Transactions on Engineering and Computing Sciences

Investigating Droop Control Contribution to Grid Resilience by Maintaining Frequency Stability During Grid Disturbances

C. N. Anyangwe — 2025-03-19

In contrast to conventional power plants, which are based on large synchronous generators with large inertia capabilities to dampen sudden disturbances, renewable energy sources, such as solar and wind, connected to the grid through power electronics converters, display low system inertia and overload limiting capabilities. Additionally, because they lack primary frequency regulation capabilities, they are unable to actively respond to the frequency response of the system. This research investigates the contribution of droop control strategies to grid resilience by focusing on their ability to maintain frequency stability during grid disturbances. The study employs a simulation-based approach using MATLAB/Simulink to model the Djoum power plant in Cameroun and implement droop control algorithm. The methodology involves designing and analyzing the system's response under sudden load changes using droop and supervisory control strategies. Parameters such as droop coefficients and control bandwidths were systematically varied to analyze their impact on frequency regulation and grid resilience. Results show that droop control maintains system operation by adjusting frequency and voltage under disturbance, while supervisory control acts as a secondary layer to fully restore parameters to their reference values thereby ensuring reliable operation during grid disturbances.

Dodecaphony and Interval Vectors

Ilhan M. Izmirli — 2025-03-19

The exploration of the profound and intrinsic cohesion between mathematics and music is certainly nothing new – it actually dates all the way back to Pythagoras (c. 570 BCE – c. 495 BCE). However, the introduction of the dodecaphonic (twelve-tone) system developed by Arnold Schoenberg (1874 – 1951) has taken this study to entirely new levels, and has instituted such concepts as set theory, ordered sets, vectors, and various types of spaces as useful tools in music theory. In this paper we will look into one of these tools, namely the notion of interval vectors.

Artificial Intelligence, Cybersecurity, and a Growing Ethical Dilemma

Steven Arvin — 2025-03-15

Currently, cybersecurity threats, particularly cyber-attacks, are a growing concern. As time goes on, it becomes increasingly challenging to hinder these attacks. Nevertheless, a new participant has entered the arena, known as artificial intelligence (AI). AI offers a way for cybersecurity experts to counteract the ever-evolving attacks. By utilizing techniques such as identifying threats and automated responses to incidents, organizations can enhance their security measures and safeguard confidential data. Despite the numerous advantages of adopting AI, it is equally important to remain vigilant about potential risks. In recent years, the rapid growth in cybersecurity threats has necessitated the development of more effective measures to protect sensitive information and systems. This paper delves into the ethical concerns of AI in cybersecurity, stressing the crucial balance between technological innovation and maintaining ethical standards.

Bridging the Gap between Business Practice and Data Science Approaches

Jiangping Wang — 2025-03-08

In data science and analytics, the driving force is not on how to perform analytics tasks or how to use advanced technology in analytics projects. Business problems and goals should always drive the overall approaches. Projects and applications in data science and analytics should serve business goals and help business decision making. In this paper, a case study that serves various directions in answering business questions is presented.

How AI is Transforming Real Estate: Insights from the Hospitality & Multifamily Sectors

F N U Marsella — 2025-03-27

Artificial intelligence (AI) is transforming the hospitality and multifamily real estate sectors, enhancing revenue management, customer experience, and operational efficiency. This paper examines how AI-driven technologies are reshaping these industries, highlighting their applications, benefits, and challenges. In the hospitality sector, AI optimizes revenue through dynamic pricing, enhances guest experiences with intelligent automation, and reduces operational costs via predictive maintenance. Meanwhile, in the multifamily sector, AI streamlines tenant screening, automates leasing, improves resident engagement, and integrates smart home technologies to enhance security and efficiency. AI-powered market analysis, rent optimization, and predictive maintenance further improve asset performance in both sectors. Despite its transformative potential, AI adoption presents challenges such as high initial costs, algorithmic bias in tenant screening and pricing models, and data privacy concerns. The integration of AI with emerging technologies like blockchain and IoT is also explored as a potential solution to enhance security, transparency, and efficiency in real estate operations. By analyzing real-world case studies and industry data in hospitality and multifamily sectors, this study explores best practices for AI implementation, provides a glimpse into what the future could hold for real estate with AI, and offers strategic recommendations for stakeholders looking to maximize efficiency, profitability, and long-term sustainability in an AI-driven real estate landscape.

Numerical Investigation of Sand Erosion Effects on the T-56 Compressor Blade with Changing Parameters

Nizar A. Qattan — 2025-03-19

This paper presents a comprehensive numerical investigation into the effects of sand erosion on the T-56 compressor blade, a critical component in turbomachinery subjected to harsh environmental conditions. Using advanced Computational Fluid Dynamics (CFD) techniques, the study examines the impact of parameters such as sand particle concentration, size, and pressure ratio on blade erosion rates and overall compressor performance. The numerical analysis is validated against established NASA Rotor 37 data, providing confidence in the accuracy of the modeling approach. Results reveal the significant influence of sand erosion on aerodynamic efficiency and highlight key operational considerations to mitigate damage in sandy environments. This research provides valuable insights for improving the design and maintenance of turbomachinery, particularly in regions like the GCC, where sand ingestion poses a severe challenge.

Crimebots and Lawbots: Cyberwarfare Powered by Generative Artificial Intelligence

Peter E Murray — 2025-03-15

Crimebots are fueling the cybercrime pandemic by exploiting artificial intelligence (AI) to facilitate crimes such as fraud, misrepresentation, extortion, blackmail, identity theft, and security breaches. These AI-driven criminal activities pose a significant threat to individuals, businesses, online transactions, and even the integrity of the legal system. Crimebots enable unjust exonerations and wrongful convictions by fabricating evidence, creating deepfake alibis, and generating misleading crime reconstructions. In response, lawbots have emerged as a counterforce, designed to uphold justice. Legal professionals use lawbots to collect and analyze evidence, streamline legal processes, and enhance the administration of justice. To mitigate the risks posed by both crimebots and lawbots, many jurisdictions have established ethical guidelines promoting the responsible use of AI by lawyers and clients. Approximately 1.34% of lawyers have been involved in AI-related legal disputes, often revolving around issues such as fees, conflicts of interest, negligence, ethical violations, evidence tampering, and discrimination. Additional concerns include fraud, confidentiality breaches, harassment, and the misuse of AI for criminal purposes. For lawbots to succeed in the ongoing battle against crimebots, strict adherence to complex AI regulations is essential. Ensuring compliance with these guidelines minimizes malpractice risks, prevents professional sanctions, preserves client trust, and upholds the ethical and legal professional standards of excellence.

A Cognitive Analysis and Life Prediction Through AI Algorithm of Control Arm Using Manufacturing and Vehicle Driving Data

Byeong Sam Kim — 2025-03-11

This study aims to enhance vehicle safety by predicting the life perdition of control arms, critical suspension components. Traditional inspection methods have limitations in accurately predicting failures, leading to unexpected accidents occurring both before and after the vehicle's expected lifespan. The increasing complexity of control arm manufacturing, coupled with the growing volume of vehicle driving data and heightened competition, necessitates a more sophisticated approach to quality and safety. This system implements autonomy and intelligence of the production system by utilizing intelligent production system, big data, and artificial intelligence technologies, and supports optimal decision-making in real time. Data collection: There collected various sensor data from the production site, system data, MES system data, etc. In data refinement, data analysis and algorithm extraction of the Control Arm are performed, and the collected data is refined and preprocessed to be processed into a form suitable for analysis. Database construction: We build a relational database or NoSQL database to systematically manage data. This study represents a crucial step towards a more proactive and data-driven approach to vehicle safety and manufacturing. By integrating AI and big data technologies, the automotive industry can move towards a future characterized by minimized accidents and optimized production processes. Finally, we derived the results of predicting and optimizing the remaining useful life prediction of the remaining product.