It can be seen from literature survey that the majority of the research were carried out on high-density polyethylene reinforced with plant and mineral fibers for evaluating its mechanical and tribological characteristics. Our study mainly focuses on incorporation of natural fibers along with nanoclay by varying the percentage of high-density polyethylene matrix, prepared via melt mixing method. The blends prepared were then compressed to thin sheets of size 150 x 150 x 2 mm³ in the compression molding machine at pressure and temperature of 200 MPa and 160℃ respectively, for 120 min followed by water cooling for 30 min. The effect of variation in high-density polyethylene content on the mechanical and tribological properties of the composites were studied using tensilometer and pin-on-disc test setup, respectively. The composites were also characterized for density, hardness, surface roughness and functional group identification respectively. It is observed that with the addition of 20 wt% silk fiber along with 1 wt% nanoclay in the high-density polyethylene matrix yielded improved results in terms of higher strength and modulus as well as good slide wear resistance along with low friction characteristics as compared to other composites. The results obtained have been assessed with the tensile fractured and wear damage morphology using scanning electron microscopy.

Legacy systems have provided the backbone for many organizations’ digital operations for years, yet legacy systems lack the ability to be as agile to current cybersecurity challenges. Hybrid cloud environments, mobile workers, and increased sophistication of threat actors present challenges that legacy identity management technologies fail to provide adequate security and regulatory compliance. Organizations are looking for solutions that will evolve with the increasing challenges presented by the digital environment. This paper presents research focused on creating an identity ecosystem capable of adapting to future challenges which are to replace legacy identity management systems. The Identity and Access Management (IAM) system has scalable capabilities, is aligned with regulatory requirements, and includes built-in intelligence to identify and address cyber threats. The IAM system has capabilities such as multiple forms of advanced authentication, real-time monitoring and policy-based controls to ensure all users have secure access across all environments. This allows organizations to better manage their identities, protect sensitive information and respond quickly to potential threats. The overall result is to provide a robust, compliant and intelligent digital identity solution for organizations in a world of rapid change.

Keywords: Identity and Access Management (IAM); Legacy Infrastructure Modernization; Zero Trust Security; Cloud-Based IAM; Automation and Robotic Process Automation (RPA); Artificial Intelligence (AI) and Machine Learning (ML); Governance; Risk; and Compliance (GRC); Privileged Access Management (PAM); Multi-Factor Authentication (MFA); Decentralized Identity (DID) and Blockchain; Policy-Based Access Control (PBAC); Quantum-Resistant Security; Edge and IoT Integration; Identity Intelligence and Trust Scoring

Generative Artificial Intelligence (AI) tools such as ChatGPT, Mistral, and Copilot are reshaping the educational landscape, particularly in programming and computer science education. Their capacity to generate, debug, and optimize code provides immediate performance benefits, yet their influence on long-term cognitive development remains uncertain. This article merges empirical evidence from a quasi-experimental study conducted with 151 first-year computer science students and critical reflection on the pedagogical foundations that have guided education for over half a century. Results indicate significant short-term performance gains in AI-assisted tasks (20-40% improvement) but weak correlations with unaided problem-solving performance (r ≈ 0.15, p > 0.05), suggesting limited long-term learning transfer. Drawing on Bloom’s taxonomy and cognitive offloading theory, this paper explores how reliance on AI may alter students’ metacognitive processes, diminish their ability to think algorithmically, and challenge the traditional models of knowledge acquisition. Beyond empirical findings, it argues for a new pedagogy of AI — one that balances digital assistance with critical reflection, autonomy, and ethical literacy. Generative AI should be viewed not as a cognitive shortcut but as a partner in co-creation, requiring structured integration to prevent the emergence of “artificial learners” with diminished cognitive depth.

Modern software teams frequently encounter delays in resolving recurring or related issues due to fragmented knowledge scattered across JIRA tickets, developer discussions, and GitHub pull requests (PRs). To address this challenge, we propose a Retrieval-Augmented Generation (RAG) framework that integrates Sentence-Transformers for semantic embeddings with FAISS-based vector search to deliver context-aware ticket resolution recommendations. The approach embeds historical JIRA tickets, user comments, and linked PR metadata to retrieve semantically similar past cases, which are then synthesized by a Large Language Model (LLM) into grounded and explainable resolution suggestions. The framework contributes a unified pipeline linking JIRA and GitHub data, an embedding and FAISS indexing strategy for heterogeneous software artifacts, and a resolution generation module guided by retrieved evidence. Experimental evaluation using precision, recall, resolution time reduction, and developer acceptance metrics shows that the proposed system significantly improves resolution accuracy, fix quality, and knowledge reuse in modern DevOps environments.

Keywords: Retrieval-Augmented Generation (RAG); Semantic Search; FAISS; Sentence Transformers; CodeBERT; Knowledge Reuse; Automated Ticket Resolution; Large Language Models (LLMs); JIRA, GitHub; DevOps Automation; Context-Aware Retrieval; Embedding Indexing; Explainable AI; Software Maintenance; Developer Productivity; Hybrid Retrieval; Data Drift; Hallucination Mitigation; AI-Augmented Triage

Developing cultural competence is essential for project managers and leaders working in increasingly globalized and multicultural environments. Cultural Intelligence (CQ) — defined as the ability to function effectively across diverse cultural contexts — is a critical competency in 21st-century leadership. This multidimensional construct includes metacognitive, cognitive, motivational, and behavioral dimensions, each contributing uniquely to effective cross-cultural interaction, decision-making, negotiation, innovation, and adjustment. Project managers should actively assess and cultivate their CQ through mindfulness and intentional practice, leveraging tools and structured development steps to enhance their cultural responsiveness and leadership effectiveness. Additionally, overcoming barriers such as limited perception, ethnocentrism, stereotyping, prejudice, and power imbalances is vital to achieving genuine cultural competence, which in turn fosters organizational effectiveness and team cohesion in diverse settings.

Keywords: Multicultural competence; project managers; cross-cultural projects; intercultural sensitivity; cultural intelligence; cultural dimensions; training effectiveness

In an era when global climate goals are becoming binding commitments rather than policy options, the steel industry stands at a decisive juncture. Responsible for nearly eight to eleven percent of global carbon dioxide emissions, steel production remains among the most carbon-intensive industrial activities. Transitioning this foundational industry towards sustainable production is therefore vital to achieving net-zero ambitions. Green steel technologies which employ low- or zero-carbon manufacturing methods are emerging as the cornerstone of this transformation.