The rapid penetration of renewable energy sources such as solar photovoltaic (PV) and wind power into modern power grids introduces significant operational challenges, including intermittency, voltage instability, harmonic distortion, and fault vulnerability. Conventional control strategies are often insufficient for handling dynamic grid disturbances and nonlinear system behavior. This study proposes an Artificial Intelligence (AI)-enhanced control framework for grid-connected renewable energy systems to enable adaptive control, predictive fault detection, and resilient operation. The proposed architecture integrates machine learning-based fault classification, adaptive inverter control, and real-time grid condition monitoring. A hybrid dataset composed of simulated grid disturbances and real operational parameters is used to train and validate the AI model. Results demonstrate improved fault detection accuracy, reduced system recovery time, enhanced voltage stability, and improved power quality under dynamic grid conditions. The proposed AI-driven framework enhances grid reliability, supports high renewable penetration, and contributes to resilient and sustainable energy infrastructure.

This research investigates the impact of an AI adaptive language assessment system, when combined with special education principles, on neurodiverse students in TESOL contexts. Although adaptive systems have been extensively debated in language learning, there has been remarkably little attention paid to students with autism spectrum disorder, dyslexia, or ADHD. To fill this research void, the study employed a sequential explanatory mixed-methods approach. In the quantitative component, 120 students were included in a 12-week quasi-experimental design comparing the impact of AI adaptive assessment with traditional testing modes. The data included standardized English proficiency test scores, test anxiety, engagement, and psychometric statistics using Item Response Theory and differential item functioning. The results demonstrated greater proficiency achievement, reduced anxiety, and increased engagement among students using the adaptive system. Reliability coefficients were high, and subgroup analysis revealed little measurement bias. In the qualitative component, teacher interviews shed light on usability and integration in the classroom. In general, the results of this study indicate that by combining adaptive assessment with organized special education principles, students with diverse cognitive abilities can be treated equitably and meaningfully in language assessment, while also offering a roadmap for future research on transparency and long-term implementation.

Industrial IoT systems rely heavily on wireless communication, yet security and regulatory compliance are often addressed separately during system development. This paper examines how wireless infrastructure security can be integrated with electromagnetic compatibility (EMC) and radio frequency (RF) regulatory requirements at the design stage. It analyzes common wireless attack vectors in industrial settings, including jamming, spoofing, and protocol exploitation, and evaluates how regulatory constraints influence hardware and network architecture decisions. A security centered device architecture is proposed where RF shielding, grounding schemes, spectrum allocation, and firmware isolation are treated as interconnected design elements. The framework incorporates zero trust communication principles within industrial wireless networks while maintaining compliance with EMC standards such as IEC 61000 and relevant RF certification requirements. The study demonstrates that early coordination between cybersecurity engineering and compliance engineering reduces redesign cycles and certification delays. The proposed model offers a structured pathway for building industrial wireless systems that meet both security and regulatory obligations without post development modifications.

Power electronic converters have been at the center of industrial systems and various energy systems such as renewable energy systems, industrial motor drives, and grid-connected power systems. The systems face harsh conditions, making reliability an essential factor for the design. The traditional procedure for the design of converters considers the reliability of the system after the parameters have been selected for the design, making it difficult to consider the parameters of the system during the design stage. This paper proposes a reliability-oriented design optimization framework for power electronic systems operating in industrial and utility-scale applications. The proposed methodology integrates electro-thermal modeling, physics-of-failure lifetime estimation, and mission-profile-based stress evaluation within a unified multi-objective optimization framework. Junction temperature profiles and thermal cycling patterns are obtained through electro-thermal simulation under realistic operating conditions. Device lifetime is then estimated using fatigue-based models, and the resulting reliability metrics are incorporated into a multi-objective optimization algorithm that considers lifetime, efficiency, and system cost. A case study involving a 500-kW grid-connected converter demonstrates the effectiveness of the proposed approach. Simulation results show that the optimized design reduces thermal stress and increases predicted semiconductor lifetime from 6.8 years to 13.6 years while maintaining high efficiency with a moderate increase in system cost. The proposed framework provides a systematic approach for reliability-oriented design of industrial power electronic systems.

Electromagnetic Bandgap Structures (EBGS) have emerged as an effective technique for suppressing undesired harmonic components in microwave and RF systems. Harmonic distortion degrades signal integrity, reduces power efficiency, and increases electromagnetic interference in communication and power electronic circuits. This research presents the design and simulation of an EBGS based microstrip bandpass filter aimed at achieving compact size, sharp selectivity, and effective harmonic suppression. The proposed structure integrates periodic defected ground plane patterns beneath a microstrip transmission line to create frequency selective stopbands while preserving passband characteristics. MATLAB based modeling and full wave electromagnetic simulations were performed to analyze S parameters, insertion loss, return loss, and harmonic rejection performance. The results demonstrate that the EBGS based bandpass filter significantly attenuates second and third harmonics while maintaining low insertion loss within the desired passband. The proposed design provides improved selectivity and compactness compared to conventional microstrip bandpass filters. The study contributes to the advancement of high-performance filtering solutions for wireless communication systems, radar applications, and RF front end modules.

In the crystalline basement terrains of Southwestern Nigeria, groundwater exploration remains a significant challenge due to the extreme lateral and vertical heterogeneity of the subsurface. This study investigates the Agbaje–Ijokodo area in Ibadan, a region historically plagued by high borehole failure rates, using an integrated geophysical approach. By combining 1D Vertical Electrical Sounding (VES) at 28 locations with 2D Electrical Resistivity Tomography (ERT) across 14 profiles, we mapped the complex architecture of the local aquifer system. The results reveal a predominant three-layer geoelectric sequence: a clayey topsoil, a weathered saprolite layer (averaging 10.8 m in thickness), and a basal fractured-to-fresh basement. Interpretation of geoelectric curves, primarily H-type (57%) and Dar-Zarrouk parameters indicates that while the weathered regolith provides storage, its productivity is often hampered by high clay content. Critical secondary porosity was identified in deep-seated fracture zones and basement depressions, particularly in the Agbaje sector, where reflection coefficients below 0.75 and longitudinal conductance values (0.2 - 0.69 mhos) suggest both high groundwater potential and moderate protective capacity. In contrast, the Ijokodo area is characterized by shallow bedrock ridges and thin overburden, explaining its poor-to-fair yield history. These findings suggest that sustainable groundwater development in the area must shift from targeting shallow saprolite to deeper, localized fracture networks. This research demonstrates that an integrated resistivity framework is indispensable for reducing the risks associated with borehole siting in complex metamorphic terrains.

Halide perovskites have emerged as significant materials for the light-absorbing layer in many optoelectronic devices due to their remarkable optoelectronic capabilities. To enhance device performance for broader acceptance, it is imperative to identify novel solutions. A viable approach is the integration of carbon nanotubes (CNTs), which have demonstrated exceptional adaptability and effectiveness. In these devices, carbon nanotubes (CNTs) fulfill many roles, such as supplying conductive substrates and electrodes, as well as enhancing charge extraction and transport. The forthcoming generation of photovoltaic devices, metal halide perovskite solar cells (PSCs), presents significant potential. Despite substantial advancements, concurrently attaining optimal efficiency, stability, and affordability continues to pose a challenge, necessitating the creation of innovative materials termed CNTs, which, due to their exceptional electrical, optical, and mechanical characteristics, have attracted significant interest as prospective materials for highly efficient PSCs. The integration of CNTs into perovskite solar cells enhances adaptability, facilitating advancements in device performance and durability for many applications. This article offers a comprehensive examination of current developments in carbon nanotube technology and its incorporation into perovskite solar cells, functioning as transparent conductive electrodes, charge transporters, interlayers, hole-transporting materials, and back electrodes. Furthermore, we identified significant obstacles and provided recommendations for future improvements in perovskite solar cells with CNTs.

Enterprise web systems support many organizational functions, including digital transactions, cloud services, data storage, and enterprise software operations. As these systems operate across distributed infrastructures, traditional security models based on static authentication and network boundaries face significant limitations. This study proposes an identity-centric security model that integrates identity authentication, identity profiling, behavioral monitoring, risk evaluation, and policy-based access control within a unified framework. The model evaluates identity activity continuously during active sessions instead of relying only on initial login verification. Identity profiles contain contextual information derived from authentication attributes, device information, location data, and historical usage patterns. Behavioral monitoring observes session activity and identifies deviations from established patterns. A risk evaluation mechanism combines authentication irregularities and behavioral deviations to calculate identity risk scores. These scores guide policy-based access decisions within enterprise applications. Experimental analysis using simulated enterprise session data indicates improved anomaly detection capability, faster response to suspicious activity, and higher accuracy in access decisions compared with traditional role-based access control systems. Continuous monitoring and adaptive policy evaluation allow enterprise platforms to react to changing identity conditions during system interaction. The findings indicate that identity-centric security frameworks provide a context-aware approach for protecting enterprise web systems.

Enterprise reporting platforms support organizational analysis through automated reports and analytical dashboards that process operational and financial data. Despite their widespread use in business intelligence environments, limited research examines the internal operational behavior of these platforms. Most studies address predictive analytics, enterprise data management, or system monitoring rather than analytical diagnosis of reporting activities. This study proposes a diagnostic analytics framework for evaluating performance within enterprise reporting systems. The framework examines report generation logs, query execution records, and system interaction data to interpret reporting behavior and identify abnormal execution patterns. The methodological process includes log data collection, preprocessing, feature extraction, and statistical anomaly detection using report execution time metrics. Several diagnostic indicators support the analysis, including query processing duration, concurrent user activity, data processing volume, and execution failure frequency. Analytical results show that most reports operate within normal execution ranges, while a smaller group demonstrates unusually long execution durations. These events correspond with high database workload and complex query operations. The results indicate that operational log data provide meaningful insight into reporting platform performance. The proposed framework offers a structured analytical approach for identifying reporting delays and evaluating system efficiency within enterprise reporting environments.

Workforce productivity remains a key factor in the performance of service-oriented organizations where employee activity directly affects service delivery and operational outcomes. Effective productivity measurement requires systematic analysis of workforce performance indicators and operational data. This study presents a workforce productivity measurement framework designed for service environments. The framework integrates workforce analytics, management information systems data, and operational performance indicators to calculate a Workforce Productivity Index (WPI). The model uses several indicators, including task completion rate, customer satisfaction score, operational efficiency index, attendance consistency, and service response time. Enterprise information systems provide operational records that support quantitative evaluation of workforce productivity across service teams. The proposed model combines these indicators through a weighted productivity formula that generates productivity scores for employees or operational units. Evaluation results show clear performance differences across workforce groups and identify productivity patterns within service operations. Higher productivity scores correspond to efficient task completion, consistent attendance, and positive service feedback. The framework provides a structured approach for productivity evaluation using operational indicators and enterprise system data. The proposed model also supports workforce performance assessment and organizational productivity analysis within service-based operations.

Digital banking platforms generate large volumes of operational information through transaction processing systems, system logs, and customer communication channels. Many studies examine transaction monitoring, fraud detection, and cybersecurity events. Customer interaction records receive less attention as a source of operational risk information. This study investigates the use of customer interaction data as indicators of operational conditions in digital banking platforms. The research examines interaction records collected from support tickets, complaint submissions, chat conversations, and service request logs. These records are analyzed together with Management Information System (MIS) event logs in order to identify recurring service issues and operational patterns. The proposed analytical framework organizes interaction data through several stages that include data collection, preprocessing, interaction pattern detection, and operational risk indicator generation. Repeated reports related to transaction delays, authentication failures, and application performance problems appear within the interaction dataset. These patterns correspond to operational events recorded in system activity logs. The study also introduces a quantitative operational risk score calculated from the frequency and severity of interaction categories. The results indicate that customer interaction datasets contain measurable signals related to operational disruptions within digital banking platforms. The analytical framework demonstrates that interaction records provide an additional information source for operational monitoring and risk analysis in digital financial services.

Cloud-based SaaS platforms now run essential services across finance, healthcare, and government sectors. Many of these systems include automated agents and decision engines that operate at high speed and scale. Identity and access governance therefore serves as a central control layer. Traditional IAM models depend on fixed roles, centralized authorization servers, and periodic reviews. Such structures struggle in distributed, multi-tenant environments that process millions of access requests each day. Prior studies address adaptive authentication, Zero Trust security, decentralized identity, anomaly detection, and cloud resilience. However, these solutions often function separately rather than within a unified framework. This paper introduces a Resilient Identity and Access Governance Architecture that integrates real time risk evaluation, distributed policy enforcement, lifecycle governance for human and machine identities, and fault tolerance in a single design. The framework defines measurable targets for availability, detection time, throughput, and policy propagation. Risk scoring occurs during live authorization decisions, and enforcement spans multiple nodes. The result is a scalable identity governance model suitable for complex SaaS ecosystems that require high availability and consistent control.

Solar photovoltaic systems are increasingly connected to smart grids, making equipment reliability a major concern. Failures in solar inverters and grid connected components reduce energy output and increase operational cost. Most existing predictive maintenance studies focus either on PV systems or on smart grid assets separately and rely mainly on centralized cloud processing. This paper proposes a unified predictive maintenance framework that integrates solar inverter and smart grid monitoring within an edge-based architecture. Electrical and thermal signals are processed locally, where time and frequency domain features are extracted and analyzed using a CNN–LSTM model for real time fault classification. A health index model is applied to estimate remaining useful life for condition-based maintenance planning. Experimental results show 96.8% classification accuracy and a reduction in inference latency from 85 ms in cloud-based processing to 18 ms at the edge. The proposed framework reduces communication load, supports faster decision making, and improves operational stability in distributed renewable energy systems.

This study presents an applied research approach aimed at implementing a continuous improvement framework for the development of commercial strategies within an emerging company. The proposed framework integrates information gathering, data analysis, and strategic design within an iterative cycle supported by project management tools. Its primary objective is to structure commercial decision-making processes and enhance sales performance through measurable and replicable mechanisms. The methodology combines qualitative data collection, analytical processing, and project planning techniques, incorporating the Earned Value Method (EVM) as a control tool to monitor execution in terms of scope, time, and cost. The results demonstrate that EVM enables objective performance tracking, identifying schedule deviations (SPI = 0.67) while maintaining cost efficiency (CPI = 1.02). This facilitated the implementation of timely corrective actions and the consolidation of commercial strategies aligned with key performance indicators.

Hybrid nanomaterials have become a revolutionary group of designed systems that combine the complementary physicochemical characteristics of various constituents on the nanoscale, presenting novel prospects in developing technologies that are sustainability-oriented. These materials incorporate organic, inorganic, and bio-inspired constituents into a single architecture, which makes the materials allow synergistic capabilities that cannot be achieved by single-component systems. Their use has grown substantially in the last few years in catalysis, environmental remediation, and advanced energy systems due to the pressing necessity to solve global problems of resource depletion, pollution and climate change. Hybrid nanomaterials in catalytic processes exhibit superior activity, selectivity and stability as a result of optimized surface interfaces and adjustable electronic structures that enable effective generation of pollutants and renewable feedstocks. Simultaneously, their use in environmental science has become mainstream due to their application in water purification, air filtration, and sensing platforms, where the high surface area and versatile use allow quick and selective removal of contaminants. These materials are also used in energy-related fields, such as supercapacitors, batteries and photocatalytic devices, where they advance the high-performance of storage and conversion systems by enhancing the charge transport, energy density and cycling stability. Even with these developments, scalability and long-term stability issues, as well as environmental impact, are a key obstacle to large-scale adoption. This review shows that in recent times, there has been an advancement in the rational design, synthesis and functional optimization of hybrid nanomaterials, with a focus on structure-property interactions and their potential application in sustainability. Moreover, it discusses new directions and the future visions targeted at closing the gap between laboratory development and industrial adoption.

The ongoing use of chatbots in healthcare, education, customer service, and mental health has made more apparent the weaknesses of entirely task-focused conversational systems that are non-emotional. Emotion detection has become an essential process of improving human-computer interaction that allows the chatbots to detect the affective states of users and react in a more human-centric and situational behalf. This paper gives a synthesis of the research on emotion-aware chatbot systems and how emotion detection methods, data modalities, and architecture can be used to enhance the quality of interaction. Fifty chosen studies were systematically analyzed to study the trends of publications, prevalent emotion detecting techniques, effectiveness of modality, and system design method. The results show that there is an increasing concentration of quality research in traditional human-computer interaction and artificial intelligence outlets, and there is a growing global concern in the last few years. The use of text-based emotion detection is the most popular in that it is more scalable, whereas the speech, visual, and multimodal detection use more emotion expressiveness and resilience in real life. Multimodal architectures can capture more complex emotional cues better than other electric stimuli, but face difficulties in terms of complexity, privacy and evaluation of the system. The review also shows that most of the current chatbot frameworks are more focused on the technical measures of performance rather than long-term, human-focused evaluation outcomes. In general, the present study provides an insight into the achievements and limitations of the existing research on emotion-sensitive chatbots and emphasizes the necessity to create ethically oriented, culturally sensitive and systematically tested conversational agents in order to promote the development of emotionally intelligent human-computer interaction.

Reliability-Centered Maintenance (RCM) plays a crucial role in minimizing operational downtime and lifecycle costs in petrochemical industries. However, conventional RCM approaches often lack dynamic failure diagnosis and prioritization capabilities under uncertain operating conditions. This study proposes an enhanced framework integrating Failure Mode and Effects Analysis (FMEA) with data-driven linguistic rule extraction to improve maintenance decision-making for centrifugal pumps. The proposed methodology utilizes OREDA-based failure classification to identify critical failure modes and introduces a weighted severity–occurrence model to overcome limitations of traditional Risk Priority Number (RPN) ranking. The framework establishes relationships between failure causes and key operational parameters such as flow rate, discharge pressure, vibration, temperature, and efficiency using linguistic variables. A rule-based diagnostic system is developed to enable real-time fault identification and maintenance scheduling. The framework is validated through a case study of centrifugal pumps in a petrochemical aromatic plant. Results demonstrate improved fault detection accuracy, reduced maintenance time, and enhanced system reliability. The proposed approach provides a scalable and intelligent decision-support tool for predictive maintenance and industrial asset management.

Standard LCDM cosmology relies on undetected "Dark Matter" and "Dark Energy" to explain gravitational anomalies. This paper introduces the Hamouda Informational Gravity Model (HIGM), a framework where gravity is not a fundamental force of mass but an emergent phenomenon of Information Entropy. By identifying Infrared (IR) radiation as the primary cosmic information carrier, we demonstrate that the resulting entropic gradients generate the pressure and curvature traditionally attributed to dark sectors. We provide empirical validation across multiple scales, including the Milky Way, Andromeda (M31), and the Hubble Tension, proving that gravitational stability is a thermodynamic necessity driven by infrared information flow.

The increasing integration of artificial intelligence (AI) into organizational environments has created new opportunities to improve information technology (IT) management processes. AI tools support managerial decision-making, automate routine and complex operational tasks, and enhance system monitoring, diagnosis, and performance optimization, enabling organizations to manage large volumes of data more efficiently and respond to operational needs more quickly and accurately. However, integrating AI into existing IT management systems presents several technical and managerial challenges, including system complexity, legacy infrastructure limitations, data governance requirements, security risks, compliance obligations, and the need for effective managerial oversight. Without a structured implementation approach, AI adoption may introduce operational risks and reduce transparency in IT processes. This paper proposes a conceptual framework for embedding AI tools into IT management systems by addressing both technical architecture and management requirements. The framework identifies key components such as data integration layers, AI analytics modules, management control interfaces, and governance mechanisms. It also highlights how predictive analytics and intelligent automation can enhance operational efficiency, risk management, and strategic planning while maintaining transparency and accountability. The study provides a structured approach to help organizations design AI-enabled IT management systems aligned with organizational objectives and effective managerial control.

Green synthesis of silver nanoparticles (AgNPs) has emerged as a sustainable, cost-effective alternative to traditional chemical and physical methods (Sati et al.,2025). This study explores the plant-mediated synthesis of AgNPs using bioactive extracts, which serve as both reducing and stabilizing agents. The resulting nanoparticles exhibit unique tunable optical properties, primarily characterized by strong Surface Plasmon Resonance (SPR). We demonstrate that these biogenic AgNPs significantly enhance the detection sensitivity of SPR-based biosensors compared to conventional substrates. Characterization through UV-Vi’s spectroscopy, SEM, and EDX confirms the formation of spherical AgNPs with high elemental purity. These findings provide a roadmap for the development of eco-friendly, high-performance diagnostic tools for environmental and biomedical monitoring.

The rapid advancement of artificial intelligence (AI) technologies has significantly transformed digital communication while simultaneously enabling sophisticated cyber threats, particularly AI-powered scams and deepfake-based deception. Deepfake technologies, which involve the generation of highly realistic synthetic audio-visual content, are increasingly exploited for impersonation, fraud, and misinformation, thereby posing serious risks to digital trust and cybersecurity. In Nigeria, the widespread adoption of digital platforms among tertiary institution students has heightened their exposure to such threats. This study examined the roles of cybersecurity awareness, digital literacy, and media literacy in shaping students’ preparedness to detect AI-powered scams and deepfakes in tertiary institutions in Enugu State, Nigeria. A descriptive survey design was adopted, involving 469 students selected through a multistage sampling technique from universities, polytechnics, and colleges of education. Data were collected using a structured Google Forms questionnaire and analysed using mean, standard deviation, and independent samples t-test at a 0.05 level of significance. The findings revealed that students possessed cybersecurity awareness, digital literacy, and media literacy to a great extent (Grand Mean = 3.34), and demonstrated preparedness against AI-powered scams and deepfakes to a great extent (Grand Mean = 3.21). However, their ability to detect manipulated media remained relatively weak. No significant difference was found between male and female students in both awareness and preparedness. The study concludes that while students demonstrate reasonable awareness, targeted educational interventions are required to improve their ability to detect sophisticated AI-driven threats. It recommends the integration of deepfake awareness and AI fraud detection strategies into tertiary institution curricula.

The increasing volume of Automatic Identification System (AIS) data generated by maritime vessels poses significant challenges in data storage, transmission, and real-time processing, particularly in bandwidth-constrained environments. Traditional trajectory compression methods often fail to preserve safety-critical information, which is essential for collision avoidance and maritime situational awareness. This study proposes a Risk-Aware Deep Learning method that integrates sequence-to-sequence Long Short-Term Memory (LSTM) models with attention mechanisms and a domain-informed risk assessment framework to compress AIS trajectories efficiently. By assigning dynamic risk scores based on proximity to other vessels, traffic density, navigational hazards, and vessel manoeuvres, the model prioritises the preservation of high-risk trajectory segments. Experimental results demonstrate that the proposed method outperforms traditional geometric, spatiotemporal, and autoencoder-based approaches in terms of compression ratio, reconstruction fidelity, and safety feature retention. With a risk preservation score of 95% and a compression ratio of 7.5, this model provides an effective solution for maritime data management and supports real-time monitoring, predictive analytics, and autonomous navigation. Future work will explore real-time deployment, federated learning, and the integration of multi-modal maritime data sources.