Daily Archives: October 7, 2026

Authors: Mashiba Alfred Kalaba, Dr. C. Maria Ugin Joseph

Abstract: The study investigated the use of graphical methods by teachers when teaching coordinate geometry in Zambian secondary school to be specific in Chainda, Chalimbana and Chongwe secondary schools in Chongwe district Lusaka province. Coordinate geometry is an important topic in mathematics that connects algebra and geometry through graphical representations. However, many learners experience difficulties in understanding the topic due to abstract teaching approaches. The study examined how teachers apply graphical techniques, the challenges they encounter, and the effects of graphical methods on learners’ understanding. A descriptive research design was used. Data were collected from mathematics teachers and learners through questionnaires, interviews and classroom observations. Findings reveal that graphical methods improve learners’ conceptual understanding, engagement, and performance in coordinate geometry. However, limited teaching resources, insufficient teacher training and time constraints hinder effective use of graphical approaches. The study recommends increased teacher training, provision of teaching aids and integration of digital tools such as GeoGebra to enhance the teaching of coordinate geometry.

DOI: http://doi.org/10.5281/zenodo.23212369

Published by: vikaspatanker

Authors: K. Prathip, G. suresh

Abstract: This study evaluates the seismic response of a G+15 reinforced-concrete framed building for three geometric configurations: a setback building on level ground, a step-back building on 30° sloping ground, and a step-setback building on 30° sloping ground. Three-dimensional models were analysed in STAAD.Pro using the equivalent static lateral-force method and linear time-history concepts in accordance with the source study's IS 1893:2002 framework. The comparative assessment covers storey drift, storey shear, time period, displacement, and column actions including shear force, bending moment, torsion and axial force under critical load combinations. The results demonstrate strong sensitivity of seismic response to vertical and plan irregularity and to the change in column geometry caused by sloping terrain. The setback configuration develops comparatively larger tensile moments in several critical cases, while short columns in the step-back and step-setback configurations require particular design attention. The complete numerical result tables, response curves, model views and comparative plots from the project study are retained in this manuscript to support transparent comparison among the three configurations.

DOI: http://doi.org/10.5281/zenodo.23211320

Published by: vikaspatanker

Authors: Rohit Agnihotri.

Abstract: The rapid proliferation of cloud-native environments, decentralized workforces, and API-driven architectures has exposed critical limitations in conventional Identity and Access Management (IAM) systems. This article examines the emergence of Agentic Artificial Intelligence (AI)-driven IAM frameworks as a transformative paradigm for enterprise security. Drawing on empirical data from industry reports, peer-reviewed studies, and real-world deployment analyses, we evaluate the architecture, operational benefits, risk dimensions, and governance requirements of AI-powered IAM systems. Our findings indicate that agentic AI integration yields measurable improvements across breach detection (up to 87%), provisioning speed (82%), and compliance automation (91%), while simultaneously introducing novel challenges in auditability, model explainability, and adversarial robustness. We propose a multi-layered governance model and research agenda to guide responsible adoption.

DOI: https://doi.org/10.5281/zenodo.23209786

Published by: vikaspatanker

Authors: Rohit Agnihotri

Abstract: The deployment of agentic Artificial Intelligence (AI) systems (autonomous, goal-directed agents capable of multi-step action, tool use, and dynamic resource acquisition) across enterprise environments has outpaced the development of the governance infrastructure required to account for, audit, and regulate their actions. While conventional Identity and Access Management (IAM) frameworks were designed for human actors executing predictable, bounded operations, agentic systems operate with a degree of autonomy, adaptability, and action scope that renders existing accountability models structurally inadequate. This article presents a comprehensive analysis of identity, access, and auditability frameworks for agentic actions, with a focus on three interdependent governance dimensions: traceability (the capacity to reconstruct the complete causal chain of an agentic action), explainability (the generation of human-intelligible justifications for agent decisions and actions), and compliance (the systematic alignment of agentic behavior with regulatory requirements and organizational policy). We introduce the Agentic Identity and Auditability Framework (AIAF), a seven-pillar governance architecture that addresses the full lifecycle of agentic identity, action attribution, and accountability. Empirical analysis demonstrates that organizations deploying formal agentic auditability frameworks achieve 72–97% traceability coverage across seven audit layers compared to 5–71% without formal frameworks, and automate 58–83% of compliance evidence generation across applicable regulatory frameworks. We further develop a five-level Agentic Autonomy Classification Model aligned with SAE automation level conventions, providing a principled basis for calibrating governance intensity to agent autonomy. The article concludes with critical research directions in cryptographic action attribution, privacy-preserving audit architectures, and the governance of self-modifying agentic systems.

DOI: https://doi.org/10.5281/zenodo.23206322

Published by: vikaspatanker

Authors: Renuka Kantilal Thange, Dr. Hitesh Sharma

Abstract: The rapid advancement of Artificial Intelligence (AI) is transforming professional photography by introducing intelligent systems that assist photographers in technical and creative decision-making. While AI-powered tools have significantly improved image enhancement and editing, limited research has explored their collaborative role in professional toddler photography, where creativity, lighting precision, child comfort, and safety are equally important. This study proposes a Human–AI collaborative framework that integrates intelligent lighting systems with the expertise of professional photographers to optimize lighting selection during toddler photography sessions. The research examines how AI-assisted recommendations can support decisions regarding natural, artificial, and hybrid lighting environments while maintaining the photographer's creative control. A mixed-method research design is proposed, involving professional photographers and parents to evaluate image quality, creativity, workflow efficiency, toddler comfort, and overall photographic outcomes. The study aims to demonstrate that Human–AI collaboration enhances decision-making rather than replacing professional expertise. The proposed framework contributes to intelligent photography practices by combining computational intelligence with artistic creativity and offers practical guidelines for integrating AI into professional toddler photography.

DOI: https://doi.org/10.5281/zenodo.23205864

Published by: vikaspatanker

Authors: Ravindra Babu Annam

Abstract: RAG enables large language models to give responses that can be enhanced with knowledge extracted from external documents. However, the retrieval of documents exposes the language models to security threats because the information retrieved may be poisoned and used for manipulation of the model. Current security mechanisms are tailored for particular attacks and hence cannot provide end-to-end protection of the entire retrieval to generation process. This study proposes a Secure Retrieval-Augmented Generation Framework (SRAGF) that enables the protection of RAG-based models against data poisoning and prompt injection attacks. It conducts document relevance assessment, source credibility evaluation, semantic anomalies recognition, suspicious pattern recognition, poisoning detection, injection detection, risk classification, context sanitization, knowledge and instruction separation, and output security evaluation. Security scores are calculated for the retrieved documents, which are classified accordingly and included in the context of generation. Documents that pose high risk are stored separately, the ones presenting medium risk undergo sanitization, and those that do not present any threat are retained for context generation. The baseline frameworks for the comparative analysis are Wang-TAD and Neural Cleanse. In the prepared evaluation results, SRAGF exhibits higher detection rates, precision, recall, and F1-score compared to the two baseline frameworks while lowering attack.

DOI: http://doi.org/10.5281/zenodo.23184152

Published by: vikaspatanker