Search Results (562)

In this work, the structures of the isolated anion and anhydrous and monohydrated sodium salts of alizarin red S (ARS) have been theoretically investigated within the density functional theory framework (B3LYP/6-311++G** calculations). The combination of calculations with the scaled quantum mechanics force field (SQMFF) methodology has allowed the assignment of the experimental infrared spectrum of ARS in the solid phase and the determination of the corresponding force constants. The structural analysis also included the investigation of the NMR and UV-visible spectra of the compound in solution in light of the undertaken quantum chemical calculations, the obtained theoretical data being in good agreement with the corresponding experimental ones. The impact of the presence of the Na⁺ counterion and hydration water on the properties of the organic ARS⁻ fragment was evaluated. Atoms in molecules theory (AIM) analysis was also undertaken to obtain further details on the electronic structure of the investigated species, and the HOMO-LUMO gap was determined to evaluate their relative reactivity. Globally, the results obtained in this work extend the available information on alizarin red S and may also be used for the fast identification of the three studied species of the compound investigated (anhydrous and monohydrated sodium salts and isolated anion). Full article

A concept towards current measurement in low and medium voltage power distribution networks is presented. The concentric magnetic field around the current-carrying conductor should be measured using a nitrogen-vacancy quantum magnetic field sensor. A bottleneck in current measurement systems is the readout electronics, which are usually based on optically detected magnetic resonance (ODMR). The idea is to have a hardware that tracks up to four resonances simultaneously for the detection of the three-axis magnetic field components and the temperature. Normally, expensive scientific instruments are used for the measurement setup. In this work, we present an electronic device that is based on a Zynq 7010 FPGA (Red Pitaya) with an add-on board, which has been developed to control the excitation laser, the generation of the microwaves, and interfacing the photodiode, and which provides additional fast digital outputs. The T1 measurement was chosen to demonstrate the ability to read out the spin of the system. Full article

Introduction: The sodium-to-potassium (Na:K) ratio in the diet is a critical biomarker for cardiovascular and metabolic health, yet global adherence to recommended levels remains poor. Objectives: The objective of this study was to identify dietary determinants of the dietary Na:K ratio and its associations with micronutrient intake and diet quality. Methods: An observational cross-sectional survey was conducted in a representative sample of manufacturing workers through a combined stratified proportional and two-stage probability sampling plan, with strata defined by company size and industrial sector from the state of Rio Grande do Norte, Brazil. Dietary intake was assessed using 24 h recalls via the Multiple Pass Method, with Na:K ratios calculated from quantified food composition data. Diet quality was assessed with the Diet Quality Index-International (DQI-I). Multiple linear regression was used to analyze associations of Na:K ratio with the study variables. Results: The survey was conducted in the state of Rio Grande do Norte, Brazil, in 921 randomly selected manufacturing workers. The sample mean age was 38.2 ± 10.7 years, 55.9% males, mean BMI 27.2 ± 4.80 kg/m². The mean Na:K ratio was 1.97 ± 0.86, with only 0.54% of participants meeting the WHO recommended target (<0.57). Fast food (+3.29 mg/mg per serving, p < 0.001), rice, bread, and red meat significantly increased the ratio, while fruits (−0.16 mg/mg), dairy, white meat, and coffee were protective. Higher Na:K ratios were associated with lower intake of calcium, magnesium, phosphorus, and vitamins C, D, and E, as well as poorer diet quality (DQI-I score: −0.026 per 1 mg/mg increase, p < 0.001). Conclusions: These findings highlight the critical role of processed foods in elevating Na:K ratios and the potential for dietary modifications to improve both electrolyte balance and micronutrient adequacy in industrial workers. The study underscores the need for workplace interventions that simultaneously address sodium reduction, potassium enhancement, and overall diet quality improvement tailored to socioeconomic and cultural contexts, a triple approach not previously tested in intervention studies. Future studies should further investigate nutritional consequences of imbalanced Na:K intake. Full article

This study proposes a novel LK-BP-AFPSO model for the nondestructive evaluation of wood mechanical properties, combining a backpropagation neural network (BP) with adaptive fractional-order particle swarm optimization (AFPSO) and Liang–Kleeman (LK) information flow theory. The model accurately predicts four key mechanical properties—longitudinal tensile strength (SPG), modulus of elasticity (MOE), bending strength (MOR), and longitudinal compressive strength (CSP)—using only nondestructive physical features. Tested across diverse wood types (fast-growing YKS, red-heart CSH/XXH, and iron-heart XXT), the framework demonstrates strong generalizability, achieving an average prediction accuracy (R²) of 0.986 and reducing mean absolute error (MAE) by 23.7% compared to conventional methods. A critical innovation is the integration of LK causal analysis, which quantifies feature–target relationships via information flow metrics, effectively eliminating 29.5% of spurious correlations inherent in traditional feature selection (e.g., PCA). Experimental results confirm the model’s robustness, particularly for heartwood variants, while its adaptive fractional-order optimization accelerates convergence by 2.1× relative to standard PSO. This work provides a reliable, interpretable tool for wood quality assessment, with direct implications for grading systems and processing optimization in the forestry industry. Full article

The textile industry is progressively shifting towards more sustainable solutions, particularly in the field of printing technologies. This study reports the development and evaluation of water-based pigment inks formulated with bio-based pigments derived from intermediates produced via bacterial fermentation. Two pigments—indigo (blue) and quinacridone (red)—were incorporated into ink formulations and applied on cotton and polyester fabrics through valve-jet inkjet printing (ChromoJet). The physical properties of the inks were analyzed to ensure compatibility with the equipment, and printed fabrics were assessed as to their color fastness to washing, rubbing, artificial weathering, and artificial light. The results highlight the good performance of the bio-based inks, with excellent light and weathering fastness and satisfactory wash and rub resistance. The effect of different pre-treatments, including a biopolymer and a synthetic binder, was also investigated. Notably, the biopolymer pre-treatment enhanced pigment fixation on cotton, while the synthetic binder improved wash fastness on polyester. These findings support the integration of biotechnologically sourced pigments into eco-friendly textile digital printing workflows. Full article

Growth traits are the most important economic traits in red tilapia (Oreochromis spp.) production, and are the main targets for its genetic improvement. Increasing salinity levels in the environment are affecting the growth, development, and molecular processes of aquatic animals. Red tilapia tolerates saline water to some degree. However, few credible genetic markers or potential genes are available for choosing fast-growth traits in salt-tolerant red tilapia. This work used genome-wide association study (GWAS) and RNA-sequencing (RNA-seq) to discover genes related to four growth traits in red tilapia cultured in saline water. Through genotyping, it was determined that 22 chromosomes have 12,776,921 high-quality single-nucleotide polymorphisms (SNPs). One significant SNP and eight suggestive SNPs were obtained, explaining 0.0019% to 0.3873% of phenotypic variance. A significant SNP peak associated with red tilapia growth traits was located on chr7 (chr7-47464467), and plxnb2 was identified as the candidate gene in this region. A total of 501 differentially expressed genes (DEGs) were found in the muscle of fast-growing individuals compared to those of slow-growing ones, according to a transcriptome analysis. Combining the findings of the GWAS and RNA-seq analysis, 11 candidate genes were identified, namely galnt9, esrrg, map7, mtfr2, kcnj8, fhit, dnm1, cald1, plxnb2, nuak1, and bpgm. These genes were involved in ‘other types of O-glycan biosynthesis’, ‘glycine, serine and threonine metabolism’, ‘glycolysis/gluconeogenesis’, ‘mucin-type O-glycan biosynthesis’ and ‘purine metabolism signaling’ pathways. We have developed molecular markers to genetically breed red tilapia that grow quickly in salty water. Our study lays the foundation for the future marker-assisted selection of growth traits in salt-tolerant red tilapia. Full article

Water status is a key determinant of physiological performance and vineyard productivity. However, its assessment through field measurements is time-consuming and labour-intensive. Remote sensing offers a fast and reliable alternative to traditional in situ methods for the monitoring of the water status in vineyards. This study aimed to assess the potential of high-resolution multispectral imagery acquired by UAVs to estimate the vine water status. The research was conducted over two growing seasons (2021 and 2022) in a commercial Merlot vineyard in Yepes (Toledo, Central Spain), under five irrigation regimes designed to generate a range of water statuses. UAV flights were performed at two times of day (09:00 and 12:00 solar time), coinciding with in-field measurements of physiological parameters. Stem water potential (SWP), chlorophyll content, and photosynthesis data were collected. The SWP consistently showed the strongest and most stable associations with vegetation indices (VIs) and the red spectral band at 12:00. A simple linear regression model using the NDVI explained up to 58% of the SWP variability regardless of the time of day or year. Multiple linear regression models incorporating the red and NIR bands yielded even higher predictive power (R² = 0.62). Stronger correlations were observed at 12:00, especially when combining data from both years, highlighting the importance of midday measurements in capturing water stress effects. These findings demonstrate the potential of UAV-based multispectral imagery as a non-destructive and scalable tool for the monitoring of the vine water status under field conditions. Full article

Salivary $\alpha$-amylase (sAA) is a widely recognized biomarker for stress and autonomic nervous system activity. However, conventional enzymatic assays used to quantify sAA are limited by time-consuming, lab-based protocols. In this study, we present a portable, AI-driven point-of-care system for automated sAA classification via colorimetric image analysis. The system integrates SCHEDA, a custom-designed imaging device providing and ensuring standardized illumination, with a deep learning pipeline optimized for mobile deployment. Two classification strategies were compared: (1) a modular YOLOv4-CNN architecture and (2) a unified YOLOv8 segmentation-classification model. The models were trained on a dataset of 1024 images representing an eight-class classification problem corresponding to distinct sAA concentrations. The results show that red-channel input significantly enhances YOLOv4-CNN performance, achieving 93.5% accuracy compared to 88% with full RGB images. The YOLOv8 model further outperformed both approaches, reaching 96.5% accuracy while simplifying the pipeline and enabling real-time, on-device inference. The system was deployed and validated on a smartphone, demonstrating consistent results in live tests. This work highlights a robust, low-cost platform capable of delivering fast, reliable, and scalable salivary diagnostics for mobile health applications. Full article

Skeletal muscle fiber type composition critically influences lamb meat quality. This study examined the relationships between muscle fiber types and key quality traits, including tenderness, color, lipid and amino acid profiles, and volatile flavor compounds. MyHC I (slow-twitch oxidative fibers) positively correlated with desirable traits such as increased redness, water-holding capacity, unsaturated fatty acids, and essential amino acids. Conversely, MyHC IIb (fast glycolytic fibers) was linked to reduced tenderness and higher levels of off-flavor compounds. MyHC IIa and IIx showed minimal effects. Untargeted metabolomics comparing muscles with high versus low slow-twitch fiber proportions revealed differential metabolites enriched in sphingolipid and arginine-proline metabolism pathways. These results suggest that a higher proportion of oxidative fibers enhances both the sensory and nutritional qualities of lamb meat by modulating lipid metabolism, amino acid availability, and flavor formation. Full article

The aim of this study is to investigate the physiological characteristics and regulatory mechanisms of porcine intramuscular fat (IMF), subcutaneous fat (take back fat (BF), for example), and visceral fat (take perienteric fat (PF), for example) to address the challenge of optimizing meat quality without excessive fat deposition. Many improved breed pigs have fast growth rates, high lean meat rates, and low subcutaneous fat deposits, but they also have low IMF content, resulting in poor meat quality. There is usually a positive correlation between intramuscular fat and subcutaneous fat deposits. This study selected eight-month-old female Tibetan pigs as experimental subjects. After slaughter, fat samples were collected. Histological differences in adipocyte morphology were observed via hematoxylin–eosin (HE) staining of tissue sections, and phenotypic characteristics of different adipose tissues were analyzed through fatty acid composition determination. Transcriptome sequencing and untargeted metabolomics were employed to perform pairwise comparisons between different fatty tissues to identify differentially expressed genes and metabolites. A siRNA interference model was constructed and combined with Oil Red O staining and lipid droplet optical density measurement to investigate the regulatory role of WNT16 in adipocyte differentiation. Comparative analysis of phenotypic and fatty acid composition differences in adipocytes from different locations revealed that IMF adipocytes have significantly smaller areas and diameters compared to other fat depots and contain higher levels of monounsaturated fatty acids. Integrated transcriptomic and metabolomic analyses identified differential expression of WNT16 and L-tyrosine, both of which are involved in the melanogenesis pathway. Functional validation showed that inhibiting WNT16 in porcine preadipocytes downregulated adipogenic regulators and reduced lipid droplet accumulation. This cross-level regulatory mechanism of “phenotype detection–multi-omics analysis–gene function research” highlighted WNT16 as a potential key regulator of site-specific fat deposition, providing new molecular targets for optimizing meat quality through nutritional regulation and genetic modification. Full article

Accurate and rapid delineation of wildfire-affected areas is essential in the era of climate-driven increases in fire frequency. This study compares and analyzes four techniques for identifying wildfire-affected areas using Sentinel-2 satellite imagery: (1) calibrated differenced Normalized Burn Ratio (dNBR); (2) differenced NDVI (dNDVI) with empirically defined thresholds (0.04–0.18); (3) supervised SVM classifiers applying linear, polynomial, and RBF kernels; and (4) unsupervised ISODATA clustering. In particular, this study proposes an SVM-based classification method that goes beyond conventional index- and threshold-based approaches by directly using the SWIR, NIR, and RED band values of Sentinel-2 as input variables. It also examines the potential of the ISODATA method, which can rapidly classify affected areas without a training process and further assess burn severity through a two-step clustering procedure. The experimental results showed that SVM was able to effectively identify affected areas using only post-fire imagery, and that ISODATA enabled fast classification and severity analysis without training data. This study performed a wildfire damage analysis through a comparison of various techniques and presents a data-driven framework that can be utilized in future wildfire response and policy-oriented recovery support. Full article

Tactical athletes and military personnel engaged in intense exercise need to consume enough quality protein in their diet to maintain protein balance and promote recovery. Plant-based protein sources contain fewer essential amino acids (EAAs), while pork loin contains a higher concentration of EAAs and creatine than most other animal protein sources. This study aimed to determine whether the ingestion of plant-based or pork-based military-style meals ready-to-eat (MREs) affects recovery from and subsequent Army Combat Fitness Test (ACFT) performance. Methods: Twenty-three (n = 23) University Corps of Cadets members participated in a randomized, double-blind, placebo-controlled, and crossover-designed study. Diets were prepared by a dietitian, food scientist, and chef to have similar taste, appearance, texture, and macronutrient content. The chef also labeled the meals for double-blind administration. Participants refrained from intense exercise for 48 h before reporting to the lab in a fasted condition with a 24 h urine sample. Participants donated a blood sample, completed questionnaires and cognitive function tests, and consumed a pre-exercise meal. After four hours, participants performed the ACFT according to military standards. Participants were fed three MREs daily while returning to the lab in a fasted condition at 0600 with 24 h urine samples after 24, 48, and 72 h of recovery. On day 3, participants repeated the ACFT four hours after consuming an MRE for breakfast. Participants resumed normal training and returned to the lab after 2–3 weeks to repeat the experiment while consuming the alternate diet. Data were analyzed using general linear model statistics with repeated measures and percent changes from baseline with 95% confidence intervals. Results: Results revealed that 3 days were sufficient for participants to replicate ACFT performance. However, those consuming the pork-based diet experienced less muscle soreness, urinary urea excretion, cortisol, inflammation, and depression scores while experiencing a higher testosterone/cortisol ratio and appetite satisfaction. There was also evidence of more favorable changes in red and white blood cells. Conversely, blood lipid profiles were more favorably changed when following a plant-based diet. Conclusions: These findings suggest that protein quality and the availability of creatine in the diet can affect recovery from intense military-style exercise. Minimally, plant-based MREs should include 6–10 g/d of EAA and 2–3 g/d of creatine monohydrate to offset dietary deficiencies, particularly in military personnel following a vegetarian diet. Registered clinical trial #ISRCTN47322504. Full article

This article sheds light on the historical dyeing traditions of rural inhabitants of the Eastern Baltic region. The 19th- and early 20th-century Estonian archival sources mention that rotted alder buckthorn (Frangula alnus Mill.) bark was used to dye woollen yarn red. The bark was rotted by leaving it outside for weeks or months before dyeing. Although dyeing red with alder buckthorn bark by fermenting it in wood ash lye is well known, the combination of rotting the bark and using the boiling method to dye red has not been reported. Practical experiments testing shorter and longer-term rotting of alder buckthorn bark both on and under the ground were conducted. Woollen yarns were dyed with rotted bark using the boiling method and were tested for lightfastness and alkaline pH sensitivity, and analysed using HPLC-DAD. The results show that rotting alder buckthorn bark has a strong effect on the achievable colours and that woollen yarns can be dyed different shades of red. The colours were sensitive to alkaline pH and their light fastness varied from very low to good. HPLC-DAD analysis showed that the pretreatment of the bark affected not only the colour but also the dye composition of the dyed wool. Full article

Infrared thermography (IRT), a widely used nondestructive testing method, is commonly employed to identify moisture in historic walls. However, its reliance on manual interpretation by experts makes the process both time-consuming and costly. This study addresses the challenge of detecting wall moisture; this issue is closely linked to the deterioration of cultural heritage structures. This study focuses on the brick walls of the Tainan Confucian Temple, the oldest Confucian temple in Taiwan. The targeted are walls coated with lime plaster mixed with red mineral pigments, a traditional finish that gives the temple its distinctive red appearance. This study proposes a system to automatically identify wall areas, mark low-temperature zones, and determine the presence and distribution of moisture. Visible and infrared thermal images of these walls are captured and preprocessed to normalize the size and enhance the features. Finally, two convolutional neural network (CNN) models are trained in this study: one for identifying wall regions and the other for detecting low-temperature areas. The proposed method achieves an accuracy of 91.18% in detecting wall moisture, representing a 24.05% improvement over conventional object recognition techniques, the accuracy of which is 73.5%. In addition, this method requires only 3 s to detect the wall moisture, representing a 99.92% reduction in processing time compared to the conventional manual method. This method not only provides a fast and objective method for assessing moisture in lime-plastered heritage walls but also significantly enhances the efficiency of restoration efforts. This method can be applied to similar wall structures in other Confucian temples, offering broad potential for heritage conservation. Full article

Eucalyptus scab disease (Elsinoë) is a harmful plant fungus that can disrupt various ecological and economic services provided by commercial forests. To effectively control and monitor the occurrence of forest pathogens, it is important to understand their spatial distribution within the infected area. Consistent monitoring, together with high-resolution imagery obtained from unmanned aerial vehicles (UAVs), has become important in forest management. Therefore, this study focuses on detecting and mapping the spatial distribution of E. masingae within commercial forests using image texture and vegetation indices (VIs) computed from a UAV sensor with machine learning (ML) and deep learning (DL) models. The fast large margin (FLM), random forest (RF), and deep learning (DL) models were used to determine which model effectively mapped the spatial distribution of the disease. The results indicated that image texture significantly increased the model accuracies (FLM = 94.8%; RF = 98.9%; DL = 98.9%) as opposed to the results without the use of image texture (FLM = 84.4%; RF = 76.1%; DL = 81.7%). Since the DL model obtained the fastest model run time and was proven to be the most significant model, it selected the mean, homogeneity, second moment, and correlation texture parameters which were predominantly determined from the red and blue bands of the UAV sensor containing visible bands. Additionally, the 3 × 3 moving window size was ideal for detecting E. masingae since the estimation of texture parameters was reduced efficiently. Overall, this study showcases the ability of UAVs to effectively map forest disease. Together with that, it has proven that the DL model outperformed the conventional ML models. Full article