Auto-infusion systems stand as instrumental entities in the medical sector, facilitating a streamlined and automated approach towards patient infusion processes. The evolution of these systems has been significantly influenced by the integration of Internet of Things (IoT) technologies [1], heralding a new era of enhanced reliability, precision, and functionality. This research aims to elucidate the development and operation of an IoT-based auto-infusion system equipped with a myriad of features optimized for real-time monitoring and responsiveness. Central to the system's operation is a peristaltic pump, meticulously regulated by a stepper motor, ensuring precise control over the infusion process. The system boasts a multitude of monitoring and alarm functionalities, such as flow rate detection, obstruction alerts, leakage notifications, low fluid level warnings, and bubble detection features. These capabilities ensure the provision of a robust and secure operational environment, minimizing potential risks and enhancing the reliability of the infusion process. An added innovative facet of the system is the incorporation of a UPS (Uninterruptible Power Supply), ensuring continuous operation even in scenarios afflicted by power outages, thereby enhancing the resilience and reliability of the infusion system.

Acoustic levitation, an emerging technology that employs sound waves to suspend solid particles and liquid droplets in mid-air, holds immense promise. Its potential applications span diverse fields, including environmental science, mechanical engineering, biochemistry, food science, and cancer therapy. The method involves manipulating matter using sound waves to create novel forms of energy and material. However, standing wave acoustic levitation faces challenges, particularly unstable soundwaves that hinder stationary levitation. This study delves into the intricacies of the standing wave acoustic levitation system. Additionally, it compares this method with near-field and parametric array levitation techniques. Each approach utilizes sound waves to create “invisible fingers” that hold objects, but they have distinct advantages and limitations. To address instability, two standing wave acoustic levitation systems are constructed and tested: a simpler version and an expert-level setup. The goal is to enhance accuracy and stability. Ultimately, this research aims to unlock the full potential of acoustic levitation.

This paper investigates the impact of sustainable development and climate change mitigation efforts by evaluating renewable energy technologies for reducing carbon emissions in residential buildings in Singapore. It begins with an extensive literature review, covering key initiatives such as the Green Mark scheme, household energy consumption patterns, and various government policies including the Carbon Tax, the Mandatory Energy Labeling Scheme (MELS), and the Minimum Energy Performance Standards (MEPS). Additionally, it addresses the embodied carbon emissions associated with residential buildings. The study then examines the existing renewable energy technologies in Singapore, focusing on Solar Photovoltaic (PV) systems, Wind Turbines, Biomass Energy, and Energy Storage solutions. Using HOMER PRO software, the research evaluates these technologies in terms of data on efficiency, costs, Net Present Cost (NPC), and the number of batteries required, incorporating sensitivity data for different sizes of residential flats through Multiple Criteria Data Analysis. Simulations conducted with RETSCREEN software for both single-family homes and multi-unit housing take into account various factors such as weather patterns, energy consumption, and financial implications. These simulations provide a comprehensive assessment of the potential for these technologies to significantly reduce carbon emissions in residential buildings, aligning with Singapore's sustainability goals and contributing to global climate change mitigation efforts.

This study explores how Artificial Intelligence (AI) can be used to enhance diversity and inclusion in engineering education. By using AI, schools can find and reduce biases, tailor learning to individual needs, and support students from underrepresented backgrounds. AI can also improve accessibility and help teachers adopt more inclusive methods. We discuss the benefits and challenges of using AI in this context, showing its potential to make engineering education more equitable.

Artificial Intelligence (AI) is transforming the educational landscape by offering personalized learning experiences that cater to individual student needs. This report delves into the psychological aspects of AI -enhanced learning experiences, focusing on their impact on student motivation, engagement, cognitive load, and emotional well-being. By analyzing various case studies and existing literature, we explore the benefits and challenges associated with integrating AI into educational settings. The findings indicate that AI-driven tools can significantly enhance motivation and engagement by providing tailored learning paths and real-time, relevant feedback. Additionally, AI can help manage cognitive load and offer emotional support, fostering a supportive and effective learning environment. However, challenges such as dependency on technology, data privacy concerns, and potential stress must be addressed to fully harness the benefits of AI in education. This report underscores the importance of strategic implementation and continuous evaluation of AI tools to ensure they contribute positively to students' psychological well-being and academic success.

This paper investigates how Collaborative Learning environments enriched by AI technologies have emerged as a pivotal advancement in modern education, promising enhanced learning experiences and outcomes. AI facilitates personalized learning pathways, fosters engagement through interactive tools, and supports peer interaction in dynamic educational settings. However, integrating AI into educational practices poses concerning challenges. Despite these challenges, AI holds immense potential to revolutionize collaborative learning by optimizing educational resources, promoting student engagement, and preparing learners for future challenges.

Signals are crucial in conveying information across various fields. This paper presents new methods for processing electromyographic (EMG) signals to create AI systems that decode muscle activity for arm movement control. Using an advanced dataset, the study focuses on enhancing prosthetic control and rehabilitation technologies through sophisticated signal processing and machine learning techniques. Various preprocessing steps improved signal quality, and a diverse set of features was extracted and classified. The results highlight the potential for more intuitive and responsive robotic arm movements, contributing to better prosthetic and rehabilitation solutions.

This paper investigates how clinical technologies focusing in radiology enriched by AI technologies have emerged as a pivotal advancement in the field of radiology, offering advancements that enhance both diagnostic accuracy and operational efficiency. These AI systems improve diagnostic performance by identifying subtle patterns that might be overlooked by human radiologists. Additionally, AI integration with Picture Archiving and Communication Systems (PACS) streamlines image management, automating routine tasks such as image analysis and report generation, which boosts workflow efficiency and productivity. Despite these benefits, challenges such as data privacy, algorithm transparency, and the need for continuous validation of AI models remain pertinent. As AI technology evolves, it holds the promise of further enhancing radiological practices, improving patient outcomes, and contributing to a more efficient healthcare system.

This paper investigates how AI-driven tools and platforms are integrated into engineering curricula to ready future engineers for the digital age. It emphasizes the advantages of improved learning experiences and tailored education, stressing the significance of updating curriculum content and training faculty. It also addresses challenges such as ethical concerns and integration complexities, highlighting the necessity for strategic planning to optimize AI's role in encouraging innovation and preparing students for careers in technology.

This paper explores the transformative potential of gamification and immersive learning experiences, enhanced by artificial intelligence (AI), in modern education. Gamification leverages game design elements to boost engagement, motivation, and learning outcomes, while immersive technologies such as virtual reality (VR) and augmented reality (AR) create interactive, experiential learning environments. AI plays a pivotal role by personalizing learning experiences, adapting content to individual needs, and providing real-time feedback. This study reviews existing literature presents case studies of successful implementations, and discusses the benefits and challenges associated with these technologies. By integrating AI with gamification and immersive learning, educators can create dynamic, engaging, and effective educational experiences. The paper also addresses ethical considerations, accessibility issues, and future research directions, ultimately highlighting the significant impact of AI-driven gamification and immersive learning on the future of education.

Wearable devices can enhance quality of life by allowing patients to live independently while monitoring vital signs remotely. The reliance on batteries, however, is a limitation, as devices stop working once the battery is depleted. Energy harvesting from human and environmental sources presents a solution, providing continuous power for wearables and portable devices. This project focuses on low-voltage energy harvesting methods like photovoltaic cells, thermoelectric generators, piezoelectric materials, and magnetic induction to power IoT systems, with stored energy in batteries.