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A hybrid regenerator is made by combining mesh matrices of different sizes in a single unit. The porous matrix, which has a larger hydraulic diameter, is placed at the warm end, while the dense matrix, which has a smaller hydraulic diameter, is placed at the cold end. In this study, a three-mesh hybrid regenerator is analyzed to reduce heat loss and improve overall performance. Special attention is given to the effect of mesh size on heat transfer and regenerator effectiveness. The performance of the three-mesh hybrid regenerator is compared with that of a homogeneous regenerator. The results show that a properly designed hybrid regenerator can achieve higher effectiveness and better thermal performance. The three-mesh hybrid regenerator performs better than both the two-mesh hybrid regenerator and the simple homogeneous regenerator. This improvement is mainly due to the gradual and more uniform decrease in hydraulic diameter along the regenerator, which helps improve heat transfer and reduce heat loss.

Coal resources in southeastern Nigeria occur across contrasting geological settings and exhibit substantial variation in rank, petrographic composition, mineral matter, moisture, volatile matter, ash yield and energy potential. This study presents an integrated geological, mineralogical and energy assessment of Ihioma lignite in the Orlu area of Imo State and the better-established sub-bituminous coal resources of the Enugu area, southeastern Nigeria. The assessment combines published geological and stratigraphic evidence with reported proximate, ultimate and mineralogical data for Ihioma lignite and published petrographic and physicochemical information for Enugu coal. Ihioma occurs within the Tertiary sedimentary succession of the Anambra Basin and represents a lower-rank coal resource than the Lower Maastrichtian coal of the Mamu Formation. Published analysis of an Ihioma lignite sample reported moisture of 6.0%, ash of 4.0%, volatile matter of 38.5% and fixed carbon of 51.5%, together with reported carbon, hydrogen, nitrogen and oxygen concentrations of 64.6%, 4.0%, 1.064% and 2.0%, respectively. Hematite was identified as the principal mineral phase, with quartz and albite among the subordinate minerals. The reported low sulphur content is potentially advantageous for thermal utilization, although broader sampling is required before regional conclusions can be made. Enugu coal, by contrast, belongs predominantly to the sub-bituminous range of the Lower Maastrichtian Mamu Formation. Earlier rank studies identified a progressive decrease in rank from Enugu toward northern coalfields, with Enugu samples reaching sub-bituminous A rank. Published studies further indicate that the Mamu Formation accumulated in a paralic to deltaic depositional system characterised by repeated alternation of sandstone, siltstone, mudstone, carbonaceous shale and coal. The contrasting rank and mineralogical characteristics of Ihioma and Enugu demonstrate that southeastern Nigerian coal resources cannot be treated as a homogeneous fuel. Ihioma lignite may have potential for low- to medium-temperature thermal applications, briquetting, industrial heat and selected non-fuel uses, whereas the higher-rank Enugu coal presents a stronger case for conventional thermal and metallurgical evaluation. Nevertheless, commercial development should be preceded by systematic drilling, proximate and ultimate analyses, petrography, trace-element characterisation, calorific-value determination, ash-fusion testing, washability studies and environmental baseline assessment. The study concludes that the greatest opportunity for southeastern Nigerian coal lies in resource-specific beneficiation and diversified utilization rather than indiscriminate combustion.

As adversarial AI systems are constantly evolving, and new attacks are being developed, they create a serious threat for cybersecurity, especially for the detection of complex, adaptive, and unseen threats. This paper introduces a quantum augmented AI system to extract knowledge from vast amounts of cybersecurity data and to build knowledge graph dynamically for enhancing adversarial threat detection. The framework takes advantage of recent developments in quantum utility by mapping high-dimensional and complex security data into quantum Hilbert spaces using Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN) to uncover hidden relationships and polymorphic attack patterns. The proposed approach leverages the ability of quantum machine learning algorithms to detect subtle adversarial behaviors, anomalous interactions, and zero-day vulnerabilities that could escape traditional machine learning defenses, by integrating data mining and knowledge graph analytics with AI. The framework offers a scalable approach to harnessing cybersecurity intelligence from large and diverse data sources and improves the security systems' strength and flexibility. The experimental analysis shows that the use of quantum-assisted models can enhance the representation of threats, pattern recognition, and proactive cyber defense, making quantum machine learning a potential direction for next-generation adversarial cyber defense solutions.

The swift development of cyber threats, especially, sophisticated and distributed attacks on software-defined networks (SDNs) have necessitated smart and proactive threat-hunting solutions. This paper suggests a Hybrid Classical-Quantum Neural Network (C-QNN) model that combines classical machine learning algorithms with the near-term quantum computing capacity to improve predictive cyber threat detection. The proposed framework uses classical data mining techniques to extract features, reduce dimensionality and preprocess traffic pattern prior to passing optimized representations to variational quantum circuits to perform more sophisticated pattern recognition. The hybrid pipeline combines classical optimization techniques and quantum embedding strategies to overcome the drawbacks of existing quantum hardware and exploit quantum-enhanced learning capabilities. Empirical analysis shows better detection accuracy and predictive capabilities in detecting complex attack patterns in high dimensional network settings. The results emphasize the prospects of hybrid quantum-classical AI systems as a viable solution to deploy scalable, intelligent cybersecurity systems without depending on full fault-tolerant quantum processors. This study helps to establish the basis of embedding quantum-enhanced analytics into the next-generation proactive systems of cyber defense.

The study area is part of the Niger Delta, comprising of Benin and its adjoining areas situated in the Gulf of Guinea. The area is located within latitudes 5°30′- 6°30′ N and longitudes 4°30′- 5°30′ E. Structural interpretation of the Aeromagnetic and Landsat Data of Benin and its Environs, Southern Nigeria was carried out to determine depth to the magnetic basement, delineate the basement morphology and relief, and delineate the structural features associated with the basin and to infer the effects of such structures to the general tectonic history and basin geodynamics of the study area. The aeromagnetic and Landsat data were subjected to various image and data enhancement and transformation routines using ILWIS 3.2 Academic and Erdas imaging softwares. Results of the study revealed that the dominant structural trend direction of the study area is in the NE-SW direction. Other lineament trend directions are in the NW-SE, N-S and E-W directions. The drainage pattern is dendritic which is indicative of lithological, structural and topographic differences and also indicative of alluvial rocks, which is typical of the geology of the area that consists mainly of sedimentary rocks. The lineament density map revealed the presence of high-density fracture zones around Atyere and Salawa-Odo areas. Results of the 2-D spectral analysis revealed a two-layer depth model.  The shallower magnetic source (D₁) has an average depth of 0.37 km while the deeper magnetic source bodies (D₂) have an average depth of 3.47 km. The shallower magnetic anomalies is as a result of basement rocks which intruded into the sedimentary rocks while, the deeper magnetic anomalies is associated with intra-basement discontinuities like faults. The average sediment thickness of 3.47 km obtained in this study is significant for accumulation and entrapment of hydrocarbons. However, there seem to be a correlation of the interpreted lineaments with areas of primary mineralization believed to be in the study area.