This study investigates priority assignment rules (PARs) for transaction processing in automated warehouses featuring a shuttle-based storage and retrieval system (SBSRS). By incorporating real-time data tracking through agent-based modeling, the research explores the unique aspect of the SBSRS design, which involves flexible travel of robotic order picker shuttles be-tween tiers. The paper proposes PARs under agent-based modeling to enhance multi-objective performance metrics, including average flow time (AFT), maximum flow time (MFT), outlier transaction AFT, and standard deviations of flow times (SD) within the system. Experimental evaluations are conducted with various warehouse designs, comparing the results against commonly used static scheduling rules. The findings demonstrate that real-time tracking policies significantly improve system performance. Specifically, prioritizing the processing of outliers based on transaction waiting time enhances MFT, SD, and other performance metrics, while minimizing adverse effects on AFT. Certain rules exhibit notable improvements in MFT and SD, while others achieve the lowest AFT values among all experiments. This paper contributes to the existing literature by presenting a multi-objective performance improvement procedure and highlighting the advantages of real-time data track-ing-based scheduling policies in automated warehousing systems.
Cardiovascular disease (CVD) risk prediction plays a significant role in clinical research since it is the key to primary prevention. As family health units follow up on a specific group of patients, particularly in the middle-aged and elderly groups, CVD risk prediction has additional importance for them. In a retrospectively collected data set from a family health unit in Turkey in 2018, we evaluated the CVD risk levels of patients based on SCORE-Turkey. By identifying additional CVD risk factors for SCORE-Turkey and grouping the study patients into 3-classes "low risk," "moderate risk," and "high risk" patients, we proposed a machine learning implemented early warning system for CVD risk prediction in family health units. Body mass index, diastolic blood pressures, serum glucose, creatinine, urea, uric acid levels, and HbA1c were significant additional CVD risk factors to SCORE-Turkey. All of the five implemented algorithms, k-nearest neighbour (KNN), random forest (RF), decision tree (DT), logistic regression (LR), and support vector machines (SVM), had high prediction performances for both the K4 and K5 partitioning protocols. With 89.7% and 92.1% accuracies for K4 and K5 protocols, KNN outperformed the other algorithms. For the five ML algorithms, while for the "low risk" category, precision and recall measures varied between 95% to 100%, "moderate risk," and "high risk" categories, these measures varied between 60% to 92%. Machine learning-based algorithms can be used in CVD risk prediction by enhancing prediction performances and combining various risk factors having complex relationships.
The aim of this study is to investigate the association between the media and the public network agendas concerning Syrian refugees in Türkiye, as an exemplar of a polarized media system within the context of a competitive authoritarian regime to gain insight into the cognitive effects of media in such a context internationally. Large-scale media and nationally representative mind-mapping survey data analysis show that the media’s network agenda is only significantly transferred to the pro-government individuals, while no significant effect was found on pro-opposition individuals from any media outlet regardless of their political stances. Implications are discussed.
Water is an invaluable substance that ensures the life cycle and causes hydrologic events worldwide. Water deficit, also known as drought, is a naturally occurring disaster that affects the hydrometeorologic and/or climatic responses in time and space. In this study, the meteorologic and hydrologic droughts in Buyuk Menderes, Kucuk Menderes, and Gediz basins in Turkey are investigated. The streamflow drought index (SDI) and standard precipitation index (SPI) are used considering different time windows. To achieve this, the monthly streamflow at Cicekli-Nif, Besdegirmenler-Dandalas, Bebekler-Rahmanlar, and Kocarli-Koprubasi hydrometric stations together with monthly precipitation at 14 meteorologic stations during 1973-2020 (47 years) are used. The SDI and SPI with 1, 3, 6, and 12 months moving average are then used to express the association between the meteorologic and hydrologic droughts in the basin. Results showed that the SDI depicts no abnormal situations, while the SPI rates in the 1980s and 2010s indicated severe droughts. It was concluded that the inner parts of the basins are prone to frequent droughts, and there is a concordance between SPI and SDI patterns at the basin level. However, minor discrepancies between SPI and SDI do exist and probably originated from temporal delays and water abstraction.
The COVID-19 pandemic has led to the introduction of unprecedented safety measures, one of them being physical distancing recommendations. Here, we assessed whether the pandemic has led to long-term effects on two important physical distancing aspects, namely interpersonal distance preferences and interpersonal touch behaviors. We analyzed nearly 14,000 individual cases from two large, cross-cultural surveys – the first conducted 2 years prior to the pandemic and the second during a relatively stable period of a decreased infection rate in May-June 2021. Preferred interpersonal distances increased by 54% globally during the COVID-19 pandemic. This increase was observable across all types of relationships, all countries, and was more pronounced in individuals with higher self-reported vulnerability to diseases. Unexpectedly, participants reported a higher incidence of interpersonal touch behaviors during than before the pandemic. We discuss our results in the context of prosocial and self-protection motivations that potentially promote different social behaviors.
The purpose of this research is to reveal the impacts of the COVID-19 outbreak on the domestic and international travel activities of Turkish academicians for academic reasons and to get insights into academicians’ opinions about how this pandemic would affect academic studies by preventing travel and socialising. An email interview technique is used. Collected data were analysed by using MAXQDA Analytics Pro 2020. As a result of content analysis, 70% of the respondents declared that their academic travel plans were cancelled due to this pandemic. The study emphasised that virtual travels seem to increase accessibility and equality for many academicians, especially those with travel restrictions.
The population of the world is increasing, and product demand is increasing, making a linear economy ineffective, and therefore, this situation makes the circular economy (CE) a requirement. Economic, social, and environmental factors all play a role in the CE. Also, logistics activities are critical parts of the CE. Hence, this study's goal is to create a framework for evaluating organizations' CE logistics performance. The main contribution of the study is the creation of a framework to assess the circularity performance, including various logistics activities, and measure the performance of logistics activities. To establish a list of criteria, 30 experts were interviewed. The criteria were ranked using fuzzy statements. The selected multi‐criteria decision‐making (MCDM) method, data envelopment analysis, is applied using IBM ILOG CPLEX Optimization Studio version 20.1.0.
With COVID-19, the importance of integrity and transparency in health systems was once again highlighted all over the world. All countries took many fiscal and non-fiscal precautions to fight against this pandemic, trying to protect the functionality of health systems and to minimize the damage to the public by keeping the spread of the pandemic under control. In addition to non-fiscal responses like curfews, travel bans, social distance, closure of public areas, isolation, etc., countries also made fiscal responses by allocating budgets only to combat the pandemic. Fiscal responses were also split into two categories: the entire budget allocated for the fight against the pandemic and the budget allocated only to health issues. The budgets under both categories were announced by many counties at the beginning of each quarter, starting from October 2020, January 2021, and April 2021. However, from the global context, it was observed that there were very serious differences between the fiscal responses namely financial support provided by the countries. Also, while some countries did not share any data in this area, it was also observed that some of the other countries did not take any fiscal responses. In this study, which will be developed by examining the data of many countries in depth and comparatively, we aim to analyze the success of countries that share their data in a transparent, regular way in managing the COVID-19 pandemic. This study highlights not only the consolidated fiscal policies but also the integrity and transparency in data sharing policies of health systems to fight with this pandemic and to decrease the possible risk in economies arising from this pandemic in the long run.
Love is a phenomenon that occurs across the world and affects many aspects of human life, including the choice of, and process of bonding with, a romantic partner. Thus, developing a reliable and valid measure of love experiences is crucial. One of the most popular tools to quantify love is Sternberg’s 45-item Triangular Love Scale (TLS-45), which measures three love components: intimacy, passion, and commitment. However, our literature review reveals that most studies (64%) use a broad variety of shortened versions of the TLS-45. Here, aiming to achieve scientific consensus and improve the reliability, comparability, and generalizability of results across studies, we developed a short version of the scale—the TLS-15—comprised of 15 items with 5-point, rather than 9-point, response scales. In Study 1 (N = 7,332), we re-analyzed secondary data from a large-scale multinational study that validated the original TLS-45 to establish whether the scale could be truncated. In Study 2 (N = 307), we provided evidence for the three-factor structure of the TLS-15 and its reliability. Study 3 (N = 413) confirmed convergent validity and test–retest stability of the TLS-15. Study 4 (N = 60,311) presented a large-scale validation across 37 linguistic versions of the TLS-15 on a cross-cultural sample spanning every continent of the globe. The overall results provide support for the reliability, validity, and cross-cultural invariance of the TLS-15, which can be used as a measure of love components—either separately or jointly as a three-factor measure.
The growing literature on corporate sustainability suggests its positive implications for organizations and stakeholders. Based on the social identity approach, the current study aims to investigate whether responsible innovation can leverage these sustainability advantages of companies to improve organizational commitment and competitiveness. Responsible innovation has been integrated into an original model as a construct that translates corporate sustainability into organizational outcomes to meet stakeholders' needs and to frame and solve the sustainability‐related tensions in an ethical way. The study also attempts to examine the mediating impacts of exploration and exploitation orientations on the proposed link between corporate sustainability and responsible innovation. The model was validated by using the partial least squares structural equation modelling method on a sample of 196 middle managers in small businesses in Turkey. The findings reveal that responsible innovation has a significant mediation effect on the proposed links, and both exploration and exploitation elicit the innovation capacity in sustainability practices. The study also supports the argument on the positive impact of exploitation on exploration.
This article investigates the dynamics of complex housing systems within the context of large-scale protracted displacement in Turkey/Türkiye. It presents new empirical findings from a qualitative study conducted in Torbalı, a rapidly growing intermediate city with a significant population of Syrian displaced people. Drawing on theoretical and conceptual insights from housing studies, urban studies and migration studies, the article assesses the ways in which displacement materialises in place through housing and contributes to city-making and urbanisation processes informally, incrementally, and in locally and historically contingent manners. We argue that the forms and dynamics of emerging housing exhibit both continuity but most markedly significant disjuncture from past housing trajectories in Torbalı. This challenges the implicit assumption of legal uniformity of self-builders common in incremental housing debates and suggests that the notion of incremental housing has limited relevance in contexts of protracted urban displacement. Furthermore, findings underline the significance of legal dimensions in energising housing informalities; in grading socio-legal statuses of resident populations; in bounding displaced people’s mobilities; in demarcating labour flows; in moulding rental markets; and in directing the flows of housing materials. These in turn shape current and future urban built environments and mould the ways in which the urbanisation of refuge manifests.
This study aims to predict the cognitive engagement rate in a Language MOOC (Massive Open Online Course) based on the features extracted from learners’ engagement behaviors within the content and activities. The features were extracted from the data of the Language MOOC “Türkçe Öğreniyorum (I learn Turkish)” which aims to provide self-paced learning materials for those interested in developing their skills in Turkish as a foreign language. After the data preprocessing processes were carried out with the data set obtained for cognitive engagement classification, feature selection processes were performed using filtering and wrapper methods. Afterward, the machine learning model trained using the Logistic Regression (LR) algorithm performed the classification with 94% accuracy. The model evaluation metrics also support the classification result obtained. Based on the extracted features and the classification results obtained, the model will be able to capture learners’ interaction behaviors with the content and activities in a Language MOOC and detect changes in learner behavior over time. Prediction accuracy is essential to offer dynamic content and activities in a Language MOOC for adjusting the individual needs of each learner, providing personalized learning experiences that are tailored to their skills, knowledge, and preferences.
Biochemical networks are usually modeled by Ordinary Differential Equations (ODEs) that describe time evolution of the concentrations of the interacting (biochemical) species for specific initial concentrations and certain values of the interaction rates. The uncertainty in the measurements of the model parameters (i.e. interaction rates) and the concentrations (i.e. state variables) is not an uncommon occurrence due to biological variability and noise. So, there is a great need to predict the evolution of the species for some intervals or probability distributions instead of specific initial conditions and parameter values. To this end, one can employ either phase portrait method together with bifurcation analysis as a dynamical system approach, or Dynamical Bayesian Networks (DBNs) in a probabilistic domain. The first approach is restricted to the case of a few number of parameters, while DBNs have recently been used for large biochemical networks. In this paper, we show that time-homogeneous ODE parameters can be efficiently estimated with Bayesian Networks. The accuracy and computation time of our approach is compared to two-slice time-invariant DBNs that have already been used for this purpose. The efficiency of our approach is demonstrated on two toy examples and the EGF-NGF signaling pathway.
In Industry 5.0, humans and machines work together, using advanced technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and automation to improve efficiency, productivity, and quality while also supporting sustainable practices and human values. There is a growing interest in learning about the challenges of Industry 5.0 and exploring these technologies to promote sustainability and responsible business practices. We need a hybrid decision model to strike a balance between technical progress, human values, and sustainable practices as we move toward Industry 5.0, which presents enormous challenges in the areas of technology, the environment, society and ethics, and business and economics. Through a literature analysis guided by the PRISMA technique and the Delphi method, the study highlighted challenges in the areas of technology, the environment, society and ethics, and business and economics, as well as solution measures to address them. The weightage of the challenges was determined using the Best Worst Method, and the ranking of the potential solutions was prioritized using the Elimination and Choice Expressing Reality method.
Digitalization and sustainability are two important concepts that affect the entire supply chain, and, therefore, they should be considered together. As one of the most critical elements of supply chains, logistics operations need consideration in terms of transforming through digitalization while taking sustainability goals into account. This study especially focuses on air logistics operations. Although sustainability in air logistics in terms of analysing environmental, social and economic impacts is a popular topic, there is a gap in the literature related to integration of sustainability and digitalization in air logistics operations. In order to fulfil the gap in the literature and to contribute in the research field, this study conducts a content analysis to reveal the current trends and future agenda for sustainable air logistics in the digital era. In order to do that, VOSViewer program is used for bibliometric analysis. At the end of the study, potential research themes are proposed.
Industrial revolutions often seek to strengthen the separation of human and machine labor by going one step further, toward automation and digitalization, and the transfer of tough and dangerous occupations to robots. As it strives to include robots in people's daily activities and work, the introduction of concepts such as I5.0 is a step forward in enhancing human-machine interaction and provides some possibilities and challenges for firms. Therefore, this article mainly focuses on studying and concretely examining the challenges faced by businesses transitioning from I4.0 to I5.0 by providing case examples from the textile and apparel supply chain. After a detailed review of the current literature related to the I5.0 challenges, the I5.0 challenges were listed in general. Then, the fuzzy Decision-making trial and evaluation laboratory approach has adopted into the challenges to reveal causal interactions between them thus, prioritizing the substantial challenges to be focused on to influence the entire textile and apparel supply chain.
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