Search Results (6,111)

The increasing demand for sustainable polymer composites has driven the development of hybrid laminates that combine natural, recycled, and synthetic reinforcements while maintaining adequate mechanical performance. However, the combined influence of stacking sequence and mineral filler addition on the mechanical behavior of such sustainable hybrid systems remains insufficiently understood. In this study, sustainable hybrid laminated composites based on epoxy reinforced with glass fiber (G), bamboo fiber (B), and flex banner (F) were fabricated with varying stacking sequences and calcium carbonate (CaCO₃) filler contents (0 and 1 wt.%). A total of nine laminate configurations were produced and evaluated through flexural and impact testing. The results demonstrate that mechanical performance is strongly governed by laminate architecture and filler addition. The bamboo-dominant G/B/B/B/G laminate containing 1 wt.% CaCO₃ exhibited the highest flexural strength (191 MPa) and impact resistance (0.766 J/mm²), indicating a synergistic effect between reinforcement arrangement and CaCO₃-induced matrix strengthening. In contrast, the lowest performance was observed for the G/F/B/F/G configuration without filler. Overall, all hybrid composites outperformed neat epoxy, highlighting the potential of bamboo–flex banner hybrid laminates with CaCO₃ filler for sustainable composite applications requiring balanced mechanical properties. This work aligns with SDG 12 by promoting resource-efficient circular-economy practices through the utilization of flex banner material and natural fibers as reinforcements in epoxy-based hybrid composites. Full article

Technological advances have influenced and changed cultural heritage in the galleries, libraries, archives, and museums (GLAM) sector by facilitating new forms of experimentation and knowledge exchange. In this context, this paper explores the evolving practice of archaeological facial depiction using AI-powered deepfake avatar creator software programs, such as Epic Games’ MetaHuman Creator (MHC), which offer new affordances in terms of agility, realism, and engagement, and build upon traditional workflows involving the physical sculpting or digital modelling of faces from the past. Through a case-based approach, we illustrate these affordances via real-world applications, including four-dimensional portraits, multi-platform presentations, Augmented Reality (AR), and enhanced audience interaction. We consider the limitations and challenges of these digital avatar systems, such as misrepresentation or cultural insensitivity, and we position this advanced technology within the broader context of digital heritage, considering both the technical possibilities and ethical concerns around synthetic representations of individuals from the past. Finally, we propose that the use of MHC is not a replacement for current practice, but rather an augmentation, expanding the potential for storytelling and public learning outcomes in the GLAM sector, as a result of increased efficiency and new forms of public engagement. Full article

Bio-based fertilisers (BBFs) derived from waste streams represent a transformative approach to sustainable agriculture, addressing the dual challenges of waste management and food security. This comprehensive review examines recent advances in BBF production technologies, nutrient recovery mechanisms, soil health impacts, and the benefits of a circular economy. This review, based on an analysis of peer-reviewed studies, demonstrates that BBFs consistently improve the physical, chemical, and biological properties of soil while reducing environmental impacts by 15–45% compared to synthetic alternatives. Advanced biological treatment technologies, including anaerobic digestion, vermicomposting, and biochar production, achieve nutrient recovery efficiencies of 60–95% in diverse waste streams. Market analysis reveals a rapidly expanding sector projected to grow from $2.53 billion (2024) to $6.3 billion by 2032, driven by regulatory support and circular economy policies. Critical research gaps remain in standardisation, long-term performance evaluation, and integration with precision agriculture systems. Future developments should focus on AI-driven optimisation, climate-adaptive formulations, and nanobioconjugate technologies. Full article

With the rapid expansion of pulsar survey data driven by advanced radio telescopes such as FAST, automated detection methods have become crucial for the efficient and accurate identification of single-pulse signals. A key challenge in this task is the extreme class imbalance between genuine pulsar pulses and radio frequency interference (RFI), which significantly hampers classifier performance—particularly in low signal-to-noise ratio (S/N) environments. To address this issue and improve detection accuracy, we propose Pulsar-WRecon, a Wasserstein GAN with Gradient Penalty (WGAN-GP)-based framework designed to generate realistic single-pulse profiles. The synthetic samples generated by Pulsar-WRecon are used to augment training data and alleviate class imbalance. Building upon the enhanced dataset, Convolutional Kolmogorov–Arnold Network (CKAN) is further introduced as a novel hybrid model that integrates convolutional layers with KAN-based functional decomposition to better capture complex patterns in pulse signals. On the three-channel pulsar images from the HTRU1 dataset, our method achieves a recall of 97.5% and a precision of 98.5%. On the DM time series image dataset, FAST-DATASET, it achieves a recall of 93.2% and a precision of 92.5%. These results validate that combining generative data augmentation with an improved model architecture can effectively enhance the precision of single-pulse detection in large-scale pulsar surveys, especially in challenging, real-world conditions. Full article

Synthetic Aperture Radar (SAR) and Interferometric SAR (InSAR) techniques have received rapid advance in recent years, and the Multi-temporal InSAR (MT-InSAR) has been widely applied in various earth observations. Distributed scatterer (DS) InSAR is one of the most advanced MT-InSAR methods, and has overcome the limitation of the lack of enough measurement points in the low coherent regions for traditional methods. While the Joint-Scatterer InSAR (JS-InSAR) is the extension of DS InSAR method, which exploited the overall information of Joint Scatterers to carry out DS identification and phase optimization. And it can avoid the inaccuracy caused by the offset errors between scatterers in complex terrain areas. However, the intensive computation and low efficiency have severely restricted the application of JS-InSAR, especially when dealing with massive and long historical SAR images. As the sequential estimator has proven to successfully improve the efficiency of MT-InAR and obtain near-time deformation time series, in this work, we proposed the sequential-based JS-InSAR (S-JSInSAR) method with flexible batches. This method has adaptively divided large single look complex (SLC) stack into different batches with flexible number and certain overlaps. Then, the JS-InSAR processing is performed on each batch, respectively, and these estimated results are integrated into the final deformation time series based on the connection mode. Thus, S-JSInSAR can efficiently process large InSAR dataset, and mitigate the decorrelation effect caused by long temporal baselines. To demonstrate the effectiveness of the S-JSInSAR, a multi-year of 145 Sentinel-1 ascending SAR images in Tangshan, China, were collected to estimate the long deformation time series. And the results compared with other methods have shown the processing time has substantially decreased without the loss of deformation accuracy, and obtain deformation spatial distribution with more details in local regions, which have well validated the efficiency and reliability of the proposed method. Full article

Water polluted by dye colorants has been on the rise in the last decade. This is due to the over reliance on the textile industry, and it is holding a high economic value in most countries. This industry is the highest consumer of fresh water whilst also discharging several natural and synthetic pollutants to the environment. Several methods have been used for the removal of these pollutants and one of the most efficient technologies to be developed includes the photocatalysis method, via advanced oxidation processes. This review highlights the developments of green iron oxide nanoparticles as photocatalysts in the last decade. It was noted that tuning and controlling the phytochemical concentration and synthesis conditions, can assist with forming uniform and non-agglomerated materials, as this has limited the vast usage of these materials in major applications. Also, upon controlling the synthesis conditions, improved surface area and charge separation efficiency was noted. Their limitations and need for modification through forming composites are highlighted. Moreover, future perspectives are given on the use of green IONPs as photocatalysts. Full article

This paper presents a two-stage explainable recommendation system for reciprocal affiliate–seller matching that uses machine learning and data science to handle voluminous data and generate personalized ranking lists for each user. In the first stage, a representation learning model was trained to create dense embeddings for affiliates and sellers, ensuring efficient identification of relevant pairs. In the second stage, a learning-to-rank approach was applied to refine the recommendation list based on user suitability and relevance. Diversity-enhancing reranking (maximal marginal relevance/explicit query aspect diversification) and popularity penalties were also implemented, and their effects on accuracy and provider-side diversity were quantified. Model interpretability techniques were used to identify which features affect a recommendation. The system was evaluated on a fully synthetic dataset that mimics the high-level statistics generated by affiliate platforms, and the results were compared against classical baselines (ALS, Bayesian personalized ranking) and ablated variants of the proposed model. While the reported ranking metrics (e.g., normalized discounted cumulative gain at 10 (NDCG@10)) are close to 1.0 under controlled conditions, potential overfitting, synthetic data limitations, and the need for further validation on real-world datasets are addressed. Attributions based on Shapley additive explanations were computed offline for the ranking model and excluded from the online latency budget, which was dominated by approximate nearest neighbors-based retrieval and listwise ranking. Our work demonstrates that high top-K accuracy, diversity-aware reranking, and post hoc explainability can be integrated within a single recommendation pipeline. While initially validated under synthetic evaluation, the pipeline was further assessed on a public real-world behavioral dataset, highlighting deployment challenges in affiliate–seller platforms and revealing practical constraints related to incomplete metadata. Full article

Microbial biological control agents are a sustainable alternative to synthetic pesticides, yet their widespread application is limited by a lack of environmental resilience of commercial products. To address this, we exploited honey—a stringent ecological niche—as a reservoir for stress-tolerant bacteria. In this study, the bioprospection utilizing five types of commercially available honeys yielded a collection of 53 bacteria and 10 fungi. All bacterial isolates were evaluated for antimicrobial activity against a laboratory-standard bacterium and yeast, and six economically relevant phytopathogenic microorganisms. Initial screening with standard laboratory organisms proved to be an efficient method to detect strains with antimicrobial potential, correlating significantly with further phytopathogen inhibition (Spearman’s r = 0.4512, p = 0.0005). Two promising strains, M2.7 and M3.18, were selected for quantitative dual-culture assays along with molecular identification using 16S rDNA and gyrA gene sequencing, classifying them as Bacillus velezensis. These strains exhibited high inhibitory effects against the pathogens (p > 0.001), often with equivalent efficacy to the commercial biocontrol strain, and also induced significant phytopathogen hyphal deformities, such as increased septation and swelling. These findings support honey as a viable source of robust biocontrol agents, offering a sustainable strategy to substitute or complement current agrochemicals. Full article

Rapid advancements in the field of deep learning have had a profound impact on a wide range of scientific studies. This paper incorporates the power of deep neural networks to learn complex relationships in longitudinal data. The novel generative approach, Variational Deep Alliance (VaDA), is established, where an “alliance” is formed across repeated measurements via the strength of Variational Auto-Encoder. VaDA models the generating process of longitudinal data with a unified and well-structured latent space, allowing outcomes prediction, subjects clustering and representation learning simultaneously. The integrated model can be inferred efficiently within a stochastic Auto-Encoding Variational Bayes framework, which is scalable to large datasets and can accommodate variables of mixed type. Quantitative comparisons to those baseline methods are considered. VaDA shows high robustness and generalization capability across various synthetic scenarios. Moreover, a longitudinal study based on the well-known CelebFaces Attributes dataset is carried out, where we show its usefulness in detecting meaningful latent clusters and generating high-quality face images. Full article

This critical review explores the transformative impact of artificial intelligence (AI), particularly machine learning (ML) and computer vision (CV), on scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS) for metallic microstructure analysis, spanning research from 2010 to 2025. It critically evaluates how AI techniques balance automation, accuracy, and scalability, analysing why certain methods (e.g., Vision Transformers for complex microstructures) excel in specific contexts and how trade-offs in data availability, computational resources, and interpretability shape their adoption. The review examines AI-driven techniques, including semantic segmentation, object detection, and instance segmentation, which automate the identification and characterisation of microstructural features, defects, and inclusions, achieving enhanced accuracy, efficiency, and reproducibility compared to traditional manual methods. It introduces the Microstructure Analysis Spectrum, a novel framework categorising techniques by task complexity and scalability, providing a new lens to understand AI’s role in materials science. The paper also evaluates AI’s role in chemical composition analysis and predictive modelling, facilitating rapid forecasts of mechanical properties such as hardness and fracture strain. Practical applications in steelmaking (e.g., automated inclusion characterisation) and case studies on high-entropy alloys and additively manufactured metals underscore AI’s benefits, including reduced analysis time and improved quality control. Extending prior reviews, this work incorporates recent advancements like Vision Transformers, 3D Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). Key challenges—data scarcity, model interpretability, and computational demands—are critically analysed, with representative trade-offs from the literature highlighted (e.g., GANs can substantially augment effective dataset size through synthetic data generation, typically at the cost of significantly increased training time). Full article

Infrared thermal field (ITF) prediction for meter boxes is crucial for the early warning of power system faults, yet this method faces three major challenges: data sparsity, complex geometry, and resource constraints in edge computing. Existing physics-informed neural network-graph neural network (PINN-GNN) approaches suffer from redundant physics residual calculations (over 70% of flat regions contain little information) and poor model generalization (requiring retraining for new box types), making them inefficient for deployment on edge devices. This paper proposes the PhysGraphIR framework, which employs an Adaptive Residual Sampling (ARS) mechanism to dynamically identify hotspot region nodes through a physics-aware gating network, calculating physics residuals only at critical nodes to reduce computational overhead by over 80%. In this study, a ‘hotspot region’ is explicitly defined as a localized area exhibiting significant temperature elevation relative to the background—typically concentrated around electrical connection terminals or wire entrances—which is critical for identifying potential thermal faults under sparse data conditions. Additionally, it utilizes a Physics Knowledge Distillation Graph Neural Network (Physics-KD GNN) to decouple physics learning from geometric learning, transferring universal heat conduction knowledge to specific meter box geometries through a teacher–student architecture. Experimental results demonstrate that on both synthetic and real-world meter box datasets, PhysGraphIR achieves a hotspot region mean absolute error (MAE) of 11.8 °C under 60% infrared data missing conditions, representing a 22% improvement over traditional PINN-GNN. The training speed is accelerated by 3.1 times, requiring only five infrared samples to adapt to new box types. The experiments prove that this method significantly enhances prediction accuracy and computational efficiency under sparse infrared data while maintaining physical consistency, providing a feasible solution for edge intelligence in power systems. Full article

Petroleum coke quality strongly influences refinery economics and downstream energy use, yet real-time control is constrained by slow quality assays and a 24–48 h lag in laboratory results. This study introduces a physics-informed combinatorial digital twin for value-optimized coking, aimed at improving energy efficiency and environmental performance through adaptive quality forecasting. The approach builds a modular library of 32 candidate equations grouped into eight quality parameters and links them via cross-parameter dependencies. A two-level optimization scheme is applied: a genetic algorithm selects the best model combination, while a secondary loop tunes parameters under a multi-objective fitness function balancing accuracy, interpretability, and computational cost. Validation on five clustered operating regimes (industrial patterns augmented with noise-perturbed synthetic data) shows that optimal model ensembles outperform single best models, achieving typical cluster errors of ~7–13% NMAE. The developed digital twin framework enables accurate prediction of coke quality parameters that are critical for its energy applications, such as volatile matter and sulfur content, which serve as direct proxies for estimating the net calorific value and environmental footprint of coke as a fuel. Full article

Cannabinoids can bind to several cannabinoid receptors and modulate cellular signaling and gene expression relevant to inflammation and lipid homeostasis. Likewise, several vitamin E analogs can modulate inflammatory signaling and foam cell formation in macrophages by antioxidant and non-antioxidant mechanisms. We analyzed the regulatory effects on the expression of genes involved in cellular lipid homeostasis (e.g., CD36/FAT cluster of differentiation/fatty acid transporter and scavenger receptor SR-B1) and inflammation (e.g., inflammatory cytokines, TNFα, IL1β) by cannabinoids (cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC)) in human THP-1 macrophages with/without co-treatment with natural alpha-tocopherol (RRR-αT), natural RRR-αTA (αTAn), and synthetic racemic all-rac-αTA (αTAr). In general, αTAr inhibited both lipid accumulation and the inflammatory response (TNFα, IL6, IL1β) more efficiently compared to αTAn. Our results suggest that induction of CD36/FAT mRNA expression after treatment with THC can be prevented, albeit incompletely, by αTA (either αTAn or αTAr) or CBD. A similar response pattern was observed with genes involved in lipid efflux (ABCA1, less with SR-B1), suggesting an imbalance between uptake, metabolism, and efflux of lipids/αTA, increasing macrophage foam cell formation. THC increased reactive oxygen species (ROS), and co-treatment with αTAn or αTAr only partially prevented this. To study the mechanisms by which inflammatory and lipid-related genes are modulated, HEK293 cells overexpressing cannabinoid receptors (CB1 or TRPV-1) were transfected with luciferase reporter plasmids containing the human CD36 promoter or response elements for transcription factors involved in its regulation (e.g., LXR and NFκB). In cells overexpressing CB1, we observed activation of NFκB by THC that was inhibited by αTAr. Full article

An efficient synthetic method for the preparation of self-assembling conjugated organic materials with a silazane anchor group based on direct hydrosilylation reaction is reported. A novel organic semiconductor molecule, NH(Si-Und-BTBT-Hex)_2, consisting of a polar silazane anchor group linked through undecylenic (Und) aliphatic spacers to conjugated blocks based on benzothieno[3,2-b][¹]benzothiophene (BTBT) and solubilizing hexyl (Hex) end groups, was synthesized. Its self-organization on the air-water interface and solid substrates into ultrathin layers obtained by the Langmuir–Schaefer or Langmuir–Blodgett methods was investigated. Monolayer organic field-effect transistors manufactured from NH(Si-Und-BTBT-Hex)₂ showed operation in the p-type mode. Full article

In this study, an Integrated Energy System (IES) with hydrogen refinement within a tiered carbon trading mechanism (TCTM) is presented to improve energy efficiency and support decarbonization. To address uncertainties in the IES, a distributionally robust optimization (DRO) approach, employing a fuzzy set framework with Kernel Density Estimation (KDE) to construct error distributions and specify output ranges for renewable energy (RE), is proposed. Latin hypercube sampling (LHS) and K-means clustering are, respectively, applied to generate original and representative scenarios. Subsequently, case studies are performed to evaluate advantages of the presented model. The results indicate that hydrogen refinement within the TCTM framework has substantial benefits for the IES. Specifically, the proposed scenario integrates hydrogen blending combustion (HBC) with synthetic methane, demonstrating significant economic and carbon benefits, with cost reductions of 7.3%, 7.1%, and 4.3% and carbon emission reductions of 6%, 3%, and 2.4% compared to scenarios with no hydrogen utilization, HBC only, and synthetic methane only, respectively. In contrast, to exclude carbon trading and include fixed-price trading, the TCTM achieves a 3.5% and 1.1% reduction in carbon emissions, respectively. Finally, a comprehensive sensitivity analysis is performed, examining factors such as the ratio of hydrogen blending, price, and growth rate of carbon trading. Full article