Rani Durgavati Vishwavidyalaya (Rani Durgavati University), also known as University of Jabalpur, is a government university in Jabalpur, Madhya Pradesh, India. It was named after the queen Rani Durgavati..
The cohomological classification of orbit spaces of free G=ℤ_2 or G=𝕊^1 actions on the mod 2 or rational cohomology product of three spheres 𝕊^n×𝕊^m ×𝕊^l,1≤ n≤ m≤ l , has been discussed in [5, 14]. In this paper, we discuss orbit spaces of free G=𝕊^3 actions on a finitistic space X whose rational cohomology is isomorphic to the product of three spheres. As an application, we determine Borsuk-Ulam type theorems.
Novel mixed-ligand copper(II) complexes, [Cu(chrys)(macac)] (1) and [Cu(chrys)(dbm)] (2) (where chrysH = chrysin, macacH = methyl acetoacetate, dbmH = dibenzoylmethane), were synthesized and characterized as potent multifunctional agents. The structural evidences were gathered from a suite of techniques: infrared (IR) spectroscopy identified key functional groups and binding modes, magnetic susceptibility measurements confirmed the mononuclear copper(II) center, and mass spectrometry confirmed the molecular ion. Additional insights were derived from electronic absorption spectra, electron spin resonance (ESR) spectroscopy, and thermogravimetric analysis (TGA). The collective results confirmed a distorted square planar geometry, which was further validated by Density Functional Theory (DFT) calculations. Theoretical calculations over molecular geometry, charge distribution, molecular orbital descriptions FMOs, absolute electronegativity (χabs), and total hardness (η) of the complex, namely [Cu(chrys)(dbm)] (1), using density functional theory were also investigated. The complexes exhibit significant superoxide dismutase (SOD) mimetic activity and demonstrate potent, dose-dependent antibacterial efficacy against pathogens like E. coli and Citrobacter, outperforming standard antibiotics at higher concentrations. This combined experimental and theoretical study highlights the potential of these complexes as promising scaffolds for developing new metallodrugs with dual antioxidant and antibacterial properties.
A disk magnet (11 & times; 2.5 mm flat) positioned over the screw cap of extraction vial to secure the position of another disc magnet (10 & times; 2 mm) with a countersink hole placed inside the cap over PTFE backed silicone septum to house the solvent drop. This configuration of headspace-single drop microextraction (HS-SDME) has been found convenient to overcome the inherent drawbacks of drop instability and limited freedom for variations in drop size, length of extraction time, temperature, stirring rate and transportation of field prepared extract to the laboratory, or queuing for injection, whilst still in the magnet hole. The success of the system has been demonstrated in the determination of phenol and chloro-and methylphenols after acetylation. The major parameters, optimized by central composite design for 2 mL of sample, containing 0.01-10 mg L-1 of fifteen phenols, were 300 mg of sodium carbonate, 100 & micro;L of acetic anhydride, 20 min at 38 degrees C for both acetylation and HS-SDME, and 15 & micro;L drop of anisole. The solvent selection for optimum extraction of analytes was optimized using the Hansen solubility parameters, employing the integral components for solubility as dispersion, polarity and hydrogen bonding. Each GC-FID run took 8 min for the separation of fifteen acetylated phenols, and the internal standard. The LODs (S/N = 3) ranged from 2.1 to 4.5 & micro;g L-1; and LOQs (S/N = 10) from 6.5 to 12.6 & micro;g L-1. In precision studies five aliquots of the sample were separately derivatized and two injections each were made. The RSD obtained within day and between three days were, respectively, in the range 2.1 to 5.0% and 3.1 to 6.6% (n = 5). The optimized method was applied to different real water samples and paper mill effluents when some of the studied phenols were detected. In the perspective of the life cycle assessment of associated input and output items the environmental impacts are significantly reduced in several categories, particularly human toxicity and ecotoxicity. The greenness index was computed as AGREEprep (0.64), ComplexMoGAPI (78), BAGI (67.5) and Sample Preparation Meric of Sustainability (7.47). The total cost per analysis was estimated as INR 284.20 (& euro; 2.70).
The present study puts forward a strong authentication and data encryption framework for e-learning platforms—including those based on the traditional ZKP-based authentication and AEES, or adaptive/advanced encryption standard, protocols to enhance data security. This ZKP is proposed to enable, “real-time” Identity Authentication with 98.5
AI is being used in employee development and career management systems, allowing businesses to offer tailored insights on skills, learning paths and career progression. Even if there is algorithm-generated information about careers available, employees' perception of such systems as useful in terms of career advancement is not guaranteed. Based on Social Cognitive Career Theory (SCCT) and the new view of personalized human resource management, this study investigates how AI Tool Adoption (AITA) turns into Career-related Outcomes (CROs) via Perceived Personalization (PP), Skill Development Support (SDS), and Career Promotion Clarity (CPC). The model also explores downstream outcomes of Career Advancement Intention (CAI) and User Satisfaction with AI Tools (USAT). Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data from 315 respondents. The model showed good reliability (Cronbach's alpha between 0.800 and 0.833), average variance extracted (0.625 and 0.666) and composite reliability (0.870 and 0.889). Perceived Personalization and Skill Development Support were significantly positively affected by AI Tool Adoption (beta=.388, p<.001; beta=.382, p<.001, respectively). Skill Development Support was more related to Career Promotion Clarity (beta=.372, p<.001) than Perceived Personalization (beta=.256, p<.001). Career Promotion Clarity was then found to be the best predictor of Career Advancement Intention (beta=.347, p<.001) and Career Advancement Intention was the best predictor of User Satisfaction (beta=.384, p<.001). The model accounted for 32.8% of the variation in User Satisfaction. It seems that the impact of AI's career value is not so much in its adoption but in transforming employee data into tailored, growth-focused, and impactful advice.
