Scopus İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395

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Advances in the computation of nmr parameters for inorganic nuclides
(ELSEVIER, 2023) Holmes, Sean T.; Alkan, Fahri; Dybowski, Cecil; 0000-0002-4046-9044; AGÜ, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü; Alkan, Fahri
In this article, we discuss practical aspects of the computation of NMR parameters of inorganic nuclides, as well as insights afforded by such calculations into the characterization of molecular-level structure and dynamics and the validation of theoretical models. An emphasis is placed on calculation of the magnetic shielding tensors of solids using cluster-based models that account for intermolecular interactions. In particular, the use of valence modification of terminal atoms using bond valence theory (VMTA/BV), which reduces net charges on clusters through terminal pseudoatoms with nonstandard nuclear charges, is demonstrated to be a robust technique for calculations on nuclei in network solids. Cluster-based calculations, including those that employ the VMTA/BV method, afford a unique opportunity to calculate magnetic shielding tensors for nuclei in solids by using density functional theory approximations beyond the generalized gradient approximation and by incorporating relativistic effects at the spin-orbit level. These developments are spurred by use of the zeroth-order regular approximation (ZORA), which provides a robust method of accounting for relativistic effects (up to the spin-orbit level) experienced by valence electrons. Calculations of NMR parameters are discussed for fluorine, cadmium, tin, tellurium, mercury, lead, and platinum, all of which have seen significant advances in recent years. These examples highlight the importance of such factors as coordination geometry, oxidation state, relativistic effects, and density functional approximations on computed magnetic shielding tensors.
A bi-criteria approach to scheduling in the face of uncertainty: Considering robustness and stability simultaneously
(Nova Science Publishers, Inc., 2014) Selcuk, Gören; Sabuncuoĝlu, Ihsan; 000-0002-5320-4213; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Selcuk, Gören; Sabuncuoĝlu, Ihsan
It is possible to scrutinize impacts of uncertainty on schedules from two different perspectives. The flrst one has to do with the fact that schedules are required to main- tain high performance in the face of uncertainty. In other words, it is desired that their performances are insensitive to negative impacts of disruptions. We refer to this view- point as the robustness perspective. The second viewpoint is about another quality: when a schedule is executed in the shop floor, the realized schedule is required not to deviate much from its initial version. This is because many activities besides pro- duction are planned based on the production schedule. It is important that unforeseen disruptions affect the plans for these activities as little as possible. We refer to this viewpoint as the stability perspective. Even though a considerable body of literature has emerged on hedging schedules against the negative effects of unforeseen disrup- tions in the last two decades, few studies address the problem of scheduling under uncertainty from both the robustness and the stability perspectives at the same time. The nature of the relation between robustness and stability, the trade-off between them, the circumstances under which they conflict or reconcile need to be thoroughly inves- tigated. To this end, we propose a bi-criteria approach to simultaneously investigate the robustness and stability of production schedules. We consider proactive schedul- ing in a single machine environment with random processing times. We use the total expected flow time and the total variance of job completion times as the robustness and stability measures, respectively. The proposed o-constraint variants are exact methods to generate the set of all Pareto-optimal schedules. We also develop an algorithm to generate a flxed number (set by the decision-maker) of near-Pareto-optimal schedules to deflne the characteristics and the shape of the trade-off curve without generating the entire Pareto set. Our computational experiments indicate that the proposed algorithms are efflcient.
Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
(CRC PRESS-TAYLOR & FRANCIS GROUP6000 BROKEN SOUND PARKWAY NW, STE 300, BOCA RATON, FL 33487-2742 USA, 2016) Kogias, Dimitris; Tuna, Gurkan; Gungor, Vehbi Cagri; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
Cognitive Radio Networks for Smart Grid Communications Potential Applications, Protocols, and Research Challenges
Communications Technologies for Smart Grid Applications: A Review of Advances and Challenges
(IGI Global, 2022) Tuna, Gurkan; Daş, Resul; Gungor, Vehbi Cagri; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Gungor, Vehbi Cagri
Smart grid is a modern power grid infrastructure for improved efficiency, reliability, and safety, with smooth integration of renewable and alternative energy sources, through automated control and modern communications technologies. The smart grid offers several advantages over traditional power grids such as reduced operational costs and opening new markets to utility providers, direct communication with customer premises through advanced metering infrastructure, self-healing in case of power drops or outage, providing security against several types of attacks, and preserving power quality by increasing link quality. Typically, a heterogeneous set of networking technologies is found in the smart grid. In this chapter, smart grid communications technologies along with their advantages and disadvantages are explained. Moreover, research challenges and open research issues are provided.
Computational Detection of Pre-microRNAs
(Humana Press Inc., 2022) Saçar Demirci, Müşerref Duygu; 0000-0003-2012-0598; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Biyomühendislik Bölümü; Saçar Demirci, Müşerref Duygu
MicroRNA (miRNA) studies have been one of the most popular research areas in recent years. Although thousands of miRNAs have been detected in several species, the majority remains unidentified. Thus, finding novel miRNAs is a vital element for investigating miRNA mediated posttranscriptional gene regulation machineries. Furthermore, experimental methods have challenging inadequacies in their capability to detect rare miRNAs, and are also limited to the state of the organism under examination (e.g., tissue type, developmental stage, stress-disease conditions). These issues have initiated the creation of high-level computational methodologies endeavoring to distinguish potential miRNAs in silico. On the other hand, most of these tools suffer from high numbers of false positives and/or false negatives and as a result they do not provide enough confidence for validating all their predictions experimentally. In this chapter, computational difficulties in detection of pre-miRNAs are discussed and a machine learning based approach that has been designed to address these issues is reviewed.
Deep Learning Based Formation Control of Drones
(Springer Science and Business Media Deutschland GmbH, 2021) Kabore, Kader M.; Güler, Samet; AGÜ, Mühendislik Fakültesi, Elektrik - Elektronik Mühendisliği Bölümü; Güler, Samet
Robot swarms can accomplish demanding missions fast, efficiently, and accurately. For a robust operation, robot swarms need to be equipped with reliable localization algorithms. Usually, the global positioning system (GPS) and motion capture cameras are employed to provide robot swarms with absolute position data with high precision. However, such infrastructures make the robots dependent on certain areas and hence reduce robustness. Thus, robots should have onboard localization capabilities to demonstrate a swarm behavior in challenging scenarios such as GPS-denied environments. Motivated by the need for a reliable onboard localization framework for robot swarms, we present a distance and vision-based localization algorithm integrated into a distributed formation control framework for three-drone systems. The proposed approach is established upon the bearing angles and the relative distances between the pairs of drones in a cyclic formation where each drone follows its coleader. We equip each drone with a monocular camera sensor and derive the bearing angle between a drone and its coleader with the recently developed deep learning algorithms. The onboard measurements are then relayed back to the formation control algorithm in which every drone computes its control action in its own frame based on its neighbors only, forming a completely distributed architecture. The proposed approach enables three-drone systems to perform in coordination indepen- dent of any external infrastructure. We validate the performance of our approach in a realistic simulation environment. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Developing New Empirical Formulae for the Resilient Modulus of Fine-Grained Subgrade Soils Using a Large Long-Term Pavement Performance Dataset and Artificial Neural Network Approach
(SAGE Publications Ltd, 2022) Fedakar, Halil Ibrahim; 0000-0002-7561-5363; AGÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü; Fedakar, Halil Ibrahim
Artificial neural network (ANN) has been successfully used for developing prediction models for resilient modulus (Mr). However, no reliable Mr formula derived from these models has been proposed in previous studies, although engineers/ researchers need empirical formulae for hand calculation of Mr. Therefore, this study aimed to propose reliable empirical formulae for the Mr of fine-grained soils using ANN. For this purpose, thousands of ANN models were developed using the long-term pavement performance (LTPP) and external datasets. The input parameters were the percentage of soil particles passing through #200 sieve (P200), silt percentage (SP), clay percentage (CP), liquid limit (LL), plasticity index (PI), maximum dry density ([rdry]max), optimum moisture content (wopt), confining pressure (sc), and nominal maximum axial stress (sz). The ANN models were compared with several constitutive models. The results indicate that the constitutive models failed to predict the Mr, and the best Mr predictions were obtained by the ANN-C9 (P200, SP, CP, LL, PI, sc, and sz), ANN-C10 (P200, SP, CP, [rdry]max, wopt, sc, and sz), and ANN-C11 (P200, SP, CP, LL, PI, [rdry]max, wopt, sc, and sz) models. Thus, the structures of these ANN models were formulated and proposed as the new empirical formulae for the Mr of fine-grained soils. Sensitivity analysis was also performed on these ANN models. It was determined that (rdry)max is the most influential parameter in the ANN-C10 model, and LL is the most influential parameter in the ANN-C9 and ANN-C11 models. On the other hand, sc and sz are the least influential parameters.
Dietary fibers
(SPRINGER LINK, 2021) Dragan, Simona Ruxanda; Damian, Georgiana; Pah, Ana Maria; Hayta, Mehmet; Kahraman, Kevser; 0000-0002-2786-3944; AGÜ, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü; Kahraman, Kevser
Dietary fibers have become increasingly popular in recent years with various applications in the food and pharmaceutical industry industries. Dietary fibers are plant-based foods which are incompletely digested by digestive enzymes. The water-soluble fibers are prebiotics, which are fermented in the colon and have a beneficial role for gut microbiota. The insoluble fibers are not affected by digestive systems and provide bulking. Fiber intake has various benefits: it reduces appetite, lowers variance in blood sugar levels, reduces the risk of cardiovascular disease, lowers the risk of diabetes, balances intestinal pH, alle¬viates constipation, and facilitates regular defecation.
The Digitized Shoulder: From Preoperative Planning to Patient-Specific Guides
(SPRINGER LINK, 2022) Sadeghi, Majid Mohammad; Kapicioglu, Mehmet; Kececi, Emin Faruk; Bilsel, Kerem; 0000-0001-8561-6960; AGÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümü; Kececi, Emin Faruk
As the information and computer technologies improve, it can change how the shoulder surgeries and treatments are done. Digitalization of the shoulder joint acquired via MR and CT (1) shows the surgeons the pathology in a more easily understandable way, (2) generates models for preoperative planning, and (3) uses special software to generate patient-specific instruments. Digitalization of the shoulder will make the shoulder disorder’s treatment easier and more accurate in the future.
Economic policy and interest groups
(De Gruyter, 2020) Schuss H.; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü
Economic policy and interest groups
Effluent treatment in denim and jeans manufacture
(ELSEVIER, 2015) Uzal, Nigmet; 0000-0002-0912-3459; AGÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümü; Uzal, Nigmet
This chapter discusses the major strategies that should be considered in the treatment of denim dyeing and jeans processing wastewater. It first gives an overview of wastewater characteristics and further elaborates on the different techniques currently available for treating wastewater. There follow the strategies to be adopted for water reuse and the recovery of dyes and chemicals. Also emphasised is the utilisation of novel technologies that provide waste minimisation, recovery and reuse opportunities and pollution prevention, instead of end of pipe approaches for treating this highly polluted wastewater.
Energy harvesting and battery technologies for powering wireless sensor networks
(ELSEVIER, 2016) Tuna, Gürkan; Güngör, Vehbi Çağrı; 0000-0003-0803-8372; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Güngör, Vehbi Çağrı
Due to the advances in wireless sensor networks (WSNs), factory and plant process automation systems are being reinvented. WSN-based industrial applications often cost much less than wired networks in both the short and long terms; automation engineers are empowering existing solutions with the new capabilities of WSNs. On the other hand, since industrial wireless sensor networks (IWSNs) consist of thousands of nodes, the problem of powering the nodes is critical. Power to the nodes is usually provided through primary batteries and this necessitates replacement when the batteries are depleted. However, the replacement may not be cost-effective or even feasible in most industrial applications.Though advancements in integrated circuit technologies help in saving more energy by leading to lower energy consumption levels, they do not eliminate the use of battery power. In this regard, energy harvesting technologies play a key role in extending the battery lifetime of the nodes. Wireless sensor nodes within industrial plants can operate from energy harvested from available energy sources such as heat, mechanical motion or vibration, indoor lighting, electromagnetic fields, and air flow. In this chapter, a review of existing energy storage technologies and various energy-harvesting techniques is given. The chapter then discusses open research issues in these topics.
Estimation of Protease Activity by Use of the Mixolab
(ELSEVIER, 2013) Kahraman, Kevser; Koksel, H.; 0000-0002-2786-3944; AGÜ, Mühendislik Fakültesi, Malzeme Bilimi ve Nanoteknoloji Mühendisliği Bölümü; Kahraman, Kevser
It is known that the preharvest attack on wheat by some heteropterous insects (Eurygaster spp., Aelia spp., and Nysius huttoni) reduces the breadmaking quality of the resulting wheat flour (Lorenz and Meredith 1988, Swallow and Every 1991, Harriri et al 2000). The problem is quite common in Mediterranean, Middle Eastern, and East European countries. Before harvest, these insects inject their salivary secretions into maturing wheat ears when they feed. Following attack by the insect, the grain matures normally, but a light-colored opaque patch surrounds the site where the insect pierced the grain. The penetration point is visible as a small dark spot in the middle of this patch. The damaged area is softer and usually collapses when pressed by a fingernail. The secretions contain proteases that break down the gluten structure during mixing and fermentation (Kretovich 1944; Every et al 1998; Sivri et al 1998, 1999).
GREEN SUPPLIER SELECTION BY USING FUZZY TOPSIS METHOD
(WORLD SCIENTIFIC PUBL CO PTE LTDPO BOX 128 FARRER RD, SINGAPORE 9128, SINGAPORE, 2016) Dogan, Ahmet; Soylemez, Ismet; Ozcan, Ugur; 0000-0002-8253-9389; AGÜ, Mühendislik Fakültesi, Endüstri Mühendisliği Bölümü; Soylemez, Ismet
With the increased environmental consciousness in customers, organizations took upon the task of redesigning their strategic goals in a more environment-sensitive way in order to fulfill their social obligations, to enable sustainability, to gain competitive advantage and to make the world more habitable. Because, the emerging conditions in the 21st century indicate that the traditional criteria - such as price, cost so on for supply chain management, supplier selection and performance measurement of suppliers are no more sufficient and there is the necessity of adding new criteria such as environmental matters. This paper deals with the problem of selecting green suppliers in an organization in Turkey that has operations in the field of accumulator. The aim is to select the greenest of 3 suppliers in Turkey, France and Bulgaria which supply the organization with the plastic material used in the production of accumulator. The problem is solved via fuzzy TOPSIS, which is a multi-criteria decision making method (MCDM), and the results are used to select the greenest supplier.
Green vehicle routing problem
(Nova Science Publishers, Inc., 2024) Madenoglu, Fatma Selen; 0000-0002-5577-4471; AGÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü; Madenoglu, Fatma Selen
The decrease in natural resources, the fact that greenhouse gases cause global warming, and consumers' sensitivity to environmental, and health issues have forced businesses to develop sustainable strategies. The idea of "green logistics," which is focused on carrying out economically sensible environmental activities when producing goods and services, has gained significant relevance for this reason. Transportation activity is one of the most basic costs of logistics activity. Due to the fuels used in transportation and the greenhouse gases released, it has become vital to develop and implement sustainable and green energy policies in logistics activities. Green logistics has become one of the areas that needs to be emphasized. A metaheuristic is presented and proposed to solve the vehicle routing problem, which is a type of vehicle routing problem involved in optimizing simultaneous pickup and delivery considering carbon emissions in logistics activities. A green logistics approach is presented in order to guide businesses that aim to produce services in accordance with sustainable and green energy policies.
The Implementation of the 4+4+4 Educational Policy in Turkey: Reflections from English Classrooms
(SPRINGER, 2023) Birdal, Tuğba; Vural, Seniye; 0000-0002-9460-3173; AGÜ, Yabancı Diller Yüksekokulu, Yabancı Dilleri Bölümü; Birdal, Tuğba
Lowering the age of English instruction has been a common language policy of many countries in response to the global status of English, as well as the benefits of learning a foreign language at a younger age. However, how congruent the policies are with actual implementations is a crucial question. That being the case, the purpose of this study was to investigate the training backgrounds, perceived pedagogical weaknesses, needs, frequent classroom practices, and challenges of primary school English teachers in Turkey after a policy change that lowered the beginning age of English instruction from 4th grade to 2nd grade. Qualitative and quantitative data were collected via questionnaires and semi-structured interviews based on a mixed-methods research design. The results indicate that most teachers express needs for their everyday classroom practices, such as games, songs, and materials, whereas theoretical or cultural constructs are of secondary importance to them. In addition, the policy change entails modifications in the curricula of teacher education programs.
Industry 4.0: A bibliometric analysis
(Nova Science Publishers, Inc., 2023) Madenoğlu, Fatma Selen; 0000-0002-5577-4471; AGÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü; Madenoğlu, Fatma Selen
With the emergence of Industry 4.0, organizations' interest in digital transformation has significantly expanded to remain competitive. As a result, organizational digital transformations have appeared. Nowadays, machine learning is built into every device, and automation has taken center stage. This paper presents a bibliometric study and in-depth research on the most recent advancements in the "Industry 4.0" field. The top 10 most cited publications and authors, main research areas, publications, and countries are all examined as part of the bibliometric study, which also extracts data on the overall number of publications and citations. The organizational transformation highlighted in the publications is then examined after a detailed analysis of the most cited publications. In this paper, the publications on Industry 4.0 are examined and in addition, publications that deal with Industry 4.0 and organizational transformation are presented.
Integrating Gene Ontology Based Grouping and Ranking into the Machine Learning Algorithm for Gene Expression Data Analysis
(SPRINGER INTERNATIONAL PUBLISHING AGGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND, 2021) Yousef, Malik; Sayici, Ahmet; Bakir-Gungor, Burcu; AGÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü; Sayici, Ahmet; Bakir-Gungor, Burcu
Recent advances in the high throughput technologies resulted in the production of large gene expression data sets for several phenotypes. Via comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc., one could identify biomarkers. As opposed to traditional gene selection approaches, integrative gene selection approaches incorporate domain knowledge from external biological resources during gene selection, which improves interpretability and predictive performance. In this respect, Gene Ontology provides cellular component, molecular function and biological process terms for the products of each gene. In this study, we present Gene Ontology based feature selection approach for gene expression data analysis. In our approach, we used the ontology information as grouping (term) information and embedded this information into a machine learning algorithm for selecting the most significant groups (terms) of ontology. Those groups are used to build the machine learning model in order to perform the classification task. The output of the tool is a significant ontology group for the task of 2-class classification applied on the gene expression data. This knowledge allows the researcher to perform more advanced gene expression analyses. We tested our approach on 8 different gene expression datasets. In our experiments, we observed that the tool successfully found the significant Ontology terms that would be used as a classification model. We believe that our tool will help the geneticists to identify affected genes in transcriptomic data and this information could enable the design of platforms to assist diagnosis, to assess patients' prognoses, and to create patient treatment plans.
Integrating infrastructure and accessibility in measures of bespoke neighbourhoods
(Edward Elgar Publishing Ltd., 2020) Osth J.; Turk U.; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü
Integrating infrastructure and accessibility in measures of bespoke neighbourhoods
Isothiocyanates as drug candidates in cancer prevention and treatment
(IGI Global, 2023) Saylan, Demet; Cebeci, Fatma; AGÜ, Yaşam ve Doğa Bilimleri Fakültesi, Moleküler Biyoloji ve Genetik Bölümü; Saylan, Demet
Cancer is the second most common cause of death worldwide. Many methods such as surgery, chemotherapy, radiation therapy, etc. are being used for treatment. Most patients have a combination treatment with chemotherapy along with surgery or radiotherapy or both. Chemotherapy kills cancer cells by preventing them from reproducing, growing, and spreading in the body. Recently, safer alternatives to chemotherapy have been discovered and developed, as most of the drugs used in cancer treatment have side effects and a serious impact on patient comfort. As an alternative, the phytochemicals found in daily consumed plants are attractive candidates for clinical/pre-clinical evaluation because of their higher safety. In this context, certain degradation products of glucosinolates (isothiocyanates) are promising agents for cancer prevention and treatment.

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