Barkatullah University is a state government university in Bhopal, India. Originally known as the University of Bhopal, and informally as Bhopal University, it was renamed in 1988 after the freedom fighter Professor Maulavi Barkatullah, who was born in the area. Barkatullah University has been accredited as an A category university by the National Assessment and Accreditation Council (NAAC).
The tremendous potential of organic–inorganic hybrid crystals in the next generation of optoelectronic and photonic technologies has led to a steady increase in their investigation recently. The semi-organic nonlinear optical single crystals of L-argininium perrhenate (LAPR) were grown with bulk size by employing the slow evaporation solution growth technique (SEST), and the crystal’s structural and physical properties were analyzed. The orthorhombic crystal structure of LAPR was revealed by the single-crystal XRD (SCXRD) method. The NMR spectral analysis was carried out for the title sample. The grown specimen of LAPR has been subjected to FTIR analysis to confirm the existing functional groups. The outcome of UV–Vis–NIR analysis reveals that the crystal has 80
In the present situation, a lot of research has been directed towards the potency of plants. These natural resources contain characteristics valuable in combat against a number of diseases. But due to lack of familiarity of these plants among human beings, an appropriate advantage of their significance cannot be drawn away. Plants also shares the certain similar characteristics of leaves like color, texture, shape or size, making them hard to classify them among others. So, to eradicate this problem, a deep learning model has been used for the purpose for classification of different plants species captured in real-time using internet of things practice. Six different plants namely Ashwagandha, Black Pepper, Garlic, Ginger, Basil, and Turmeric has been selected for this purpose. Our proposed convolutional neural network (CNN) model achieved higher performance with an accuracy of 99
This study investigates the behavior of normal shock waves in high-temperature, compressible turbulent flows influenced by chemical reactions. Understanding such reacting shocks is essential for the design of hypersonic vehicles, propulsion systems, and magnetohydrodynamic energy devices, where heat release strongly alters shock strength. A modified Rankine–Hugoniot framework is developed by incorporating temperature-dependent chemical heat addition, enabling unified analysis of exothermic and endothermic effects on shock jumps. Artificial Neural Network methods are applied in combination with an optimised back-propagation algorithm employing the Levenberg–Marquardt approach to forecast shock intensity and Mach number to improve predictive performance. The combined consideration of turbulence and chemical heat-release effects is the unique aspect of this work. This study also determines entropy generation and shock parameters like Mach number, velocity variation and ultimate compression. In contrast to exothermic processes, shock jumps for endothermic reactions are observed to increase as the Arrhenius number rises. This paper compares analytical and numerical results and concludes that the linearized model is valid up to Ar=5 for exothermic reactions and Ar=7 for endothermic reactions with maximum errors of 27
Crape jasmine is a beautiful evergreen flowering shrub and is native to India. During a survey in 2025, severe yellowing symptoms with stunted growth of the plant was observed on the crap jasmine plant at Bhopal, Central India. For the identification of 'Candidatus phytoplasma species' on crap jasmine, symptomatic and asymptomatic leaf samples were collected and total DNA was extracted. The phytoplasma 16S rRNA gene was successfully amplified by direct and nested PCR using isolated DNA and phytoplasma-specific primers from three symptomatic plants. The amplified similar to 1.2 kb nested PCR products from three samples were sequenced, and the resulting 16S rRNA gene nucleotide sequences were submitted to GenBank under the accession numbers PX442245, PX890108 and PX890109. Sequence analysis of all three isolates (PX442245, PX890108 and PX890109) revealed the highest nucleotide sequence identity (99%) and close phylogenetic relationships with reference strains of brinjal little leaf phytoplasma, 'Candidatus Phytoplasma trifolii' (16SrVI group), predominantly belonging to the 16SrVI-D subgroup. In silico RFLP analysis of the 16S rRNA gene sequences of the phytoplasma isolates under study showed the highest similarity to the reference strain of the 16SrVI-D subgroup, with similarity coefficients ranging from 0.93 to 0.86. To the best of our knowledge, this is the first report of a 'Candidatus Phytoplasma trifolii'-related strain (16SrVI-D subgroup) associated with yellows disease of crape jasmine (Tabernaemontana divaricata) in India.
The increasing use of renewable energy, particularly photovoltaic (PV) systems, creates issues for grid stability and reactive power management. Variable loads and fluctuating solar irradiation can lead to voltage instability, power losses, and low quality power. Traditional energy storage and control strategies often struggle under changing system conditions. To address these issues, this paper proposes a hybrid method for improving grid stability reactive power management in PV and Superconducting Magnetic Energy Storage Inverters (SMES) using a hybrid approach. The proposed hybrid approach is a combined performance of both the Giraffe Kicking Optimization Algorithm (GKOA) and Higher-Order Topological Neural Networks (HOTNN), named GKOA-HOTNN. The primary aim of the proposed approach is to distribute the necessary reactive power from the PV and SMES power inverters locally. The proposed GKOA algorithm is employed to optimize the reactive power distribution and enhance the performance of grid stability in PV and superconducting magnetic energy storage systems. HOTNN is used to estimate the charging and discharging process of superconducting magnetic storage systems and electric vehicles (EVs). The performance of the proposed technique is evaluated and compared with other existing methods on the MATLAB platform, including Random Forest Cuckoo Search Optimization (RF-CSO), Particle Swarm Optimization (PSO), and Scaled Conjugate Artificial Neural Network (SC-ANN), proposed method achieves a power loss of 1 kW and an efficiency of 98
