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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.

Studied were the cerebral hemodynamics and various brain blood flow autoregulation circuits in patients with initial manifestations of the chronic cerebral ischemia (IMCCI) using Doppler sonography.  Forty-eight patients (29 women and 19 men) in the age of 28 to 55 years with IMCCI caused by arterial hypertension (AH) in 30 patients, and by cerebral atherosclerosis (CA) in 18 patients were investigated. The controls were 25 healthy adults matching as to gender and age.

               The cerebral hemodynamics was studied by measuring the linear blood flow velocity (BFV) in extra- and intracranial vesssels on a Doppler sonography. The state of various autoregulation circuits was carried out by functionally loading the patients with:  hypercapnia, calculating the reactivity coefficient CrCO2, nitroglycerine, calculating the reactivity coefficient CrFNT, and orthostatic loading, determining the orthostatic instability. The investigation of cerebral hemodynamics has revealed isolated hemodynamically insignificant lesions of the carotids (63.7 %) or vertebral (36.3 %) arteries in 28.4 % patients with IMCCI accompanied by the BFV interhemispheric asymmetries (20-30 %) in the Willis’ arteries in 68 % of the patients. The investigation of CVR has shown that the patients with IMCCI have disorders of the autoregulative response as to metabolic and myogenic circuits with the tendency to hyperreactivity in the patients with AH and hyporeactivity in those with CA.

               The patients also have the inverted reactions to nitroglycerine and orthostatic loads, this inversion being more pronounced in the patients with CA with the tendency to increase in the groups of older patients. The analysis of obtained data makes it possible to suppose that the CVR changes at various levels of autoregulation characterising the dysfunctioning of homeostatic range support mechanisms are the triggering mechanism of cerebral hemodynamic disorders in the patients with IMCCI. Unifying various values of the CVR as to the Doppler sonography data makes it possible to optimise diagnosing and treatment tactics for the patients with IMCCI.

This study examined the relationship between End-of-Life Product Recovery Practices (EPRP) and Supply Chain Performance (SCP) among oil and gas firms in the South-South region of Nigeria, with particular emphasis on the moderating role of organizational policy. this study investigated how four dimensions of End-of-Life Product Recovery Practices remanufacturing design, product reconfiguration, product recycling, and product redesign influence key supply chain performance indicators, namely delivery lead time, delivery reliability, and customer satisfaction. The study adopted a quantitative research design using a survey method to collect primary data from oil and gas firms operating within the South-South region of Nigeria. The target population consisted of managers and officers involved in supply chain and operations management across selected oil and gas firms. A structured questionnaire was used as the primary data collection instrument, and a total of 276 valid responses were obtained for analysis. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the relationships among the constructs, while descriptive statistics were conducted using SPSS software. The findings revealed that the influence of End-of-Life Product Recovery Practices on supply chain performance varies across operational dimensions. Product recycling and product reconfiguration showed significant relationships with delivery lead-time, while product redesign significantly influenced delivery reliability. In addition, product recycling and product reconfiguration demonstrated significant relationships with customer satisfaction, whereas remanufacturing design showed no significant relationship with delivery lead-time and customer satisfaction. The results also indicated that organizational policy plays a moderating role in strengthening the relationship between End-of-Life Product Recovery Practices and Supply Chain Performance, although the magnitude of the moderating effect was relatively weak. The study concludes that while End-of-Life Product Recovery Practices contribute to improving supply chain performance in the oil and gas sector, their effects are generally modest and depend largely on supportive organizational policies and effective supply   chain management systems. The study recommends that oil and gas firms strengthen sustainability policies, invest in recycling and product flexibility systems, and integrate recovery practices into broader supply chain strategies in order to achieve improved operational efficiency and environmental sustainability.

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.