Category Archives: Uncategorized

Authors: Dr. Shivani Budhkar, Chavan Krushna Rameshwar

 

 

Abstract: This paper investigates the role of Artificial Intelligence (AI) tools in modern workplaces, focusing on their potential to boost efficiency and overall productivity. AI-driven technologies provide organizations with advanced capabilities to streamline workflows, improve decision-making, and foster innovation. Drawing on existing research, industry reports, and case studies, the study highlights both the opportunities and challenges involved in adopting AI across different workplace settings. By analyzing real-world implementations and practical applications, this research offers actionable insights for organizations aiming to leverage AI as a means of achieving operational excellence and long-term strategic goals in the digital era.

DOI: http://doi.org/

Published by: vikaspatanker

Authors: Rahul Kumar Satbhaiya, Jitendra

 

 

Abstract: High-rise buildings are particularly susceptible to lateral forces in seismically active regions. The primary consideration in their design is ensuring adequate resistance to these lateral stresses, as insufficient stability may lead to excessive displacement, structural instability, or even collapse. To mitigate such risks, buildings must be designed with effective lateral load–resisting mechanisms that enhance overall stability and serviceability. Among the commonly employed methods, steel bracing and masonry infill within reinforced concrete (RC) frames are recognized for their efficiency in resisting lateral loads. Steel bracing systems are advantageous due to their ease of installation, minimal space requirements, and ability to provide significant stiffness and strength with considerable design flexibility. Similarly, masonry infill can be executed efficiently with skilled labor and contributes to the overall lateral resistance of the structure. This study investigates the seismic performance of a reinforced concrete high-rise building of configuration R+12 (13 stories in total). Three structural configurations are evaluated: (i) bare frame, (ii) in filled frame with solid masonry, and (iii) frame with X-braced corner supports. The building model was developed and analyzed using CSI ETABS software, considering a three-dimensional asymmetric layout with a floor height of 3 m. Dynamic analysis was carried out using the response spectrum method for seismic Zone V under soft soil conditions, as specified by Indian seismic design guidelines. The results demonstrate that external steel bracing provides superior stability and reduced displacement compared to masonry infill and bare frames. In terms of both resistance and moment capacity, the steel bracing system proved to be the most effective lateral load–resisting system. Furthermore, from a cost-performance perspective, the steel tie system was found to be the most economical, followed by solid masonry infill, while the bare frame exhibited the least efficiency.

DOI: http://doi.org/

 

 

Published by: vikaspatanker

Authors: Yogeesh G Gowda, Vishwas G V, Santosh Patil, Pawan P R, Kumaraswamy S

Abstract: The integration of blockchain technology and smart contracts represents a transformative advancement in auction systems, offering efficient, transparent, and secure decentralized platforms. This project leverages these technologies to facilitate a range of auction functionalities, from placing bids and managing the auction lifecycle to ensuring fair outcomes without reliance on intermediaries. By offering enhanced transparency and immutability of records, decentralized auctions built on blockchain have the potential to reduce administrative burdens, improve trust among participants, and ensure continuity of the auction process. However, challenges such as scalability, gas costs, and regulatory considerations necessitate robust design and compliance mechanisms. This paper explores the capabilities, benefits, and limitations of this decentralized auction system, emphasizing its potential to revolutionize auction processes while addressing critical gaps in traditional centralized models.

DOI:

 

Published by: vikaspatanker

Authors: Noor jahan Fatima, Dr sarabjit kaur

Abstract: This study explores the structural characteristics of selected deformed nuclei using theoretical frameworks, focusing on their shapes, energy levels, and quadrupole moments. Nuclear deformation results from the complex interplay between shell effects and the strong nuclear force, causing deviations from spherical symmetry. Advanced models, including the Nilsson model, Hartree-Fock-Bogoliubov (HFB) theory, and collective models, are employed to examine the influence of deformation on nuclear structure. The key findings emphasize the role of deformation in shaping rotational spectra and intrinsic quadrupole moments, with significant results for nuclei such as 152Sm, 238U, and 240Pu. Calculations of quadrupole deformation parameters and energy levels show strong agreement with experimental data, validating the theoretical approaches. The study also investigates the stabilization of heavy nuclei through deformation, which redistributes charge density and mitigates Coulomb repulsion. These findings have important applications in nuclear astrophysics, particularly in the rapid neutron capture process (r-process), as well as in nuclear technology, where insights into deformed nuclei contribute to isotope development and reactor design. A deeper understanding of deformed nuclei in this research advances both fundamental nuclear physics and practical applications

DOI: http://doi.org/10.5281/zenodo.16941086

Published by: vikaspatanker

Authors: Suresh kumar, Dr Vandana, Shashikant Sheoran, Vandana Mahlawat

Abstract: This study explores the structural characteristics of selected deformed nuclei using theoretical frameworks, focusing on their shapes, energy levels, and quadrupole moments. Nuclear deformation results from the complex interplay between shell effects and the strong nuclear force, causing deviations from spherical symmetry. Advanced models, including the Nilsson model, Hartree-Fock-Bogoliubov (HFB) theory, and collective models, are employed to examine the influence of deformation on nuclear structure. The key findings emphasize the role of deformation in shaping rotational spectra and intrinsic quadrupole moments, with significant results for nuclei such as 152Sm, 238U, and 240Pu. Calculations of quadrupole deformation parameters and energy levels show strong agreement with experimental data, validating the theoretical approaches. The study also investigates the stabilization of heavy nuclei through deformation, which redistributes charge density and mitigates Coulomb repulsion. These findings have important applications in nuclear astrophysics, particularly in the rapid neutron capture process (r-process), as well as in nuclear technology, where insights into deformed nuclei contribute to isotope development and reactor design. A deeper understanding of deformed nuclei in this research advances both fundamental nuclear physics and practical applications.

DOI: http://doi.org/10.5281/zenodo.16940701

Published by: vikaspatanker

Authors: Suresh kumar, Dr Vandana

Abstract: Deformed nuclei, characterized by deviations from spherical symmetry, exhibit unique structural properties that are critical to understanding nuclear stability, reaction dynamics, and excitation phenomena. This theoretical study investigates the structural properties of selected deformed nuclei using advanced nuclear models and computational approaches. Employing Density Functional Theory (DFT) with Skyrme and Gogny interactions, alongside Hartree-Fock-Bogoliubov (HFB) calculations, the research analyses deformation effects on nuclear binding energy, charge distributions, and level densities. Transitional and neutron-rich nuclei are emphasized to explore the evolution of deformation, triaxiality, and nuclear softness. The results reveal significant impacts of deformation on nuclear moment of inertia and energy spectra, particularly in rare-earth and actinide regions. The inclusion of triaxiality further enhances the accuracy of predictions for level densities and excitation spectra. Comparisons with experimental data from gamma-ray spectroscopy and Coulomb excitation validate the robustness of the theoretical frameworks employed. This study addresses key gaps in understanding nuclear deformation, particularly for isotopic chains near the neutron drip line and transitional regions. The findings provide critical insights for refining existing nuclear models and guiding future experimental investigations. Furthermore, this research highlights the importance of incorporating pairing correlations and deformation effects to predict properties of nuclei far from stability. The study contributes to the broader understanding of nuclear structure and its applications in nuclear energy, astrophysics, and particle physics. These findings underscore the role of theoretical models in complementing experimental efforts and advancing nuclear physics research.

DOI: http://doi.org/10.5281/zenodo.16940412

 

Published by: vikaspatanker

Authors: Vengadeshwaran B

 

 

Abstract: Conventional keyword-based search systems lack contextual understanding and often return irrelevant or incomplete results. In enterprise environments, this becomes a bottleneck when users attempt to extract precise information from large and complex documents. This paper introduces AskMyDoc, a scalable project management tool and smart document querying system that leverages semantic search, large language models (LLMs), and vector databases. Unlike traditional SQL databases susceptible to human errors during updates, AskMyDoc processes and indexes documents using embedding techniques and retrieves answers using generative AI, thereby ensuring accuracy, speed, and consistency. The system is built with LangChain, FAISS, Sentence Transformers, and OpenAI's GPT models, supporting real-time natural language querying even across gigabyte-scale documents.

Published by: vikaspatanker

Authors: Gopichand Talluri

Abstract: The increase in the complexity of financial regulation and the increase in the number of unstructured financial data have presented significant challenges to the conventional compliance systems. The potential solution could be the intelligent automation and processing of financial data via context, since nowadays the development of Large Language Models (LLMs) allows using smart automation and processing of financial information. The paper creates an outline of the operationalization of the LLMs to the financial compliance in managed cloud AI platforms. The proposed system will include pre-processing of the data, inference with the help of the LLM, compliance analysis and continuous monitoring to guarantee the scalability, reliability, and compliance. Experimental analysis of simulated data shows that the proposed model shows better results compared to current methods, including FinBERT, BloombergGPT, and FinGPT in accuracy, efficiency, and compliance score. The findings indicate that the integration of the capabilities of LLM and cloud-based infrastructure is effective in tackling real-world financial compliance issues. This piece of work is a step in the right direction of establishing scalable, reliable, and smart compliance systems in contemporary financial settings.

DOI: https://doi.org/10.5281/zenodo.19732571

Published by: vikaspatanker

Authors: Santhosh SG, Sampath Kumar

Abstract: As digital data rapidly increases, there will be a corresponding depletion of textural data for various uses, as the number of image-based documents with usable text continues to rise. But too often, this is complicated by the obstacles of storing images with distortion, algorithmic font types, misaligned printed text, random text orientation and other forms of noise. For considering image-based documents with bilingual documents like government forms, educational transcripts, medical records, and business receipts with multiple integrated languages across a single document, become more complex and piled on these particular layers of challenges. The research paper "Optimizing Evaluation Metrics with PSNR, AMBE, and F1 Score to ensure Consistency in Document Improvement and Consistency in Classification Accuracy" is intended to examine some of the issues that both have been examined and expressed. This effort confronted the problems this work has discussed and expressed through consistent and reliable classification accuracy; AMBE determines the interference with the brightness distribution; and PSNR applies a clarity "score" to determine the clarity of an image. Combined, the three metrics present a possible framework to enhance the reliability and consistency of document processing system.

DOI:

 

 

Published by: vikaspatanker

Authors: Ravichandra Mulpuri

Abstract: This article examines the transformative potential of integrating Large Language Models (LLMs) into Salesforce to create conversational enterprises capable of dynamic decisioning. It explores how natural language interfaces and AI-driven intelligence reshape CRM systems from passive repositories of customer data into active engines of contextual insights and guided action. The discussion highlights the evolution of Salesforce toward an augmented ecosystem that democratizes access to analytics, accelerates decision-making, and enhances enterprise agility. Key use cases across sales, service, marketing, and leadership demonstrate the breadth of impact, while architectural principles emphasize integration, governance, security, scalability, and user experience as foundational requirements. Challenges such as data quality, AI reliability, compliance, and cultural adoption are also addressed, underscoring the need for careful strategy and governance. Looking ahead, the article outlines future directions including autonomous decision support, multimodal integration, interoperability across federated cloud environments, and the emergence of new professional roles.

DOI: http://doi.org/10.5281/zenodo.16925791

Published by: vikaspatanker