Search Results (6,580)

Background: Pregnancy represents a major identity transition, yet most perinatal assessments focus primarily on emotional symptoms rather than on how women integrate the maternal role into their broader identity and life context. Difficulties in maternal role integration may constitute an early vulnerability factor for psychological distress. This study aimed to develop and validate the Maternal Role Integration Questionnaire—pregnancy version (MRIQ-P), a brief instrument designed to assess maternal identity and role balance during pregnancy, and to examine its clinical relevance for perinatal mental health. Methods: A sequential mixed-methods design was employed. Phase 1 involved focus groups with pregnant women (n = 17) and cognitive debriefing to generate and refine items. Phase 2 included expert evaluation of content validity. In Phase 3, the MRIQ-P was psychometrically validated in a sample of pregnant women (n = 256), randomly divided into exploratory (n = 83) and confirmatory (n = 173) subsamples. Exploratory and confirmatory factor analyses were conducted, along with reliability analyses, tests of convergent, discriminant, incremental, and measurement invariance validity. Results: Analyses supported a bifactor structure comprising a general factor of maternal role integration and two specific dimensions: Maternal Identity and Balance of the Maternal Role. The final 8-item version demonstrated excellent internal consistency for the total score (α = 0.96) and subscales (α = 0.98 for Maternal Identity and α = 0.98 for Balance of the Maternal Role), as well as measurement invariance across primiparous and multiparous women. Higher maternal role integration was associated with greater self-esteem, positive affect, and life satisfaction, and with lower anxiety, depression, prenatal distress, and maternal ambivalence. Importantly, MRIQ dimensions explained additional variance in antenatal depression and dispositional guilt beyond established psychological predictors, supporting its incremental and potential clinical utility. Conclusions: The MRIQ is a brief, psychometrically robust, and clinically relevant instrument for assessing maternal role integration during pregnancy. By capturing identity- and role-related processes that are not directly addressed by symptom-based screening tools, it may contribute to early identification of vulnerability and to more comprehensive perinatal psychological assessment in healthcare settings. Full article

The rapid electrification of transportation and proliferation of distributed energy resources (DERs) are transforming distribution grids into highly dynamic, data-intensive, and cyber-physical systems. While reinforcement learning (RL), multi-agent coordination, and edge computing offer powerful tools for adaptive control, their deployment in safety-critical utility environments raises concerns regarding stability, certification compatibility, cyber-resilience, and regulatory acceptance. This paper presents an architecture-centric framework for edge-intelligent and cyber-resilient coordination of electric vehicles (EVs) and DERs that reconciles adaptive learning with deterministic safety guarantees. The proposed hierarchical edge–cloud architecture integrates multi-agent system (MAS) coordination, constraint-invariant reinforcement learning, and embedded cybersecurity mechanisms within a structured control hierarchy. Learning-enabled edge agents operate exclusively within standards-compliant safety envelopes enforced through supervisory constraint projection, control barrier functions, and Lyapunov-consistent stability safeguards. Protection-critical functions remain deterministic and isolated from adaptive layers, preserving compatibility with IEEE 1547 and existing utility protection schemes. The framework further incorporates anomaly triggered policy freezing, fail-safe fallback modes, and communication-aware resilience mechanisms to prevent unsafe transient behavior in non-stationary, distributed environments. Unlike simulation-only learning approaches, the architecture embeds progressive validation through software-in-the-loop (SIL), hardware-in-the-loop (HIL), and power hardware-in-the-loop (PHIL) testing to empirically verify transient stability, constraint compliance, and cyber-resilience under realistic timing and disturbance conditions. Beyond technical performance, the paper situates edge intelligence within standards evolution, governance structures, workforce transformation, techno-economic assessment, and equitable deployment pathways. By framing adaptive control as a bounded, auditable augmentation layer rather than a disruptive replacement for certified infrastructure, the proposed architecture provides a pragmatic roadmap for evolutionary modernization of distribution systems. Full article

Seasonal human coronaviruses (HCoVs) continue to undergo adaptive evolution under structural and immune-mediated constraints. We investigated the molecular epidemiology and spike (S) protein structural variation of circulating coronaviruses in Riyadh, Saudi Arabia, during the 2025–2026 winter season, with particular emphasis on genotype persistence and glycosylation architecture in HCoV-OC43. Among 293 nasopharyngeal aspirates (NPAs) collected from hospitalized patients with acute respiratory illness, HCoV-OC43 was detected in 26 cases (8.87%), whereas other seasonal coronaviruses were not identified. Partial sequencing of the S gene revealed 97.84–98.23% nucleotide identity relative to the prototype strain VR-759, with amino acid substitutions distributed at discrete positions rather than within extended variable domains, indicating structural conservation. Phylogenetic reconstruction demonstrated that all Riyadh isolates clustered within genotype C, together with previously circulating local strains, supporting sustained endemic persistence and in situ evolution. In silico analysis of the S protein glycosylation landscape identified four invariant N-linked glycosylation motifs (N-X-S/T) at residues 46, 121, 134, and 190, reflecting strong structural constraints on glycan-dependent folding and antigenic configuration. A genotype-associated K68N substitution generated an additional N-glycosylation motif (68NGTD) in multiple Riyadh isolates, potentially modifying local glycan shielding without disrupting the overall glycosylation framework. The preservation of core glycosylation sites alongside selective motif acquisition suggests evolutionary fine-tuning of S surface topology rather than large-scale structural remodeling. Collectively, these findings indicate that genotype C persistence in Riyadh is accompanied by conserved S architecture and subtle glycosylation adjustments that may modulate immune recognition while maintaining structural integrity. Continued high-resolution molecular surveillance will be critical for defining the functional consequences of S microevolution in endemic HCoVs. Full article

Cross-domain scene classification aims to mitigate the distribution discrepancy between domains through domain adaptation techniques. With the rapid advancement of Vision–Language Models (VLMs), utilizing them for cross-domain scene classification has emerged as a promising research direction. Current methods utilize domain-specific prompts to facilitate domain adaptation through the CLIP model. However, for remote sensing images, the considerable differences in visual features across domains pose significant challenges for learning domain-specific prompts, leading to suboptimal cross-domain performance. In addition, they cannot reduce the domain shift that exists between the source domain and the target domain. To address the above challenges, we propose a novel cross-domain scene classification method, DIPVR (Domain-Invariant Prompts and Visual Representations), which enhances model performance by learning domain-invariant features for both prompts and visual representations. Specifically, we propose learning domain-invariant prompts and introducing prior knowledge to guide the prompt-learning process. To learn domain-invariant visual representations, we propose a Visual Invariant Learning module that adaptively extracts the shared features between the source and target domains. Finally, visual features are matched with context features to align the domain distributions between the source and target domains. The experimental results on the cross-domain scene classification datasets demonstrate that our proposed method outperforms the baseline methods, achieving optimal cross-domain transfer performance. Full article

This paper presents a deterministic embedded control architecture for an industrial electroplating line. The validated system includes two autonomous trolleys, 18 station-aligned process positions, shared-track motion, and redundant grouped baths. The proposed controller addresses the limitations of rigid sequential automation by combining asynchronous finite-state trolley execution, runtime allocation of equivalent technological stations, dwell-time-preserving retrieval, distributed thermal supervision, and layered fail-safe protection within a single ATmega2560-based implementation. The core contribution is the integration of virtual process groups and temporal FIFO logic into a compact plant-side embedded controller. This enables adaptive bath selection and process-completion-based retrieval without reliance on a real-time operating system or a computationally heavy supervisory runtime. The architecture also incorporates predictive pre-start validation, runtime software arbitration, hardware-wired interlocks, binary-coded trolley positioning, and a distributed 1-Wire thermal measurement network. Validation was performed in a controller-centered hardware-in-the-loop representation of an 18-station zinc electroplating line. Over a 100-batch horizon, the proposed architecture reduced makespan from 1642 min to 1244 min, corresponding to a 24.2% throughput improvement. Average trolley idle time decreased from 18.4 min/batch to 4.1 min/batch. Grouped-bath utilization increased from 64% to 91%, while tracked bottleneck incidents decreased from 18 to 2. These results show that adaptive, resource-aware, and safety-layered electroplating control can be realized effectively on a compact embedded platform in an industry-representative HIL setting, while preserving dwell-time integrity and controller-level safety invariants. Full article

Skeleton-based action recognition is a critical technology for intelligent sports analysis. Although the human skeletal structure exhibits inherent bilateral symmetry, sensor noise on resource-constrained edge devices frequently induces geometric distortion and topological asymmetry. Consequently, achieving a balance between high accuracy and real-time performance remains a significant challenge. To this end, we propose EMS-GCN, an Efficient Multi-scale Shift Graph Convolutional Network that integrates geometric priors. Specifically, we design a Gaussian kernel-driven topology refinement module to mitigate structural noise inherent in sensor data. By leveraging geometric symmetry and Gaussian distances among nodes, this module dynamically constrains graph topology learning, thereby effectively rectifying the structural asymmetry and ambiguity induced by noise. Furthermore, we construct a Multi-scale Shift Linear Attention (MSLA) module to replace computationally intensive temporal convolutions. Leveraging temporal shift invariance, this module captures multi-scale contexts via parameter-free shift operations. Furthermore, we introduce a linear temporal attention mechanism to model global temporal dependencies with linear complexity, effectively resolving the information asymmetry inherent in long-range interactions. Finally, EMS-GCN incorporates a dual-branch attention structure to adaptively calibrate feature responses. Extensive experiments demonstrate that our model maintains high recognition accuracy with only 0.56M parameters, representing a reduction of over 60% compared to mainstream baselines. These results validate the efficacy of leveraging geometric and temporal symmetries to enhance real-time sports analysis. Full article

In this paper, we introduce two analytic deformations of probability measures that unify and extend two classical deformations from free probability theory, namely the $\mathbb{T}=(s,t)$-deformation $U^{\mathbb{T}}$ and the $T_a$-deformation, where $a,t\in \mathbb{R}$ and $s>0$. The corresponding operators, denoted by ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$, are defined via a functional equation involving the Cauchy–Stieltjes transform (CST). This framework recovers the classical cases as particular instances, specifically ${\mathbb{Y}}_{(0,s,t)}^{\prime }={\mathbb{Y}}_{(0,s,t)}^{\prime \prime }=U^{\mathbb{T}}$ and ${\mathbb{Y}}_{(a,1,1)}^{\prime }={\mathbb{Y}}_{(a,1,1)}^{\prime \prime }=T_a$. We analyze the analytic and structural properties of the operators ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$ within the concept of Cauchy–Stieltjes kernel (CSK) families, with particular emphasis on their action on variance functions (VFs). In particular, we derive explicit formulas for the VFs associated with measures deformed by ${\mathbb{Y}}_{(a,s,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,s,t)}^{\prime \prime }$. As an application, we establish an invariance property showing that the class of free Meixner family (FMF) is stable under both deformations. Furthermore, by restricting the parameters to ${\mathbb{Y}}_{(a,1,t)}^{\prime }$ and ${\mathbb{Y}}_{(a,1,t)}^{\prime \prime }$, we obtain two new characterizations of the semicircle law. These results highlight the role of symmetry in the analytic deformation and in the stability properties of fundamental distributions in free probability. Full article

This study modeled the factor structure of the Generalized Anxiety Disorder-7 (GAD-7), quantified its internal consistency, tested for longitudinal invariance, and estimated associations with measures of depression, parent stress, family functioning, and child psychopathology. Data were from 200 parents enrolled in an on-going study of children with chronic health conditions recruited from a pediatric hospital. Exploratory and confirmatory factor analysis (CFA) modeled the GAD-7 factor structure, and multiple-group CFA tested longitudinal invariance over 48 months. A one-factor model showed the best fit to the data, and the omega hierarchical was 0.89 and 0.88 at baseline and 48 months, respectively. The GAD-7 demonstrated longitudinal invariance. Internal consistency was good at both assessments (α > 0.75). Correlations with other measures were significant and at least small in magnitude. Known-group validity (parents with vs. without depression) showed very large effects (d > 2.0). The GAD-7 is psychometrically robust in parents of children with chronic health conditions. Full article

Background: Physical literacy is a multidimensional construct encompassing physical competence, confidence, motivation, knowledge, and lifelong engagement in physical activity. The Perceived Physical Literacy Instrument (PPLI) has been widely used internationally; however, previous adolescent validations have been based on a reduced 9-item version originally developed for teachers. This study aims to evaluate the validity and test–retest reliability of a Spanish adaptation of the original 18-item PPLI in Spanish adolescents aged 11–18 years. Methods: A multi-phase validation study was conducted with 869 Spanish adolescents (421 females). The procedure included: (1) translation and cultural adaptation, (2) Exploratory Factor Analysis (EFA; n = 290), Confirmatory Factor Analysis (CFA; n = 579) and invariance analyses, and (3) test–retest reliability assessment. Results: EFA supported a three-factor solution comprising 15 items. CFA showed standardized factor loadings ranging from 0.62 to 0.89, indicating that the latent constructs were adequately represented. Although the 15-item model showed acceptable fit, a 5-item unidimensional short form was developed due to limitations in the three-dimensional models. This short form demonstrated good model fit (scaled RMSEA = 0.073; scaled CFI = 0.992; SRMR = 0.026), adequate convergent validity (AVE = 0.558), high reliability (ω = 0.821), moderate test–retest stability (ICC = 0.69), and full configural, metric, and scalar longitudinal invariance. Conclusions: The 15-, 9-, and 5-item versions of the PPLI are valid and reliable options. The 15-item version allows comprehensive assessment and domain-level interpretation. The 9-item version facilitates comparability with previous international research. The 5-item version may be useful in contexts with time constraints but may not be the preferred choice for comprehensive assessment of physical literacy in clinical or detailed pedagogical diagnostic settings. Full article

Differential operators often admit multiple algebraically equivalent symbolic formulations, yet those formulations can differ in the organization of their internal structure prior to solution analysis. A reproducible symbolic framework is introduced to compare such formulations at the level of operator expressions. Within a declared symbolic specification consisting of a fixed grammar, an admissible weight class, canonical compression rules, and an admissible family of reformulations, we define four encoding-relative structural descriptors: structural strain τ, structural curvature κ, compressibility σ, and the balance ratio Γ = κ/τ. Structural strain compares an encoding to a designated reference representation, while compressibility measures reduction under canonical symbolic compression. These quantities are deterministic descriptors within the declared encoding class rather than coordinate-free invariants of the underlying operator. The structural length functional underlying these descriptors is developed, canonical compression is formalized, and finite symbolic comparison is distinguished from pathwise symbolic deformation. A robustness theorem shows that, away from the threshold surface Γ = σ, sufficiently small admissible perturbations preserve the induced diagnostic label. A supporting weight-robustness result further shows that qualitative labels persist across a local admissible family of weight choices under corresponding nondegeneracy conditions. The framework serves as a reproducible diagnostic for operator representations alongside Lyapunov, spectral, pseudospectral, and energy-based stability theories. Examples of representative ordinary and partial differential operators illustrate how the descriptors are computed and how they behave under admissible re-expression, while the appendices provide the technical backbone of the paper: formal definitions, reproducibility protocol, extended perturbation arguments, and explicit failure-mode analysis. Additional sensitivity checks regarding encoding, weights, and threshold variation clarify the method’s scope, and explicit failure modes delineate the boundary cases in which the descriptors cease to apply. The main contribution of this study is a formally delimited and reproducible symbolic framework for comparing differential operators under a fixed, declared specification, together with robustness results and worked examples that clarify the method’s scope. Full article

Background: Although the concepts of meteoropathy and meteosensitivity are not included in official classifications, such as the ICD-11 or DSM-5, they are increasingly being studied as potential symptom complexes linking weather variability to health status. The METEO-Q questionnaire, originally developed in Italy, has been adapted in Japan and Turkey, where it has demonstrated satisfactory reliability parameters, although the authors emphasized the need for further verification of the tool’s temporal stability. The present study aimed to adapt METEO-Q to the Polish language and conduct a critical assessment of its factor structure, measurement invariance, and validity in clinical groups. Methods: This cross-sectional study involved 1128 adults: healthy individuals (n = 711), cardiac outpatients (n = 194), and subclinical group with diagnosed mental disorders (n = 223). Data from healthy participants were divided into a training sample (n = 426) for exploratory factor analysis (EFA) and a test sample (n = 285) for confirmatory factor analysis (CFA). Measurement invariance was assessed in the clinical groups. Validity was verified through correlations with a list of 21 symptoms and measures of anxiety and worry about climate change. Results: A two-factor model (meteoropathy and meteosensitivity) was better fitted to the data than a one-factor model, which is consistent with findings from Italian, Japanese, and Turkish studies. However, absolute fit indices in the test sample indicated significant model misfit [CFA: χ² (43) = 210.192, p < 0.001, RMSEA = 0.120, CFI = 0.927], suggesting the presence of local errors in the tool’s structure. The reliability of the subscales was high (α from 0.86 to 0.93). Multi-group analyses suggested metric and scalar invariance. Patients with mental disorders obtained the highest scores, while cardiac outpatients reported a lower level of meteoropathy (M = 6.13) than healthy individuals (M = 7.24). Conclusions: METEO-Q demonstrates a stable two-factor structure and high internal consistency. The obtained RMSEA index (0.12), although indicative of some misfit, is similar to results obtained in other adaptations, such as the Japanese (RMSEA = 0.10) and the Turkish (RMSEA = 0.11), which suggests it is a consistent feature of this tool across different cultural contexts. Accordingly, the instrument is suitable for research purposes; however, its clinical application requires considerable caution and further work to optimize the model. Full article

The critical brain hypothesis proposes that neural systems operate near a phase transition to optimize information processing. A key method for investigating this hypothesis is the phenomenological renormalization group (pRG), which looks for scale-invariant features across levels of coarse-graining. One such feature is the power-law scaling of eigenvalues of covariance matrices of coarse-grained variables. However, the estimation of this scaling exponent, $\mu$, often relies on linear regression over arbitrarily selected ranges of the plot of eigenvalues versus rank. This heuristic “eyeballing” introduces uncontrolled bias and complicates the interpretation of observed scaling relationships. In order to obtain a more robust estimation of $\mu$, we do not fit the standard eigenvalue-vs-rank relationship, but rather the density of eigenvalues, for which standard protocols exist for fitting power laws to empirical data distributions. We demonstrate this approach using a synthetic model that replicates the scaling signatures of neural data while providing control over the system’s exponents as well as neural data obtained from publicly available Neuropixels recordings. We also establish standards for the minimal data required to quantify power-law behavior in a pRG eigenvalue analysis. Our approach contributes a tool for understanding the fundamental limitations imposed by spatial and temporal constraints of experimental datasets, which is required to rigorously assess the neural criticality hypothesis. Full article

This study introduces a deterministic fractional-penalty refinement of Vogel’s Approximation Method (VAM) for generating high-quality initial basic feasible solutions (IBFS) in classical transportation problems. Unlike the traditional additive regret measure employed in VAM, the proposed method uses a multiplicative contrast ratio between the two smallest admissible costs in each row and column. This modification preserves the allocation structure of VAM while introducing scale-invariant prioritization that improves sensitivity to relative cost differences.The method was evaluated on thirty-four benchmark transportation problems drawn from the literature and self-constructed large-scale instances (up to $10\times 20$). Performance was assessed using percentage optimality gaps relative to optimal solutions obtained via the Stepping–Stone and MODI procedures. Across all instances, the proposed approach achieved a mean optimality gap of $2.78\%$, compared to $5.22\%$ for classical VAM, $14.97\%$ for the Least Cost Method (LCM), and $45.78\%$ for the Northwest Corner Method (NWCM). Dispersion of deviations was also reduced, indicating improved robustness across heterogeneous cost structures Statistical validation confirms the improvement over VAM: the paired t-test yielded $t=-3.17$ ($p=0.00163$, one-sided), and the Wilcoxon signed-rank test produced $p=6.10\times {10}^{-5}$. Computational experiments further show that the refinement does not increase runtime relative to classical IBFS procedures.The proposed method therefore constitutes a structured enhancement of VAM that improves initial solution quality while maintaining computational simplicity. Full article

We identify a fundamental tension between general relativity and spectral geometry arising from the global, nonlocal character of spectral data versus the local causal dynamics of spacetime. To resolve this, we postulate spectral invariance, $\delta {\Lambda }_n=0$, requiring the eigenvalues of the Laplace–Beltrami operator to remain fixed under physical evolution. This condition yields a compensatory relation between metric deformations and eigenfunction amplitudes, suggesting a kinematic coupling linking energy distribution to spacetime curvature. From the second variation of the associated energy functional, we derive a rank-4 tensor proportional to the inverse DeWitt supermetric on superspace and a contracted rank-2 tensor proportional to the spacetime metric, and we recover a invariance law of the energy functional in configuration space. Spectral invariance may suggest a framework in which geometry and energy are co-defined through fixed spectral data. Full article

Land subsidence in the Yellow River Floodplain, approaching 60 mm/year, is severely exacerbated by annual groundwater oscillations of 3 to 8 m. Conventional hydro-mechanical models, which primarily rely on effective stress principles, often struggle to fully capture the moisture-induced structural degradation of calcareous cemented soils under such hydraulic disturbances. To address this theoretical gap, we conducted a multifactor orthogonal triaxial experiment to quantitatively decouple the macroscopic factors governing the hydro-mechanical degradation. The results reveal that moisture content acts as the absolute dominant driver, accounting for 81.65% of the variance in macroscopic shear strength variance and completely overwhelming the mechanical advantages provided by initial compaction. A generalized dual-path water-sensitive damage model was explicitly derived, mathematically uncovering a fundamental asynchronous degradation mechanism. Cohesion exhibits an inward-concave, brittle fracture trajectory, which is macroscopically inferred to be associated with the water-induced softening of calcareous bonds (phase-transition parameter 0.81, maximum allocation 75.1%). Conversely, the internal friction angle demonstrates an outward-convex, hysteretic decline (parameter 1.59), maintaining structural interlocking until severe water-film lubrication occurs. By decoupling highly state-dependent initial strength parameters from invariant degradation operators, the modified Mohr–Coulomb model achieved exceptional forward blind-prediction accuracy. Validations across distinct initial skeletal structures constrained relative prediction errors strictly between −19.3% and +13.7% without any subjective parameter recalibration. The quantified extreme vulnerability theoretically proves that minor water infiltration can instantly eradicate over 75% of cohesive strength, necessitating a paradigm shift from shallow mechanical compaction to stringent waterproofing in regional engineering practices. Full article