Using high-precision observations from the space-based \textit{Gaia} and \textit{TESS} missions, complemented by ground-based spectroscopic data and multi-band photometric surveys, we perform a detailed investigation of the Galactic open cluster NGC~2506. We present a new analysis of the intermediate-age open cluster NGC~2506, using joint fits to the radial velocities (RVs) and spectral energy distributions (SEDs) of five double-lined binary systems, including two eclipsing binaries. The analysis yields self-consistent estimates of the cluster's age, distance, and extinction, based on 18 free parameters: 10 stellar masses, 5 orbital inclinations, and common values for age, distance, and $A_V$. The SED fitting incorporates stellar isochrones, and the resulting parameters are examined through HR diagrams (R--$T_{\rm eff}$, R--M, and M--$T_{\rm eff}$) to assess evolutionary consistency. The age we derive for the cluster is $1.94 \pm 0.03$ Gyr for an assumed [Fe/H] = -0.30, and a fitting formula is given for extrapolation to other metallicities. The distance we find from the SED fitting is $3189 \pm 53$ pc, and this is to be compared with our own inference from the Gaia data which is $3105 \pm 75$ pc, based on 919 stars identified as cluster members. Our results demonstrate the power of binary systems in tightly constraining cluster-wide age and distance at this evolutionary stage. This approach represents one of the most accurate characterizations of an intermediate-age open cluster using multiple binary systems.

(119951) 2002 KX14 is a large classical TNO with limited previous observations and unresolved questions regarding its physical properties. Five stellar occultations by 2002 KX14 were observed from 2020 to 2023, involving multiple telescopes across different locations in Europe and the Americas. The five occultations resulted in 15 positive chords, accurately measuring the 2002 KX14's shape and size. The projected ellipse has semi-major and semi-minor axes of $241.0 \pm 7.2$ km and $157.1 \pm 5.2$ km, respectively, corresponding to an average area-equivalent diameter of $389.2 \pm 8.7$ km. The geometric albedo was estimated at $11.9 \pm 0.7\%$.

In this work, product tables in invoices are obtained autonomously via a deep learning model, which is named as ExTTNet. Firstly, text is obtained from invoice images using Optical Character Recognition (OCR) techniques. Tesseract OCR engine [37] is used for this process. Afterwards, the number of existing features is increased by using feature extraction methods to increase the accuracy. Labeling process is done according to whether each text obtained as a result of OCR is a table element or not. In this study, a multilayer artificial neural network model is used. The training has been carried out with an Nvidia RTX 3090 graphics card and taken $162$ minutes. As a result of the training, the F1 score is $0.92$.

Red giant stars play a key role in advancing our understanding of stellar mass loss. However, its initial mass and the amount of mass lost during this phase remain uncertain. In this study, we investigate the asteroseismic signatures of mass loss and the parameters that influence it. We examine six stars identified as red giant branch (RGB) stars in the APOKASC-2 catalog. Assuming these stars are on their first ascent of the RGB, we construct interior models. The resulting model ages are significantly older than the age of the Galaxy, indicating that these stars are likely experiencing mass loss and evolving toward the red clump (RC) phase. The minimum possible initial masses are estimated using the mass-metallicity diagram, from which we infer that the minimum mass lost by these stars ranges from $0.1$-$0.3{\rm M}_{\odot}$. Models constructed with an initial minimum mass yield the maximum possible age of the star. The ages of these models fall within the range of 9-9.5Gyr. For two stars, asteroseismic parameters confirm RC classification. Due to degeneracies in the HR diagram, distinguishing between internal structure models is challenging; however, asteroseismic constraints provide clear differentiation. Although mass-loss and mass-conservation models have similar $M$, $R$, and $T_{\rm eff}$ values, $\Delta\nu$s determined from the $l$=0 modes in the mass-loss models are 5-10$\%$ higher than observed. This discrepancy may arise from differences in internal structure. Finally, evolutionary model grids are used to examine how initial mass and $Z$ affect mass loss. Mass loss increases with increasing metallicity and decreases with increasing initial mass, regardless of the adopted value of $\eta$.

The concept of domination in graphs plays a central role in understanding structural properties and applications in network theory. In this study, we focus on the paired disjunctive domination number in the context of middle graphs, a transformation that captures both adjacency and incidence relations of the original graph. We begin by investigating this parameter for middle graphs of several special graph classes, including path graphs, cycle graphs, wheel graphs, complete graphs, complete bipartite graphs, star graphs, friendship graphs, and double star graphs. We then present general results by establishing lower and upper bounds for the paired disjunctive domination number in middle graphs of arbitrary graphs, with particular emphasis on trees. Additionally, we determine the exact value of the parameter for middle graphs obtained through the join operation. These findings contribute to the broader understanding of domination-type parameters in transformed graph structures and offer new insights into their combinatorial behavior.

Pulsating detached eclipsing binary systems are crucial for studying the internal structure of oscillating stars. These systems are advantageous because binary effects on pulsations are minimal, allowing for more accurate determinations of fundamental stellar parameters such as mass and radius. They serve as unique laboratories for detailed investigations of pulsating stars. In this study, we focused on four detached eclipsing binaries exhibiting $\delta$ Scuti-type oscillations: HD 117476, 205 Dra, HY Vir, and V1031 Ori. Our preliminary investigation showed that all binary components of these targets lie within the $\delta$ Scuti instability strip. Therefore, we aimed to determine which components are pulsating and which are not, and to explore the differences between them. To achieve this, we analyzed TESS photometric data and high-resolution spectra of the targets. Radial velocity variations were measured, and atmospheric parameters for each component were derived using spectral disentangling or synthetic composite spectra. We also modeled the binary light and radial velocity curves to determine the fundamental physical parameters of the components. Furthermore, we examined pulsation properties using three different approaches to identify the pulsating components. The evolutionary status of the targets was also assessed. Our analysis revealed that, in each system, only one component exhibits $\delta$ Scuti-type pulsations, while the others are non-pulsating. Interestingly, we found that the key difference between pulsating and non-pulsating components within the same binary is metallicity: the metal-rich components were found to be non-pulsators, supporting theoretical studies on the effect of metallicity on $\delta$ Scuti-type pulsations.

Asteroseismology provides a direct observational window into the structure and evolution of stars. While spectroscopic and photometric methods only provide information about the surface properties of stars, asteroseismology, through the analysis of oscillation frequencies, offers comprehensive information about the deep stellar interior as well as the surface. The scattering of effective temperature (Teff) determined from the spectrum and degeneracy in the Hertzsprung Russell diagram poses challenges in developing a unique interior model for a single star. Although observational asteroseismic data partially lift this degeneracy, the best model that meets all asteroseismic constraints is not obtained. Most models reported in the literature typically address the large separation Dnu constraint between oscillation frequencies, which is a critical issue, especially in post main sequence stars. Reference frequencies, influenced by helium ionisation zone induced glitches in oscillation frequencies, are instrumental in refining models. Using the high metallicity derived from the colors of the Kepler Legacy star KIC 7747078, we obtain the masses of models M as 1.208 Msun and 1.275 Msun using the reference frequencies and individual frequencies as constraints, respectively. By applying the chi2 method using these reference frequencies, Dnu, and surface metallicity determined from the spectrum, we develop a unique star model with a mass of 1.171 pm 0.019 Msun, a radius of 1.961 pm 0.011 Rsun, an effective temperature of 5993 K, an initial metallicity of 0.0121, and an age of 5.15 pm 0.29 Gyr. A significant advantage of this method is that Teff emerges as an output, not a constraint. The mixed mode oscillation frequencies of this model align well with the observations.

Trumpler 5 is a moderately old, dust-obscured metal-poor open cluster. In this study, high-resolution near-infrared spectroscopic data of seven giant stars from the Trumpler 5 cluster were analyzed to derive chemical abundances for 20 elements and $^{12}C/^{13}C$ ratios. Color-magnitude diagram (CMD) analysis of BV and Gaia photometry has also been performed for a comprehensive study of the cluster. Thanks to the methodology employed, some targets are studied for the first time. Additionally, it provides a detailed color-magnitude diagram analysis using photometric and spectroscopic data. We gathered high-resolution spectra for seven Trumpler 5 red giants in the near-infrared H and K wavelength domains, using the Immersion Grating INfrared Spectrometer (IGRINS). We introduced a method to initially estimate the stellar surface gravity (log g) by using calibrated equivalent widths of the Ti II line at 15873 {\AA} from a large sample. We performed standard spectroscopic analyses to refine the model atmospheric parameters of our targets and determined the chemical abundances primarily through spectrum synthesis. We also performed color-magnitude diagram analyses to extract differential reddening correction to compare cluster parameters both with and without corrections. We derived stellar parameters for seven members of Trumpler 5 with our method and the results are consistent with both the literature and other methods. We also inferred elemental abundances for more than 20 species, along with the $^{12}C/^{13}C$ ratios. The elemental abundances are in good agreement with the literature values for similar targets. Through CMD analysis, we found the reddening value, E(B-V)$\simeq$0.76 and estimated the age of the cluster to be approximately 2.50 Gyr.

In a recent paper [\textit{M. Cristelli, A. Zaccaria and L. Pietronero, Phys. Rev. E 85, 066108 (2012)}], Cristelli \textit{et al.} analysed relation between skewness and kurtosis for complex dynamical systems and identified two power-law regimes of non-Gaussianity, one of which scales with an exponent of 2 and the other is with $4/3$. Finally the authors concluded that the observed relation is a universal fact in complex dynamical systems. Here, we test the proposed universal relation between skewness and kurtosis with large number of synthetic data and show that in fact it is not universal and originates only due to the small number of data points in the data sets considered. The proposed relation is tested using two different non-Gaussian distributions, namely $q$-Gaussian and Levy distributions. We clearly show that this relation disappears for sufficiently large data sets provided that the second moment of the distribution is finite. We find that, contrary to the claims of Cristelli \textit{et al.} regarding a power-law scaling regime, kurtosis saturates to a single value, which is of course different from the Gaussian case ($K=3$), as the number of data is increased. On the other hand, if the second moment of the distribution is infinite, then the kurtosis seems to never converge to a single value. The converged kurtosis value for the finite second moment distributions and the number of data points needed to reach this value depend on the deviation of the original distribution from the Gaussian case. We also argue that the use of kurtosis to compare distributions to decide which one deviates from the Gaussian more can lead to incorrect results even for finite second moment distributions for small data sets, whereas it is totally misleading for infinite second moment distributions where the difference depends on $N$ for all finite $N$.

Galactic open and globular clusters (OCs, GCs) appear to inhabit separate regions of the age-mass plane. However, the transition between them is not easily defined because there is some overlap between high-mass, old OCs and low-mass, young GCs. We are exploring the possibility of a clear-cut separation between OCs and GCs using an abundance feature that has been found so far only in GCs: (anti)correlations between light elements. Among the coupled abundance trends, the Na-O anticorrelation is the most widely studied. These anticorrelations are the signature of self-enrichment, i.e., of a formation mechanism that implies multiple generations of stars. Here we concentrate on the old, massive, metal-rich OC NGC 6791. We analyzed archival Keck/HIRES spectra of 15 NGC 6791 main sequence turn-off and evolved stars, concentrating on the derivation of C, N, O, and Na abundances. We also used WIYN/Hydra spectra of 21 evolved stars (one is in common). Given the spectral complexity of the very metal-rich NGC 6791 stars, we employed spectrum synthesis to measure most of the abundances. We confirmed the cluster super-solar metallicity and abundances of Ca and Ni that have been derived in past studies. More importantly, we did not detect any significant star-to-star abundance dispersion in C, N, O and Na. Based on the absence of a clear Na-O anticorrelation, NGC 6791 can still be considered a true OC, hosting a single generation of stars, and not a low-mass GC.

Locally rotationally symmetric (LRS) Bianchi Type I cosmological models are examined in the presence of dynamically anisotropic dark energy and perfect fluid. We assume that the dark energy (DE) is minimally interacting, has dynamical energy density, anisotropic equation of state parameter (EoS). The conservation of the energy-momentum tensor of the DE is assumed to consist of two separately additive conserved parts. A special law is assumed for the deviation from isotropic EoS, which is consistent with the assumption on the conservation of the energy-momentum tensor of the DE. Exact solutions of Einstein's field equations are obtained by assuming a special law of variation for the mean Hubble parameter, which yields a constant value of the deceleration parameter. Geometrical and kinematic properties of the models and the behaviour of the anisotropy of the dark energy has been carried out. The models give dynamically anisotropic expansion history for the universe that allows to fine tune the isotropization of the Bianchi metric, hence the CMB anisotropy.

We present a binary evolution study of cataclysmic variables (CVs) and related systems with white dwarf accretors, including for example, AM CVn systems, classical novae, supersoft X-ray sources, and systems with giant donor stars. Our approach intentionally avoids the complications associated with population synthesis algorithms thereby allowing us to present the first truly comprehensive exploration of all of the subsequent binary evolution pathways that ZACVs might follow (assuming fully non-conservative, Roche-lobe overflow onto an accreting WD) using the sophisticated binary stellar evolution code MESA. The grid consists of 56,000 initial models, including 14 white dwarf accretor masses, 43 donor-star masses ($0.1-4.7$ $M_{\odot}$), and 100 orbital periods. We explore evolution tracks in the orbital period and donor-mass ($P_{\rm orb}-M_{\rm don}$) plane in terms of evolution dwell times, masses of the white dwarf accretor, accretion rate, and chemical composition of the center and surface of the donor star. We report on the differences among the standard CV tracks, those with giant donor stars, and ultrashort period systems. We show where in parameter space one can expect to find supersoft X-ray sources, present a diagnostic to distinguish among different evolutionary paths to forming AM CVn binaries, quantify how the minimum orbital period in CVs depends on the chemical composition of the donor star, and update the $P_{\rm orb}(M_{\rm wd})$ relation for binaries containing white dwarfs whose progenitors lost their envelopes via stable Roche-lobe overflow. Finally, we indicate where in the $P_{\rm orb}-M_{\rm don}$ the accretion disks will tend to be stable against the thermal-viscous instability, and where gravitational radiation signatures may be found with LISA.

We first observe that the (co)domains of the q-deformed functions are some subsets of the (co)domains of their ordinary counterparts, thereby deeming the deformed functions to be incomplete. In order to obtain a complete definition of $q$-generalized functions, we calculate the dual mapping function, which is found equal to the otherwise \textit{ad hoc} duality relation between the ordinary and escort stationary distributions. Motivated by this fact, we show that the maximization of the Tsallis entropy with the complete $q$-logarithm and $q$-exponential implies the use of the ordinary probability distributions instead of escort distributions. Moreover, we demonstrate that even the escort stationary distributions can be obtained through the use of the ordinary averaging procedure if the argument of the $q$-exponential lies in (-$\infty$, 0].

Accurate and fast recognition of forgeries is an issue of great importance in the fields of artificial intelligence, image processing and object detection. Recognition of forgeries of facial imagery is the process of classifying and defining the faces in it by analyzing real-world facial images. This process is usually accomplished by extracting features from an image, using classifier algorithms, and correctly interpreting the results. Recognizing forgeries of facial imagery correctly can encounter many different challenges. For example, factors such as changing lighting conditions, viewing faces from different angles can affect recognition performance, and background complexity and perspective changes in facial images can make accurate recognition difficult. Despite these difficulties, significant progress has been made in the field of forgery detection. Deep learning algorithms, especially Convolutional Neural Networks (CNNs), have significantly improved forgery detection performance. This study focuses on image processing-based forgery detection using Fake-Vs-Real-Faces (Hard) [10] and 140k Real and Fake Faces [61] data sets. Both data sets consist of two classes containing real and fake facial images. In our study, two lightweight deep learning models are proposed to conduct forgery detection using these images. Additionally, 8 different pretrained CNN architectures were tested on both data sets and the results were compared with newly developed lightweight CNN models. It's shown that the proposed lightweight deep learning models have minimum number of layers. It's also shown that the proposed lightweight deep learning models detect forgeries of facial imagery accurately, and computationally efficiently. Although the data set consists only of face images, the developed models can also be used in other two-class object recognition problems.

The diagnosis, prognosis, and treatment of patients with musculoskeletal (MSK) disorders require radiology imaging (using computed tomography, magnetic resonance imaging(MRI), and ultrasound) and their precise analysis by expert radiologists. Radiology scans can also help assessment of metabolic health, aging, and diabetes. This study presents how machinelearning, specifically deep learning methods, can be used for rapidand accurate image analysis of MRI scans, an unmet clinicalneed in MSK radiology. As a challenging example, we focus on automatic analysis of knee images from MRI scans and study machine learning classification of various abnormalities including meniscus and anterior cruciate ligament tears. Using widely used convolutional neural network (CNN) based architectures, we comparatively evaluated the knee abnormality classification performances of different neural network architectures under limited imaging data regime and compared single and multi-view imaging when classifying the abnormalities. Promising results indicated the potential use of multi-view deep learning based classification of MSK abnormalities in routine clinical assessment.

Zinc Oxide (ZnO) semiconductor is ideal candidates for ultra-violet (UV) photodetector due to its promising optoelectronic properties. Photodetectors based on ZnO nanostructures show very high photoconductivity under UV light, but they are plagued by slow photo-response time as slow as several tens of hours, even more. Most of the studies claimed that atmospheric adsorbates such as water and oxygen create charge traps states on the surface and remarkably increase both the photoconductivity and response time, but there are also limited studies that claiming the defect induced states acting as hole trap centers responsible for these problems. However, the underlying physical mechanism is still unclear. Here we study the effects of both adsorbates and defect-related states on the photo-response character of Pulsed Electron Deposited ZnO thin films. In order to distinguish between these two mechanisms, we have compared the time-dependent photo-response measurements of bare-ZnO and SiO2 encapsulated-ZnO thin film samples taken under UV light and high vacuum. We show that the dominant mechanism of photo-response in ZnO is the adsorption/desorption of oxygen and water molecules even when the measurement is performed in high vacuum. When the samples are encapsulated by a thin SiO2 layer, the adsorption/desorption rates can significantly improve, and the effects of these molecules partially removed.

We present time-resolved photometry of six faint (V>17mag) cataclysmic variables (CVs); one of them is V849 Oph and the others are identified from the Sloan Digital Sky Survey (SDSS J0920+0042, SDSS J1327+6528, SDSS J1227+5139, SDSS J1607.02+3623, SDSS J1457+5148). The optical CCD photometric observations of these objects were performed at the TÜBİTAK National Observatory (Turkey) between February 2006 and March 2009. We aimed to detect short time scale orbital variability arisen from hot-spot modulation, flickering structures which occur from rapid fluctuations of material transferring from red star to white dwarf and orbital period changes for selected short-period (P<4h) CVs at quiescence. Results obtained from eclipse timings and light curves morphology related to white dwarf stars, accretion disks and hot-spots are discussed for each system. Analysis of the short time coverage of data, obtained for SDSS J1227+5139 indicates a cyclical period change arisen from magnetic activity on the secondary star. Photometric period of SDSS J1607+3623 is derived firstly in this study, while for the other five systems light elements are corrected using the previous and new photometric observations. The nature of SDSS J1457+5148 is not precisely revealed that its light curve shows any periodicity that could be related to the orbital period.

Segmentation of thigh tissues (muscle, fat, inter-muscular adipose tissue (IMAT), bone, and bone marrow) from magnetic resonance imaging (MRI) scans is useful for clinical and research investigations in various conditions such as aging, diabetes mellitus, obesity, metabolic syndrome, and their associated comorbidities. Towards a fully automated, robust, and precise quantification of thigh tissues, herein we designed a novel semi-supervised segmentation algorithm based on deep network architectures. Built upon Tiramisu segmentation engine, our proposed deep networks use variational and specially designed targeted dropouts for faster and robust convergence, and utilize multi-contrast MRI scans as input data. In our experiments, we have used 150 scans from 50 distinct subjects from the Baltimore Longitudinal Study of Aging (BLSA). The proposed system made use of both labeled and unlabeled data with high efficacy for training, and outperformed the current state-of-the-art methods with dice scores of 97.52%, 94.61%, 80.14%, 95.93%, and 96.83% for muscle, fat, IMAT, bone, and bone marrow tissues, respectively. Our results indicate that the proposed system can be useful for clinical research studies where volumetric and distributional tissue quantification is pivotal and labeling is a significant issue. To the best of our knowledge, the proposed system is the first attempt at multi-tissue segmentation using a single end-to-end semi-supervised deep learning framework for multi-contrast thigh MRI scans.

The impact of wall roughness on fully developed laminar pipe flow is investigated numerically. The roughness is comprised of square bars of varying size and pitch. Results show that the inverse relation between the friction factor and the Reynolds number in smooth pipes still persists in rough pipes, regardless of the rib height and pitch. At a given Reynolds number, the friction factor varies quadratically with roughness height and linearly with roughness pitch. We propose a single correlation for the friction factor that successfully collapses the data.