Daily Archives: September 8, 2025

Authors: Nikhil Gaur, Dr. Jyoti Yadav

Abstract: To investigate the lateral ultimate bearing capacity of cantilever rigid piles subjected to large horizontal displacement, this paper analyzes the distribution characteristics of soil resistance along the pile side and explores calculation methods for lateral bearing capacity of pile foundations using both numerical simulation and theoretical approaches. The results indicate that, under large displacement conditions, the soil in front of the pile yields progressively from top to bottom. Once the soil adjacent to the pile reaches its limit displacement, the lateral soil resistance no longer increases with further displacement. The ultimate lateral bearing capacity of cantilever rigid piles under large horizontal displacement is determined based on the ultimate displacement of the side soil. Among the tested approaches, the modified “m” method demonstrates the best fitting accuracy. However, further investigation is required to define the applicable range of foundation coefficient distribution.

DOI: https://doi.org/10.5281/zenodo.17081470

 

 

Published by: vikaspatanker

Authors: Nikhil Gaur, Dr. Jyoti Yadav

Abstract: Most of the sub-structures of new railway river bridges in India are built with solid mass concrete gravity piers and abutments. These piers, designed without steel reinforcement, rely on the assumption that they are not subjected to tensile stresses under regular loading. However, during high-magnitude earthquakes, their safety becomes a critical concern, particularly in seismically active regions of India. This study assesses the seismic vulnerability of solid gravity bridge piers, which are key components of railway bridges, since they transfer loads between the substructure and the superstructure. Seven existing piers from the state of Gujarat were analyzed using free vibration analysis and nonlinear static (pushover) analysis in ABAQUS. Free vibration analysis revealed that the fundamental mode mass participation was always below 50%, while the cumulative participation of the first six modes remained under 80%, demonstrating significant contributions from higher vibration modes. Pushover analysis results confirmed the limited ductility of solid piers and highlighted their susceptibility under seismic excitations. The study emphasizes the need for seismic strengthening strategies to ensure the safety and serviceability of such piers.

DOI: https://doi.org/10.5281/zenodo.17081435

 

 

Published by: vikaspatanker

Authors: ER Sandeep Tandon

Abstract: This paper presents a detailed simulation and analysis of unsymmetrical faults (LG, LL, LLL) in a three-phase grid interconnection using MATLAB/Simulink. The model includes two voltage sources representing grid ends, connected via a two-pi section transmission line to simulate realistic power transfer conditions. The system response to each fault type is analyzed in terms of voltage and current distortions. Separate fault simulations are carefully modeled using practical parameters. Output data is collected using Workspace blocks and statistically analyzed to extract minimum, maximum, and mean values. Comparative tables and waveform plots illustrate the behavior of each fault type. Additionally, the paper discusses the theoretical basis of fault currents using symmetrical components. The study aims to serve as a base model for educators, researchers, and developers of AI-based fault detection systems.

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Published by: vikaspatanker

Authors: Sharma Pankaj Kanhaiya, Professor Devendra Sharma, Professor Saurabh Gupta

Abstract: The integration of photovoltaic (PV) systems with battery and supercapacitor storage in DC microgrids demands efficient energy management to enhance system stability, reliability, and operational efficiency. This research presents an optimized neural network-based energy management approach tailored for a standalone DC microgrid incorporating PV panels, lithium-ion batteries, and supercapacitors. The neural network model is specifically designed to handle the nonlinear characteristics of the microgrid, optimize power flow, and maintain the state of charge (SoC) of energy storage devices within safe limits. By utilizing advanced training algorithms inspired by optimization techniques such as artificial rabbit optimization, the proposed system achieves improved prediction accuracy and load balancing. The approach also integrates a fuzzy logic control mechanism to facilitate real-time adaptive responses to dynamic load changes and renewable generation variability. Simulation results demonstrate enhanced voltage stability, reduced power fluctuations, and efficient energy distribution compared to conventional methods. This optimized neural network strategy effectively mitigates the challenges inherent in hybrid energy storage management, promoting longer battery life, quicker response times from supercapacitors, and overall system resilience. The study contributes significant insights toward the development of intelligent energy management systems for sustainable and autonomous DC microgrid applications ((PDF) Artificial Rabbits Optimized Neural Network-Based Energy …, 2024).

Published by: vikaspatanker

Authors: Sagar Gupta

Abstract: Crowdsourcing has emerged as a powerful mechanism for harnessing distributed human intelligence at scale, enabling diverse applications such as data annotation, collective problem solving, and decision-making across domains. With the advent of neural networks, crowdsourced activity has been both a source of critical training data and an arena for deploying advanced artificial intelligence systems to optimize participation, reliability, and outcome quality. This paper explores the intersection between crowdsourced activity and neural networks, emphasizing how neural architectures are applied to classify, validate, and enhance crowd contributions. The discussion spans natural language processing, computer vision, recommendation systems, quality assurance, and hybrid human–AI collaboration frameworks. The review concludes with challenges in scalability, bias mitigation, and ethical considerations, highlighting emerging opportunities for integrating neural networks to reshape crowdsourced ecosystems.

DOI: http://doi.org/10.5281/zenodo.17075867

Published by: vikaspatanker

Authors: Sagar Gupta

Abstract: Enterprise Resource Planning (ERP) systems have long served as the backbone of organizational information systems, integrating finance, operations, human resources, supply chains, and customer-facing processes into unified platforms. Traditionally, ERP implementations relied on rule-based configurations and deterministic workflows. However, the evolution of neural networks has introduced adaptive, data-driven intelligence into ERP ecosystems. Neural architectures are increasingly being deployed to enhance demand forecasting, anomaly detection, process optimization, and user personalization within ERP systems. This paper traces the evolution of neural networks in ERP implementations, from early adoption in predictive analytics to contemporary applications in autonomous process automation and decision intelligence. It also explores case studies, challenges, and future research directions, highlighting the transformative potential of neural networks in reshaping the ERP landscape

DOI: http://doi.org/10.5281/zenodo.17075766

Published by: vikaspatanker

Authors: Sagar Gupta

Abstract: The financial domain is inherently dynamic, stochastic, and complex, making it one of the most fertile grounds for the application of advanced machine learning techniques. Among these, Recurrent Neural Networks (RNNs) have emerged as particularly well-suited for modeling sequential and temporal dependencies in financial data. This paper explores the role of RNNs in complex finance applications, tracing their evolution from basic time-series forecasting to modern variants such as Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs). The discussion highlights applications in algorithmic trading, credit risk assessment, fraud detection, portfolio optimization, and regulatory compliance. Case studies are presented to illustrate both the potential and the limitations of RNNs in finance. The paper concludes with a critical discussion of challenges such as interpretability, overfitting, adversarial risks, and future research directions, including hybrid neuro-symbolic architectures and transformer-RNN hybrids for financial intelligence

DOI: http://doi.org/10.5281/zenodo.17075673

Published by: vikaspatanker

Authors: Sagar Gupta

Abstract: Recent advances in Natural Language Processing (NLP) have been driven by the widespread adoption of large-scale pretrained language models (LMs). While generic NLP models such as GPT, BERT, and T5 exhibit strong zero-shot and few-shot performance across diverse tasks, specialized NLP models (e.g., BioBERT, FinBERT, SciBERT) are fine-tuned on domain-specific corpora to achieve superior performance in targeted applications. With the emergence of prompt engineering as a method to guide large language models (LLMs), a new research challenge arises: can prompt engineering narrow the performance gap between generic and specialized models, or does domain-specific pretraining remain necessary? This paper provides a comparative analysis of generic and specialized NLP models under different prompt-engineering strategies, focusing on domains such as finance, healthcare, and legal text processing. Experimental findings indicate that while prompt engineering enhances the adaptability of generic LMs, specialized models continue to outperform in precision-critical tasks. The study underscores the complementary role of prompt design and domain-specific adaptation in the next generation of NLP systems

DOI: http://doi.org/10.5281/zenodo.17075611

Published by: vikaspatanker