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23 pages, 5320 KiB  
Article
The Association Between the Built Environment and Insufficient Physical Activity Risk Among Older Adults in China: Urban–Rural Differences and Non-Linear Effects
by Bo Qin, Tian Tian, Wangsheng Dou, Hao Wu and Meizhu Hao
Sustainability 2025, 17(9), 4035; https://doi.org/10.3390/su17094035 - 30 Apr 2025
Viewed by 257
Abstract
The built environment has been widely recognized as a critical determinant of physical activity among older adults. However, urban–rural disparities and the non-linear effects of environmental features remain underexplored. Using interpretable machine learning (random forest model) on nationwide representative data from 2526 older [...] Read more.
The built environment has been widely recognized as a critical determinant of physical activity among older adults. However, urban–rural disparities and the non-linear effects of environmental features remain underexplored. Using interpretable machine learning (random forest model) on nationwide representative data from 2526 older adults in the China Health and Retirement Longitudinal Study (CHARLS) database, this study identified both common and distinct risk factors for insufficient moderate-to-vigorous physical activity (MVPA) across diverse urban and rural contexts. The results revealed a location-based gradient in physical activity insufficiency: rural areas < suburban areas < central urban areas. Rural older adults faced greater constraints from safety concerns and transportation accessibility limitations. In comparison, urban older adults would benefit from targeted improvements in built environment quality, particularly elevator accessibility and diverse public activity spaces. Furthermore, non-linear relationships were observed between built environment features and physical activity, elucidating the “density paradox”: while moderate urban compactness promoted active behaviors, excessive density (>24,000 persons/km2), perceived overcrowding, and over-proximity to specific facilities (<1 km) were linked to reduced MVPA. These findings underscore the necessity for differentiated policy interventions in urban and rural settings to address the distinct environmental needs of older adults. Meanwhile, in urban planning, it is crucial that we balance spatial compactness and functional diversity within optimal thresholds for creating sustainable and inclusive built environments. Although a compact design may enhance mobility, equal attention must be paid to preventing spatial disorder from over-densification. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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45 pages, 2927 KiB  
Review
Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods
by Yuxiao Gao, Yang Jiang, Yanhong Peng, Fujiang Yuan, Xinyue Zhang and Jianfeng Wang
Tomography 2025, 11(5), 52; https://doi.org/10.3390/tomography11050052 - 30 Apr 2025
Viewed by 415
Abstract
Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their [...] Read more.
Medical image segmentation is a critical application of computer vision in the analysis of medical images. Its primary objective is to isolate regions of interest in medical images from the background, thereby assisting clinicians in accurately identifying lesions, their sizes, locations, and their relationships with surrounding tissues. However, compared to natural images, medical images present unique challenges, such as low resolution, poor contrast, inconsistency, and scattered target regions. Furthermore, the accuracy and stability of segmentation results are subject to more stringent requirements. In recent years, with the widespread application of Convolutional Neural Networks (CNNs) in computer vision, deep learning-based methods for medical image segmentation have become a focal point of research. This paper categorizes, reviews, and summarizes the current representative methods and research status in the field of medical image segmentation. A comparative analysis of relevant experiments is presented, along with an introduction to commonly used public datasets, performance evaluation metrics, and loss functions in medical image segmentation. Finally, potential future research directions and development trends in this field are predicted and analyzed. Full article
(This article belongs to the Section Artificial Intelligence in Medical Imaging)
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20 pages, 7885 KiB  
Article
Fault Diagnosis Method for Transformer Winding Based on Differentiated M-Training Classification Optimized by White Shark Optimization Algorithm
by Guochao Qian, Kun Yang, Jin Hu, Hongwen Liu, Shun He, Dexu Zou, Weiju Dai, Haozhou Wang and Dongyang Wang
Energies 2025, 18(9), 2290; https://doi.org/10.3390/en18092290 - 30 Apr 2025
Viewed by 245
Abstract
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. [...] Read more.
Transformers, serving as critical components in power systems, are predominantly affected by winding faults that compromise their operational safety and reliability. Frequency Response Analysis (FRA) has emerged as the prevailing methodology for the status assessment of transformer windings in contemporary power engineering practice. To mitigate the accuracy limitations of single-classifier approaches in winding status assessment, this paper proposes a differentiated M-training classification algorithm based on White Shark Optimization (WSO). The principal contributions are threefold: First, building upon the fundamental principles of the M-training algorithm, we establish a classification model incorporating diversified classifiers. For each base classifier, a parameter optimization method leveraging WSO is developed to enhance diagnostic precision. Second, an experimental platform for transformer fault simulation is constructed, capable of replicating various fault types with programmable severity levels. Through controlled experiments, frequency response curves and associated characteristic parameters are systematically acquired under diverse winding statuses. Finally, the model undergoes comprehensive training and validation using experimental datasets, and the model is verified and analyzed by the actual transformer test results. The experimental findings demonstrate that implementing WSO for base classifier optimization enhances the M-training algorithm’s diagnostic precision by 8.92% in fault-type identification and 8.17% in severity-level recognition. The proposed differentiated M-training architecture achieves classification accuracies of 98.33% for fault-type discrimination and 97.17% for severity quantification, representing statistically significant improvements over standalone classifiers. Full article
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15 pages, 4722 KiB  
Article
Differing Manifestations of Spatial Curvature in Cosmological FRW Models
by Meir Shimon and Yoel Rephaeli
Universe 2025, 11(5), 143; https://doi.org/10.3390/universe11050143 - 30 Apr 2025
Viewed by 190
Abstract
We found statistical evidence for a mismatch between the (global) spatial curvature parameter K in the geodesic equation for incoming photons and the corresponding parameter in the Friedmann equation that determines the time evolution of the background spacetime and its perturbations. The mismatch, [...] Read more.
We found statistical evidence for a mismatch between the (global) spatial curvature parameter K in the geodesic equation for incoming photons and the corresponding parameter in the Friedmann equation that determines the time evolution of the background spacetime and its perturbations. The mismatch, hereafter referred to as ‘curvature slip’, was especially evident when the SH0ES prior of the current expansion rate was assumed. This result is based on joint analyses of cosmic microwave background (CMB) observations with the PLANCK satellite (P18), the first year results of the Dark Energy Survey (DES), baryonic oscillation (BAO) data, and at a lower level of significance, the Pantheon SNIa (SN) catalog as well. For example, the betting odds against the null hypothesis were greater than 107:1, 1400:1 and 1000:1 when P18+SH0ES, P18+DES+SH0ES and P18+BAO+SH0ES were considered, respectively. Datasets involving SNIa weakened this curvature slip considerably. Notably, even when the SH0ES prior was not imposed, the betting odds for the rejection of the null hypothesis were 70:1 and 160:1 in cases where P18+DES and P18+BAO were considered. When the SH0ES prior was imposed, the global fit of the modified model (that allows for a nonvanishing ‘curvature slip’) strongly outperformed that of ΛCDM, being manifested by significant deviance information criterion (DIC) gains ranging between 7 and 23, depending on the dataset combination considered. Even in comparison with KΛCDM, the proposed model resulted in significant, albeit smaller, DIC gains when SN data were excluded. Our finding could possibly be interpreted as an inherent inconsistency between the (idealized) maximally symmetric nature of the FRW metric and the dynamical evolution of the GR-based homogeneous and isotropic ΛCDM models. As such, this implies that there is apparent tension between the metric curvature and the curvature-like term in the time evolution of the redshift. Full article
(This article belongs to the Section Cosmology)
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18 pages, 260 KiB  
Article
Evaluating the Performance of DenseNet in ECG Report Automation
by Gazi Husain, Ayesha Siddiqua and Milan Toma
Electronics 2025, 14(9), 1837; https://doi.org/10.3390/electronics14091837 - 30 Apr 2025
Viewed by 192
Abstract
Ongoing advancements in machine learning show great promise for automating medical data interpretation, potentially saving valuable time in life-threatening situations. One such area is the analysis of electrocardiograms (ECGs). In this study, we investigate the effectiveness of using a DenseNet121 encoder with three [...] Read more.
Ongoing advancements in machine learning show great promise for automating medical data interpretation, potentially saving valuable time in life-threatening situations. One such area is the analysis of electrocardiograms (ECGs). In this study, we investigate the effectiveness of using a DenseNet121 encoder with three decoder architectures: Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), and a Transformer-based approach. We utilize these models to generate automated ECG reports from the publicly available PTB-XL dataset. Our results show that the DenseNet121 encoder paired with a GRU decoder yields higher performance than previously achieved. It achieves a METEOR (Metric for Evaluation of Translation with Explicit Ordering) score of 72.19%, outperforming the previous best result of 55.53% from a ResNet34-based model that used LSTM and Transformer components. We also discuss several important design choices, such as how to initialize decoders, how to use attention mechanisms, and how to apply data augmentation. These findings offer valuable insights into creating more robust and reliable deep learning tools for ECG interpretation. Full article
(This article belongs to the Special Issue Digital Intelligence Technology and Applications)
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16 pages, 2371 KiB  
Article
Improving Data Quality with Advanced Pre-Processing of MWD Data
by Alla Sapronova and Thomas Marcher
Geotechnics 2025, 5(2), 28; https://doi.org/10.3390/geotechnics5020028 - 30 Apr 2025
Viewed by 105
Abstract
In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially [...] Read more.
In geotechnical engineering, an accurate prediction is essential for the safety and effectiveness of construction projects. One example is the prediction of over/under-excavation volumes during drill and blast tunneling. Using machine learning (ML) models to predict over-excavation often results in low accuracy, especially in complex geological settings. This study explores how the pre-processing of measurement while drilling (MWD) data impacts the accuracy of ML models. In this work, a correlational analysis of the MWD data is used as the main pre-processing procedure. For each drilling event (single borehole), correlation coefficients are calculated and then supplied as inputs to the ML model. It is shown that the ML model’s accuracy improves when the correlation coefficients are used as inputs to the ML models. It is observed that datasets made from correlation coefficients help ML models to obtain higher generalization skills and robustness. The informational content of datasets after different pre-processing routines is compared, and it is shown that the correlation coefficient dataset retains information from the original MWD data. Full article
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23 pages, 555 KiB  
Article
Digital Transformation, CEO Compensation, and ESG Performance: Evidence from Chinese Listed Companies
by Caiming Nie, Dor Kushinsky and Ting Ren
Sustainability 2025, 17(9), 4033; https://doi.org/10.3390/su17094033 - 30 Apr 2025
Viewed by 237
Abstract
As sustainability reporting and ESG disclosure gain global importance, understanding the factors influencing ESG outcomes becomes crucial for policymakers, investors, and corporate decision-makers. China, a major player in the global economy, has recently taken steps to align its stock exchanges with international ESG [...] Read more.
As sustainability reporting and ESG disclosure gain global importance, understanding the factors influencing ESG outcomes becomes crucial for policymakers, investors, and corporate decision-makers. China, a major player in the global economy, has recently taken steps to align its stock exchanges with international ESG reporting standards. In this context, the study examines the individual and joint effects of digital transformation and CEO compensation on ESG performance, considering moderating factors such as firm size, state ownership, and CEO age and gender. The research employs a comprehensive dataset containing 16,205 firm-year observations from 2018 to 2022, combining financial data, ESG ratings, and a matrix of word frequencies related to digital transformation extracted from annual reports. The study adopts a firm-year two-way fixed effect model, utilizing panel data and control variables to address potential endogeneity concerns and unobserved firm heterogeneity. The findings provide evidence supporting the positive impact of digital transformation and CEO compensation on ESG performance. The level of digital transformation is positively associated with ESG performance. This relationship is stronger for larger firms and firms with older CEOs, while state-owned enterprises show mixed results compared to non-SOEs. However, the effect of CEO compensation and ESG performance is stronger for male CEOs. This study thus contributes to the growing literature on ESG performance, digital transformation, and executive compensation by providing insights into their relationships in the context of Chinese listed companies. Full article
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17 pages, 4365 KiB  
Article
Genotypic Characterization of Human Respiratory Syncytial Viruses Detected in Mexico Between 2021 and 2024
by Nadia Martínez-Marrero, Juan Carlos Muñoz-Escalante, Rosa Maria Wong-Chew, Pedro Torres-González, Miguel Leonardo García-León, Patricia Bautista-Carbajal, Pedro Antonio Martínez-Arce, María del Carmen Espinosa-Sotero, Verónica Tabla-Orozco, Fabian Rojas-Larios, Susana Juárez-Tobías, Ana María González-Ortiz, Ángel Gabriel Alpuche-Solís and Daniel E. Noyola
Viruses 2025, 17(5), 651; https://doi.org/10.3390/v17050651 - 30 Apr 2025
Viewed by 210
Abstract
Human respiratory syncytial virus (HRSV) is a leading cause of severe respiratory infections among children, older adults, and immunocompromised individuals. The COVID-19 pandemic and the non-pharmacological interventions to mitigate it resulted in significant changes in HRSV epidemiology and seasonality patterns. Worldwide, there was [...] Read more.
Human respiratory syncytial virus (HRSV) is a leading cause of severe respiratory infections among children, older adults, and immunocompromised individuals. The COVID-19 pandemic and the non-pharmacological interventions to mitigate it resulted in significant changes in HRSV epidemiology and seasonality patterns. Worldwide, there was a considerable reduction in the number of HRSV infections during that period, and the impact of those changes on genotype distribution is still not fully understood. In this work, we analyzed the genotypic characteristics of HRSV strains detected between 2021 and 2024 in Mexico with the aim of identifying changes in circulating lineages. HRSV positive samples collected in five states in Mexico were used. The complete viral attachment glycoprotein gene was sequenced, and phylogenetic inference was performed using datasets including all sequences available at GenBank and GISAID until 30 June 2024. We obtained 114 HRSV sequences (63.2% HRSV-A and 36.8% HRSV-B); 19 were from the 2021–2022 season, 53 from 2022–2023, and 42 from 2023–2024. All HRSV-A sequences clustered with sequences from other countries within A.D lineages, including A.D.1, A.D.3, A.D.5.1, and A.D.5.2 lineages. All HRSV-B sequences clustered in the B.D.E.1 lineage with sequences collected between 2020 and 2024. In conclusion, the characterization of HRSV viruses circulating in Mexico during and after the SARS-CoV-2 pandemic and comparison to all available sequences reported to date corroborates that, on a global scale, HRSV-A viruses of several A.D lineages circulate simultaneously, while HRSV-B viruses are restricted to the B.D.E.1 lineage. Full article
(This article belongs to the Section Human Virology and Viral Diseases)
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21 pages, 630 KiB  
Article
Hybrid Deep Learning Framework for Continuous User Authentication Based on Smartphone Sensors
by Bandar Alotaibi and Munif Alotaibi
Sensors 2025, 25(9), 2817; https://doi.org/10.3390/s25092817 - 30 Apr 2025
Viewed by 160
Abstract
Continuous user authentication is critical to mobile device security, addressing vulnerabilities associated with traditional one-time authentication methods. This research proposes a hybrid deep learning framework that combines techniques from computer vision and sequence modeling, namely, ViT-inspired patch extraction, multi-head attention, and BiLSTM networks, [...] Read more.
Continuous user authentication is critical to mobile device security, addressing vulnerabilities associated with traditional one-time authentication methods. This research proposes a hybrid deep learning framework that combines techniques from computer vision and sequence modeling, namely, ViT-inspired patch extraction, multi-head attention, and BiLSTM networks, to authenticate users continuously from smartphone sensor data. Unlike many existing approaches that directly apply these techniques for specific recognition tasks, our method reshapes raw motion signals into ViT-like patches to capture short-range patterns, employs multi-head attention to emphasize the most discriminative temporal segments, and then processes these enhanced embeddings through a bidirectional LSTM to integrate broader contextual information. This integrated pipeline effectively extracts local and global motion features specific to each user’s unique behavior, improving accuracy over conventional Transformer, Informer, CNN, and LSTM baselines. Experiments on the MotionSense and UCI HAR datasets show accuracies of 97.51% and 89.37%, respectively, indicating strong user-identification performance. Full article
(This article belongs to the Section Intelligent Sensors)
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23 pages, 1288 KiB  
Review
AI-Driven Advancements in Orthodontics for Precision and Patient Outcomes
by David B. Olawade, Navami Leena, Eghosasere Egbon, Jeniya Rai, Aysha P. E. K. Mohammed, Bankole I. Oladapo and Stergios Boussios
Dent. J. 2025, 13(5), 198; https://doi.org/10.3390/dj13050198 - 30 Apr 2025
Viewed by 695
Abstract
Artificial Intelligence (AI) is rapidly transforming orthodontic care by providing personalized treatment plans that enhance precision and efficiency. This narrative review explores the current applications of AI in orthodontics, particularly its role in predicting tooth movement, fabricating custom aligners, optimizing treatment times, and [...] Read more.
Artificial Intelligence (AI) is rapidly transforming orthodontic care by providing personalized treatment plans that enhance precision and efficiency. This narrative review explores the current applications of AI in orthodontics, particularly its role in predicting tooth movement, fabricating custom aligners, optimizing treatment times, and offering real-time patient monitoring. AI’s ability to analyze large datasets of dental records, X-rays, and 3D scans allows for highly individualized treatment plans, improving both clinical outcomes and patient satisfaction. AI-driven aligners and braces are designed to apply optimal forces to teeth, reducing treatment time and discomfort. Additionally, AI-powered remote monitoring tools enable patients to check their progress from home, decreasing the need for in-person visits and making orthodontic care more accessible. The review also highlights future prospects, such as the integration of AI with robotics for performing orthodontic procedures, predictive orthodontics for early intervention, and the use of 3D printing technologies to fabricate orthodontic devices in real-time. While AI offers tremendous potential, challenges remain in areas such as data privacy, algorithmic bias, and the cost of adopting AI technologies. However, as AI continues to evolve, its capacity to revolutionize orthodontic care will likely lead to more streamlined, patient-centered, and effective treatments. This review underscores the transformative role of AI in modern orthodontics and its promising future in advancing dental care. Full article
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17 pages, 622 KiB  
Article
Forecasting Forex EUR/USD Closing Prices Using a Dual-Input Deep Learning Model with Technical and Fundamental Indicators
by Abolfazl Saghafi, Maryam Bagherian and Farhad Shokoohi
Mathematics 2025, 13(9), 1472; https://doi.org/10.3390/math13091472 - 30 Apr 2025
Viewed by 252
Abstract
Predicting foreign exchange prices is a challenging yet important task due to the complex, volatile, and fluctuating nature of the data. Although deep learning models are efficient, accurate predictions of closing prices and future price directions remain difficult. This study proposes a dual-input [...] Read more.
Predicting foreign exchange prices is a challenging yet important task due to the complex, volatile, and fluctuating nature of the data. Although deep learning models are efficient, accurate predictions of closing prices and future price directions remain difficult. This study proposes a dual-input deep-learning long short-term memory (LSTM) model for forecasting the EUR/USD closing price and predicting price direction using both fundamental and technical indicators. The model outperforms the second-best model, achieving a 29% reduction in mean absolute error (MAE) and root mean squared error (RMSE) in the training set and reductions of 24% and 23% in MAE and RMSE, respectively, in the test set. These results are confirmed through forecasting simulations, where performance metrics are consistent with those from the training phase. Finally, the model generates reliable three-day price forecasts, providing valuable insights into price direction. Full article
(This article belongs to the Special Issue Statistical Methods for Forecasting and Risk Analysis)
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25 pages, 28786 KiB  
Article
Text-Conditioned Diffusion-Based Synthetic Data Generation for Turbine Engine Sensor Analysis and RUL Estimation
by Luis Pablo Mora-de-León, David Solís-Martín, Juan Galán-Páez and Joaquín Borrego-Díaz
Machines 2025, 13(5), 374; https://doi.org/10.3390/machines13050374 - 30 Apr 2025
Viewed by 302
Abstract
This paper introduces a novel framework for generating synthetic time-series data from turbine engine sensor readings using a text-conditioned diffusion model. The approach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized signals into [...] Read more.
This paper introduces a novel framework for generating synthetic time-series data from turbine engine sensor readings using a text-conditioned diffusion model. The approach begins with dataset preprocessing, including correlation analysis, feature selection, and normalization. Principal Component Analysis (PCA) transforms the normalized signals into three components, mapped to the RGB channels of an image. These components, combined with engine identifiers and cycle information, form compact 19 × 19 × 3 pixel images, later scaled to 512 × 512 × 3 pixels. A variational autoencoder (VAE)-based diffusion model, fine-tuned on these images, leverages text prompts describing engine characteristics to generate high-quality synthetic samples. A reverse transformation pipeline reconstructs synthetic images back into time-series signals, preserving the original engine-specific attributes while removing padding artifacts. The quality of the synthetic data is assessed by training Remaining Useful Life (RUL) estimation models and comparing performance across original, synthetic, and combined datasets. Results demonstrate that synthetic data can be beneficial for model training, particularly in the early epochs when working with limited datasets. Compared to existing approaches, which rely on generative adversarial networks (GANs) or deterministic transformations, the proposed framework offers enhanced data fidelity and adaptability. This study highlights the potential of text-conditioned diffusion models for augmenting time-series datasets in industrial Prognostics and Health Management (PHM) applications. Full article
(This article belongs to the Section Turbomachinery)
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10 pages, 184 KiB  
Study Protocol
Pre-Clinical Models of Penetrating Brain Injury: Study Protocol for a Scoping Review
by Cindy K. Wong, Jennifer E. Dinalo, Patrick D. Lyden, Gene Sung and Roy A. Poblete
NeuroSci 2025, 6(2), 37; https://doi.org/10.3390/neurosci6020037 - 30 Apr 2025
Viewed by 187
Abstract
Penetrating brain injuries (PBI) constitute a significant subset of traumatic brain injuries, characterized by high morbidity and mortality due to their unique pathophysiological mechanisms. Despite its clinical prevalence in civilian and military settings, progress in translational research remains limited due to a lack [...] Read more.
Penetrating brain injuries (PBI) constitute a significant subset of traumatic brain injuries, characterized by high morbidity and mortality due to their unique pathophysiological mechanisms. Despite its clinical prevalence in civilian and military settings, progress in translational research remains limited due to a lack of well-characterized pre-clinical models that accurately replicate human PBI. Existing models often fail to adequately simulate critical aspects such as ballistic dynamics, tissue cavitation, and secondary injury cascades, limiting their translational relevance and hindering therapeutic advancements. This scoping review aims to systematically evaluate existing pre-clinical models, including animal, computational, ballistic, and hybrid simulations, to assess their methodological rigor, translational applicability and reported outcome measures. Using PRISMA-ScR guidelines, we will conduct a comprehensive literature search across multiple databases, extracting data on model characteristics, injury induction techniques, histopathological findings, biomolecular markers, and functional assessments. Additionally, bibliometric analyses will provide insights into research trends and gaps in PBI modeling, particularly concerning replicating real-world injury mechanisms and long-term functional outcomes. Through this evaluation, we aim to identify optimal experimental frameworks for studying PBI pathophysiology and recovery mechanisms while informing future model development for therapeutic advancements. The findings from this review will serve as a foundation for advancing pre-clinical PBI research, guiding future model development and therapeutic innovations, and ultimately enhancing treatment strategies and patient outcomes. Full article
18 pages, 3933 KiB  
Article
Creativity and Awareness in Co-Creation of Art Using Artificial Intelligence-Based Systems in Heritage Education
by Francesca Condorelli and Francesca Berti
Heritage 2025, 8(5), 157; https://doi.org/10.3390/heritage8050157 - 30 Apr 2025
Viewed by 264
Abstract
The article investigates a learning setting contextualising the use of artificial intelligence in heritage education, with a particular focus on AI systems utilising text-to-image processes. The setting is the one of a university interdisciplinary seminar in communication in South Tyrol, a border region [...] Read more.
The article investigates a learning setting contextualising the use of artificial intelligence in heritage education, with a particular focus on AI systems utilising text-to-image processes. The setting is the one of a university interdisciplinary seminar in communication in South Tyrol, a border region in the north of Italy shaped by a strong cultural identity. The paper illustrates a didactic experience introducing a highly technical and, for most of the students in the chosen context, challenging topic, such as AI. The teaching addresses a critical approach to AI, such as dataset constraints, sustainability, and authorship, and focuses on text-to-image algorithms and artistic co-creation, namely, the shifting role of the artist from sole creator to initiator/collaborator shaping the AI system’s output. The aim of the paper is to contribute to the debate in heritage education on teaching and learning using AI-based systems. The latter are seen as a potential tool for the engagement of students in understanding heritage and its safeguarding and in the relationship between community, territory, and active participation, as emphasised by both the “UNESCO Convention on Intangible Cultural Heritage” and the “Council of Europe Framework Convention on the Value of Cultural Heritage for Society”. However, the current boundaries of AI, particularly in terms of bias and limitations of datasets, must be addressed and reflected on. Full article
(This article belongs to the Special Issue Progress in Heritage Education: Evolving Techniques and Methods)
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19 pages, 18440 KiB  
Article
Rotating Bending Fatigue Behavior of AlSi10Mg Fabricated by Powder Bed Fusion-Laser Beam: Effect of Layer Thickness
by Lu Liu, Shengnan Wang and Yifan Ma
Crystals 2025, 15(5), 422; https://doi.org/10.3390/cryst15050422 - 30 Apr 2025
Viewed by 206
Abstract
A single batch of AlSi10Mg powder was used to fabricate two groups of round bars via horizontal printing, employing an identical strategy except for one parameter in the process of powder bed fusion-laser beam. The parameter is layer thickness, set at 50 and [...] Read more.
A single batch of AlSi10Mg powder was used to fabricate two groups of round bars via horizontal printing, employing an identical strategy except for one parameter in the process of powder bed fusion-laser beam. The parameter is layer thickness, set at 50 and 80 μm for Group-1 and Group-2, respectively, resulting in laser energy densities of 29.95 and 18.72 J/mm3. Both materials exhibit similar microstructures; Group-1 has fewer and smaller defects than Group-2, leading to higher strength and ductility. Fatigue performance of low-cycle and long-life up to 108 cycles under rotating bending was assessed, and the fracture surfaces were carefully examined under scanning electron microscopy. The S-N data converge to a single slope followed by a horizontal asymptote, indicating the occurrence of very-high-cycle fatigue (VHCF) in both cases. Group-1 shows higher fatigue strength in the range of 104 to 108 cycles, and a greater failure probability in VHCF regime than Group-2. This is attributed to the larger defect size in Group-2, where the smaller control volume in rotating bending greatly increases the likelihood of encountering large defects compared to Group-1. At the defect edge, the microstructure shows distinct resistance to crack propagation under high and low loads. Full article
(This article belongs to the Special Issue Fatigue and Fracture of Crystalline Metal Structures)
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