What happens when engineering students are given not just a project, but a competition? In the Microcontrollers Laboratory at the University of Balamand, students discovered the answer firsthand. Microcontrollers courses form the foundation of embedded systems education, yet theory alone is often not enough to fully grasp real-world applications. Hands-on experience is essential to bring these concepts to life. Students were challenged to design a four-wheel-drive (4WD) line-tracking robot using the PIC18F45K22 microcontroller. The project combined sensor interfacing, motor control, and real-time decision-making in a practical robotics application. To further enhance engagement, the project was conducted in a competitive setting, motivating students to optimize performance in terms of accuracy, speed, and reliability. This article presents the design and implementation of the robot and highlights how project-based and competition-driven learning can transform a routine embedded systems assignment into a rich engineering experience.
As artificial intelligence (AI) moves off our screens into the physical world, the behavior of autonomous agents is no longer an abstract computation but an action with real consequences. Robots today navigate warehouses, assist in hospitals, and explore unpredictable outdoor environments, yet even minor mistakes can escalate into costly failures. Traditional learning systems, such as supervised perception models, behavior-cloning policies, and offline-trained reinforcement learning controllers excel when everything goes according to plan, but they often lack the flexibility humans rely on every day, which is the capability of sensing danger and recovering before failure occurs. As embodied AI becomes woven into society, understanding how machines can monitor their own safety and correct their trajectories is becoming a central question in robotics and human–AI interaction. This article explores a maze environment, revealing a deeper challenge of building agents that not only act intelligently but also recover when faced with risks.
Generative artificial intelligence (GenAI) is rapidly changing how digital content is created and consumed. Two widely used GenAI technologies are deepfakes and large language models (LLMs). Deepfakes generate or modify images, videos, audio, and text that imitate real people, while LLMs provide robust language understanding, reasoning, and multimodal coordination. When combined, these technologies significantly increase the realism, speed, and accessibility of synthetic media, raising concerns about misinformation, impersonation, and loss of digital trust. At the same time, the same reasoning capabilities that enable deepfake generation can also be leveraged for detection, verification, and mitigation. This article explores how LLMs strengthen deepfake generation by enabling realistic scripts, coordinated multimodal outputs, and scalable automation. Furthermore, it highlights how LLMs can also be used to fight deepfakes through semantic analysis, cross-modal verification, and provenance-based safeguards. By examining this dual role in an agentic AI setting, the article emphasizes why LLMs are central to both the deepfake problem and its defense.
Critical infrastructure (CI), including healthcare, transportation, energy management, and water supply, becomes increasingly digitized to improve efficiency. At the same time, this digital transformation increases CI’s vulnerability to cyberthreats. On one hand, integrating CI with artificial intelligence (AI) and machine learning (ML) algorithms can enable real-time response to attacks and highly accurate threat detection, enhancing cybersecurity. However, these novel technologies introduce unprecedented risks, including attacks that target and misuse AI models. This article explores the multifaceted nature of using AI in CI cybersecurity, presenting the ways it can be used as a powerful tool and a potential threat vector at the same time. The most appropriate ML algorithms for CI use cases are examined, and the vulnerabilities of AI systems are analyzed, discussing ethical, operational, and data-related challenges. Moreover, suggested frameworks for governments, policy makers, and regulations for ethical and secure AI integration to CI cybersecurity systems are provided. This article offers a comprehensive overview for students and professionals interested in efficiently securing CI through AI technology, while also identifying future research and implementation directions.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Blockchain is a distributed ledger that is simultaneously decentralized. Nodes on various blockchain networks store the whole transaction history. The immutability of blockchain technology lowers the danger of fraud. This technology provides a safe and practical answer for a wide range of educational needs, including digital certification and recording. General concerns that regularly emerge in education can also be addressed through the use of blockchain technology. The educational architecture based on blockchain has the potential to drastically reduce academic dishonesty. This study takes a complete approach, focusing on the evaluation of blockchain-based educational design, the features it employs, and the educational services produced as a result of this research. Blockchain technology is used with the Algorand platform, which is one of three important components that lead to the growth of its use in the education sector. The Algorand language code’s implementation in education exemplifies beneficial principles such as uniform decision making through decentralization. This technique can be applied to a wide range of topics, including education system governance, learning management, and current trends at the elementary, secondary, and university levels.
Provides society information that may include news, reviews or technical notes that should be of interest to practitioners and researchers.
Smart factories are no longer the exclusive domain of multinational manufacturing corporations with extensive infrastructure and costly automation systems. Engineering students can now design, test, validate, and optimize entire smart factory ecosystems within their laboratories using advanced simulation platforms such as FlexSim and AnyLogic. Free trial versions, academic toolkits, online training, and international simulation conferences provide industrial-grade modeling environments to students. By combining system-level simulation, industrial performance, and artificial intelligence (AI), a new professional profile, the AI process engineer, can be developed. This article delves into how simulation-driven education is reshaping student learning. It also underscores the necessity for students to advance beyond traditional operational skills toward predictive, analytical, and AI-enabled system design.
Organizations worldwide are accelerating their investment in artificial intelligence (AI), yet many struggle to define what success actually looks like once a system goes live. This article introduces a practical, four-dimensional framework for measuring true AI adoption, covering workf low efficiency, decision quality, cultural adoption, and responsible AI practices. Grounded in established technology acceptance theory and sociotechnical systems thinking, the framework is operationalized through an AI Adoption Maturity Assessment (AAMA) framework that generates targeted recommendations for data team leaders. The goal is to give students, early-career engineers, and organizational leaders a clear, actionable approach to evaluating AI initiatives beyond deployment milestones.
The rapid increase in data generated by connected devices has created a pressing need for privacy-preserving techniques in distributed learning. This article examines methods that enable collaborative machine learning (ML) while maintaining data security and user privacy. Key approaches such as federated learning (FL), differential privacy (DP), secure multiparty computation (SMPC), and homomorphic encryption (HE) are analyzed for their unique capabilities and various applications. FL facilitates model training across decentralized data sources, ensuring data remain local, while DP mitigates privacy risks by adding controlled noise. SMPC and HE support secure computations on encrypted data, maintaining confidentiality during processing. Despite their effectiveness, these techniques face challenges related to computational complexity, scalability, and regulatory compliance. The article reviews current advancements, practical implementations, and future directions, emphasizing the need for optimized, accessible solutions to enhance data security in distributed systems.
Advanced hashing algorithms (AHAs) are crucial for modern cryptography. They can ensure data integrity, security, and efficient data management. In this article, we explore key aspects of these algorithms, including their fundamental principles, diverse applications, and ongoing research challenges. We delve into the intricacies of cryptographic hash functions, highlighting their role in digital signatures, password storage, and blockchain technology. We discuss the evolving landscape of postquantum hash functions, designed to withstand attacks from quantum computers. We emphasize the importance of ongoing research in developing even more secure and efficient hashing algorithms to address the growing demands of the digital world.
Blockchains are decentralized systems that allow for transactions among parties to be securely processed without the need for a central intermediary, such as a bank in the case of currency exchange. Since the inception of Bitcoin (the first implementation of blockchain) in 2009, considerable advancements have been made in the field of blockchain, spanning applications to software development, gaming, and digital artwork. In parallel, queueing theory, a field of applied mathematics and statistics, which studies the efficiency of waiting lines, has been used for understanding the dynamics of blockchains, albeit with limited applications in the literature. Readers without exposure to these fields will develop a grasp of blockchains and queueing models and learn how the intersection of these fields is studied in the literature. This article lays groundwork for readers to understand the future literature in these fields.
This article explores the impact of collaborative innovation on science, technology, engineering, and mathematics (STEM) education through initiatives implemented at the Universiti Malaysia Pahang Al-Sultan Abdullah (UMPSA) STEM Lab. Operating under a quadruple helix collaboration framework involving academia, industry, government, and the community, the lab implements outreach programs that emphasize digital making skills and experiential STEM learning. These programs integrate hands-on learning modules, mentorship by university students, and partnerships with industry and government agencies to support STEM education initiatives. The outreach activities engage school students and teachers through project-based learning experiences, including robotics, game programming, Internet of Things (IoT), and web development projects. During the COVID-19 pandemic, the UMPSA STEM Lab adapted its outreach activities through virtual learning modules, remote mentorship, and the distribution of learning kits supported by online instruction. Case studies of student projects demonstrate practical applications of STEM concepts and the development of programming, engineering, and digital making skills. By sharing insights from these initiatives, this article highlights the role of collaborative partnerships in supporting scalable STEM outreach programs and provides reference practices for institutions seeking to implement similar STEM education initiatives.
In recent decades, society has witnessed significant advancements in emerging mobility systems. These systems refer to transportation solutions that incorporate digital technologies, such as automation and connectivity, to support sustainability goals, creating safer, more efficient, and user-centered mobility. Examples include connected and automated vehicles (CAVs), shared mobility services (e.g., carpooling), electrified mobility solutions, and mobility-as-a-service platforms such as Whim, Moovit, and Uber. These innovations have the potential to greatly impact different aspects of mobility such as safety, pollution, comfort, travel time, and fairness. In this article, we explore the current landscape of CAVs. We discuss their role in daily life and their future potential, while also examining the challenges they may introduce. We further review practical challenges in CAVs research, with a focus on simulation and real-world testing of CAV algorithms. Then, existing solutions that aim to overcome these limitations are presented. Finally, we provide an accessible introduction to modeling CAVs using basic kinematic principles and offer an open source tutorial to motivate interested students to begin exploring the field.
This article explores the integration of biodegradable nanorobots and edge computing as a sustainable approach to environmental monitoring. Traditional cloud-based systems consume extensive energy for data transmission and computation, creating a paradox where digital progress contributes to ecological degradation. By embedding computation directly into eco-friendly, biodegradable robotic systems, engineers can achieve localized, energy-efficient intelligence that minimizes environmental impact. This article presents the principles, architecture, applications, and ethical considerations of biodegradable nanorobots powered by edge intelligence, emphasizing their potential in soil restoration, pollution detection, and sustainable agriculture.
