The engineering challenges of the 21st century demand solutions that are not only innovative but also environmentally responsible. Two fields-Internet of Things (IoT) technologies and green and sustainable chemistry- are now converging to transform how industries operate, how societies respond to crises, and how we safeguard public health and the environment.

The application of IoT in fire safety and disaster response illustrates this promise. My work on an IoT-enabled firefighting robot demonstrated how robotics can be deployed to protect human life in high-risk environments. However, the broader significance lies in coupling such devices with sustainable fire suppression agents and eco-friendly materials, thereby reducing the environmental impact of emergency interventions. This dual approach underscores a new paradigm: engineering solutions must protect lives while minimizing harm to ecosystems.

The onset of natural convection in a vertical Hele-Shaw configuration under the action of mechanical vibration is studied. Vibration is considered to be a harmonic one and its direction is considered parallel to the temperature gradient. Linear stability analysis for small Pr number leads us to the Mathieu equation. The so-called time averaged method is applied to this equation which gives us a closed form relation for the critical Ra_T number for the harmonic (synchronous) solution. The results are in a perfect agreement with published numerical results.

Artificial intelligence (AI) has emerged as a critical set of computational methods and engineered systems that support discovery across science, technology, engineering, and mathematics (STEM) disciplines. While initially developed within computer science, AI applications are now widely utilized to achieve monumental advances across multiple domains, including:

1. Biology and Medicine: AI-based models have enabled real-time protein folding predictions that enhance drug discovery and precision healthcare.
2. Climate Science: Machine learning techniques have improved environmental modeling and long-term forecasting.
3. Materials Engineering: Computational algorithms have accelerated the design of alloys, polymers, and nanomaterials; and
4. Astrophysics: Large-scale data analysis methods support the interpretation of complex astronomical observations.

This paper examines the engineering foundations enabling AI-driven advancements. Relative emphasis was placed on the integral role of infrastructure, algorithm design, and governance frameworks in ensuring that AI applications remain scalable, ethical, and resilient. Current research indicates that AI-enabled engineering not only accelerates innovation but also redefines the competencies required for the future STEM workforce.

By addressing opportunities and challenges, including issues of data quality, bias, and regulatory oversight, this work positions AI’s influence across STEM as a paradigm shift with far-reaching implications. The analysis highlights the importance of interdisciplinary collaboration among engineers, scientists, and educators in navigating the ethical and societal complexities of AI integration, ensuring that innovation is both responsible and sustainable.

The increasing sophistication in cyber threats, it requires advanced AI and ML based solutions that go beyond the historical security measures. AI and ML have become an essential part of cybersecurity as they can analyze Real-time attack risks and respond accordingly. AI plays a Critical role in detecting and preventing attacks, keeping businesses on the cutting edge of cybersecurity barriers.

This paper discusses the role of ML algorithms in anomaly detection, intrusion detection, malware classification, and phishing attack prevention. AI amplifies cybersecurity by detecting patterns and anomalies in network traffic and user behaviour that may indicate a potential cyberattack. Through Cutting - edge data analysis and predictive modelling, AI can proactively prevent attacks by recognizing potential risks before they happen. By analyzing past patterns of attacks and determining similarities, AI systems take proactive measures against breaches before they happen.

One of the other vital responsibilities of Artificial Intelligence in cybersecurity is the development of automatic incident response systems. This kind of system will examine data, identify potential threats, and take instantaneous actions to either contain or mitigate cyberattacks, thus minimizing damage and interferences. Due to the large volume of data processing it can handle in real-time, AI is one of the most important tools in ensuring efficient cybersecurity in the modern digital age.

This paper illustrates the role of AI-driven vulnerability detection in cybersecurity frameworks, in light of such challenges as adversarial AI, data privacy issues, and explainable AI in cybersecurity.

Keywords: AI in Cyber Security; Machine Learning; Intrusion Detection; Anomaly Detection; Malware Classification; Threat Detection

In a rapidly evolving global economy driven by technological disruption, innovation, and skill-based employment, a critical disconnect between academia and industry continues to hinder progress. While academic institutions remain the bedrock of knowledge creation, industries are the engines of economic application. The synergy between these two pillars is essential for sustainable development. Yet, misaligned goals, outdated curricula, and limited collaboration have created a gap that threatens both graduate employability and industrial competitiveness. Bridging this gap is no longer an academic concern; it is a strategic imperative. Nations that succeed in integrating education with enterprise are better poised to foster innovation, generate employment, and maintain a competitive edge in the global knowledge economy.

Engineering managers in industries now rely on AI, a move that is shifting how things have been done before. As organizations work to keep up with the needs for agility, sustainability and efficiency, AI has become very valuable for making decisions, managing resources and improving the way things are done. Engineering management which mostly depended on human experience and traditional data tools has now added efficient smart systems that rapidly handle a lot of information. With this progress, companies experience better productivity and also find new opportunities for innovation in manufacturing, construction, energy and logistics. Integrating AI in both overall planning and daily project work helps engineering managers meet the rapid changes in the world economy.