International Journal of Innovation, Management and Technology (IJIMT) is an international academic open access journal which gains a foothold in Singapore, Asia and opens to the world. It aims to promote the integration of innovation management and techn
Emotional dysregulation is a common challenge in children with autism spectrum disorder (ASD), often present as sudden tantrums or mood swings. Continuous monitoring of physiological signals offers a nonintrusive way to understand and potentially anticipate these episodes. This paper presents a work-in-progress case study that provides a longitudinal analysis of three months of multimodal physiological data collected from a 10-year-old child diagnosed with ASD using the Empatica EmbracePlus wearable device. Key signals were analyzed, including electrodermal activity (EDA), heart rate (HR), and accelerometry, to identify patterns linked to emotional dysregulation. To validate events, the caregiver installed video cameras throughout the home and reviewed footage whenever the wearable system flagged abnormal physiological activity. This retrospective confirmation enabled accurate annotation of heightened arousal episodes and supported the use of the system as a real-time assistive tool for a working parent to monitor their child's emotional state. Temporal and arousal-based features were extracted using rolling-window and peak-detection methods. Unsupervised anomaly detection Algorithms were employed to differentiate heightened arousal states from baseline behavior. Results revealed consistent physiological signatures, particularly in EDA and HR, that preceded periods of elevated arousal by several minutes.
Project-based learning (PBL) is a student-centered approach in which students learn by solving real-life problems in a teamwork environment. Little is known about how students in different academic levels perceive the effectiveness of PBL approach. This study aims to compare and analyze students' perceptions of the use of PBL in different undergraduate- and graduate-level civil engineering courses. Students' perceptions of the effectiveness of the partial use of PBL in civil engineering courses to improve their understanding of course topics and develop their life skills were collected through a questionnaire distributed at the end of the semester. Data was collected from 104 students enrolled in five different undergraduate and graduate civil engineering courses. Results showed that students of the junior-level course held more positive attitude towards collaboration and teamwork rather than students of senior-level or graduate-level courses. The mean scores of the survey questions related to teamwork, collaboration, and communication skills tended to decrease with an increase in the course level. Junior-level students exhibited the highest degree of satisfaction in response to the survey question related to the degree of enjoyment when working in groups. Due to the difficulty in scheduling meetings, ineffective communications were noted by graduate students. They recorded the lowest mean scores for the survey questions related to teamwork/collaboration skills and degree of enjoyment when working in groups. Survey results indicated, however, that PBL was more effective in improving understanding of course topics, self-regulation and self-learning skills of the graduate students rather than undergraduate students.
Capstone projects are integrated into engineering curricula to combine various subjects and impart essential professional skills that may be difficult to teach solely through traditional lecture-based courses. These projects play a crucial role in preparing students for their future roles as professional engineers, thereby significantly impacting a university’s industry reputation and ranking. The challenge in engineering education lies in aligning the teaching approach of educators with the diverse learning styles of their students. This study aims to examine the impact of the learning style of the students measured by their watching-doing scores using the 4MAT tool, on the attainment of the benefits of the graduation project (GP). The Bayesian Belief Networks (BBN) approach was adopted in this study to analyse the data collected from 271 students enrolled in both GP1 and GP2 semesters in the engineering department of United Arab Emirates University. Results show that regardless of learning style, both watching and doing category students share similar perspectives on various aspects of the GP course, such as the optimal team performance ratio. However, when assessing the overall effectiveness of the GP programme, doing students exhibit a higher level of agreement than watching students. The study provides valuable insights to faculty members, helping them navigate the optimal balance between providing mentorship and fostering students’ independence during the different stages of their final-year design capstone projects. These findings underscore the importance of tailored educational strategies to accommodate diverse learning styles, contributing to more effective engineering education and better-prepared graduates.
There have been numerous research efforts to minimize construction defects and a variety of suggestions have been provided. However, while all of these suggestions are valuable and have the potential to prevent defects, a construction company may have difficulty adopting them due to financial and practical constraints. Thus, this calls for the identification and characterization of the most influential causes of defects, in order to prioritize defect prevention strategies. To address this necessity, this paper aims to identify the most important causes of defects in terms of frequency, magnitude, and pathogenicity. For this goal, a questionnaire survey of 106 industry professionals was conducted to examine 30 causes of defects, collected through an extensive literature review. High frequency and high magnitude causes were identified and traced back to their initiating causes. Accordingly, the five most pathogenic causes were found to be (1) organizational culture, (2) time pressure and constraints, (3) workplace quality system, (4) financial constraints on operational expenses, and (5) inadequate employee training or learning opportunities. This paper is valuable to researchers in terms of developing a theoretical foundation to analyze and visualize the complex mechanisms of defect generation in construction. Further, this paper is of value to practitioners in terms of providing an effective tool to set defect prevention strategies and prioritize investment areas for quality improvements.
It is common for a construction schedule to deviate from its original-planned baseline, as uncertainty is inherent in all construction activities. Accordingly, planners are required to perform periodic schedule updates that learn from retrospective progress to more accurately schedule remaining activities and draw optimum recovery plans. This research proposes a method that utilizes a neural network regression model to forecast upcoming productivity rates based on retrospective progress and accordingly updates the schedule on a regular time interval with the required resource adjustments to meet the planned end date of the project with optimal cost. The method was tested on brickwork activities at a residential complex construction project in the UAE, using retrospective progress data of 1487 working days for 132 masons, and was found to be 98% accurate in predicting labor productivity, which was thus used as a basis to draw schedule recovery plans according to the proposed framework. In essence, this research provides a platform toward an automated self-recovering scheduling system, which serves construction managers in proactively preventing potential schedule deficiencies.
Purpose To proactively draw efficient maintenance plans, road agencies should be able to forecast main road distress parameters, such as cracking, rutting, deflection and International Roughness Index (IRI). Nonetheless, the behavior of those parameters throughout pavement life cycles is associated with high uncertainty, resulting from various interrelated factors that fluctuate over time. This study aims to propose the use of dynamic Bayesian belief networks for the development of time-series prediction models to probabilistically forecast road distress parameters. Design/methodology/approach While Bayesian belief network (BBN) has the merit of capturing uncertainty associated with variables in a domain, dynamic BBNs, in particular, are deemed ideal for forecasting road distress over time due to its Markovian and invariant transition probability properties. Four dynamic BBN models are developed to represent rutting, deflection, cracking and IRI, using pavement data collected from 32 major road sections in the United Arab Emirates between 2013 and 2019. Those models are based on several factors affecting pavement deterioration, which are classified into three categories traffic factors, environmental factors and road-specific factors. Findings The four developed performance prediction models achieved an overall precision and reliability rate of over 80%. Originality/value The proposed approach provides flexibility to illustrate road conditions under various scenarios, which is beneficial for pavement maintainers in obtaining a realistic representation of expected future road conditions, where maintenance efforts could be prioritized and optimized.
To deal with the complexity of today's engineering demands, leading universities intend to investigate educational advances and facilities to provide students a creative academic experience that integrates disciplinary knowledge and student transition to the world of the competitive job environment. By tackling a challenging design problem, the capstone project (CP) harnesses creative learning and transfer hands-on abilities into the industrial environment. Effective coaching on the understanding of course objectives aids both advisors and students to properly plan their tasks in CP design. Students and advisors must have a shared understanding of their roles and responsibilities, as well as the extent of independence anticipated from students. This study identifies specific areas in the capstone project that require direct advisor mentoring, aside with other areas in which students are anticipated to work independently, through a questionnaire survey consisting of 206 participants from both segments: advisors and final year engineering students. The survey results outline the discrepancies and consensus of perceptions amongst both segments, provide insights about current CP delivery, and highlight potential modifications that may be considered for improving the delivery of the capstone project where optimum levels of knowledge and skills are obtained by students. The results of this study serve as basis for guiding faculty members about finding the right balance of mentorship and students' independence, as students progress throughout different stages of their final year design capstone projects.
Design, operation, maintenance and rehabilitation of mass mobility projects require holistic environmental, energy and monetary action; transit solutions depend on public authorities' influence over user-adoptability and sustainability. New technically advanced automated vehicles (AVs) and alternate fuel technology require consideration by planners, where such urban-settlement transportation systems are increasingly charged to be efficient, reliable, interconnected traffic pathways marked by in-out nodes able to direct people effectively. Prototype mass-transit alternative(s) choice is complex, with the infrastructure decisions largely bound to the different objectives of consumers, policy and government agencies. Three factors must be considered: user-stakeholder appraisal; and both environment; and, economic factors during the entire asset life-cycle of production, construction, operation, maintenance, rehabilitation (OM&R) and final disposal or salvage value. To assess this life-cycle impact, a new research project is presented that proposes a framework consisting of: (i) an analysis of traffic flow patterns within an intra-city settlement, addressing the suitability of any preferred alternative for direct real-world application alongside user-opinion; (ii) the life-cycle cost and environmental in/out-flows for any proposed holistic AV-based alternative(s) assessed against existing systems; and, (iii) stakeholder expert-opinion involvement through a multi-criteria decision-making (MCDM) framework assigning weightage to cost, energy and emissions. Towards this, user-preference data collected from intra-city bus passengers in Abu Dhabi notes a majority of passengers as full-time workers, in which the notion of reduced fare level and an increased network coverage was not homogenously supported, thus it is argued that municipal agency and policy-makers need to target work commutes supported by innovative solutions to enhance user-experience by reducing journey time and fluctuating service frequency around office hours. It is suggested that regional road transport system energy, cost and emission issues may be resolved by using the proposed framework that builds upon life-cycle impact strategies integrated directly into decision-analyses. It's argued that decision-making frameworks, if they are to be successfully implemented, must allow ongoing feedback loops included in this framework.
