds were structurally annotated at Metabolomics Standards Initiative (MSI) Levels 1 and 2, of which 42 matched compounds with reported bioactivity in a curated literature-based reference database. Seventeen compounds—including the NAD+ precursor trigonelline and multiple B vitamins—were detected across all five products. The number and diversity of compounds with reported bioactivity varied substantially by product and correlated with botanical ingredient complexity. Conclusions: This work presents a qualitative molecular survey of the RTD beverage category using standardized, dual-mode untargeted metabolomics, providing a reference dataset for future targeted quantitation studies. Full article

RNA-binding proteins (RBPs) are essential regulators of all aspects of RNA metabolism, including splicing, stability, localisation, translation, and degradation. Through their ability to recognise specific cis-elements in target transcripts, often via RNA-recognition motifs or other conserved domains, RBPs enable rapid cellular adaptation to stress and maintain proteostasis, particularly in post-mitotic tissues with limited transcriptional flexibility. Accumulating evidence positions RBPs as both modulators and drivers of the molecular hallmarks of ageing, including genomic instability, loss of proteostasis, mitochondrial dysfunction, cellular senescence, and chronic inflammation. This review synthesises peer-reviewed studies on the multifaceted roles of RNA-binding proteins in organismal ageing and age-related diseases. Key themes include the tissue- and age-dependent changes in expression of turnover and translation regulatory RBPs such as HuR (ELAVL1), AUF1 (HNRNPD), TIA-1, and tristetraprolin (ZFP36), which alter the stability of mRNAs encoding cell-cycle regulators, pro-inflammatory cytokines, and stress-response proteins. Systematic downregulation of core splicing factors, including PTBP1 and several heterogeneous nuclear ribonucleoproteins, drives widespread senescence-associated splicing alterations in pathways governing cell division, autophagy, DNA repair, and mitochondrial function, suggesting a causal contribution to the senescent phenotype. Prion-like RBPs such as TDP-43 and FUS exhibit age-dependent mislocalisation, nuclear depletion, and cytoplasmic aggregation, contributing to splicing defects, impaired RNA transport, and neurodegeneration in amyotrophic lateral sclerosis, frontotemporal dementia, and limbic-predominant age-related TDP-43 encephalopathy. Interactions between RBPs and non-coding RNAs, together with disrupted liquid–liquid phase separation dynamics, further exacerbate age-related decline. By integrating mechanistic studies from cellular and animal models with observations in human cohorts, this review underscores RBPs as central nodes linking multiple ageing hallmarks and highlights their potential as biomarkers and therapeutic targets to promote healthy ageing. Limitations of current models and priorities for future translational research are discussed. Full article

Selecting the appropriate academic track is a critical decision for students, as misalignment between program requirements and individual cognitive, personality, and competency profiles can significantly impact academic performance, persistence, and overall educational outcomes. Traditional educational recommender systems often rely solely on skill matching or on the correlation of interests, failing to account for the dimension of competency that is required for success in specific academic tracks. This paper introduces a novel Multi-Dimensional Psychometric Alignment (MDPA) algorithm that moves beyond simple rank-order correlation between skills and programs by jointly integrating multiple psychometric perspectives and evaluating both preference similarity and competency sufficiency. Based on a structured synthesis of Cognitive Preferences (MBTI), Cognitive Modalities (Gardner’s Multiple Intelligences), and Personality Stability (Big Five), the proposed profile captures complementary dimensions of student readiness that are usually examined separately in prior educational recommender systems. Then applies an alignment algorithm-which is based on a hybrid similarity metric that fuses Spearman’s Rank Correlation (Interest Shape) with Weighted Euclidean Distance (Competency Magnitude), enforced by non-linear threshold penalties for critical traits- in order to find the best options for students. This approach constitutes a deterministic, explainable recommender system whose novelty lies in combining heterogeneous psychometric evidence with an explicit magnitude–shape matching mechanism and threshold-based academic viability constraints. Our approach is validated through a case study of university students in Kazakhstan, and the results demonstrate how “academic fit” is better modeled as a function of both interest pattern and trait sufficiency, offering a robust alternative to “black-box” skill-based recommenders. Full article

Background: Cancer cachexia is a serious condition during the last stages of the disease, which is characterized by the loss of muscle and fat mass in patients with cancer. There are no effective treatments for cancer cachexia, and new treatment interventions are urgently needed. We have previously demonstrated that 5 mg/kg/day bisoprolol and 1.7 µg/kg/day ARA 284, a small non-erythropoietic tissue protective peptide, separately have positive effects in a rat model of cancer cachexia. Methods: We investigated the compound effects of both bisoprolol and ARA 284 by targeting multiple pathways in the Yoshida hepatoma rat model of cancer cachexia. Rats were randomly allocated to one of the following treatment groups: bisoprolol (5 mg/kg/day), ARA 284 (1.7 µg/kg/day), a 25% combination (1.25 mg/kg/day bisoprolol + 0.425 µg/kg/day ARA 284), a 75% combination (3.75 mg/kg/day bisoprolol + 1.275 µg/kg/day ARA 284), or placebo. Results: The combination of 3.75 mg/kg/day bisoprolol and 1.275 µg/kg/day ARA 284 showed the strongest overall effects compared with the respective effective monotherapies, respectively, or placebo across multiple endpoints, including body weight, lean mass, food intake, spontaneous activity, and cardiac function in a rat model of cancer cachexia (p < 0.01, respectively). Furthermore, this combination therapy had the strongest effects on survival against the placebo (hazard ratio 0.08, 95% confidence interval 0.04 to 0.17, p < 0.001). Conclusions: Our findings show that the combination of bisoprolol and ARA 284 is beneficial in a hepatoma cachexia model and may provide greater overall effects than either monotherapy alone. Full article

Background: Robust perception involves processing heterogeneous sensory signals, such as facial expressions, vocal prosody, and language, particularly in noisy environments. In computational modeling, a key challenge is integrating these diverse inputs while actively filtering uninformative variations. While recent deep learning models address this integration through complex fusion architectures, they typically aggregate features without explicit filtering modules analogous to inhibitory control. In this study, we propose Multi-modal Information Disentanglement (MInD), a computational framework designed to test the hypothesis that algorithmic noise isolation facilitates robust multisensory integration. Methods: Drawing conceptual inspiration from cognitive theories of modularity, our model decomposes sensory inputs into amodal (modality-invariant) and modal-specific pathways. Furthermore, we introduce an adversarial noise isolation mechanism to serve as an algorithmic analog to cognitive inhibition. Given that our model operates on pre-extracted high-level features, this mechanism functions to isolate latent distributional variance—uninformative fluctuations that persist after initial feature extraction—guiding the network to separate task-relevant affective cues from irrelevant feature variance. Results: Empirical evaluations on standard emotion recognition benchmarks indicate that this purification-before-fusion strategy is associated with competitive performance and stability across multiple metrics. Notably, the framework attains these results using simple linear integration layers, suggesting that separating representations prior to fusion may reduce the computational complexity required for subsequent integration. Conclusions: These observations highlight the computational utility of algorithmic noise suppression, illustrating how cognitive inspiration can inform efficient machine learning architectures without claiming direct neurobiological validation. Full article

This paper addresses a persistent divide in water governance: critical frameworks reveal power dynamics and ontological diversity but lack operational guidance, while operational frameworks prioritize technical management at the expense of ontological plurality and social legitimacy. We introduce Water-Important Socioecological Systems (WISe) as a prescriptive framework that integrates political ontology with hydrosocial territory analysis to inform more reflexive and inclusive water governance. WISe designates specific zones where ecological functions for water sustainability are concentrated and where social practices, productive livelihoods, and symbolic meanings coexist inseparably with biophysical processes. Unlike Integrated Water Resources Management (IWRM), which treats social and ecological dimensions as separate pillars, and the Ostrom Social-Ecological Systems framework, which undertheorizes ontological plurality, WISe explicitly centers the coexistence of multiple ways of understanding and relating to water as a governance principle. The framework was developed through a five-phase mixed-methods conceptual inquiry combining a systematic literature review (202 documents), an exploratory stakeholder survey of 223 participants across six Colombian hydrographic basins, and an analysis of designated water-strategic ecosystems. The findings reveal that ontological diversity is distributed across all stakeholder groups: hydrological supply framings predominate (36.4–45.8%), yet territorial-integrated perspectives appear in all groups, with government actors (22.9%) showing the highest proportion. The majority (56.1%) perceive WISe as exclusively state-managed, revealing a dominant ontological position that reduces socioecological territories to objects of administrative control. This article presents WISe as a conceptual and prescriptive framework informed by exploratory empirical evidence. Rather than offering a definitive empirical validation of the model, this study provides initial analytical grounding for its development and identifies indicative patterns that warrant further testing across other geographical and institutional contexts. WISe offers a framework comprising six defining characteristics and five operational dimensions that bridge theoretical understandings with governance-oriented analysis, treating ontological difference not as an obstacle but as essential knowledge for more reflexive and equitable water governance. Full article

Objectives: This study aimed to evaluate the influence of tumor geometry on dose distribution and delivery accuracy, and to assess the impact of the Automatic Lower Dose Objective (ALDO) function on dosimetric performance. Methods: Computed tomography images of a Rando anthropomorphic phantom were used to simulate intracranial multiple metastases. Two contour groups were generated on the same CT dataset, consisting of two spherical targets with diameters of 1 cm and 2 cm, respectively. For each group, target pairs were created with edge-to-edge separation distances ranging from 1 to 6 cm. Automated single-isocenter stereotactic radiosurgery plans were generated using the HyperArc workflow, both with and without the ALDO function. Dosimetric performance was evaluated using the RTOG conformity index, Paddick conformity index, gradient index, and homogeneity index. Patient-specific quality assurance was performed using electronic portal imaging device-based verification and radiochromic film dosimetry. Gamma analysis with multiple criteria was applied to assess the impact of target size and geometric separation on delivery accuracy. Results: The use of ALDO improved dose conformity and gradient performance but resulted in increased dose heterogeneity and higher hot spots. In non-ALDO configurations, the agreement between EPID portal dosimetry and film measurements varied according to target size, gamma criteria, and spatial position. For the 2 cm targets, EPID portal dosimetry generally demonstrated higher gamma passing rates than film measurements, whereas the 1 cm targets showed mixed results depending on measurement position and gamma criteria. These differences likely reflect the distinct detector characteristics and spatial sensitivities of the two QA methodologies. Larger discrepancies were observed under stricter gamma criteria and at off-axis positions, indicating potential influences of target geometry and high-dose gradient regions within the simplified phantom configurations evaluated in this study. Conclusions: Within the simplified two-target phantom configurations evaluated in this study, tumor geometric distribution significantly affects both dosimetric characteristics and QA outcomes in HyperArc SRS. Film measurements provide greater sensitivity, whereas EPID-PD alone may be insufficient for evaluating small-target high-gradient regions under strict gamma criteria. Full article

The distributed energy system emerges as a promising and valuable technology. However, various factors are hindering the development of the algorithm, including the diversity of units and their respective output constraints. Additionally, the multiple-layer algorithm still faces difficulties searching for the best coordination between integer and continuous variables. To address these challenges, this paper proposes a simultaneous optimization method to aid the design of distributed energy systems. Considering the influence of the outputs of the different units, the proposed method introduces a dual-cycle structure that separates the storage energy units and other units according to the output mode. Additionally, the proposed method yields the hourly outputs of the different units and their respective rated capacities simultaneously. At the same time, an originally designed random walk algorithm with compulsive evolution is integrated into the proposed method. Moreover, a time-series optimization method is applied for the storage energy device to enhance the computational efficiency. To validate the proposed method, the configuration of the distributed energy system (DES) and the hourly output of the different units in three scenarios are analyzed in detail. Quantitative results show that the proposed RWCE-DC reduces the average daily total cost compared to a standard differential evolution algorithm with penalty functions (from 413,628 to 241,716 CNY/day in Scenario II). Across three grid-interaction scenarios, RWCE-DC yields daily costs of 243,271 CNY/day (Scenario I), 209,716 CNY/day (Scenario II), and 178,896 CNY/day (Scenario III) while automatically removing redundant units (e.g., gas boiler in Scenario I) and strictly respecting storage state-of-charge constraints without penalty functions. However, the analysis has several limitations. First, the economic model uses a simplified annualized cost approach without taxes, subsidies, inflation, or discount rate variations. Second, only one geographic location with specific solar and load profiles is considered. Third, the current algorithm focuses on single-objective cost minimization and does not yet incorporate multi-objective trade-offs. These factors should be considered when interpreting the absolute cost values and when applying the method to other regions or policy contexts. These results confirm that the proposed dual-cycle method provides an efficient and numerically validated optimization approach for DES synthesis. Full article

Existing research on quality gain-loss functions predominantly focuses on single variables or separable quality characteristics, overlooking the correlations among multiple quality attributes and the complexity of spatiotemporal factors. To address this issue, this study proposes a multivariate and multidimensional quality gain-loss function model based on a nonseparable Gaussian process (NSGP). A spatiotemporal interaction term is constructed using the Matérn kernel function, while the Kalman filtering and smoothing algorithms are introduced to improve computational efficiency. In addition, the signal-to-noise ratio is employed to determine the joint gain-loss weights, thereby establishing the multivariate and multidimensional quality gain-loss function model. Taking hydraulic concrete construction as the research background, simulation experiments and a practical engineering case are used to examine the performance and applicability of the proposed model. The results indicate that, compared with conventional machine learning methods, the NSGP model achieves superior predictive accuracy and can effectively characterize the spatiotemporal evolution patterns of concrete slump and segregation resistance. However, the interval coverage probability in the dam concrete case study remains lower than the nominal level, indicating that uncertainty quantification requires further improvement. The proposed model does not require prior determination of covariance separability during computation. Under the given dataset and assumptions, it provides an exploratory quantitative tool for point prediction, multivariate quality evaluation, and parameter optimization of selected fresh concrete indicators. Full article

Achieving climate-neutral aviation requires propulsion systems capable of reducing emissions and noise while maintaining high aerodynamic efficiency. Distributed Electric Propulsion (DEP) represents a promising solution; however, accurately predicting the unsteady aerodynamic interactions between multiple propellers and lifting surfaces remains challenging. This work investigates the aerodynamic performance of two Distributed Propulsion (DP) configurations using FLOWUnsteady, a meshless Computational Fluid Dynamics (CFD) solver based on the reformulated Vortex Particle Method (rVPM) within a Large-Eddy Simulation (LES) framework. The Lagrangian particle formulation eliminates mesh generation and limits numerical dissipation. Two layouts—a twin wingtip-mounted arrangement and a four-propeller configuration including inboard units are analyzed and compared with a clean wing baseline as functions of propeller position, inflow speed (20 and 33 m/s), and angle of attack. Beyond global aerodynamic performance metrics, the rVPM–LES framework provides a time-resolved and spatially resolved characterization of local propeller–wing interference in multi-propulsor configurations, highlighting differences in loading and torque demand between inboard and wingtip propellers that are not typically captured by low- to mid-fidelity modeling approaches. The results show that distributed propulsion increases lift and reduces drag relative to the clean wing by accelerating the local flow, delaying separation, and enhancing wing circulation. Thrust and torque coefficients exhibit a clear dependence on rotational speed and angle of attack: inboard propellers experience stronger aerodynamic interference and higher torque demand, whereas wingtip propellers maintain more uniform loading. These findings confirm the capability of the meshless rVPM approach to accurately and efficiently capture unsteady interactions in distributed propulsion systems, supporting its application to the analysis and design of future DEP aircraft. Full article

In advanced oxide materials, additive selection is increasingly constrained by the simultaneous requirements of functional response, phase stability, morphology control, processing tolerance, scalability, and critical raw material security. This study develops a ZnO-centered framework to compare boron-based strategies (direct B doping, B₄C/ZnO composite formation, and h-BN/ZnO interface engineering) with rare-earth strategies (Ce/CeO₂, La/La₂O₃, and Y/Y₂O₃). Structural, morphological, chemical-state, and vibrational evidence from XRD, FE-SEM/EDX, XPS, Raman, and FT-IR studies is interpreted through an evidence hierarchy that separates lattice incorporation, surface/grain-boundary segregation, and deliberate secondary-phase or heterointerface formation. The synthesis shows that boron-containing routes usually provide broader phase retention, lower agglomeration tendency, more gradual defect modulation, and greater processing robustness, whereas rare-earth routes offer stronger oxygen-vacancy regulation, redox activity, luminescence tuning, and heterojunction-assisted function but require tighter process control and more rigorous verification of incorporation mode. Reanalysis of seven primary experimental pathways indicates that B₄C/ZnO and h-BN/ZnO are mechanistically non-equivalent: B₄C supports rigid composite-interface growth, while h-BN promotes sheet-mediated interface multiplication and Maxwell–Wagner–Sillars polarization. Türkiye is treated as an illustrative boron-rich producer case within a transferable producer/importer decision model. Dopant selection is therefore framed as a multi-criteria decision involving performance thresholds, reproducibility, technology-readiness potential, and supply-security exposure, not peak output alone. Full article

Rhizosphere microorganisms play central roles in nutrient cycling and contaminant transformation in sediment-associated freshwater systems, yet their responses to newer pesticides remain insufficiently characterized. In this study, a 28-day Vallisneria natans–rhizosphere sediment microcosm was used to compare the effects of trifluenfuronate and fluopyram at nominal concentrations of 0.01, 0.1, and 1 mg L⁻¹. Bacterial community composition was assessed using 16S rRNA gene sequencing, and shotgun metagenomic data were used to evaluate relative functional annotation patterns. Plant physiological traits and rhizosphere sediment enzyme activities were measured as ecological context for interpreting microorganism-associated responses. Fluopyram, particularly at 1 mg L⁻¹, produced clearer ordination-level shifts in rhizosphere bacterial community composition than trifluenfuronate, although pairwise treatment separation was not statistically resolved after multiple-testing correction. Annotation-based metagenomic profiles also differed between the two pesticides: stronger exposure was associated with reduced relative signals for several xenobiotic-, transport-, and regulation-related annotations, while high-dose fluopyram showed a methane-metabolism-related annotation signal and high-dose trifluenfuronate showed relative enrichment of secondary-metabolism-related annotations. These microbial and annotation-profile responses coincided with stronger inhibition of V. natans growth and greater suppression of rhizosphere sediment enzyme activities under fluopyram exposure. Overall, fluopyram induced more consistent microorganism-associated response patterns than trifluenfuronate in the tested rooted macrophyte–sediment microcosm. The results highlight the sensitivity of rhizosphere microbial communities and metagenomic annotation profiles to pesticide exposure in sediment-associated freshwater systems. Full article

Cooperative localization enables teams of robots to maintain better positioning in GNSS-denied environments by sharing state estimates and inter-robot range measurements to reduce the rate of proprioceptive odometry drift. In scenarios without fixed navigation beacons or pre-surveyed reference nodes, each robot functions as both a positioning client and a mobile ranging peer. A critical vulnerability in this architecture is silent fault propagation. A robot with a degraded localization solution may broadcast an incorrect, often overconfident position estimate, corrupting its peers’ localization. Classical Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM) methods are ineffective in this context because meter-scale inter-robot separations introduce strong geometric nonlinearity and unstable Geometric Dilution of Precision (GDOP), resulting in scattered subset solutions rather than the coherent, biased clusters that RAIM is designed to detect. This paper addresses this vulnerability by proposing a two-stage distributed Fault Detection and Identification (FDI) architecture for peer-to-peer ranging-based cooperative localization. The first stage applies a global chi-square test on Weighted Least-Squares trilateration residuals to detect the presence of a fault. The second stage identifies the faulty robot by computing Leave-One-Out and Leave-Two-Out subset solutions, which are then partitioned using a clustering algorithm. The cluster that exempts measurements from the faulty robot is identified using either a maximum-cardinality or a minimum-variance criterion. A decentralized voting protocol that requires at least two independent corroborations is then employed for network-wide fault declaration. Monte Carlo simulations show that the clustering-based identification method outperforms classical residual-based methods across multiple fault types, with results reported for the planar (2D) case. No single clustering configuration dominates in terms of identification performance across all tested fault conditions, as performance varies with the fault profile. The proposed architecture operates fully in a distributed manner, requiring only the exchange of position estimates, covariances, and binary votes. Full article