Virtual training platform allows interactive and engaging learning through practice without exposing trainees to hazards. In the recent pandemic (COVID-19) situation, online training is gaining importance as it allows learning with social distancing. This research study develops two online training modes—slide-based and virtual world—and assesses them on factors such as knowledge retention, engagement, and attention. Fire safety and emergency evacuation procedures were selected for online training development, focusing on a university community. A Lean Startup methodology was employed to develop training content for virtual and slide-based safety training (SBST). A virtual university building was developed with 15 learning objectives on fire safety. An empirical evaluation of the training modes was conducted with 143 participants. The results validated that a Virtual Safety World (VSW) can provide the same knowledge as SBST but can do so in a more engaging manner. Retention of concepts after a month was higher in VSW participants. The participants’ attention levels, measured by employing qEEG, showed that participants exhibited better-sustained attention while in VSW than in SBST mode. In addition, initial studies of the virtual training platform, designed to be adaptive to the user, are performed using deep learning and qEEG.

With 212 locations serving a geographical area three times the size of the state of Texas, our international client has been experiencing unexpected failures in his portfolio of thousands of refrigeration units, resulting in an associated loss of $1.4 million. These losses have been attributed to lapses in food quality caused by improper refrigeration, and the adopted reactive maintenance strategy of the refrigeration units. To assist our client in complying with the cold-chain regulations of food safety and mitigate such losses, the agent-based simulation was used to model and assess the viability of two distinct massively populated Internet of Things (IoT) alternatives. The first (alternative A) involves monitoring the availability of each of the refrigeration units and signaling for the initiation of repair processes and food removal from failed units when the cooling temperature differs from a specified threshold and a more sophisticated and expensive alternative B that involves the adoption of the added capability of a condition-based predictive maintenance strategy to reduce unplanned downtime and mitigate or eliminate the causes of failure. Using historical data, a simulation of the current operations was first modeled, validated, and then augmented with capabilities to address the operational characteristics of the proposed IoT implementations. Results of financial analysis results including the probabilistic risk analyses, that account for the variations and the probability distributions of the assumption parameters, showed alternative A to be superior, with a mean net present value (NPV) of $416,703, and a modified IRR of about 18.77% exceeding the project cost of capital of 12.80% with complete certainty. The client was also advised that a 30% reduction in the acquisition cost would make investment B as viable as investment A.

In recent times, significant advancements have been evidenced in efforts to enhance connectivity and security within intelligent buildings (IBs). An IB is a contemporary structure that leverages state-of-the-art computer technologies to autonomously control the environmental conditions within a building to enhance occupants’ comfort, safety, work efficiency, and wellbeing while also ensuring optimal energy consumption. In the access aspect of a telecommunication network for IBs, the Main Distribution Frame (MDF) is in the central office. Intermediate Distribution Frames (IDFs) act as the isolation points for additional troubleshooting and further distribute the lines. The MDF is essentially a circuit switch that is typically operated through manual intervention because requests to establish or remove connections are sporadic and occasional. This can be tackled by utilizing Agent Based Systems (ABS) and Internet of Things (IoT) monitoring systems to develop a more holistic perspective of the process by which IDFs and MDF rooms are maintained. Furthermore, it will generate a process overview if we eradicate the process of physically checking a room that is currently used for IoT monitoring. In this research, a case study is presented to determine the feasibility of developing an IoT monitoring system. Currently, the monitoring applied in the case study is long, and over 40% of the equipment has maintenance failures. Distributed simulation of ABS is used to simulate the use of IoT for this project. Its data forecast can be very useful in determining the appropriate actions to improve the process of IDF and MDF and operational cost.

The adoption of the Internet of Things (IoT) and its related technologies has transformed the manufacturing industry and has significantly changed the traditional linear manufacturing supply chains into dynamic and interconnected systems. However, the lack of an approach to assess the economic feasibility and return uncertainties of an IoT system implementation, is blamed as the culprit for hindering its adoption rate. Using two distinctive case studies, this research investigates the use of distributed simulation of agent-based model (ABM) to address such gap in the literature. The first involves the economic feasibility of an IoT implementation in a very large retail warehouse facility, while the second case study proposes a framework able to generate and assess ideal or near-ideal manufacturing configurations and capabilities, and in establishing appropriate information messaging protocols between the various system components by using ABM in distributed simulation.