Engineering &

This study investigates the impact of using multiple inquiry levels to enhance programming skills in software engineering education. A system called MILOS (Multiple Inquiry Levels Ontology-driven System) was developed using the Activity-Oriented Design Method (AODM) to engage students through confirmation, structured, guided, and open inquiry levels. MILOS was evaluated with 54 first-year software engineering students, while Sololearn, a popular programming app, was tested with 55 students. Both were assessed using the M3 evaluation framework’s Micro (usability) and Meso (performance) levels. Results showed that MILOS significantly improved students’ learning outcomes, demonstrating the effectiveness of multi-level inquiry-based learning in programming education.

 Contributors

• Millennium Institute of Technology & Entrepreneurship, Karachi, Pakistan
• Bahria University, Karachi, Pakistan
• Testing Centre of Excellence, Habib Bank Limited, Karachi, Pakistan
• COMSATS University Islamabad, Lahore, Pakistan
• Cardiff Metropolitan University, Cardiff, United Kingdom
• Multimedia University, Cyberjaya, Malaysia

This study focuses on the design and development of a truncated spherical dielectric lens antenna aimed at enhancing directivity and beamwidth performance. Using Polytetrafluoroethylene (PTFE) as the lens material, simulations were conducted in CST Studio Suite within the 8–12 GHz range. Experimental validation was performed using a Vector Network Analyzer and anechoic chamber. Optimal performance was achieved with a lens of 24.34 mm thickness, 55 mm center of curvature, and 110 mm aperture, resulting in 21.9 dBi directivity and 13.1° beamwidth. The lens’s compact size and simple design make it well-suited for RF applications requiring focused energy transmission.

Contributors

• Millennium Institute of Technology and Entrepreneurship, Karachi, Pakistan
• Federal Urdu University Arts and Science Technology, Karachi, Sindh, Pakistan
• King Abdulaziz University, Jeddah, Saudi Arabia
• Gachon University Seongnam-si, South Korea

With the rapid growth of the Internet of Medical Things (IoMT), vast amounts of real-time health data are now generated and transmitted for remote monitoring. This study presents a novel approach called Association Rule Mining for Risk Prediction (ARMR), which integrates IoMT infrastructure with Association Rule Mining (ARM) to predict health risks, particularly heart disease. Using a dataset from multiple hospitals, ARMR identifies key associations among demographic, physiological, and lifestyle factors. The system not only predicts disease risk effectively but also uncovers unexpected patterns in patient data, providing valuable insights to support accurate diagnosis and more informed medical decision-making.

Contributors

• Millennium Institute of Technology and Entrepreneurship, Karachi, Pakistan
• DHA Suffa University, Karachi, Pakistan
• Walton Institute for Information and Communication Systems Science, Waterford, Ireland

The imperative need for mobile sensor nodes in Underwater Wireless Sensor Networks (UWSNs) has been evident for decades, driven by applications in underwater warfare, autonomous underwater vehicle (AUV) exploration, and remote-operated vehicle (ROV) operations. While the development of protocols for ad hoc mobile UWSNs (AMUWSNs) is challenging due to the dynamic nature of the underwater environment and the inherent limitations of acoustic communication, there remains a dearth of suitable solutions. This paper introduces the Self-Organized Ad Hoc Mobile (SOAM) routing protocol for AMUWSNs. SOAM is a reactive, cluster-based approach that leverages Received Signal Strength (RSS) for distance estimation. By employing a beacon-based mechanism, the protocol establishes forwarding paths between cluster heads and a gateway, enabling efficient communication among ordinary sensor nodes.

Self-Organized Ad Hoc Mobile (SOAM) UWSNs: A novel routing protocol for dynamic underwater environments.

Contributors

• Millennium Institute of Technology & Entrepreneurship, Karachi, Pakistan
• University of Malaga, Málaga, Spain
• Sindh Madressatul Islam University, Karachi, Pakistan

Underwater Sensor Networks (UWSNs) hold immense potential for monitoring aquatic environments and detecting geological events. However, the unique challenges posed by underwater conditions, such as limited bandwidth, significant latency, and dynamic topologies, necessitate innovative solutions. This paper introduces a novel energy-efficient routing protocol that adapts to the dynamic nature of UWSNs. By employing the Fuzzy Analytical Hierarchical Process (FAHP) under Multi-Criteria Decision Making (MCDM), our protocol intelligently selects optimal routes based on factors like hop count, distance to the sink, and node connectivity. Through rigorous evaluation, the proposed protocol demonstrated comparable performance to existing fuzzy-based routing schemes like SPRINT.

Analysis of maximum distance in a 3D box, dodecahedron geometry, collision frequency based on node density, and simulation results.

Contributors

• Millennium Institute of Technology & Entrepreneurship, Karachi, Pakistan
• University of Malaga, Málaga, Spain
• Dawood University of Engineering and Technology, Karachi, Pakistan

The IoT revolutionizes agriculture by addressing challenges like pest infestation, weather unpredictability, inefficient irrigation, and remote farm management. This research proposes an IoT-based solution to optimize crop yield and quality. By integrating sensors for pest monitoring, environmental conditions, and irrigation control, coupled with GSM and VNC for remote management, our system offers a cost-effective and efficient approach. Real-time data is collected and analyzed, enabling informed decision-making. This architecture holds the potential to significantly enhance agricultural practices, particularly in regions like Pakistan where traditional methods persist.

Proposed framework and key IoT application areas within the research.

Contributors

• Millennium Institute of Technology & Entrepreneurship, Karachi, Pakistan
• Macquarie University, Sydney, New South Wales, Australia
• Mehran University, Jamshoro, Sindh, Pakistan

This study presents an end-to-end IoT-based architecture for real-time monitoring and categorization of general anxiety risks, particularly during pandemics like COVID-19. A prototype system, named X-DASH, was developed using Node-MCU and physiological sensors to collect data (heart rate, blood pressure, SPO2) from 500 patients at a cardiac clinic in Karachi. X-DASH categorizes anxiety risk levels into six categories based on threshold values and displays them on a centralized dashboard. Validation with physicians showed over 90% accuracy in risk assessment. Communication protocols were evaluated, with MQTT recommended for its reliability despite higher delays compared to CoAP and Modbus.

Contributors

• Millennium Institute of Technology and Entrepreneurship, Karachi, Pakistan
• DHA Suffa University, Karachi, Pakistan
• University of Missouri, Kansas, Missouri, USA
• Health Security Partners, Bahawalpur, Pakistan
• University of Doha for Science and Technology, Doha, Qatar
• Walton Institute for Information and Communications Systems Science, Waterford, Ireland

Muhammad Iftikhar Umrani is a research scholar at the Walton Institute, SETU, Waterford, Ireland. He has made significant contributions to AI-driven cybersecurity for UAV systems in emerging 6G networks. His work in the UAVSec project, which has used GANs and GNNs, has improved threat detection, secured UAV operations, and ensured system reliability. Mr. Umrani collaborates with other researchers such as Dr. Steven Davy, Dr. Bernard Butler, and Dr. Aisling O’Driscoll, thus exemplifying multidisciplinary research excellence.

Mr. Umrani visited MiTE as a part of ongoing EU staff exchange project, COALESCE, for which SETU is the coordinator. During his visit, Mr. Umrani spoke at the MiTE 1^st  International Conference on Evolving Technologies in Computing as an international speaker on “The Critical Role of Cybersecurity in Unmanned Aerial Vehicles”. He discussed UAVs’ possible applications in logistics, agriculture, disaster management, and surveillance, and discussed significant cybersecurity risks such as GPS jamming, malware injection, sensor spoofing, and unauthorized access. He emphasized the use of robust security measures, including rolling encryption keys, AI-driven anomaly detection, and multifactor authentication, and provided real-world examples to practically address the question of how UAV systems can be protected.

Mr. Umrani also proposed forward-looking strategies, such as drone forensics for tracing cyberattacks and predictive analytics for preventing exploitation of vulnerabilities. He called for collective action from all stakeholders: manufacturers should be encouraged to embrace security-by-design principles, while operators should prioritize ongoing cybersecurity training and adherence to standards. His integrative approach underlined the importance of building a secure and sustainable UAV ecosystem.

His deliverables at the  1^st MiTE International Conference was very smoothly, connecting the dot between theoretical research and practical implementation.

Madia Safdar is playing an important role towards energy systems engineering. Her current Ph.D. work at Lappeenranta University of Technology, Finland, has the focus of optimization of Battery Energy Storage Systems for grid stability and efficient use of renewable energies. She played an important role in the demand response projects as a research assistant at Aalto University. Partnerships with Kuwait College of Science and Technology allowed her to bridge cross-border intellectual exchanges and convert theoretical knowledge into real solutions. With her collaborative research, Madia has advanced various frameworks for energy-efficient systems especially designed for industries and residential usages that answer global energy needs with localized solutions.

Laboratory and Hands-on Experiences

Madia’s lab experience has been refined to give her technical skills on energy storage and grid technologies. At Aalto University, she collaborated with leading faculty to develop advanced skills in using General Algebraic Modeling System (GAMS) and data analytics for optimizing battery storage under real-world constraints. Her role as an instructional assistant at Kuwait College of Science and Technology further strengthened her mentorship skills, guiding students in applying engineering principles to renewable energy integration and storage experiments. These hands-on experiences, combined with her collaborations with foreign faculty, have enriched her multidisciplinary research approach, enabling her to contribute to energy solutions that integrate electrical engineering, economics, and environmental science.

Conference Presentations and International Engagements

She Visited 1^st MiTE International Conference 2024 as a part of EU funded project COALESCE, where she deliver the keynote speech on the subject of “Optimal Sizing of Battery Energy Storage Capacity for Industrial Loads” Here, she brought into focus a peak demand charges and grid reliability-enhancing optimization model using GAMS. She proposed scalable solutions for industries on how to cut costs while sustaining the environment, addressing constraints like battery limits, load balance, and electricity pricing. The presentation received positive comments from global researchers and industry stakeholders, opening the door for collaborations on real-world applications.

Through her active engagements in international forums, Madia has developed cooperation with institutions within Finland, Pakistan, and Kuwait. Her interactions with cold climates have resulted in models that provide variable demand with fluctuating price scenarios. Additionally, Madia gives lectures at MITE, where she tells students about new technology and inspires them to pursue technological innovations. Madia demonstrates her willingness to share her knowledge and explore sustainable energy applications through her participation in lecture and in 1^st MiTE International Conference.

International Faculty Lecture at MiTE

Madia Safdar delivered lecture, introducing students to optimization and the sizing of battery energy storage capacity. She explained the importance of optimizing energy storage systems, especially in terms of reducing energy costs and managing energy more effectively.

During the lecture, Madia was focusing on General Algebraic Modeling System, or General Algebraic Modeling System (GAMS), which is a software tool for solving complex optimization problems. She gave an overview of General Algebraic Modeling System (GAMS) and explained how it simplifies the process of modeling and optimizing energy systems.

Madia elaborated on how General Algebraic Modeling System (GAMS) can be used to mitigate energy costs, which include very beneficial results such as easing energy bills, peak shifting/shaving, and better management of ancillary services. She said that these benefits optimize the operation of energy systems with cost and energy consumption at an optimal level.

In addition, Madia has performed a sensitivity analysis of demand charge values, which shows how GAMS can determine the effect of different demand charges on peak demands. This analysis proves to be a key determinant of how changes in demand charges affect the total cost of energy, thus promoting better decision-making for optimizing storage systems. General Algebraic Modeling System General Algebraic Modeling System (GAMS) Madia’s lecture not only presented its general aspects but also showed its practical application in the field of energy management, linking students with sound optimization techniques and with the role the software has in real-world energy solutions.

Madia Safdar presented a thorough lecture on GAMS, taking the lecture of the week one step ahead with the basics of GAMS presented in the previous lecture. She explained deeply about how optimization is done by using GAMS, mainly focusing on the application area of battery energy storage for minimizing cost of energy.

Madia went through the advanced features of GAMS and explained how it models and solves optimization problems for energy systems. She also elaborated in detail the types of energy optimizations that can be achieved using GAMS, including energy bill reductions, peak shifting/shaving, and optimizing ancillary services. This made it easier for the students to grasp how GAMS applies to real-world energy challenges.

To reinforce these theoretical concepts, Madia demonstrates the practical uses of General Algebraic Modeling System (GAMS) using live demonstrations as she walks through them with her students. This time, for example, the demonstration is a simple energy optimization, where one can see firsthand how General Algebraic Modeling System (GAMS) would minimize energy cost and optimize energy storage system performance.

Madia further offered students a hands-on approach with General Algebraic Modeling System (GAMS). Each student had the chance to interact with the software in exploring how the energy optimization models are built and run. In this practical session, they would apply what they learned, making it valuable experience for them in working with energy optimization models and understanding how General Algebraic Modeling System (GAMS) could be used in real-life applications for such tasks.

By the end of the lecture, students learned the theoretical and practical knowledge of energy optimization with General Algebraic Modeling System (GAMS) to apply in future projects and studies.