Search Results (1,084)

The preparation and use of iron oxide magnetic nanoparticles for water remediation is a widely investigated research field. To improve the efficacy of such nanomaterials, different synthetic processes and functionalization methods have been developed in the framework of green chemistry to exploit their magnetic properties and adsorption capacity in a sustainable way. In this work, iron oxide magnetic nanoparticles embedded in cross-linked sodium alginate beads designed to clean water from metal ions were magnetically characterized. In particular, the effect of copper adsorption on their magnetic properties was investigated. The magnetic characterization in a DC field of the beads before adsorption showed the presence of a superparamagnetic state at 300 K—a state that was also preserved after copper adsorption. The main differences in terms of magnetic properties before and after Cu²⁺ adsorption were the reduction of the magnetic signal (observed by comparing the saturation magnetization) and a different shape of the blocking temperature distribution obtained by magnetization versus temperature measurements. The evaluation of the reduction in magnetization can be important from the application perspective since it can affect the efficiency of the beads’ removal from the water medium after treatment. Full article

Privacy preservation poses significant challenges in third-party data sharing, particularly when handling table data containing personal information such as demographic and behavioral records. Synthetic table data generation has emerged as a promising solution to enable data analysis while mitigating privacy risks. While Generative Adversarial Networks (GANs) are widely used for this purpose, they exhibit limitations in modeling table data due to challenges in handling mixed data types (numerical/categorical), non-Gaussian distributions, and imbalanced variables. To address these limitations, this study proposes a novel adversarial learning framework integrating gradient boosting trees for synthesizing table data, called Adversarial Gradient Boosting Decision Tree (AGBDT). Experimental evaluations on several datasets demonstrate that our method outperforms representative baseline models regarding statistical similarity and machine learning utility. Furthermore, we introduce a privacy-aware adaptation of the framework by incorporating k-anonymization constraints, effectively reducing overfitting to source data while maintaining practical usability. The results validate the balance between data utility and privacy preservation achieved by our approach. Full article

To address the issues in existing 3D point cloud facet generation networks, specifically, the tendency to produce a large number of empty facets and the uncertainty in facet count, this paper proposes a novel deep learning framework for robust facet segmentation. Based on the generated facet set, two exploratory applications are further developed. First, to overcome the bottleneck where inaccurate empty-facet detection impairs the downsampling performance, a facet-abstracted downsampling method is introduced. By using a learned facet classifier to filter out and discard empty facets, retaining only non-empty surface facets, and fusing point coordinates and local features within each facet, the method achieves significant compression of point cloud data while preserving essential geometric information. Second, to solve the insufficient precision in organ segmentation within crop point clouds, a facet growth-based segmentation algorithm is designed. The network first predicts the edge scores for the facets to determine the seed facets. The facets are then iteratively expanded according to adjacent-facet similarity until a complete organ region is enclosed, thereby enhancing the accuracy of segmentation across semantic boundaries. Finally, the proposed facet segmentation network is trained and validated using a synthetic dataset. Experiments show that, compared with traditional methods, the proposed approach significantly outperforms both downsampling accuracy and instance segmentation performance. In various crop scenarios, it demonstrates excellent geometric fidelity and semantic consistency, as well as strong generalization ability and practical application potential, providing new ideas for in-depth applications of facet-level features in 3D point cloud analysis. Full article

Cell-free biosensors harness the selectivity of cellular machinery without living cells’ constraints, offering advantages in environmental monitoring, medical diagnostics, and biotechnological applications. This review examines recent advances in cell-free biosensor development, highlighting their ability to detect diverse analytes including heavy metals, organic pollutants, pathogens, and clinical biomarkers with high sensitivity and specificity. We analyze technological innovations in cell-free protein synthesis optimization, preservation strategies, and field deployment methods that have enhanced sensitivity, and practical applicability. The integration of synthetic biology approaches has enabled complex signal processing, multiplexed detection, and novel sensor designs including riboswitches, split reporter systems, and metabolic sensing modules. Emerging materials such as supported lipid bilayers, hydrogels, and artificial cells are expanding biosensor capabilities through microcompartmentalization and electronic integration. Despite significant progress, challenges remain in standardization, sample interference mitigation, and cost reduction. Future opportunities include smartphone integration, enhanced preservation methods, and hybrid sensing platforms. Cell-free biosensors hold particular promise for point-of-care diagnostics in resource-limited settings, environmental monitoring applications, and food safety testing, representing essential tools for addressing global challenges in healthcare, environmental protection, and biosecurity. Full article

Hydrogels, characterized by their high water content, tunable mechanical properties, and excellent biocompatibility, have emerged as a promising material platform for the preservation of cultural heritage. Their unique physicochemical features enable non-invasive and adaptable solutions for environmental regulation, structural stabilization, and antifungal protection. This review provides a comprehensive overview of recent progress in hydrogel-based strategies specifically developed for the conservation of cultural relics, with a particular focus on antifungal performance—an essential factor in preventing biodeterioration. Current hydrogel systems, composed of natural or synthetic polymer networks integrated with antifungal agents, demonstrate the ability to suppress fungal growth, regulate humidity, alleviate mechanical stress, and ensure minimal damage to artifacts during application. This review also highlights future research directions, such as the application prospects of novel materials, including stimuli-responsive hydrogels and self-dissolving hydrogels. As an early exploration of the use of hydrogels in antifungal protection and broader cultural heritage conservation, this work is expected to promote the wider application of this emerging technology, contributing to the effective preservation and long-term transmission of cultural heritage worldwide. Full article

The growing need for secure and privacy-preserving identity management in industrial environments has exposed the limitations of traditional, centralized authentication systems. In this context, SIBERIA was developed as a modular solution that empowers users to control their own digital identities, while ensuring robust protection of critical services. The system is designed in alignment with European standards and regulations, including EBSI, eIDAS 2.0, and the GDPR. SIBERIA integrates a Self-Sovereign Identity (SSI) framework with a decentralized blockchain-based infrastructure for the issuance and verification of Verifiable Credentials (VCs). It incorporates multi-factor authentication by combining a voice biometric module, enhanced with spoofing-aware techniques to detect synthetic or replayed audio, and a behavioral biometrics module that provides continuous authentication by monitoring user interaction patterns. The system enables secure and user-centric identity management in industrial contexts, ensuring high resistance to impersonation and credential theft while maintaining regulatory compliance. SIBERIA demonstrates that it is possible to achieve both strong security and user autonomy in digital identity systems by leveraging decentralized technologies and advanced biometric verification methods. Full article

Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021–2023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R², SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R² = 86.8–99.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R² = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap. Full article

This study evaluates the efficacy of commercial clean-label additives, specifically fermentates, in inhibiting mold growth in vitro and extending the shelf life of preservative-free bread. The mold growth on selected bread was modeled using the time-to-growth approach. The pH, a_w, and moisture content of fresh bread were determined. In addition, selected fermentates were characterized physicochemically. Fermentates, defined as liquid or powdered preparations containing microorganisms, their metabolites, and culture supernatants, were tested at varying concentrations (1% to 12%) to assess their antimicrobial performance and impact on bread quality parameters, including moisture content, water activity, and pH. The results showed significant differences in fermentate efficacy, with Product A as the best mold growth inhibitor in vitro and a clear dose-dependent response. For Penicillium corylophilum, inhibition increased from 51.90% at 1% to 62.60% at 4%, while P. chrysogenum had an inhibition ranging from 32.26% to 34.49%. Product F exhibited moderate activity on both molds at 4%, inhibiting between 28.48% and 46.27%. The two molds exhibited differing sensitivities to the fermentates, with P. corylophilum consistently more susceptible to inhibition. Product A displayed a low pH (2.61) and high levels of lactic acid (1053.6 mmol/L) and acetic acid (1061.3 mmol/L). Product F presented a similar pH but lower levels of lactic and acetic acid. A time-to-growth model, validated by significant coefficients (p < 0.05) and high predictive accuracy (R² > 0.95), was employed to predict the appearance of mold on bread loaves. The model revealed that higher concentrations of fermentates A and F delayed mold growth, with fermentate A demonstrating superior efficacy. At 2% concentration, fermentate A delayed mold growth for 8 days, compared to 6 days for fermentate F. At 8% concentration, fermentate A prevented mold growth for over 25 days, significantly outperforming the control (4 days). Additionally, fermentates influenced bread quality parameters, with fermentate A improving crust moisture retention and reducing water activity at higher concentrations. These findings highlight the potential of fermentates as sustainable, consumer-friendly alternatives to synthetic preservatives, offering a viable solution to the challenge of bread spoilage while maintaining product quality. Full article

Introduction: Biodegradable Temporizing Matrix (BTM) is a new synthetic dermal substitute suitable for wound closure and tissue regeneration. The data in paediatric population remain limited. The study purpose is to review the indications for BTM application in paediatric patients, evaluate the short-term and long-term results, including complications and functional outcomes, as well as to share some unique observations regarding the use of BTM in paediatric population. Patients and Methods: Patients undergoing reconstructive surgery and BTM application during the last three years were included. Data collected included patient demographics, primary diagnosis, previous surgical management, post-operative complications and final outcomes. BTM was used in 32 patients. The indications varied including epidermolysis bullosa (n = 6), burns (n = 4), trauma (n = 7), infection (n = 4), ischemia or necrosis (n = 11). Results: The results were satisfying with acceptable aesthetic and functional outcomes. Complications included haematoma underneath the BTM leading to BTM removal and re-application (n = 1), BTM infection (n = 1) and split-thickness skin graft failure on top of BTM requiring re-grafting (n = 2). Conclusions: BTM can be a good alternative to large skin grafts, locoregional flaps or even free flaps. The big advantages over other dermal substitutes or skin grafts are that BTM is less prone to infection and offers excellent scarring by preserving the normal skin architecture. Specifically in children, BTM might not require grafting, resulting in spontaneous healing with good scarring. In critically ill patients, BTM reduces the operation time and there is no donor site morbidity. BTM should be considered in the reconstructive ladder when discussing defect coverage options in children and young people. Full article

Digital image quality is crucial for reliable analysis in applications such as medical imaging, satellite remote sensing, and video surveillance. However, traditional denoising methods struggle to balance noise removal with detail preservation and lack adaptability to various types of noise. We propose a novel three-module architecture for image denoising, comprising a generator, a dual-path-UNet-based denoiser, and a discriminator. The generator creates synthetic noise patterns to augment training data, while the dual-path-UNet denoiser uses multiple receptive field modules to preserve fine details and dense feature fusion to maintain global structural integrity. The discriminator provides adversarial feedback to enhance denoising performance. This dual-path adversarial training mechanism addresses the limitations of traditional methods by simultaneously capturing both local details and global structures. Experiments on the SIDD, DND, and PolyU datasets demonstrate superior performance. We compare our architecture with the latest state-of-the-art GAN variants through comprehensive qualitative and quantitative evaluations. These results confirm the effectiveness of noise removal with minimal loss of critical image details. The proposed architecture enhances image denoising capabilities in complex noise scenarios, providing a robust solution for applications that require high image fidelity. By enhancing adaptability to various types of noise while maintaining structural integrity, this method provides a versatile tool for image processing tasks that require preserving detail. Full article

Food waste has emerged as a critical worldwide concern, resulting in environmental deterioration and economic detriment. Bio-based natural polymer coatings and films have emerged as a sustainable solution to food preservation challenges, particularly in reducing postharvest losses and extending shelf life. Compared to their synthetic counterparts, these polymers, such as chitosan, starch, cellulose, proteins, and alginate, are derived from renewable sources that are biodegradable, safe, and functional. Within this context, this review examines the various bio-based natural polymer coatings and films as biodegradable, edible alternatives to conventional packaging solutions. It examines the different fabrication methods, like solution casting, electrospinning, and spray coating, and incorporates antimicrobial agents to enhance performance. Emphasis is placed on their mechanical, barrier, and antimicrobial properties, their application in preserving fresh produce, how they promote food safety and environmental sustainability, and accompanying limitations. This review highlights the importance of bio-based natural polymer coatings and films as a promising, eco-friendly solution to enhancing food quality, safety, and shelf life while addressing global sustainability challenges. Full article

This study presents a DIKWP-driven artificial consciousness framework for IoT-enabled smart healthcare, integrating a Data–Information–Knowledge–Wisdom–Purpose (DIKWP) cognitive architecture with a software-defined IoT infrastructure. The proposed system deploys DIKWP agents at edge and cloud nodes to transform raw sensor data into high-level knowledge and purpose-driven actions. This is achieved through a structured DIKWP pipeline—from data acquisition and information processing to knowledge extraction, wisdom inference, and purpose-driven decision-making—that enables semantic reasoning, adaptive goal-driven responses, and privacy-preserving decision-making in healthcare environments. The architecture integrates wearable sensors, edge computing nodes, and cloud services to enable dynamic task orchestration and secure data fusion. For evaluation, a smart healthcare scenario for early anomaly detection (e.g., arrhythmia and fever) was implemented using wearable devices with coordinated edge–cloud analytics. Simulated experiments on synthetic vital sign datasets achieved approximately 98% anomaly detection accuracy and up to 90% reduction in communication overhead compared to cloud-centric solutions. Results also demonstrate enhanced explainability via traceable decisions across DIKWP layers and robust performance under intermittent connectivity. These findings indicate that the DIKWP-driven approach can significantly advance IoT-based healthcare by providing secure, explainable, and adaptive services aligned with clinical objectives and patient-centric care. Full article

Internal crack defects in high-strength low-alloy (HSLA) steels during continuous casting pose significant challenges to downstream processing and product reliability. However, due to the inherent class imbalance in industrial defect datasets, conventional machine learning models often suffer from poor sensitivity to minority class instances. This study proposes a predictive framework that integrates conditional tabular generative adversarial network (CTGAN) for synthetic minority sample generation and CatBoost for classification. A dataset of 733 process records was collected from a continuous caster, and 25 informative features were selected using mutual information. CTGAN was employed to augment the minority class (crack) samples, achieving a balanced training set. Feature distribution analysis and principal component visualization indicated that the synthetic data effectively preserved the statistical structure of the original minority class. Compared with the other machine learning methods, including KNN, SVM, and MLP, CatBoost achieved the highest metrics, with an accuracy of 0.9239, precision of 0.9041, recall of 0.9018, and F1-score of 0.9022. Results show that CTGAN-based augmentation improves classification performance across all models. These findings highlight the effectiveness of GAN-based augmentation for imbalanced industrial data and validate the CTGAN–CatBoost model as a robust solution for online defect prediction in steel manufacturing. Full article

In recent years, data augmentation techniques have become the predominant approach for addressing highly imbalanced classification problems in machine learning. Algorithms such as the Synthetic Minority Over-sampling Technique (SMOTE) and Conditional Tabular Generative Adversarial Network (CTGAN) have proven effective in synthesizing minority class samples. However, these methods often introduce distributional bias and noise, potentially leading to model overfitting, reduced predictive performance, increased computational costs, and elevated cybersecurity risks. To overcome these limitations, we propose a novel architecture, FADEL, which integrates feature-type awareness with a supervised discretization strategy. FADEL introduces a unique feature augmentation ensemble framework that preserves the original data distribution by concurrently processing continuous and discretized features. It dynamically routes these feature sets to their most compatible base models, thereby improving minority class recognition without the need for data-level balancing or augmentation techniques. Experimental results demonstrate that FADEL, solely leveraging feature augmentation without any data augmentation, achieves a recall of 90.8% and a G-mean of 94.5% on the internal test set from Kaohsiung Chang Gung Memorial Hospital in Taiwan. On the external validation set from Kaohsiung Medical University Chung-Ho Memorial Hospital, it maintains a recall of 91.9% and a G-mean of 86.7%. These results outperform conventional ensemble methods trained on CTGAN-balanced datasets, confirming the superior stability, computational efficiency, and cross-institutional generalizability of the FADEL architecture. Altogether, FADEL uses feature augmentation to offer a robust and practical solution to extreme class imbalance, outperforming mainstream data augmentation-based approaches. Full article

Background/Objectives: In routine practice, colonoscopy findings are saved as unstructured free text, limiting secondary use. Accurate named-entity recognition (NER) is essential to unlock these descriptions for quality monitoring, personalized medicine and research. We compared named-entity recognition (NER) models trained on real, synthetic, and mixed data to determine whether privacy preserving synthetic reports can boost clinical information extraction. Methods: Three Spark NLP biLSTM CRF models were trained on (i) 100 manually annotated Romanian colonoscopy reports (ModelR), (ii) 100 prompt-generated synthetic reports (ModelS), and (iii) a 1:1 mix (ModelM). Performance was tested on 40 unseen reports (20 real, 20 synthetic) for seven entities. Micro-averaged precision, recall, and F1-score values were computed; McNemar tests with Bonferroni correction assessed pairwise differences. Results: ModelM outperformed single-source models (precision 0.95, recall 0.93, F1 0.94) and was significantly superior to ModelR (F1 0.70) and ModelS (F1 0.64; p < 0.001 for both). ModelR maintained high accuracy on real text (F1 = 0.90), but its accuracy fell when tested on synthetic data (0.47); the reverse was observed for ModelS (F1 = 0.99 synthetic, 0.33 real). McNemar χ² statistics (64.6 for ModelM vs. ModelR; 147.0 for ModelM vs. ModelS) greatly exceeded the Bonferroni-adjusted significance threshold (α = 0.0167), confirming that the observed performance gains were unlikely to be due to chance. Conclusions: Synthetic colonoscopy descriptions are a valuable complement, but not a substitute for real annotations, while AI is helping human experts, not replacing them. Training on a balanced mix of real and synthetic data can help to obtain robust, generalizable NER models able to structure free-text colonoscopy reports, supporting large-scale, privacy-preserving colorectal cancer surveillance and personalized follow-up. Full article