Category Archives: Uncategorized

Authors: Dr Saumya Bahadur

Abstract: This paper examines the Laṅkāvatāra Sūtra, a foundational text of Yogācāra Buddhism, through the lens of adversarial pedagogy and compares it with contemporary machine learning models, particularly Generative Adversarial Networks (GANs). The Sūtra is notable for its dialogical structure, in which the bodhisattva Mahāmati poses questions, challenges, and objections to the Buddha, who systematically deconstructs these conceptual formulations. This adversarial exchange is not merely rhetorical but functions as a pedagogical process: erroneous views and dualistic constructs are generated, tested, refuted, and refined until the practitioner’s reliance on conceptual elaboration collapses. In this way, the teaching method itself resembles an adversarial learning model, where insight emerges through continuous confrontation with errors. In this paper the author explores the method of learning where adversarial views are used to engage in deep learning and transcendence. GANs provide a modern analogue: they consist of two competing networks—a generator that produces synthetic outputs and a discriminator that evaluates their authenticity. Through iterative feedback and critique, both models improve in tandem, eventually producing outputs indistinguishable from real data. Similarly, the Buddha’s adversarial dialogues expose the “generated illusions” of discriminative thinking, while the “discriminator” function is represented by wisdom (prajñā), which identifies and dismantles conceptual fabrications. The comparison highlights both parallels and divergences. While GANs aim at convergence toward increasingly realistic outputs within representational constraints, the Buddhist adversarial method seeks not fidelity to appearances but the transcendence of representational frameworks altogether, pointing toward non-dual realization and liberation from suffering. This contrast underscores how ancient epistemic practices may resonate with modern computational paradigms while also exceeding them in scope, embedding cognitive, ethical, and soteriological dimensions absent in machine learning. The paper thus proposes that reading the Laṅkāvatāra Sūtra as an adversarial pedagogy provides fertile ground for interdisciplinary inquiry, bridging Buddhist philosophy, cognitive science, and artificial intelligence research.

Published by: vikaspatanker

Authors: Esakkiammal N, Deebika S

Abstract: Artificial Intelligence (AI) chatbots have become integral to modern communication, providing services ranging from customer support to mental health assistance. Their rapid adoption raises critical questions about their psychological influence on users. This study investigates the impact of AI chatbots on human emotional well-being, emphasizing psychological mechanisms such as emotional regulation, social support, companionship, and dependency. Using a mixed-methods approach—combining surveys and semi-structured interviews—the study examines the extent to which chatbots contribute to emotional support and their potential to induce dependency or social withdrawal. Results suggest that while AI chatbots can positively influence emotional well-being by providing accessible support, they may also create challenges, such as emotional over-reliance and diminished real-world social engagement. The paper concludes with practical, ethical, and design recommendations for AI chatbot developers, emphasizing the importance of balancing technology with human-centric emotional care.

Published by: vikaspatanker

Authors: Mallesh Miryala

Abstract: In integration-heavy Salesforce environments, release reliability depends less on the deployment tool and more on engineering controls: correct metadata scoping and ordering, explicit boundary contracts, retry-safe synchronization, and operational feedback. This article presents an end-to-end delivery model that (i) represents metadata, code, and access controls as a dependency graph to build deterministic delta packages and select relevant tests; (ii) treats system boundaries as executable API and event contracts, verified in CI by both providers and consumers to prevent drift; and (iii) implements integrations as idempotent, retry-safe state machines using external identifiers, payload digests, and bounded deduplication windows. We show how layered quality gates—static analysis, targeted suites, contract checks, and observability signals—create a control loop that reduces change-failure rate and shortens recovery time. The result is an implementation-oriented guide, with algorithms, diagrams, and reference patterns for teams operating Salesforce alongside middleware such as MuleSoft and legacy systems including GIS, ERP, and data platforms.

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Authors: Ms. Nirmala R G, Pratap K V, Nithilan I

Abstract: The growing adoption of electric vehicles (EVs), renewable-energy microgrids, and portable power systems has intensified the need for efficient and reliable battery management strategies. Conventional passive balancing circuits in lithium-ion battery packs dissipate excess energy as heat, resulting in low efficiency, poor scalability, and thermal stress. This paper presents an Active Cell Balancing Battery Management System (ACB-BMS) employing a bidirectional buck–boost converter topology integrated with an Extended Kalman Filter (EKF)-based state-of-charge (SOC) estimation algorithm. The system dynamically redistributes charge between cells, achieving faster equalization and significantly reduced energy loss compared with resistor-based methods. The EKF enables accurate real-time tracking of each cell’s SOC, improving safety and charge control under varying load and temperature conditions. A complete MATLAB/Simulink simulation model of the proposed system has been developed and validated, demonstrating superior voltage uniformity, faster balancing response, and enhanced energy efficiency. The proposed approach forms a practical foundation for next- generation intelligent BMS architectures suitable for electric vehicles and hybrid renewable-energy storage. Future hardware implementation is planned to extend the technology toward commercial-grade embedded platforms.

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Authors: Hanumesha S T

Abstract: Percolation theory provides a mathematically elegant and practically powerful framework for modeling connectivity transitions in random media, with applications ranging from porous materials and composite conductivity to epidemics, network robustness, and transport in disordered systems. A central quantity is the percolation threshold p_c, the critical occupation probability at which macroscopic connectivity emerges with nontrivial scaling. Although p_c is known exactly for a few planar cases and lattices, many practical scenarios require estimation under finite-size, boundary, and uncertainty constraints. This paper develops a rigorous and computation-oriented methodology for percolation threshold estimation that couples (i) probabilistic inequalities and bracketing arguments (crossing probabilities, monotonicity, sharp-threshold heuristics, and finite-size scaling), with (ii) simulation-based estimators (spanning probability curves, union-find connectivity, confidence intervals, and extrapolation). We emphasize a "two-engine" approach: bounds that constrain plausible threshold locations and simulation that refines the estimate while quantifying uncertainty. We also introduce an uncertainty-aware parameterization using intuitionistic fuzzy sets and (hyper)graph abstractions to represent ambiguous occupancy mechanisms and heterogeneous coupling patterns; this is motivated by real settings where the effective "open probability" is not a crisp scalar but a range informed by measurement noise or multi-factor criteria. The final manuscript provides a Word-ready, mathematics-forward exposition, with figures and tables embedded to illustrate lattice configurations, spanning curves, scaling collapse, and probabilistic bracketing.

DOI: http://doi.org/10.5281/zenodo.18092138

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Authors: Vishal Ranjan, Dr. Jyoti Yadav

Abstract: High-Performance Concrete (HPC) is an advanced form of cement concrete where ingredients are selected and proportioned to enhance various properties of the concrete in both fresh and hardened states. One key feature of HPC is its higher strength, which offers significant structural advantages. The primary components contributing to the cost of a structural member are concrete, steel reinforcement, and formwork. This paper compares these components when higher-grade concrete, specifically HPC, is used, and highlights how high-strength concrete provides the most economical solution for designing load-bearing members, particularly in carrying vertical loads to the building foundation through columns. The mix design variables critical to concrete strength include the water-cementitious material ratio, total cementitious material, cement-admixture ratio, and superplasticizer dosage, which are analyzed to achieve the desired high-grade concrete mix.

DOI: https://doi.org/10.5281/zenodo.18092124

 

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Published by: vikaspatanker

Authors: Vishal Ranjan, Dr. Jyoti Yadav

Abstract: High-Performance Concrete (HPC) has become an essential material in modern construction due to its superior mechanical properties, durability, and environmental benefits. This paper explores the use of HPC in building structures within India, with a focus on its performance, advantages, and the impact on the construction industry. By reviewing recent studies, case studies, and performance data, this research demonstrates the role of HPC in enhancing structural integrity, reducing maintenance costs, and contributing to sustainability. The paper also discusses the challenges and potential future directions for the use of HPC in India’s infrastructure development.

DOI: https://doi.org/10.5281/zenodo.18091902

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Authors: Ms.Dhanushni.N, Ms.Vivisha Catherin.P

Abstract: Alzheimer’s disease (AD) is a pressing global issue, It’s known as the severe neuron disease. They Mainly damages the Brain cells, which leads to permanent lose of memory which is also called dementia. Many people die due to this disease every year because it is not curable but the early detection can prevent from spreading. Alzheimer’s are most commonly found in the elder peoples or from the age of (60 and above). It requires an efficient and automated system which can detect the disease and classify it in the basis of Alzheimer’s stages like Mild Demented(MD), Moderate Demented(MOD), Non Demented(ND), Very Mild Demented(VMD). For the prediction we use Machine learning and deep learning Algorithm’s like convolutional neural networks for imaging data(CNNs), Random forest and Gradient Boosting(XGBoost / LightGBM), Support Vector Machines(SVM) Which is much more efficient from the preexisting models of the Alzheimer Detection. Of relying on methods, like CNNs and SVM for our model design like Random Forest and XGBoost do typically with fixed structures and manual feature selection processes; we take a different approach thats more intricate and advanced by utilizing transfer learning through the InceptionV3 network already trained on ImageNet for its robust feature extraction abilities. To boost our models effectiveness in handling datasets adequately; we integrate various data augmentation methods such as adjusting image angles and proportions along, with mirroring techniques. Address the issue of class distribution by adjusting the weights for classes to focus more on identifying cases of Alzheimers disease accurately. In addition, to this adjustment in class weighting strategy consider implementing techniques like dropout regularization method and early stopping along with model checkpoint mechanism to prevent the model from learning noise and improve generalization. This holistic strategy leads to a model that's proficient in reducing both positives and false negatives which is crucial, in accurate medical diagnosis.

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Authors: Mallesh Miryala

Abstract: Large organizations run on operational data that changes every hour: people join and leave, locations are renamed, incidents unfold, and responsibilities shift. In practice, the hardest part is not storing records; it is keeping the records consistent enough that policy decisions and workflows remain trustworthy. This paper proposes a practical design pattern called the policy- aware operational graph. The pattern treats people, organizational units, locations, requests, and tasks as a connected graph with explicit ownership and audit history. It combines three ideas that are often built separately: identity lifecycle management, rule-driven routing, and cross-system transaction safety. The design is informed by field experience maintaining a continuity platform at a large public university and building high-volume fulfillment workflows at a national telecom. The paper contributes a reference architecture, a repeatable identity hygiene loop for key contacts, and an efficient duplicate-detection method that routes only uncertain cases to human review. A small reference implementation is provided to demonstrate how blocking keys and union-find can scale to large datasets without excessive memory or quadratic comparisons.

Published by: vikaspatanker