Risk-based inspection planning of rail infrastructure considering operational resilience

This research proposes a response model for a disrupted railway track inspection plan. The proposed model takes the form of an active acceptance risk strategy while having been developed under the disruption risk management framework. The response model entails two components working in a series; an integrated Nonlinear Autoregressive model with eXogenous input Neural Network (iNARXNN), alongside a risk-based value measure for predicting track measurements data and an output valuation. The neural network fuses itself to Bayesian inference, risk aversion and a data-driven modelling approach, as a means of ensuring the utmost standard of prediction ability. Testing on a real dataset indicates that the iNARXNN model provides a mean prediction accuracy rate of 95%, while also successfully preserving data characteristics across both time and frequency domains. This research also proposes a network-based model that highlights the value of accepting iNARXNN’s outputs. The value is formulated as the ratio of rescheduling cost to a change in the risk level from a missed opportunity to repair a defective track, i.e., late defect detection. The value model demonstrates how the resilience action is useful for determining a rescheduling strategy that has (negative) value when dealing with a disrupted track inspection pla

Content analysis of problem-based learning on engineering students’ unemployment awareness

Qualitative content analysis can be applied to analysing textual words written in different types of reflection assignments. Thus, the aim of this research is to investigate the adoption of problem-based learning (PBL) learning to understanding the knowledge and level of awareness of chemical engineering students regarding graduate unemployment in Malaysia. Participants are 88 second-year students enrolled on the Differential Equation course at the National University of Malaysia. With technological breakthroughs in computerised text analysis that enable researchers to collect and analyse large amounts of textual data, content analysis employing an inductive approach is used to analyse students’ PBL reflective writing which expresses whether all the main factors of graduate unemployment can be identified. The result reveals that almost 80% of participants were unable to meet the requirement of the first stage of PBL, which is to identify the critical learning issue i.e., unemployment factors. This group of students only managed to identify person-related factors associated to unemployment: working experience and soft skills. Only 15 participants, however, have successfully identified all six factors influencing graduate employability. Practical learning, entrepreneurial skills, internal audit and basic qualifications of graduates, along with soft skills and working experience are the main components of the higher education curriculum that have a significant impact on the unemployment problem of graduates in Malaysia if they are revised. The findings have important implications for how the institution can properly plan and take appropriate actions to raise unemployment awareness among students at an early stage of their study