Search Results (285)

The Manicouagan impact structure in northeastern Canada is one of the largest, well-documented impact sites among Phanerozoic structures. Once considered a candidate for the cause of end-Triassic extinctions, radioisotopic dating of impact melt rock has established the age of the impact as middle to late Norian. In contrast to the clearly defined association between the Chicxulub structure and the K-Pg boundary, however, the sedimentary record of the Manicouagan impact is unusually sparse, with verified ejecta deposits currently limited to a single deep-marine occurrence (Japan) and one well-documented deposit in a continental (fluvial) setting (England). Sedimentary layers at the top of a widespread seismically deformed zone in a continental sequence in the Upper Triassic (Norian) Blomidon Formation, Fundy Basin, contain sparse, potentially impact-derived grains (shocked quartz and spherulitic grains) that are interpreted as impact ejecta that were reworked within a playa-lacustrine environment. The presence of these ejecta suggests that the seismic deformation resulted indirectly from the Manicouagan impact via reactivation of a nearby fault system. Paleomagnetic correlation of the ejecta-bearing strata in the Blomidon Formation to the Newark astrochronostratigraphic polarity time scale suggests a temporal discrepancy in the correlation of the Newark time scale to the magnetostratigraphic record of the Upper Triassic. This hypothesis is supported by recent correlations of the geomagnetic polarity time scale to the Newark time scale. Full article

Multilayer and free-standing films self-assembled from water-soluble anionic azo dye acid yellow 9 (AY9) and both poly(allylamine hydrochloride) (PAH) and chitosan (CS) cationic polyelectrolytes were fabricated from water solution using a layer-by-layer (LbL) technique and characterized by UV–Vis and Raman spectroscopy. Observations were made of a strong, unexpected, and highly unusual colour change from deep red to a distinct dark blue upon exposure of the multilayer films to an acidic environment. The colour change was attributed to the multilayer films only and was not observed either for the polymer or the dye alone, or their mixture in water solution, nor when cast as free-standing films. The significant shift to blue colour of the absorption peaks was quantified with UV–Vis spectroscopy, and a proposed explanation is presented based on density functional theory (DFT) calculations exploring possible and most likely acid-base equilibria configurations of the azo dye that result from being self-assembled. Full article

This paper describes a crowdsourced experiment in which participants were asked to judge which of two simultaneously presented facial images (one real, one AI-generated) was fake. With the growing presence of synthetic imagery in digital environments, cognitive systems must adapt to novel and often deceptive visual stimuli. Recent developments in cognitive science propose that some mental processes may exhibit quantum-like characteristics, particularly in their context sensitivity. Drawing on Tezzin’s “generalized fair coin” model, this study applied Contextuality-by-Default (CbD) theory to investigate whether human judgments of human faces exhibit quantum-like contextuality. Across 20 trials, each treated as a “generalized coin”, bootstrap resampling (10,000 iterations per coin) revealed that nine trials demonstrated quantum-like contextuality. Notably, Coin 4 exhibited strong context-sensitive causal asymmetry, where both the real and synthetic faces elicited inverse judgments due to their unusually strong resemblance to one another. These results support the growing evidence that cognitive judgments are sometimes quantum-like contextual, suggesting that adopting comparative strategies, such as evaluating unfamiliar faces alongside known-real exemplars, may enhance accuracy in detecting synthetic images. Such pairwise methods align with the strengths of human perception and may inform future interventions, user interfaces, or educational tools aimed at improving visual judgment under uncertainty. Full article

The widespread emergence of resistant pathogenic microorganisms are diminishing the effectiveness of existing antimicrobial drugs, posing an enormous threat to global public health. This phenomenon, known as antimicrobial resistance (AMR), is primarily driven by the misuse and overuse of antimicrobial drugs. Natural product researchers around the globe, in response to antibiotics resistance, are searching for new antimicrobial lead compounds from unexplored or underexplored ecological niches such as the marine environment. In order to isolate new antimicrobial lead compounds, two Streptomyces spp. were isolated from marine sediment samples by a serial dilution technique and subsequently cultured in modified Bennett’s broth medium. Repeated chromatographic steps of ethyl acetate (EtOAc) extracts obtained from the culture broth led to the isolation of a new compound with an unusual chemical skeleton, streptopiperithiazol (1), and a synthetically known (2) compound. These compounds were characterized by the extensive analysis of 1D and 2D spectroscopic as well as HRMS data. The absolute configuration of 1 was unresolved due to limited yield and lack of proper facilities for taking CD and ECD spectra. In vitro activity study of compounds 1 and 2 revealed that these compounds had better activity against Gram-positive bacteria than Gram-negative bacteria and yeast. Full article

Inland aquatic ecosystems, encompassing lakes, reservoirs, and ponds, serve as vital repositories of water resources and provide essential ecological, social, and cultural services. Water color, a key indicator of water quality, reflects the complex interactions among physicochemical, biological, and environmental drivers. Heilong Pool (HP) in Southwest China, which consists of a Clear Pool (CP) and a Turbid Pool (TP), has recently exhibited an anomalous reddish-brown “pumpkin soup” phenomenon in the CP, while the TP remains unchanged. This unusual phenomenon has raised widespread public concern regarding water resource security and its potential association with geological disasters. To elucidate the ecological and geochemical mechanisms of this phenomenon, we employed a multifaceted analytical approach that included assessing nutrient elements, quantifying heavy metal concentrations, analyzing dissolved substances, characterizing algal community composition, and applying δD-δ¹⁸O isotope analytical models. Our findings illustrated that while Bacillariophyta predominate (>79.3% relative abundance) in the algal community of HP, they were not the primary determinant of water color changes. Instead, Fe(OH)₃ colloidal particles, originating from groundwater–surface water interactions and controlled by redox environment dynamics periodically, emerged as the principal factors of the reddish-brown discoloration. The genesis of the “pumpkin soup” water coloration was attributed to the precipitation-induced displacement of anoxic groundwater from confined karst conduits. Subsequent exfiltration and atmospheric exposure facilitate oxidative precipitation, forming authigenic rust-hued Fe(OH)₃ colloidal complexes. This study provides new insights into the geochemical and hydrological mechanisms underlying water color anomalies in karst-dominated catchments. Full article

The growth and sustainability of today’s global economy heavily relies on smooth maritime operations. The increasing security concerns to marine environments pose complex security challenges, such as smuggling, illegal fishing, human trafficking, and environmental threats, for traditional surveillance methods due to their limitations. Artificial intelligence (AI), particularly deep learning, has offered strong capabilities for automating object detection, anomaly identification, and situational awareness in maritime environments. In this paper, we have reviewed the state-of-the-art deep learning models mainly proposed in recent literature (2020–2025), including convolutional neural networks, recurrent neural networks, Transformers, and multimodal fusion architectures. We have highlighted their success in processing diverse data sources such as satellite imagery, AIS, SAR, radar, and sensor inputs from UxVs. Additionally, multimodal data fusion techniques enhance robustness by integrating complementary data, yielding more detection accuracy. There still exist challenges in detecting small or occluded objects, handling cluttered scenes, and interpreting unusual vessel behaviours, especially under adverse sea conditions. Additionally, explainability and real-time deployment of AI models in operational settings are open research areas. Overall, the review of existing maritime literature suggests that deep learning is rapidly transforming maritime domain awareness and response, with significant potential to improve global maritime security and operational efficiency. We have also provided key datasets for deep learning models in the maritime security domain. Full article

Part I of this two-part documentation of the ECMWF ensemble (ECEPS) new tropical cyclone track and intensity forecasting challenges during the 2024 western North Pacific season described four typhoons that started well to the south of an unfavorable external environment north of 20° N. In this Part II, five other 2024 season typhoons that formed and intensified near and north of 20° N are documented. One change is that the Cooperative Institute for Meteorological Satellite Studies ADT + AIDT intensities derived from the Himawari-9 satellite were utilized for initialization and validation of the ECEPS intensity forecasts. Our first objective of providing earlier track and intensity forecast guidance than the Joint Typhoon Warning Center (JTWC) five-day forecasts was achieved for all five typhoons, although the track forecast spread was large for the early forecasts. For Marie (06 W) and Ampil (08 W) that formed near 25° N, 140° E in the middle of the unfavorable external environment, the ECEPS intensity forecasts accurately predicted the ADT + AIDT intensities with the exception that the rapid intensification of Ampil over the Kuroshio ocean current was underpredicted. Shanshan (11 W) was a challenging forecast as it intensified to a typhoon while being quasi-stationary near 17° N, 142° E before turning to the north to cross 20° N into the unfavorable external environment. While the ECEPS provided accurate guidance as to the timing and the longitude of the 20° N crossing, the later recurvature near Japan timing was a day early and 4 degrees longitude to the east. The ECEPS provided early, accurate track forecasts of Jebi’s (19 W) threat to mainland Japan. However, the ECEPS was predicting extratropical transition with V_max ~35 kt when the JTWC was interpreting Jebi’s remnants as a tropical cyclone. The ECEPS predicted well the unusual southward track of Krathon (20 W) out of the unfavorable environment to intensify while quasi-stationary near 18.5° N, 125.6° E. However, the rapid intensification as Krathon moved westward along 20° N was underpredicted. Full article

Outlier detection plays a key role in data analysis by improving data quality, uncovering data entry errors, and spotting unusual patterns, such as fraudulent activities. Choosing the right detection method is essential, as some approaches may be too complex or ineffective depending on the data distribution. In this study, we explore a simple yet powerful approach using the range distribution to identify outliers in univariate data. We compare the effectiveness of two range statistics: we normalize the range by the standard deviation ($\sigma$) and the interquartile range (IQR) across different types of distributions, including normal, logistic, Laplace, and Weibull distributions, with varying sample sizes (n) and error rates ($\alpha$). An evaluation of the range behavior across multiple distributions allows for the determination of threshold values for identifying potential outliers. Through extensive experimental work, the accuracy of both statistics in detecting outliers under various contamination strategies, sample sizes, and error rates ($\alpha =0.1,0.05,0.01$) is investigated. The results demonstrate the flexibility of the proposed statistic, as it adapts well to different underlying distributions and maintains robust detection performance under a variety of conditions. Our findings underscore the value of an adaptive method for reliable anomaly detection in diverse data environments. Full article

Pituitary neuroendocrine tumors (PitNETs) represent a complex pathology based on numerous incompletely elucidated molecular mechanisms. Beyond tumor cells, analyzing the tumor microenvironment may help identify novel prognostic markers and therapies. A key component of this environment is the folliculo-stellate (FS) cell. We examined FS cells in 77 PitNETs obtained by transsphenoidal surgery, using glial fibrillary acidic protein (GFAP) as an immunohistochemical marker. Immunohistochemistry for anterior pituitary hormones and transcription factors was performed to accurately classify the tumors. Our study included 19 somatotroph, 16 mammosomatotroph, 5 plurihormonal PIT-1 positive, 7 corticotroph, 14 gonadotroph, 11 unusual plurihormonal, and 5 null cell PitNETs. FS cells were observed in 55 of the cases, distributed isolated, in small groups or diffuse networks. A considerable number of tumors immunopositive for more than one hormone (including associations between GH/PRL, but also unusual combinations like GH/ACTH) also contained FS cells (p < 0.01), suggesting their involvement in tumor lineages differentiation. In 27 tumors, GFAP-positive cells clustered in highly vascularized areas. Additionally, in 11 of these cases a direct interaction between endothelial cells and FS cells was noted, sustaining their potential role in tumor angiogenesis. Given their complexity, FS cells may be crucial for understanding tumorigenesis mechanisms. Full article

LK-99, with chemical formula Pb_10−xCu_x(PO₄)₆O, was recently reported to be a room-temperature superconductor. While this claim has met with little support in a flurry of ensuing work, a variety of calculations (mostly based on density-functional theory) have demonstrated that the system possesses some unusual characteristics in the electronic structure, in particular flat bands. We have established previously that within DFT, the system is insulating with many characteristics resembling the classic cuprates, provided the structure is not constrained to the P3(143) symmetry nominally assigned to it. Here we describe the basic electronic structure of LK-99 within self-consistent many-body perturbative approach, quasiparticle self-consistent GW (QSGW) approximation and their diagrammatic extensions. QSGW predicts that pristine LK-99 is indeed a Mott/charge transfer insulator, with a bandgap gap in excess of 3 eV, whether or not constrained to the P3(143) symmetry. When Pb₉Cu(PO₄)₆O is hole-doped, the valence bands modify only slightly, and a hole pocket appears. However, two solutions emerge: a high-moment solution with the Cu local moment aligned parallel to neighbors, and a low-moment solution with Cu aligned antiparallel to its environment. In the electron-doped case the conduction band structure changes significantly: states of mostly Pb character merge with the formerly dispersionless Cu d state, and high-spin and low spin solutions once again appear. Thus we conclude that with suitable doping, the ground state of the system is not adequately described by a band picture, and that strong correlations are likely. Irrespective of whether this system class hosts superconductivity or not, the transition of Pb₁₀(PO₄)₆O from being a band insulator to Pb₉Cu(PO₄)₆O, a Mott insulator, and multi-determinantal nature of doped Mott physics make this an extremely interesting case-study for strongly correlated many-body physics. Full article

Zoharite (IMA 2017-049), (Ba,K)₆ (Fe,Cu,Ni)₂₅S₂₇, and gmalimite (IMA 2019-007), ideally K₆□Fe²⁺₂₄S₂₇, are two new sulfides of the djerfisherite group. They were discovered in an unusual gehlenite–wollastonite paralava with pyrrhotite nodules located in the Hatrurim pyrometamorphic complex, Negev Desert, Israel. Zoharite and gmalimite build grained aggregates confined to the peripheric parts of pyrrhotite nodules, where they associate with pentlandite, chalcopyrite, chalcocite, digenite, covellite, millerite, heazlewoodite, pyrite and rudashevskyite. The occurrence and associated minerals indicate that zoharite and gmalimite were formed at temperatures below 800 °C, when sulfides formed on external zones of the nodules have been reacting with residual silicate melt (paralava) locally enriched in Ba and K. Macroscopically, both minerals are bronze in color and have a dark-gray streak and metallic luster. They are brittle and have a conchoidal fracture. In reflected light, both minerals are optically isotropic and exhibit gray color with an olive tinge. The reflectance values for zoharite and gmalimite, respectively, at the standard COM wavelengths are: 22.2% and 21.5% at 470 nm, 25.1% and 24.6% at 546 nm, 26.3% and 25.9% at 589 nm, as well as 27.7% and 26.3% at 650 nm. The average hardness for zoharite and for gmalimite is approximately 3.5 of the Mohs hardness. Both minerals are isostructural with owensite, (Ba,Pb)₆(Cu,Fe,Ni)₂₅S₂₇. They crystallize in cubic space group Pm$\overline{3}$m with the unit-cell parameters a = 10.3137(1) Å for zoharite and a = 10.3486(1) Å for gmalimite. The calculated densities are 4.49 g·cm⁻³ for the zoharite and 3.79 g·cm⁻³ for the gmalimite. The primary structural units of these minerals are M₈S₁₄ clusters, composed of MS₄ tetrahedra surrounding a central MS₆ octahedron. The M site is occupied by transition metals such as Fe, Cu, and Ni. These clusters are further connected via the edges of the MS₄ tetrahedra, forming a close-packed cubic framework. The channels within this framework are filled by anion-centered polyhedra: SBa₉ in zoharite and SK₉ in gmalimite, respectively. In the M₈S₁₄ clusters, the M atoms are positioned so closely that their d orbitals can overlap, allowing the formation of metal–metal bonds. As a result, the transition metals in these clusters often adopt electron configurations that reflect additional electron density from their local bonding environment, similar to what is observed in pentlandite. Due to the presence of shared electrons in these metal–metal bonds, assigning fixed oxidation states—such as Fe²⁺/Fe³⁺ or Cu⁺/Cu²⁺—becomes challenging. Moreover, modeling the distribution of mixed-valence cations (Fe²⁺/³⁺, Cu⁺/²⁺, and Ni²⁺) across the two distinct M sites—one located in the MS₆ octahedron and the other in the MS₄ tetrahedra—often results in ambiguous outcomes. Consequently, it is difficult to define an idealized end-member formula for these minerals. Full article

Flavone derivatives have emerged as promising antiviral agents, with baicalein demonstrating the potent inhibition of the SARS-CoV-2 main protease (Mpro). In this study, the unique electronic and structural properties of 3-hydroxyflavone (3-HF) were investigated using the density functional theory (B3PW91/cc-pVTZ), providing insights into its potential as a bioactive scaffold. Among mono-hydroxyflavone (n-HF) isomers, 3-HF exhibits an extensive intramolecular hydrogen-bonding network linking the phenyl B-ring to the A- and γ-pyrone C-rings, enabled by the distinctive C3-OH substitution. Despite a slight non-planarity (dihedral angle: 15.4°), this hydrogen-bonding network enhances rigidity and influences the electronic environment. A ¹³C-NMR chemical shift analysis revealed pronounced quantum mechanical effects of the C3-OH group, diverging from the trends observed in other flavones. A natural bond orbital (NBO) analysis highlighted an unusual charge distribution, with predominantly positive charges on the γ-pyrone C-ring carbons, in contrast to the typical negative charges in flavones. These effects impact C1s orbital energies, suggesting that the electronic structure plays a more significant role in ¹³C-NMR shifts than simple ring assignments. Given the established antiviral activity of hydroxylated flavones, the distinct electronic properties of 3-HF may enhance its interaction with SARS-CoV-2 Mpro, making it a potential candidate for further investigation. This study underscores the importance of quantum mechanical methods in elucidating the structure–activity relationships of flavones and highlights 3-HF as a promising scaffold for future antiviral drug development. Full article

Since pre-industrial times, anthropogenic methane emissions have increased and are partly responsible for a changing global climate. Natural gas and oil extraction activities are one significant source of anthropogenic methane. While methods have been developed and refined to quantify onshore methane emissions, the ability of methods to directly quantify emissions from offshore production facilities remains largely unknown. Here, we review recent studies that have directly measured emissions from offshore production facilities and critically evaluate the suitability of these measurement strategies for emission quantification in a marine environment. The average methane emissions from production platforms measured using downwind dispersion methods were 32 kg h⁻¹ from 188 platforms; 118 kg h⁻¹ from 104 platforms using mass balance methods; 284 kg h⁻¹ from 151 platforms using aircraft remote sensing; and 19,088 kg h⁻¹ from 10 platforms using satellite remote sensing. Upon review of the methods, we suggest the unusually large emissions, or zero emissions observed could be caused by the effects of a decoupling of the marine boundary layer (MBL). Decoupling can happen when the MBL becomes too deep or when there is cloud cover and results in a stratified MBL with air layers of different depths moving at different speeds. Decoupling could cause: some aircraft remote sensing observations to be biased high (lower wind speed at the height of the plume); the mass balance measurements to be biased high (narrow plume being extrapolated too far vertically) or low (transects miss the plume); and the downwind dispersion measurements much lower than the other methods or zero (plume lofting in a decoupled section of the boundary layer). To date, there has been little research on the marine boundary layer, and guidance on when decoupling happens is not currently available. We suggest an offshore controlled release program could provide a better understanding of these results by explaining how and when stratification happens in the MBL and how this affects quantification methodologies. Full article

Time-series anomaly detection is a critical task in various domains, including industrial control systems, where the early detection of unusual patterns can prevent system failures and ensure operational reliability. This paper introduces ChaMTeC (CHAnnel Mixing and TEmporal Convolution Network), a novel deep learning framework designed for time-series anomaly detection. ChaMTeC integrates an inverted embedding strategy, multi-layer temporal encoding, and a Mean Squared Error (MSE)-based feedback mechanism with dynamic thresholding to enhance anomaly detection performance. The framework is particularly tailored for industrial environments, where anomalies are rare and often subtle, making detection challenging. We evaluate ChaMTeC on six publicly available datasets and a newly introduced dataset, WaterLog, which is specifically designed to reflect real-world industrial control system scenarios with reduced anomaly rates. The experimental results demonstrate that ChaMTeC outperforms state-of-the-art models, achieving superior performance in terms of F1-CPA (Coverage-based Point-Adjusted F1) scores. The WaterLog dataset, which has been made publicly available, provides a more realistic benchmark for evaluating anomaly detection systems in industrial settings, addressing the limitations of existing datasets that often contain frequent and densely packed anomalies. Our findings highlight the effectiveness of combining channel-mixing techniques with temporal convolutional networks and dynamic thresholding for detecting anomalies in complex industrial environments. The proposed framework offers a robust solution for real-time anomaly detection, contributing to the reliability and sustainability of critical infrastructure systems. Full article

Background: The increasing prevalence of cyber threats in industrial cyber–physical systems (ICPSs) necessitates advanced solutions for threat detection and analysis. This research proposes a novel GPT-based framework for assessing cyber threats, leveraging artificial intelligence to process and analyze large-scale cyber event data. Methods: The framework integrates multiple components, including data ingestion, preprocessing, feature extraction, and analysis modules such as knowledge graph construction, clustering, and anomaly detection. It utilizes a hybrid methodology combining spectral residual transformation and Convolutional Neural Networks (CNNs) to identify anomalies in time-series cyber event data, alongside regression models for evaluating the significant factors associated with cyber events. Results: The system was evaluated using 9018 cyber-related events sourced from 44 global news portals. Performance metrics, including precision (0.999), recall (0.998), and F1-score (0.998), demonstrate the framework’s efficacy in accurately classifying and categorizing cyber events. Notably, anomaly detection identified six significant deviations during the monitored timeframe, starting from 25 September 2023 to 25 November 2024, with a sensitivity of 75%, revealing critical insights into unusual activity patterns. The fully deployed automated model also identified 11 correlated factors and five unique clusters associated with high-rated cyber incidents. Conclusions: This approach provides actionable intelligence for stakeholders by offering real-time monitoring, anomaly detection, and knowledge graph-based insights into cyber threats. The outcomes highlight the system’s potential to enhance ICPS security, supporting proactive threat management and resilience in increasingly complex industrial environments. Full article