Search Results (1,045)

Perseus A (3C 84), a powerful radio source located at the centre of the giant elliptical galaxy NGC 1275—classified as a Seyfert type II AGN and the dominant member of the X-ray bright Abell 426 cluster–exhibits radio emission variability over a wide range of timescales, from decades to hours. This study investigates intraday variability (IDV) in the 6.5 GHz radio emission of 3C 84 using the RT-32 radio telescope in Zolochiv, Ukraine. A novel low-amplitude azimuthal scanning method enabled quasi-simultaneous measurements of antenna and system temperatures, allowing for separation of intrinsic source variations from propagation effects. During an observation session in August 2021, a burst with a peak intensity of 13.5 Jy above the background was detected, likely corresponding to a Fast Radio Burst (FRB). Additionally, quasi-periodic low-amplitude variations with timescales from 0.3 to 6 h were observed. These fluctuations correlate strongly with local atmospheric changes, such as dew formation on the telescope structure, and, to a lesser extent, with ionospheric acoustic–gravity waves. The findings highlight the importance of accounting for propagation conditions when interpreting short-timescale radio variability in AGNs and suggest the need for multi-station, multi-frequency monitoring campaigns to distinguish between intrinsic and environmental modulation of AGN flux densities. Full article

Dam break problem-induced floods can trigger hazardous dike overtopping, demanding predictions that are fast, accurate, and interpretable. We pursue two objectives: (i) introducing a new alpha evolution (AE) optimization scheme to improve tree-model predictive accuracy, and (ii) developing a cluster-wise modeling strategy in which regimes are defined by wave characteristics. Using a dataset generated via physical model experiments and smoothed particle hydrodynamics (SPH) numerical simulations, we first group samples via hierarchical clustering (HC) on the Froude number $\left(\mathrm{Fr}\right)$, wave nonlinearity $\left(R\right)$, and relative distance to the dike $\left(D\right)$. We then benchmark CatBoost, XGBoost, and ExtraTrees within each cluster and select the best-performing CatBoost as the baseline, after which we train standard CatBoost and its AE-optimized variant. Under random train–test splits, AE-CatBoost achieves the strongest generalization for predicting relative run-up distance $H_m$ (testing dataset $R^2=0.9803$, $\mathrm{RMSE}=0.0599$), outperforming particle swarm optimization (PSO) and grid search (GS)-tuned CatBoost. We further perform TreeSHAP analyses on AE-CatBoost for global, local, and interaction attributions. SHAP analysis yields physics-consistent explanations: D dominates, followed by H and L, with a weaker positive effect of $\mathrm{Fr}$ and minimal influence of R; $H\times D$ is the strongest interaction pair. Overall, AE optimization combined with HC-based cluster-wise modeling produces accurate, interpretable overtopping predictions and provides a practical route toward field deployment. Full article

Active restoration structures such as microtopographic water-harvesting designs are widely implemented in dryland ecosystems to improve soil moisture, reduce erosion, and promote vegetation recovery. We assessed the combined effects of planted species identity, planting diversity (mono-, bi- and multi-species mixtures), and micro-catchment (half-moon) structures on seedling performance and spontaneous natural regeneration in a hyper-arid restoration pilot site in Sharaan National Park, northwest Saudi Arabia. Thirteen native plant species, of which four—Ochradenus baccatus, Haloxylon persicum, Haloxylon salicornicum, and Acacia gerrardii—formed the dominant planted treatments, were established in 18 half-moons and monitored for survival, growth, and natural recruitment. Seedling survival after 20 months differed significantly among planting treatments, increasing from 58% in mono-plantings to 69% in bi-plantings and 82% in multi-plantings (binomial GLMM, p < 0.001), indicating a positive effect of planting diversity on establishment. Growth traits (height, collar diameter, and crown dimensions) were synthesized into an Overall Growth Index (OGI) and an entropy-weighted OGI (EW-OGI). Mixed-effects models revealed strong species effects on both indices (F_12,369 ≈ 7.2, p < 0.001), with O. baccatus and H. persicum outperforming other taxa and cluster analysis separating “fast expanders”, “moderate growers”, and “decliners”. Trait-based modeling showed that lateral crown expansion was the main driver of overall performance, whereas stem thickening and fruit production contributed little. Between 2022 and 2024, half-moon soils exhibited reduced electrical conductivity and exchangeable Na, higher organic carbon, and doubled available P, consistent with emerging positive soil–plant feedbacks. Spontaneous recruits were dominated by perennials (≈67% of richness), with perennial dominance increasing from mono- to multi-plantings, although Shannon diversity differences among treatments were small and non-significant. The correlation between OGI and spontaneous richness was positive but weak (r = 0.29, p = 0.25), yet plots dominated by O. baccatus hosted nearly two additional spontaneous species relative to other plantings, highlighting its strong facilitative role. Overall, our results show that half-moon micro-catchments, especially when combined with functionally diverse native plantings, can simultaneously improve soil properties and promote biotic facilitation, fostering a transition from active intervention to passive, self-sustaining restoration in hyper-arid environments. Full article

The rapid global expansion of genetically modified (GM) crops requires fast, on-site detection methods. Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR-associated (CRISPR/Cas) systems offer a promising platform for decentralized GM organism (GMO) monitoring. This review focuses specifically on the application of this technology in agriculture and food supply chains, diverging from previous reviews centered on clinical diagnostics. We examine the mechanisms of key CRISPR effectors (e.g., Cas12a, Cas13a) and their integration into diagnostic platforms (e.g., DETECTR, SHERLOCK) for detecting transgenic elements (e.g., CaMV35S promoter). A dedicated comparison of signal readout modalities, including fluorescence, lateral flow, and electrochemical sensing, highlights their suitability for different GMO detection scenarios, from field screening to laboratory confirmation. Finally, we discuss current challenges, including multiplexing and standardization, and outline future directions, such as the engineering of novel Cas variants and integration with smartphone technology. CRISPR-based diagnostics are poised to become indispensable tools for decentralized, efficient, and reliable GMO detection. Full article

La-doped ZnO nanoclusters confined within mesoporous SBA-15 were synthesized using an impregnation–calcination method and evaluated for their visible-light-driven photocatalytic degradation of Rhodamine B (RhB). Small-angle X-ray diffraction (XRD) and transmission electron microscopy (TEM) confirmed the preservation of the 2D hexagonal mesostructure of SBA-15 post-loading. In contrast, wide-angle XRD and Fourier-transform infrared spectroscopy (FT-IR) analyses revealed that the incorporated ZnO existed predominantly as highly dispersed amorphous or ultrafine clusters within the mesopores. N₂ adsorption–desorption measurements exhibited Type IV isotherms with H1 hysteresis loops. Compared to pristine SBA-15, the specific surface area and pore volume of the composites decreased from 729.35 m² g⁻¹ to 521.32 m² g⁻¹ and from 1.09 cm³ g⁻¹ to 0.85 cm³ g⁻¹, respectively, accompanied by an apparent increase in the average pore diameter from 5.99 nm to 6.55 nm, attributed to non-uniform pore occupation. Under visible-light irradiation, the photocatalytic performance was highly dependent on the La doping level. Notably, the 5% La-ZnO/SBA-15 sample exhibited superior activity, achieving over 99% RhB removal within 40 min and demonstrating the highest apparent rate constant (k = 0.1152 min⁻¹), surpassing both undoped ZnO/SBA-15 (k = 0.0467 min⁻¹) and other doping levels. Reusability tests over four consecutive cycles showed a consistent degradation efficiency exceeding 93%, with only a ~7 percentage-point decline, indicating excellent structural stability and recyclability. Radical scavenging experiments identified h⁺, ·OH, and ·O₂⁻ as the primary reactive species. Furthermore, photoluminescence (PL) quenching observed at the optimal 5% La doping level suggested suppressed radiative recombination and enhanced charge carrier separation. Collectively, these results underscore the synergistic effect of La doping and mesoporous confinement in achieving fast, efficient, and recyclable photocatalytic degradation of organic pollutants. Full article

Background: The positive identification of a source of tissue as human plays an important role in various contexts. It is particularly important for investigations concerning tissue and organ trafficking, since unequivocal confirmation is required for legal proceedings involving such cases. While deoxyribonucleic (DNA) methods are considered the gold standard for tissue identification, issues such as degraded DNA or the presence of chemical preservatives can hinder performance and positive identification using DNA techniques. Objectives: The aim of this study was to develop a simple method for presumptive identification of human tissue using standard bottom-up proteomics data. Methods: We identified proteins isolated from human kidney, lung and spleen tissues by bottom-up proteomics and database search using Proteome Discoverer and Sequest HT algorithms. The list of identified proteins was sorted based on liquid chromatography (LC)–mass spectrometry (MS) data metrics such as the number of unique peptides used to identify each protein and the % sequence coverage of an identified protein to determine if any parameter would cluster proteins annotated as human in a distinct category. We found that eliminating proteins identified with fewer than two unique peptides and those with less than 5% sequence coverage resulted in a final list where at least half of the remaining proteins are annotated as human. We applied this data filtration process to blinded LC–MS/MS data from 26 previous experiments to assess accuracy. Results: Using bottom-up proteomics data and the filtration rules established, we identified tissue samples (n = 10), including kidney, spleen, lung, formalin-fixed paraffin-embedded uterus, frozen breast tissue, dry blood and dry saliva as human, and tissue (n = 16) from rat, mouse, bovine, and sheep as non-human, resulting in 100% sensitivity and specificity. Conclusions: The results demonstrate that the list of identified proteins following a standard bottom-up proteomics experiment could be filtered and potentially used as a fast and simple method for presumptive human tissue identification. Full article

Adversarial learning has been attracting more and more attention thanks to the fast development of machine learning and artificial intelligence. However, due to the complicated structure of most machine learning models, the mechanism of adversarial attacks is not well interpreted. How to measure the effect of attacks is still not quite clear. In this paper, we investigate the adversarial learning from the perturbation analysis point of view. We characterize the robustness of learning models through the calmness of the solution mapping. In the case of convex clustering models, we identify the conditions under which the clustering results remain the same under perturbations. When the noise level is large, it leads to an attack. Therefore, we propose two bilevel models for adversarial learning where the effect of adversarial learning is measured by some deviation function. Specifically, we systematically study the so-called $\delta$-measure and show that under certain conditions, it can be used as a deviation function in adversarial learning for convex clustering models. Finally, we conduct numerical tests to verify the above theoretical results as well as the efficiency of the two proposed bilevel models. Full article

Background/Objectives: Segmental non-mass enhancement (NME) is the breast MRI distribution pattern with the highest positive predictive value (PPV) for malignancy. Despite its diagnostic relevance, its imaging characteristics have rarely been examined in isolation, leaving uncertainty in clinical practice. This multicenter retrospective cohort study aimed to evaluate multiparametric MRI features—including internal enhancement pattern, dynamic contrast-enhanced (DCE) kinetics, and diffusion restriction—in segmental NME to identify malignancy predictors. Methods: This retrospective cohort review included 14,834 breast MRI reports from five institutions (September 2017–February 2024), identifying 103 women (mean age, 44.4 ± 9.9 years) with segmental NME (70 malignant, 33 benign). MRI was performed at 1.5 T or 3 T using standardized protocols. Two breast radiologists, blinded to pathology, assessed internal enhancement, DCE kinetics, diffusion restriction, and short tau inversion recovery (STIR) features according to BI-RADS. Statistical analyses included chi-square/Fisher’s tests and logistic regression. Results: Clustered ring enhancement (CRE) was significantly associated with malignancy (p = 0.004). Fast initial-phase enhancement (p < 0.001) and delayed-phase washout (p = 0.011) also correlated with malignancy. On multivariate analysis, fast initial-phase enhancement remained an independent predictor (odds ratio [OR] = 5.133, p = 0.031), whereas slow enhancement predicted benignity (OR = 0.194, p = 0.020). Histologies included ductal carcinoma in situ, invasive ductal carcinoma, granulomatous mastitis, and benign hyperplastic lesions. Conclusions: This study, focusing exclusively on segmental NME, identifies CRE, fast initial-phase enhancement, and washout kinetics as reliable imaging biomarkers. Incorporating these features into breast MRI interpretation may improve diagnostic accuracy, risk stratification, and management decisions. Full article

Background: Metabolic syndrome (MetS) comprises a cluster of metabolic abnormalities that increase the risk of type 2 diabetes mellitus (T2DM) and cardiometabolic morbidity. Although widely recognized, evidence on its documentation and follow-up in primary care is limited. This study aimed to evaluate the extent of MetS documentation in electronic medical records (EMRs), examine follow-up patterns and metabolic changes over time, and assess the incidence and predictors of new-onset T2DM according to baseline MetS severity. Methods: A retrospective cohort study was conducted on 8170 adults aged 30–50 years, insured by Clalit Health Services in Southern Israel, who met ATP III criteria for MetS in 2008 and were followed through 2015. MetS severity was classified as mild (three components), moderate (four), or severe (five). Changes in metabolic indices were assessed longitudinally, and predictors of T2DM were analyzed using Kaplan–Meier survival and multivariable Cox regression models. Results: Although all participants met the diagnostic criteria, only 1.6% had a recorded MetS diagnosis. Over the eight years of follow-up, 26% developed T2DM, with incidence increasing from 21% among those with mild MetS to 49% among those with severe MetS (p < 0.0001). Fasting plasma glucose rose significantly (median +13 mg/dL, p < 0.001), BMI remained stable, and modest improvements were observed in blood pressure and lipid levels. Elevated fasting glucose (HR 2.13, p < 0.001), higher BMI (HR 1.33, p = 0.010), and lower HDL (HR 1.26, p = 0.045) independently predicted diabetes onset. Conclusions: MetS remains markedly under-documented and insufficiently integrated into primary care follow-up. Despite regular clinical follow-up, improvements in metabolic indices were limited. These findings highlight the need for structured strategies to enhance MetS recognition and long-term management within routine practice. Full article

To determine the optimal arrangement of vertical-axis wind turbines (VAWTs) within wind farms, we previously developed a technique (method-1) that constructs a flow field based on two-dimensional (2D) velocity data derived from computational fluid dynamics (CFD) simulations. In this study, we introduce an improved approach (method-2), which follows the same fundamental concept as method-1 but incorporates a more efficient algorithm for generating the flow field. Comparative analyses confirmed that method-2 produces results equivalent to those of method-1 while significantly reducing computational time and cost. Method-2 reduces the computation time of method-1 by approximately 50% for parallel layouts (θ = 0°) and up to 60% for slanted layouts (θ = ±45°). Using method-2, we further investigated the performance of a wind farm composed of eight VAWT rotors arranged in a linear configuration under the assumption of a 2D flow. The results highlighted two important aspects. First, the predicted power output is unaffected by the order in which the flow fields are superimposed during calculation; second, the method exhibits high sensitivity to even small variations in rotor placement within the layout when the spacings between rotors are short. Additionally, we examined how rotor spacing affects the distribution of power generation across the rotor array. These findings of this study validate the efficiency of method-2 and offer practical insights for designing optimized VAWT layouts. Full article

Background: We examined the prevalence and correlates of energy drink (ED) consumption in Polish adults using an archival, nationally sourced dataset. Methods: Cross-sectional analysis of 120,000 adults from the archival 2024 National Health Test of Poles (computer-assisted web interview). ED consumption was assessed by frequency and dichotomized for regression (ever vs. never). Multivariable logistic regression estimated adjusted odds ratios (aOR) with 95% confidence intervals; an age cut-off was derived using ROC/Youden. Owing to the cross-sectional design, all estimates are interpreted as associations rather than causal effects. Results: In this national sample, 16.9% of adults reported ever consuming energy drinks, while regular (weekly or more frequent) consumption was rare (2.8%). After multivariable adjustment, the strongest independent correlates of ever consuming an energy drink were an age ≤53 years (aOR 3.80, 95% CI 3.61–4.01), male sex (aOR 3.17, 95% CI 3.03–3.32), frequent fast-food consumption (aOR 2.63, 95% CI 2.51–2.76), and being a current smoker (aOR 2.49, 95% CI 2.23–2.77). In contrast to the initial hypothesis, consumption was not found to be independently associated with education level. A strong, dose-dependent relationship was observed between consumption frequency and an increased prevalence of sleep disturbances, depression, and somatic complaints like headaches and chest pain. Conclusions: Energy drink consumption in Poland is concentrated within a high-risk demographic of young to middle-aged men and is deeply embedded within a cluster of adverse health behaviors. These findings underscore the need for comprehensive public health interventions that address the entire lifestyle pattern, rather than focusing solely on energy drink use. Full article

As one of the vital research directions in hyperspectral image (HSI) processing, anomaly detection is dedicated to identifying anomalous pixels in HSIs that have significant spectral differences from the surrounding background, and it has attracted extensive attention from numerous scholars in recent years. Anomaly detectors based on collaborative representation have achieved favorable performance in this field. Based on CRD, scholars have proposed many different variants. However, most of these methods only focus on the spectral information of HSIs, and they suffer from slow detection speed and poor robustness. In this paper, we combine the Extended Multi-Attribute Profile (EMAP) with the CRD algorithm, propose a fast collaborative representation anomaly detection algorithm based on the extended multi-attribute profile. First, we use EMAP to extract the spatial structural information of the HSI. Then, before the anomaly detection, we employ the k-means clustering algorithm to separate anomalous pixels with similar features, and obtain a reconstructed background dictionary matrix. This further separates the background from anomalies and improves the robustness of anomaly detection. Finally, we apply a collaborative representation-based anomaly detector to detect anomalies. The proposed method is compared with other algorithms through experiments on four real HSI datasets and one synthetic HSI dataset. The experimental simulation results verify the effectiveness of our proposed method. Full article

The aim of this research is to develop and implement artificial intelligence models for the automatic detection of defects in the microstructures of austempered ductile iron (ADI). Our research used three different approaches, representing various categories of machine learning tasks: image classification (ResNet), pixel-wise segmentation (UNet), and object detection (YOLO). Each of the models were adapted to the specific characteristics of the dataset and tested on a collection of microstructural images prepared within the scope of the research. The data preparation process included clustering using the k-means method, morphological operations, generation of binary masks, conversion of labels into formats required by each architecture, and data augmentation to increase the diversity of training samples. The results demonstrated that ResNet achieved very high classification accuracy but did not provide spatial information about defect localization. UNet produced precise segmentation masks of martensitic regions, allowing for quantitative analysis of samples, although it required significantly higher computational resources and struggled with detecting very small defects. YOLO, in turn, enabled fast detection of defects in the form of bounding boxes. In summary, each model proved effective in a different context: ResNet for preliminary classification, UNet for detailed laboratory analysis, and YOLO for industrial detection tasks. Full article

These days, the prices of fruits and vegetables fluctuate significantly, causing issues in the supply chain and for perishable products. This study aimed to use hybrid machine learning methods to cluster regional Chilean produce from 2015 to 2023 based on market analysis and address fluctuations in price and demand for agricultural products. The hybrid model employed in this research included substantial noise reduction with interquartile range (IQR), Z-score, and Hampel filters; temporal-spectral feature extraction through additive decomposition and Fast Fourier Transform (FFT); principal component analysis (PCA) for reducing dimensions; Gaussian mixture models (GMMs) for probabilistic clustering; and regime-shift detection using cumulative sum (CUSUM) and Bayesian online change-point detection (BOCPD). Finally, a sensitivity analysis demonstrated the approach’s reliability and robustness. The novelty of this research lies in the introduction of a hybrid model for forecasting agricultural product prices across Chile’s various regions. Full article

To clarify the relationship between muscle development and meat quality in Ziwuling black goats, this study used the longissimus dorsi muscle of 6-month-old and 12-month-old goats as samples. With HE staining, fast–slow myofiber immunofluorescence double staining, and transcriptome sequencing, this study analyzed muscle structure, myofiber type transformation, and molecular regulation. Results showed that 6-month-olds had higher myofiber density and smaller diameter; 12-month-olds showed myofiber hypertrophy (larger diameter); immunofluorescence revealed more fast-twitch myofibers (Type II) at 6 months and increased slow-twitch ones (Type I) at 12 months. Transcriptome sequencing identified 387 differentially expressed genes (DEGs: 156 upregulated, 231 downregulated). GO analysis indicated that DEGs are involved in skeletal muscle growth, cAMP biosynthesis, etc.; KEGG analysis showed enrichment in arginine–proline metabolism and AMPK/MAPK signaling pathways (AMPK regulates fatty acid metabolism genes like ACACB/CPT1A; arginine–proline metabolism relates to muscle maturation). WGCNA clustered genes into nine modules (MEblue correlated with myofiber density/MAPK; MEgreen correlated negatively with diameter but positively with density, involving PPARGC1A/AMPK). In conclusion, protein nutrition at 6 months (promote myofiber proliferation) and regulating energy intake at 12 months (improve meat quality) are recommended, and 12 months is the optimal slaughter age. Full article