Search Results (624)

This study examined the mediating role of problem-solving attitudes in the relationship between catastrophic thinking and environmental awareness among university students using structural equation modeling. Two samples of undergraduate students from Al-Azhar University, Egypt, participated: a psychometric validation sample (N = 670) and a main study sample (N = 989). Participants completed three validated instruments assessing catastrophic thinking, problem-solving attitudes, and environmental awareness. Results revealed that catastrophic thinking was significantly negatively associated with environmental awareness both directly (β = −0.266) and indirectly through problem-solving attitudes (β = −0.172), with the indirect pathway accounting for approximately 39% of the total effect. The structural model demonstrated excellent fit to the data, and all hypothesized relationships were statistically significant. These findings suggest that catastrophic cognitions are associated with reduced environmental awareness both directly and through their negative relationship with problem-solving orientations that facilitate engagement with complex issues including environmental challenges. The study highlights the importance of addressing trait-level cognitive distortions alongside environmental content in education programs, as general catastrophic thinking patterns may impair environmental awareness even among students without climate-specific anxiety. Full article

Systematic radiometric anomalies, manifesting as non-physical range-direction oscillations, significantly compromise the quality of Miniature Radio Frequency (Mini-RF) S-band SAR imagery and its scientific application in the lunar south polar region. In this study, we analyzed 1262 scenes from the Mini-RF archive in south polar regions. By employing a statistical screening method based on fitting the relationship of backscattering signal and off-nadir angle, 377 scenes (29.9%) were identified as radiometrically anomalous scenes with systematic errors. To correct these errors, a physics-based radiometric correction framework has been proposed by reconstructing the effective antenna gain pattern (AGP) of Mini-RF. Referenced relationship between the backscattering signal and the local incidence angle was established using normal scenes. For each anomalous scene, a simulation-driven gradient descent optimization approach is developed to estimate the offset of the AGP. Subsequently, the derived offset is applied to realign the AGP of the anomalous scene, effectively compensating for the systematic range-direction oscillations and restoring the true backscatter intensity. Using the proposed method, systematic errors in anomalous scenes have been eliminated effectively, reducing the Root Mean Square Error (RMSE) relative to the reference radiometric curve from 2.11 to 1.21 and decreasing the image entropy from 2.83 to 2.29. By eliminating systematic banding artifacts, the proposed method has significantly improved the radiometric fidelity of Mini-RF data. Furthermore, a temporal periodicity was found in the gain offsets, suggesting dynamic instrument distortion driven by variations in the orbital thermal environment. Full article

High-entropy alloys (HEAs) represent one of the most promising emerging material families, particularly for advanced surface engineering applications. In this work, a near-high-entropy alloy (near-HEA) coating was produced on a 316L stainless steel substrate using laser metal deposition (LMD) from a powder mixture of Inconel 625, Cr and Mo, without the intentional addition of Fe. Due to dilution from the substrate, the resulting alloy contained elevated Fe content while maintaining Cr, Ni and Mo concentrations within the generally accepted compositional range of HEAs. The deposited layer exhibited a dual-phase microstructure consisting of a face-centered cubic (FCC) phase and a highly distorted tetragonal phase forming a periodic network with a characteristic length scale of several hundred nanometers. The hardness of the coating increased to approximately three times that of the substrate, reaching values of 600–700 HV. To further modify the surface properties, laser-induced periodic surface structures (LIPSS) were generated on the polished coating using femtosecond pulsed laser irradiation at different energy densities. The morphology and subsurface structure of the resulting periodic patterns were investigated by scanning electron microscopy. LIPSS with characteristic dimensions ranging from the micrometer to nanometer scale were successfully produced. Cross-sectional analyses revealed that the underlying dual-phase microstructure remained continuous within the laser-structured regions, indicating that LIPSS formation occurred predominantly via metallic ablation without significant phase transformation or amorphization. These results demonstrate the combined applicability of LMD and femtosecond laser structuring for producing mechanically enhanced, micro- and nanostructured near-HEA coatings with potential for advanced surface-related functionalities. Full article

Raster-stereography and Moiré Fringe Topography are widely recognized as effective techniques for surface screening. Traditionally, these methods have been applied in various medical and clinical contexts, such as assessing human body symmetry, analyzing spinal deformities, evaluating scapular positioning, and predicting trunk-related abnormalities. Both techniques have proven to be reliable tools for examining the human body surface and identifying health-related issues. However, in these techniques, line grids projected onto non-uniform surfaces often break or distort, complicating curvature detection. Capturing and digitizing these distortions through photographymeans further reducing accuracy due to low contrast between background and projected lines. In this paper, we present a modified, i.e., dotted-based, approach to Moiré Fringe Topography construction, offering a simpler, more accurate, and efficient method for recording human body surface curvatures. The proposed technique significantly reduces the complexity of the data acquisition process while maintaining precision in surface analysis. A Single-Photon Avalanche Diode (SPAD) image sensor was used to capture the Moiré patterns. Full article

Objective: To synthesize current evidence on the relationships between adolescent idiopathic scoliosis (AIS), stress-related mechanisms, neuroanatomical asymmetry, and mental disorders, and to propose an integrative conceptual framework describing their interaction. Materials and Methods: A comprehensive literature review was conducted using the PubMed, Web of Science, and Scopus databases. Search terms targeted the etiology and pathogenesis of adolescent idiopathic scoliosis, hemispheric lateralization, stress responses, body schema disturbances, and associated mental disorders. The review was reported in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations. A structured qualitative synthesis of 225 relevant publications was performed. Results: The analyzed studies revealed several complementary conceptual approaches to AIS pathogenesis. Emerging evidence suggests that atypical hemispheric lateralization, potentially associated with right-hemisphere (RH) dysfunction, may contribute to susceptibility to AIS. Such patterns of lateralization have been linked to specific stress-related coping strategies, including harm avoidance, as well as to disturbances of body schema and an increased prevalence of certain mental disorders. Gender-related differences in stress responses and in the development and progression of AIS were consistently reported across studies. Collectively, these findings support the hypothesis that neuropsychological and stress-related mechanisms, including phenomena described as the “distorting mirror effect”, may contribute to the persistence and progression of spinal deformity in vulnerable individuals. Conclusions: AIS appears to be a multifactorial condition in which atypical neuroanatomical asymmetry, stress-related processes, and altered body representation interact. This integrative perspective generates hypotheses suggesting that prevention and treatment strategies for AIS could benefit from incorporating approaches aimed at modulating stress responses and enhancing brain neuroplasticity. Further interdisciplinary studies integrating clinical, neuroimaging, and neurobiological methods are warranted to clarify underlying mechanisms. Full article

This paper investigates an artificial intelligence (AI)-enhanced visible light communication (VLC) system for body area networks (BANs) based on a hybrid modulation framework that jointly employs quadrature amplitude modulation (QAM) and pulse-position modulation (PPM). The dual-modulation strategy leverages the high spectral efficiency of QAM together with the robustness of PPM to light-emitting diode (LED) nonlinearity and timing distortions, enabling simultaneous high-rate and reliable communication, two essential requirements in BAN applications. To address the nonlinear response of light-emitting diodes and the variability in indoor optical channels, the system integrates classical predistortion techniques with a deep learning equalizer combining convolutional neural network (CNN)–transformer layers. This hybrid model captures both local and long-range distortion patterns, improving symbol reconstruction for both modulation branches. The study further examines pilot-assisted equalization and adaptive bit loading, showing that these strategies strengthen link robustness under diverse channel conditions while enhancing spectral efficiency. The proposed architecture demonstrates that combining dual modulation with AI-driven equalization and adaptive transmission strategies leads to a more resilient and efficient VLC system, well-suited for the dynamic constraints of wearable and body-centric communication environments. Full article

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔE_int ≈ −22.6 to −31.1 kcal·mol⁻¹), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔE_gap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity. Full article

Street layout has a significant effect on accessibility and intelligibility, which ultimately affects navigation and movement efficiency. While previous research has examined planned and unplanned street patterns, most studies focus on single-scale analyses or isolated typologies, limiting understanding of how hybrid networks function across multiple spatial levels. Addressing this gap, this study investigates the effects of hybrid planned and organically evolved street layouts on spatial accessibility in Mandalay, Myanmar. The research employs space syntax analysis to assess the citywide, township-level, and micro-scale networks through measures of angular integration, choice, axial connectivity, and intelligibility. Using the Four-Point Star Model to identify Mandalay’s distinct spatial features, a global accessibility assessment compares it to 50 other cities. The results show that grid-based layouts with central townships exhibit the highest integration and connectivity, while organic and fragmented networks, particularly in Amarapura, reduce spatial coherence and accessibility. Micro-scale analysis indicates that hybrid layouts with cul-de-sacs and distorted grids can improve accessibility when they connect effectively with secondary roads. By analysing street networks across multiple spatial scales, this research presents significant implications for efficient accessibility and transport planning in mixed-pattern cities. Full article

To investigate the hydraulic instability mechanisms of low-specific-speed pump–turbines operating in turbine mode, this study experimentally characterized the pressure distribution and pulsation evolution on the guide vanes of a model unit (ns = 28) using an embedded sensor technique. By overcoming the accessibility limitations of traditional measurement methods, this research reveals the distinct pressure response mechanisms on the guide vane Front Side (upstream-facing) and Back Side (runner-facing). The results demonstrate that the time-averaged pressure distribution is highly sensitive to the Guide Vane Opening (GVO). Specifically, pressure on the Front Side increases with GVO, dominated by the improvement of flow pattern and stagnation effect, whereas pressure on the Back Side decreases monotonically, governed by the Bernoulli effect. Increasing the GVO significantly improves pressure uniformity, reducing the surface pressure gradient by 55%. Regarding dynamic characteristics, pressure fluctuation intensity on the Back Side is significantly higher than that on the Front Side. Furthermore, fluctuations are notably amplified near the tongue, confirming that flow distortion induced by the tongue is a key factor driving circumferential non-uniformity. Spectral analysis identifies the Blade Passing Frequency (BPF) as the dominant frequency, verifying Rotor–Stator Interaction (RSI) as the primary excitation source, while the guide vane channel exhibits a significant low-pass filtering effect on high-order harmonics. These findings provide a solid theoretical foundation and data support for the optimal design and stability control of pump–turbine guide vanes. Full article

Existing 3D point cloud enhancement methods typically rely on artificially designed geometric transformations or local blending strategies, which are prone to introducing illogical deformations, struggle to preserve global structure, and exhibit insufficient adaptability to diverse degradation patterns. To address these limitations, this paper proposes SFD-ADNet—an adaptive deformation framework based on a dual spatial–frequency domain. It achieves 3D point cloud augmentation by explicitly learning deformation parameters rather than applying predefined perturbations. By jointly modeling spatial structural dependencies and spectral features, SFD-ADNet generates augmented samples that are both structurally aware and task-relevant. In the spatial domain, a hierarchical sequence encoder coupled with a bidirectional Mamba-based deformation predictor captures long-range geometric dependencies and local structural variations, enabling adaptive position-aware deformation control. In the frequency domain, a multi-scale dual-channel mechanism based on adaptive Chebyshev polynomials separates low-frequency structural components from high-frequency details, allowing the model to suppress noise-sensitive distortions while preserving the global geometric skeleton. The two deformation predictions dynamically fuse to balance structural fidelity and sample diversity. Extensive experiments conducted on ModelNet40-C and ScanObjectNN-C involved synthetic CAD models and real-world scanned point clouds under diverse perturbation conditions. SFD-ADNet, as a universal augmentation module, reduces the mCE metrics of PointNet++ and different backbone networks by over 20%. Experiments demonstrate that SFD-ADNet achieves state-of-the-art robustness while preserving critical geometric structures. Furthermore, models enhanced by SFD-ADNet demonstrate consistently improved robustness against diverse point cloud attacks, validating the efficacy of adaptive space-frequency deformation in robust point cloud learning. Full article

This study investigates how financial growth connects to regional environmental performance within the framework of policies aimed at reducing carbon emissions. It uses a comprehensive panel dataset covering the period from 2010 to 2024. Although Kazakhstan has set ambitious targets, significant differences in financing levels and institutional development across regions pose substantial obstacles to achieving the target emissions reductions. Employing regional panel data, we use a random-effects model to assess links among banking loans, governmental funding metrics, employment statistics, and pollution measurements. Principal component analysis is utilized to tackle potential collinearity and reveal fundamental patterns. This approach reflects the inherent differences between regions rather than evolutionary shifts. The obtained empirical data demonstrate a significant relationship between high levels of bank loans and reduced carbon emissions. Regions with better access to financial services are better positioned to invest in energy efficiency, green infrastructure, and green innovation. Conversely, increases in regional budgets are associated with rising emissions, as tax revenue growth primarily comes from industries most dependent on fossil fuels. Dependence on the national budget for subsidies exacerbates distortions in regional budgets’ relationship with the regions’ transition to low-carbon development. The findings confirm the importance of regional financial management in determining the path to reducing greenhouse gas emissions. Based on this, it is proposed to transform the mechanism of interbudgetary relations to grant regions greater financial autonomy and to localize credit resources at the regional level to accelerate the transition to a low-carbon economy in Kazakhstan. Full article

Background/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models. Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations. Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics. Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability. Full article

Background: Feigned restriction of shoulder joint movement for secondary gain is clinically relevant and may misdirect care, distort disability determinations, and inflate system costs. Distinguishing feigning from structural pathology and from functional or psychosocial presentations is difficult because pain is subjective, performance varies, and no single sign or test is definitive. This comprehensive review hypothesizes that the systematic integration of clinical examination, objective biomechanical and neurophysiological testing, and emerging technologies can substantially improve detection accuracy and provide defensible medicolegal documentation. Methods: PubMed and reference lists were searched within a prespecified time frame (primarily 2015–2025, with foundational earlier works included when conceptually essential) using terms related to shoulder movement restriction, malingering/feigning, symptom validity, effort testing, functional assessment, and secondary gain. Evidence was synthesized narratively, emphasizing objective or semi-objective quantification of motion and effort (goniometry, dynamometry, electrodiagnostics, kinematic sensing, and imaging). Results: Detection is best approached as a stepwise, multidimensional evaluation. First-line clinical assessment focuses on reproducible incongruence: non-anatomic patterns, internal inconsistencies, distraction-related improvement, and mismatch between claimed disability and observed function. Repeated examinations and documentation strengthen inference. Instrumented strength testing improves quantification beyond manual testing but remains effort-dependent; repeat-trial variability and atypical agonist–antagonist co-activation can indicate submaximal performance without proving intent. Imaging primarily tests plausibility by confirming lesions or highlighting discordance between claimed limitation and minimal pathology, while recognizing that normal imaging does not exclude pain. Diagnostic anesthetic injections and electrodiagnostics can clarify pain-mediated restriction or exclude neuropathic weakness but require cautious interpretation. Motion capture and inertial sensors can document compensatory strategies and context-dependent normalization, yet validated standalone thresholds are limited. Conclusions: Feigned shoulder impairment cannot be confirmed by any single test. The desirable strategy combines structured assessment of inconsistencies with objective biomechanical and neurophysiologic measurements, interpreted within the whole clinical context and rigorously documented; however, prospective validation is still needed before routine implementation. Full article

This study analyzes the relationship between macroeconomic distress and financial dollarization in Türkiye using annual regional panel data for 26 Nomenclature of Territorial Units for Statistics 2 regions over the period 2005–2021. Macroeconomic distress is captured using the misery index, computed as the compound of inflation and unemployment rates, while the share of foreign-currency-denominated deposits in total deposits measures financial dollarization. Applying second-generation panel econometric models that account for regional heterogeneity, we investigate both long-run equilibrium relationships and short-run interactions. Panel cointegration tests show a long-run connection between macroeconomic distress and dollarization. Short-run effects estimated using a Panel Vector Error Correction Model and a Cross-Sectionally Augmented ARDL framework point to bidirectional causality. Long-run coefficient estimates obtained via Dynamic Ordinary Least Squares indicate an apparent asymmetry. Increases in dollarization exert a substantial and economically significant effect on macroeconomic distress, whereas the long-run impact of distress on dollarization is comparatively modest. The findings suggest that dollarization functions not only as a response to macroeconomic instability but also as a structural element that intensifies inflationary pressures and labor market distortions over time. Focusing on regional patterns rather than national aggregates, the paper provides new evidence on the spatial dimension of the dollarization–instability link. Full article

Reconstructing 3D human motion from monocular video presents challenges when frames contain occlusions or blur, as conventional approaches depend on features extracted within limited temporal windows, resulting in structural distortions. In this paper, we introduce a novel framework that combines temporal probability guidance with graph topology learning to achieve robust 3D human mesh reconstruction from incomplete observations. Our method leverages topology-aware probability distributions spanning entire motion sequences to recover missing anatomical regions. The Graph Topological Modeling (GTM) component captures structural relationships among body parts by learning the inherent connectivity patterns in human anatomy. Building upon GTM, our Temporal-alignable Probability Distribution (TPDist) mechanism predicts missing features through probabilistic inference, establishing temporal coherence across frames. Additionally, we propose a Hierarchical Human Loss (HHLoss) that hierarchically regularizes probability distribution errors for inter-frame features while accounting for topological variations. Experimental validation demonstrates that our approach outperforms state-of-the-art methods on the 3DPW benchmark, particularly excelling in scenarios involving occlusions and motion blur. Full article