Yaşar University

About: Yaşar University is a education organization based out in Izmir, Turkey. It is known for research contribution in the topics: Exergy & Job shop scheduling. The organization has 760 authors who have published 1436 publications receiving 20813 citations. The organization is also known as: Yaşar Üniversitesi.

Development and Validation of the Mukbang Addiction Scale

Abstract: Recent literature has speculated that some individuals spend lots of time watching mukbang (i.e., combination of the South Korean words ‘eating’ [‘meokneun’] and ‘broadcast’ [‘bangsong’] that refers to eating broadcasts where a person eats a large portion of food on camera whilst interacting with viewers) and compensate different needs using this activity. However, compensating unattained offline needs using a specific online activity could lead to the addictive use of that activity. The present study investigated problematic mukbang watching by developing and validating the Mukbang Addiction Scale (MAS). An online survey was administered to 236 university students (Mage = 20.50 years; 62% female) who had watched mukbang at least once. Construct validity, criterion validity, and reliability analyses indicated that the MAS had good psychometric properties. Exploratory and confirmatory factor analyses confirmed the unidimensional structure of the scale. The Cronbach’s alpha (α = .95) and composite reliability (CR = .92) suggested that the MAS had excellent internal consistency. Latent class analyses (LCA) revealed two primary profiles, one with high endorsement and one with low endorsement of the items assessed. Item response theory (IRT) findings also indicated a good model fit. IRT findings provisionally supported a cut-off scale raw score of 22 (out of 30). Assessment and clinical-related implications of the findings are illustrated in accordance with other excessive behaviours.

Ensemble of metaheuristics for energy-efficient hybrid flowshops: Makespan versus total energy consumption

Abstract: Due to its practical relevance, the hybrid flowshop scheduling problem (HFSP) has been widely studied in the literature with the objectives related to production efficiency. However, studies regarding energy consumption and environmental effects have rather been limited. This paper addresses the trade-off between makespan and total energy consumption in hybrid flowshops, where machines can operate at varying speed levels. A bi-objective mixed-integer linear programming (MILP) model and a bi-objective constraint programming (CP) model are proposed for the problem employing speed scaling. Since the objectives of minimizing makespan and total energy consumption are conflicting with each other, the augmented epsilon (e)-constraint approach is used for obtaining the Pareto-optimal solutions. While close approximations for the Pareto-optimal frontier are obtained for small-sized instances, sets of non-dominated solutions are obtained for large instances by solving the MILP and CP models under a time limit. As the problem is NP-hard, two variants of the iterated greedy algorithm, a variable block insertion heuristic and four variants of ensemble of metaheuristic algorithms are also proposed, as well as a novel constructive heuristic. The performances of the proposed seven bi-objective metaheuristics are compared with each other as well as the MILP and CP solutions on a set of well-known HFSP benchmarks in terms of cardinality, closeness, and diversity of the solutions. Initially, the performances of the algorithms are tested on small-sized instances with respect to the Pareto-optimal solutions. Then, it is shown that the proposed algorithms are very effective for solving large instances in terms of both solution quality and CPU time.

Approximation of simulation-derived visual comfort indicators in office spaces: a comparative study in machine learning

Abstract: In performance-oriented architectural design, the use of advanced computational simulation tools may provide valuable insight during design. However, the use of such tools is often a bottleneck in the design process, given that computational requirements are usually high. This is a fact that mostly affects the early conceptual stage of design, where crucial decisions mainly occur, and available time is limited. In order to deal with this, decision-makers frequently resort to drawing conclusions from experience, and, as such, valuable insight that advanced computational methods have to offer is lost. This paper explores an alternative approach, which builds on machine-learning algorithms that inductively learn from simulation-derived data, yielding models that approximate to a good degree and are orders of magnitude faster. We focus on visual comfort of office spaces. This is a type of space that specifically requires visual comfort more than others. Three machine-learning methods are compared with respect...