Daily Archives: September 29, 2025

Authors: Umudi E.Q, Ekpenyong I.O, Sani M.I, Onwugbuta G C, Ikechukwu S.C, Obruche E.K

Abstract: Zinc Oxide (ZnO) nanowires featuring a hexagonal configuration were successfully synthesized through the chemical bath deposition technique. The characterization of the nanowires was conducted using scanning electron microscopy (SEM), X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), and a spectrophotometer. The SEM images revealed that the diameters of the ZnO nanowires varied from 170.3 to 481 nm, indicating that a bath solution pH of 8.1 is optimal for the formation of hexagonal ZnO nanowires. The XRD patterns validated that the ZnO nanowires exhibit a hexagonal crystallite structure, with the crystallite size, determined via Scherrer’s equation, increasing with elevated annealing temperatures (0.536 nm, 0.541 nm, and 0.557 nm at 100°C, 150°C, and 200°C, respectively). EDX analysis yielded insights into the elemental composition of the samples, confirming the presence of Zn and O. Results from optical analysis demonstrated that ZnO nanowires possess high absorbance in the ultraviolet and infrared spectra while exhibiting significant transmittance in the visible spectrum. Furthermore, the absorbance of the nanowires was found to increase with higher annealing temperatures. Their notable absorbance in the ultraviolet range indicates potential applications as solar harvesters for capturing solar energy for photovoltaic panels, which can convert sunlight directly into electricity for commercial or industrial use.

DOI: http://doi.org/10.5281/zenodo.17225255

Published by: vikaspatanker

Authors: CEng. Shreekant Patil

Abstract: Providing inclusive and equitable quality education and fostering lifelong learning opportunities for everyone is the essence of Sustainable Development Goal 4 (SDG 4). In India, where a large youth population is both challenge and opportunity, developing entrepreneurial skills through quality education is central to economic prosperity and social integration. This research analyzes the crucial function of startup mentorship in promoting SDG 4 through skill building among Indian youth and adults. Capitalizing on the deep mentorship pool within India's dynamic startup ecosystem, this research investigates how mentorship initiatives enhance the quality of education by combining academic theory with experiential entrepreneurial skills, empowering excluded groups, and promoting innovation-based livelihoods. The paper also highlights the need for policy environment, ecosystem assistance, and inclusive mentorship to build scalable impact consistent with India's economic and social goals.

DOI: http://doi.org/10.5281/zenodo.17225033

Published by: vikaspatanker

Authors: Anushree, Dr. P. Lalitha Associate Professor, Dr. S. Suja Assistant Professor

Abstract: With the rapidly evolving business environment, the traditional forms of workforce planning can no longer be used to manage uncertainty, skill upheaval and rapidly shifting talent requirements. This paper provides a new data-driven framework based on Reinforcement Learning (RL) to reach the objective of Adaptive Strategic Workforce Planning (ASWP). The approach proposed is the RL-based approach, unlike the rest of the statical models because it is not based on the forecast, and even the changing conditions in all the cases of optimizing the talent decisions, instead it constantly uses the data of the organization and the forecast, it makes the adjustment itself. The conceptual model illustrates the incorporation of the key workforce planning intent such as planning talent requirements, bridging skill shortages, succession planning, and workforce cost-efficiency into a Reinforcement Learning (RL) system. The agent is addressing such factors as skill profiles, role transition, and labor market in this stage. The method of reward functions measures the extent to which the actions, such as hiring, upskilling, redeployment, and promotion are aligned with the objective of cost-efficiency, business alignment, and workforce agility. The flexibility of the model is fundamentally by the one of the complex situations and ongoing feedbacks assist in learning the ideal policies. The reward maximization, speed of convergence, ability to generalize to workforce situations and ability to scale to various organizational situations are among the most important measurement factors. Strategic results are quantified with the help of better accuracy of forecasts, reduction of talent gaps, better use of resources and better alignment to long-term business objectives. By contributing to the dynamic, robust, and interpretable planning tool used in organizations that operate in volatile labor markets, this research paper improves the use of artificial intelligence in human resource management and the workforce analytics field. The proposed method helps HR leaders to make decisions based on data about the talent path of the future. This causes the workforce planning to be a proactive strategic asset instead of a responsive program.

Published by: vikaspatanker

Authors: Salihu Sarki Ubayi, Mahmud Danladi, Abbas Sani, Habibu Idris, Salisu Mannir Ubayi, Idris Zakariyya Ishaq, Umar Shehu Ibrahim

Abstract: Structural engineering education has traditionally relied on textbooks, classroom lectures, and two-dimensional diagrams. However, students often struggle to translate these abstract resources into an understanding of real-world structural behavior. This limitation hinders their ability to connect theory with practice. To address this challenge, this paper proposes StructARLearn, a novel software platform derived from Structure + AR (Augmented Reality) + Learning. StructARLearn is an Augmented Reality (AR)-based platform designed to provide immersive, interactive, and experiential learning opportunities in structural engineering. It integrates AR visualizations, real-time finite element simulations, and interactive modules that enable students to apply loads, visualize deformations, and observe structural responses in real-world contexts through mobile devices or AR glasses. By bridging theoretical knowledge with practice, the platform improves comprehension, retention, and engagement. This paper presents the conceptualization and development methodology of StructARLearn, reviews related literature on AR in engineering pedagogy, outlines the framework of the platform, and discusses its anticipated benefits, challenges, and implications for large-scale adoption.

DOI: http://doi.org/10.5281/zenodo.17222735

Published by: vikaspatanker

Authors: Ajay Damor, Dr Nidhi Tiwari, Professor Madhavi S Bhanwar

Abstract: With the surge of data demands, ultra-reliable low-latency communications (URLLC), and massive connectivity envisioned in 6G networks, accurate and efficient channel state information (CSI) acquisition becomes critically important. Traditional channel estimation techniques often struggle under high mobility, wide bandwidths, and dense multi-user environments—especially when Non-Orthogonal Multiple Access (NOMA) is employed to improve spectral efficiency. This review surveys recent advances in hybrid techniques combining NOMA and Artificial Intelligence (AI) for channel estimation in 6G spectrum, and proposes a novel framework that leverages their complementary strengths. First, we examine the challenges in channel estimation under NOMA-based systems in 6G, including pilot contamination, interference due to superposition coding, and dynamic channel variation in mmWave/THz bands. Next, we analyze state-of-the-art AI methods—such as deep neural networks (CNNs, LSTM), graph neural networks, and reinforcement learning—that have been applied either alone or in combination with conventional estimation algorithms. We pay particular attention to hybrid approaches that integrate AI with compressive sensing, sparse recovery, or signal processing‐based beamforming to reduce estimation error and computational overhead. We then propose a hybrid AI-NOMA channel estimation model tailored for 6G, which includes: (i) user clustering and power‐domain assignment to mitigate inter-user interference in NOMA; (ii) an AI estimator (e.g., a CNN or LSTM) that refines a coarse initial estimate; and (iii) dynamic adaptation between AI and conventional methods based on channel conditions. Simulation results (or theoretical analysis) show that this hybrid approach reduces mean squared error (MSE), improves spectral efficiency, and maintains robustness under imperfect CSI and high mobility, exceeding benchmarks set by LS, MMSE, or pure AI‐based estimators. Finally, we discuss implementation considerations: training data requirements, model complexity, latency, and compatibility with existing 6G architectures. Open research directions are identified, including transfer learning across channel environments, online learning to adapt to changing spectrum conditions, and integrating with other 6G technologies such as Reconfigurable Intelligent Surfaces (RIS) and ultra-massive MIMO.

Published by: vikaspatanker