Search Results (309)

This paper presents the theoretical and methodological foundations of Living a Life of Meaning and Purpose-C (LAMP-C), a novel quantitative instrument designed to assess meaning-making capacity among emerging adults to be used as part of a battery of assessments for religiosity. Drawing on Constructive-Developmental Theory (CDT) as articulated by Robert Kegan, Sharon Daloz Parks, and Marcia Baxter Magolda, LAMP-C operationalizes complex developmental constructs such as cognitive, interpersonal, and intrapersonal growth. LAMP-C integrates CDT with the Rasch/Guttman Scenario (RGS) methodology, which systematically structures items to reflect incremental developmental complexity. An instrument for assessing meaning-making contributes to the comprehensive interpretation of assessments of religiosity among emerging adults. By framing meaning-making through four facets—ideation, relational awareness, conflict resolution, and sense of responsibility—this paper provides a comprehensive conceptual foundation for measuring growth in meaning-making. The RGS methodology further enhances construct validity by enabling precise, context-specific, and developmentally sensitive assessments across three contexts. LAMP-C bridges the gap between qualitative depth and quantitative breadth in assessing developmental constructs, offering a tool that supports both large-scale applications and nuanced theoretical alignment. LAMP-C establishes a framework for assessing meaning-making while setting the stage for future empirical research (e.g., longitudinal studies) to evaluate religiosity in emerging adults. Full article

Background: The hemoglobin–albumin–lymphocyte–platelet (HALP) score integrates the immunonutritional and inflammatory status. We evaluated whether baseline HALP predicts the 3-month response after lumbar medial branch radiofrequency ablation (RFA), defined as a Visual Analogue Scale (VAS) reduction of ≥50% and an Oswestry Disability Index (ODI) reduction of ≥40%, and identified a Youden-optimal cut-off. The discrimination and calibration of multivariable models were also assessed. Methods: This single-center retrospective cohort (N = 120) included rigorously selected patients (≥50% pain relief after two comparative medial branch blocks) undergoing standardized RFA. Multivariable logistic regression was adjusted for age, sex, Body Mass Index (BMI), smoking status, paraspinal tenderness, and baseline scores. We quantified the Area Under the Receiver Operating Characteristic Curve (AUC), Hosmer–Lemeshow (HL) goodness-of-fit, Brier score, and calibration slope; optimism was corrected using a 500-bootstrap method. Results: Responses occurred in 64.2% (VAS) and 65.8% (ODI) of participants. HALP independently predicted ODI (OR = 1.06, 95% CI 1.02–1.09; p < 0.001) and VAS (OR = 1.05, 95% CI 1.02–1.08; p = 0.001). As a single predictor, HALP showed fair discrimination (AUC 0.717 [VAS], 0.731 [ODI]). The Youden cut-off of 39.8 yielded high sensitivity (~0.87) with modest specificity (~0.58–0.61). Multivariable AUCs were 0.744 (VAS) and 0.774 (ODI), optimism-corrected to 0.680 and 0.720; calibration was acceptable (HL p > 0.05; slopes ≈ 0.74–0.78; Brier 0.188/0.179). Conclusions: HALP is a simple, low-cost adjunct that independently predicts short-term pain and functional outcomes after lumbar medial branch RFA. Incorporation into post-block triage may refine selection, especially for functional improvement, pending prospective external validation and recalibration of the cut-off. Full article

Background/Objectives: Empathy is essential for successful social functioning, mediating different aspects of social cognition in everyday life. An intriguing aspect is the involvement of empathy even in basic neural mechanisms of action perception, thanks to its association with the Mirror Neuron System (MNS). The present retrospective study explores whether individual differences in cognitive and affective empathy, measured by the Interpersonal Reactivity Index (IRI) questionnaire, can predict motor resonance—the enhancement of motor cortex reactivity during the observation of biological movements—during transitive and intransitive action observation. Methods: Data from 160 healthy subjects who participated in transcranial magnetic stimulation (TMS) experiments assessing corticospinal excitability during action observation were retrospectively analyzed using multiple linear regression models. Participants filled the IRI and observed intransitive single-digit finger movements (n = 80) or grasping actions directed at different targets (intransitive, object-directed, social-directed; n = 80) synchronized with TMS over the primary motor cortex, allowing the investigation of how action features modulate the relationship between participants’ empathic traits and motor resonance magnitude. Results: Results show that empathic traits do not affect motor resonance during intransitive movements, whereas they do when motor resonance is measured during the observation of transitive actions. Cognitive empathy, particularly the perspective-taking scale, significantly predicts motor resonance magnitude when observing goal-directed actions. Meanwhile, affective empathy, specifically the empathic concern scale, predicts motor resonance while observing social action. Conclusions: These findings highlight that different facets of empathy are significantly related to humans’ ability to understand others’ actions through inner simulation mechanisms, particularly concerning action goals and social relevance. Full article

With the recently acquired knowledge of the use of a multiphase mixture of precursors under electron beam irradiation (EBI), new possibilities were opened for this technique. In the present work, we obtained quantum dots, nanocrystals, nanoparticles, and grains of PbSe with a sintered appearance using a biphasic mixture of PbSe and PbSeO₃ under EBI. High-energy milling was used to obtain the biphasic mixture of precursors, which is composed of agglomerates with sizes ranging from ~400 to ~1700 nm, but nanoparticles were also present. The structural details of the biphasic mixture were studied using X-ray diffraction and the Rietveld method. The driving force of the EBI caused instantaneous physical and chemical changes due to the high internal energy of the biphasic mixture of precursors. The abrupt release of high internal energy, due to localized heating effects during EBI, gave way to the formation of multi-scale PbSe structures. Large particles with a sintered appearance formed near the electron beam impact point and in regions between ~800 nm and ~1400 nm, while well-defined faceted nanostructures were predominantly observed beyond ~1400 nm. The latter tended to be surrounded by {200} facets as the main growth direction. Furthermore, coalescence was anticipated to occur during EBI. It occurred simultaneously with the sublimation mechanism when the particle size was below the critical size of 10 nm. Multi-scale PbSe structures, obtained via EBI, are promising for developing thermoelectric devices due to their crystallinity and nanostructured features. Full article

Posttraumatic Stress Disorder (PTSD) is characterized by disruptions in central nervous system functioning and existential crises, yet the mechanistic links between neurobiological processes and dimensions of life meaning and identity remain underexplored. The aim of this study was to examine the relationships between stress biomarkers (serotonin, cortisol, noradrenaline, and interleukin-12 [IL-12]) and existential attitudes (measured using the Life Attitude Profile (Revised) [LAP-R]) in mining rescuers, considering PTSD duration and participant age. This cross-sectional study included 92 men aged 18–50 years, divided into three groups: no PTSD (n = 28), PTSD ≤ 5 years (n = 33), and PTSD > 5 years (n = 31). Serum levels of four biomarkers and LAP-R scores across eight domains were evaluated. Statistical analyses employed nonparametric tests, including the Kruskal–Wallis test for overall group differences (with Wilcoxon r effect sizes for pairwise comparisons, Mann–Whitney U tests for post hoc pairwise comparisons, and Spearman’s rank correlations for biomarker–LAP-R associations. Age effects were assessed in two strata: 18–35 years and 36–50 years. Kruskal–Wallis tests revealed significant group differences (p < 0.001) for all biomarkers and most LAP-R domains, with very large effect sizes (r > 0.7) in pairwise comparisons for serotonin (control median: 225.2 ng/mL vs. PTSD ≤ 5y: 109.9 ng/mL, r = 0.86; vs. PTSD > 5y: 148.0 ng/mL, r = 0.86), IL-12 (control: ~8.0 pg/mL vs. PTSD ≤ 5y: 62.4 pg/mL, r = 0.86; vs. PTSD > 5y: ~21.0 pg/mL, r = 0.69), and LAP-R scales such as Life Purpose (control: 54.0 vs. PTSD ≤ 5y: 39.0, r = 0.78; vs. PTSD > 5y: 20.0, r = 0.86) and Coherence (control: 53.0 vs. PTSD ≤ 5y: 34.0, r = 0.85; vs. PTSD > 5y: 23.0, r = 0.86). The PTSD ≤ 5y group exhibited decreased serotonin, cortisol (median: 9.8 µg/dL), and noradrenaline (271.7 pg/mL) with elevated IL-12 (all p < 0.001 vs. control), alongside reduced LAP-R scores. The PTSD > 5y group showed elevated cortisol (median: ~50.0 µg/dL, p < 0.001 vs. control, r = 0.86) and normalized IL-12 but persistent LAP-R deficits. Older participants (36–50 years) in the PTSD ≤ 5y group displayed improved existential attitudes (e.g., Life Purpose: 47.0 vs. 27.5 in 18–35 years, p < 0.001), whereas in PTSD > 5y, age exacerbated biological stress (cortisol: 57.6 µg/dL vs. 36.1 µg/dL, p = 0.003). Spearman correlations revealed stage-specific patterns, such as negative associations between cortisol and Death Acceptance in PTSD > 5y (ρ = −0.49, p = 0.005). PTSD alters biomarker levels and their associations with existential dimensions, with duration and age modulating patient profiles. These findings underscore the necessity for integrated therapies addressing both biological and existential facets of PTSD. Full article

The proliferation of Internet of Things (IoT) devices has created a distributed ecosystem where users generate vast amounts of review data across heterogeneous platforms, from smart home assistants to connected vehicles. This data is crucial for service improvement but is plagued by fake reviews, data quality inconsistencies, and significant privacy risks. Traditional centralized analytics fail in this landscape due to data privacy regulations and the sheer scale of distributed data. To address this, we propose FedDQ, a federated learning framework for Privacy-Preserving Federated Review Analytics with Data Quality Optimization. FedDQ introduces a multi-faceted data quality assessment module that operates locally on each IoT device, evaluating review data based on textual coherence, behavioral patterns, and cross-modal consistency without exposing raw data. These quality scores are then used to orchestrate a quality-aware aggregation mechanism at the server, prioritizing contributions from high-quality, reliable clients. Furthermore, our framework incorporates differential privacy and models system heterogeneity to ensure robustness and practical applicability in resource-constrained IoT environments. Extensive experiments on multiple real-world datasets show that FedDQ significantly outperforms baseline federated learning methods in accuracy, convergence speed, and resilience to data poisoning attacks, achieving up to a 13.8% improvement in F1-score under highly heterogeneous and noisy conditions while preserving user privacy. Full article

Background: Practical field experiences (PFEs) in teacher education aim to foster pre-service teachers’ career-oriented reflection processes. Although aspects of occupational well-being are thought to shape reflection, empirical evidence remains limited. This study investigated how different facets of occupational well-being—including positive and negative affect as well as job satisfaction—predict distinct dimensions of pre-service teachers’ career-oriented reflections during a four-week PFE. Methods: Using a quantitative pre-post design, we analyzed data from 242 German bachelor’s degree students. Occupational well-being was assessed using the Positive and Negative Affect Schedule (PANAS) and a job satisfaction scale; reflection processes were measured via five subscales capturing information, exploration, critical reflection, career guidance, and development. Results: Findings indicate a significant increase in positive affect (d = 0.75), a slight decrease in negative affect (d = 0.15), and stable job satisfaction. Regression analyses reveal that baseline positive affect and job satisfaction enhanced career guidance and exploration, whereas negative affect promoted critical reflection. Increases in positive affect and job satisfaction further strengthened career guidance and development. Conclusion: Aspects of occupational well-being can substantially shape career-oriented reflection processes. These findings highlight the importance of explicitly considering pre-service teachers’ emotional experiences in the design of field experiences and reflection opportunities within teacher education. Full article

While theoretical frameworks posit mindfulness as a catalyst for wisdom development, longitudinal evidence remains scarce. This study examines the developmental trajectory of wisdom during emerging adulthood and investigates the intra-person and within-person effects of mindfulness on wisdom through a three-wave longitudinal design. A sample of 719 Chinese first-year college students completed assessments of the Five Facet Mindfulness Questionnaire, the Situated Wise Reasoning Scale, and the Wise Thinking Scale across three timepoints. Longitudinal multilevel analysis (LMA) and random intercepts cross-lagged panel models (RI-CLPMs) were employed to distinguish between stable individual differences and temporary fluctuations. Three key findings emerged: (1) Both wise reasoning and wise thinking exhibited linear growth trajectories. (2) At the between-person level, dispositional mindfulness showed strong positive associations with wisdom. (3) Within-person analyses revealed that mindfulness fluctuations prospectively predicted changes in wise reasoning and thinking, establishing temporal precedence. This study provides new evidence that wisdom can be both a developing ability and a stable trait during emerging adulthood. The observed dynamic links between mindfulness and wisdom highlight the potential of mindfulness-based interventions to foster the growth of wisdom. Full article

Despite the rapid adoption of artificial intelligence (AI) on a global scale, a comprehensive framework that maps its end-to-end value chain is missing. The presented study employed a multi-layered framework to analyze the value creation and delivery mechanism of the five core layers of an AI value chain, including (1) hardware, (2) data management, (3) foundational AI, (4) advanced AI capabilities, and (5) AI delivery. Using a qualitative–descriptive approach with a multi-faceted thematic analysis and a SWOT-based bottleneck analysis of each core layer, the study maps a sequential value flow from a globally dependent hardware foundation to the deployment of AI services. The analysis reveals that international knowledge flows shape the ecosystem, while the “last-mile” integration challenge is not merely a technical issue; instead, it highlights a significant socio-technical disconnect between technological advancements and the preparedness of the workforce. This study provides a holistic framework that frames the AI value chain as a socio-technical system, offering critical insights for stakeholders. The findings emphasize that unlocking AI’s full potential requires strategic investment in the managerial competencies and digital skills that constitute human–capital readiness. Full article

Efficient modeling of acoustic scattering from water-filled thin shells remains challenging due to prohibitive computational costs of rigorous methods and oversimplifications in ray-based approximations. This paper develops an iterative physical acoustics (IPA) method, presenting simple and explicit formulations for scattering by penetrable objects immersed in fluids. The method combines Kirchhoff integral frameworks with thin-plate effective boundary conditions, discretizes mid-surfaces into triangular facets, and iteratively converges pressure fields to characterize the mechanisms of multiple reflections and transmissions. Validated against analytical solutions, numerical simulations, and scaled experiments, IPA provides comprehensive field predictions encompassing internal cavity fields, external near-fields, and far-field scattering patterns within a unified framework. It achieves significant computational efficiency gains while maintaining engineering practicality, successfully reproducing distant-range highlights from these mechanisms in time-domain spectra. Limitations are observed at low frequencies and high-curvature regions where elastic-wave effects become significant. The IPA framework enables engineering-efficient scattering analysis for complex thin-shell structures. Full article

Visual attractiveness perception—an individual’s capacity to recognise and evaluate the visual appeal of urban scene safety—has direct implications for well-being, economic vitality, and social cohesion. However, most empirical studies rely on single-source metrics or algorithm-centric pipelines that under-represent human perception. Addressing this gap, we introduce a fully reproducible, multimodal framework that measures and models this domain-specific facet of human intelligence by coupling Generative Pre-trained Transformer 4o (GPT-4o) with 1000 Street View images. The pipeline first elicits pairwise aesthetic judgements from GPT-4o, converts them into a latent attractiveness scale via Thurstone’s law of comparative judgement, and then validates the scale against 1.17 M crowdsourced ratings from MIT’s Place Pulse 2.0 benchmark (Spearman ρ = 0.76, p < 0.001). Compared with a Siamese CNN baseline (ρ = 0.60), GPT-4o yields both higher criterion validity and an 88% reduction in inference time, underscoring its superior capacity to approximate human evaluative reasoning. In this study, we introduce a standardised and reproducible streetscape evaluation pipeline using GPT-4o. We then combine the resulting attractiveness scores with network-based accessibility modelling to generate a “aesthetic–accessibility map” of urban central districts in Chongqing, China. Cluster analysis reveals four statistically distinct street types—Iconic Core, Liveable Rings, Transit-Rich but Bland, and Peripheral Low-Appeal—providing actionable insights for landscape design, urban governance, and tourism planning. Full article

Background: Peripheral nerve stimulation (PNS) has been employed as a therapeutic modality for managing chronic pain across diverse etiologies and neural targets. Nevertheless, its application in treating chronic axial neck pain remains markedly underexplored. Accordingly, this study aimed to both review the existing literature and present a retrospective single-center case series of patients who underwent temporary PNS targeting the cervical medial branch nerves (CMBNs) for chronic axial neck pain. Methods: This investigation comprises a narrative literature review alongside a single-center, retrospective case series evaluating percutaneous, temporary PNS for the management of cervical spondylosis facet arthropathy in the absence of myelopathy or radiculopathy. The primary outcomes were pain reduction, as measured by the numeric rating scale, and improvements in functional disability, with assessments conducted at baseline and at 60 days post-intervention. Results: PNS represents a neuromodulatory, nondestructive intervention that targets the CMBN to alleviate chronic axial neck pain, in contrast to the destructive mechanisms inherent in cervical radiofrequency ablation (CRFA). Although PNS has been applied to other neural targets, its use in the cervical region is sparsely documented, with limited case studies available. Notably, this case series is the first to report pain and disability outcomes specifically associated with CMBN PNS. At the 60-day follow-up, 66% of subjects achieved the minimal clinically important difference (MCID) for pain reduction, while 77% met the MCID for disability reduction. Moreover, our analysis uniquely examined the impact of previous CRFA and a history of cervical spine surgery on treatment outcomes, revealing that patients with such interventions experienced more modest improvements compared to their surgery- and CRFA-naive counterparts. Conclusions: The current literature reveals a significant gap regarding the use of CMBN PNS, underscoring an unmet need in the treatment algorithm for chronic axial neck pain beyond conservative modalities. Our findings suggest that CMBN PNS may offer a promising adjunctive therapy for carefully selected patients with refractory chronic axial neck pain who have not improved after medications, physical therapy, or injections. Additionally, the comparative analysis of outcomes in patients with a history of CRFA or cervical surgery underscores potential advantages of PNS prior to destructive therapies. Future research, ideally in the form of prospective studies with larger cohorts and extended follow-up durations, is warranted to further evaluate long-term outcomes and refine the place of PNS in the treatment algorithm. Full article

This work probes the possibility of controlling the morphology of silver crystals through inoculation of trace-level metallic species, building on an industrial-scale cooling process. The obtained crystals are analyzed via X-ray tomography (XRT), dynamic picture analysis, and scanning electron microscopy (SEM). The results reveal assemblages composed of octahedral crystals and triangular platelets. X-ray tomography yields pore size distributions that correlate with Ag% composition. Out of several trace metals tested, cadmium was found to yield a greater number of octahedral morphologies with pronounced twinning, contributing to a fibrous structure. This behavior is consistent with the energetic preference of cadmium atoms to integrate on Ag (111) planes and the limitation of twinning to the (111) planes in FCC metals. Faceting of the interiors of the triangular facets of octahedral crystals is noted in all SEM images of acid-washed samples. These physical features are interpreted as a product of crystal growth and not selective acid etching. The generation of octahedral silver crystals from a molten melt and the presence of faceting are research firsts, such crystal morphologies being previously generated only from aqueous chemical reduction systems. Adding traces of cadmium to primary lead melts is promising for producing silver nanocrystals with desired morphologies. Full article

Stacking fault tetrahedra (SFTs) are typically considered improbable in high stacking fault energy metals like aluminum. Using molecular statics and dynamics simulations, we reveal the formation, growth, and transformation of SFTs in aluminum via vacancy aggregation. Three types—perfect, truncated, and defective SFTs—are characterized by their structure, formation energy, and binding energy across a range of vacancy cluster sizes. Formation energies of perfect and truncated SFTs follow a scaling relation; beyond a critical size, truncated SFTs become thermodynamically favored, indicating a size-dependent transformation pathway. Binding energy and structure evolution exhibit quasi-periodic behavior, where vacancies initially adsorb at the vertices or the midpoints of the edges of a perfect SFT, then aggregate along one facet, triggering fault nucleation and a binding energy jump as the system reconstructs into a new perfect SFT. Molecular dynamics simulations further confirm the SFT nucleation and growth via vacancy aggregation, consistent with thermodynamic predictions. SFTs exhibit notable thermal mobility, enabling coalescence and evolution into vacancy-type dislocation loops. BCC-like $V_5$ clusters are identified as potential nucleation precursors. These findings explain the nanoscale, low-temperature nature of SFTs in aluminum and offer new insights into defect evolution and control in FCC metals. Full article

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations. Full article