Search Results (120)

In this work, the light curve of the Seyfert galaxy Mrk 6 constructed from photometric observations in the B, V, and $R_{\mathrm{c}}$ filters over the period from 5 April 2016 to 1 February 2026 is presented and analyzed. Over the entire monitoring interval (2016–2026), the variability amplitude of the light curve reaches $\mathrm{\Delta }B=1.9$ mag, $\mathrm{\Delta }V=1.5$ mag, and $\mathrm{\Delta }{R}_{\mathrm{c}}=1.4$ mag. During 2024–2026, the galaxy exhibits synchronous photometric variability in the B, V, and $R_{\mathrm{c}}$ filters with an amplitude of ∼0.3 mag. The study also uses spectroscopic observations obtained on 15 and 22 November 2025 and 16 February 2026 at the Shamakhy Astrophysical Observatory (Azerbaijan), as well as on 9 January 2026 at the Fesenkov Astrophysical Institute (Kazakhstan). The fluxes in the H$\beta$ emission line were calibrated using the [O III] $\lambda 5007$ Å line, ensuring consistent relative calibration of the spectral data. A comparison of the optical spectra reveals a pronounced transformation of the H$\beta$ line profile between November 2025 and January 2026. The broad component, clearly present in November 2025, becomes strongly suppressed and nearly disappears in January 2026, while the narrow emission lines remain stable. This behavior is consistent with a changing-look transition, indicating a temporary weakening of the broad-line region emission. The radius of the broad-line region $R_{\mathrm{BLR}}$ was taken to be equal to the average time delays (lags), amounting to ≈20 light days for the H$\beta$ emission and ≈28 light days for the H$\alpha$. Full article

The increasing frequency of disasters caused by landslides, mainly due to climate change leading to more intense extreme events, requires reliable predictive models for risk mitigation. Italy, in particular, is a country at high risk of landslides, but the lack of an updated catalogue of landslide activation dates poses a significant challenge for defining reliable activation thresholds. This study develops a methodology for mapping landslide susceptibility based on events in a pilot area of central Italy, integrating a database of landslides with known activation dates with predisposing and triggering parameters. Two statistical techniques were compared to assess their predictive performance in discriminating landslide from non-landslide conditions during extreme precipitation events. A comparison between binary logistic regression (BLR) and decision trees (QUEST) revealed the clear superiority of the BLR model, which achieved excellent predictive accuracy (AUC = 0.913). The model identified clay-rich lithology, gentle slopes (0–16°) and maximum daily precipitation as the most significant controlling factors. This result led to the generation of three derivative products: a susceptibility map, a hazard map for an extreme precipitation scenario with a 100-year return period, and a spatially distributed map of activation thresholds. This threshold map quantifies the intensity of precipitation required to exceed a critical probability of landslide initiation (p > 0.7) at any point in the territory. The susceptibility map highlights critical areas within the study area, while the hazard map also includes the return period of the event. The threshold map is a direct and operational tool for early warning systems, transforming a statistical model into a guide for real-time risk management. The study area serves as a pilot area that could allow this methodology to be replicated. With the integration of real-time meteorological data, it could function as a real-time warning system. The proposed framework therefore provides a directly actionable tool for civil protection agencies, land-use planning authorities, and emergency managers, enabling location-specific rainfall alert thresholds to be issued rather than a single regional value, with the potential to reduce both false alarms and missed warnings. Full article

The Epi2SensA method is a method similar to the validated EpiSensA assay for assessing the skin sensitization potential of chemicals. The Epi2SensA protocol includes adaptation (changes to exposure conditions and the controls) for using an alternative reconstructed human epidermis (RhE) model, the EpiDerm™ model. The interlaboratory validation study evaluated the reliability and predictive capacity of Epi2SensA according to OECD Performance Standards. Four laboratories (Mattek, Now Part of Sartorius, Eurofins Munich, Burleson Research Technologies, Inc., and Food and Drug Safety Center) conducted blinded testing of 20 coded reference substances representing various chemical categories and sensitization potencies. Statistical analysis using modified acceptance criteria (a 60% cell viability threshold) and a modified prediction model (requiring at least two positive gene markers) demonstrated substantially improved performance compared to the original EpiSensA criteria. The between-laboratory reproducibility (BLR) was 85%, the average within-laboratory reproducibility (WLR) was 83.3%, and the average predictivity parameters were 88.1% for sensitivity, 88.9% for specificity, and 88.3% for accuracy. Epi2SensA achieved performance metrics comparable to the validated reference method (EpiSensA), supporting regulatory acceptance of the Epi2SensA assay using the EpiDerm™ model (Mattek Corporation, Now Part of Sartorius, Ashland, MA, USA) as an alternative RhE source for OECD TG 442D skin sensitization testing. Full article

Classifying optical water types (OWTs), particularly concerning different phytoplankton bloom types, is critically important because dominant phytoplankton groups govern key marine ecosystem functions and biogeochemical processes, including nutrient cycling and carbon export. This study refines a recent OWT classification method developed for the Second-Generation Global Imager (SGLI), which was originally proposed to discriminate dinoflagellate and diatom blooms. By employing binary logistic regression (bLR) with independent in situ data from Karenia selliformis (dinoflagellate) blooms off the Kamchatka Peninsula and Skeletonema spp. (diatom) blooms in Tokyo Bay, this study establishes more robust and statistically meaningful boundaries between OWTs. The analysis confirms the diagnostic spectral shapes from SGLI data: a trough at 490 nm for K. selliformis blooms and a peak at 490 nm for diatom blooms, validating the consistency of this spectral criterion. The updated method reliably identifies waters dominated by coloured dissolved organic matter and different phytoplankton functional types in mesotrophic waters, and successfully detected a Karenia mikimotoi bloom in the Gulf St. Vincent, South Australia, demonstrating its potential for the global monitoring of red tides. By providing a reliable, satellite-based tool to distinguish between ecologically distinct phytoplankton groups, this refined OWT classification offers a valuable data product to improve the accuracy of marine ecosystem and carbon cycle models, moving beyond bulk chlorophyll-a parameterizations. Full article

Background/Objectives: This study aimed to evaluate the prognostic value of ¹⁸F-FDG PET/CT-based secondary lymphoid organ metabolic ratios—spleen/liver (SLR), bone marrow/liver (BLR), and ileocecal region/liver (ILR)—and hematological inflammation markers (neutrophil/lymphocyte ratio [NLR] and systemic immune-inflammation index [SII]) obtained before nivolumab treatment in relation to survival in patients with advanced non-small cell lung cancer (NSCLC). Methods: This retrospective single-center study included 79 advanced NSCLC patients who were treated with nivolumab monotherapy at Marmara University Faculty of Medicine Hospital between 2022 and 2024. Pretreatment SLR, BLR, and ILR ratios were calculated from ¹⁸F-FDG PET/CT examinations; NLR and SII values were obtained from hematological data. Survival outcomes were analyzed using the Kaplan–Meier method, and prognostic factors were assessed using Cox proportional hazards regression analysis. In a subset of patients, an exploratory longitudinal analysis was performed using early follow-up PET/CT to assess follow-up-to-baseline changes in immune-organ metabolic ratios in relation to overall survival. Results: High NLR and SII levels were significantly associated with shorter progression-free survival and overall survival. In contrast, no significant associations were observed between PET/CT-derived metabolic ratios (SLR, BLR, and ILR) and survival. Multivariate analysis identified the presence of liver metastases and a high NLR as independent adverse prognostic factors for overall survival. Conclusions: In this homogeneous real-world cohort treated exclusively with single-agent nivolumab, PET/CT-derived secondary lymphoid organ metabolic ratios showed limited prognostic value at baseline and during early on-treatment assessment. In contrast, hematological inflammation markers, especially high NLR levels, are strong prognostic indicators of survival and may complement established clinical factors in risk stratification. Full article

Background: Granuloma Annulare (GA) is a chronic inflammatory dermatosis with diverse presentations, yet comprehensive data integrating histopathology, long-term prognosis, and systemic inflammatory markers remain limited. This study aimed to analyze these clinicopathological characteristics and identify biomarkers for chronicity and recurrence. Methods: In this retrospective cohort study, we evaluated 34 patients with histopathologically confirmed GA diagnosed between 2013 and 2023. Demographic data, clinical features, comorbidities, treatment outcomes, and laboratory parameters were analyzed. Histopathological findings were re-evaluated to assess pattern types and the presence of specific markers. Statistical correlations between clinical parameters, inflammatory markers, and histopathological features were assessed. Results: The interstitial pattern was the predominant histopathological subtype (64.7%), with universal mucin deposition (100%). A statistically significant positive correlation was observed between lesion duration and the presence of multinucleated giant cells (r = 0.456, p = 0.033), suggesting a time-dependent granuloma maturation. Clinically, generalized GA cases demonstrated significantly longer recovery times compared to localized forms. The overall recurrence rate was 23.5%. A lower BLR was significantly associated with disease recurrence (r = −0.539, p = 0.021). At least one comorbidity was present in 76.5% of the patients. Conclusions: Our findings suggest that giant cell formation serves as a histopathological marker of lesion chronicity, supporting the concept of granuloma remodeling over time. Furthermore, a suppressed BLR may serve as a novel surrogate marker associated with disease recurrence. Given the high burden of comorbidities observed in both localized and generalized forms, GA should be approached as a cutaneous manifestation of underlying systemic dysregulation, suggesting that clinicians should consider routine metabolic screening for these patients. Full article

Background: Adjuvant anti-PD-1 therapy improves recurrence-free survival (RFS) in high-risk melanoma, but many patients experience adverse events. 2-deoxy-2-[¹⁸F]fluoro-D-glucose positron emission tomography with computed tomography [¹⁸F]FDG-PET/CT has been proposed to identify biomarkers that may predict outcome of treatment. Objectives: The aim of this register-based study was to investigate the prognostic value of immune-related adverse events (irAEs), spleen-to-liver ratio (SLR), and bone marrow-to-liver ratio (BLR), detected by [¹⁸F]FDG-PET/CT. Methods: This retrospective, register-based cohort study included 122 patients with radically resected stage III–IV melanoma treated with adjuvant anti-PD-1. Patient data were extracted from a Danish register, and measurements for SLR and BLR were made using an AI model. Cox regression models were made on irAEs and BLR to assess associations with RFS and overall survival (OS). Results: Over half of the patients experienced recurrence, and one quarter died during follow-up of 4 ¾ years. Seventy-four percent exhibited at least one PET-detected irAE. This study found no association between irAEs and OS. Regarding RFS, our findings suggest an increased risk of recurrence for the presence of irAEs within the first 1.5 years of follow-up (HR: 2.93, CI: 1.10–7.84, p = 0.032). BLR and SLR were not associated with RFS or OS in multivariable models. Conclusions: This study did not confirm the findings of a positive association between irAEs and survival found in previous studies. PET-detected irAEs were common in the study population, but did not predict OS, while early-onset irAEs were linked to increased recurrence risk. Neither SLR nor BLR demonstrated prognostic value. Further research is needed to clarify the clinical utility of PET-derived biomarkers, especially in the adjuvant setting. Full article

This study aimed to investigate the effects of Levilactobacillus brevis, Lacticaseibacillus paracasei, and Lacticaseibacillus rhamnosus strains isolated from Mihalic cheese, also known as “weeping cheese”, on fermentation kinetics, microbial viability, and textural and aromatic properties of the butter matrix. The effects of the isolates were determined on acidification kinetics (V_max, T_vmax, pH_vmax), viability proportion index (VPI), textural parameters (firmness, work of shear, stickiness, work of adhesion), and volatile aroma compounds (GC-MS) formation. This study found that the BLR sample containing Lacticaseibacillus rhamnosus maintained its limited viability under acidic stress conditions despite its high fermentation rate and low pH_vmax values. The BLP sample containing Lacticaseibacillus paracasei exhibited high viability due to its low acidification rate and limited pH change. Determining the chemical classes to which the aroma compounds in the BLP sample belonged revealed a composition rich in fatty acids. The BLB sample containing Levilactobacillus brevis produced a high ΔpH value and an aroma profile rich in aldehyde compounds. Examination of the macro-structural properties of the butter samples revealed that the sample containing Lacticaseibacillus rhamnosus, similar to the control sample (BMC), was more compact and rigid during storage. In contrast, samples containing Lacticaseibacillus paracasei and Levilactobacillus brevis had a softer/spreadable texture. These findings demonstrate the potential of lactic acid bacteria isolates from the traditional Mihalic cheese microbiota as biological catalysts for the development/improvement of texture, aroma, and sensory quality in high-fat dairy products and for the industrial production of products modified to meet consumer preferences. Full article

Cost is a key performance indicator for evaluating the success of architectural construction projects. While previous studies have relied on quantitative data and statistical models to evaluate cost performance, recent advancements in methods have enabled analysis using unstructured data. Unstructured data, particularly in construction supervision reports, can be considered the significant variables for performance evaluation, as they represent independent third-party monitoring of the construction project’s execution. This study aims to present a framework that supports cost performance evaluation using unstructured data and random forests (RFs), a representative method of machine learning. Specifically, association rule analysis and social network analysis were used to identify the main keywords, and an RF model was applied to these data to evaluate cost performance. The tuning of hyper-parameters in the RF was implemented by the Bayesian optimization technique with the augmentation of the original dataset. The accuracy of cost performance evaluation was 59% for the traditional logistic regression (LR), 74% for the regularization-based logistic regression (BLR) designed to prevent overfitting, and 76% for the RF model utilizing augmented data. The complementary utility of the models consisting of the proposed framework can be useful for deriving various evaluation explanations about cost performance. The applicability is expected to increase as more data become available in the future. Full article

The high-redshift blazars are important cosmological probes for exploring the early universe and unraveling the fundamental emission processes and the structure of the active galactic nuclei. The high-energy GeV gamma-ray emissions of 38 high-redshift blazars (z > 2.5) observed by Fermi-LAT were analyzed. Along with the Archive multiwavelength data, we employ one-zone leptonic external Compton (EC) models to reproduce the spectral energy distributions (SEDs) of 38 sources. Both the external photons from the molecular torus (MT) and the broad-line region (BLR) are considered. We obtained the best-fitting parameters for describing the characteristics of the jets and accretion disks. The results indicate that high-redshift blazars exhibit higher $\gamma$-ray luminosities, energy densities, jet powers, kinetic powers, accretion disk luminosities, black hole (BH) masses, radiation efficiencies, and mass accretion rates compared to low-redshift blazars. For high-redshift blazars, the influence of the accretion rate on jet power appears to weaken, and in most cases, the jet power exceeds the total accretion power. We speculate that for high-redshift blazars, rapid accretion may lead to magnetic field saturation, thereby reducing the effectiveness of the Blandford–Payne (BP) process. Consequently, the Blandford–Znajek (BZ) process is likely to play a more dominant role in powering jets in high-redshift blazars compared to low-redshift blazars. Naturally, we acknowledge that selection effects cannot be fully eliminated. Full article

Recent studies have greatly furthered our understanding of the Southern Bantu languages, but questions about the internal relationships of the Southern Bantu language subgroups and the validity of the clade as a whole still remain. This study attempts to reconstruct the consonant inventory of one proposed genetic clade, that of Tsonga-Copi (S50–S60). Using published dictionaries and reference works for each language of the subgrouping, a corpus of cognate vocabulary was assembled. Each term was then matched, where possible, to a reconstruction in the Bantu Lexical Reconstructions 3 (BLR3) database. Sound correspondences were identified and used to reconstruct the consonant inventory of Proto-Tsonga-Copi. In addition to the discovery of several typologically unusual sound changes, the results of this study also lend support to existing and developing hypotheses about both the internal relationships of Southern Bantu clades, as well as the nature of language contact in (pre)historic Southern Africa, particularly the influence of Khoisan and other Bantu languages. Full article

Human–environment interactions in antiquity were fundamentally shaped by environmental constraints, with spatial patterns of human construction works reflecting strategic resource optimization. This study employed Geographic Information System (GIS) and binary logistic regression (BLR) to analyze the siting principles of ancient postal buildings in Fujian, China, integrating related environmental factors of elevation, slope, relief amplitude, and distance to rivers. The results revealed significant spatial differentiation, with elevation exhibiting the strongest influence on siting preference, followed by slope, relief amplitude, and distance to rivers. Clustering patterns along coasts and rivers indicated a strategic balance between transmission efficiency and military defense needs. The applicability of the integrated GIS–BLR approach in studying the ancient postal system demonstrates its extensibility to other ancient settlement systems while offering insights for contemporary conservation practice and sustainable development. Full article

Active galactic nuclei (AGNs) often exhibit broad-line regions (BLRs), populated by high-velocity clouds in approximately Keplerian orbits around the central supermassive black hole (SMBH) at subparsec scales. During episodes of intense accretion at super-Eddington rates, the accretion disk can launch a powerful, radiation-driven wind. This wind may overtake the BLR clouds, forming bowshocks around them. Two strong shocks arise: one propagating into the wind, and the other into the cloud. If the shocks are adiabatic, electrons and protons can be efficiently accelerated via a Fermi-type mechanism to relativistic energies. In sufficiently dense winds, the resulting high-energy photons are absorbed and reprocessed within the photosphere, while neutrinos produced in inelastic $pp$ collisions escape. In this paper, we explore the potential of super-accreting AGNs as neutrino sources. We propose a new class of neutrino emitter: an AGN lacking jets and gamma-ray counterparts, but hosting a strong, opaque, disk-driven wind. As a case study, we consider a supermassive black hole with $M_{\mathrm{BH}}={10}^6{M}_{\odot }$ and accretion rates consistent with tidal disruption events (TDEs). We compute the relevant cooling processes for the relativistic particles under such conditions and show that super-Eddington accreting SMBHs can produce detectable neutrino fluxes with only weak electromagnetic counterparts. The neutrino flux may be observable by the next-generation IceCube Observatory (IceCube-Gen2) in nearby galaxies with a high BLR cloud filling factor. For galaxies hosting more massive black holes, detection is also possible with moderate filling factors if the source is sufficiently close, or at larger distances if the filling factor is high. Our model thus provides a new and plausible scenario for high-energy extragalactic neutrino sources, where both the flux and timescale of the emission are determined by the number of clouds orbiting the black hole and the duration of the super-accreting phase. Full article

This study explores the mechanical behavior of polymer and composite specimens fabricated using fused deposition modeling (FDM), focusing on three material configurations: acrylonitrile butadiene styrene (ABS), carbon fiber-reinforced polyphthalamide (PPA/Cf), and a sandwich-structured composite. A systematic experimental plan was developed using the Box–Behnken design (BBD) to investigate the effects of material type (MT), infill pattern (IP), and printing direction (PD) on tensile and flexural strength. Experimental results showed that the PPA/Cf material with a “Cross” IP printed “Flat” yielded the highest mechanical performance, achieving a tensile strength of 75.8 MPa and a flexural strength of 102.3 MPa. In contrast, the lowest values were observed in ABS parts with a “Grid” pattern and “Upright” orientation, recording 37.8 MPa tensile and 49.5 MPa flexural strength. Analysis of variance (ANOVA) results confirmed that all three factors significantly influenced both outputs (p < 0.001), with MT being the most dominant factor. Machine learning (ML) algorithms, Bayesian linear regression (BLR), and Gaussian process regression (GPR) were employed to predict mechanical performance. GPR achieved the best overall accuracy with R² = 0.9935 and MAPE = 11.14% for tensile strength and R² = 0.9925 and MAPE = 12.96% for flexural strength. Comparatively, the traditional BBD yielded slightly lower performance with MAPE = 13.02% and R² = 0.9895 for tensile strength. Validation tests conducted on three unseen configurations clearly demonstrated the generalization capability of the models. Based on actual vs. predicted values, the GPR yielded the lowest average prediction errors, with MAPE values of 0.54% for tensile and 0.45% for flexural strength. In comparison, BLR achieved 0.79% and 0.60%, while BBD showed significantly higher errors at 1.76% and 1.32%, respectively. Full article

Sex determination is a fundamental component of forensic identification and medicolegal investigations. Several studies have investigated sexual dimorphism through mandibular osteometric measurements, including the position of anatomical foramina such as the mandibular and mental foramen (MF), reporting population-specific discrepancies. This study assessed the reliability and predictive ability of specific anthropometric mandibular measurements for sex estimation using three-dimensional (3D) cone beam computed tomography (CBCT) surface reconstructions. Methods: CBCT scans from 204 Greek individuals (18–70 years) were analyzed. Records were categorized by sex and age. Five linear measurements were traced on 3D reconstructions using ViewBox 4 software: projections of the inferior points of the right and left mental and mandibular foramina and the linear distance between mental foramina projections. A binary logistic regression (BLR) model was employed. All measurements showed statistically significant sex differences, with males presenting higher mean values. The final model achieved accuracy of 66.7% in sex prediction, with two vertical measurements—distances from the right mandibular foramen and the left mental foramen—identified as the strongest predictors of sex. The positions of the mandibular and mental foramina demonstrate sex-related dimorphism in this Greek sample, supporting their forensic relevance in population-specific applications. Full article