Search Results (12,821)

This paper introduces flourishing circularity as a transformative approach to resource assessment that transcends both traditional Resource-Based View (RBV) theory and conventional circular economy concepts. We demonstrate RBV’s fundamental limitations in addressing the polycrisis of breached planetary boundaries and social inequities. Similarly, while the circular economy focuses on resource reuse and recycling, it often merely delays environmental degradation rather than reversing it. Flourishing circularity addresses these shortcomings by reconceptualizing natural and social capital not as externalities but as foundational sources of all value creation. We develop a comprehensive framework for assessing resources within an open systems perspective, where competitive advantage increasingly derives from a firm’s ability to regenerate the systems upon which all business depends. The paper introduces novel assessment tools that capture the dynamic interplay between organizational activities and coevolving social and ecological systems. We outline the core competencies required for flourishing circularity: regenerative approaches to social and natural capital, and systems thinking with cross-boundary collaboration capabilities. These competencies translate into competitive advantage as stakeholders increasingly favor organizations that enhance system health. The framework provides practical guidance for transforming resource assessment from extraction to regeneration, enabling business models that create value through system enhancement rather than depletion. Full article

While the international influence of the Programme for International Student Assessment (PISA) on education policy debates is well recognized, the degree to which PISA findings drive actual policy reforms and classroom practices remain debated. Using PISA as a case, this article examines how educational research is translated into policy responses and practices in German federalism, focusing specifically on immigrant students—a key group within German education reform discourse. It analyzes the reflection of PISA findings from the 2000, 2018, and 2022 assessments on immigrant student performance in the resolutions of the Standing Conference of Ministers of Education and Cultural Affairs, the process of implementation by the federal states (Länder), and the effect on school-level practice. Framed by research knowledge mobilization theory, the article investigates the relationships among research production, mediation, and usage, clarifying the interplay between educational research, policy, and practice in Germany’s federal system. Historical analysis exposes consistent gaps between research-derived recommendations and binding, actionable change at both policy and practice levels, often due to challenges in developing evidence-based and consistently applied policy measures across the Länder. The article concludes with practical recommendations for improving the impact of interdisciplinary, policy-oriented research on policy and practice, considering the complexities of Germany’s federal governance. Full article

(1) Background: Developing cultural competence and reflective communication skills remains a challenge in nursing education. Traditional teaching methods often provide limited opportunities for safe practice of culturally sensitive interactions in emotionally complex situations. Artificial intelligence (AI)–based patient simulations may offer a scalable approach to experiential and reflective learning. (2) Aim: This study explored the educational potential of AI-based patient simulations in supporting nursing students’ self-assessed cultural competence, reflective awareness, and communication confidence. (3) Methods: A convergent mixed-methods pre–post study was conducted among 24 s-cycle nursing students. Participants engaged in individual AI-based patient simulations with simulated patients representing diverse cultural contexts. Quantitative data were collected using an exploratory cultural competence self-assessment scale administered before and after the simulation. Qualitative data included post-simulation reflection forms and AI-student interaction transcripts, analysed using inductive thematic analysis. (4) Results: A statistically significant increase in overall self-assessed cultural competence was observed (Wilcoxon signed-rank test: Z = 4.05, p < 0.001, r = 0.59), with the greatest improvements in communication adaptability and perceived communication sufficiency. Qualitative findings indicated an emotional shift from uncertainty to engagement, heightened awareness of cultural complexity, reflective reassessment of assumptions, and high perceived educational value of AI simulations. (5) Conclusions: AI-based patient simulations represent a promising pedagogical tool for fostering reflective and communication-oriented learning in culturally complex nursing contexts. Their primary value lies in supporting experiential learning, emotional engagement, and the development of cultural humility, suggesting their potential role as a complementary educational strategy in advanced nursing education. Full article

The article examines the implementation of 3D printing in civil protection and crisis management with a focus on the educational process, while 3D printing technology enables the creation of various teaching aids that streamline teaching and enrich theoretical knowledge. The empirical part of the study is based on a quantitative questionnaire survey among students of the Faculty of Safety Engineering of the University of Žilina in Žilina, with hypotheses set in advance and forming the basis for the construction of the questionnaire. The questionnaire collected data on the subjective evaluation of 3D printing through continuous, nominal, and ordinal responses and was completed by 277 students. Statistical methods of simple and group classification, as well as t-test, ANOVA, Kruskal–Wallis and Pearson’s correlation analysis were used to evaluate the data. Statistical significance was used to determine whether observed differences and relationships were unlikely to have arisen by chance. In addition, effect size measures were used in correlation and regression analyses to assess the strength and practical relevance of statistically significant relationships. The results of the study show that 3D printing significantly contributes to improving education and preparedness in civil protection, as it allows for more material-efficient and flexible production of educational aids compared to traditional custom production. Thus, it supports the development of more resilient communities and contributes to long-term sustainability. The findings confirmed that 3D printing is a suitable tool for improving public preparedness for emergencies. Full article

Accurate detection of structural damage is essential for ensuring the safety and reliability of bridges. However, traditional vibration-based approaches often struggle to capture rich feature representations and adequately model spatial dependencies among sensors. This study proposes a novel bridge damage detection framework, LGSTA-GNN, which integrates local–global spatiotemporal learning with graph neural networks. The framework first extracts multi-scale temporal–frequency features using a multi-scale feature extraction module. A local graph feature extraction module then models intrinsic spatial relationships through graph convolutions, while a global graph attention module adaptively captures inter-sensor dependencies by emphasizing structurally informative nodes. A benchmark dataset generated from a scaled bridge model under progressive damage states is used to evaluate the proposed method. Extensive experiments demonstrate that LGSTA-GNN outperforms multiple graph neural network variants and conventional deep learning techniques, achieving superior accuracy, precision, recall, and F1-score. The confusion matrix and t-SNE visualization further verify its enhanced discriminative capability and robustness. Ablation studies confirm the contribution of each module, highlighting the effectiveness of global attention in identifying subtle structural deterioration. Overall, LGSTA-GNN provides an effective and interpretable solution for intelligent bridge damage detection, with strong potential for practical structural health monitoring and real-time safety assessment. Full article

Flooding remains one of the most disruptive and costly natural hazards worldwide. Conventional approaches for estimating flood damage cost rely on empirical loss curves or historical insurance data, which often lack spatial resolution and predictive robustness. This study develops a data-driven framework for estimating flood damage costs across the contiguous United States, where comprehensive hydrologic, climatic, and socioeconomic data are available. A database of 17,407 flood events was compiled, incorporating approximately 38 parameters obtained from the National Oceanic and Atmospheric Administration (NOAA), the National Water Model (NWM), the United States Geological Survey (USGS NED), and the U.S. Census Bureau. Data preprocessing addressed missing values and outliers using the interquartile range and Walsh tests, followed by partitioning into training (70%), testing (15%), and validation (15%) subsets. Four modeling configurations were examined to improve predictive accuracy. The optimal hybrid regression–classification framework achieved correlation coefficients of 0.97 (training), 0.77 (testing), and 0.81 (validation) with minimal bias (−5.85, −107.8, and −274.5 USD, respectively). The findings demonstrate the potential of nationwide, event-based predictive approaches to enhance flood-damage cost assessment, providing a practical tool for risk evaluation and resource planning. Full article

Active involvement in creative activities, known as creative health, has been shown to enhance wellbeing, with museums serving as unique spaces for health promotion; however, visitors often require guidance to derive significant benefits from these institutions. This study, part of the larger ASBA (Anxiety, Stress, Brain-friendly museum Approach) project, evaluates the first phase of an intervention specifically focused on a Mindfulness protocol adapted to museum contexts. It has employed a single-group pre–post design with 79 healthy adults recruited from the non-clinical population. Participants were involved in a 15 min standardized mindfulness practice adapted from Mindfulness-Based Stress Reduction (MBSR) in either an art or science museum. State anxiety (SAI) and mood (VAS) were assessed at baseline and post-intervention, alongside personality traits (BFI-10) and interest measures to identify individual moderators of treatment response. The practice appeared to reduce state anxiety significantly in both settings, with large effect sizes. Specific moderators emerged: openness to experience predicted anxiety reduction in the art museum, whereas science interest predicted outcomes in the science setting. These findings suggest that brief, standardized mindfulness protocols implemented through the ASBA framework can provide promising immediate benefits for visitor wellbeing across diverse museum environments. Full article

Improved survival rates for paediatric cancer patients represent a major medical achievement, but they have simultaneously brought the long-term sequelae of oncological treatments into sharp focus. Cardiotoxicity stands out as one of the most serious complications, being the leading cause of non-relapse-related morbidity and mortality among childhood cancer survivors. This comprehensive review analyses the current landscape, highlighting the significant gap in evidence that hinders optimal care. This paper constitutes a comprehensive narrative and scoping review based on a critical analysis of current clinical guidelines, landmark studies, and consensus papers in paediatric cardio-oncology. Crucially, it assesses the heterogeneity and limitations of existing evidence regarding standardized surveillance protocols, primary prevention strategies, and acute/late-onset cardiovascular complication management. The review then identifies and critically discusses key areas for future research and clinical development. A critical gap in evidence persists in paediatric cardio-oncology, leading to significant variability in clinical practice and the underdiagnosis/undertreatment of cardiovascular risk factors in this vulnerable population. To bridge this gap, there is an urgent need for international collaborative research. The overarching goal is to transform paediatric cardio-oncology into a predictive and preventive speciality, ensuring that all childhood cancer survivors achieve not only extended life expectancy but also improved cardiovascular quality of life. Full article

Background/Objectives: Accurate and reproducible grading of lumbar spinal stenosis (LSS) is clinically critical for guiding treatment decisions and patient management, yet manual assessment remains challenging due to imaging variability and inter-observer subjectivity. To address these limitations, this study aimed to evaluate the generalizability of deep learning–based feature extraction methods—VGG19, ConvNeXt-Tiny, and DINOv2—combined with classical machine learning classifiers for automated multi-grade LSS assessment. Automated grading enables objective, reproducible, and scalable assessment of lumbar spinal stenosis severity, addressing key limitations of manual interpretation. Methods: Axial MRI images were processed using pretrained VGG19, ConvNeXt-Tiny, and DINOv2 models to extract deep features. Logistic Regression, Support Vector Machine (SVM), and LightGBM were trained on internal datasets and externally validated using MRI data from the University of Phayao Hospital. Performance was assessed using accuracy, precision, recall, F1-score, confusion matrices, and multi-class ROC curves. Results: VGG19-based features yielded the strongest external performance, with Logistic Regression achieving the highest accuracy (0.9556) and F1-score (0.9558). External validation further demonstrated excellent discrimination, with AUC values ranging from 0.994 to 1.000 across all severity grades. SVM (0.9333 accuracy) and LightGBM (0.9222 accuracy) also performed well. ConvNeXt-Tiny showed stable cross-model performance, while DINOv2 features exhibited reduced generalizability, especially with LightGBM (accuracy 0.6222). Most classification errors occurred between adjacent grades. Conclusions: Deep convolutional features—particularly VGG19—combined with classical machine learning classifiers provide robust and generalizable LSS grading across external MRI data. Despite advances in modern architectures, CNN-based feature extraction remains highly effective for spinal imaging and represents a practical pathway for clinical decision support. Full article

Background/Objectives: Precise implant positioning is critical for successful prosthetic rehabilitation, particularly in completely edentulous patients where anatomical landmarks are lost. The aim of this study was to assess the accuracy of implant placement in the edentulous maxilla and mandible using computer-assisted planning and a bone-supported stackable surgical guide protocol. Methods: This retrospective clinical study included 15 completely edentulous patients who received a total of 60 implants. A dual-scan protocol was utilized for planning. The surgical protocol involved a base guide fixed to the bone with pins, serving as a rigid foundation for stackable components used for osteotomy and implant insertion. Postoperative CBCT scans were superimposed onto the preoperative plan to calculate angular deviations, 3D linear deviations at the implant neck and apex, and depth deviations. Results: The analysis demonstrated high accuracy with a mean angular deviation of 1.25° ± 0.80°. The mean 3D linear deviation was 0.96 ± 0.57 mm at the implant neck and 1.07 ± 0.56 mm at the apex. Depth deviation showed a mean discrepancy of 0.37 ± 0.58 mm. All measured parameters were statistically significantly lower (p < 0.05) than the pre-established clinical safety thresholds. Conclusions: Within the limitations of this study, the bone-supported stackable surgical guide protocol proved to be a highly accurate method for full-arch rehabilitation. By eliminating mucosal resilience and ensuring rigid fixation, this approach enables predictable implant placement and facilitates the passive fit of screw-retained bar-supported prostheses, representing a reliable alternative to dynamic navigation in daily clinical practice. Full article

Background/Objectives: Non-muscle invasive bladder cancer (NMIBC) management is increasingly complex due to conflicting guideline-based risk classifications, ongoing Bacillus Calmette–Guérin (BCG) shortages, and emerging alternative therapies. Computational Histology Artificial Intelligence (CHAI) tests are clinically available, providing insights from tumor specimens including predicting BCG responsiveness and individualized recurrence and progression risks, which may support precision medicine. This technology features biomarkers purpose-built for clinically unmet needs and has practical advantages including a fast turnaround time and no need for consumption of tissue or other specimens. We assessed the impact of such tests on physicians’ decision-making in routine, real-world NMIBC management. Methods: Physicians at six centers ordered CHAI tests (Vesta Bladder) at their discretion during routine NMIBC care. Tumor specimens were processed by a CLIA/CAP-accredited laboratory (Valar Labs, Houston, TX, USA) where H&E-stained slides were analyzed with the CHAI assay to extract histomorphic features of the tumor and microenvironment, which were algorithmically assessed to generate biomarker test results. For each case from 24 June 2024 to 18 July 2025, ordering physicians were surveyed to assess pre- and post-test management plans and post-test result usefulness. Results: Among 105 high-grade NMIBC cases with complete survey results available, primary management changed in 67% (70/105). Changes included modality shifts (n = 7; three to radical cystectomy with high prognostic risk scores; four avoiding cystectomy with low scores) and intravesical agent change (n = 63). Surveillance was intensified in 7%, predominantly among those with ≥90th percentile risk scores. The therapeutic agent changed in 80% (40/50) of predictive biomarker-present (indicative of poor response to BCG) tumors vs. 48% (23/48) of biomarker-absent tumors. Conclusions: In two thirds of cases, CHAI biomarker results influenced clinical decision-making during routine care. BCG predictive biomarker results frequently guided intravesical agent selection. These results have implications for optimizing clinical outcomes, especially in the setting of ongoing BCG shortages. Prognostic risk stratification results guided treatment escalation vs. de-escalation, including surveillance intensification and surgical vs. bladder-sparing decisions. CHAI biomarkers are currently utilized in routine clinical care and informing precision NMIBC management. Full article

Background/Objectives: Exercise-based interventions are strongly recommended for managing temporomandibular disorders (TMDs). However, conventional approaches have limited capacity to address symptoms associated with mandibular kinematic abnormalities and often lack sufficient logical clarity for reproducible clinical applications. Furthermore, although current diagnostic criteria and imaging modalities primarily assess static anatomical conditions, traditional three-dimensional motion analysis is difficult to implement in routine practice. This study aimed to evaluate the effectiveness of a personalized, exercise-based intervention optimized to patients’ lateral excursion (LE) characteristics using an artificial intelligence (AI)-assisted motion analysis system. Methods: An AI-based two-dimensional motion analysis platform was used to quantify maximum mouth opening (MMO) and LE in three patients with TMD. Individualized interventions—including massage, stretching, resistance exercises, coordination training, and breathing exercises—were provided over 3 weeks based on each patient’s clinical presentation and movement patterns identified through the kinematic analysis. Results: All three patients successfully completed the intervention. Average pain intensity declined across all cases. Mandibular function improved: the mean MMO increased by 38.92% on average, and LE decreased by −1.55 mm on average. Conclusions: This study demonstrates that a personalized, exercise-based intervention guided by AI-assisted mandibular kinematic analysis was associated with reductions in pain and improvements in dynamic mandibular function. This approach provides a logically clear and objective framework that may support physical therapy in TMD management, advancing beyond conventional static assessment methods. Full article

This study presents a statistically robust quality-engineering evaluation of an industrial cathodic electrodeposition (CED) process applied to large electric-charging station components. In contrast to predominantly laboratory-scale studies, the analysis is based on 1250 thickness measurements, enabling reliable assessment of process uniformity, positional effects, and long-term stability under real production conditions. The mean coating thickness was specified at 21.84 µm with a standard deviation of 3.14 µm, fully within the specified tolerance window of 15–30 µm. One-way ANOVA revealed statistically significant but technologically small inter-station differences (F(49, 1200) = 3.49, p < 0.001), with an effect size of η² ≈ 12.5%, indicating that most variability originates from inherent within-station common causes. Shewhart X^¯–R–S control charts confirmed process stability, with all subgroup means and dispersions well inside the control limits and no evidence of special-cause variation. Distribution tests (χ², Kolmogorov–Smirnov, Shapiro–Wilk, Anderson–Darling) detected deviations from perfect normality, primarily in the tails, attributable to the superposition of slightly heterogeneous station-specific distributions rather than fundamental non-Gaussian behaviour. Capability and performance indices were evaluated using Statistica and PalstatCAQ according to ISO 22514; the results (Cp = 0.878, Cpk = 0.808, Pp = 0.797, Ppk = 0.726) classify the process as conditionally capable, with improvement potential mainly linked to reducing positional effects and centering the mean closer to the target thickness. To complement the statistical findings, an AIAG–VDA FMEA was conducted across the entire value stream. The highest-risk failure modes—surface contamination, incorrect bath chemistry, and improper hanging—corresponded to the same mechanisms identified by SPC and ANOVA as contributors to thickness variability. Proposed corrective actions reduced RPN values by 50–62.5%, demonstrating strong potential for capability improvement. A predictive machine-learning model was implemented to estimate layer thickness and successfully reproduced the global trend while filtering process-related noise, offering a practical tool for future predictive quality control. Full article

Background/Objectives: Artificial intelligence (AI) is an emerging technology for diagnosing liver fibrosis in Metabolic-Dysfunction-Associated Steatotic Liver Disease (MASLD), but a comprehensive synthesis of its performance is lacking. This systematic review (SR) aimed to evaluate the current evidence of AI models for diagnosing or staging liver fibrosis in patients with MASLD compared to conventional diagnostic tools. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, ScienceDirect, Embase, LILACS, IEEE Series, and Association for Computing Machinery (ACM). Primary studies applying AI to diagnose fibrosis in adults with MASLD were included. Risk of bias was assessed using the QUADAS-2 tool, and methodological reporting was evaluated according to the MINimum Information for Medical AI Reporting (MINIMAR) guideline. A narrative synthesis was performed, grouping studies by data type (clinical/laboratory vs. imaging) and summarizing diagnostic performance and clinical application. A frequency-based analysis was applied to identify the most recurrent predictive features, and an analysis of the AI architecture and application was reported. The review was registered in PROSPERO (CRD420251035919). Results: Twenty-one studies were included, encompassing 19,221 patients and 5237 images. Across studies, AI models consistently outperformed non-invasive scores such as Fibrosis-4 Index (FIB-4) and NAFLD Fibrosis Score (NFS). The most frequent predictive variables were identified. Despite an overall low risk of bias, methodological transparency and external validation were limited. Conclusions: AI is feasible for the non-invasive diagnosis of liver fibrosis in MASLD, demonstrating superior accuracy to standard clinical scores. Broader clinical application is limited by the lack of external validation and high heterogeneity among the studies. Prospective validation in diverse, multicenter cohorts is essential before AI can be integrated into routine clinical practice. Full article

Background/Objectives: Birth preparedness and complication readiness (BPCR) is a cornerstone of maternal health strategies designed to minimize the “three delays” in seeking, reaching, and receiving skilled care. In Ethiopia, uptake of BPCR remains insufficient, and little evidence exists on how individual- and community-level factors interact to shape preparedness. This study assessed the determinants of BPCR among women of reproductive age in Hawela Lida district, Sidama Region. Methods: A community-based cross-sectional study was conducted among 3540 women using a multistage sampling technique. Data were analyzed with multilevel mixed-effect negative binomial regression to account for clustering at the community level. Adjusted prevalence ratios (APRs) with 95% confidence intervals (CIs) were reported to identify determinants of BPCR. Model fitness was assessed using Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC), and log-likelihood statistics. Results: At the individual level, women employed in government positions had over three times higher expected BPCR scores compared with farmers (AIRR = 3.11; 95% CI: 1.89–5.77). Women with planned pregnancies demonstrated higher BPCR preparedness (AIRR = 1.66; 95% CI: 1.15–3.22), as did those who participated in model family training (AIRR = 2.53; 95% CI: 1.76–4.99) and women exercising decision-making autonomy (AIRR = 2.34; 95% CI: 1.97–5.93). At the community level, residing in urban areas (AIRR = 2.78; 95% CI: 1.81–4.77) and in communities with higher women’s literacy (AIRR = 4.92; 95% CI: 2.32–8.48) was associated with higher expected BPCR scores. These findings indicate that both personal empowerment and supportive community contexts play pivotal roles in enhancing maternal birth preparedness and readiness for potential complications. Random-effects analysis showed that 19.4% of the variance in BPCR was attributable to kebele-level clustering (ICC = 0.194). The final multilevel model demonstrated superior fit (AIC = 2915.15, BIC = 3003.33, log-likelihood = −1402.44). Conclusions: Both individual- and community-level factors strongly influence BPCR practice in southern Ethiopia. Interventions should prioritize women’s empowerment and pregnancy planning, scale-up of model family training, and address structural barriers such as rural access and community literacy gaps. Targeted, multilevel strategies are essential to accelerate progress toward improving maternal preparedness and reducing maternal morbidity and mortality. Full article