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Learning critical concepts that are centered on the analysis, design, and maintenance of transportation infrastructure systems poses a measure of difficulty for undergraduates in engineering. Therefore, hands-on learning pedagogy should be an excellent precursor to increase understanding of these concepts, since the pedagogy incorporates real-life experience in the delivery. This paper describes how a hands-on learning pedagogy called experiment-centric pedagogy (ECP) has been used to teach these concepts to undergraduate students at a historically Black university. The research questions are as follows: (1) How well can ECP improve students’ understanding of concepts essential to the analysis and design of transportation infrastructure systems? (2) How has the ECP facilitated the achievement of the learning objectives of these concepts? and (3) Does an ECP increase the engagement of undergraduate students in their transportation infrastructure engineering learning and lead to measurable lasting gains? To answer these research questions, ECP was implemented and assessed when used to teach the concepts of stress and strain utilized in the analysis of bridges and other transportation infrastructure, sound used in the development and design of noise barriers, moisture content in controlling compaction of highway infrastructure systems, and degradation of infrastructure systems exposed to various environmental settings. Assessment results from 92 undergraduates reveal an increase in students’ motivation and cognitive understanding of the relevant concepts, as well as learning gains and an improved success rate compared to the traditional method of teaching. 

Security and trust of Information Systems are critical in its design as they directly influence users' view and acceptance of such systems. Security can be said to be a contextual and dynamic term as there has not been a holistic, universal, and eternal security measure to date. Recent years have seen a lot of confidential and sensitive information being sent, received, and analyzed on the Internet, and a plethora of investigations on ways of developing comprehensive security solutions like encryptions, pattern recognition, and anomaly detection. This work reviews the human inspired algorithms that are particularly employed in pattern recognition and anomaly detection problems. The work discusses the components of the immune system that inspired the artificial Immune System (AIS) based algorithms for pattern and intrusion detection (IDS) problems. A detailed comparison is made between negative selection, clonal selection, and dendritic cell algorithms (danger theory) which are the three major AIS algorithms. AIS is ubiquitous in computer and information security because it is based on the theories developed through years of study and understanding of the human immune system by immunologist. The strengths and weaknesses of these algorithms are also discussed, and possible improvement suggested.