Search Results (7,175)

Every year about fifty volcanoes erupt on average, posing a serious threat for populations living in the neighboring areas. To mitigate the volcanic risk, many satellite monitoring systems have been developed. Information from the medium infrared (MIR) and thermal infrared (TIR) bands of sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) is commonly exploited for this purpose. However, the potential of daytime shortwave infrared (SWIR) observations from the Sea and Land Surface Temperature Radiometer (SLSTR) aboard Sentinel-3 satellites in supporting the near-real-time monitoring of thermal volcanic activity has not been fully evaluated so far. In this work, we assess this potential by exploring the contribution of a normalized hotspot index (NHI) in the monitoring of the recent Home Reef (Tonga Islands) eruption. By analyzing the time series of the maximum NHI_SWIR value, computed over the Home Reef area, we inferred information about the waxing/waning phases of lava effusion during four distinct subaerial eruptions. The results indicate that the first eruption phase (September–October 2022) was more intense than the second one (September–November 2023) and comparable with the fourth eruptive phase (June–August 2024) in terms of intensity level; the third eruption phase (January 2024) was more difficult to investigate because of cloudy conditions. Moreover, by adapting the NHI algorithm to daytime SLSTR SWIR data, we found that the detected thermal anomalies complemented those in night-time conditions identified and quantified by the operational Level 2 SLSTR fire radiative power (FRP) product. This study demonstrates that NHI-based algorithms may contribute to investigating active volcanoes located even in remote areas through SWIR data at 500 m spatial resolution, encouraging the development of an automated processing chain for the near-real-time monitoring of thermal volcanic activity by means of night-time/daytime Sentinel-3 SLSTR data. Full article

Photodynamic and photothermal therapies with IR780 have gained exponential interest, and their photophysical properties have demonstrated promise for use in antitumor and antimicrobial chemotherapy. IR780 and its derivatives are valuable in labeling nanostructures with different chemical compositions for in vitro and in vivo fluorescence monitoring studies in the near-infrared (NIR) spectrum. The current literature is abundant on this topic, particularly with applications in the treatment of different types of cancer using laser illumination to produce photodynamic (PDT), photothermal (PTT), and, more recently, sonodynamic therapy (SDT) approaches for cell death. This review aims to update the state of the art concerning IR780 photosensitizer as a theranostic agent for PDT, PTT, SDT, and photoacoustic (PA) effects, and fluorescence imaging monitoring associated with different types of nanocarriers. The literature update concerns a period from 2017 to 2024, considering, more specifically, the in vivo effects found in preclinical experiments. Some aspects of the labeling stability of nanostructured systems will be discussed based on the evidence of IR780 leakage from the nanocarrier and its consequences for the reliable analysis of biological data. Full article

Background/Objectives: Liposomal drug formulations improve anticancer treatment efficacy and reduce toxicity by altering pharmacokinetics and biodistribution. Indocyanine Green (ICG), an FDA-approved near-infrared imaging agent, exhibits photosensitivity, photothermal effects, and potential ferroptosis induction, enhancing anticancer activity. Doxorubicin (DOX), widely used for treating breast, ovarian, and liver cancers, is limited by cardiotoxicity, requiring dosage control. Incorporating ICG and DOX into liposomes enables medical imaging, controlled drug release, reduced administration frequency, and fewer side effects. This study aims to develop liposomes encapsulating both ICG and DOX and evaluate their theranostic potential in in vitro and in vivo lung adenocarcinoma models. Methods: Liposomes containing ICG and DOX (Lipo-ICG/DOX) were synthesized using an active loading method and characterized for size (~140 nm), lipid, and drug concentrations. In vitro studies using A549 lung cancer cells assessed liposome uptake via fluorescence microscopy, while in vivo xenograft models evaluated therapeutic efficacy. Results: Lipo-ICG/DOX showed uptake in A549 cells, with ICG localizing in lysosomes and DOX in nuclei. Treatment reduced cell viability significantly by day three. In vivo imaging demonstrated the retention of liposomes in tumor sites, with ICG signals observed in the liver and intestines, indicating metabolic routes. When combined with 780 nm light exposure, liposomes slowed tumor growth over 12 days. Mechanistic studies revealed combined ferroptosis and apoptosis induction. Conclusions: Lipo-ICG/DOX demonstrates strong theranostic potential, integrating imaging and therapy for lung adenocarcinoma. This multifunctional formulation offers a promising strategy for improving cancer treatment efficacy while minimizing side effects. Full article

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection. Full article

The traditional plastic sorting process primarily relies on manual operations, which are inefficient, pose safety risks, and result in suboptimal separation efficiency for mixed waste plastics. Near–infrared (NIR) spectroscopy, with its rapid and non–destructive analytical capabilities, presents a promising alternative. However, the analysis of NIR spectra is often complicated by overlapping peaks and complex data patterns, limiting its direct applicability. This study establishes a comprehensive machine learning–based NIR spectroscopy model to distinguish polypropylene (PP) at different aging stages. A dataset of NIR spectra was collected from PP samples subjected to seven simulated aging stages, followed by the construction of a classification model to analyze these spectral variations. The aging of PP was confirmed using Fourier–transform infrared spectroscopy (FTIR). Mechanical property analysis, including tensile strength and elongation at break, revealed a gradual decline with prolonged aging. After 40 days of accelerated aging, the elongation at the break of PP dropped to approximately 30%, retaining only about one–sixth of its original mechanical performance. Furthermore, various spectral preprocessing methods were evaluated to identify the most effective technique. The combination of the second derivative method with a linear –SVC achieved a classification accuracy of 99% and a precision of 100%. This study demonstrates the feasibility of the accurate identification of PP at different aging stages, thereby enhancing the quality and efficiency of recycled plastics and promoting automated, precise, and sustainable recycling processes. Full article

This study examines the effects of three traditional Chinese mind–body exercises—Tai Chi (TC), Baduanjin (BD), and Health Qigong Yijinjing (YJJ)—on learning abilities, executive functions, and prefrontal brain connectivity in children with learning difficulties. Seventy-two children (aged 9–11) with learning difficulties were randomly assigned to TC, BD, YJJ, or a control group (CON). Intervention groups practiced for 12 weeks (45 min, three times per week), while the control group maintained their regular physical education. Assessments included Academic Performance Ranking (APR), Pupil Rating Scale (PRS), and executive functions. Granger causality analyses were conducted on the functional near-infrared spectroscopy data to derive the effective connectivity at the brain region levels. Post-intervention, all intervention groups showed significant improvements over the control group in PRS and APR scores (p < 0.05), with the TC group achieving higher PRS scores than the BD group. The TC group also demonstrated superior improvements in executive functions, particularly in inhibition and working memory. Additionally, the TC group exhibited significantly enhanced effective connectivity from the left and right dorsolateral prefrontal cortex to Brodmann area 8, indicating improved brain communication. Traditional Chinese mind–body exercises, particularly Tai Chi, improve academic performance, executive functions, and prefrontal cortex connectivity in children with learning difficulties. Tai Chi demonstrates superior outcomes, supporting its potential as an effective intervention for cognitive and academic development. Full article

Lossless image compression is vital for missions with limited data transmission bandwidth. Reducing file sizes enables faster transmission and increased scientific gains from transient events. This study compares two wavelet-based image compression algorithms, CCSDS 122.0 and JPEG 2000, used in the European Space Agency Comet Interceptor and Hera missions, respectively, in varying scenarios. The JPEG 2000 implementation is sourced from the JasPer library, whereas a custom implementation was written for CCSDS 122.0. The performance analysis for both algorithms consists of compressing simulated asteroid images in the visible and near-infrared spectral ranges. In addition, all test images were noise-filtered to study the effect of the amount of noise on both compression ratio and speed. The study finds that JPEG 2000 achieves consistently higher compression ratios and benefits from decreased noise more than CCSDS 122.0. However, CCSDS 122.0 produces comparable results faster than JPEG 2000 and is substantially less computationally complex. On the contrary, JPEG 2000 allows dynamic (entropy-permitting) reduction in the bit depth of internal data structures to 8 bits, halving the memory allocation, while CCSDS 122.0 always works in 16-bit mode. These results contribute valuable knowledge to the behavioral characteristics of both algorithms and provide insight for entities planning on using either algorithm on board planetary missions. Full article

Colloidal quantum dots (QDs) have emerged as promising materials for the development of infrared photodetectors owing to their tunable band gaps, cost-effective manufacturing, and ease of processing. This paper provides a comprehensive overview of the fundamental properties of quantum dots and the operating principles of various infrared detectors. We review the latest advancements in short-wave infrared (SWIR), mid-wave infrared (MWIR), and long-wave infrared (LWIR) detectors employing colloidal quantum dots. Despite their potential, these detectors face significant challenges compared to conventional infrared technologies. Current commercial applications are predominantly limited to the near-infrared and short-wave bands, with medium- and long-wave applications still under development. The focus has largely been on lead and mercury-based quantum dots, which pose environmental concerns, underscoring the need for high-performance, non-toxic materials. Looking forward, the development of large array and small pixel detectors and improving compatibility with readout circuits are critical for future progress. This paper discusses these hurdles and offers insight into potential strategies to overcome them, paving the way for next-generation infrared sensing technologies. Full article

Background: Indocyanine green (ICG) fluorescence has seen extensive application across medical and surgical fields, praised for its real-time navigation capabilities and low toxicity. Initially employed to assess liver function, ICG fluorescence is now integral to liver surgery, aiding in tumor detection, liver segmentation, and the visualization of bile leaks. This study reviews current protocols and ICG fluorescence applications in liver surgery, with a focus on optimizing timing and dosage based on clinical indications. Methods: Following PRISMA guidelines, we systematically reviewed the literature up to 27 January 2024, using PubMed and Medline to identify studies on ICG fluorescence used in liver surgery. A systematic review was performed to evaluate dosage and timing protocols for ICG administration. Results: Of 1093 initial articles, 140 studies, covering a total of 3739 patients, were included. The studies primarily addressed tumor detection (40%), liver segmentation (34.6%), and both (21.4%). The most common ICG fluorescence dose for tumor detection was 0.5 mg/kg, with administration occurring from days to weeks pre-surgery. Various near-infrared (NIR) camera systems were utilized, with the PINPOINT system most frequently cited. Tumor detection rates averaged 87.4%, with a 10.5% false-positive rate. Additional applications include the detection of bile leaks, lymph nodes, and vascular and biliary structures. Conclusions: ICG fluorescence imaging has emerged as a valuable tool in liver surgery, enhancing real-time navigation and improving clinical outcomes. Standardizing protocols could further enhance ICG fluorescence efficacy and reliability, benefitting patient care in hepatic surgeries. Full article

With the advancement of technology, biometric recognition technology has gained widespread attention in identity authentication due to its high security and convenience. Finger vein recognition, as a biometric technology, utilizes near-infrared imaging to extract subcutaneous vein patterns, offering high security, stability, and anti-spoofing capabilities. Existing research primarily focuses on improving recognition accuracy; however, this often comes at the cost of increased model complexity, which, in turn, affects recognition efficiency, making it difficult to balance accuracy and speed in practical applications. To address this issue, this paper proposes a high-accuracy and high-efficiency finger vein recognition model called Faster Multi-Scale Finger Vein Recognition Network (FMFVNet), which optimizes recognition speed through the FasterNet Block module while ensuring recognition accuracy with the Multi-Scale Convolutional Attention (MSCA) module. Experimental results show that on the FV-USM and SDUMLA-HMT datasets, FMFVNet achieves recognition accuracies of 99.80% and 99.06%, respectively. Furthermore, the model’s inference time is reduced to 1.75 ms, representing a 20.8% improvement over the fastest baseline model and a 62.7% improvement over the slowest, achieving more efficient finger vein recognition. Full article

This study presents a novel approach for filament recognition in fused filament fabrication (FFF) processes using a multi-spectral spectroscopy sensor module combined with machine learning techniques. The sensor module measures 18 wavelengths spanning the visible to near-infrared spectra, with a custom-designed shroud to ensure systematic data collection. Filament samples include polylactic acid (PLA), thermoplastic polyurethane (TPU), thermoplastic copolyester (TPC), carbon fibre, acrylonitrile butadiene styrene (ABS), and ABS blended with Carbon fibre. Data are collected using the Triad Spectroscopy module AS7265x (composed of AS72651, AS72652, AS72653 sensor units) positioned at three measurement distances (12 mm, 16 mm, 20 mm) to evaluate recognition performance under varying configurations. Machine learning models, including k-Nearest Neighbors (kNN), Logistic Regression, Support Vector Machine (SVM), and Multi-Layer Perceptron (MLP), are employed with hyperparameter tuning applied to optimise classification accuracy. Results show that the data collected on the AS72651 sensor, paired with the SVM model, achieves the highest accuracy of 98.95% at a 20 mm measurement distance. This work introduces a compact, high-accuracy filament recognition module that can enhance the autonomy of multi-material 3D printing by dynamically identifying and switching between different filaments, optimising printing parameters for each material, and expanding the versatility of additive manufacturing applications. Full article

After attending both the “Decade to Educate in the Sustainable Development and the Agenda 30 of the UNESCO” and the “ACS GCI Pharmaceutical Roundtable”, which focused on sustainable chemistry, in this article, a green chemistry contribution to the Kamal Qureshi protocol is offered; thus, DIM^® and several of its analogs (3,3′-diindolylmethanes) were suitably produced under the green chemistry protocol. In the first stage, the substrate indol-3-yl carbinol was evaluated using mechanochemistry (the best mode) in comparison to other activating methods (near-infrared and microwave electromagnetic irradiation and ultrasound), wishing to highlight the employment of both TAFF^®, an excellent and well-characterized natural catalyst (bentonitic clay), and acetone, a green solvent, in addition to the analysis of the procedures in real-time. In the second stage, the mechanochemical methodology was extended to produce a set of fifteen DIMs, in the last stage, the use of a green metric exhibited the greenness of the approach, with it being important to highlight that, to our knowledge, after a search in the literature, this is the first time that the process has been evaluated to demonstrate its greenness. Full article

With growing consumer concerns about food safety, developing methods for the field-based, non-destructive, and rapid detection of pesticide residues is becoming increasingly critical. This study introduces a field-based, non-destructive, and rapid method for detecting pesticide residues on kumquat surfaces. Initially, spectral data from the visible/near-infrared (VNIR) light bands were collected using a handheld spectrometer from kumquats treated with three pesticides at various gradient concentrations and water. The data were then preprocessed and analyzed using machine learning (SPA-SVM) and deep learning models (1D-CNN, 1D-ResNet) to determine the optimal model. Features from the convolutional layer of the 1D-ResNet model were extracted for visualization and analysis, highlighting significant differences in features between the different pesticides and across varying concentrations. The results indicate that the 1D-ResNet model achieved 97% overall accuracy, with a macro average of 0.96 and a weighted average of 0.97, and that precision, recall, and F1-score approached 1.00 for most pesticide treatment gradients. The results of this research verified the feasibility of the handheld spectrometer combined with 1D-Resnet for the detection of pesticide residues on the surface of kumquat, realized the visualization of pesticide residue characteristics, and also provided a reference for the detection of pesticide residues on the surface of other fruits. Full article

Sensing distance and speed have crucial effects on the data of active and passive sensors, providing valuable information relevant to crop growth monitoring and environmental conditions. The objective of this study was to evaluate the effects of sensing speed and sensor height on the variation in proximal canopy reflectance data to improve rice vegetation monitoring. Data were collected from a rice field using active and passive sensors with calibration procedures including downwelling light sensor (DLS) calibration, field of view (FOV) alignment, and radiometric calibration, which were conducted per official guidelines. The data were collected at six sensor heights (30–130 cm) and speeds (0–0.5 ms^–1). Analyses, including peak signal-to-noise ratio (PSNR) and normalized difference vegetation index (NDVI) calculations and statistical assessments, were conducted to explore the impacts of these parameters on reflectance data variation. PSNR analysis was performed on passive sensor image data to evaluate image data variation under varying data collection conditions. Statistical analysis was conducted to assess the effects of sensor speed and height on the NDVI derived from active and passive sensor data. The PSNR analysis confirmed that there were significant impacts on data variation for passive sensors, with the NIR and G bands showing higher noise sensitivity at increased speeds. The NDVI analysis showed consistent patterns at sensor heights of 70–110 cm and sensing speeds of 0–0.3 ms^–1. Increased sensing speeds (0.4–0.5 ms^–1) introduced motion-related variability, while lower heights (30–50 cm) heightened ground interference. An analysis of variance (ANOVA) indicated significant individual effects of speed and height on four spectral bands, red (R), green (G), blue (B), and near-infrared (NIR), in the passive sensor images, with non-significant interaction effects observed on the red edge (RE) band. The analysis revealed that sensing speed and sensor height influence NDVI reliability, with the configurations of 70–110 cm height and 0.1–0.3 ms^–1 speed ensuring the stability of NDVI measurements. This study notes the importance of optimizing sensor height and sensing speed for precise vegetation index calculations during field data acquisition for agricultural crop monitoring. Full article

The early detection of bronze disease is a significant challenge not only in conservation science but also in various industrial fields that utilize copper alloys (i.e., shipbuilding and construction). Due to the aggressive nature of this corrosion pathway, developing methods for its early detection is pivotal. The presence of copper trihydroxychlorides is the main key indicator of the ongoing autocatalytic process. Commonly used for pigment identification, reflectance imaging spectroscopy (RIS) or fiber optics reflectance spectroscopy (FORS) was recently employed for mapping atacamite distribution in extended bronze corrosion patinas. In this work, we detected the onset of bronze disease using visible–near-infrared (VIS-NIR) multispectral reflectography, which allowed for disclosing features that were poorly detectable to the naked eye. The image cube was analyzed using the spectral correlation mapper (SCM) algorithm to map the distribution of copper trihydroxychlorides. FORS and Raman spectroscopy were employed to characterize the patina composition and validate RIS data. A set of bronze samples, representative of Florentine Renaissance workshops, was specifically realized for the present study and artificially aged at different corrosion stages. Full article