Search Results (6,997)

This narrative review delves into the molecular mechanisms of hyaluronic acid (HA) and re-epithelializing agents in the context of periodontal regeneration. Periodontitis, characterized by chronic inflammation and the destruction of tooth-supporting tissues, presents a significant challenge in restorative dentistry. Traditional non-surgical therapies (NSPTs) sometimes fail to fully manage subgingival biofilms and could benefit from adjunctive treatments. HA, with its antibacterial, antifungal, anti-inflammatory, angiogenic, and osteoinductive properties, offers promising therapeutic potential. This review synthesizes the current literature on the bioactive effects of HA and re-epithelializing agents, such as growth factors and biomaterials, in promoting cell migration, proliferation, and extracellular matrix (ECM) synthesis. By modulating signaling pathways like the Wnt/β-catenin, TGF-β, and CD44 interaction pathways, HA enhances wound healing processes and tissue regeneration. Additionally, the role of HA in facilitating cellular crosstalk between epithelial and connective tissues is highlighted, as it impacts the inflammatory response and ECM remodeling. This review also explores the combined use of HA with growth factors and cytokines in wound healing, revealing how these agents interact synergistically to optimize periodontal regeneration. Future perspectives emphasize the need for further clinical trials to evaluate the long-term outcomes of these therapies and their potential integration into periodontal treatment paradigms. Full article

Nanoscale resonant devices based on optical tweezers are widely used in the field of precision sensing. In the process of driving the nanoresonator based on the Coulomb force, the real-time, precise regulation of the charge carried by the charged resonator is essential for continuous manipulation. However, the accuracy of the existing charge measurement methods for levitated particles is low, and these methods cannot meet the needs of precision sensing. In this study, a novel net charge measurement protocol for levitated particles based on spatial speed statistics is proposed. High-precision mass measurement based on Maxwell’s rate distribution law is the basis for improving the accuracy of charge measurement, and accurate measurement of net charge can be achieved by periodic electric field driving. The error of net charge measurement is less than 7.3% when the pressure is above 0.1 mbar, while it can be less than 0.76% at 10 mbar. This proposed method features real-time, high-precision, non-destructive, and in situ measurement of the net charge of particles in the medium vacuum, which provides new solutions for practical problems in the fields of high-precision sensing and nano-metrology based on levitated photodynamics. Full article

Due to the high gain, wide bandwidth, and directional radiation characteristics of Vivaldi antennas, this paper conducted relevant research on the feasibility of non-destructive blood glucose detection based on Vivaldi antennas. The research included finite element method (FEM) simulation and glucose concentration monitoring. In the simulation stage, the power transmission and reflection characteristics, radiation characteristics, and electric field distribution characteristics of the antenna were described in detail. In the test stage, the S₁₁ response of the antenna to variation in glucose concentration in the range of 0–6.11 mg/mL was measured, including the S₁₁ amplitude and phase. The experimental results show that there is a high linear correlation between the S₁₁ response and glucose concentration, and the sensitivity of the S₁₁ amplitude response to the variation in glucose concentration is close to 0.3445 (dB/(mg/mL)) at 14.2556 GHz, and the sensitivity of the S₁₁ phase response to the variation in glucose concentration is about 0.5652 (degree/(mg/mL)) at 14.37 GHz. In addition, the predicted results of the glucose concentration based on linear regression are discussed. Full article

The aim of this article is to analyse the global environmental impact of wind farms, i.e., the effects on human health and the local ecosystem. Compared to conventional energy sources, wind turbines emit significantly fewer greenhouse gases, which helps to mitigate global warming. During the life cycle of a wind farm, 86% of CO₂ emissions are generated by the extraction of raw materials and the manufacture of wind turbine components. The water consumption of wind farms is extremely low. In the operational phase, it is 4 L/MWh, and in the life cycle, one water footprint is only 670 L/MWh. However, wind farms occupy a relatively large total area of 0.345 ± 0.224 km²/MW of installed capacity on average. For this reason, wind farms will occupy more than 10% of the land area in some EU countries by 2030. The impact of wind farms on human health is mainly reflected in noise and shadow flicker, which can cause insomnia, headaches and various other problems. Ice flying off the rotor blades is not mentioned as a problem. On a positive note, the use of wind turbines instead of conventionally operated power plants helps to reduce the emission of particulate matter 2.5 microns or less in diameter (PM 2.5), which are a major problem for human health. In addition, the non-carcinogenic toxicity potential of wind turbines for humans over the entire life cycle is one of the lowest for energy plants. Wind farms can have a relatively large impact on the ecological system and biodiversity. The destruction of animal migration routes and habitats, the death of birds and bats in collisions with wind farms and the negative effects of wind farm noise on wildlife are examples of these impacts. The installation of a wind turbine at sea generates a lot of noise, which can have a significant impact on some marine animals. For this reason, planners should include noise mitigation measures when selecting the site for the future wind farm. The end of a wind turbine’s service life is not a major environmental issue. Most components of a wind turbine can be easily recycled and the biggest challenge is the rotor blades due to the composite materials used. Full article

Background/Objectives—Parkinson’s disease (PD) is the second most common neurodegenerative disorder caused by the destruction of neurons in the substantia nigra of the brain. Clinical diagnosis of this disease, based on monitoring motor symptoms, often leads to a delayed start of PD therapy and control, where over 60% of dopaminergic nerve cells are damaged in the brain substantia nigra. The search for simple and stable characteristics of EEG recordings is a promising direction in the development of methods for diagnosing PD and methods for diagnosing the preclinical stage of PD development. Methods—42 subjects participated in work, of which 4 female/10 male patients were included in the group of patients with non-motor disorders, belonging to the risk group for developing PD (median age: 62 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 143 and 80)/(median age: 68 years, height: 170 cm, weight: 73.9 kg, pulse: 75, BPsys and BPdia: 143 and 82). The first control group of healthy participants included 6 women (median age: 33 years, height: 161 cm, weight: 66 kg, pulse: 80, BPsys and BPdia: 110 and 80)/8 men (median age: 36.3 years, height: 175 cm, weight: 69 kg, pulse: 78, BPsys and BPdia: 120 and 85). The second control group of healthy participants included 8 women (median age: 74 years, height: 164 cm, weight: 70 kg, pulse: 70, BPsys and BPdia: 145 and 82)/6 men (median age: 51 years, height: 172 cm, weight: 72.5 kg, pulse: 74, BPsys and BPdia: 142 and 80). Wavelet oscillatory pattern estimation is performed on patients’ nocturnal sleep recordings without separating them into sleep stages. Results—Amplitude characteristics of oscillatory activity in patients without motor disorders and the prodromal PD stage are significantly reduced both in terms of changes in the number of patterns and in terms of their duration. This pattern is especially pronounced for high-frequency activity, in frequency ranges close to 40 Hz. Conclusions—The success of the analysis of the electrical activity of the brain, performed over the entire duration of the night recording, makes it promising to further use during daytime monitoring the concept of oscillatory wavelet patterns in patients with non-motor disorders, belonging to the risk group for developing PD. The daytime monitoring system can become the basis for developing screening tests to detect neurodegenerative diseases as part of routine medical examinations. Full article

The presence of gray mold can seriously affect the yield and quality of strawberries. Due to their susceptibility and the rapid spread of this disease, it is important to develop early, accurate, rapid, and non-destructive disease identification strategies. In this study, the early detection of strawberry leaf diseases was performed using hyperspectral imaging combining multi-dimensional features like spectral fingerprints and vegetation indices. Firstly, hyperspectral images of healthy and early affected leaves (24 h) were acquired using a hyperspectral imaging system. Then, spectral reflectance (616) and vegetation index (40) were extracted. Next, the CARS algorithm was used to extract spectral fingerprint features (17). Pearson correlation analysis combined with the SPA method was used to select five significant vegetation indices. Finally, we used five deep learning methods (LSTMs, CNNs, BPFs, and KNNs) to build disease detection models for strawberries based on individual and fusion characteristics. The results showed that the accuracy of the recognition model based on fused features ranged from 88.9% to 96.6%. The CNN recognition model based on fused features performed best, with a recognition accuracy of 96.6%. Overall, the fused feature-based model can reduce the dimensionality of the classification data and effectively improve the predicting accuracy and precision of the classification algorithm. Full article

To enhance lychee quality assessment and address inconsistencies in post-harvest pest detection, this study presents a multi-source fusion approach combining hyperspectral imaging, X-ray imaging, and visible/near-infrared (Vis/NIR) spectroscopy. Traditional single-sensor methods are limited in detecting pest damage, particularly in lychees with complex skins, as they often fail to capture both external and internal fruit characteristics. By integrating multiple sensors, our approach overcomes these limitations, offering a more accurate and robust detection system. Significant differences were observed between pest-free and infested lychees. Pest-free lychees exhibited higher hardness, soluble sugars (11% higher in flesh, 7% higher in peel), vitamin C (50% higher in flesh, 2% higher in peel), polyphenols, anthocyanins, and ORAC values (26%, 9%, and 14% higher, respectively). The Vis/NIR data processed with SG+SNV+CARS yielded a partial least squares regression (PLSR) model with an R² of 0.82, an RMSE of 0.18, and accuracy of 89.22%. The hyperspectral model, using SG+MSC+SPA, achieved an R² of 0.69, an RMSE of 0.23, and 81.74% accuracy, while the X-ray method with support vector regression (SVR) reached an R² of 0.69, an RMSE of 0.22, and 76.25% accuracy. Through feature-level fusion, Recursive Feature Elimination with Cross-Validation (RFECV), and dimensionality reduction using PCA, we optimized hyperparameters and developed a Random Forest model. This model achieved 92.39% accuracy in pest detection, outperforming the individual methods by 3.17%, 10.25%, and 16.14%, respectively. The multi-source fusion approach also improved the overall accuracy by 4.79%, highlighting the critical role of sensor fusion in enhancing pest detection and supporting the development of automated non-destructive systems for lychee stem borer detection. Full article

Background: In the Sahel, one of the largest semi-arid areas in the world, pastoral livestock is the main source of protein for the local population. The quantification of herbaceous biomass in the Sahelian rangelands is of major importance since it provides food for the livestock. The main method used to monitor the biomass consists of cutting, drying, and weighting it. However, indirect methods are available and allow a reliable biomass estimation. Methods: In this study, we developed a non-destructive method for estimating herbaceous biomass for the Sahelian rangelands based on measurements of its height and coverage. Results: Results show that the fit is better in the fenced area. The volume index (height × coverage) provides a better biomass prediction with relative differences between measured and predicted biomass of 11% in 2017 and 8% in 2019. Conclusions: Monitoring herbaceous biomass without destroying it is possible by measuring only its height and coverage. Full article

In recent years, ablation of the buffer layer in high-voltage cables has become a prevalent issue compromising the reliability of power transmission systems. Given the internal location of these faults, direct monitoring and assessment are challenging, resulting in numerous undetected ablation hazards. Previous practice has demonstrated that detecting buffer layer ablation through surface temperature distribution changes is feasible, offering a convenient, efficient, and non-destructive approach. However, the variability in heat generation and the subtle temperature differences in thermal infrared images, compounded by noise interference, can impair the accuracy and timeliness of fault detection. To overcome these challenges, this paper introduces an adaptive enhancement method for the thermal infrared imaging of high-voltage cable buffer layer ablation. The method involves an Average Gradient Weighted Guided Filtering (AGWGF) technique to decompose the image into background and detail layers, preventing noise amplification during enhancement. The background layer, containing the primary information, is enhanced using an improved Contrast Limited Adaptive Histogram Equalization (CLAHE) to accentuate temperature differences. The detail layer, rich in high-frequency content, undergoes improved Adaptive Bilateral Filtering (ABF) for noise reduction. The enhanced background and detail layers are then fused and stretched to produce the final enhanced thermal image. To vividly depict temperature variations in the buffer layer, pseudo-color processing is applied to generate color-infrared thermal images. The results indicate that the proposed method’s enhanced images and pseudo-colored infrared thermal images provide a clearer and more intuitive representation of temperature differences compared to the original images, with an average increase of 2.17 in information entropy and 8.38 in average gradient. This enhancement facilitates the detection and assessment of buffer layer ablation faults, enabling the prompt identification of faults. Full article

Stainless steel core plates (SSCPs) show great potential for modular construction due to their superiority of excellent mechanical properties, light weight, and low cost over traditional concrete and honeycomb structures. During the brazing process of SSCP joints which connect the skin panel and core tubes, it is difficult to keep an even heat flow of inert gas in the vast furnace, which can lead to partially missing solder defects in brazing joints. Pulsed eddy current imaging (PECI) has demonstrated feasibility for detecting missing solder defects, but various factors including lift-off variation and image blurring can deteriorate the quality of C-scan images, resulting in inaccurate evaluation of the actual state of the brazed joints. In this study, a differential pulsed eddy current testing (PECT) probe is designed to reduce the lift-off noise of PECT signals, and a mask-based image segmentation and thinning method is proposed to eliminate the blurring effect of C-scan images. The structure of the designed probe was optimized based on finite element simulation and the positive peak of the PECT signal was selected as the signal feature. Experiments with the aid of a scanning device are then carried out to image the interrogated regions of the SSCP specimen. The peak values of the signals were collected in a matrix to generate images of the scanned brazing joints. Results show that lift-off noise is significantly reduced by using the differential probe. Image blurring caused by the convolution effect of the probe’s point spread function with the imaging object was eliminated using a mask-based image segmentation and thinning method. The restored C-scan images enhance the sharpness of the profiles of the brazing joints and the opening in the images accurately reflect the missing solder of the brazed joints. Full article

The nutritional quality of bananas and their geographical origin authenticity are very important for trade. There is an urgent need for rapid, non-destructive testing to improve the origin and quality assurance for importers, distributors, and consumers. In this study, 99 banana samples from a range of producing countries were collected. Hyperspectral data were combined with chemometric methods to construct quantitative and qualitative models for bananas, predicting soluble solids content (SSC), potassium content (K), and country of origin. A second derivative analysis combined with competitive adaptive weighted sampling (CARS) and random frog jumping (RF) was selected as the best pre-treatment method for the prediction of SSC and K content, respectively. Partial least squares (PLS) models achieved R²_p values of 0.8012 and 0.8606 for SSC and K content, respectively. Chinese domestic and imported bananas were classified with a prediction accuracy of 95.83% using partial least squares-discriminant analysis (PLS-DA) and an RF method that screened the spectral variables after a second pretreatment. These results showed that hyperspectral imaging technology could be effectively used to non-destructively predict the nutrient contents of bananas and identify their geographical origin. In the future, this technology can be applied to determine the nutritional quality composition and geographical origin of bananas from other countries. Full article

Egg freshness is a critical factor that influences the egg’s nutritional value, safety, and overall quality; consequently, it is a priority for both producers and consumers. This review examines the factors that affect egg freshness, and it evaluates both traditional and modern methods for assessing egg freshness. Traditional techniques, such as the Haugh unit test and candling, have long been utilized; however, they have limitations, which are primarily due to their destructive nature. The review also highlights advanced non-destructive methods, including Vis-NIR spectroscopy, ultrasonic testing, machine vision, thermal imaging, hyperspectral imaging, Raman spectroscopy, and NMR/MRI technologies. These techniques offer rapid and accurate assessments while preserving the integrity of the eggs. Despite the current challenges related to calibration and implementation, integrating artificial intelligence (AI) and machine learning with these innovative technologies presents a promising avenue for the improvement of freshness evaluation. This development could revolutionize quality control processes in the egg industry, ensuring consistently high-quality eggs for consumers. Full article

Anthocyanins are flavonoid substances of plant origin with potential antioxidant effects. Because of their intense colors, they are used as natural dyes in food. However, their stability in food matrices is limited. This study aimed to verify the effect of selected carbohydrates on the stability of anthocyanins (cyanidin-3-O-β-glucopyranoside, cyanidin-3-O-β-galactopyranoside, cyanidin-3-O-β-rutinoside and delphinidin-3-O-β-rutinoside) during the accelerated storage test, since carbohydrates help to preserve the typical color of anthocyanins, increase their shelf-life and availability in the organism, and reduce losses during processing. Moreover, the kinetic parameters of anthocyanin degradation (E_a, k, t_1/2) were determined. Sucrose was found to have the greatest potential for retarding anthocyanin degradation during storage, whereas fructose exerted an accelerating effect. Glycosidation of anthocyanin aglycone had no significant effect in terms of their stability. Anthocyanin degradation was significantly positively correlated with the change in the a* parameter (redness), and subsequently, a significant positive correlation was observed in the determination of the kinetic parameters for anthocyanins and the a* parameter. The highest stability of anthocyanins was observed in the presence of sucrose and their degradation can be predicted by the value of the a* parameter, which would also be a very fast and non-destructive method for food processing companies. Full article

Glioblastoma (GBM) is the most prevalent malignant brain tumor. Current standard-of-care treatments offer limited benefits for patient survival. Virotherapy is emerging as a novel strategy to use oncolytic viruses (OVs) for the treatment of GBM. These engineered and non-engineered viruses infect and lyse cancer cells, causing tumor destruction without harming healthy cells. Recent advances in genetic modifications to OVs have helped improve their targeting capabilities and introduce therapeutic genes, broadening the therapeutic window and minimizing potential side effects. The efficacy of oncolytic virotherapy can be enhanced by combining it with other treatments such as immunotherapy, chemotherapy, or radiation. Recent studies suggest that manipulating the gut microbiome to enhance immune responses helps improve the therapeutic efficacy of the OVs. This narrative review intends to explore OVs and their role against solid tumors, especially GBM while emphasizing the latest technologies used to enhance and improve its therapeutic and clinical responses. Full article

As machine learning continues to evolve, traditional image steganography techniques find themselves increasingly unable to meet the dual challenge of deceiving both the human visual system and machine learning models. In response, this paper introduces the Visually Robust Image Steganography (VRIS) model, specifically tailored for this dual deception task. The VRIS model elevates visual security through the use of specialized feature extraction and processing methodologies. It meticulously conducts feature-level fusion between secret images and cover images, ensuring a high level of visual similarity between them. To effectively mislead machine learning models, the VRIS model incorporates a sophisticated strategy utilizing random noise factors and discriminators. This involves adding controlled amounts of random Gaussian noise to the encrypted image, thereby enhancing the difficulty for machine learning frameworks to recognize it. Furthermore, the discriminator is trained to discern between the noise-infused encrypted image and the original cover image. Through adversarial training, the discriminator and VRIS model refine each other, successfully deceiving the machine learning systems. Additionally, the VRIS model presents an innovative method for extracting and reconstructing secret images. This approach safeguards secret information from unauthorized access while enabling legitimate users to non-destructively extract and reconstruct images by leveraging multi-scale features from the encrypted image, combined with advanced feature fusion and reconstruction techniques. Experimental results validate the effectiveness of the VRIS model, achieving high PSNR and SSIM scores on the LFW dataset and demonstrating significant deception capabilities against the ResNet50 model on the Mini-ImageNet dataset, with an impressive misclassification rate of 99.24%. Full article