Prompt Gamma Neutron Activation Analysis is a nuclear-based technique that can be used in explosives detection. It relies on bombarding unknown samples with neutrons emitted from a neutron source. These neutrons interact with the sample nuclei emitting the gamma spectrum with peaks at specific energies, which are considered a fingerprint for the sample composition. Analyzing these peaks heights will give information about the unknown sample material composition. Shielding the sample from gamma rays or neutrons will affect the gamma spectrum obtained to be analyzed, providing a false indication about the sample constituents, especially when the shield is unknown. Here we show how using deep neural networks can solve the shielding drawback associated with the prompt gamma neutron activation analysis technique in explosives detection. We found that the introduced end-to-end framework was capable of differentiating between explosive and non-explosive hydrocarbons with accuracy of 95% for the previously included explosives in the model development data set. It was also, capable of generalizing with accuracy 80% over the explosives which were not included in the model development data set. Our results show that coupling prompt gamma neutron activation analysis with deep neural networks has a good potential for high accuracy explosives detection regardless of the shield presence.

One year and a half after the start of the COVID-19 pandemic, it became suitable to rethink the design of the engineering education systems to remain sustainable and resilient. The paper aims to identify the most important aspects of the system, as well as the most vulnerable ones and the extent to which the system meets the sustainability requirements of the society. The Balanced Scorecard approach is used to ensure that the system remains sustainable and resilient. The indicators to measure the aspects of this design are developed. A Quality Function Deployment approach is used to identify the extent to which a designed system satisfies the sustainability requirements of the society. The problem is formulated as an engineering design problem in which the customer requirements are presented using a sustainability Triple Bottom Line framework. The results indicate that a well-designed engineering education system is capable of addressing the majority of the 17 sustainable development goals (SDGs) identified by the United Nations. The most important aspect of this system is its commitment to quality assurance and continuous improvement. Such a system is a key player to achieve the SDGs, particularly those of economic growth, quality education, good health and wellbeing, and industry innovation.

A project-based active/cooperative design course is planned, implemented, assessed and evaluated to achieve several desired engineering outcomes. The course allows freshman-level students to gain professional hands-on engineering design experience through an opportunity to practise teamwork, quality principles, communication skills, life-long learning, realistic constraints and awareness of current domestic and global challenges. Throughout successive design reports and in-class assignments, the students are required by the end of the semester to communicate, clearly and concisely, the details of their design both orally and in writing through a functional artefact/prototype, a design notebook, an A0 project poster and a final oral presentation. In addition to these direct assessment tools, several indirect measures are used to ensure triangulation. Assignments are based on customer expectations using a detailed checklist. This paper shows the direct and indirect assessment tools that indicated a high level of achievement of course learning outcomes and a high level of student satisfaction.