AUIQ Technical Engineering Science

Microsoft Power BI and Power Automate enhances project tracking by improving data accuracy, reducing manual
effort, and delivering timely insights. The automation-driven approach leads to significant time savings and fewer
reporting errors, contributing to more efficient and informed project management. Despite the availability of digital
tools, there is limited research on unified frameworks that combine automation, data visualization, and real-time
feedback for holistic project management. This study addresses this gap by evaluating an integrated system applied to a
real-world project over one fiscal year, offering practical insights and measurable outcomes. Present study explores how
Power BI enhances data accuracy and efficiency in project management through automation and visualization. Through
the analysis of case studies and real-world examples, the positive impact of Microsoft power automate is illustrated on
key project management domains such as budget tracking, timeline management, and risk mitigation. Additionally, the
study delves into the integration of Microsoft power automate with power business intelligence, measure the time taken
to retrieve data, report rendering, and evaluate the system’s responsiveness in processing user inputs while maintaining
data accuracy, cross-validating with other tools, gathering user feedback on consistency. Despite acknowledging the
challenges and limitations of Microsoft power automate, the study offers practical recommendations for successful
implementation of Microsoft power automate for efficient project management. This led to time savings of 40%,
depending on the complexity and frequency of reporting tasks. Moreover, it also reduces the likelihood of manual errors
associated with data entry, calculations, and report generation. Error reductions decline to 37.5%, depending on the
complexity and volume of data handled. The findings offer a practical roadmap for organizations to modernize project
management by adopting integrated automation and analytics tools, ultimately enhancing decision-making, reducing
operational inefficiencies, and improving project outcomes.Microsoft Power BI and Power Automate enhances project tracking by improving data accuracy, reducing manual
effort, and delivering timely insights. The automation-driven approach leads to significant time savings and fewer
reporting errors, contributing to more efficient and informed project management. Despite the availability of digital
tools, there is limited research on unified frameworks that combine automation, data visualization, and real-time
feedback for holistic project management. This study addresses this gap by evaluating an integrated system applied to a
real-world project over one fiscal year, offering practical insights and measurable outcomes. Present study explores how
Power BI enhances data accuracy and efficiency in project management through automation and visualization. Through
the analysis of case studies and real-world examples, the positive impact of Microsoft power automate is illustrated on
key project management domains such as budget tracking, timeline management, and risk mitigation. Additionally, the
study delves into the integration of Microsoft power automate with power business intelligence, measure the time taken
to retrieve data, report rendering, and evaluate the system’s responsiveness in processing user inputs while maintaining
data accuracy, cross-validating with other tools, gathering user feedback on consistency. Despite acknowledging the
challenges and limitations of Microsoft power automate, the study offers practical recommendations for successful
implementation of Microsoft power automate for efficient project management. This led to time savings of 40%,
depending on the complexity and frequency of reporting tasks. Moreover, it also reduces the likelihood of manual errors
associated with data entry, calculations, and report generation. Error reductions decline to 37.5%, depending on the
complexity and volume of data handled. The findings offer a practical roadmap for organizations to modernize project
management by adopting integrated automation and analytics tools, ultimately enhancing decision-making, reducing
operational inefficiencies, and improving project outcomes.

The agriculture industry has evolved toward intelligent, data-driven processes due to the growing need for food worldwide, environmental sustainability, and effective resource use. A thorough analysis of smart agriculture as a game-changing element of the industry 4.0 revolution is provided in this study, with a focus on the incorporation of Internet of Things (IoT)-based technologies for sustainable farming. To optimize agricultural processes including irrigation, crop health monitoring, climate and weather tracking, animal management, and disease detection, it investigates the functions and uses of smart sensors and IoT devices. The study demonstrates how smart agriculture may meet important issues like food shortages, environmental degradation, and wasteful resource use by combining the latest scientific developments with practical applications. The study also covers the enabling technologies that facilitate real-time agricultural monitoring and decision-making, including artificial intelligence (AI), cloud computing, and cellular networks. It ends by outlining the main obstacles, existing constraints, and potential avenues for further study in the development of robust, technologically advanced agricultural systems.

The development of soft-polymeric contact lenses (SPCLs) has significantly enhanced vision correction by integrating advanced polymeric materials with optimized optical properties. This study examines the impact of refractive index (RI), oxygen permeability (Dk/t), and water content (EWC%) on visual quality and ocular health, correlating material properties with user-reported experiences. A survey of 5,585 medical SPCL users revealed that 31% experienced ocular fatigue and redness, often linked to optical aberrations and replacement frequency. Notably, commercially available SPCLs exhibit RI values ranging from 1.37 to 1.43, deviating from the natural ocular tissue RI (∼1.33), which contributes to light distortions and reduced visual clarity. The study also confirms that higher water content lenses are favored for daily use, while those with lower water content but higher oxygen permeability are more suitable for extended wear, minimizing corneal hypoxia risks. Brands such as Biotrue ONEday and Acuvue Moist 1-Day employ advanced polymeric formulations, reducing optical aberrations. Additionally, materials like poly(2-hydroxyethyl methacrylate) (PHEMA) and novel silicone hydrogels offer superior oxygen permeability and hydration balance, mitigating phase separation issues and mechanical degradation. These findings underscore the critical role of polymer engineering in ophthalmic applications, supporting the design of next-generation SPCLs that enhance optical performance, user comfort, and long-term biocompatibility

technique for developing intelligent industrial systems as industrial automation improves. This narrative review brings
together more than 70 research sources on industrial PLC, Node-RED, IoT, and IIoT applications. Case studies, experimental implementations, and industry reports are used to discover trends, challenges, and opportunities. The review focuses
on three main topics: PLCs in modern industrial systems and their evolution and integration with IoT/IIoT; Node-RED
as a middleware for industrial automation and its ability to connect PLCs to cloud and edge computing; and IIoT and
smart manufacturing and how it improves data acquisition, processing, and analytics for optimized operations. This
study review elucidates the technical interplay of PLCs, Node-RED, and the Industrial Internet of Things (IIoT) in smart
industrial systems. It delineates essential integration trends, emphasizes current problems, and specifies prospective
trajectories for the advancement of automation, connection, and system intelligence.

Energy supply is a key indicator of nation's development, hence, the need to shift attention towards its sustainability. In this study, a reliability, availability, maintainability and dependability-(RAMD)-based approach is utilized to determine subsystem components that impede sustainable water supply. Mathematical models using the Markovian birth-death process and transition diagrams are developed for each component/subsystem for the energy supply system. Chapman-Kolmogorov differential equations for each component are also formulated. The failure and repair time random variables are statistically independent and follow exponential distribution. Three different cases of energy supply system are considered. The components/subsystems are solar photovoltaic (PV), wind, diesel generator (DG), inverter, pump, and tank respectively. RAMD analysis result reveals that the subsystem pump is the most critical with minimum reliability of 0.548811636 after 10 months and availability of 0.92 for all cases. The results also reveal that at a period of 10 months, the reliability of the systems are 0.299778623 for case I, 0.284193481 for case II and 0.257385582 for case III respectively. Based on the numerical results, it can be inferred that case I is the best among other cases and subsystem pump is the most critical subsystem component. The findings of this work would be very useful for designers and maintenance engineers.

Breast cancer remains one of the leading causes of mortality worldwide, emphasizing the critical need for accurate and efficient diagnostic tools. This study investigates the effectiveness of combining linear and non-linear feature selection methods-Principal Component Analysis (PCA), Pearson Correlation Coefficient (PCC), and Backpropagation Neural Networks (BNN) to improve breast cancer classification using machine learning models. We utilized the Wisconsin Breast Cancer Dataset to evaluate the performance of five classifiers-Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Decision Tree (DT), Naïve Bayes (NB) and Artificial Neural Network (ANN). The results demonstrated that BNN-selected features consistently outperformed PCA and PCC across all classifiers, with SVM achieving the highest classification accuracy. The results showed that BNN-selected features consistently outperformed PCA and PCC, with SVM achieving up to 97.3% accuracy and 98.1% precision, and KNN reaching 97.1% accuracy and 96.9% precision. Stacking ensemble models further improved performance: the non-linear SVM-KNN-ANN ensemble attained perfect classification metrics of 100% accuracy, precision, recall, specificity, and F1-score demonstrating the superior synergy between BNN and ensemble learning. These findings highlight the diagnostic advantage of combining non-linear feature selection with meta-learning approaches and support the development of robust, high-accuracy breast cancer detection systems.

The global shift to sustainable energy has heightened the demand for smart, robust, and efficient monitoring systems. Wireless Sensor Networks (WSNs) play a key role by enabling real-time data collection, decentralized control, and flexible communication in renewable energy applications. This review presents a comprehensive analysis of WSN architectures, routing protocols, and deployment strategies tailored to photovoltaic (PV) arrays, wind energy systems, hydroelectric plants, and smart microgrids. Core operational challenges are examined through a critical evaluation of energy-aware routing protocols including LEACH, PEGASIS, TEEN, ZRP, and advanced variants like EE-OLEACH and PT-Hybrid. Real-world applications, such as offshore wind farms, rooftop PV installations, and smart grid in underserved regions, underscore the practical value of WSN-based monitoring. Emerging solutions such as AI-enhanced clustering, blockchain-secured data integrity, and federated learning for decentralized intelligence demonstrate the convergence of WSNs with next-generation technologies. Additionally, the integration of edge computing and future 6G frameworks is explored for ultra-reliable, low-latency communication. However, gaps remain in technological readiness, large-scale deployment validation, and the need for cross-layer protocol optimization. The review concludes that while WSNs are already central to modern renewable energy systems, ongoing innovations in energy harvesting, secure networking, and intelligent analytics will further elevate their role in creating autonomous, self-healing, and high-performance energy infrastructure.

Corrosion leads to global economic losses exceeding $2.5 trillion annually and accounts for up to 30% of infrastructure failures. Conventional corrosion inhibitors face limitations such as aquatic toxicity (LC50 <100 mg/L), moderate efficiencies (65–82%), and poor long-term durability (<500 hours). This review critically evaluates hybrid organic–inorganic corrosion inhibitors, focusing on their composition, mechanisms, performance metrics, and eco-safety. Hybrid systems comprising organic molecules integrated with inorganic matrices like sol–gel networks, nanocomposites, and layered double hydroxides (LDHs) demonstrate enhanced functionality through barrier effects, self-healing, and controlled release. These materials achieve inhibition efficiencies of 88–98.5%, outperforming conventional systems by 18–35%. Sol–gel coatings protect carbon steel at 92–98% efficiency in 3.5% NaCl, while LDHs sustain 94–97% protection over 1200–1500 hours. Nanocomposites reduce corrosion rates from 0.95 mm/year to 0.002–0.004 mm/year. Electrochemical techniques (EIS, PDP) show increased charge transfer resistance (500–2500 Ω·cm2) and coating resistance up to 108 Ω·cm2. Surface analysis reveals smoother films (Ra = 15–35 nm), higher contact angles (95–125°), and adhesion strengths of 12–18 MPa. Case studies report 90–96% protection in marine settings, 88–94% in acidic media, and over 92% under atmospheric conditions. Durability tests confirm >90% efficiency after 1000 hours, significantly higher than the <60% for conventional coatings. Overall, hybrid inhibitors offer a high-performance, eco-conscious alternative for corrosion control, combining efficiency, environmental safety, and scalability. Their superior metrics and adaptability position them as next-generation solutions for diverse industrial applications.

This study aims to optimize the architectural integration and energy performance of nano-ceramic window films in a three-story Baghdad commercial building having an overall area of 2,000 m2. In a 40% window-to-wall ratio (WWR) and climate-responsive orientation, films were selected for solar exposure; north façade overhanging high-transmittance movies, east and west vertical fins on medium-transmittance movies, and south horizontal louvers on low-transmittance movies. Architectural strategies for daylighting, comfort, and façade appearance with low solar heat gain (SHG) were considered. MATLAB 2022 simulations evaluated energy savings, daylight transmission, and solar rejection. The most effective film (either film, HD-IR05100) achieved 29,750 kWh of annual energy savings. The project combines environmental design and the latest façade articulation, proving it possible to achieve passive solar control without compromising visual transparency. 2D technical illustrations and 3D photorealistic renderings support the aesthetic and functional benefits, endorsing nano-ceramic films as a design-led, sustainable solution in tropical climates.

Cancer classification is important for early diagnosis and effective treatment. This study proposes a hybrid framework that combines Fisher Discriminant analysis (FD) with Linear Support Vector Machine (LSVM) and Long Short-Term Memory (LSTM) networks. MicroRNAs (miRNAs) expression profiles for three types of cancer are used to evaluate the model. These are: lung- adenocarcinoma, ovary and pancreas cancers. The classification process is done using 5-fold cross valuation. Results show significant performance for LSTM compared to LSVM with and without features reduction. However, using FD analysis results in high accuracy reached 0.97and 0.96 for LSTM and LSVM respectively, using 50 miRNAs. The accuracy of all models improved with increased feature number sets. Another test has been done to find the relation between these types of cancers based on the classification process. Most pancreas cancer samples are classified as lung- adenocarcinoma cancer compared to ovary cancer in an unseen test. Overall, the results demonstrate the effects of LSTM -FD as a dependable tool for cancer diagnosis. Further, our model detects original cancer like the ovary and pancreas accurately.