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Article Citation - WoS: 10Citation - Scopus: 11Analysis of Space Efficiency in High-Rise Timber Residential Towers(Mdpi, 2024) Ilgin, Hueseyin Emre; Aslantamer, Ozlem NurHigh-rise timber residential towers (>= eight-stories) represent a burgeoning and auspicious sector, predominantly due to their capability to provide significant ecological and financial advantages throughout their lifecycle. Like numerous other building types, spatial optimization in high-rise timber residential structures stands as a pivotal design factor essential for project viability. Presently, there exists no comprehensive investigation on space efficiency in such towers. This study analyzed data from 51 case studies to enhance understanding of the design considerations influencing space efficiency in high-rise timber residential towers. Key findings included (1) the average space efficiency within the examined cases was recorded at 83%, exhibiting variances ranging from 70% to 93% across different cases, (2) the average percentage of core area to gross floor area (GFA) was calculated at 10%, demonstrating fluctuations within the range of 4% to 21% across diverse scenarios, and (3) no notable distinction was observed in the effect of various core planning strategies on spatial efficiency when properly designed, and similar conclusions were drawn regarding building forms and structural materials. This research will aid in formulating design guidelines tailored for various stakeholders such as architectural designers involved in high-rise residential timber building developments.Article Citation - WoS: 7Citation - Scopus: 10Space Efficiency in North American Skyscrapers(Mdpi, 2024) Ilgin, Huseyin Emre; Aslantamer, Ozlem NurSpace efficiency in North American skyscrapers is crucial due to financial, societal, and ecological reasons. High land prices in major cities require maximizing every square foot for financial viability. Skyscrapers must accommodate growing populations within limited spaces, reducing urban sprawl and its associated issues. Efficient designs also support environmental sustainability and enhance city aesthetics, while optimizing infrastructure and services. However, no comprehensive study has examined the key architectural and structural features impacting the space efficiency of these towers in North America. This paper fills this gap by analyzing data from 31 case study skyscrapers. Findings indicated that (1) central core was frequently employed in the organization of service core; (2) most common forms were setback, prismatic, and tapered configurations; (3) outriggered frame and shear walled frame systems were mostly used; (4) concrete was the material in most cases; and (5) average space efficiency was 76%, and the percentage of core area to gross floor area (GFA) averaged 21%, from the lowest of 62% and 13% to the highest of 84% and 31%. It is expected that this paper will aid architectural and structural designers, and builders involved in shaping skyscrapers in North America.Review Citation - WoS: 7Citation - Scopus: 9A Survey of Covid-19 Diagnosis Using Routine Blood Tests With the Aid of Artificial Intelligence Techniques(Mdpi, 2023) Habashi, Soheila Abbasi; Koyuncu, Murat; Alizadehsani, RoohallahSevere Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), causing a disease called COVID-19, is a class of acute respiratory syndrome that has considerably affected the global economy and healthcare system. This virus is diagnosed using a traditional technique known as the Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. However, RT-PCR customarily outputs a lot of false-negative and incorrect results. Current works indicate that COVID-19 can also be diagnosed using imaging resolutions, including CT scans, X-rays, and blood tests. Nevertheless, X-rays and CT scans cannot always be used for patient screening because of high costs, radiation doses, and an insufficient number of devices. Therefore, there is a requirement for a less expensive and faster diagnostic model to recognize the positive and negative cases of COVID-19. Blood tests are easily performed and cost less than RT-PCR and imaging tests. Since biochemical parameters in routine blood tests vary during the COVID-19 infection, they may supply physicians with exact information about the diagnosis of COVID-19. This study reviewed some newly emerging artificial intelligence (AI)-based methods to diagnose COVID-19 using routine blood tests. We gathered information about research resources and inspected 92 articles that were carefully chosen from a variety of publishers, such as IEEE, Springer, Elsevier, and MDPI. Then, these 92 studies are classified into two tables which contain articles that use machine Learning and deep Learning models to diagnose COVID-19 while using routine blood test datasets. In these studies, for diagnosing COVID-19, Random Forest and logistic regression are the most widely used machine learning methods and the most widely used performance metrics are accuracy, sensitivity, specificity, and AUC. Finally, we conclude by discussing and analyzing these studies which use machine learning and deep learning models and routine blood test datasets for COVID-19 detection. This survey can be the starting point for a novice-/beginner-level researcher to perform on COVID-19 classification.Article Citation - WoS: 5Citation - Scopus: 7High-Rise Timber Offices: Main Architectural and Structural Design Parameters(Mdpi, 2024) Ilgin, Hueseyin Emre; Aslantamer, Ozlem NurHigh-rise office structures constructed using timber material (with a minimum of eight stories) signify a burgeoning and favorable sector, mainly owing to their ability to offer substantial environmental and economic advantages across their lifespan. However, it is crucial to recognize that the current corpus of scholarly literature lacks a thorough investigation into vital aspects concerning the architectural and structural planning of these sustainable structures. In an effort to fill this gap and augment the understanding of advancing international tendencies, this paper delved into data originating from 27 high-rise offices on a worldwide scale. The primary findings were: (i) Central core arrangements were the most popular, accounting for 67%, followed by peripheral types at 22%. (ii) Prismatic designs were the most frequently used at 85%, with free forms making up 11%. (iii) Material combinations involving timber and concrete were widely prevalent, making up 70% of composite constructions, which were 74% of the sample group, with pure timber constructions at 26%. (iv) Structural systems predominantly utilized shear walled frame systems, comprising 85% of the total. This article serves as a valuable resource for architectural designers, offering guidance on planning and executing future sustainable developments in the domain of high-rise timber office.Article Citation - WoS: 14Citation - Scopus: 25Automatic Classification of UML Class Diagrams Using Deep Learning Technique: Convolutional Neural Network(Mdpi, 2021) Gosala, Bethany; Chowdhuri, Sripriya Roy; Singh, Jyoti; Gupta, Manjari; Mishra, AlokUnified Modeling Language (UML) includes various types of diagrams that help to study, analyze, document, design, or develop any software efficiently. Therefore, UML diagrams are of great advantage for researchers, software developers, and academicians. Class diagrams are the most widely used UML diagrams for this purpose. Despite its recognition as a standard modeling language for Object-Oriented software, it is difficult to learn. Although there exist repositories that aids the users with the collection of UML diagrams, there is still much more to explore and develop in this domain. The objective of our research was to develop a tool that can automatically classify the images as UML class diagrams and non-UML class diagrams. Earlier research used Machine Learning techniques for classifying class diagrams. Thus, they are required to identify image features and investigate the impact of these features on the UML class diagrams classification problem. We developed a new approach for automatically classifying class diagrams using the approach of Convolutional Neural Network under the domain of Deep Learning. We have applied the code on Convolutional Neural Networks with and without the Regularization technique. Our tool receives JPEG/PNG/GIF/TIFF images as input and predicts whether it is a UML class diagram image or not. There is no need to tag images of class diagrams as UML class diagrams in our dataset.Article Citation - WoS: 2Citation - Scopus: 3Space Efficiency in European High-Rise Timber Buildings(Mdpi, 2024) Ilgin, Huseyin Emre; Aslantamer, Ozlem NurAs towering wooden edifices (>= 8 stories) become a rapidly expanding and promising field, they provide substantial environmental and economic advantages throughout their entire lifespans, leading to their increasing popularity, especially in the European context. Similar to various other construction forms, spatial efficiency is a vital design criterion in timber buildings to guarantee the viability of a project. Currently, there is no thorough study on spatial efficiency in these towers in Europe, which is home to the majority of the world's timber towers. This paper examined data from 56 cases to improve comprehension of the planning factors affecting space efficiency in these buildings. The main findings showed that the average space efficiency across the analyzed examples was documented at 82%, with deviations spanning from 70% to 90%, the average core area to gross floor area (GFA) ratio was determined to be 11%, ranging from 4% to 21%, and no substantial difference was noted in the impact of core arrangements on space efficiency, and parallel findings were observed for forms and construction materials. This article aspires to provide architectural designers with essential perspectives, assisting and directing them in the conception and realization of upcoming ventures both across Europe and internationally in this domain.Article Citation - WoS: 3Citation - Scopus: 4Anti-Inflammatory, Antioxidant, and Anti-Atherosclerotic Effects of Natural Supplements on Patients With Fmf-Related Aa Amyloidosis: a Non-Randomized 24-Week Open-Label Interventional Study(Mdpi, 2022) Romano, Micol; Garcia-Bournissen, Facundo; Piskin, David; Rodoplu, Ulkumen; Piskin, Lizzy; Elzagallaai, Abdelbaset A.; Demirkaya, ErkanWe aimed to evaluate the effect of a combination of natural products on parameters related to inflammation, endothelial dysfunction, and oxidative stress in a cohort of familial Mediterranean fever (FMF) patients with Serum Amyloid A amyloidosis, in a non-randomized, 24-week open-label interventional study. Morinda citrifolia (anti-atherosclerotic-AAL), omega-3 (anti-inflammatory-AIC), and extract with Alaskan blueberry (antioxidant-AOL) were given to patients with FMF-related biopsy-proven AA amyloidosis. Patients were >18 years and had proteinuria (>3500 mg/day) but a normal estimated glomerular filtration rate (eGFR). Arterial flow-mediated dilatation (FMD), carotid intima media thickness (CIMT), and serum biomarkers asymmetric dimethylarginine (ADMA), high sensitivity C-reactive protein (hs-CRP), pentraxin (PTX3), malondialdehyde (MDA), Cu/Zn-superoxide dismutase (Cu/Zn-SOD), and glutathione peroxidase (GSH-Px) were studied at baseline and after 24 weeks of treatment. A total of 67 FMF-related amyloidosis patients (52 male (77.6%); median age 36 years (range 21-66)) were enrolled. At the end of a 24-week treatment period with AAL, AIC, and AOL combination therapy, ADMA, MDA, PTX3, hsCRP, cholesterol, and proteinuria were significantly decreased compared to baseline, while CuZn-SOD, GSH-Px, and FMD levels were significantly increased. Changes in inflammatory markers PTX3, and hsCRP were negatively correlated with FMD change, and positively correlated with decreases in proteinuria, ADMA, MDA, cholesterol, and CIMT. Treatment with AAL, AIC and AOL combination for 24 weeks were significantly associated with reduction in inflammatory markers, improved endothelial functions, and oxidative state. Efficient control of these three mechanisms can have long term cardiovascular and renal benefits for patients with AA amyloidosis.Article Citation - WoS: 3Citation - Scopus: 7Some Results on S-contractions of Type e(Mdpi, 2018) Fulga, Andreea; Karapinar, ErdalIn this manuscript, we consider the compositions of simulation functions and E-contraction in the setting of a complete metric space. We investigate the existence and uniqueness of a fixed point for this composite form. We give some illustrative examples and provide an application.Article Citation - WoS: 10Citation - Scopus: 20A Comparison of the Ballistic Performances of Various Microstructures in Mil-A Armor Steel(Mdpi, 2020) Konca, ErkanDue to their advantageous properties, there is a growing interest in developing armor steels containing fully or partially bainitic microstructures. In this study, bainitic and martensitic microstructures were obtained in rolled homogeneous armor (RHA) steel samples and their ballistic protection performances were investigated. RHA (MIL-A-12560) steel samples were subjected to isothermal heat treatments at three different temperatures, where one temperature (360 degrees C) was above the martensite formation start (Ms) temperature of 336 degrees C while the other two (320 degrees C and 270 degrees C) were below. For the assessment of the ballistic protection performance, the kinetic energy losses of the 12.7 mm bullets fired at the test samples were determined. The promising nature of the bainite microstructure was confirmed as the sample isothermally treated at 360 degrees C provided approximately 10% higher ballistic protection as compared to the regular RHA sample of tempered martensite microstructure. However, the ballistic performances of the isothermally treated samples decreased as the treatment temperature went below the Ms temperature. Following the ballistic tests, hardness measurements, impact tests at -40 degrees C, and macro- and microstructural examinations of the samples were performed. No correlation was found between the hardness and impact energies of the samples and their ballistic performances.Article Citation - WoS: 18Citation - Scopus: 35Distributed Centrality Analysis of Social Network Data Using Mapreduce(Mdpi, 2019) Behera, Ranjan Kumar; Rath, Santanu Kumar; Misra, Sanjay; Damasevicius, Robertas; Maskeliunas, RytisAnalyzing the structure of a social network helps in gaining insights into interactions and relationships among users while revealing the patterns of their online behavior. Network centrality is a metric of importance of a network node in a network, which allows revealing the structural patterns and morphology of networks. We propose a distributed computing approach for the calculation of network centrality value for each user using the MapReduce approach in the Hadoop platform, which allows faster and more efficient computation as compared to the conventional implementation. A distributed approach is scalable and helps in efficient computations of large-scale datasets, such as social network data. The proposed approach improves the calculation performance of degree centrality by 39.8%, closeness centrality by 40.7% and eigenvalue centrality by 41.1% using a Twitter dataset.
