Utilizing comprehensive experimental data on charmed mesons, we systematically investigate masses of the higher radial and orbital excitations of the $D$ and $D_s$ meson families using the relativistic flux tube model. Our study employs mass splitting induced by spin-dependent interactions within the j-j coupling scheme. Our predicted masses align well with the experimental measurements for the well-established $D$ and $D_s$ states. However, anomalous resonances such as $D_{s0}(2317)$ and $D_{s1}(2460)$ do not align with conventional meson states within our theoretical framework. Based on our reliable mass predictions for low-lying states, we propose spectroscopic assignments for several recently observed high-mass resonances: $D_2(2740)^{0}$, $D^*_3(2750)$, $D_0(2550)^{0}$, $D^*_1(2600)^{0}$, $D_1^*(2760)^0$, $D^*_J(3000)$, $D_J(3000)$, $D^*_2(3000)$, $D^*_{s1}(2860)^{\pm}$ and $D^*_{s3}(2860)^{\pm}$.Additionally, the resonance $D_{sJ}(3040)^+$ is identified as a $2P$ excitation with spin-parity quantum numbers $J^P = 1^+$. Extending our model, we also calculate the mass spectra of doubly charmed $\Xi_{cc}$ and $\Omega_{cc}$ baryons within the heavy-diquark-light-quark picture. These theoretical predictions provide crucial guidance for ongoing and future experimental searches for higher radial and orbital excitations in the charmed meson and doubly charmed baryon sectors.

NASA's all-sky survey mission, the Transiting Exoplanet Survey Satellite (TESS), is specifically engineered to detect exoplanets that transit bright stars. Thus far, TESS has successfully identified approximately 400 transiting exoplanets, in addition to roughly 6000 candidate exoplanets pending confirmation. In this study, we present the results of our ongoing project, the Validation of Transiting Exoplanets using Statistical Tools (VaTEST). Our dedicated effort is focused on the confirmation and characterization of new exoplanets through the application of statistical validation tools. Through a combination of ground-based telescope data, high-resolution imaging, and the utilization of the statistical validation tool known as \texttt{TRICERATOPS}, we have successfully discovered eight potential super-Earths. These planets bear the designations: TOI-238b (1.61$^{+0.09} _{-0.10}$ R$_\oplus$), TOI-771b (1.42$^{+0.11} _{-0.09}$ R$_\oplus$), TOI-871b (1.66$^{+0.11} _{-0.11}$ R$_\oplus$), TOI-1467b (1.83$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-1739b (1.69$^{+0.10} _{-0.08}$ R$_\oplus$), TOI-2068b (1.82$^{+0.16} _{-0.15}$ R$_\oplus$), TOI-4559b (1.42$^{+0.13} _{-0.11}$ R$_\oplus$), and TOI-5799b (1.62$^{+0.19} _{-0.13}$ R$_\oplus$). Among all these planets, six of them fall within the region known as 'keystone planets,' which makes them particularly interesting for study. Based on the location of TOI-771b and TOI-4559b below the radius valley we characterized them as likely super-Earths, though radial velocity mass measurements for these planets will provide more details about their characterization. It is noteworthy that planets within the size range investigated herein are absent from our own solar system, making their study crucial for gaining insights into the evolutionary stages between Earth and Neptune.

Automated segmentation of vascular map in retinal images endeavors a potential benefit in diagnostic procedure of different ocular diseases. In this paper, we suggest a new unsupervised retinal blood vessel segmentation approach using top-hat transformation, contrast-limited adaptive histogram equalization (CLAHE), and 2-D Gabor wavelet filters. Initially, retinal image is preprocessed using top-hat morphological transformation followed by CLAHE to enhance only the blood vessel pixels in the presence of exudates, optic disc, and fovea. Then, multiscale 2-D Gabor wavelet filters are applied on preprocessed image for better representation of thick and thin blood vessels located at different orientations. The efficacy of the presented algorithm is assessed on publicly available DRIVE database with manually labeled images. On DRIVE database, we achieve an average accuracy of 94.32% with a small standard deviation of 0.004. In comparison with major algorithms, our algorithm produces better performance concerning the accuracy, sensitivity, and kappa agreement.

In this work, we obtained analytical bound state solution of the Schrödinger equation with Manning Rosen plus exponential Yukawa Potential using parametric Nikiforov-Uvarov method (NU). We obtained the normalized wave function in terms of Jacobi polynomial. The energy eigen equation was determined and presented in a compact form. The study also includes the computations of partition function and other thermodynamics properties such as vibrational mean energy ({\mu}), vibrational heat capacity (c), vibrational entropy (s) and vibrational free energy (F). Using a well design maple programme, we obtained numerical bound state energies for different quantum states with various screening parameters: {\alpha}=0.1,0.2,0.3,0.4 and 0.5. The numerical results showed that the bound state energies increase with an increase in quantum state while the thermodynamic plots were in excellent agreement to work of existing literature.

Two simple yet powerful optimization algorithms, named the Best-Mean-Random (BMR) and Best-Worst-Random (BWR) algorithms, are developed and presented in this paper to handle both constrained and unconstrained optimization problems. These algorithms are free of metaphors and algorithm-specific parameters. The BMR algorithm is based on the best, mean, and random solutions of the population generated for solving a given problem, and the BWR algorithm is based on the best, worst, and random solutions. The performances of the proposed two algorithms are investigated by implementing them on 26 real-life nonconvex constrained optimization problems given in the Congress on Evolutionary Computation (CEC) 2020 competition, and comparisons are made with those of the other prominent optimization algorithms. The performances on 12 constrained engineering problems are also investigated, and the results are compared with those of very recent algorithms (in some cases, compared with more than 30 algorithms). Furthermore, computational experiments are conducted on 30 unconstrained standard benchmark optimization problems, including 5 recently developed benchmark problems with distinct characteristics. The results demonstrated the superior competitiveness and superiority of the proposed simple algorithms. The optimization research community may gain an advantage by adapting these algorithms to solve various constrained and unconstrained real-life optimization problems across various scientific and engineering disciplines. The codes of the BMR and BWR algorithms are available at this https URL

Identifying the finer details of a book's genres enhances user experience by enabling efficient book discovery and personalized recommendations, ultimately improving reader engagement and satisfaction. It also provides valuable insights into market trends and consumer preferences, allowing publishers and marketers to make data-driven decisions regarding book production and marketing strategies. While traditional book genre classification methods primarily rely on review data or textual analysis, incorporating additional modalities, such as book covers, blurbs, and metadata, can offer richer context and improve prediction accuracy. However, the presence of incomplete or noisy information across these modalities presents a significant challenge. This paper introduces IMAGINE (Intelligent Multi-modal Adaptive Genre Identification NEtwork), a framework designed to address these complexities. IMAGINE extracts robust feature representations from multiple modalities and dynamically selects the most informative sources based on data availability. It employs a hierarchical classification strategy to capture genre relationships and remains adaptable to varying input conditions. Additionally, we curate a hierarchical genre classification dataset that structures genres into a well-defined taxonomy, accommodating the diverse nature of literary works. IMAGINE integrates information from multiple sources and assigns multiple genre labels to each book, ensuring a more comprehensive classification. A key feature of our framework is its resilience to incomplete data, enabling accurate predictions even when certain modalities, such as text, images, or metadata, are missing or incomplete. Experimental results show that IMAGINE outperformed existing baselines in genre classification accuracy, particularly in scenarios with insufficient modality-specific data.

The Koopmanization embeds the bilinearization via the action of the infinitesimal stochastic Koopman operator on the observables associated with the controlled nonlinear It\^o stochastic differential system without explicit linearizations. The stochastic evolutions of controlled Markov processes assume the structure of controlled nonlinear It\^o stochastic differential equations. This paper sketches a Koopman operator framework for the filtering of the controlled nonlinear It\^o stochastic differential system. The major ingredients of this paper are the construction of the eigenfunctions, action of the infinitesimal stochastic Koopman operator, multi-dimensional It\^o differential rule and filtering concerning the controlled nonlinear It\^o stochastic differential system. In this paper, we illustrate the filtering in the Koopman setting for a polynomial system and compare with the filtering in the Carleman setting.

Numerous studies have shown that existing Face Recognition Systems (FRS), including commercial ones, often exhibit biases toward certain ethnicities due to under-represented data. In this work, we explore ethnicity alteration and skin tone modification using synthetic face image generation methods to increase the diversity of datasets. We conduct a detailed analysis by first constructing a balanced face image dataset representing three ethnicities: Asian, Black, and Indian. We then make use of existing Generative Adversarial Network-based (GAN) image-to-image translation and manifold learning models to alter the ethnicity from one to another. A systematic analysis is further conducted to assess the suitability of such datasets for FRS by studying the realistic skin-tone representation using Individual Typology Angle (ITA). Further, we also analyze the quality characteristics using existing Face image quality assessment (FIQA) approaches. We then provide a holistic FRS performance analysis using four different systems. Our findings pave the way for future research works in (i) developing both specific ethnicity and general (any to any) ethnicity alteration models, (ii) expanding such approaches to create databases with diverse skin tones, (iii) creating datasets representing various ethnicities which further can help in mitigating bias while addressing privacy concerns.

Motivated by the discovery of several charm states exhibiting tetraquark-like characteristics at BESIII and LHCb, this study investigates the spectroscopy and decay properties of D mesons and tetraquark states with quark content $Cq\bar{q}\bar{q}$ within the diquark - antidiquark framework. The analysis is performed using a potential model based on the Cornell potential, considering color antitriplet - triplet configurations. Mass spectra are computed for both mesons and tetraquarks, while their decay behaviors are examined using the factorization approach for D mesons and Fierz rearrangement for tetraquark decays. Theoretical results are compared with experimentally observed resonances to improve our understanding of charm quark bound systems.

We investigate endoartinian modules, which satisfy the descending chain condition on endoimages, and establish new characterizations that unify classical and generalized chain conditions. Over commutative rings, endoartinianity coincides with rings satisfying the strongly ACCR* with dim(R) = 0 and strongly DCCR* conditions. For principally injective rings, the endoartinian and endonoetherian rings are equivalent. Addressing a question of Facchini and Nazemian, we provide a condition under which isoartinian and Noetherian rings coincide, and we classify semiprime endoartinian rings as finite products of matrix rings over a division ring. We further show that endoartinianity is equivalent to the Kothe rings over principal ideal rings with central idempotents, and characterize such rings as finite products of artinian uniserial rings.

In this paper, we emphasise the recent observational findings from the Dark Energy Spectroscopic Instrument Data Release 2 (DESI DR2), which provide compelling evidence for a possible deviation from the standard $\Lambda$CDM (Cold Dark Matter) cosmology, suggesting the presence of a dynamically evolving effective dark energy component. Motivated by this, we construct a theoretical framework in which a massive cosmological vector field, $B^{\mu}$, couples non-minimally to the background curvature through marginal interactions, offering a controlled mechanism to realise the deviation from the $\Lambda$CDM model. A detailed analysis of the effective Equation of State (EoS) parameter $w(\tilde H)$ reveals a narrow region of parameter space consistent with current cosmological observations presented by DESI. The analysis yields a stringent upper bound for the coupling constant $\lambda$ to be \lambda<2.98\times10^{-11}, a very strong bound on mass $3.1356\times10^{-66}~\text{g} \leq m \leq 3.3627\times10^{-66}~\text{g},$ and the admissible range $-0.405 \leq \log_{10}\tilde\gamma \leq -0.38$ for which present-day value $w_0 = w(\tilde H = 1)$ corresponding to a deviation $\delta = w_0 + 1$ that lies within the region $0.107 \leq \delta \leq 0.217$. This interval reproduces the deviation inferred from the combined DESI, Cosmic Microwave Background (CMB), and Pantheon+ data, reflecting a controlled departure from the $\Lambda$CDM behaviour. In summary, the results suggest that the proposed framework of a massive vector field can account for the departure from $\Lambda$CDM behaviour highlighted by DESI in the current cosmic acceleration. Furthermore, the framework approaches the $\Lambda$CDM behaviour in late-time $t\gtrsim28$ Gyr, establishing a direct phenomenological link between the underlying parameters and the observed dynamical nature of dark energy.

The language identification task is a crucial fundamental step in NLP. Often it serves as a pre-processing step for widely used NLP applications such as multilingual machine translation, information retrieval, question and answering, and text summarization. The core challenge of language identification lies in distinguishing languages in noisy, short, and code-mixed environments. This becomes even harder in case of diverse Indian languages that exhibit lexical and phonetic similarities, but have distinct differences. Many Indian languages share the same script, making the task even more challenging. Taking all these challenges into account, we develop and release a dataset of 250K sentences consisting of 23 languages including English and all 22 official Indian languages labeled with their language identifiers, where data in most languages are newly created. We also develop and release baseline models using state-of-the-art approaches in machine learning and fine-tuning pre-trained transformer models. Our models outperforms the state-of-the-art pre-trained transformer models for the language identification task. The dataset and the codes are available at this https URL and in Huggingface open source libraries.

The rise of computation-based methods in thermal management has gained immense attention in recent years due to the ability of deep learning to solve complex 'physics' problems, which are otherwise difficult to be approached using conventional techniques. Thermal management is required in electronic systems to keep them from overheating and burning, enhancing their efficiency and lifespan. For a long time, numerical techniques have been employed to aid in the thermal management of electronics. However, they come with some limitations. To increase the effectiveness of traditional numerical approaches and address the drawbacks faced in conventional approaches, researchers have looked at using artificial intelligence at various stages of the thermal management process. The present study discusses in detail, the current uses of deep learning in the domain of 'electronic' thermal management.

The memory wall problem arises due to the disparity between fast processors and slower memory, causing significant delays in data access, even more so on edge devices. Data prefetching is a key strategy to address this, with traditional methods evolving to incorporate Machine Learning (ML) for improved accuracy. Modern prefetchers must balance high accuracy with low latency to further practicality. We explore the applicability of utilizing Kolmogorov-Arnold Networks (KAN) with learnable activation functions,a prefetcher we implemented called KANBoost, to further this aim. KANs are a novel, state-of-the-art model that work on breaking down continuous, bounded multi-variate functions into functions of their constituent variables, and use these constitutent functions as activations on each individual neuron. KANBoost predicts the next memory access by modeling deltas between consecutive addresses, offering a balance of accuracy and efficiency to mitigate the memory wall problem with minimal overhead, instead of relying on address-correlation prefetching. Initial results indicate that KAN-based prefetching reduces inference latency (18X lower than state-of-the-art ML prefetchers) while achieving moderate IPC improvements (2.5\% over no-prefetching). While KANs still face challenges in capturing long-term dependencies, we propose that future research should explore hybrid models that combine KAN efficiency with stronger sequence modeling techniques, paving the way for practical ML-based prefetching in edge devices and beyond.

Astrometric observations of S-stars provide a unique opportunity to probe the nature of Sagittarius-A* (Sgr-A*). In view of this, it has become important to understand the nature and behavior of timelike bound trajectories of particles around a massive central object. It is known now that whereas the Schwarzschild black hole does not allow the negative precession for the S-stars, the naked singularity spacetimes can admit the positive as well as negative precession for the bound timelike orbits. In this context, we study the perihelion precession of a test particle in the Kerr spacetime geometry. Considering some approximations, we investigate whether the timelike bound orbits of a test particle in Kerr spacetime can have negative precession. In this paper, we only consider low eccentric timelike equatorial orbits. With these considerations, we find that in Kerr spacetimes, negative precession of timelike bound orbits is not allowed.

The resonance mass spectra have been studied through a non-relativistic hypercentral Constituent Quark Model (hCQM) using a linear potential. Also, the effects of higher order correction terms (${\cal{O}}(\frac{1}{m})$, ${\cal{O}}(\frac{1}{m^{2}})$) have been studied for improvisation of the results. Other baryonic properties such as Regge trajectories, magnetic moment and decay widths have been considered. A detailed comparison with other approaches are discussed in the present review.

In this paper, we construct the rotating Janis-Newman-Winicour (JNW) naked singularity spacetime using Newman-Janis Algorithm (NJA). We analyse NJA with and without complexification methods and find that the energy conditions do satisfied when we skip the complexification step. We study the shadows cast by rotating JNW naked singularity and compare them with the shadows cast by the Kerr black hole. We find that the shadow of the rotating naked singularity can be distinguished from the shadow of the Kerr black hole. While we analyse the precession of timelike bound orbits in rotating JNW spacetime, we find that it can have a negative (or opposite) precession, which is not present in the Kerr black hole case. These novel signatures of the shadow and orbital precession in rotating JNW naked singularity spacetime could be important in the context of the recent observation of the shadow of the M87 galactic center and the stellar dynamics of `S-stars' around Milkyway galactic center.

Excited states masses of the strange singly charmed baryons are calculated using the non-relativistic approach of hypercentral Constituent Quark Model (hCQM). The hyper-Coulomb plus screened potential is used as a confinement potential with the first order correction. The spin-spin, spin-orbit and spin-tensor interaction terms are included perturbatively. Our calculated masses are allowed to construct the Regge trajectories in both $(n_r, M^2)$ and $(J, M^2)$ planes. The mass spectra and the Regge trajectories study predict the spin-parity of $\Xi{_c(2970)^{+/0}}$, $\Xi{_c(3080)^{+/0}}$, $\Xi{_c(3123)^+}$, $\Omega{_c}(3000)^0$ and $\Omega{_c}(3119)^0$ baryons. Moreover, the strong one pion decay rates of the isodoublet states of $\Xi{_c(2645)}$, $\Xi{_c(2790)}$ and $\Xi{_c(2815)}$ are analyzed in the framework of Heavy Hadron Chiral Perturbation Theory (HHChPT). Also, the ground state magnetic moments and the radiative decay rates based on the transition magnetic moments are calculated in the framework of constituent quark model.

With data samples collected with the BESIII detector at seven energy points at $\sqrt{s}= 3.68 - 3.71$ GeV, corresponding to an integrated luminosity of 333 pb$^{-1}$, we present a study of the $\Lambda$ transverse polarization in the $e^+e^-\to\Lambda\bar\Lambda$ reaction. The significance of polarization by combining the seven energy points is found to be 2.6$\sigma$ including the systematic uncertainty, which implies a non-zero phase between the transition amplitudes of the $\Lambda\bar\Lambda$ helicity states. The modulus ratio and the relative phase of EM-$psionic$ form factors combined with all energy points are measured to be $R^{\Psi} =$ 0.71$^{+0.10}_{-0.10}$ $\pm$ 0.03 and $\Delta\Phi^{\Psi}$ = (23$^{+8.8}_{-8.0}$ $\pm$ 1.6$)^\circ$, where the first uncertainties are statistical and the second systematic.

In this work, density functional theory is performed to investigate the phonon dispersion, elastic, thermodynamic, and thermoelectric properties of half-Heusler alloy CoHfSi in Quantum espresso software using First-principles calculations. The alloy is found to be semiconducting with a band gap of 1.13 eV. The material is mechanically stable, as satisfied by the elastic properties in the thermo_pw package using Born-Huang stability criteria. Positive phonon frequencies determine that the material is dynamically stable. BoltzTrap code is utilized to determine the thermoelectric properties. The higher value of the Seebeck coefficient (150{\mu}V/K-250{\mu}V/K) is required for more conversion efficiency. zT increases with the temperature increase and reaches a maximum value of 3 at 850K.