| 1 |
Author(s):
Ogwu Gabriel Elojo, Adewuyi Benjamin O., Talabi H. K., Efozia Ngozi Fidelia.
Page No : 1-11
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Prospective Revisions on the Use of Machine Learning in the Aluminum Alloy Development: A Review on Techniques and Future Applications.
Abstract
Alloy design is the major driving force of next-generation materials technology. Traditionally alloy design has relied on empirical rules and iterative trial-and-error experimentation, with the process of identifying novel compositions being time-consuming, costly, and inefficient. The landscape has recently been revolutionized by advances in machine learning (ML) that enable data-driven methods to improve the efficiency of sophisticated alloy design, selection, and property prediction. ML algorithms can learn effectively the relationships between composition, processing, structure, and properties from existing data and thus guide the discovery of novel alloys with target properties. In this review, a survey of ML approaches employed in alloy design is provided, including supervised and unsupervised learning, feature engineering, and combination with physical modeling frameworks such as CALPHAD.
| 2 |
Author(s):
Ifekanandu Chukwudi Christian (Ph.D.).
Page No : 12-27
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Automated Material Handling and Operational Efficiency of Industrial Goods Manufacturing Companies in South-West, Nigeria.
Abstract
This study examined automated material handling and operational efficiency of industrial goods manufacturing companies in South-West Nigeria. The study adopted the correlational research design and the quantitative research approach. The population of the study consisted of 42 registered industrial goods manufacturing companies in South-West Nigeria. Thirty-one (31) industrial goods manufacturing companies were selected for the study using purposive sampling method. The sampling unit consisted of managers of the 31 selected industrial goods manufacturing companies in South-West Nigeria. A sample size of 155 managers was drawn from the 31 selected industrial goods manufacturing companies on the ratio of 5 managers per company. A structured questionnaire was used as the main instrument for data collection. The data collected were analyzed statistically while the hypotheses were tested using the Spearman Rank Order Correlation Coefficient (rho). The SPSS version 24 was used for data processing. The findings revealed that automated material storage has significant relationship with operational efficiency (time efficiency and cost efficiency) of industrial goods manufacturing companies. The study also found a significant relationship between automated material movement and operational efficiency (time efficiency and cost efficiency) of industrial goods manufacturing companies. Based on these findings, it was concluded that automated material handling such as automated material storage and movement is significantly related to operational efficiency of industrial goods manufacturing companies in South-West Nigeria. Therefore, it was recommended that industrial goods manufacturing companies in Nigeria particularly those that are yet to apply modern technology in their material handling processes should automate their material handling processes as it would enhance operational efficiency.
| 3 |
Author(s):
Abubakar Jibrin, Aliyu Sani Ahmad.
Page No : 28-40
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Enhancing Security in Cloud Computing Environment.
Abstract
Cloud computing is the most emerging technology that becomes the demanding architecture for IT enterprise. It exhibits remarkable potentials to offer cost-effective and more flexible service to the customers over the network. A vast number of big organizations like google, Facebook, Dropbox etc. all depends upon this type of computing. It dynamically increases the capability of the organization without training new people. Cloud computing moves its database and applications on various data centers across various countries where management of data and its security is the major concern. The dynamic and scalable nature of cloud computing creates security challenges in their management examining policy failure or malicious activity. In this paper, we examine the detailed design of cloud computing architecture in which service models, deployment models, cloud security are exploded. Furthermore, this study identifies the security challenges in cloud computing during the transfer of data into the cloud and provides a viable solution to address the potentials threats.
| 4 |
Author(s):
Elkhidir Tayallah Yousif, Abdelfattah Hafiz MohammedAhmed.
Page No : 41-47
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Theoretical Analysis and Stability Assessment of Machine Learning-Enhanced Numerical Solvers for Nonlinear UAV Flight Dynamics.
Abstract
Accurate numerical modelling of nonlinear unmanned aerial vehicle (UAV) flight dynamics remains a critical challenge, particularly under strongly nonlinear operating conditions where classical solvers require small integration step sizes to maintain stability. This study presents a hybrid numerical framework that integrates a fourth-order Runge-Kutta (RK4) solver with a learning-based correction mechanism to enhance accuracy while reducing computational cost.
The proposed approach is analytically investigated in terms of numerical error decomposition, convergence behaviour, and stability characteristics. A multilayer perceptron (MLP) is employed to learn the residual numerical error and provide corrective updates while preserving the underlying physical structure of the system. Numerical validation is performed using flight trajectory datasets representing realistic UAV operations. The results demonstrate that the proposed framework achieves a Root Mean Square Error (RMSE) of 1.82 × 10⁻³ rad in pitch angle prediction while reducing computational cost by approximately 38% compared to the baseline RK4 solver. The findings confirm that hybrid learning-based numerical schemes can provide a practical balance between accuracy, efficiency, and numerical robustness.
| 5 |
Author(s):
Fatade Oluwayemisi Boye, Kuyoro Folashade, Sanusi Funmilayo, Okesola Kikelomo, Oluwasefunmi Famodimu.
Page No : 48-59
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From Correlation to Causation: A Conceptual Framework for Applying Causal AI to Adolescent Depression Research.
Abstract
Adolescent depression is a growing global concern, yet most neuroimaging and machine learning research in this domain remains correlational, limiting understanding and clinical translation. Existing studies predominantly use cross-sectional designs and black-box predictive models that identify associations but cannot determine whether observed brain features represent causes, consequences, or correlates depressive symptoms. This article proposes a conceptual framework for advancing from correlation to causation through the integration of longitudinal multimodal data and modern causal AI techniques. We outline key methodological limitations in current research including poor reproducibility, lack of temporal resolution, confounding development factors, and limited reproducibility; and describe how causal approaches such as Directed Acyclic Graphs, Granger causality, counterfactual reasoning, Mendelian Randomization, and causal graph neural networks can address these gaps. Building on these tools, we propose a three-phase framework consisting of: (a) Data foundations emphasizing longitudinal neuroimaging, symptom-level phenotyping, and multimodal integration; (2) Causal modeling pipelines using causal discovery, causal GNNs, and counterfactual simulations to identify mechanisms; and (3) translational pathways for Personalized interventions, and early-risk prediction grounded in causal pathways. Ethical considerations related to privacy, consent, fairness, and data governance are also examined, especially given adolescents’ vulnerability and the sensitivity of neural and digital phenotyping data. Collectively, this framework provides a systematic and feasible roadmap for leveraging causal AI to undercover mechanistic pathways, guide interventions, and strengthen the scientific foundation of adolescent depression research.
