Search Results (3,011)

Autonomous underwater vehicles (AUVs) play a pivotal role in the exploration and monitoring of the sea floor. A primary challenge in surveying AUVs is consistently obtaining high-quality optical imagery data. A major cause of quality reduction is turbid water, which both attenuates and scatters light. The effects of turbidity can be minimized by lowering the operational altitude of the AUV, at the cost of increased survey duration and cost. Consequently, before conducting a survey, a trade-off must be made between the risk of acquiring suboptimal images and the additional time required to cover an area. In this research, we develop a computer-vision-based technique and control system that dynamically adjusts the altitude of an AUV based on real-time estimates of turbidity from collected images. Our testing in a simulated environment demonstrates that this system reliably improves the efficiency and quality of image collection. Full article

Background: One of the most widely used metal nanoparticles in biological applications is gold, which has unique physicochemical characteristics. Strong localized surface plasmon resonance (LSPR) endows them with exceptional optical properties that facilitate the development of innovative methods for biosensing, bioimaging, and cancer research, particularly in the context of photothermal and photodynamic therapy. Methods: This study marked the first time that Mandragora autumnalis ethanolic extract (MAE) was utilized in the environmentally friendly synthesis of gold nanoparticles (AuNPs). Several characterization methods, including dynamic light scattering analysis (DLS), scanning electron microscopy (SEM), X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, thermogravimetric analysis (TGA), and biological methods, were used to emphasize the anti-cancerous activity of the biogenic AuNPs. Results: MAE-AuNPs showed a surface plasmon resonance band at 570 nm. DLS and SEM demonstrated the synthesis of small, spherical AuNPs with a zeta potential of −19.07 mV. The crystalline nature of the AuNPs was confirmed by the XRD pattern, and data from FTIR and TGA verified that MAE-AuNPs played a part in stabilizing and capping the produced AuNPs. In addition, the MAE-AuNPs demonstrated their potential effectiveness as antioxidant and anticancer therapeutic agents by demonstrating radical scavenging activity and anticancer activity against a number of human cancer cell lines, specifically triple-negative breast cancer cells. Conclusions: Green synthesis techniques are superior to other synthesis methods because they are simple, economical, energy-efficient, and biocompatible, which reduces the need for hazardous chemicals in the reduction process. This article highlights the significance of characterizing MAE-AuNPs and evaluating their antioxidant and anticancer properties. Full article

Three-dimensional (3D) surface-enhanced Raman scattering (SERS) substrates have demonstrated remarkable abilities of ultrasensitive and reproducible molecular detection. The combination of both electromagnetic and chemical enhancement processes, light trapping, and multiple scattering effects of 3D structures are what enhance their performance. The principles of underlying enhancements are summarized systematically, and the main types of 3D substrates—vertically aligned nanowires, dendritic and fractal nanostructures, porous frameworks and aerogels, core–shell and hollow nanospheres, and hierarchical hybrid structures—are categorized in this review. Advances in fabrication techniques, such as template-assisted growth, electrochemical and galvanic deposition, dealloying and freeze-drying, self-assembly, and hybrid integration, are critically evaluated in terms of structural tunability and scalability. Novel developments in the field of biosensing are also highlighted, including non-enzymatic glucose sensing, tumor biomarker sensing, and drug delivery. The remaining limitations, such as low reproducibility, mechanical stability, and substrate standardization, are also noted, and future directions, such as stimuli-responsive designs, multifunctional hybrid platforms, and data-driven optimization strategies of SERS technologies, are also included. Full article

Multimodal imaging techniques have the potential to enhance the interpretation of histology by offering additional molecular and structural information beyond that accessible through hematoxylin and eosin (H&E) staining alone. Here, we present a quantitative approach for comparing the information content of different image modalities, such as H&E and multimodal imaging. We used a combination of deep learning and radiomics-based feature extraction with different information markers, implemented in Python 3.12, to compare the information content of the H&E stain, multimodal imaging, and the combined dataset. We also compared the information content of individual channels in the multimodal image and of different Coherent Anti-Stokes Raman Scattering (CARS) microscopy spectral channels. The quantitative measurements of information that we utilized were Shannon entropy, inverse area under the curve (1-AUC), the number of principal components describing 95% of the variance (PC95), and inverse power law fitting. For example, the combined dataset achieved an entropy value of 0.5740, compared to 0.5310 for H&E and 0.5385 for the multimodal dataset using MobileNetV2 features. The number of principal components required to explain 95 percent of the variance was also highest for the combined dataset, with 62 components, compared to 33 for H&E and 47 for the multimodal dataset. These measurements consistently showed that the combined datasets provide more information. These observations highlight the potential of multimodal combinations to enhance image-based analyses and provide a reproducible framework for comparing imaging approaches in digital pathology and biomedical image analysis. Full article

The Yuanjiang River, situated in the upper reaches of the Red River, is a crucial component of a biodiversity hotspot in the mountains of southwestern China, supporting a high diversity of fish species. Nevertheless, systematic research on fish diversity in the Yuanjiang River is scarce, scattered, and outdated. In our study, we produced 764 DNA barcodes belonging to 64 fish morphospecies to evaluate fish diversity in the Yuanjiang River. Barcoding gap analysis and DNA-based delimitation approaches achieved a high identification success rate (>93%), indicating that DNA barcoding is a practical approach for delimiting fish in the Yuanjiang River. However, four species were characterized by high levels of intraspecific divergence, generating multiple clades and/or molecular operational taxonomic units (MOTUs), suggesting that these species might comprise undetected species. Meanwhile, two closely related species within the genus Schistura, i.e., S. callichroma and S. caudofurca, cannot be delimited by the DNA barcoding technique, which is indicative of recent speciation. In summary, this study established a reliable DNA barcode reference library for fish species in the Yuanjiang River and revealed previously unknown fish diversity. Full article

Maritime navigation safety relies on high-precision perception systems. However, hazy weather often significantly compromises system performance, particularly by reducing image quality and increasing navigational risks. Although image dehazing techniques provide an effective solution, the lack of dedicated overwater dehazing datasets limits the generalization of dehazing algorithms. To overcome this problem, we present a large-scale overwater paired image dehazing dataset: Overwater-Haze. The dataset contains 21,000 synthetic overwater hazy images generated based on the atmospheric scattering model (ASM), categorized into Mist, Moderate, and Dense subsets based on varying haze concentrations, and 500 real overwater hazy images, which form the Real-Test portion of the test set. In order to meet the requirements for background interference mitigation, image diversity, and high quality, we performed extensive data augmentation and developed a comprehensive dataset creation pipeline. Our evaluation of five dehazing algorithms shows that models trained on Overwater-Haze achieve 9.96% and 10.47% lower Natural Image Quality Evaluator (NIQE) and Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) scores than pre-trained models on real overwater scenes, demonstrating the value of Overwater-Haze in assessing algorithm performance in overwater environments. Full article

To address the growing need for field calibration of the optical properties of pedestrian targets used in autonomous emergency braking (AEB) tests, a novel three-dimensional multi-faceted standard body (TDMFSB) was developed. A camera-based analytical algorithm was proposed to evaluate the bidirectional reflectance distribution function (BRDF) characteristics of pedestrian targets. Additionally, a field calibration method applied in AEB testing scenarios (CPFAO and CPLA protocols) on one new and one aged typical pedestrian target of the same type revealed a 21% decrease in the BRDF uniformity of the aged target compared to the new one, confirming optical degradation due to repeated “crash–scatter–reassembly” cycles. The surface wear of the aged target on the side facing the vehicle produced a smoother surface, increasing its BRDF magnitude by 25% compared to the new target and making it easily detectable by the vehicle’s perception system. This led to “reverse scoring,” a safety risk in performance evaluation, necessitating timely calibration of AEB pedestrian targets to ensure reliable test results. The findings provide valuable insights into the development of regulatory techniques, evaluation standards, and technical specifications for test targets and offer a practical path toward full-life-cycle traceability and quality control. Full article

Stable crop yields require an appropriate supply of essential soil nutrients such as nitrogen (N), phosphorus (P), and potassium (K) based on the accurate diagnosis of soil nutrient status. Traditional laboratory analysis of soil nutrients is often complicated and time-consuming and does not provide real-time nutrient status. Visible–near-infrared (Vis-NIR) spectroscopy has emerged as a non-destructive and rapid method for estimating soil nutrient levels. Vis-NIR spectra reflect sample characteristics as the peak intensities; however, they are often affected by various artifacts and complex variables. Since Vis-NIR spectroscopy does not directly measure nutrient levels in soil, improving estimation accuracy is essential. For spectral preprocessing, the most important aspect is to develop an appropriate preprocessing strategy based on the characteristics of the data and identify artifacts such as noise, baseline drift, and scatter in the spectral data. Machine learning-based modeling techniques such as partial least-squares regression (PLSR) and support vector machine regression (SVMR) enhance estimation accuracy by capturing complex patterns of spectral data. Therefore, this review focuses on the use of Vis-NIR spectroscopy for evaluating soil properties including soil water content, organic carbon (C), and nutrients and explores its potential for real-time field application through spectral preprocessing and machine learning algorithms. Vis-NIR spectroscopy combined with machine learning is expected to enable more efficient and site-specific nutrient management, thereby contributing to sustainable agricultural practices. Full article

Surface-enhanced Raman scattering (SERS) technology, leveraging its single-molecule-level detection sensitivity, molecular fingerprint recognition capability, and capacity for rapid, non-destructive analysis, has emerged as a pivotal analytical tool in food science, life sciences, and environmental monitoring. This review systematically summarizes recent advancements in SERS technology, encompassing its enhancement mechanisms (synergistic effects of electromagnetic and chemical enhancement), innovations in high-performance substrates (noble metal nanostructures, non-noble metal substrates based on semiconductors/graphene, and hybrid systems incorporating noble metals with functional materials), and its interdisciplinary applications. In the realm of food safety, SERS has enabled the ultratrace detection of pesticide residues, mycotoxins, and heavy metals, with flexible substrates and intelligent algorithms significantly enhancing on-site detection capabilities. Within biomedicine, the technique has been successfully applied to the rapid identification of pathogenic microorganisms, screening of tumor biomarkers, and viral diagnostics. For environmental monitoring, SERS platforms offer sensitive detection of heavy metals, microplastics, and organic pollutants. Despite challenges such as matrix interference and insufficient substrate reproducibility, future research directions aimed at developing multifunctional composite materials, integrating artificial intelligence algorithms, constructing portable devices, and exploring plasmon-catalysis synergy are poised to advance the practical implementation of SERS technology in precision diagnostics, intelligent regulation, and real-time monitoring. Full article

Non-invasive diagnostics play a crucial role in medicine, and they ensure both contamination safety and patient comfort. The proposed study integrates hyperspectral imaging with advanced image fusion, enabling non-invasive, diagnostic procedure within tissue. It utilizes near-infrared (NIR) wavelength vision that is suitable for reflections from objects within a dispersive layer, enabling the reconstruction of internal tissue layers images. It can detect objects, including cancerous tumors (presented as phantoms), inside human tissue. This involves processing data from multiple images taken in different NIR bands and merging them through image fusion techniques. Our research demonstrates evident data about objects within the diffusive media, visible only in the reconstructed images. The experimental results demonstrate a significant correlation with the samples employed in the study’s experimental design. Full article

The Karnak Temples are considered one of Egypt’s most significant archaeological sites, dating back to the Middle Kingdom (c. 2000–1700 BC) and were continuously expanded until the Ptolemaic period (305–30 BC). As the second most visited UNESCO World Heritage archaeological site in Egypt after the Giza Pyramids, Karnak faces severe deterioration processes due to prolonged exposure to environmental impacts, mechanical damage, and historical interventions. This study employs a multidisciplinary approach integrating non-destructive testing (NDT) methods to assess the physical and mechanical condition and degradation mechanisms of scattered sandstone blocks at the site. Advanced documentation techniques, including Reflectance Transformation Imaging (RTI), photogrammetry, and Infrared Thermography (IRT), were used to analyze surface morphology, thermal stress effects, and weathering patterns. Ultrasonic Pulse Velocity (UPV) testing provided internal structural assessments, while spectral and gloss analysis quantified chromatic alterations and surface roughness. Additionally, the Karsten Tube test determined the water absorption behavior of the sandstone, highlighting variations in porosity and susceptibility to salt crystallization. In this sense, the results indicate that climatic factors such as extreme temperature fluctuations, wind erosion, and groundwater infiltration contributed to sandstone deterioration. Thermal cycling leads to microcracking and granular disintegration, while high capillary water absorption accelerates chemical weathering processes. UPV analyses showed substantial internal decay, with low-velocity zones correlating with fractures and differential cementation loss. Finally, an interventive conservation plan was proposed. Full article

Seed priming is an innovative pre-planting technique to improve germination and accelerate early seedling growth, offering a sustainable and eco-friendly alternative to chemical treatments. In this study, silver nanoparticles (AgNPs) were synthesized using flower extracts of neem plants for the first time, alongside the conventional neem leaf extract-based AgNPs, and their comparative efficacy was evaluated in wheat seed priming. The biosynthesized AgNPs were characterized through UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive Spectroscopy (EDS), Dynamic Light Scattering (DLS), and zeta potential analysis to confirm their formation, stability, and surface functionality. Wheat seeds were primed with varying concentrations (25, 50, 75, 100 mg/L) of flower-mediated nanoparticles (F-AgNPs) and leaf-mediated nanoparticles (L-AgNPs). Effects on seed germination, seedling growth, plant pigments, secondary metabolites, and antioxidant enzyme activities were systematically investigated. The results indicated that F-AgNP priming treatment significantly enhanced wheat seedlings’ performances in comparison to L-AgNPs, which could be attributed to the difference in phytochemical profiles in the extracts. This study contributes a comparative experimental analysis highlighting the potential of biogenic AgNPs—particularly those derived from neem flower extract—offering a promising strategy for enhancing seedling establishment in wheat through seed priming. Full article

This study employs the improved modified extended tanh method (IMETM) to derive exact analytical solutions of a higher-order nonlinear Schrödinger (HNLS) model, incorporating $\beta$-fractional derivatives in both time and space. Unlike classical methods such as the inverse scattering transform or Hirota’s bilinear technique, which are typically limited to integrable systems and integer-order operators, the IMETM offers enhanced flexibility for handling fractional models and higher-order nonlinearities. It enables the systematic construction of diverse solution types—including Weierstrass elliptic, exponential, Jacobi elliptic, and bright solitons—within a unified algebraic framework. The inclusion of fractional derivatives introduces richer dynamical behavior, capturing nonlocal dispersion and temporal memory effects. Visual simulations illustrate how fractional parameters $\alpha$ (space) and $\beta$ (time) affect wave structures, revealing their impact on solution shape and stability. The proposed framework provides new insights into fractional NLS dynamics with potential applications in optical fiber communications, nonlinear optics, and related physical systems. Full article

Microrheology, a branch of rheology, focuses on studying the flow and deformation of matter at micron length scales, enabling the characterization of materials using minute sample volumes. This review article explores the principles and advancements of microrheology, covering a range of techniques that infer the viscoelastic properties of soft materials from the motion of embedded tracer particles. Special emphasis is placed on methods employing optical tweezers, which have emerged as a powerful tool in both passive and active microrheology thanks to their exceptional force sensitivity and spatiotemporal resolution. The review also highlights complementary techniques such as video particle tracking, magnetic tweezers, dynamic light scattering, and atomic force microscopy. Applications across biology, materials science, and soft matter research are discussed, emphasizing the growing relevance of particle tracking microrheology and optical tweezers in probing microscale mechanics. Full article

Various gels are integral for the food industry, providing unique textural and mechanical properties essential for the quality and functions of products. These properties are fundamentally governed by the gels’ nanostructural organization. This review investigates the mechanisms of nanostructure formation in food gels, the methods for their characterization and control, and how precise tuning of these nanostructures enables targeted food applications. We examine the role of various building blocks, including biopolymers, lipids, and particles, and the gelation mechanisms leading to specific nanostructures. Advanced techniques (e.g., microscopy, scattering, spectroscopy, and rheology) are discussed for their insights into nano-/microstructures. Strategies for tuning nanostructures through chemical composition adjustments (e.g., concentration, pH, ionic strength) and physical processing controls (e.g., temperature, shear, ultrasound) are presented. Incorporating nanostructures like nanoparticles and nanofibers to enhance gel properties is also explored. The review links these nanostructures to key functional properties, including mechanical strength, water-holding capacity, optical characteristics, and bioactive delivery. By manipulating nanostructures, products can achieve tailored textures, improved stability, and controlled nutrient release. Applications enabled by nanostructure tuning include tailored sensory experiences, fat reduction, innovative food structures, and smart packaging solutions. Although significant progress has been made, precise structural control and a comprehensive understanding of complex nanoscale interactions in food gels remain challenging. This review underscores the importance of nanostructure tuning in food gels, highlighting its potential to drive future research that unlocks innovative, functional food products. Full article