Career Point University (CPU) is a private university located near Bhoranj in Hamirpur district, Himachal Pradesh, India. The university was established in 2012 by the Gopi Bai Foundation Trust through the Career Point University (Establishment & Regulation) Act, 2012. Gopi Bai Foundation Trust has also set up Career Point University, Kota in Rajasthan. Both universities are backed by the Career Point group.
In this study of Fe2-xCoxMnAl Heusler alloys, we analysed several key properties such as electronic, magnetic and optical properties including the formation energy, electronic energy band gap, magnetic moment and spin polarization (SP). These properties were studied to understand the half-metallic stability as the lattice parameter changed. The alloys consistently maintained the total magnetic moments (MTotal) predicted by the Slater-Pauling rule (SPR), with values of 2.0 μB for x = 0.0, 2.5 μB for x = 0.5, 3.0 μB for x = 1.0, 3.5 μB for x = 1.5, and 4.0 μB for x = 2.0, across a wide range of lattice variations. Value of SP was observed 100
The algorithm incorporation of artificial intelligence into the system of Environmental, Social and Governance (ESG) may be viewed as a radical shift in the sustainable financial field, including the jurisdictional and contractual one in green investment law. AI-based applications support advanced processing of data, predictive analytics, and real-time monitoring, which are useful in improving the evaluation of ESG risks, identifying cases of greenwashing, automating compliance with new regulatory tools such as the Corporate Sustainability Reporting Directive (CSRD) and the AI Act and allocating capital to low-carbon initiatives more efficiently. Such technical abilities are offering investors the opportunity to meet high levels of risk-adjusted returns in addition to their further development towards broader sustainability targets, such as the achievement of net-zero transitions and the United Nations Sustainable Development Goals.
Due to the exponential growth of data, there is a demand for knowledge that can be utilized derived from massive and massively varied datasets in diverse domains. This paper presents an Optimized Hybrid Framework which combines benefits of data mining techniques and contemporary machine learning algorithms to improve efficiency, effectiveness of information extraction. The methodology is based on two hybrid ensembles of unsupervised learning system. a combination of the Supervised Learning algorithm: Logistig regression; K-nearest Neighbors; Decision tree; Random Forest; Ad boosting; Ligh Gradient Boosting Machine and eXtreme Gradient Boosting. The former is developed with traditional classifiers, while the latter is constructed by gradient boosted automatic learning models. These normalization, feature scaling and encoding are parts of the general preparation pipeline that assures that you correctly prepare your data. Cross-validation and hyperparameter tuning are used to speed up training, avoid its overfitting, aid generalization, and enhance a model’s performance. Standard measures, accuracy, precision, recall, F1 score as well as AUC-ROC are employed to assess the performance and computational efficiency. Experimental Results The experiments show that the hybrid models can significantly improve classification accuracy as well as robustness compared to single classifiers on many datasets. At the single model level, accuracies went from 82.5
This study reports the hydrothermal synthesis of silver-anchored binary (PNBC–Ag/TiO₂, PNBC–Ag/Mg(OH)₂) and ternary (PNBC–Ag/TiO₂/Mg(OH)₂) nanocomposites using activated biochar (PNBC) derived from dead pine needles as a sustainable support. Structural, optical and surface analyses using XRD, FTIR, XPS, FESEM/HRTEM, and UV–Vis DRS confirmed the successful integration of Ag, TiO₂, and Mg(OH)₂ within the biochar matrix, producing nanocrystalline composites with improved light-absorption properties. The ternary nanocomposite demonstrated superior photocatalytic performance, achieving 98.4
The environmental remediation and renewable energy production belong to the concept of sustainable multifunctionality, which minimizes environmental harm. In this study, ternary layered double hydroxide (LDH) of M1M2FeCO3 [M1, M2--Ni/Zn/Co] has been synthesized via a co-precipitation method. Structural analysis (XRD, FTIR, TEM, SEM, EDX, BET, UV-Vis, and XPS) of synthesized confirmed well-ordered layered structures of materials with crystallinity, having nanosheet like morphology with average particle size (6.10-7.48 nm), large specific surface areas (32.42-119.95 m2/g) and tunable band gaps (1.37-2.20 eV) of materials also analysed. XPS analysis revealed the presence of Ni2+, Co2+/Co3+, Zn2+, and Fe3+ species with strong M-O bonding in the synthesized LDH materials. Notably, ZnCoFe-LDH achieved 99.04 % degradation efficiency of congo red (CR) dye within 48 min under sonophotocatalytic irradiation process, following pseudo-first-order reaction kinetics. The integration of ZnCoFe-LDH as a photoanode in a turmeric dye-sensitized solar cell (DSSC) resulted in a photovoltaic (PV) efficiency 2.68 %, which is higher than that of NiCoFe and NiZnFe-LDH based devices. Overall, these findings establish ZnCoFe-LDH as a sustainable multifunctional catalytic material that is capable of applicability in environmental remediation and solar light harnessing, thereby offering a pathway towards amalgamating the eco-friendly technologies.
