Search Results (6,925)

Population ageing poses growing challenges to social protection systems, particularly in contexts characterized by informal labour trajectories and uneven pension coverage. This study examines the association between current employment and multidimensional well-being among older adults of retirement age in Ecuador, focusing on heterogeneity across well-being domains and the relative importance of economic sufficiency and institutional access. A quantitative, non-experimental, cross-sectional study was conducted with a non-probability sample of 365 adults aged 65 years or older across the seven parishes of Rumiñahui canton. Multidimensional well-being was constructed as an equally weighted composite of four standardized domains: Health, Urban Environment and Accessibility, Relational Well-Being, and Household Conditions. Ordinary least squares models with HC3 robust standard errors were estimated, adjusting for sociodemographic characteristics, log-transformed monthly income, and access to health care and public transportation. Employment showed heterogeneous domain-specific associations: it was positively associated with Health (β = 0.359, Holm-adjusted p < 0.001) and Relational Well-Being (β = 0.155, Holm-adjusted p = 0.008), negatively associated with Urban Environment and Accessibility (β = −0.293, Holm-adjusted p < 0.001), and not significantly associated with Household Conditions after multiple-testing correction (β = 0.085, Holm-adjusted p = 0.067). In the fully adjusted global model, employment remained positively associated with Global Well-Being (β = 0.124, p = 0.014), although income showed the strongest standardized association (β = 0.397, p < 0.001), followed by health-care access (β = 0.254, p < 0.001); public-transport access was not independently associated with the global outcome. These findings indicate that employment at retirement age is associated with later-life well-being in a domain-specific rather than uniform manner and that economic sufficiency and health-care access are particularly salient correlates of overall well-being. Given the cross-sectional design, the findings should be interpreted as associations rather than causal effects. Full article

This study proposes a lightweight domain-assisted deep learning framework for binary human presence detection using ultra-wideband (UWB) radar. The proposed methodology processes raw UWB radar signals through statistically screened, physics-grounded signal features including Fast Fourier Transform (FFT)-based frequency-domain statistics and Hilbert Transform (HT)-derived envelope statistics which are selected via a per-subject Cohen’s d screening step and stacked as auxiliary input channels alongside the raw signal for a lightweight two-dimensional convolutional neural network (2D-CNN). A cross-subject evaluation protocol (train-on-one-subject, test-on-the-other) is adopted to assess generalization across individuals rather than relying on a pooled, sample-level split. Among the candidate features, a Frequency Standard Deviation (FSTD) is shown to match or exceed the performance of every multi-feature combination tested, indicating that targeted feature selection is more consequential than input fusion for this task. To further improve deployment efficiency, post-training INT8 quantization is applied, reducing the model to approximately 23 KB while preserving classification performance for quantization-robust configurations. Hardware-in-the-loop benchmarking on the STEdgeAI platform indicates on-device inference times ranging from approximately 0.88 ms on AI-enabled STM32N6 hardware to 117–130 ms on STM32H7-class microcontrollers; these figures reflect model inference only and exclude radar acquisition and preprocessing time. Experiments are conducted on a two-subject (one male, one female) indoor dataset; the reported cross-subject results are presented as a relative comparison across feature and quantization configurations rather than as an estimate of population-level generalization. The findings nonetheless illustrate the feasibility of combining principled feature selection with quantization-aware, hardware-validated deployment on embedded artificial intelligence (AI) platforms. Full article

Thermal runaway (TR) initiation and propagation tests are essential for evaluating the safety of lithium-ion batteries (LIBs). However, differences among existing standards in application scenarios, test levels, initiation methods, boundary conditions, and acceptance criteria hinder direct comparison and cross-level transfer of safety evidence. This study reviews representative standards and experimental research concerning road vehicles, industrial batteries, stationary energy storage, and railway transportation. External heating, nail penetration, overcharge, built-in devices, and induction heating are compared in terms of target failures, energy input, structural disturbance, and source-term characterization. The analysis shows that TR initiation methods, together with cell chemistry, state of charge, capacity, and format, determine thermal, gaseous, and ejecta source terms. Cell spacing, electrical connections, thermal management, mechanical constraints, and enclosure ventilation and pressure relief further govern propagation pathways and system-level consequences. Accordingly, a multilevel evidence chain is proposed, encompassing target-failure definition, cell-level initiation-method qualification, module- or representative-propagation-unit validation, pack/system-level consequence assessment, and product-change review. A six-risk-domain equivalence-assessment method is also established to address source terms, topology, thermal pathways, protection, enclosure boundaries, and personnel exposure. The framework provides a traceable basis for interpreting cross-standard results, validating large-scale LIB systems hierarchically, and inheriting safety evidence following product changes. Full article

Existing Non-Functional Requirement (NFR) classification approaches typically classify NFR statements independently, overlooking the semantic information provided by their associated Functional Requirements (FRs). Moreover, most existing models rely on general-purpose language representations and sentence-level evaluation protocols that may overestimate model generalization. This paper proposes a context-aware NFR classification framework that integrates FR context with the domain-specialized representations learned by the domain-specialized Stage-3 Re-Distill encoder. The proposed framework is evaluated under a strict project-level protocol designed to assess generalization across previously unseen software projects, together with a supplementary sentence-level 10-fold cross-validation experiment for comparison with existing studies. Experimental results demonstrate that the proposed framework achieves 0.86 Accuracy and 0.80 Macro-F1 under the project-level evaluation protocol while consistently outperforming generic pretrained transformer encoders. The experiments further reveal that the contribution of FR context is category-dependent, substantially improving Security and Usability classification while providing little or even negative benefit for Availability. Under sentence-level cross-validation, the same framework achieves a Macro-F1 score of 0.97, highlighting the strong influence of evaluation protocols on reported performance. These findings demonstrate that domain-specialized representations and FR context provide complementary benefits for NFR classification while highlighting the importance of realistic evaluation protocols for assessing model generalization. Full article

Accurate detection of early minor faults in electric vehicle traction batteries is important for preventing thermal runaway under complex operating conditions. Aging-related capacity degradation and measurement noise can mask the weak voltage distortions caused by early faults, leading to false alarms in data-driven diagnostic models. To improve robustness to these disturbances, this paper develops an early multi-fault decoupling and diagnosis framework based on PatchTSSA, a lightweight Transformer architecture that adapts time-series patching and Token Statistics Self-Attention (TSSA) to battery diagnostic sequences. The framework combines static–dynamic feature fusion with time-series patching to capture both global voltage drift and local morphological gradients. Within this adapted framework, TSSA replaces quadratic dot-product attention with second-order moment pooling, giving linear complexity with respect to the number of tokens and supporting future investigation of embedded Battery Management System (BMS) implementation. A physics-informed fault-injection strategy is used to construct a five-class dataset comprising the healthy state (E00), minor internal short circuit (E01), severe internal short circuit (E02), penetration fault (E03), and sensor drift (E04) from public Center for Advanced Life Cycle Engineering (CALCE) and National Aeronautics and Space Administration (NASA) battery-aging data. Across five raw-cycle-grouped splits and training seeds under 5 mV Gaussian white noise, the complete PatchTSSA configuration achieves $91.5\pm 1.9\%$ overall accuracy, $94.1\pm 1.2\%$ macro recall, and $85.4\pm 5.3\%$ E01 recall for the simulated fault patterns. The direct E01–E04 confusion rate is $0.09\pm 0.20\%$, whereas the E00-to-E01 false-alarm rate is $15.1\pm 6.9\%$. CALCE–NASA protocol differences are used only to describe cross-dataset domain shift; no transfer-performance claim is made without a matched capacity-free evaluation. The results indicate the potential of the framework for online fault-pattern discrimination, while validation using real fault data and embedded hardware remains necessary. Full article

Background: Hydroxylapatite is a biocompatible calcium-phosphate ceramic used in regenerative biomaterials. Calcium hydroxylapatite (CaHA) fillers combine immediate mechanical support with subsequent extracellular matrix remodeling, and their material behavior depends on microsphere morphology, mineral composition, carrier interactions, and rheology. Objectives: To determine whether a CaHA formulation prepared using Lattice Pore Formation technology (Facetem) exhibits a distinct and internally consistent material profile across microsphere geometry, lattice-pore surface architecture, mineral composition, formulation-level sedimentation, and rheological behavior across dilution ratios, with selected morphology and sedimentation characteristics compared with Radiesse. Methods: Facetem (marketed in the Republic of Korea as DCLASSY) was evaluated as the test product, with Radiesse used as the comparator product. Microspheres were examined using field-emission scanning electron microscopy and laser-diffraction particle-size analysis. Surface morphology was assessed before and after 12 weeks of phosphate-buffered saline (PBS) incubation. Mineral composition was evaluated using X-ray diffraction and inductively coupled plasma optical emission spectroscopy. Extrusion continuity and sedimentation after saline dilution were evaluated, and rheological properties were measured across eight dilution ratios using saline, non-cross-linked hyaluronic acid, and semi-cross-linked hyaluronic acid. Results: Facetem had a mean particle diameter of 34.60 μm, with higher circularity (0.95 versus 0.88) and roundness (0.96 versus 0.85), a lower aspect ratio, and fewer particles below 20 μm (2.10% versus 14.50%) than Radiesse. Micrograin domains formed a lattice-pore surface that showed morphological changes after 12 weeks of PBS incubation while the spherical contour remained recognizable. Hydroxylapatite represented 99.09% of the mineral phase, with a Ca/P ratio of 1.67. Facetem extruded as a continuous strand and showed greater supernatant clarification after dilution. Storage modulus declined with dilution, and semi-cross-linked hyaluronic acid retained more elastic resistance than saline at equivalent ratios. Conclusions: Facetem demonstrated a consistent microsphere population, an organized lattice-pore surface, hydroxylapatite stoichiometry, and diluent-dependent rheology. The integrated analysis defines its physicochemical profile; the PBS findings should be interpreted as morphological stability under non-biological buffer conditions rather than in vivo degradation. Full article

Project experience is distributed across individuals, project artifacts, team interactions, and organizational arrangements, making its transfer across project and temporal boundaries a multilevel systems challenge. Although after-action reviews, debriefs, retrospectives, postmortems, project reviews, and lessons-learned practices provide organized means of learning from experience, the interdependent components, transformation processes, and feedback relationships through which they generate reusable experiential knowledge remain insufficiently explained. This study systematically reviewed 106 English-language scholarly publications selected from 4157 records retrieved from the Web of Science Core Collection and Scopus. The evidence was synthesized through functional classification of evidence, typological analysis, thematic synthesis, and systems-oriented interpretive synthesis. The review establishes a common analytical domain for structured review practices based on four defining attributes and identifies four ideal-type configurations distinguished by their primary triggering mechanisms and knowledge boundaries. Across these configurations, four interconnected clusters of key cognitive tasks transform project experience materials into a shared interpretive foundation, reusable experiential knowledge, and a basis for future action. These transformations constitute a multilevel feedback architecture comprising short within-project and long cross-project loops, but may break down at five points under task–cognitive, social–political, and organizational–technological conditions. This study reframes structured review practices as a multilevel socio-technical learning system that connects actors, evidence, cognitive processes, knowledge artifacts, organizational uptake, and feedback from subsequent action. It thereby provides a systems-oriented conceptual basis for the design and diagnosis of structured reviews and for future research on human–AI support in project-based organizations. Full article

Radiation-shielding materials are critically important for protecting human health in nuclear energy, medical imaging, and radiotherapy applications. Due to the toxicity and environmental disadvantages of traditional lead-based materials, boron-doped glasses represent a promising alternative because of their radiation-shielding characteristics, optical transparency, and relatively low toxicity. This study evaluated whether a systematically selected artificial neural network (ANN) provides a meaningful predictive advantage over multiple linear regression (MLR) and support vector regression (SVR) for broad-spectrum linear attenuation coefficient (LAC) estimation while quantifying energy-dependent prediction error, input-importance uncertainty, and transferability limits. After removing one duplicated 0.0221 MeV record per glass, the final Phy-X/PSD-derived computational dataset comprised 546 observations from six glass compositions evaluated at 91 unique photon-energy points over 0.015–15 MeV. The 2–10–1/tansig ANN was selected using photon-energy-grouped cross-validation and the one-standard-error rule with parsimony. It achieved pooled out-of-fold RMSE = 0.020453 cm⁻¹, MAE = 0.004061 cm⁻¹, R² = 0.999915, and MAPE = 0.854%, outperforming MLR and RBF-SVR under the common validation protocol. Perturbation analysis identified photon energy as the dominant predictive input (95.75%), while B₂O₃ concentration, interpreted as a compositional descriptor of the S1–S6 series, contributed 4.25% model-specific importance. Repeated-split analysis supported strong within-domain interpolation for most partitions, whereas leave-one-energy-interval-out testing showed poor boundary-energy extrapolation and leave-one-composition-out testing revealed strongly nonuniform composition transferability. The ANN should therefore be used only as a preliminary computational screening and decision-support tool within the represented domain, with independent experimental validation required before engineering or safety-critical use. Full article

This study investigated sulfoaluminate cement grout intended for water-rich tunnel applications and developed machine-learning models for predicting its fresh-state properties. The experimental programme included a 31-mixture base series varying the water-to-binder ratio, Class F fly ash, steel slag, and polycarboxylate ether (PCE) superplasticizer dosage; a hydroxyethyl cellulose (HEC)-modified series with a fixed binder composition; and a static-water turbidity series used to evaluate particle dispersion resistance. Flowability, setting time, compressive strength, and turbidity were measured. The results showed that the water-to-binder ratio, fly ash, steel slag, PCE, and HEC affected the balance between workability, setting behaviour, strength development, and static-water dispersion resistance. Increasing the HEC dosage from 0.004 to 0.006 reduced turbidity by 84.1–94.7% under static-water conditions, whereas an HEC dosage of 0.007 reduced the 7 and 28 d compressive strengths by 51.5% and 40.7%, respectively. Machine-learning models trained on the 31-mixture HEC-free dataset provided exploratory predictions of flowability and initial and final setting times. Extra Trees, support vector regression, and Extra Trees gave the lowest nested leave-one-out cross-validation (LOOCV) errors for flowability, initial setting time, and final setting time, with R² values of 0.819, 0.743, and 0.767, respectively. Shapley Additive Explanations (SHAP) analysis indicated that the water-to-binder ratio dominated flowability prediction, whereas fly ash contributed strongly to setting-time prediction. The results provide complementary experimental and data-driven evidence for comparing sulfoaluminate cement grout formulations within the investigated composition domain. Full article

Background: Medical students who volunteer during armed conflict may help sustain overwhelmed health services while experiencing concurrent social, academic, psychological, and physical consequences. However, no context-sensitive instrument has been available to assess these multidimensional benefits and burdens in active conflict settings. This study primarily aimed to develop and conduct an initial psychometric evaluation of the Volunteering Impact Scale (VIS) among medical students volunteering in Gaza and, secondarily, to characterize their volunteering experiences. Methods: A cross-sectional survey was conducted among 169 medical students who volunteered in hospitals in Gaza during the ongoing war. An initial 28-item pool was generated from a focused literature review, relevant theoretical frameworks, and consultations with Gaza-based medical students and clinicians. A multidisciplinary team reviewed the items for relevance, clarity, redundancy, and contextual appropriateness. Following item refinement and psychometric evaluation, the final 16-item VIS comprised five domains: social impact, mental burden, physical burden, academic development, and wellbeing and fulfillment. Its structure was assessed using exploratory factor analysis with principal axis factoring and Promax rotation, followed by confirmatory factor analysis. Internal consistency and convergent and discriminant validity were also examined. Domain scores and their associations with participant, volunteering, and war-exposure characteristics were subsequently analyzed. Results: Exploratory factor analysis revealed a five-factor structure explaining 51.2% of the variance. Parallel analysis favored three factors, whereas a theoretically informed five-domain solution was retained for further evaluation. Primary factor loadings ranged from 0.23 to 0.89. Most items met the prespecified loading criterion of 0.40; however, three Social items demonstrated weaker loadings and were retained provisionally because of their conceptual relevance to the construct. Confirmatory analysis provided support for the proposed measurement structure (RMSEA = 0.059, SRMR = 0.075, CFI = 0.911, and TLI = 0.888). Cronbach’s alpha coefficients ranged from 0.64 to 0.75, and discriminant validity was supported by heterotrait–monotrait ratios below 0.85. Participants reported perceived social (M = 4.31/5), academic (M = 4.19), and wellbeing-related (M = 3.96) benefits. Mean reverse-scored Mental and Physical scores were 2.71 and 2.60, respectively, with lower scores indicating greater burden. Longer volunteering was associated with greater physical strain but also with stronger academic and wellbeing outcomes. Conclusions: The VIS demonstrated a conceptually coherent five-domain structure and promising initial psychometric properties for assessing the concurrent benefits and burdens of healthcare volunteering in a war-affected setting. Further validation in independent and more diverse samples is required. The findings also indicate that volunteer programs should combine meaningful clinical participation with structured supervision, recovery time, role rotation, and context-sensitive psychosocial support. Full article

Background/Objectives: Intensive care units (ICUs) are acoustically demanding environments in which nurses are continuously exposed to alarms, medical equipment, and staff activity. This study assessed ICU nurses’ perceived internal and external noise levels and their perceived noise-related adverse effects, and examined whether these perceptions differed according to demographic and work-related characteristics. Methods: A cross-sectional design was used with 244 ICU nurses in Saudi Arabia. Data were collected using a structured questionnaire adapted to assess perceived internal and external ICU noise and four effect domains (subjective, emotional, physiological, and work performance). Results: Perceived ICU noise was moderate (internal 64.68; external 64.51). Effect-domain means were 50.95 (subjective), 54.67 (emotional), 54.74 (physiological), and 51.28 (work performance). Female nurses scored significantly higher than male nurses for internal and external noise and for subjective, emotional, and physiological effects (all p < 0.05). Staff nurses, rotating-shift nurses, and nurses with <5 years of ICU experience reported higher effects in several domains; significant differences were also found by age group, marital status, and work-shift pattern. In multivariable models adjusting for all characteristics simultaneously, staff-nurse role, female gender and rotating shift remained independently associated with higher scores, whereas age group and years of ICU experience did not. Conclusions: ICU noise was perceived as a significant occupational stressor, with certain nurse subgroups reporting greater adverse effects. Healthcare organizations should prioritize comprehensive noise-reduction policies, effective alarm management, and targeted support strategies to mitigate noise-related impacts on nurses and improve the ICU work environment. Full article

The design and analysis of electrical filters rely on a wide range of numerical and symbolic software environments, each providing different computational and analytical capabilities. However, comprehensive comparative studies that evaluate both approaches using identical filter configurations and common performance criteria remain limited. This paper presents a systematic comparative evaluation of representative active electrical filter structures using SPICE, TINA-TI, MATLAB, MAPLE, ECAP, SYSEG, and TFSYG. The investigated filters are analyzed using a unified methodology based on frequency-domain, time-domain, and symbolic indicators, including magnitude and phase responses, cutoff frequencies, pole-zero distributions, transient responses, and transfer functions. The main contribution is a reproducible cross-platform framework comparing numerical and symbolic environments using equivalent filter models, identical component values, and common analysis criteria. Unlike tool- or topology-specific evaluations, the methodology applies the same comparative procedure to representative active-filter families, distinguishing software-dependent differences from those associated with the underlying circuit model. The results show close agreement for directly comparable quantities while highlighting the complementary capabilities of numerical and symbolic analysis. The proposed methodology provides practical guidelines for selecting appropriate software tools for electrical filter analysis, design, optimization, and engineering education, supporting the combined use of numerical and symbolic techniques in analog circuit development. Full article

With the rapid development of graph neural networks and physics-informed machine learning for critical infrastructure risk, reliable assessment of submarine telecommunication cables has become both a practical resilience requirement and a demanding cross-domain learning problem. These cables carry more than 99% of international data traffic, yet fault risk modeling faces four explicit challenges: (1) modality misalignment, because marine evidence combines gridded environmental fields with irregular vessel trajectories; (2) relational heterogeneity, because hazards interact through semantically distinct spatial links; (3) physical inconsistency, because unconstrained predictions may violate seabed geomechanics under geographic shift; and (4) decision uncertainty, because safety-critical inspection and routing require uncertainty rather than point estimates alone. The closest approaches leave identifiable gaps. Makrakis and colleagues optimized static cable routes without learned hazard interactions or uncertainty; Taghizadeh and colleagues constrained flood graph predictions without cable-specific heterogeneous entities; and Guo and colleagues fused maritime trajectories without forecasting cable faults or screening routes. No existing approaches combine these missing capabilities under geographically held-out cable basins. To address this gap, the present paper proposes MarineGuard-GNN, a physics-informed multimodal heterogeneous graph neural network. Its Cross-Modal Spatiotemporal Tokenizer maps GEBCO bathymetry, CMEMS ocean fields, and NOAA AIS trajectories into a shared 256-dimensional space; a relation-aware Heterogeneous Graph Transformer represents four semantic node types and four physical relation types; a differentiable Mohr–Coulomb loss regularizes geomechanical consistency; and a Monte Carlo dropout risk head estimates segment-level epistemic uncertainty for inspection and routing. Under 4-fold geographic cross-validation at natural prevalence on 15,110 cable nodes (847 faults), MarineGuard-GNN attains cross-basin AUC-ROC, average-precision, and F1 ranges of $0.72$–$0.76$, $0.18$–$0.24$, and $0.29$–$0.36$, respectively; average precision corresponds to a $3.21$–$4.28\times$ lift over the $0.0561$ no-skill prevalence baseline. Paired basin-stratified bootstrap analysis and Holm-corrected tests confirm improvements over the strongest tabular and graph baselines ($\Delta$AUC-ROC $\ge 0.02$, $\Delta$AP $\ge 0.03$; $p_{\mathrm{adj}}<0.05$). Matched ablation shows that removing the corrected mechanics term reduces AUC-ROC by $0.005$ and average precision by $0.010$ without improving calibration. Across three densely sampled public cable corridors, uncertainty-aware routing reduces mean predicted risk by at least $5\%$ while limiting distance overhead to below $3\%$; the conclusion remains stable for risk thresholds from $0.45$ to $0.60$. These results demonstrate statistically supported cross-basin generalization, establishing the proposed framework as a reproducible decision support method. Full article

Background/Objective: Gait symmetry in children is increasingly understood as a development-dependent continuum rather than a fixed endpoint. This narrative review extends that framework into a multidomain model of gait divergence in cerebral palsy (CP), asking whether asymmetry in hemiplegic and diplegic CP fails to reach the normative curve or diverges from it in phenotype-specific ways that cannot be captured by a single index. Methods: Literature was identified through a semantic search (Elicit; April–August 2026) supplemented by gap-directed searches, yielding 50 references, synthesized across four domains—interlimb symmetry, kinematic deviation, neuromuscular/coordination control, and functional balance/postural control—drawing predominantly on cross-sectional and cross-study evidence, with fewer longitudinal and intervention studies. Results: Hemiplegic CP shows lateralized divergence with compensatory changes in the nominally unaffected limb and partial improvement in asymmetry over time. Diplegic CP frequently shows preserved or near-normal left–right symmetry coexisting with progressive, growth-linked deterioration in sagittal-plane impairments hidden beneath a stable global gait score, alongside a more coupled, less variable coordination strategy not matched by comparable neuromuscular maturation; whether this coupling explains the preserved symmetry remains an untested hypothesis. These domains do not move in parallel across phenotypes; therefore, no single metric is sufficient. Conclusions: Because the hemiplegia–diplegia contrast is currently better supported as a cross-sectional distinction than as evidence of differing longitudinal slopes, we propose a provisional, non-summative Trajectory Divergence Framework—without numerical scoring—as a hypothesis-generating structure for future multi-domain monitoring, identifying the absence of longitudinal, multi-domain, multi-phenotype cohorts as the principal evidence gap in the field. Full article

Background/Objectives: Alexithymia may constrain emotion regulation, but its relevance may differ across personality configurations. This study examined whether narcissistic Admiration and Rivalry are embedded in distinct early maladaptive schema and alexithymia profiles. Methods: A non-clinical adult sample (N = 311) completed the Narcissistic Admiration and Rivalry Questionnaire, the Young Schema Questionnaire–Short Form 3, and the Toronto Alexithymia Scale–20. Analyses examined 18 schemas and three alexithymia components—Difficulty Identifying Feelings (DIF), Difficulty Describing Feelings (DDF), and Externally Oriented Thinking (EOT)—using zero-order correlations, within-domain regressions, hierarchical regressions, and structural equation models. Results: Admiration was associated mainly with Approval/Recognition Seeking and Unrelenting Standards and with lower Social Isolation/Alienation, Defectiveness/Shame, Failure, Subjugation, Emotional Inhibition, and Insufficient Self-Control. Rivalry showed a threat-related profile involving Defectiveness/Shame, Subjugation, Emotional Inhibition, Negativity/Pessimism, and Insufficient Self-Control. Entitlement/Grandiosity was positively associated with both dimensions and did not reliably differentiate them. Alexithymia was essentially unrelated to Admiration and did not improve its schema model. In contrast, adding DIF, DDF, and EOT increased explained variance in Rivalry by 5.5%, a small-to-moderate increment; DIF and EOT made independent contributions, whereas DDF did not. A parsimonious Rivalry structural model reproduced these associations but showed mixed global fit. Conclusions: These cross-sectional findings indicate a modest, dimension-specific contribution of alexithymic processing to Rivalry rather than a general association with narcissistic self-regulation. The EOT findings require particular caution because of the subscale’s limited reliability. Longitudinal and experimental studies are needed to test the proposed vulnerability–stress process. Full article