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Open Access
Accelerating discovery of COFs for CO2 capture and H2 purification using structurally guided computational screening
(Elsevier, 2022-01-01) Aksu, G. O.; Fındıkçı, İlknur Eruçar; Haslak, Z. P.; Keskin, S.; Mechanical Engineering; FINDIKÇI, Ilknur Eruçar
Screening of hypothetical covalent organic framework (hypoCOF) database enables to go beyond the current synthesized structures to design high-performance materials for CO2 separation. In this work, we followed a structurally guided computational screening approach to find the most promising candidates of hypoCOF adsorbents and membranes for CO2 capture and H2 purification. Grand canonical Monte Carlo (GCMC) simulations were used to evaluate CO2/H2 separation performance of 3184 hypoCOFs for pressure-swing adsorption (PSA) and vacuum-swing adsorption (VSA) processes. CO2/H2 adsorption selectivities and CO2 working capacities of hypoCOFs were calculated in the range of 6.13–742 (6.39–954) and 0.07–8.68 mol/kg (0.01–3.92 mol/kg), achieving higher values than those of experimentally synthesized COFs at PSA (VSA) conditions. Density functional theory (DFT) calculations revealed that the strength of hydrogen bonding between CO2 and the functional group of linkers is an important factor for determining the CO2 selectivity of hypoCOFs. The most predominant topologies and linker types were identified as bor and pts, linker91 (a triazine linker) and linker92 (a benzene linker) for the top-performing hypoCOF adsorbents, respectively. Molecular dynamics (MD) simulations of 794 hypoCOFs showed that they exceed the Robeson's upper bound by outperforming COF, zeolite, metal organic framework (MOF), and polymer membranes due to their high H2/CO2 selectivities, 2.66–6.14, and high H2 permeabilities, 9×105–4.5×106 Barrer. Results of this work will be useful to guide the synthesis of novel materials by providing molecular-level insights into the structural features of hypothetical COFs to achieve superior CO2 separation performance.
Open Access
CL-FedFR: Curriculum learning for federated face recognition
(SciTePress , 2024-02-29) Dube, D. C.; Eroğlu Erdem, Ciğdem ; Korcak, Ö.; Electrical & Electronics Engineering; ERDEM, Çiğdem Eroğlu
Face recognition (FR) has been significantly enhanced by the advent and continuous improvement of deep learning algorithms and accessibility of large datasets. However, privacy concerns raised by using and distributing face image datasets have emerged as a significant barrier to the deployment of centralized machine learning algorithms. Recently, federated learning (FL) has gained popularity since the private data at edge devices (clients) does not need to be shared to train a model. FL also continues to drive FR research toward decentralization. In this paper, we propose novel data-based and client-based curriculum learning (CL) approaches for federated FR intending to improve the performance of generic and client-specific personalized models. The data-based curriculum utilizes head pose angles as the difficulty measure and feeds the images from “easy” to “difficult” during training, which resembles the way humans learn. Client-based curriculum chooses “easy clients” based on performance during the initial rounds of training and includes more “difficult clients” at later rounds. To the best of our knowledge, this is the first paper to explore CL for FR in a FL setting. We evaluate the proposed algorithm on MS-Celeb-1M and IJB-C datasets and the results show an improved performance when CL is utilized during training.
Open Access
Deep reinforcement learning approach for trading automation in the stock market
(IEEE, 2022) Kabbani, Taylan; Duman, Ekrem; Industrial Engineering; DUMAN, Ekrem; Kabbani, Taylan
Deep Reinforcement Learning (DRL) algorithms can scale to previously intractable problems. The automation of profit generation in the stock market is possible using DRL, by combining the financial assets price 'prediction' step and the 'allocation' step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with their environment to make optimal decisions through trial and error. This work represents a DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem as a Partially Observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm reporting a 2.68 Sharpe Ratio on unseen data set (test data). From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of DRL in financial markets over other types of machine learning and proves its credibility and advantages in strategic decision-making.
Open Access
Experimental and numerical modal characterization for additively manufactured triply periodic minimal surface lattice structures: Comparison between free-size and homogenization-based optimization methods
(Wiley, 2023-06) Özdemir, Mirhan; Simsek, U.; Kuşer, Engin; Gayir, C. E.; Celik, A.; Şendur, Polat; Mechanical Engineering; ŞENDUR, Polat; Özdemir, Mirhan; Kuşer, Engin
Homogenization-based topology optimization (HMTO) is one of the most extensively used grading methods to generate functionally graded lattice structures (FGLs). However, it requires a precharacterization of the lattices, which is time-consuming. As a remedy, free-size optimization-based graded lattice generation (FOGLG) is explored as an alternative method to generate the FGLs. This article builds on the authors’ previous work in which the HMTO and FOGLG approaches are studied to improve the dynamic characteristic of a design by using a single lattice type, namely, double gyroid (DG) structure. To show applicability of the proposed methods, different lattice types including diamond (D), gyroid (G), and I-WP are employed to create FGLs herein. The frequency response analysis is performed, and the results from HMTO and FOGLG are compared in terms of their accuracy and efficiency. The optimized designs are then reconstructed by relative density mapping (RDM) and enhanced relative density mapping (ERDM) methods. The fabricated test samples made of cobalt–chromium using the direct metal laser melting (DMLM) technique are then experimentally validated using a laser vibrometer. The results reveal that HMTO and FOGLG can be used on the lattice types with a variety of configurations and relative densities.
Open Access
Exploring the performance limits of MOF/polymer MMMs for O2/N2 separation using computational screening
(Elsevier, 2021-01-15) Dağlar, H.; Fındıkçı, İlknur Eruçar; Keskin, S.; Mechanical Engineering; FINDIKÇI, Ilknur Eruçar
Air separation is one of the most challenging separations because of the very similar molecular dimensions of gas molecules. We used a high-throughput computational screening approach to identify the upper performance limits of metal organic framework (MOF) membranes and MOF/polymer mixed matrix membranes (MMMs) for O2/N2 separation. Gas permeabilities and selectivities were calculated for 5629 MOF membranes and 78,806 different types of MOF/polymer MMMs, which represent the largest number of MOF-based membranes studied to date for air separation. Our results showed that many MOF membranes exceed the upper bound established for polymer membranes due to their high permeabilities and/or selectivities. The maximum achievable O2 permeability and O2/N2 selectivity of MOF/polymer MMMs were computed as 2710.8 Barrer and 19.8, respectively. Results revealed that MOF/polymer MMMs can outperform MMMs composed of traditional fillers, such as zeolites, in terms of O2 permeability and O2/N2 selectivity. The impacts of purity of air mixture and the structural flexibility of MOFs on the gas separation performances of MMMs were also discussed. These results provide molecular-level insights into adsorption and diffusion behaviors of O2 and N2 in MOF membranes in addition to presenting structure-performance relations of MOFs that can lead to high-performance membranes and fillers for MMMs.
Open Access
The impact of leadership styles on performance and mediating effect of organizational culture: A study in flight schools
(Elsevier, 2022) Gökalp, Pınar Horasanlı; Soran, Semih; Professional Flight Program; SORAN, Semih; HORASANLI, Pinar
Nowadays, the aviation sector is growing rapidly and the need for human resources in the airline industry is increasing. In order to meet the increasing pilot need, the number of flight schools and universities that provide pilot training with the support of relevant authorities is also increasing. This increase in the number of student pilots is accompanied by questions about how to ensure the continuity of quality and safety in flight training. In this context, it is very important for the flight schools to evaluate the variables that may affect the student pilot performance and to take the necessary precautions. In our study teacher leadership and organizational culture are assessed as important variables and examined. In aviation literature, there is a very small number of explanatory studies on flight performance and leadership. Additionally, no study has been found on teacher leadership of student pilots. In this context, how leadership styles affect student pilots' performances positively and negatively was investigated in our research. Organizational culture is considered as an important predictor of performance in today's organizations. In this context, it has emerged that studies have been carried out that the organizational culture is an important variable in improving organizational and individual performance. Leadership styles of teacher pilots and organizational culture have been explored in the student pilots' performance process. In this context, our work has included performance, leadership styles and organizational culture. Regarding the methodology part of this study, 151 student pilots in the flight training organizations formed the universe and a survey was conducted, analysed and explained. Considering the effect of dynamic leadership, the present study analysed the impact of leadership and culture on flight performance. It is assumed that the most effective leadership styles can be found within group dynamics consisting of members who have diverse and individual cultural orientations. Individual differences that are caused by cultural norms can be considered as the outcomes of leadership behaviour. It was concluded that flight crew leadership cannot be analysed without considering the culture variable since behaviour is influenced by both individual and environmental factors. Moreover, since the performance outcomes of the crew resource management were evaluated, the cultural attitude of the crew and leader was considered.
Metadata only
Psikoloji araştırmalarında kelime edinim yaşı: Kuramlar, yöntemler ve uygulama alanları
(Sanat ve Dil Araştırmaları Enstitüsü - SADA, 2021-12) Arıkan, K.; Tarakçı, Bahar; Akırmak, Ü.; Psychology; TARAKÇI, Bahar
Edinim yaşı bir kelimenin ilk kez öğrenildiği yaşı ifade eder. Araştırmalar, edinim yaşının, kelime işleme görevlerinde oldukça önemli bir değişken olduğunu göstermektedir. Edinim yaşı etkisi, bilginin beyinde nasıl depolandığından ve erişildiğinden ortaya çıkar ve zihinle ilgili teorik ve pratik araştırma sorularına cevap aramak için kullanılır. Örneğin, klinik psikoloji alanındaki araştırmalar beyin hasarı veya nörolojik bozuklukları olan katılımcılarda (örneğin, Alzheimer hastalığı, afazi, anlamsal demans ve disleksi) bu bozuklukları olmayan katılımcılara göre edinim yaşı etkisinin farklılaştığını göstermektedir. Sinirbilim alanındaki çalışmalar ise erken ve geç edinilen kelimelerin farklı beyin aktivasyonları ile ilişkili olduğunu göstermektedir. Uluslararası alanyazınında oldukça uzun ve zengin bir tarihe sahip olmasına rağmen Türkçe alanyazınında edinim yaşına ilişkin çok az ampirik çalışmanın olduğu görülmektedir. Bu derlemede, edinim yaşı üzerine temel bulgular ve kuramlar incelenmiş, edinim yaşı normlarının öznel ve nesnel prosedürleri ile temel bulgular karşılaştırmalı olarak sunulmuştur. Alandaki teorik ve metodolojik problemlerin incelenmesinin ardından edinim yaşının klinik psikoloji, sinirbilim ve ikinci dil edinimini kapsayan uygulama alanları tartışılmış ve gelecek araştırmalara yönelik öneriler sunulmuştur. / Age of acquisition refers to the age at which a specific word is learned for the first time. Research shows that age of acquisition is a significant variable in lexical processing tasks. The age of acquisition effect emerges from how information is stored and accessed in the brain and is used to seek answers to theoretical and practical research questions about the mind. For example, studies in the clinical psychology field show that the age of acquisition effect differs in participants with brain damage or neurological disorders (e.g., Alzheimer's disease, aphasia, semantic dementia, and dyslexia) compared to participants without these disorders. Research in neuroscience shows that early and late acquired words are associated with different brain activations. Although it has a long and rich history in international literature, there are very few empirical studies on the age of acquisition effect in Turkish literature. In this review, basic findings and theories are discussed, and the subjective and objective procedures of collecting age of acquisition norms are presented comparatively. After examining the theoretical and methodological issues in the field, the application areas of the age of acquisition, including clinical psychology, neuroscience, and second language acquisition, are discussed, and suggestions for future studies are presented.
Open Access
Simulation of vehicles’ gap acceptance decision at unsignalized intersections using SUMO
(Elsevier, 2022) Bagheri, Mohammad; Bartın, Bekir Oğuz; Ozbay, K.; Civil Engineering; BARTIN, Bekir Oğuz; Bagheri, Mohammad
In this paper, an artificial neural network (ANN)-based gap acceptance behavior model was proposed. The feasibility of implementing this model in a microscopic simulation tool was tested using the application programming interface of Simulation of Urban Mobility (SUMO) simulation package. A stop-controlled intersection in New Jersey was selected as a case study. The simulation model of this intersection was calibrated using ground truth data extracted during the afternoon peak hours. The ANN-based SUMO model was compared to SUMO model with default gap acceptance parameters and the SUMO model with calibrated gap acceptance parameters. The comparison was based on wait time and accepted gap values at the minor approach of the intersection. The results showed that the ANN-based model produced superior results based on the selected outputs. The analysis results also indicated that the ANN-based model leads to significantly more realistic driving behavior of vehicles on the major approach of the intersection.
Open Access
Stochastic production planning with flexible manufacturing systems and uncertain demand: A column generation-based approach
(Elsevier, 2022) Elyasi, Milad; Özener, Başak Altan; Ekici, Ali; Özener, Okan Örsan; Yanıkoğlu, İhsan; Economics; Industrial Engineering; ÖZENER, Başak Altan; EKİCİ, Ali; ÖZENER, Okan Örsan
The ongoing pandemic, namely COVID-19, has rendered widespread economic disorder. The deficiencies have delayed production at manufacturers in several industries on the supply side. The effects of disruption were more notable for industries with longer supply chains, especially reaching East Asia. Regarding the demand, sectors can be divided into three categories: i) the ones, like e-commerce companies, that experienced augmented demand; ii) the ones with a plunged demand, like what hotels and restaurants experience; iii) the companies experiencing a roller-coaster-ride business. After mitigation efforts, the economy started recovering, resulting in increased demand. However, regardless of their struggles, the companies have not fully returned to their pre-pandemic levels. One of the strategies to gain resilience in its supply chain and manage the disruptions is to employ flexible/hybrid manufacturing systems. This paper considers a flexible/hybrid manufacturing production setting with typically dedicated machinery to satisfy regular demand and a flexible manufacturing system (FMS) to handle surge demand. We model the uncertainty in demand using a scenario-based approach and allow the business to make here-and-now and wait-and-see decisions exploiting the cost-effectiveness of the standard production and responsiveness of the FMS. We propose a column generation-based algorithm as the solution approach. Our computational analysis shows that this hybrid production setting provides highly robust response to the uncertainty in demand, even with high fluctuations.
Open Access
Symbolic knowledge extraction for explainable nutritional recommenders
(Elsevier, 2023-06) Magnini, M.; Ciatto, G.; Cantürk, Furkan; Aydoğan, Reyhan; Omicini, A.; Computer Science; AYDOĞAN, Reyhan; Cantürk, Furkan
Background and objective: This paper focuses on nutritional recommendation systems (RS), i.e. AI-powered automatic systems providing users with suggestions about what to eat to pursue their weight/body shape goals. A trade-off among (potentially) conflictual requirements must be taken into account when designing these kinds of systems, there including: (i) adherence to experts’ prescriptions, (ii) adherence to users’ tastes and preferences, (iii) explainability of the whole recommendation process. Accordingly, in this paper we propose a novel approach to the engineering of nutritional RS, combining machine learning and symbolic knowledge extraction to profile users—hence harmonising the aforementioned requirements. MethodsOur contribution focuses on the data processing workflow. Stemming from neural networks (NN) trained to predict user preferences, we use CART Breiman et al.(1984) to extract symbolic rules in Prolog Körner et al.(2022) form, and we combine them with expert prescriptions brought in similar form. We can then query the resulting symbolic knowledge base via logic solvers, to draw explainable recommendations. ResultsExperiments are performed involving a publicly available dataset of 45,723 recipes, plus 12 synthetic datasets about as many imaginary users, and 6 experts’ prescriptions. Fully-connected 4-layered NN are trained on those datasets, reaching ∼86% test-set accuracy, on average. Extracted rules, in turn, have ∼80% fidelity w.r.t. those NN. The resulting recommendation system has a test-set precision of ∼74%. The symbolic approach makes it possible to devise how the system draws recommendations. Conclusions Thanks to our approach, intelligent agents may learn users’ preferences from data, convert them into symbolic form, and extend them with experts’ goal-directed prescriptions. The resulting recommendations are then simultaneously acceptable for the end user and adequate under a nutritional perspective, while the whole process of recommendation generation is made explainable.
Open Access
Unveiling the dynamics of emotions in society through an analysis of online social network conversations
(Springer Nature, 2023-09-11) Sener, B.; Akpinar, E.; Ataman, Mehmet Berk; Business Administration; ATAMAN, Mehmet Berk
Social networks can provide insights into the emotions expressed by a society. However, the dynamic nature of emotions presents a significant challenge for policymakers, politicians, and communication professionals who seek to understand and respond to changes in emotions over time. To address this challenge, this paper investigates the frequency, duration, and transition of 24 distinct emotions over a 2-year period, analyzing more than 5 million tweets. The study shows that emotions with lower valence but higher dominance and/or arousal are more prevalent in online social networks. Emotions with higher valence and arousal tend to last longer, while dominant emotions tend to have shorter durations. Emotions occupying the conversations predominantly inhibit others with similar valence and dominance, and higher arousal. Over a month, emotions with similar valences tend to prevail in online social network conversations.

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