Context. To understand the formation of planetary systems, it is necessary to observe and study systems at different evolutionary stages and in different environments. This paper presents new data and analyses of the AT Pyx system, a disk-hosting young star located in a cometary globule in the Gum Nebula. This radiation-driven structure is an unusual environment for observations of planet formation, and differs greatly from the low-mass star-forming regions disks are most commonly observed in. Aims. Aided by a collection of visual and spectroscopic data available for this system, our aim is to infer the possibility of embedded planets existing within the disk and how the system's environment may affect its disk morphology. Methods. Using data from the VLT's instruments XSHOOTER, ESPRESSO, and most prominently SPHERE, along with data from ALMA, we made a variety of measurements (geometric, photometric, and otherwise) to characterise the observed disk features and attributes such as spiral arms and eccentricity. Mapping of the velocity components was also undertaken using the ALMA gas line data to characterise disk orientation and determine the likelihood that the system is experiencing a late-stage infall event. Results. The disk is measured to have a position angle of 28.06 +/- 0.02 degrees and an inclination of 42.5 +/- 0.5 degrees. The disk is found to be eccentric with measured e approximate to 0.626 when deprojected. Under the assumption that the formation of a planet is wholly responsible for the primary and secondary spiral arms, we find the mass of such a planet can range between 0.004 and 3 Jupiter masses. Measurements of the velocities associated with nearby globule cloud material return reasonable velocities for a late-stage infall event. We estimate the far-ultraviolet (FUV) field strength at AT Pyx's location to be low in comparison to other surveyed disks. We also find that AT Pyx is possibly a binary system. Conclusions. AT Pyx is the first disk within a cometary globule to be spatially resolved, and is now the first such disk to be investigated to this extent. The work of this paper could potentially be a first step into the further study of disks in the moderate FUV environment of the Gum Nebula and its globules.
Vanadium pentoxide (V2O5), nickel oxide (NiO), and V2O5/NiO nanocomposites (NCs) with molar ratios of 1 : 0.5, 1 : 1, and 1 : 1.5 were synthesized by a solution-combustion (SC) method and characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and X-ray photoelectron spectroscopy (XPS). Electrochemical properties were examined in a 1 M KOH electrolyte through cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and galvanostatic charge-discharge (GCD) using two electrode types: a carbon-paste electrode (CPE) and a nickel-foam electrode (NFE). Among all compositions, the V2O5/NiO (1 : 1) NC delivered the highest specific capacitance, achieving 2367 F g-1 on the CPE and 1177 F g-1 on the NFE at 10 mV s-1, with low charge-transfer resistance and excellent cycling stability. The CPE, prepared with 70% graphite powder, 15% V2O5/NiO nanocomposite powder and 15% silicone oil, provides a three-dimensional porous architecture that allows a greater fraction of electrochemically active material to participate compared to the NFE. This architecture enhances ion diffusion, electron transport, and rate capability, demonstrating that V2O5/NiO NCs on a CPE offer a practical pathway to high-energy-density supercapacitors.
The oscillatory onset of buoyancy-driven convection in an Oldroyd-B fluid-saturated porous layer with linearly depth-dependent permeability is investigated. The viscoelastic rheology introduces elastic memory into the momentum balance through an extended Darcy law. Both increasing and decreasing permeability gradients are considered to examine how permeability stratification modifies elastic instability mechanisms. The resulting linear stability eigenvalue problem is solved using a Galerkin method to determine the critical conditions for instability over a wide range of relaxation, retardation, and variable permeability parameters. The onset of oscillatory convection is advanced by elastic relaxation and by permeability decreasing with depth, whereas retardation effects and permeability increasing with depth delay the instability. The retardation threshold at which the instability switches from oscillatory to stationary mode increases with the relaxation parameter and is only weakly influenced by the variable permeability parameter, irrespective of its sign. In the Maxwell limit, removal of stress retardation promotes instability and favors shorter-wavelength modes, highlighting the qualitative distinction between Maxwell and Oldroyd-B viscoelastic behavior. In the Newtonian limit, the classical results for convection with depth-dependent permeability are recovered.
The first Zagreb index M-1(G) of a graph G is equal to the sum of squares of the degree of vertices, and the second Zagreb index M-2(G) is the sum of the products of the degrees of pairs of adjacent vertices of G. This paper uses the subdivision concept to study the Zagreb indices and coindices of the line graph and line cut-vertex graph of a particular class of unicyclic graphs, called cycle-star graph.
Nowadays, the Internet of Things (IoT) is proliferating, bringing worldwide alarm for the security of interconnected networks, which requires an effective threat detection model to lessen cyber threats. Traditional threat detection models often struggle to identify complex attacks happening in an IoT network due to heterogeneous traffic features, data imbalance, and limited feature learning. This study aims to design an accurate threat detection in an IoT network using an attention-based deep learning (DL) algorithm that enhances spatial and temporal feature learning. Also, data-balancing and feature selection methods are employed in the research to improve accuracy and robustness. The proposed model was tested on two widely used datasets: IoT-23 and N-BaIoT, which provide realistic, diverse data on IoT network traffic and botnet attacks that are crucial for developing robust security systems. The experimental findings demonstrated that our model shows remarkable performance for threat detection compared to existing techniques by reaching a maximum accuracy of 99.93
