bluuebunny/crossref_metadata_2025 huggingface dataset:
Predicting Wear Rate and Friction Coefficient of Li2Si2O5 Dental Ceramic Using Optimized Artificial Neural Networks
Authors: Pantić Marko , Jovanović Saša , Djordjevic Aleksandar , Petrović Savić Suzana , Radenković Milan , Šarkoćević Živče
Published: February 2025

The tribological properties of dental materials, such as wear and friction, are crucial for ensuring their long-term reliability and performance. Traditional experimental approaches, while accurate, are often resource intensive and time consuming, prompting a need for efficient computational methods. This study explores the application of artificial neural networks (ANNs) to predict the tribological behavior of dental ceramic lithium disilicate (IPS e.max Cad). A genetic algorithm (GA) was used to optimize the ANN’s hyperparameters, improving its ability to model complex, nonlinear relationships between input variables, including normal load and velocity and output properties such as wear rate and friction coefficients. By integrating experimental data with an ANN, this study identifies key factors influencing tribological performance, reducing the dependency on extensive experimental testing. The results demonstrate that the optimized ANN model accurately predicts tribological behavior, offering a robust framework for material optimization. These findings emphasize the potential of combining ANNs and GAs to enhance the understanding and design of dental materials, accelerating innovation while addressing the challenges of traditional evaluation methods. This research underscores the transformative role of advanced computational approaches in tribology and material science.
Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
Authors: Cui Xining , Fang Yi , Gu Chaochen
Published: February 2025

Industrial parts are increasingly being designed to be more lightweight in modern manufacturing for energy saving and material cost reduction. However, the high-speed motion of flexible systems tends to excite severe residual vibrations that result in positioning accuracy degradation and loss of productivity. This study proposes a closed-form trajectory optimization method for vibration suppression based on trapezoidal velocity profiles, which are most widely used in industrial robots and machines. First, the formulation and minimum time solution under actuator limits of the motion profile are defined. Then, the relationship between the trajectory parameters and the vibration response is investigated. It is shown that residual vibration can be eliminated by properly tuning the acceleration/deceleration switching times according to the natural frequency. Based on the derived vibration suppression conditions, a tuning procedure for time parameters compliant with actuator limits is established to generate fast and precise movement. A main advantage of the proposed method is easy implementation for general machines without requiring extra computational resources or modification to the control system. The effectiveness and practicality of the proposed approach are verified through experiments conducted on a robot. The experimental results show that the optimized trajectory achieves superior residual vibration reduction performance.
Large Language Models in Computer Science Classrooms: Ethical Challenges and Strategic Solutions
Authors: Azoulay Rina , Hirst Tirza , Reches Shulamit
Published: February 2025

The integration of large language models (LLMs) into educational settings represents a significant technological breakthrough, offering substantial opportunities alongside profound ethical challenges. Higher education institutions face the widespread use of these tools by students, requiring them to navigate complex decisions regarding their adoption. This includes determining whether to allow the use of LLMs, defining their appropriate scope, and establishing guidelines for their responsible and ethical application. In the context of computer science education, these challenges are particularly acute. On the one hand, the capabilities of LLMs significantly enhance the tools available to developers and software engineers. On the other hand, students’ over-reliance on LLMs risks hindering their development of foundational skills. This study examines these challenges and proposes strategies to regulate the use of LLMs while upholding academic integrity. It focuses on the specific impact of LLMs in programming education, where dependence on AI-generated solutions may erode active learning and essential skill acquisition. Through a comprehensive literature review and drawing on teaching experience and guidelines from global institutions, this study contributes to the broader discourse on the integration of these advanced technologies into educational environments. The goal is to enhance learning outcomes while ensuring the development of competent, ethical software professionals.
JAVIS Chat: A Seamless Open-Source Multi-LLM/VLM Deployment System to Be Utilized in Single Computers and Hospital-Wide Systems with Real-Time User Feedback
Authors: Aguirre Javier , Cha Won Chul
Published: February 2025

The rapid advancement of large language models (LLMs) and vision-language models (VLMs) holds enormous promise across industries, including healthcare but hospitals face unique barriers, such as stringent privacy regulations, heterogeneous IT infrastructures, and limited customization. To address these challenges, we present the joint AI versatile implementation system chat (JAVIS chat), an open-source framework for deploying LLMs and VLMs within secure hospital networks. JAVIS features a modular architecture, real-time feedback mechanisms, customizable components, and scalable containerized workflows. It integrates Ray for distributed computing and vLLM for optimized model inference, delivering smooth scaling from single workstations to hospital-wide systems. JAVIS consistently demonstrates robust scalability and significantly reduces response times on legacy servers through Ray-managed multiple-instance models, operating seamlessly across diverse hardware configurations and enabling real-time departmental customization. By ensuring compliance with global data protection laws and operating solely within closed networks, JAVIS safeguards patient data while facilitating AI adoption in clinical workflows. This paradigm shift supports patient care and operational efficiency by bridging AI potential with clinical utility, with future developments including speech-to-text integration, further enhancing its versatility.
Exploring Cooling Effects of Land Cover Type Local Climate Zones in Relation to Built-Up Areas: A County Perspective
Authors: Liu Yihui , Xu Jing , Sun Shiqiang , Li Tianyu , Cao Jianfei
Published: February 2025

Evaluating cooling effects within local climate zones (LCZs) is vital for urban planning, especially in countering urban heat islands and enhancing thermal comfort. While prior research has primarily focused on the influence of land cover type LCZs on land surface temperature in large cities, the role of LCZ characteristics across diverse urban environments in smaller regions remains insufficiently explored. This study investigates the cooling effects of land cover type LCZs within the urban built-up areas at the county level using ECOSTRESS land surface temperature data. We utilized a random forest regression model to assess the impact of LCZ factors—namely, landscape shape index, percentage of landscape, and nearest neighbor distance among six built type LCZs and three land cover type LCZs—on cooling effects. The key findings are as follows: (1) Densely constructed LCZs demonstrated higher land surface temperature due to increased heat retention, with LCZ2 showing the highest mean land surface temperature (37.64 °C), whereas land cover type LCZs, particularly LCZG, recorded significantly lower mean land surface temperature (31.86 °C). (2) Land cover type LCZs were crucial in reducing land surface temperature, with LCZG presenting the most significant cooling effects, ranging from 0.31 to 79.43. LCZA and LCZD displayed comparable cooling effects, with average values of 6.54 and 6.46, respectively. (3) The landscape shape index of the land cover type LCZs themselves contributed most significantly to cooling effects. The percentage of landscape and landscape shape index of LCZ3 and LCZ6 notably influenced cooling effects in LCZA and LCZD, while LCZ5 predominantly affected LCZG. (4) The interactions between key factors and cooling effects were found to be complex and nonlinear. U-shaped effects were observed (e.g., central percentage of landscape affecting LCZA and LCZD, and percentage of landscape in LCZ6 affecting LCZG), alongside inverted U-shaped effects (e.g., landscape shape index in LCZ6 affecting LCZG) and combined effects (e.g., landscape shape index of LCZ3 affecting LCZA). The results emphasize the significance of maintaining contiguous and well-structured land cover type LCZs to optimize cooling effects.
Modelling of X-Ray Spectra Originating from the He- and Li-like Ni Ions for Plasma Electron Temperature Diagnostics Purposes
Authors: Kozioł Karol , Brosławski Andrzej , Rzadkiewicz Jacek
Published: February 2025

The multi-configurational Dirac–Hartree–Fock method has been used to examine the electron correlation effect on wavelengths and transition rates for L→K transitions occurring in He- and Li-like nickel ions. The collisional-radiative modelling approach has been used to simulate the X-ray spectra, in a 1.585–1.620 Å wavelength range, originating from the He-like nickel ions and their dielectronic Li-, Be-, and B-like satellites for various electron temperature values in the 2 keV to 8 keV range. The presented results may be useful in improving the plasma electron temperature diagnostics based on nickel spectra.
Supervised Learning Fuzzy Matrix Based on Input–Output Fuzzy Vectors
Authors: Ye Meili , Wang Nianliang , Yu Xianfeng , Wang Xiao , Liu Wuniu
Published: February 2025

Fuzzy matrices play a crucial role in fuzzy logic and fuzzy systems. This paper investigates the problem of supervised learning fuzzy matrices through sample pairs of input–output fuzzy vectors, where the fuzzy matrix inference mechanism is based on the max–min composition method. We propose an optimization approach based on stochastic gradient descent (SGD), which defines an objective function by using the mean squared error and incorporates constraints on the matrix elements (ensuring they take values within the interval [0, 1]). To address the non-smoothness of the max–min composition rule, a modified smoothing function for max–min is employed, ensuring stability during optimization. The experimental results demonstrate that the proposed method achieves high learning accuracy and convergence across multiple randomly generated input–output vector samples.
CXR-Seg: A Novel Deep Learning Network for Lung Segmentation from Chest X-Ray Images
Authors: Din Sadia , Shoaib Muhammad , Serpedin Erchin
Published: February 2025

Over the past decade, deep learning techniques, particularly neural networks, have become essential in medical imaging for tasks like image detection, classification, and segmentation. These methods have greatly enhanced diagnostic accuracy, enabling quicker identification and more effective treatments. In chest X-ray analysis, however, challenges remain in accurately segmenting and classifying organs such as the lungs, heart, diaphragm, sternum, and clavicles, as well as detecting abnormalities in the thoracic cavity. Despite progress, these issues highlight the need for improved approaches to overcome segmentation difficulties and enhance diagnostic reliability. In this context, we propose a novel architecture named CXR-Seg, tailored for semantic segmentation of lungs from chest X-ray images. The proposed network mainly consists of four components, including a pre-trained EfficientNet as an encoder to extract feature encodings, a spatial enhancement module embedded in the skip connection to promote the adjacent feature fusion, a transformer attention module at the bottleneck layer, and a multi-scale feature fusion block at the decoder. The performance of the proposed CRX-Seg was evaluated on four publicly available datasets (MC, Darwin, and Shenzhen for chest X-rays, and TCIA for brain flair segmentation from MRI images). The proposed method achieved a Jaccard index, Dice coefficient, accuracy, sensitivity, and specificity of 95.63%, 97.76%, 98.77%, 98.00%, and 99.05%on MC; 91.66%, 95.62%, 96.35%, 95.53%, and 96.94% on V7 Darwin COVID-19; and 92.97%, 96.32%, 96.69%, 96.01%, and 97.40% on the Shenzhen Tuberculosis CXR Dataset, respectively. Conclusively, the proposed network offers improved performance in comparison with state-of-the-art methods, and better generalization for the semantic segmentation of lungs from chest X-ray images.
Seasonal Shifts of Morphological Traits and Dietary of Mactra veneriformis (Bivalvia: Mactridae) Populations in the Northern Yellow River Delta’s Intertidal Zone
Authors: Xu Shuangfeng , Li Ang , Zhu Ling , Wu Biao , Liu Lulei , Jiao Minghui , Li Jiaqi , Xue Suyan , Mao Yuze
Published: February 2025

In order to examine the seasonal variations in the morphological characteristics and diet of Mactra veneriformis in the Northern Yellow River Delta’s intertidal zone and provide a scientific basis for its resource conservation and population restoration, tested clams were collected in four consecutive seasons from summer of 2022 to spring of 2023. Morphological traits were measured, and the DNA of the stomach contents was analyzed using high-throughput sequencing. The tidal differences and seasonal variations in the northern habitat of the Yellow River Estuary significantly affect the morphological characteristics and growth of M. veneriformis. Among the four seasons, significant differences in the morphological characteristics of M. veneriformis were observed between the middle-tide and low-tide zones (p < 0.05). In both middle-tide and low-tide zones, the morphological characteristics and body wet weight of M. veneriformis in winter were significantly higher than those in other seasons (p < 0.05). Moreover, the morphological characteristics of M. veneriformis were extremely significantly influenced by the interaction between tide and season (two-way interaction, p < 0.001). In all seasons, M. veneriformis in the middle- and low-tide zones exhibited positive allometric growth. While there was no significant difference in the stomach content between the spring and summer samples in the same tidal zone (p > 0.05), there was a significant difference between the middle- and low-tide samples in winter (p < 0.05). This suggests that seasonal variations, rather than tidal differences, had a larger impact on the diet of M. veneriformis in the northern Yellow River estuary and that feeding differences may be related to changes in environmental factors, such as temperature. The findings of this study provide initial insights into the feeding ecology of M. veneriformis and offer a scientific foundation for the conservation and management of its resources.
Phospholipids and Sphingolipids in Osteoarthritis
Authors: Steinmeyer Juergen
Published: February 2025

Many studies now emphasize the intricate relationship between lipid metabolism and osteoarthritis (OA), a leading cause of disability. This narrative review examines alterations in the levels of phospholipids (PLs) and sphingolipids (SLs) in synovial fluid (SF), plasma, serum, and articular tissues; discusses their role in joint lubrication, inflammation, and cartilage degradation; and describes their potential as diagnostic markers and therapeutic targets. Key findings include stage-dependent elevated levels of specific PLs and SLs in the SF, blood, and tissue of OA patients, implicating them as possible biomarkers of disease severity and progression. Studies suggest that beyond the involvement of these lipids in joint lubrication, individual species, such as lysophosphatidylcholine (LPC) 16:0, lysophosphatidic acid (LPA), ceramide-1-phosphate (C1P), and sphingosine-1-phosphate (S1P), contribute to pain, inflammation, and degradation of joints through various signaling pathways. Cross-species comparisons suggest that dogs and mice experience similar lipidomic changes during OA as humans, rendering them valuable models for studying lipid-related mechanisms. PLs and SLs in SF appear to originate primarily from the synovial blood capillaries through diffusion. In addition, lipids that are produced locally by fibroblast-like synoviocytes (FLSs) are influenced by cytokines and growth factors that regulate the biosynthesis of PLs for joint lubrication. Emerging research has identified genes such as UGCG and ESYT1 as regulators of lipid metabolism in OA. Further, we examine the suitability of lipids as biomarkers of OA and the potential of targeting the PL and SL pathways to treat OA, emphasizing the need for further research to translate these findings into clinical applications.
Blood-Based EWAS of Asthma Polygenic Burden in The Netherlands Twin Register
Authors: Van Asselt Austin J. , Pool René , Hottenga Jouke-Jan , Beck Jeffrey J. , Finnicum Casey T. , Johnson Brandon N. , Kallsen Noah , Viet Sarah , Huizenga Patricia , de Geus Eco , Boomsma Dorret I. , Ehli Erik A. , van Dongen Jenny
Published: February 2025

Asthma, a chronic respiratory condition characterized by airway inflammation, affects millions of individuals worldwide. Challenges remain in asthma prediction and diagnosis from its complex etiology involving genetic and environmental factors. Here, we investigated the relationship between genome-wide DNA methylation and genetic risk for asthma quantified via polygenic scores in two cohorts from the Netherlands Twin Register; one enriched with asthmatic families measured on the Illumina EPIC array (n = 526) and a general population cohort measured on the Illumina HM450K array (n = 2680). We performed epigenome-wide association studies of asthma polygenic scores in each cohort with results combined through meta-analysis (total samples = 3206). The EWAS meta-analysis identified 63 significantly associated CpGs, (following Bonferroni correction, α = 0.05/358,316). An investigation of previous mQTL associations identified 48 mQTL associations between 24 unique CpGs and 48 SNPs, of which two SNPs have previous associations with asthma. Enrichment analysis using the 63 significant CpGs highlighted previous associations with ancestry, smoking, and air pollution. A dizygotic twin within-pair analysis of the 63 CpGs revealed similar directional effects between the two cohorts in 33 of the 63 CpGs. These findings further characterize the intricate relationship between DNA methylation and genetics relative to asthma.
Microvascular Dysfunction Following Cardioplegic Arrest and Cardiopulmonary Bypass: Impacts of Diabetes and Hypertension
Authors: Kanuparthy Meghamsh , Manthana Rishik , Kaushik Himanshu , Xiang Kathy , Hamze Jad , Marimekala David , Feng Jun , Sellke Frank W.
Published: February 2025

Cardioplegic arrest and cardiopulmonary bypass (CP/CPB) are known to engender microvascular dysfunction in patients undergoing cardiac surgery. These effects are significantly varied by patient comorbidities including diabetes and hypertension. Both diabetes and hypertension are associated with worse outcomes after cardiac surgery, partly related to increased microvascular complications. In this review, we examine several key facets of microvascular dysfunction after CP/CPB: microvascular endothelial and vasomotor dysfunction, altered gene and protein expression, endothelial adherens junction dysfunction, and programmed cell death as they relate to diabetes and hypertension. This review examines both classical techniques, including microvessel reactivity assays, and modern multiomic approaches to characterizing these microvascular changes.
Botulinum Neurotoxins as Two-Faced Janus Proteins
Authors: Chimienti Silvia , Di Spirito Maria , Molinari Filippo , Rozov Orr , Lista Florigio , D’Amelio Raffaele , Salemi Simonetta , Fillo Silvia
Published: February 2025

Botulinum neurotoxins are synthetized by anaerobic, spore-forming bacteria that inhibit acetylcholine release at the level of the neuromuscular and autonomic cholinergic junctions, thus inducing a series of symptoms, the most relevant of which is flaccid paralysis. At least seven serotypes and over 40 subtypes are known, and they are among the most poisonous natural substances. There are different forms of botulism according to the route of contamination, but the clinical manifestation of descending symmetric flaccid paralysis is consistent, regardless of the route of contamination. It is very severe and potentially lethal. The induced paralysis lasts as long as the toxin is active, with variable length, according to the serotype of the toxin. This transient activity, as well as the precise mechanism of action, are the basis for the rationale behind use of the toxin in therapy for several clinical conditions, particularly, spastic conditions, as well as chronic migraine and axillary hyperhidrosis. The toxin has also been approved for the reduction in facial wrinkles; all these clinical applications, coupled with the toxin’s risks, have earned botulinum the title of a two-faced Janus protein. No approved vaccines are currently available, andthe only approved antidotes are the human specific intravenous immunoglobulins for infant botulism and the heptavalent equine immunoglobulins/(F(ab’)2 for adults. Nanobodies, which show great promise, may penetrate neuronal cells to inactivate the toxin within the cytoplasm, and Ebselen, a non-toxic, economic, small-molecule inhibitor, has the characteristic of inhibiting the toxin irrespective of the serotype.
Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis
Authors: Ling Xiao Chun , Chen Henry Shen-Lih , Yeh Po-Han , Cheng Yu-Chun , Huang Chu-Yen , Shen Su-Chin , Lee Yung-Sung
Published: February 2025

Purpose: To evaluate the performance of deep learning (DL) in diagnosing glaucoma and predicting its progression using fundus photography and retinal optical coherence tomography (OCT) images. Materials and Methods: Relevant studies published up to 30 October 2024 were retrieved from PubMed, Medline, EMBASE, Cochrane Library, Web of Science, and ClinicalKey. A bivariate random-effects model was employed to calculate pooled sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver operating characteristic curve (AUROC). Results: A total of 48 studies were included in the meta-analysis. DL algorithms demonstrated high diagnostic performance in glaucoma detection using fundus photography and OCT images. For fundus photography, the pooled sensitivity and specificity were 0.92 (95% CI: 0.89–0.94) and 0.93 (95% CI: 0.90–0.95), respectively, with an AUROC of 0.90 (95% CI: 0.88–0.92). For the OCT imaging, the pooled sensitivity and specificity were 0.90 (95% CI: 0.84–0.94) and 0.87 (95% CI: 0.81–0.91), respectively, with an AUROC of 0.86 (95% CI: 0.83–0.90). In predicting glaucoma progression, DL models generally showed less robust performance, with pooled sensitivities and specificities ranging lower than in diagnostic tasks. Internal validation datasets showed higher accuracy than external validation datasets. Conclusions: DL algorithms achieve excellent performance in diagnosing glaucoma using fundus photography and OCT imaging. To enhance the prediction of glaucoma progression, future DL models should integrate multimodal data, including functional assessments, such as visual field measurements, and undergo extensive validation in real-world clinical settings.
Streamlit Application and Deep Learning Model for Brain Metastasis Monitoring After Gamma Knife Treatment
Authors: Buga Răzvan , Buzea Călin Gh. , Agop Maricel , Ochiuz Lăcrămioara , Vasincu Decebal , Popa Ovidiu , Rusu Dragoș Ioan , Știrban Ioana , Eva Lucian
Published: February 2025

Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was developed using Streamlit to provide real-time, AI-driven predictions for treatment monitoring. Methods: MRI scans from 60 patients (3194 images) were analyzed using a transfer learning-enhanced AlexNet deep learning model. Class imbalance was mitigated through dynamic class weighting and data augmentation to ensure equitable performance across all classes. Optimized preprocessing pipelines ensured dataset standardization. Model performance was evaluated using accuracy, precision, recall, F1-scores, and AUC, with 95% confidence intervals. Additionally, a comparative analysis of Gamma Knife radiosurgery (GKRS) outcomes and predictive modeling demonstrated strong correlations between tumor volume evolution and treatment response. The AI predictions and visualizations were integrated into a Streamlit-based application to ensure clinical usability and ease of access. The AI-driven approach effectively classified progression and regression patterns, reinforcing its potential for clinical integration. Results: The transfer learning model achieved flawless classification accuracy (100%; 95% CI: 100–100%) along with perfect precision, recall, and F1-scores. The AUC score of 1.0000 (95% CI: 1.0000–1.0000) indicated excellent discrimination between progression and regression cases. Compared to the baseline AlexNet model (99.53% accuracy; 95% CI: 98.90–100.00%), the TL-enhanced model resolved all misclassifications. Tumor volume analysis identified the baseline size as a key predictor of progression (Pearson r = 0.795, r = 0.795, r = 0.795, p < 0.0001, p < 0.0001, and p < 0.0001). The training time (420.12 s) was faster than ResNet-50 (443.38 s) and EfficientNet-B0 (439.87 s), while achieving equivalent metrics. Despite 100% accuracy, the model requires multi-center validation for generalizability. Conclusions: This study demonstrates that transfer learning with dynamic class weighting provides a highly accurate and reliable framework for monitoring brain metastases post-GKRS. The Streamlit-based AI application enhances clinical decision-making by improving diagnostic precision and reducing variability. Explainable AI techniques, such as Grad-CAM visualizations, improve interpretability and support clinical adoption. These findings emphasize the transformative potential of AI in personalized treatment strategies, extending applications to genomic profiling, survival modeling, and longitudinal follow-ups for brain metastasis management.
The Psychosocial Safety Climate’s Influence on Safety Behavior and Employee Engagement: Does Safety Leadership Really Count?
Authors: Dera Abdurrahman Khalifa , Adedokun Muri Wole , Iyiola Kolawole
Published: February 2025

The importance of safety behavior (SB) for workplace safety, especially in high-risk industries such as the construction sector, cannot be overstated. However, there exists limited research that has examined how the psychosocial safety climate (PSC) influences SB. This research examines the effect of the PSC on SB among Libyan construction workers. The mediating role of work engagement (WE) and the moderating role of safety leadership (SL) were also observed. We obtained 366 valid responses through a cross-sectional research design to verify the aforementioned relationships. The findings indicate that PSC has a positive influence on SB, PSC has a positive influence on WE, and WE has a positive influence on SB. The link between PSC and SB is partially mediated by WE. At a high level of safety leadership, the link between PSC and WE is further strengthened for construction firms whose employees perceive a high level of safety leadership. At a high level of safety leadership, the link between PSC and SB is further strengthened for construction firms whose employees perceive a high level of safety leadership. The findings underscore the importance of exploring the PSC’s influence on safety outcomes, such as safety behavior. It also underscores the importance of having organizational resources, such as PSC, improving employee engagement, and the crucial role of safety leadership in the construction context.
Precision Size Control of Supported Pd and Pt Nanoparticles via Controlled Electroless Deposition
Authors: Zhou Haiying , Xiong Wen , Shakouri Abolfazl , Lu Yu , Regalbuto John R. , Monnier John R. , Tengco John Meynard M.
Published: February 2025

Well-defined supported metal nanoparticle catalysts, with high uniformity in particle sizes of the dispersed metal, are crucial for studying their catalyzed reactions that exhibit structure sensitivity. For such catalysts, conventional methods of preparation may prove unsuitable in controlling the nanoparticle size and distribution. In this work, the systematic growth of supported Pd and Pt particles was achieved through the method of electroless deposition (ED), in which additional metal was deposited on preexisting particles of the same metal. The ED process was investigated by varying the pump time, pump speed, and molar ratios of the reagents during the continuous addition of the metal precursor, as well as the reducing agent and stabilizer, which were hydrazine and ethylenediamine, respectively. This allowed for the precise control of deposition rates, thus regulating the supported metal particle size, size distribution, and particle density. A slower deposition rate was achieved by increasing the amount of the ethylenediamine stabilizer and lowering the pumping speed. Slower rates of deposition resulted in smaller particle sizes and tighter size distributions compared to other preparations with the same metal weight loading, as characterized via X-ray diffraction (XRD), chemisorption, and scanning-transmission electron microscopy (STEM) methods.
Pyrolysis-GCMS of Plastic and Paper Waste as Alternative Blast Furnace Reductants
Authors: Jones Eurig Wyn , Steer Julian , Ojobowale Fawaz , Marsh Richard , Holliman Peter J.
Published: February 2025

This paper reports studies on the thermal chemistry of the flash pyrolysis (heating rate of 20,000 °C/s up to 800 °C) of non-fossil fuel carbon (NFF-C) waste (or refuse-derived fuel, RDF) in the context of using this as an alternative reductant for blast furnace ironmaking. Gas chromatography–mass spectrometry (GCMS) analysis linked to the pyrolyser was used to simulate the thermal processes that take place during injection in the blast furnace raceway, where material experiences extreme temperature (ca. 1000 °C) over very short residence times (<300 ms). Species identification and qualitative analysis of evolved species generated are reported. Whilst the pyrolyser uses flash heating of a static sample, a drop tube furnace was also employed to study a sample moving rapidly through a pre-heated furnace held at 1000 °C to enable reductant burnout rates to be measured. The overarching aim of this piece of work is to study the suitability of replacing fossil fuel with non-recyclable plastic and paper as blast furnace reductants.
Enhanced Corrosion Resistance and Corrosion Identification Function of the Rare-Earth Fluorescence-Waterborne Epoxy Zinc-Rich Coatings
Authors: Zhang Minghui , Yang Chun , Bai Wansong , Hao Benke , Luo Yide , Qiao Xinglong , Zhang Shuguang , Song Dan
Published: February 2025

This study explored the preparation of the rare earth complex phosphor Eu(PTA)1.5phen, which was used to modify zinc-rich protective coatings. The methods employed in this study included FTIR spectroscopy, SEM, EDS, EIS, fluorescence spectroscopy, XRD, and XPS to examine the impact of varying concentrations of Eu(PTA)1.5phen on Fe3⁺ sensing, fluorescence quenching, and the performance of the coating. The results showed that Eu(PTA)1.5phen exhibits excellent fluorescence properties, with a maximum emission intensity of 1.8 × 108 and a quantum yield of 89.26%. Fluorescence quenching by Fe3⁺ allows for the quantification of steel corrosion. Corrosion tests revealed that adding Eu(PTA)1.5phen enhanced the compactness of the zinc-rich coatings. The optimal performance was obtained when using 3 wt.% Eu(PTA)1.5phen, leading to a corrosion current density of 6.76 × 10⁻7 A/cm2. The XRD and XPS analyses indicated that introducing Eu3⁺ does not influence the corrosion products present in the coating. This research showed that zinc-rich coatings enhanced with rare earth fluorescence not only safeguarded the steel substrate but also allow for the real-time tracking of Fe3⁺ concentrations in both the coating and the substrate. This approach offers a method for timely and effective corrosion prevention and corrosion identification, providing new insights for the development of advanced protective coatings and practical applications.
Atom-Probe Tomographic Characterization of Nanoscale Precipitates in Copper-Bearing Ultra-Low-Carbon High-Strength Steel Tempered at Different Temperatures
Authors: Liang Fengrui , Su Hang , Luo Xiaobing , Wang Zemin , Chai Feng , Xu Yuanyuan
Published: February 2025

Ultra-low-carbon, high-strength steels have gained significant attention due to their exceptional mechanical properties. To enhance the performance of the steel, understanding the precipitation behavior of strengthening precipitates is crucial. In this study, the precipitation behavior of ultra-low-carbon high-strength steel strengthened by nanoscale copper (Cu)-rich precipitates (CRPs) and carbonitride (CN) atomic clusters was characterized using atom-probe tomography after tempering at 400, 450, 600, and 650 °C for 2 h. The results revealed that the nanoscale copper CRPs and the CN atomic clusters were the main strengthening precipitates. The CRPs, enriched only in Cu, were observed at 400 °C. Segregation of nickel (Ni) and manganese (Mn) to the CRPs occurred at 450 °C, and the number densities of CRPs achieved the maximum value, leading to the highest strengthening effects. The size of the CRPs increased with increasing temperature; however, the size of the clusters of the carbide-forming atoms remained at almost ~1.6 nm. At 650 °C, the concentration of Cu, Ni, and Mn atoms in the CRPs was about 85.4, 4.5, and 4 at.%, respectively; however, that of Fe decreased significantly. In the lath boundaries, the size of 10% C and 0.4% C iso-surfaces was relatively larger than that in the matrix. In a reverted austenite region at 600 °C, the concentration of Ni in the reverted austenite, CRPs, and matrix was about 15, 2.5, and 2.5 at.%, respectively.
Exploring the Effects of Dulbecco’s Modified Eagle’s Medium on Irradiated Layers of Magnesium-Doped Hydroxyapatite in a Chitosan Matrix for Biomedical Applications
Authors: Bleotu Coralia , Iconaru Simona Liliana , Ciobanu Carmen Steluta , Groza Andreea , Predoi Daniela
Published: February 2025

In this paper, we present the development of magnesium-doped hydroxyapatite in chitosan matrix (MHA_Ch) powder by an adapted coprecipitation method. The MHA_Ch powder was then deposited as thin layers by radio frequency magnetron sputtering. The MHA_Ch layers were exposed to various irradiation doses and immersed in Dulbecco’s Modified Eagle’s Medium (DMEM) for various time intervals. We report, for the first time, the effects of DMEM on irradiated layers of magnesium-doped hydroxyapatite in a chitosan matrix. The surface morphology of the layers before and after irradiation and immersion in DMEM was evaluated by SEM, AFM, and MM studies. Additionally, data about the functional groups present in the layers and the changes induced by exposure of the layers to irradiation and DMEM were obtained by FTIR studies. In vitro biological assays were conducted using an MG63 cell line (ATCC CRL1427). Our results suggest that the magnesium-doped hydroxyapatite in chitosan matrix layers may be suitable candidates for applications in the biomedical domain.
One-Pot Strategies for Lithium Recovery from Beta-Spodumene and LTA-Type Zeolite Synthesis
Authors: dos Santos Leonardo Leandro , do Nascimento Rubens Maribondo , Pergher Sibele Berenice Castellã
Published: February 2025

This study presents a groundbreaking method for extracting lithium from beta-spodumene while simultaneously achieving the sustainable synthesis of LTA-type zeolite, designated as LPM-15, without relying on organic solvents or calcination. Lithium extraction was efficiently performed using sodium salts, accompanied by the recycling of the mother liquor, with lithium content in the supernatant precisely quantified via atomic absorption spectroscopy (AAS). The optimized synthesis route enables the concurrent production of Li2CO3 and LPM-15, distinguished by a powdered appearance without a well-defined geometric framework and a unique cubic morphology with spherical facets, respectively. To gain deeper insights into the process, density functional theory (DFT) simulations were conducted to analyze how different cation exchanges (Na+ replacing Al3+, NH4+ replacing Al3+, and Ca2+ replacing Al3+) influence the structural stability and diffusion dynamics within the zeolitic pores of LPM-15. Additionally, cation-exchange capacity (CEC) measurements further assessed ion mobility within the LPM-15 framework. This integrative approach not only sheds light on the fundamental mechanisms underpinning LTA-type zeolite synthesis but also demonstrates their versatile applications, with particular emphasis on water purification technologies.
Regulating the Mechanical and Corrosion Properties of Mg-2Zn-0.1Y Alloy by Trace SiC with Different Morphologies
Authors: Guo Furong , Nie Kaibo , Deng Kunkun , Li Yanan
Published: February 2025

Traditional magnesium structural materials are used widely due to their light weight; however, their corrosion resistance is poor. In order to address this problem and improve the strength simultaneously, SiCp-, SiCnp-, and SiCnw-reinforced Mg-2Zn-0.1Y (wt. %, MZY alloy) matrix composites (SiC/MZY composites) with the same contents (0.3 wt. %) were prepared and extruded at low temperature in this paper. The effects of SiC morphology on the microstructure, mechanical properties and corrosion resistance of MZY alloy were studied. The results show that the grain size can be refined by adding SiC reinforcement. Compared with the unreinforced MZY alloy, the strengths of the SiC/MZY composites were all improved, with a yield strength of more than 440 MPa and an ultimate tensile strength of more than 450 MPa. However, only the corrosion rate of the composites reinforced by submicron SiCp was improved significantly. The hydrogen evolution corrosion rate (PH) was reduced by 81% relative to the MZY alloy. This can be attributed to the decreased galvanic corrosion pairs, as well as the decreased potential difference between the second phase and the matrix in the SiCp/MZY composite. Additionally, a compact product film on the surface of the SiCp/MZY composite can also protect the matrix. The materials prepared in this study showed excellent strength and high corrosion resistance at relatively low cost, providing valuable insights and design ideas for the development and application of those materials in marine and offshore engineering applications.
The Revolution of Small Snails and the Early Modern Evolutionary Fauna
Authors: Dominici Stefano
Published: February 2025

The species richness of major clades and functional groups among gastropods, a key element of Modern Evolutionary Fauna (MEF), underlines the dominant role of carnivorous Caenogastropoda and Heterobranchia, including small ectoparasites and micrograzers, at modern tropical latitudes. Neogastropoda are active predators that radiated in the Cretaceous, but their early Mesozoic MEF roots are poorly understood. The escalation hypothesis emphasises prey–predator interactions as gastropods’ macroevolutionary drivers during the Mesozoic Marine Revolution but overlooks the significance of highly diversified smaller forms. The tropical fossil record of the Permian–Triassic mass extinction (PTME) and the Triassic rise of MEF suggests that non-carnivorous species dominated gastropod fauna immediately before and after the PTME: Permian micrograzers mainly fed on sponges and waned during the rise of MEF, while ectoparasites and micrograzing carnivores diversified starting from the Ladinian period. Patterns of gastropod species richness, size, and form, the fossil record of reef builders and other benthic invertebrates, and an analysis of stem neogastropods jointly suggest a Middle Triassic revolution of small-sized gastropods, triggered by the emergence of scleractinian corals and the diversification of echinoderms. Habitat heterogeneity and new food sources offered niches for the early radiation of modern gastropod clades.
Research on Risk Avoidance Path Planning for Unmanned Vehicle Based on Genetic Algorithm and Bezier Curve
Authors: Xie Gaoyang , Fang Liqing , Su Xujun , Guo Deqing , Qi Ziyuan , Li Yanan , Che Jinli
Published: February 2025

In the process of autonomous driving, the identification and avoidance of risk points is of great significance for the safe and efficient navigation of unmanned vehicles. To solve this problem, a new strategy combining a Bezier curve and the genetic algorithm is proposed in this paper. Firstly, in order to make the curvature of the path continuous, the design uses two symmetric Bezier curves as the path curves. Then, in order to describe the influence range of risk points more accurately, the artificial potential field model is used to describe the risk points, and the integral of the curve path in the potential field is calculated. Finally, an improved genetic algorithm is designed. The limit of the path and the risk value of the path are added to the fitness function, and the selection operator and the mutation operator are improved. It can be seen from the results of simulation and real vehicle experiments that this new strategy can provide an effective path planning method to avoid risk points.
Cloud Model-Based Adaptive Time-Series Information Granulation Algorithm and Its Similarity Measurement
Authors: Chen Hailan , Gao Xuedong , Wu Qi , Huang Ruojin
Published: February 2025

To efficiently reduce the dimensionality of time series and enhance the efficiency of subsequent data-mining tasks, this study introduces cloud model theory to propose a novel information granulation method and its corresponding similarity measurement. First, we present an information granulation validity index of time series (IGV) based on the entropy and expectation of the cloud model. Taking IGV as the granulation target for time series, an adaptive information granulation algorithm for time series (CMAIG) is proposed, which can transform a time series into a granular time series consisting of several normal clouds without pre-specifying the number of information granules, achieving efficient dimensionality reduction. Then, a new similarity measurement method (CMAIG_ECM) is designed to calculate the similarity between two granular time series. Finally, the hierarchical clustering algorithm based on the proposed time series information granulation method and granular time series similarity measurement method (CMAIG_ECM_HC) is carried out on some UCR datasets and a real stock dataset, and experimental studies demonstrate that CMAIG_ECM_HC has superior performance in clustering time series with different shapes and trends.
From Gretel to Strudelcity: Empowering Teachers Regarding Generative AI for Enhanced AI Literacy with CollectiveGPT
Authors: Brünner Benedikt , Schön Sandra , Ebner Martin
Published: February 2025

In the era of transformative technologies, generative artificial intelligence (genAI) offers profound opportunities and challenges for education. This study explores the development and execution of an interactive workshop designed to equip educators with foundational genAI literacy. Using a design-based research (DBR) framework, the workshop leverages interactivity and contextual relevance to introduce genAI concepts, prompting strategies and ethical considerations. Participants engaged in a scripted learning workshop design, comparing human and AI responses, exploring genAI’s probabilistic foundations, context dependency, and vulnerability to manipulation. Conducted across 12 workshops with 191 participants in Austria, this study revealed significant improvements in self-perceived genAI understanding, with 70% of participants reporting better grades in post-assessment evaluations. Feedback emphasized the workshop’s strengths in interactivity and relevance, alongside recommendations for deeper school-specific applications. Scalability analysis showed that workshop duration remained consistent regardless of group size, suggesting potential for broader implementation. The findings highlight the effectiveness of scripted learning workshop design in fostering critical AI literacy, preparing educators to critically evaluate and ethically integrate genAI into pedagogical practices. This adaptable model contributes to the discourse on professional development in AI-enhanced education.
Ready and Healthy for Kindergarten: A Collaborative Multilingual Family Involvement Program Created by Teachers, Pediatricians, and Parents
Authors: Lakata Lucia , Mandel Morrow Lesley , Lattmer Maria , Perez-Cortes Silvia , Ramachandran Usha , Pai Shilpa , Lima Daniel , Bator Alicja , Crabtree Benjamin F. , Pellerano Maria B. , Ohman Strickland Pamela , Uthirasamy Nila , Guerrero Kevin , Mendoza Caroline , Jimenez Manuel E.
Published: February 2025

Ready and Healthy for Kindergarten is a Spanish/English multilingual family involvement program that is grounded in sociocultural and family literacy theories since it focuses on health and literacy development. The program’s development reflects a collaborative partnership of teachers, pediatricians, families, and an advisory board of multilingual parents dedicated to preparing preschoolers and their families for kindergarten. Health themes are presented to introduce foundational literacy and social-emotional skills, and the program comprises eight one-hour virtual sessions intended to engage both children and their families through active participation in learning. The program highlights how parents, educators, and pediatricians can come together to align their expertise and impact family knowledge around child development needs. With this strong collaboration when designing and implementing the Ready and Healthy for Kindergarten program, we were able to successfully provide guidance and support for families, which helped them to establish routines that promote health and well-being, along with some literacy skills.
The Application of Active Methodologies in Spain: An Investigation of Teachers’ Use, Perceived Student Acceptance, Attitude, and Training Needs Across Various Educational Levels
Authors: Cabanillas-García Juan Luis
Published: February 2025

Active methodologies emphasize student participation, autonomy, and skill development, offering an innovative approach to education. However, their implementation in Spain faces challenges, including limited teacher training, resource shortages, institutional constraints, and resistance from both educators and students. This research aims to explore teachers’ perceptions regarding the integration of active methodologies into their teaching practices. The research employed a quantitative, non-experimental, descriptive, and cross-sectional survey design to systematically analyze population characteristics. The study utilized a validated questionnaire with Likert-scale items to assess teachers’ use of active methodologies. Data were collected anonymously via Google Forms, ensuring confidentiality and a robust reliability (Cronbach’s alpha 0.942). The study sampled answers provided by 994 Spanish teachers aged 20–65 using stratified probabilistic sampling, ensuring representation across educational levels, disciplines, and urban–rural contexts. This study shows that teachers prefer traditional active methodologies, such as cooperative learning, while emerging technologies like artificial intelligence face limited adoption due to training gaps. Women show a greater inclination towards active methodologies, with minimal impact from geographical context. These findings underscore the importance of targeted teacher training and support the idea that it is of paramount importance to bridge the gaps in active methodology implementation, fostering inclusive, innovative, and student-centered learning environments across diverse educational contexts.