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Universities have developed various informal learning experiences, such as design challenges, hackathons, startup incubator competitions, and accelerator programs that engage students in real-world challenges and enable environments for creative problem-solving. However, limited studies explain the extent and nature of the impact of student innovation competitions and programs (ICPs) on participating students' innovation mindset. Current literature was analysed using network analytics techniques to discover relations among ICPs and innovation skills. Using an online instrument, 194 students from two universities categorised and ranked skills/abilities they gained as the most or least improved due to participating in ICPs and their challenges during ICPs. The collected data was analysed to gain insight into the student's experiences and perceptions. The findings of this study showed that overall, students rated technical and problem-solving skills higher than some innovation mindset skills. However, the findings also suggested that incorporating more entrepreneurial elements in ICPs may improve the innovation mindset learning outcomes of ICPs. The findings contribute to how ICPs can be better designed to foster an innovation mindset, mitigate challenges that students come across, and increase the participation of all students. [ABSTRACT FROM AUTHOR]

This paper describes the process of creating a global survey of experts to evaluate drought resilience indicators. The lessons learned include five main points: (1) the heterogeneity in the conceptual background should be minimized before the construction of the survey; (2) large numbers of indicators decrease the engagement of respondents through the survey, and ways to apportion indicators whilst maintaining reliability should be considered; (3) it is necessary to design the survey to balance response rate and accuracy; (4) the survey questions should have clear statements with a logical and flowing structure; and (5) reaching experts with different domain experience and representing different regions is difficult but crucial to minimize biased results. [ABSTRACT FROM AUTHOR]

Aim/Purpose: This paper utilizes the Technology Acceptance Model (TAM) to examine the extent to which acceptance of Remote Virtual Computer Laboratories (RVCLs) is affected by students' technological backgrounds and the role of collaborative work. Background: RVCLs are widely used in information technology and cyber security education to provide students with hands-on experimentation. However, students may not exploit their full benefits if they do not accept RVCLs as a viable educational technology. Methodology: In order to study the impact of collaborative work on technology acceptance, an empirical study was conducted using collaborative and individual versions of an introductory level computer networking exercise in an RVCL. Trials for the empirical study included students from technology intensive and non-technology intensive programs. Contribution: The relationship between the technological background of students and their acceptance of an RVCL and the effect of collaborative work on this relationship were explored for the first time in the literature. Findings: The findings of the study supported that collaborative work could improve non-technology students' acceptance of RVCLs. However, no significant effect of collaborative work on technology acceptance was observed in the case of technology students. Recommendations for Practitioners: Educators should consider the benefits of collaborative work while introducing a new technology to students who may not have background in the technology introduced. Recommendation for Researchers: In this study, student technological background was found to be a significant factor for technology acceptance; hence, it is recommended that technological background is included in TAM studies as an external factor. Future Research: Repeating similar studies with multiple exercises with varying degrees of challenge is required for a better understanding of how collaborative work and student technological background affect technology acceptance.

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems. [ABSTRACT FROM AUTHOR]

Drought resilience indexes are essential tools for an evidence-based decision-making process. They can be constructed by enquiring experts about the most relevant indicators. This communication presents the challenges found a priori of creating a global survey with experts in drought. The lessons learned include: (1) the heterogeneity of the conceptual background should be minimized prior the construction of the survey; (2) large number of indicators decreases the engagement of respondents; (3) the format should balance response rate and accuracy, (4) raising diverse experts by knowledge areas, experience and regional representations is difficult, but crucial to minimize biased results. [ABSTRACT FROM AUTHOR]

Drought resilience indexes are essential tools for an evidence-based decision-making process. They can be constructed by enquiring experts about the most relevant indicators. This communication presents the challenges found a priori of creating a global survey with experts in drought. The lessons learned include: (1) the heterogeneity of the conceptual background should be minimized prior the construction of the survey; (2) large number of indicators decreases the engagement of respondents; (3) the format should balance response rate and accuracy, (4) raising diverse experts by knowledge areas, experience and regional representations is difficult, but crucial to minimize biased results. [ABSTRACT FROM AUTHOR]

Undergraduate engineering students are expected to develop an understanding that engineering solutions can have global implications. Perceptions on engineering solutions by people from different cultures can be very different, and globally aware engineers need to understand and anticipate these perception differences. As engineering programs strive to instill the requisite learning to produce this outcome, a fundamental question to address is whether global awareness can be assessed as a part of engineering students' progress in the engineering curriculum. This article proposes and validates a new instrument, based on a developmental model--the Model of Domain Learning (MDL)--to gauge engineering students' growth in global awareness. Presented research responds to the following research questions: (i) Does engineering students' global awareness improve throughout their education? (ii) Do the expected relations among components of the MDL (i.e., knowledge, strategic processing, and interest) hold for the domain of global awareness? A total of425 students, enrolled in 18 different engineering programs in a US land-grant university, participated in this study. The study findings supported that, (i) as they progress in their education, engineering students' knowledge, strategic processing, and interest increase in tandem for the domain of global awareness, (ii) the MDL can serve as a framework for assessing engineering students' development of global awareness. [ABSTRACT FROM AUTHOR]

Continuing care facilities are a rapidly growing segment of senior living communities providing end-to-end solutions comprising independent living, assisted living, nursing home care, and ultimately hospice. All these establishments contain (in addition to other facilities associated with living, exercising, learning, activities, etc.) dining services managed by an interdisciplinary (finance, nutrition, dietitian, kitchen operations, hospitality, and procurement) team of executives, each with their own objective while cognizant of the overarching organizational, operational, and financial metrics. The residents of these facilities consume most of their meals at these dining facilities, necessitating that the food served meets the complete nutrition, dietary, cost, and operational requirements. Thus, the menu (often rotating every few weeks) of food items must be carefully chosen to be efficiently procured, processed, and served, all the while meeting the nutritional, dietary, and patron satisfaction constraints each put forth by the corresponding stakeholder. We address this complex, unwieldy, and large multiobjective optimization problem using mixed integer linear programming. We demonstrate how menu planners and chefs can analyze their decisions regarding menu structures and evaluate alternative menu interventions to improve menus' nutritional value while ensuring their residents' autonomy in making food choice decisions. Along the way, we interviewed various stakeholders, identified their objectives and constraints, gathered the necessary data, formulated and solved the resulting optimization problems, and produced demonstrably effectivemenus. [ABSTRACT FROM AUTHOR]

In this article, a new genetic algorithm (GA), called the Nash equilibrium sorting genetic algorithm (NESGA,) is introduced to identify Nash equilibria for the competitive maximal covering location problem with two and three competitors, which is a combinatorial game theory problem where it is computationally intractable to enumerate all decision options of the competitors. Although GAs are widely used in combinatorial optimization, their applications to non-cooperative, simultaneous games have been limited owing to challenges in guiding the evolutionary search to Nash equilibria, which are not necessarily on the Pareto front of the search space and cannot be found by current multi-objective GAs. A novel fitness assignment strategy is proposed to help the NESGA to converge to multiple Nash equilibria. Computational experiments show that the NESGA can discover multiple Nash equilibria in a single run and outperform other game-theoretic GAs. [ABSTRACT FROM AUTHOR]

This paper presents a biobjective genetic algorithm (GA) to design reliable two-node connected telecommunication networks. Because the exact calculation of the reliability of a network is NP-hard, network designers have [...]