This work presents an unsteady analysis of the hydromagnetic flow of an exothermic fluid with thermal characteristics through a microchannel. Following the discretization of the partial differential equations, the modelled time-dependent governing equations are solved using the implicit finite-difference technique (IFDM). Graphs depicting the influence of key parameters are created, and the findings are thoroughly described. As the temperature rises due to the exothermic process, the buoyant force frequently overcomes the Darcy resistance. This creates a rise in fluid velocity, especially if the medium is highly permeable. The interconnecting ligaments (pores) significantly improve the fluid-solid contact area over a simple microchannel. Furthermore, it is discovered that varying thermal conductivity has a substantial impact on temperature and hydromagnetic fluid in the microchannel. This study's findings will benefit applications in biomedical and chemical engineering, including catalytic packed-bed reactors, bioreactors, and waste treatment. In these systems, the porous structure functions as both a flow regulator and a thermal stabilizer. Bio-microfluidic systems and heat management in microelectronics, where properties vary fast with temperature, can all benefit from the findings of this study.

The current paper is a full statistical study of solar power generation and capacity utilisation using a huge sample (161,864 half-hourly observations) of the photovoltaic grid supply infrastructure in the United Kingdom. The primary goal is to analyse the dynamics in the production of solar energy and compare the effectiveness of using capacity under seasonal and diurnal variations. The information, acquired with the help of the Kaggle open-data platform, represents the data about the actual generation in megawatts (MW), the actual capacity, lower and upper confidence limits of the generation forecast, and the inferred data, such as the percentage of the capacity utilisation, and the range of the prediction interval. The statistical tests on descriptive statistics, Pearson correlation analysis, one-way analysis of variance (ANOVA) with a post hoc Tukey HSD, and multiple linear regression were done using IBM SPSS Statistics. The results show that the mean solar output varies significantly across the seasons, with the highest mean (M = 1959.49 MW, SD = 2362.23) and lowest (M = 492.23 MW, SD = 1057.79) in summer and winter, respectively. Diurnal analysis indicated the afternoon hours showed the highest generation (M = 2866.20 MW), and low generation in the night (M = 11.17 MW). The overall capacity utilisation was just 10.04, and this implies that the installed photovoltaic infrastructure was not well used. The outcomes present some practical information that may be utilised by grid operators, renewable energy planners and energy policymakers to utilise solar energy optimally in the national power systems.

Reverse engineering is a foundational technique in cybersecurity that enables analysts to study executable software without access to its source code in order to understand program logic, functionality, and potential security weaknesses. As malicious software and complex applications continue to evolve rapidly, the ability to accurately analyze binary executables has become essential for malware detection, vulnerability assessment, and incident response. This research presents a comprehensive experimental study of both static and dynamic reverse engineering techniques applied to Linux executables within a controlled Kali Linux environment. A sample executable was deliberately developed to mimic real-world application behavior and security-related scenarios. Static analysis was performed without executing the program, employing file identification tools, string extraction methods, and binary disassembly to investigate the executable’s structure, embedded data, and instruction flow. Dynamic analysis involved running the program in a monitored environment and observing runtime behavior through system call tracing, library function monitoring, and interactive debugging. These approaches facilitated a thorough examination of how the executable interacts with the operating system, processes user input, and manages program execution flow. The experimental results show that static analysis offers quick insights into binary composition and potential indicators of sensitive data, whereas dynamic analysis uncovers real-time behavior, functional logic, and hidden execution paths that may be missed by static review alone. Employing both methods in tandem enhances analytical accuracy, reduces the likelihood of incorrect assumptions, and improves the interpretation of software behavior. This study underscores the practical value of reverse engineering techniques for strengthening cybersecurity operations, advancing malware investigation capabilities, and supporting secure software development practices.

Large scale urban infrastructure projects such as metro systems, tunnels, highways, and bridges require strict quality management during construction. These projects often involve complex subsurface conditions, dense urban surroundings, and multiple construction activities occurring simultaneously. Quality assurance (QA), quality control (QC), and geotechnical monitoring therefore remain central components of construction supervision. Conventional monitoring practices rely on inspection reports, laboratory testing records, and field instrumentation systems that often operate within separate information platforms. This separation restricts coordinated evaluation of construction quality and ground behavior during project execution. This study presents a Digital Twin enabled Geo-BIM framework for integrated QA, QC, and geotechnical compliance monitoring in urban infrastructure construction. The proposed framework links geotechnical investigation data, monitoring sensors, QA inspection documentation, and QC testing results within a unified digital environment. A Geotechnical Compliance Index (GCI) model is introduced to evaluate construction conditions and identify zones requiring inspection attention. The framework was examined through a simulation scenario representing common urban infrastructure construction activities. Results indicate that the integrated system supports continuous monitoring, automated compliance evaluation, and inspection prioritization based on geotechnical performance. The proposed framework provides a structured digital approach for managing construction quality and subsurface monitoring in complex infrastructure projects.

Environmental monitoring requires rapid and stable methods for detecting chemical contaminants in water and air systems. Optical biosensor platforms offer a practical sensing approach because they convert molecular interactions into measurable optical signal changes, including absorbance, fluorescence, luminescence, and refractive index variation. This paper evaluates the performance of optical biosensor platforms for environmental contaminant detection under controlled laboratory conditions. The study focuses on two performance measures: detection sensitivity and measurement stability. Laboratory experiments used blank, low concentration, and higher-concentration exposure conditions, and the resulting optical signals were examined through baseline comparison, normalized response analysis, and repeatability assessment. The results showed stable baseline signals and clear response shifts after contaminant exposure. Low-concentration samples remained distinguishable from blank conditions, while higher concentrations produced stronger optical variation. Repeated measurements also showed acceptable consistency across exposure levels. These findings indicate that optical biosensor platforms can support low-level environmental contaminant detection when signal response and stability are evaluated together. The study presents a structured framework for assessing optical biosensor suitability in environmental monitoring applications.

The rapid growth of packaging consumption has increased waste generation and placed pressure on existing waste management systems. Current research often treats packaging design, material selection, and recycling processes as separate domains, which limits effectiveness in practical settings. This study aims to develop a system level framework that connects sustainable packaging design with waste management processes across the full lifecycle. The proposed framework integrates material selection, production, logistics, consumption, and recovery stages within a unified structure. It incorporates a feedback mechanism in which waste system performance informs design decisions, supporting continuous adjustment based on observed conditions. Lifecycle mapping and performance evaluation are used to examine interactions among system components and to assess the impact of design choices on recovery efficiency and environmental outcomes. The results show that packaging systems achieve higher recovery rates, lower contamination levels, and improved material compatibility when design parameters reflect waste processing capabilities and user behavior patterns. The study also identifies the role of stakeholder coordination in improving system performance. The framework provides a structured method for evaluating and improving packaging systems within practical constraints and offers a basis for decision-making in sustainable packaging design and waste management integration.

The fast usage of Internet of Things (IoT) device in industrial and consumer settings has dramatically expanded on the attack surface of embedded systems. This paper explores firmware security through reverse engineering and analysis of a firmware image of an IoT style with two open-source tools: Binwalk and Ghidra. An artificial representation of the structure of typical Linux-based IoT firmware was produced by a controlled firmware image which had a SquashFS file system and compiled binaries. Embedded file systems and binaries were extracted using Binwalk and Ghidra was used to do the static analysis and decompilation of extracted executable files. The vulnerability analysis showed that there are a number of deliberately introduced security flaws such as hard-coded credentials, unsecured input handling functions and insecure configuration practices. The success of the method was shown by the successful recovery of the firmware filesystem and detection of these types of vulnerabilities with the help of the strict reverse-engineering tool. The paper shows the possible contribution of open-source tools to the analysis of firmware-level vulnerabilities and enhancing security testing of embedded IoT systems.

This paper presents a secure communication infrastructure for smart meter networks in electric utilities. The study addresses a major limitation in current advanced metering infrastructure research: communication security, monitoring, attack detection, and service continuity are often handled as separate topics. In actual utility operation, smart meter networks function within distributed environments that include field devices, gateways, concentrators, edge nodes, utility control platforms, and cloud-connected services. Such a structure creates exposure to unauthorized access, false data injection, message interception, privacy loss, and communication failure. To address these issues, the paper proposes a multi-layer framework that combines protected data transmission, distributed traffic monitoring, edge-level packet inspection, federated threat detection, and continuity support within one system model. The methodology evaluates the framework through communication, security, and reliability measures, including end-to-end delay, packet trust, detection accuracy, service availability, and recovery time. The discussion shows that the proposed framework maintains stable communication performance while improving attack detection and preserving partial operation during gateway failure, cloud disruption, and denial-of-service conditions. The results indicate that secure smart meter communication must be treated as a combined problem involving transmission protection, monitoring visibility, anomaly detection, and continuity of operation. The paper provides a practical model for future smart grid communication research and utility deployment planning.

In In the era of cyber threats evolving at lightning speed, the multinational companies (MNCs) must also incorporate an AI-driven cybersecurity framework to detect the threat, prevent intrusion, and manage the data security to continue to stay afloat. Using federated learning-based security models combined with ABSorbed ML, ABSorbed DL, and ABSorbed NLP, the AI-powered three-phase cybersecurity architecture is presented in this research for data management, intrusion detection, and real-time threat intelligence. In addition to the NSL, CICIDS, and UNSW-NB15 datasets, several AIs are used to train the AI using the AI, viz., Random Forest, XGBoost, CNN_LSTM Hybrid, Autoencoders, and Federated Learning AI in order to experiment with the effectiveness of intrusion detection. Federated Learning greatly outperformed standard security protocols: they found that Federated Learning had a collection of values of 99.0 percent accuracy and a minimum false positive rate. Few algorithms employing the use of NLP and AI for automated threat analysis had enabled proactive security intelligence, reduced detection reaction time by orders of magnitude, and enhanced IDS for intrusion detection systems. In addition, federation encryption methods also reduced the cost of computation by 2.5% and ensured high-performance data protection with homomorphic encryption and zero trust architecture (ZTA). Even in learning cybersecurity using AI-based frameworks, the adversarial attacks had suffered strong resistance, and through the usage of federated learning, the attack success rate under PGD attacks was lowest, with just a success rate of 8.5%. There are, however, several important subjects related to AI related to ethical issues, regulatory compliance, and responsibility. It leads research aimed at enhancing improved AI governance models, explainable AI (XAI), and adversarial AI defensive mechanisms for strengthening cybersecurity infrastructures in multinational corporations. After all, if used well, an AI-integrated cybersecurity framework can be utilized by MNCs to create scalable, flexible, and resilient security architecture with solid cyberthreat prevention and safe data management capabilities. Future research can also encompass a study on the federated AI cybersecurity protocols, quantum-safe cryptographic AI models, and improvements in the real-time monitoring tools in order to boost the performance of AI-driven cybersecurity defenses.

This research investigates how artificial intelligence (AI) might aid data security protocols in custodian banks. The paper evaluates custodian bankers' preparedness to adopt AI-based security solutions and the role that AI can play in securing data. To collect quantitative information from attitudes, difficulties, and readiness to integrate AI, sixty-two custodian bankers were asked to answer a structured survey. AI significantly increases the data security in risk management and fraud detection, and the majority of respondents (86.67%) agreed with this finding. It is proven that organizational readiness and financial limits have a large influence on the adoption of AI. Respondents reported being moderately to well prepared for AI, although the greatest obstacle to its deployment was budgetary restrictions. Using t-tests to test hypotheses, we were able to find that using AI actually helped data security with a mean score of 4.25 out of 5. In regression analysis, the impact of institutional readiness and budgetary limits on opinions concerning AI's ability to attract investments was identified. Cluster analysis identified three separate custodian bank groups that had different financial capabilities and preparedness. Overall, the results suggest that custodian banking needs particular tactics focused on overcoming financial obstacles and making organizations AI-ready to promote adoption of AI.

This study presents the optimization of a Heat Recovery Steam Generator (HRSG) system with integrated low-temperature heat recovery to enhance thermal efficiency and promote sustainable energy utilization in a combined cycle gas turbine (CCGT) power plant. The research addresses key industrial challenges, including high exhaust flue gas temperatures (~200°C), dependence on electric heating, and underutilization of waste heat. A secondary heat exchanger (Preheater-2) is introduced downstream of the Make-Up Water Heater (MUWH) to reduce the flue gas exit temperature to 60°C while recovering energy for potable water heating. The system was modeled and optimized using Aspen Plus. Results show that the proposed configuration can offset approximately 550 units of 3 kW electric heaters, resulting in a daily energy saving of 11.55 MWh. Thermodynamic performance improved, with HRSG heat duty increasing from 2.546 MW to 3.711 MW and overall thermal efficiency rising from 61.25% to 64.76%. Sensitivity analysis identified an optimal potable water flow rate range of 60,000–70,000 kg/h, yielding stable outlet temperatures of about 80°C. Exergy analysis confirmed reduced system irreversibility. The low sulphur content of Nigerian natural gas supports safe low-temperature heat recovery without corrosion risk. The system offers a scalable solution for industrial waste heat recovery, with applications in process heating, domestic hot water generation, and energy cost reduction.

Industrial facilities frequently rely on large steel structures exposed to wind and seismic forces. Structural safety depends on accurate load estimation and appropriate design methods. This study examines modeling approaches used in steel industrial facilities, including manufacturing plants and energy systems. The analysis considers load combinations, frame stability, and connection behavior under combined loading conditions. Finite element simulation tools are applied to evaluate structural response, including displacement patterns, stress distribution, and potential failure zones. Wind loads are determined using geometric and exposure characteristics, while seismic effects are analyzed through response spectrum methods to represent dynamic behavior. The results show that detailed structural modeling leads to reduced displacement, improved load transfer, and more stable structural performance. The use of bracing systems and properly designed connections increases resistance to lateral and dynamic forces. The study also identifies critical areas where stress concentration and deformation may occur under different loading scenarios. These findings provide a structured approach for analyzing steel industrial structures under multiple hazards. The proposed framework supports consistent evaluation of structural performance and contributes to improved design practices for industrial facilities subjected to wind and seismic effects.

Service failure detection in distributed microservice platforms remains difficult because fault symptoms often appear in services other than the one where the problem begins. Traditional monitoring methods usually examine metrics, logs, or traces separately, which limits their ability to identify partial degradation, fault propagation, and cascading disruption. This paper proposes a multi-source, dependency aware framework for service failure detection in distributed microservice platforms. The method integrates distributed traces, service level metrics, and structured log events into a unified service state representation and interprets these signals through a dynamic service dependency graph. A hybrid failure scoring model identifies degraded or failed services, while a root cause ranking stage estimates the most likely origin of the incident. The framework captures both local anomalies and propagated effects across connected services. Experimental analysis compares the proposed method with metrics only, trace only, and logs only baselines under latency inflation, timeout propagation, service crash, resource exhaustion, and silent degradation scenarios. Results show that the proposed approach achieves stronger detection accuracy, lower detection delay, and better root cause ranking performance, particularly in cascading failure cases where single source methods often misidentify affected services as the source of the incident. These findings indicate that observability fusion with dependency aware analysis provides a more reliable basis for service level diagnosis in cloud native microservice systems.

The liberation size of Muro iron ore was determined by obtaining iron ore samples from Toto Local Government Area, Nasarawa State. The collected samples were subjected to crushing and grinding to reduce their size. The elemental composition and mineralogical characteristics of the ground sample were then examined via SEM-EDS analysis. Particle size analysis of the homogenised iron ore sample was conducted, and the sieve fractions obtained were chemically analysed with the X-ray Fluorescence Spectrometer (XRF). The SEM-EDS analysis revealed that iron (Fe) and silicon (Si) were the most predominant elemental constituents with atomic percentages of 38.7% and 51.2%, respectively. The iron-bearing grains are most abundant at grain sizes less than 100µm. The mesh of grind (D80 value) of the iron ore was found to be 276 µm, the actual liberation size of the ore is -1180 + 850 µm, having the highest recovery of iron (Fe) at 44.85%. The 50% intersection, which indicates the economic liberation size of the ore is at -75 + 53 µm. Whenever Muro iron ore is ground in preparation for the concentration process, the economic liberation size of -75 + 53 µm should be adopted to prevent energy wastage through over-grinding and poor recovery due to under-grinding.

This paper examines how digital technologies can bridge the critical gap between data availability and decision-making effectiveness in capital project execution. Despite the exponential growth in data generation and the widespread adoption of digital tools, capital projects frequently suffer from cost overruns and schedule delays. This paradox suggests that the primary value of digitalization lies not in data collection, but in the ability to translate raw data into timely, actionable insights that drive proactive management. Using a conceptual framework supported by industry observations and case studies, this research illustrates how digital technologies and its components bring impact to the Capital project performance. The findings reveal that successful digital transformation is less a technological challenge and more an organizational one, requiring robust data governance, cultural alignment, and clear decision accountability. The paper concludes with practical recommendations for implementing digital solutions that move project controls from retrospective reporting to forward-looking insight generation, ultimately improving project performance and reducing execution uncertainty.