Category Archives: Uncategorized

Authors: Pankaj Kumar Rai, Dr. P. N. Ahirwar

Abstract: Additive manufacturing (AM) processes qualifies in producing high-performance, complex design component with an efficient use of material. However, processing of fusion based additive manufacturing processes such as Laser Powder Bed Fusion Processes (LPBF) generates thermal stresses due to rapid heating and cooling cycles. The accumulation of these residual stresses in the printed component is undesirable and may result in dimensional distortion, anisotropy, and premature failure of components during service. Aluminium alloys such as AlSi10Mg are processed through LPBF route of AM due its excellent printability and its application in aerospace applications due to its superior fly to weight ratio. However, the printed AlSi10Mg faces challenges due to its high thermal conductivity and residual stresses. These stresses hinder dimensional tolerances and worsen mechanical performance. This review provides the overview of additive manufacturing processes with the physics of residual stress development and residual stresses in AlSi10Mg. A detailed discussion on residual stress generation, measurement and management are presented. The residual measurement strategies involving destructive, semi-destructive, and non-destructive and state-of-the-art numerical modeling approaches, including finite element–based and data-driven methods. This review aims to provide a comprehensive insight of the residual stress in additively manufactured AlSi10Mg to help in designing of component for practical application.

DOI: http://doi.org/10.5281/zenodo.18279481

Published by: vikaspatanker

Authors: Rashi N. Sadadiwala, Ashwani Raj, Dr. Krupesh A Chauhan

Abstract: Surat, a historically significant urban center in western India, is undergoing rapid urbanization, placing considerable pressure on its cultural heritage assets such as Surat Castle (Old Fort), the Dutch Cemetery, and several other historic precincts. Renowned for its textile and diamond industries, the city contributes substantially to the national economy and today spans approximately 461.60 sq. km, accommodating a population of nearly 8 million. Constructed in 1540—41 as a defensive bastion against Portuguese incursions, Surat Fort has transitioned through multiple regimes—serving as a Mughal military stronghold, a British administrative establishment, and later State Government offices. Each occupation period has left architectural and spatial imprints, collectively narrating the city's evolving political and cultural trajectory. Recognizing the fort's heritage value, the Surat Municipal Corporation (SMC) initiated a comprehensive retrofitting and conservation program under the Smart City Mission launched in 2015. The Surat Municipal Corporation (SMC) carried out the development in three distinct phases. Phase 1 focusing on foundation and primary restoration, phase 2 focusing on adaptive reuse and cultural hub whereas phase 3 focusing on heritage square and urban integration. Quantitative analysis of visitor data from January 2019 to November 2025 demonstrates a significant shift in tourism dynamics. This study critically evaluates the impacts of these interventions, particularly how heritage conservation can be harmonized with contemporary urban renewal strategies. The retrofitting works have catalyzed the revitalization of the Chowk precinct, enhanced tourism potential, and strengthened civic identity. The transformation of the one-kilometer radius urban fabric surrounding the fort, thereby reinforcing Surat's image as a dynamic yet culturally rooted urban environment. The Chowk area now emerges as a city center and heritage square, seamlessly integrating with key urban nodes—Andrews Library, J.J. Training College, the Old Civil Hospital, the Anglican Church, Gandhi Baug, local bazaars, and the SMC Muglisara institutional cluster—alongside the newly developed metro station. This research also provides a structured repository of recorded observations and spatial analyses, serving as a reference framework for future scholars and practitioners. Overall, Surat Fort stands as a model for adaptive reuse. The study further aids policymakers, administrators, consultants, and researchers in replicating similar heritage-led retrofitting initiatives in other cities.

Published by: vikaspatanker

Authors: Dr. N.R.Santhi Maheswari, L.Subhalakshmi

Abstract: A graph G is (2, r2(r − 1))-support regular, if the 2-support of every vertex is r2(r − 1), where r is the number of vertices at distance one and r(r − 1) is the number of vertices at distance 2 for every vertex. This paper deals with the concept that, for r > 1, a r-regular graph of girth ≥ 5 is (2, r2(r − 1)) support regular. The (2, r(r − 1)2) support regular graph’s existence is also proved and illustrated. Construction of such graphs is also given with some of its properties

Published by: vikaspatanker

Authors: Arona Mumtaz

Abstract: Cybercrime has emerged as one of the most significant threats in the digital era, particularly impacting vulnerable groups such as women and children. With the growing use of the internet, cybercriminals exploit anonymity to engage in illegal activities that range from harassment to defamation and pornography. This paper examines various forms of cybercrime, with a particular focus on crimes targeting women, such as cyber harassment, cyber stalking, and cyber pornography. It discusses notable cases and the legislative framework in India aimed at combating these crimes. Despite existing laws, the paper highlights gaps in enforcement and the challenges posed by anonymity on the internet. Additionally, empirical evidence is presented to highlight the prevalence of cybercrime, its impact on victims, and the challenges in enforcement. The article concludes by offering suggestions for improving legal enforcement, public awareness, and privacy protection to combat the rising tide of cybercrime.

DOI: https://doi.org/10.5281/zenodo.18266294

 

Published by: vikaspatanker

Authors: Oksana Anatolyevna Malysheva

Abstract: They've opened up the door to instant fund transfers and around-the-clock availability. But at the same time made us more exposed to scammers and money laundering schemes, when real-time payments became a norm. Coming racing up against the tight timeframes and limited space to go back and correct anything that's gone wrong, the old way of doing things just isn't working anymore. This paper takes a hard look at the analytical and infrastructure-related issues surrounding the detection of fraud and money laundering in real-time payment systems, where speed, accuracy and meeting regulations all need to be juggled at the same time. Well-known techniques won’t cut it anymore in the world of real-time, so the researchers here take a more applied approach, merging real-time analytics systems, cutting-edge fraud detection and money laundering models. They lay out a comprehensive blueprint for real-time transaction analysis, fine-tuning features for ultra-fast decision-making, hybrid rule-based and AI-driven systems and risk-scoring that’s tailored to the flow of instant payments. When evaluating the performance of fraud and money laundering systems in real-time, this paper looks beyond the traditional measure of accuracy and zeroes in on things like response times, scalability and false positives, which are pretty critical in the real-time world. The real contributions of this work are threefold: a crystal-clear picture of the threats facing real-time payments, a logical analytical framework that gets the balance between detection models, real-world timeframes and regulatory expectations, and some down-to-earth advice on how to run your fraud and money laundering systems in a way that is not only effective but also explainable and scalable.

Published by: vikaspatanker

Authors: James Anderson

Abstract: The use of artificial intelligence technologies in tax administration is becoming increasingly widespread worldwide to increase efficiency and detect fraud. Tools such as chatbots, risk ratings and predictive analytics optimise workflows, but their wider use in administrative decision-making raises legal and structural challenges. There is a critical difference between decision-supporting and autonomous artificial intelligence. Over-reliance on automated systems risks eroding legal expertise and obscuring decision-making, making it difficult for taxpayers to seek redress. Taxpayer profiling carries the risk of discriminatory treatment, so rigorous testing and minimisation of bias are necessary. In terms of methodological foundations, the study used dogmatic and transdisciplinary analysis to examine the opportunities and risks from an international perspective. The advantages of artificial intelligence include the real-time analysis of large amounts of data, which helps to filter out tax avoidance schemes and reduce the administrative burden on taxpayers (e.g. pre-filled tax returns). At the same time, the "black box" phenomenon violates the principle of transparency. The US and the OECD aim to improve efficiency and develop taxpayer services using artificial intelligence tools. The EU takes a risk-based approach, imposing strict requirements on high-risk artificial intelligence systems and emphasising the need for human oversight and legal remedies. Australian examples (Robodebt, Pintarich cases) highlight the legal and human rights risks of faulty algorithms, underlining the need for accountability. Success lies in striking a balance: while exploiting technological efficiency, it is necessary to guarantee human oversight, the accountability of algorithms and the protection of taxpayers' fundamental rights. Artificial intelligence should support fair law enforcement, not replace it.

DOI: http://doi.org/10.5281/zenodo.18265078

Published by: vikaspatanker

Authors: Pavan M, Pavan V S, Pranav H Nayak, Sanath G, Dr. R Manjunatha

Abstract: Glaucoma is a serious eye disease that leads to irreversible vision loss if not detected at an early stage. It is primarily caused by increased intraocular pressure, which damages the optic nerve. Early diagnosis plays a crucial role in preventing permanent blindness; however, traditional diagnostic methods are time-consuming and require expert ophthalmologists. This project presents an automated glaucoma detection system using digital image processing techniques applied to retinal fundus images. The proposed system focuses on extracting key features such as the optic disc, optic cup, and calculating the cup-to-disc ratio (CDR), which is a significant indicator of glaucoma. Image preprocessing techniques including noise removal, contrast enhancement, and segmentation are employed to improve accuracy. The extracted features are then analyzed to classify the eye as normal or glaucomatous. The system aims to provide a cost-effective, efficient, and reliable method for early glaucoma screening, thereby assisting ophthalmologists in diagnosis and reducing the risk of vision loss.

Published by: vikaspatanker

Authors: Asst. Prof Anil Choubey

Abstract: This paper presents the estimation of harmonics in a voltage source converter based HVDC (VSC-HVDC) system for designing AC side filters. The extended VSC and PSO is well known for estimating amplitude, phase, frequency, and harmonic content of a signal corrupted with noise. However, the algorithm suffers from instability due to linearization and costly calculation of Jacobian matrices, and its performance deteriorates when the signal model is highly nonlinear. This paper, therefore, proposes an unscented to overcome these difficulties of linearization and derivative calculations for robust tracking of harmonics in VSC-HVDC system. The model and measurement error covariance matrices Q and R along with the VSC parameters are selected using a modified particle swarm optimization (PSO) algorithm. To circumvent the problem of premature convergence and local minima, a dynamically varying inertia weight based on the variance of the population fitness is used. This results in a better local and global searching ability of the particles, which improves the convergence of the velocity and better parameters. Various simulation results for harmonic signals corrupted with noise obtained from VSC-HVDC system reveal significant improvement in noise rejection and speed of convergence and accuracy.

Published by: vikaspatanker

Authors: Suman Lata, Dr. Upendra Kumar Srivastava

Abstract: Generative models such as GANs and diffusion systems enable the creation of highly realistic fake videos, eroding confidence in online content across social, political, and legal domains. Synthesizing insights from more than 85 scholarly articles published between 2018 and 2025, this work categorizes detection methods into spatial CNNs that identify frame-level flaws, temporal RNNs/LSTMs for motion inconsistencies, RPG-based physiological cues, and fused audio-video approaches. Evaluations on datasets like Celebs and Wild Deepfake yield accuracies above 95% in controlled settings, but cross-dataset generalization and defenses against advanced forgeries falter. Hybrid architectures with transformers emerge as leaders, revealing critical gaps in real-time efficiency and edge-device applicability to steer forthcoming innovations.

DOI: https://doi.org/10.5281/zenodo.18253039

 

Published by: vikaspatanker

Authors: Vishrut Nath Jha, Joanne Anto, Athira KK

Abstract: Recent advancements in vector databases and embedding-based retrieval have transformed how large unstruc- tured datasets are indexed and searched. However, the perfor- mance of emerging storage and retrieval systems varies widely depending on their architectural design and optimization goals. This study presents a comparative evaluation of three distinct approaches MemVid, Qdrant, and Amazon S3 Vectors using a dataset of 10,417 medical-text chunks derived from research documents. Each system was assessed in terms of indexing efficiency, retrieval accuracy, latency, and resource utilization. Experimental results demonstrate that MemVid, which stores em- beddings in a video-encoded FAISS-based format, achieved lower query latency and higher retrieval precision for this corpus, while Qdrant exhibited superior scalability and flexibility in handling dynamic updates and metadata filtering. Amazon S3 Vectors, though currently in preview, offered cloud-native durability and seamless AWS integration with moderate performance overhead. The analysis reveals that no single vector system universally outperforms others; rather, each excels under specific workload conditions. These findings provide practical guidance for selecting appropriate vector storage backend based on corpus size, update frequency, and deployment environment.

DOI: http://doi.org/10.5281/zenodo.18243168

Published by: vikaspatanker