Search Results (136)

Amid the accelerating spread of antibiotic resistance, medicinal and aromatic plants stand out as powerful natural reservoirs of bioactive compounds, offering innovative prospects for next-generation antimicrobial therapies. To explore its therapeutic potential, this study evaluated the antimicrobial and antioxidant activities of Matricaria pubescens from Southeastern Morocco, supported by a thorough chemical profiling of its essential oils. The oils were obtained by steam distillation and analyzed using gas chromatography–mass spectrometry (GC–MS). The results revealed two distinct chemotypes, with isochrysanthemic ethyl ester (32.7%) as the dominant compound in chemotype EO1 and α-ocimene (19.62%) as the major constituent in chemotype EO2. Antioxidant activities were assessed using DPPH, ABTS, and reducing power assays, while antimicrobial activities were evaluated against bacteria, fungi, and yeasts using both disc diffusion and broth microdilution methods. Both oils exhibited notable antioxidant activities. Significant antimicrobial effects were observed, with Bacillus subtilis, Escherichia coli, and Staphylococcus aureus being the most sensitive strains, whereas Pseudomonas aeruginosa exhibited the highest resistance among all tested microorganisms, with the lowest MIC recorded for B. subtilis (0.612 mg/mL). These findings emphasize that M. pubescens could serve as a valuable source of biologically active compounds, particularly in the development of agents to combat microbial resistance, and further support its potential applications in pharmaceutical, cosmetic, and food industries. Full article

Volume electron microscopy (vEM) provides nanometer-scale 3D imaging, yet its axial (z) resolution is often much lower than the in-plane ($xy$) resolution, yielding anisotropic volumes that hinder segmentation and connectomic reconstruction. We present a two-stage cross-axial super-resolution framework for isotropic reconstruction that combines a conditional diffusion model and domain-specific self-supervised pretraining of a vision transformer (ViT). First, the student–teacher self-distillation paradigm of DINOv3 is adopted to learn representations from large sets of high-resolution $xy$ sections, capturing vEM-specific texture statistics and ultrastructural patterns. Second, a conditional diffusion denoiser is trained with supervised anisotropic degradation simulated by z-downsampling, while a perceptual loss based on frozen ViT feature distances constrains generated slices to match real-section distributions. These constraints recover axial high-frequency details and reduce hallucinated textures and inter-slice drift, improving cross-slice consistency. Experiments on two public vEM datasets show improved fidelity, perceptual quality, and membrane-boundary continuity over interpolation and learning-based baselines. Full article

Diffusion models have emerged as the state-of-the-art generative paradigm, surpassing GANs in synthesizing high-fidelity images, videos, and audio. However, their reliance on iterative denoising processes imposes substantial computational burdens and memory overheads, creating a significant barrier to their deployment on resource-constrained edge devices. Unlike existing surveys that broadly cover general methodologies, this paper provides a focused review with a specific emphasis on efficient and lightweight diffusion models. We systematically analyze the trade-offs between generation quality and computational cost, categorizing acceleration techniques into sampling optimization, architectural compression, and knowledge distillation. Furthermore, we explore the integration of diffusion models with emerging architectures (e.g., Mamba) and their evolution towards general-purpose world simulators. This survey aims to provide a roadmap for “Green AI,” bridging the gap between high-end academic research and practical, real-world applications. Full article

Speech-driven face generation aims to synthesize a face image that matches a speaker’s identity from speech alone. However, existing methods typically trade identity fidelity for visual quality and rely on large end-to-end generators that are difficult to train and tune. We propose Vox2Face, a speech-driven face generation framework centered on an explicit identity space rather than direct speech-to-image mapping. A pretrained speaker encoder first extracts speech embeddings, which are distilled and metric-aligned to the ArcFace hyperspherical identity space, transforming cross-modal regression into a geometrically interpretable speech-to-identity alignment problem. On this unified identity representation, we reused an identity-conditioned diffusion model as the generative backbone and synthesized diverse, high-resolution faces in the Stable Diffusion latent space. To better exploit this prior, we introduce a discriminator-free diffusion self-consistency loss that treats denoising residuals as an implicit critique of speech-predicted identity embeddings and updates only the speech-to-identity mapping and lightweight LoRA adapters, encouraging speech-derived identities to lie on the high-probability identity manifold of the diffusion model. Experiments on the HQ-VoxCeleb dataset show that Vox2Face improves the ArcFace cosine similarity from 0.295 to 0.322, boosts R@10 retrieval accuracy from 29.8% to 32.1%, and raises the VGGFace Score from 18.82 to 23.21 over a strong diffusion baseline. These results indicate that aligning speech to a unified identity space and reusing a strong identity-conditioned diffusion prior is an effective method to jointly improve identity fidelity and visual quality. Full article

Since the introduction of denoising diffusion probabilistic models (DDPM) in 2020, diffusion-based image generation has achieved remarkable quality but remains computationally demanding for resource-constrained environments. This survey systematically analyzes over 100 publications from 2020 to 2025, presenting a four-layer optimization stack that encompasses model architecture, controllable mechanisms, sampling algorithms, and model compression. We address the fundamental “quality–efficiency–control” trilemma through three research questions: (1) the architectural complexity gap between U-shaped network (UNet) and diffusion transformer (DiT) models, (2) the parameter overhead spectrum of control mechanisms from ControlNet (42%) to NanoControl (0.024%), and (3) the theoretical impact of quantization and bit-width reduction on information loss. Our analysis reveals that instant image generation is achievable through algorithmic innovations such as step distillation and architectural pruning, reducing the sampling steps from 50 to 4–8 (or even 1) and computational cost by over 90%. We utilize the floating point operations (FLOPs) efficiency ratio (FER) to highlight the discrepancy between theoretical FLOPs reduction and actual efficiency, pointing towards the need for system-level optimization. Key findings demonstrate that DiT architectures exhibit high computational density (FER > 1.6) and low-bit quantization such as 8-bit weight, and activation (W8A8) maintains an optimal balance between compression and quality (Fréchet inception distance degradation ΔFID < 1.0), and lightweight control mechanisms enable sophisticated image control with a negligible parameter overhead. This survey provides a comprehensive algorithmic optimization roadmap for practitioners targeting efficient on-device image generation. Full article

Onions represent one of the most widely consumed vegetables within the Allium genus, generating a significant amount of waste. Onion waste is mainly composed of non-edible fractions, including roots, outer dry skins, and the outer fleshy scale. This study aimed to valorize red onion waste (ROW) and assess its total phenolic content and antioxidant activity using a water bath extraction method with distilled water as the extraction solvent. response surface methodology was employed to optimize the extraction parameters, namely temperature and time. The phenolic composition of freeze-dried ROW extract was analyzed using liquid chromatography–mass spectrometry analysis. The antibacterial activity was assessed using the disk diffusion and broth dilution methods against Gram-positive and Gram-negative bacteria, and the anticancer activity was tested against H460 lung, Caco-2 colon, and HT-29 colorectal cancer cell lines. The ideal extraction parameters were 1:40 g/mL, 98 °C for 27 min. The major detected compounds were isorhamnetin (55.32%), hyperoside (19.44%), and quercetin (13.65%). ROW extract showed antibacterial activity against Bacillus cereus, Staphylococcus aureus, Listeria monocytogenes, Escherichia coli, Pseudomonas aeruginosa, and Salmonella Typhimurium, with the highest activity observed against S. aureus. A cytotoxic effect against H460 and Caco-2 was shown by the ROW extract, with IC₅₀ values detected within the tested concentrations. The results suggest that ROW extract has potential for effective application as an antioxidant, antibacterial, and anticancer agent, demonstrating its potential for incorporation into functional foods and nutraceutical development. Full article

Essential oils (EOs) have emerged as promising natural antimicrobials for food safety applications. However, their direct use on food-contact surfaces—such as wood and plastic, commonly employed in artisanal cheese production—has been scarcely explored. This study aimed to evaluate the antibacterial effects of dill (Anethum graveolens) and nettle (Urtica dioica) essential oils against Listeria monocytogenes and Escherichia coli, both in culture media and on inert surfaces. EOs were extracted via steam distillation and characterized by gas chromatography–mass spectrometry (GC-MS). Antimicrobial activity was assessed using agar diffusion and minimum inhibitory concentration (MIC) assays. In addition, bacterial reduction was quantified following EO application to contaminated wooden and plastic surfaces for 40 min. Dill EO exhibited a high anethole content (63.66%), while nettle EO was dominated by limonene (38.73%). Dill EO produced larger inhibition zones against E. coli (13.7 ± 1.5 mm) and L. monocytogenes (12.3 ± 1.5 mm) compared to nettle EO (6.3 ± 0.6 mm and 8.0 ± 1.7 mm, respectively). On plastic, both EOs achieved complete inhibition of E. coli (100%) and greater than 92% reduction in L. monocytogenes. On wood, dill EO maintained high efficacy (up to 87.9%), whereas nettle EO showed limited reduction (29.3%) against L. monocytogenes. These results demonstrate that EO efficacy is influenced by both surface type and target microorganism, supporting the potential of dill EO as a natural antimicrobial agent for surface sanitation in artisanal cheese production. Full article

Controlling the rheological properties of liquids allows for the regulation of effective movement, transport of substances, and processes in biological systems. This work presents an experimental investigation into the influence of the proton-exchange polymer membrane Nafion on the surface tension coefficient (STC) of distilled water, aqueous solutions of two methylene blue (MB) forms, and ascorbic acid (AA). Immediately upon membrane immersion in the solutions, a sharp decrease in the surface tension of distilled water, as well as of the oxidized and reduced forms of MB, occurs. The observed narrow time interval is associated with the formation of an exclusion zone near the membrane–solution interface, containing dissociated sulfonate groups $\left(\mathrm{S}{\mathrm{O}}_3^{-}\right)$. The value of the time interval depends on the type of aqueous solution. At long soaking of the membrane in solutions, we obtained: for the aqueous solution of Mb⁺ (blue-coloured solution) the STC value eventually increases by about 5%, and for the reduced form of methylene blue MbH⁰-colourless solution, the STC value decreases by 4%. The STC value of the solutions formed during diffusion into the membrane has a significantly lower value compared to the STC of distilled water by 20% for the Mb⁺ form and by 24% for the MbH⁰ form of MB. The presence of the membrane in the aqueous AA solution causes only an increase in the STC value of the solution. Ultimately, for the solution with a concentration of 5 g/L, this increase reached 15% relative to the STC value of the original AA solution. The change in surface tension of the investigated solutions in the presence of the membrane is due to their adsorption onto the membrane surface. Fourier-transform infrared (FTIR) spectroscopy investigation of distilled water, MB, and AA solution diffusion into the membrane across the range (370–7800) cm⁻¹ confirms the process nonlinearity and enables identification of distinct time intervals corresponding to membrane swelling stages. The positions of IR transmission minima for membranes containing water and solution components remain unchanged; only the numerical values of the transmission coefficients vary. Using spectrophotometry, absorption lines of the membrane with adsorbed components of MB and AA solutions were identified in the range of (190–900) nm. The absorption spectra of dried membranes with adsorbed Mb⁺ and AA solutions show a redshift to the IR region for the Nafion with Mb⁺ and a shift to the UV region for the Nafion soaked in an aqueous ascorbic acid solution. A surface tension gradient at the membrane–solution interface can induce concentration-capillary convection in the liquid. Full article

Automated ultrasonic testing (AUT) serves as a vital method for evaluating critical infrastructure in industries such as oil and gas. However, a significant challenge in deploying artificial intelligence (AI)-based interpretation methods for AUT data lies in improving their reliability and effectiveness, particularly due to the inherent scarcity of real-world defective data. This study directly addresses data scarcity in a weld defect classification task, specifically concerning the detection of lack of fusion (LOF) defects in weld inspections using a proprietary industrial ultrasonic B-scan image dataset. This paper leverages state-of-the-art generative models, including Generative Adversarial Networks (GANs) and Denoising Diffusion Probabilistic Models (DDPM) (StyleGAN3, VQGAN with an unconditional transformer, and Stable Diffusion), to produce realistic B-scan images depicting LOF defects. The fine-tuned Transformer-based models, including ViT-Base, Swin-Tiny, and MobileViT-Small classifiers, on the regular B-scan image dataset are then applied to retain only high-confidence positive synthetic samples from each method. The impact of these synthetic images on the classification performance of a ResNet-50 model is evaluated, where it is fine-tuned with cumulative additions of synthetic images, ranging from 10 to 200 images. Its accuracy on the test set increases by 38.9% relative to the baseline with the addition of either 80 synthetic images using VQGAN with an unconditional transformer or 200 synthetic images by StyleGAN3 to the training set, and by 36.8% with the addition of 150 synthetic images by Stable Diffusion. This also outperforms Transformer-based vision models that are trained on regular training data. Concurrently, knowledge distillation experiments involve training ResNet-50 as a student model, leveraging the expertise of ViT-Base and Swin-Tiny as teacher models to demonstrate the effectiveness of adding the synthetic data to the training set, where the greatest enhancement is observed to be 34.7% relative to the baseline. This work contributes to advancing robust, AI-assisted tools for critical infrastructure inspection and offers practical pathways for enhancing available models in resource-constrained industrial environments. Full article

The growing global population has resulted in a higher demand for energy, leading researchers to prioritize the development of alternative energy sources and the improvement of current technologies. Nanofluids (NFs) are a promising method for enhancing heat transfer and efficiently utilizing solar thermal energy. This study describes the preparation of four NFs: two mono NFs of SiC and HfC containing nanoparticle concentrations ranging from 0.10–1.0 wt.%. Moreover, two hybrid NFs were synthesized within the same concentration range (0.10–1.0 wt.%) of SiC-HfC nanocomposites in proportions of 60 wt.% SiC-40 wt.% HfC and 40 wt.% SiC-60 wt.% HfC, all dispersed in a mixture of ethylene glycol (EG) and distilled water (50EG-50H₂O). The materials were synthesized by carbothermal reduction, and the NFs were prepared using the two-step method. The NFs showed stable dispersion, with HfC and 40SiC-60HfC systems exhibiting the higher zeta potential (ζ) values. Viscosity remained largely unaffected by particle addition. The thermal diffusivity of the NFs was measured by the thermal lens spectroscopy (TLS) technique using 1:20 diluted samples. The hybrid nanofluid 40SiC-60HfC improved diffusivity by 66.93%, presenting a synergistic effect in its performance, highlighting its potential in clean energy systems. Full article

Cloud contamination, especially thick cloud cover, severely limits the usability of optical remote sensing imagery by obscuring surface information. Due to the strong penetrability of microwave signals, Synthetic Aperture Radar (SAR) has emerged as an effective source for thick cloud removal. While SAR-assisted deep learning methods, such as CNNs and GANs, have made notable progress, the quality of generated imagery still requires improvement. Diffusion models, which offer strong potential for enhancing generation fidelity, could address this limitation but suffer from slow sampling speeds that constrain practical use and underscore the need for greater efficiency. To simultaneously enhance both reconstruction quality and sampling efficiency, this paper proposes a fast-sampling SAR-conditioned consistency model based on consistency distillation, named CM-CR, which adopts a teacher–student architecture to divide the reconstruction process into a rapid coarse prediction stage and a detailed refinement stage, significantly reducing per-scene processing time while maintaining high reconstruction fidelity. Specifically, a SAR-Conditioned Score-Based Diffusion Model (SCSBD) is first developed as the teacher network for learning a SAR-conditioned optical image generation model. Consistency distillation is then used to derive the student network SAR-conditioned consistency model (SCCM), which enables a rapid coarse prediction through single-step sampling. Finally, a Progressive Denoising via Multistep Resampling (PDMSR) strategy is introduced to iteratively refine the single-step output, producing fine-grained reconstructions. Comparative experiments conducted on the widely used cloud removal benchmark dataset SEN12MS-CR demonstrate that the proposed CM-CR method achieves state-of-the-art (SOTA) performance across all image quality metrics. Notably, although its design uses approximately 80 times more parameters compared with a standard Denoising Diffusion Probabilistic Model (DDPM), it delivers up to a 40-fold acceleration at inference. Full article

Background: Gross tumor volume (GTV) segmentation of Nasopharyngeal Carcinoma (NPC) crucially determines the precision of image-guided radiation therapy (IGRT) for NPC. Compared to other cancers, the clinical delineation of NPC is especially challenging due to its capricious infiltration of the adjacent rich tissues and bones, and it routinely requires multimodal information from CT and MRI series to identify its ambiguous tumor boundary. However, the conventional deep learning-based multimodal segmentation method suffers from limited prediction accuracy and frequently performs as well as or worse than single-modality segmentation models. The limited multimodal prediction performance indicates defective information extraction and integration from the input channels. This study aims to develop a 3D Gaussian-prompted Diffusion Model (3DG-PDM) for more clinically targeted information extraction and effective multimodal information integration, thereby facilitating more accurate and clinically interpretable GTV segmentation for NPC. Methods: We propose a 3D-Gaussian-Prompted Diffusion Model (3DGS-PDM) that operates NPC tumor contouring in multimodal clinical priors through a guided stepwise process. The proposed model contains two modules: a Gaussian Initialization Module that utilizes a 3D-Gaussian-Splatting technique to distill 3D-Gaussian representations based on clinical priors from CT, MRI-t2 and MRI-t1-contract-enhanced-fat-suppression (MRI-t1-cefs), respectively, and a Diffusion Segmentation Module that generates tumor segmentation step-by-step from the fused 3D-Gaussians prompts. We retrospectively collected data on 600 NPC patients from four hospitals through paired CT, MRI series and clinical GTV annotations, and divided that dataset into 480 training volumes and 120 testing volumes. Results: Our proposed method can achieve a mean dice similarity cofficient (DSC) of 84.29 ± 7.33, a mean average symmetric surface distance (ASSD) of 1.31 ± 0.63, and a 95th percentile of Hausdorff (HD₉₅) of 4.76 ± 1.98 on primary NPC tumor (GTVp) segmentation, and a DSC of 79.25 ± 10.01, an ASSD of 1.19 ± 0.72 and an HD₉₅ of 4.76 ± 1.71 on metastasis NPC tumor (GTVnd) segmentation. Comparative experiments further demonstrate that our method can significantly improve the multimodal segmentation performance on NPC tumors, with superior advantages over five other state-of-the-art comparative methods. Visual evaluation on the segmentation prediction process and a three-step ablation study on input channels further demonstrate the interpretability of our proposed method. Conclusions: This study proposes a performant and interpretable multimodal segmentation method for GTV of NPC, contributing greatly to precision improvement for NPC therapy treatment. Full article

Background: Significant progress has been made in the field of machine learning, enabling the development of methods for automatic interpretation of medical images that provide high-quality diagnostics. However, most of these methods require access to confidential data, making them difficult to apply under strict privacy requirements. Existing privacy-preserving approaches, such as federated learning and dataset distillation, have limitations related to data access, visual interpretability, etc. Methods: This study explores the use of generative models to create synthetic medical data that preserves the statistical properties of the original data while ensuring privacy. The research is carried out on the VinDr-Mammo dataset of digital mammography images. A conditional generative method using Latent Diffusion Models (LDMs) is proposed with conditioning on diagnostic labels and lesion information. Diagnostic utility and privacy robustness are assessed via cancer classification tasks and re-identification tasks using Siamese neural networks and membership inference. Results: The generated synthetic data achieved a Fréchet Inception Distance (FID) of 5.8, preserving diagnostic features. A model trained solely on synthetic data achieved comparable performance to one trained on real data (ROC-AUC: 0.77 vs. 0.82). Visual assessments showed that synthetic images are indistinguishable from real ones. Privacy evaluations demonstrated a low re-identification risk (e.g., mAP@R = 0.0051 on the test set), confirming the effectiveness of the privacy-preserving approach. Conclusions: The study demonstrates that privacy-preserving generative models can produce synthetic medical images with sufficient quality for diagnostic task while significantly reducing the risk of patient re-identification. This approach enables secure data sharing and model training in privacy-sensitive domains such as medical imaging. Full article

In the detection of small targets such as insulator defects and flashovers, the existing YOLOv11 has problems such as insufficient feature extraction and difficulty in balancing model lightweight and detection accuracy. We propose a lightweight architecture called FocusNet based on YOLOv11n. To improve the feature expression ability of small targets, Aggregation Diffusion Neck is designed to achieve deep integration and optimization of features at different levels through multiple rounds of multi-scale feature fusion and scale adaptation, and Focus module is introduced to focus on and strengthen the key features of small targets. On this basis, to achieve efficient deployment, the Group-Level First-Order Taylor Expansion Importance Assessment Method is proposed to eliminate channels that have little impact on detection accuracy to streamline the model structure. Then, Channel Distribution Distillation compensates for the slight accuracy loss caused by pruning, and finally achieves the dual optimization of high accuracy and high efficiency. Furthermore, we analyze the interpretability of FocusNet via heatmaps generated by KPCA-CAM. Experiments show that FocusNet achieves 98.50% precision and 99.20% mAP@0.5 on a proprietary insulator defect detection database created for this project using only 3.80 GFLOPs. This research provides reliable technical support for insulator monitoring in power systems. Full article

The uncertainty and confrontational nature of war itself pose significant challenges to the collection and storage of aerial battlefield temporal data. To address the issue of insufficient training of intelligent models caused by the scarcity of air battlefield situation data, this paper designs an air battlefield time series data augmentation model based on a lightweight denoising diffusion probabilistic model (LDMKD-DA). Considering the advantages of a denoising diffusion probabilistic model (DDPM) in processing images, this paper transforms 1D time series data into image data. 1D univariate time series data, such as High-resolution Range Profile dataset, are transformed by Gramian angular fields and Markov transition fields. Multivariate time series data, such as the air target intention dataset, are transformed by matrix expansion. Then, the data augmentation model is constructed based on the denoising diffusion probabilistic model. Considering the need for miniaturization and intelligence in future combat platforms, the depthwise separable convolution is introduced to lighten the DDPM, and, at the same time, the improved knowledge distillation method is introduced to accelerate the sampling process. The experimental results show that LDMKD-DA is capable of generating synthetic data similar to real data with high quality while significantly reducing FLOPs and params, while having significant advantages in univariate and multivariate time series data amplification. Full article