Sri Krishnadevaraya University is a public university in Anantapur, Andhra Pradesh, India, founded on 25 July 1981. The university is named after a patron of learning and the arts, Sri Krishnadevaraya, of the Vijayanagara empire of the 16th century.
Cobalt-zinc spinel ferrites are promising multifunctional materials due to their tunable magnetic, dielectric, and electrical properties governed by cation distribution and lattice defects. In this work, Co1-xZnxFe2O4 (x = 0.1-0.9) nanocrystalline spinel ferrites were synthesized via a hydrothermal method to investigate the relationship between microstructural strain and functional properties. X-ray diffraction confirmed the formation of a single-phase cubic spinel structure with lattice parameters increasing from 8.329 to 8.401 & Aring; with Zn2 + substitution. Microstructural analysis using Scherrer, Williamson-Hall, Halder-Wagner, and size-strain plot models revealed crystallite sizes of 2-7 nm along with notable variations in lattice microstrain and defect density. Scanning electron microscopy showed agglomerated particle clusters with sizes between 167 and 311 nm, while energy dispersive x-ray spectroscopy confirmed the presence of Fe, Co, Zn, and O without detectable impurities. Vibrating sample magnetometry measurements indicated soft magnetic behavior with saturation magnetization values ranging from 10.76 to 15.33 emu g- 1, influenced by Zn-induced cation redistribution and surface spin disorder. Dielectric analysis revealed strong low-frequency permittivity and Maxwell-Wagner polarization behavior. The composition x = 0.5 exhibited optimal magnetic ordering and dielectric response, indicating potential for high-frequency electronics and electromagnetic interference shielding applications.
The rise of more interconnected systems and digital exchanges has escalated the occurrence and intricacy of various kinds of cyber threats. In this regard, it is imperative to ensure smart, effective detection to ensure the security and reliability of networks. However, existing methods for intrusion detection are found to be deficient in terms of precision and generalization. Feature representation tends to be clunky, and security information filtering from traffic logs, users, and other sources poses another level of challenge. From this perspective, there is a clear need to develop an automated, precise, fast-adapting intrusion detection system that can assess threats and respond in real time. This article proposes an automated model for intrusion detection that consists of several state-of-the-art components. These include Stochastic Triangular Fuzzy Number Normalization (STFNN) for data normalization. Additionally, it includes a Deep Complex Convolutional Transformer Network (DCCTN) for extracting relevant features. Finally, there is a Quantum Dual-Domain Convolutional Neural Network (QDDCNN) trained using the Farmer Ants Optimization Algorithm (FAOA). The efficacy of QDDCN-FAOA can be ascertained by comparing its performance with existing models, as indicated by performance tests conducted on two popular benchmarks, NSL-KDD and UNSW-NB15, which reveal a better accuracy rate, predicted to be as high as 99.43
The Ba1−xCoxTiO3 (x = 0.2–0.8) (BCT) perovskite nanoparticles are prepared via hydrothermal method. The X-ray diffraction (XRD) confirmed the cubic perovskite structure while specifically, the x = 0.4 sample shows the mixed structure. The microstructure, and selected area electron diffraction patterns clearly show the formation of quantum dots (QDs) for x = 0.4 having the average size of 4 nm which can be suggested for quantum technology applications. The Tauc’s plots obtained from UV-Visible spectral analysis confirmed the decrease of optical bandgap (Eg) from 2.514 to 2.300 eV with increase in Co-content in the barium titanate system. The metal oxide bonds (Ba-O, Co-O, and Ti-O) are confirmed using IR-spectra. The Raman spectra of BCT evidences the formation of perovskite structure disclosing the lattice vibrations, and Raman modes of BCT. Further, the photoluminescence (PL) spectra of x = 0.2–0.8 show the perovskite formation pertaining to the emission of different colours (wavelengths). The dielectric constant, and loss dependence of x = 0.2–0.8 as a function of crystallite is elucidated. The U-shaped ac-electrical conductivity behavior as a function of frequency for x = 0.2–0.8 indicates the transition from hopping dominant conduction to polarization dominant mechanism. The relaxation dynamics is illustrated using the dielectric modulus formalism. The Cole-Cole plots reveal that the electrical conduction mechanism of BCT happened alone through grains rather than the grain boundaries.
The study focuses on the rainfall distribution in Tirupati district, Andhra Pradesh, with an emphasis on the implications of climate change on agricultural practices and water resource management. Focusing on 2000, 2010 and 2025. Secondary data from the Indian Meteorological Department, which provides high-resolution gridded rainfall datasets, are used to analyze rainfall patterns across all 36 mandals of the district. The methodology employs Geographic Information System techniques and standard statistical tools to evaluate rainfall variability and its implications for agriculture and water resource management. The results indicate considerable temporal variability in rainfall patterns. In 2000, the district primarily experienced moderate rainfall (826-894 mm), supporting agricultural stability. By 2010, rainfall levels increased, resulting in both low (1200-1320 mm) and high (1460-1580 mm) categories, which suggests a shift toward more extreme weather conditions. Projections for 2025 indicate a slight decrease in moderate rainfall (1250-1410 mm) and an increase in high rainfall events (1420-1570 mm), raising concerns regarding flooding and water resource management. The analysis identifies distinct trends between urban and rural areas. Tirupati Urban exhibits increased rainfall variability, reflecting the influence of urbanization and climate change. In contrast, rural areas have experienced significant increases in both moderate and high rainfall categories, presenting challenges for agricultural practices and water resource management. The study highlights the need for modified adaptation strategies to address the specific challenges posed by changing rainfall patterns in both urban and rural contexts.
Background: Obesity induced by prolonged high-fat-diet consumption is associated with insulin resistance, dyslipidaemia, oxidative imbalance, and hepatic injury. Aim: The present study evaluated the effects of Caralluma fimbriata extract (CFE) on fat deposition, oxidative stress, inflammation, and fibrosis in the liver of high-fat diet (HFD)-induced obese rats. Method: Thirty male Wistar rats were divided into five groups (n = 6): control (C), control+CFE, HFD, HFD+CFE (200 mg/kg/day for 150 days; preventive model), and obese+CFE (OBS+CFE; 200 mg/kg/day after 90 days of HFD feeding for the subsequent 60 days; therapeutic model). Results: HFD-fed rats showed significant increases in body weight, plasma glucose, insulin resistance (HOMA-IR), leptin, lipid-profile parameters, and hepatic markers. Elevated lipid peroxidation and protein oxidation, together with reduced antioxidant enzyme activities (GPx, GR, GST, SOD, and CAT) and HDL cholesterol levels, were also observed. CFE supplementation significantly improved body weight, hyperglycaemia, insulin resistance, hyperleptinaemia, and dyslipidaemia. It increased HDL-C levels and restored antioxidant enzyme activities in both the preventive and therapeutic groups. Additionally, CFE significantly (p < 0.05) reduced plasma and hepatic lipids, including triglycerides, total cholesterol, and free fatty acids, while improving phospholipid levels. Conclusion: Histopathological findings supported these results. Overall, CFE ameliorated liver dysfunction in HFD-fed rats by reducing oxidative stress and inflammation, suggesting potential protective and therapeutic effects.
