Mangalore Institute of Technology and Engineering (MITE) is a Engineering and Management Institution located in Mangalore, India, established by the Rajalaxmi Education Trust under the leadership of Rajesh Chouta in 2007. The institute is affiliated to the Visvesvaraya Technological University, Belgaum and approved by the All India Council of Technical Education (AICTE), New Delhi.
This work explores the geometry and observational signatures of a Schwarzschild black hole immersed in a background composed of a string cloud and a quintessential scalar field, within the broader context of Finsler geometry. The string cloud component introduces anisotropic radial pressure through a dimensionless parameter 𝔞 , while the quintessence field, characterized by ω _𝔮 = -2/3 as the equation of state parameter, modifies the space-time in a way consistent with late-time cosmic acceleration. We further extend this configuration by embedding it in a Finslerian framework via the anisotropy parameter ϵ , taking into account potential violations of Lorentz invariance and incorporating directional dependence in the space-time structure. Our approach centers on understanding the path of photons around the black hole by analyzing null geodesics, the photon sphere, and the effective potential landscape. We derive the shadow radius and impact parameter as functions of 𝔞 , γ , and ϵ , and investigate how each modifies the shadow structure. Constraining these parameters using Event Horizon Telescope observations of M87* and Sagittarius A*, we identify viable bounds consistent with observed shadow diameters. The results indicate that Finslerian corrections influence the bending of light and the deformation of the black hole shadow, suggesting a more general and flexible framework for probing deviations from general relativity in strong-field regimes.
The expansion of digital media across diverse platforms has heightened the need for reliable methods of content protection and authentication. Digital watermarking and fingerprinting provide robust mechanisms to preserve data integrity and establish rightful ownership. To ensure protection of digital data, techniques such as these can be employed. Digital watermarking embeds hidden information within digital media to verify ownership and detect unauthorized tampering, making it an essential tool for copyright protection and authentication of medical images. In contrast, digital fingerprinting extracts unique identifiers from content to enable user identification, traceability, and monitoring of distribution. Together, these approaches address critical challenges in content identification, copy protection, copyright control, and forensics. As the volume of unlawfully redistributed digital data continues to rise, organizations face growing risks of financial and intellectual property loss. The integration of watermarking and fingerprinting enhances overall security–watermarking safeguards content integrity, while fingerprinting ensures accountability and privacy. Recognizing the importance of combining these complementary techniques, this paper reviews recent research that equally emphasizes both digital watermarking and fingerprinting, providing a comparative analysis of their objectives, methodologies, and applications in securing digital information.
As solar photovoltaic installations increase rapidly, the demand for optimized supply chains becomes critical. However, most previous photovoltaic module supply chain studies rely on fixed parameters and rarely integrate machine learning-driven parameter estimation into system-wide optimization frameworks. Therefore, this paper proposes a hybrid framework that integrates machine learning with mixed-integer linear programming mathematical modeling to improve the efficiency of the photovoltaic module supply chain and reduce its costs. Critical parameters, including production quantity, production costs, energy usage, and transportation, were forecasted with machine learning models, such as CatBoost, LightGBM, and Random Forest, trained on datasets derived from industry reports and scholarly articles. Two parallel models were examined: a baseline model with constant parameter values and a machine learning-enhanced model that employs estimated inputs. The results showed that the machine learning-enhanced model reduced total photovoltaic module supply chain cost by 9.50
Longitudinal fins cooled by advanced nanofluids have emerged as an effective solution for enhancing heat transfer in modern thermal systems. This study presents a novel transient thermal analysis and fin efficiency of wetted longitudinal porous fin cooled by a trihybrid nanofluid under the influence of a magnetic field, which has not been previously explored. The longitudinal fin with rectangular, convex and triangular profiles employing trihybrid nanofluid composed of Fe_3 O_4 , Au , and Zn nanoparticles suspended in blood has been investigated. It incorporates internal heat generation and magnetic field effects with Darcy’s law describing the porous medium. The governing nonlinear partial differential equation is solved numerically via the finite difference method after being nondimensionalized. A comprehensive parametric analysis is carried out to investigate the influence of key dimensionless parameters on the fin’s temperature distribution and thermal efficiency, including the Peclet number, wet porous parameter, convective parameter, radiative parameter, power index, generation number, internal heat generation, Hartmann number and ambient temperature. A 400
An antenna is an essential element of wireless communication systems, used to send and receive electromagnetic waves. Out of several types of antennas, reconfigurable antennas are particularly interesting. They can change their operating parameters based on changing needs. An antenna that can be reconfigured can alter its operating parameters dynamically applying external control signals. This flexibility minimizes the use of multiple antennas in one system, thereby conserving space, cost, and complexity. A bow-tie-shaped microstrip patch antenna with frequency reconfiguration is suggested in this research. It aims to provide wide frequency coverage while keeping compact dimensions and efficient operation. The antenna is made on an FR4 epoxy substrate, which is chosen for its low cost and good electrical properties. The structure is simulated using HFSS software to study and improve its electromagnetic performance. The antenna uses a coplanar waveguide (CPW) feed and includes two PIN diodes along with a shorting pin for frequency switching. Varactor diodes are also added for continuous tuning of the resonant frequency. The antenna can operate at different frequencies—4.55 gigahertz, 7.45 gigahertz, 4.51 gigahertz, and 6.88 gigahertz —demonstrating its reconfigurability with respect to frequency. Additionally, using varactor diodes expands the antenna's tuning range from 3.04 GHz to 5.89 GHz.
