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Article Citation - WoS: 50Citation - Scopus: 70Deep Learning Based Fall Detection Using Smartwatches for Healthcare Applications(Elsevier Sci Ltd, 2022) Sengul, Gokhan; Karakaya, Murat; Misra, Sanjay; Abayomi-Alli, Olusola O.; Damasevicius, RobertasWe implement a smart watch-based system to predict fall detection. We differentiate fall detection from four common daily activities: sitting, squatting, running, and walking. Moreover, we separate falling into falling from a chair and falling from a standing position. We develop a mobile application that collects the acceleration and gyroscope sensor data and transfers them to the cloud. In the cloud, we implement a deep learning algorithm to classify the activity according to the given classes. To increase the number of data samples available for training, we use the Bica cubic Hermite interpolation, which allows us to improve the accuracy of the neural network. The 38 statistical data features were calculated using the rolling update approach and used as input to the classifier. For activity classification, we have adopted the bi-directional long short-term memory (BiLSTM) neural network. The results demonstrate that our system can detect falling with an accuracy of 99.59% (using leave-one-activityout cross-validation) and 97.35% (using leave-one-subject-out cross-validation) considering all activities. When considering only binary classification (falling vs. all other activities), perfect accuracy is achieved.Article Citation - WoS: 93Citation - Scopus: 129Improving Sustainability in the Tourism Industry Through Blockchain Technology: Challenges and Opportunities(Elsevier Sci Ltd, 2022) Erol, Ismail; Neuhofer, Irem Onder; Dogru, Tarik (Dr. True); Oztel, Ahmet; Searcy, Cory; Yorulmaz, Ali C.The tourism industry is extremely important to the world economy; yet, the industry falls short when it comes to economic, social, and environmental issues. Blockchain as an information technology can be utilized to help solve these issues and establish sustainable tourism globally. However, the challenges to blockchain adoption in the tourism industry have not yet been examined systematically. The goal of this study, therefore, is three-fold: we first identify the challenges to blockchain using literature review and expert opinions. Then, we examine them using the proposed rough Interpretive Structural Modeling - Cross-Impact Matrix Multiplication based on expert judgments. Finally, we link these challenges to diffusion of innovation theory. The results suggest that "lack of technical maturity" and "lack of interoperability" are the most important challenges of blockchain in the tourism industry. The findings of the study support macro- and micro-level decision-making in tourism industry's prospective applications of blockchain.Article Innovative 3d Modeling of an Old Oil Field for Sustainable Production: Case Study of Katin-Barbes Oil Field (kbof), Se Anatolia- Turkey(Elsevier Sci Ltd, 2023) Ozer, Zafer; Kamaci, Zuheyr; Aydemir, AttilaThe goal of this study is to establish a workflow for the re-interpretation of almost depleted fields targeting the long-term sustainable oil production; in particular, for the oil fields in Turkey and the neighboring Middle Eastern countries located on the fold and thrust belts of the Zagros Mountains. It also fills some of the gaps in our understanding of the northern part of the Arabian Platform by describing the seismic characteristics of the Cretaceous reservoirs that were deposited during the Aptian to Turonian. The Katin-Barbes oil field (KBOF) in SETurkey was used as a case study. In this area, 3D seismic data were used for structural interpretation and remodeling of Cretaceous carbonates sedimented in the complex tectonic region. The well logs from 55 wells in the field were used to create a compilation of formation tops and were used as reference points for two separate sets of 3-D seismic data, acquired in 1991 and 2017. The quality of seismic data was improved with interpretation filters. The structural model was obtained by using various qualifiers from the seismic cubes and seismic facies changes were identified by analyzing a number of seismic attributes. Therefore, seismic data and velocities from the borehole measurements were combined to form a velocity model in building a structural model. Seismic attributes and well logs were used to create a porosity model. Consequently, top and base of two reservoir units; the Derdere and Sabunsuyu Formations have been clarified and re-defined, and potential new well locations were identified. Depending on the results of this investigation, 9 new wells were drilled in the potential areas in the KBOF, recently. Except the last one drilled on the NE boundary of the northern block, all wells have been completed as the "oil producing wells" and top of the reservoir units were encountered at almost the same depths in our depth model. Therefore, results and proposed methods in this research are confirmed by the real, borehole data. This research will be an examplary study for the re-evaluation of older and/or almost depleted oil fields, either in Turkey or in the other Middle Eastern countries.Article Citation - WoS: 202Citation - Scopus: 307Co-Lstm: Convolutional Lstm Model for Sentiment Analysis in Social Big Data(Elsevier Sci Ltd, 2021) Behera, Ranjan Kumar; Jena, Monalisa; Rath, Santanu Kumar; Misra, SanjayAnalysis of consumer reviews posted on social media is found to be essential for several business applications. Consumer reviews posted in social media are increasing at an exponential rate both in terms of number and relevance, which leads to big data. In this paper, a hybrid approach of two deep learning architectures namely Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) (RNN with memory) is suggested for sentiment classification of reviews posted at diverse domains. Deep convolutional networks have been highly effective in local feature selection, while recurrent networks (LSTM) often yield good results in the sequential analysis of a long text. The proposed Co-LSTM model is mainly aimed at two objectives in sentiment analysis. First, it is highly adaptable in examining big social data, keeping scalability in mind, and secondly, unlike the conventional machine learning approaches, it is free from any particular domain. The experiment has been carried out on four review datasets from diverse domains to train the model which can handle all kinds of dependencies that usually arises in a post. The experimental results show that the proposed ensemble model outperforms other machine learning approaches in terms of accuracy and other parameters.Article Citation - WoS: 4Citation - Scopus: 6A Study on Microstructural Characterization of the Interface Between Apatite-Wollastonite Based Glass Ceramic and Feldspathic Dental Porcelain(Elsevier Sci Ltd, 2016) Pekkan, Gurel; Pekkan, Keriman; Park, Jongee; Ozturk, AbdullahIn this study, the contact area between the glass ceramic containing apatite [Ca-10(PO4)6(O,F-2)] and wollastonite [CaO center dot SiO2] crystals (A-W glass ceramic) and feldspathic dental porcelain was characterized using scanning electron microscope and energy dispersive spectroscopy. Alumina-added A W glass ceramics were prepared by sintering glass compacts in the MgO-CaO-SiO2-P2O5-Al2O3 system at 1100 degrees C. Commercially available dental porcelains for alumina frameworks were applied on the A-W glass ceramic specimen by brushing and carving, and then fired at 960 degrees C using an electrically heated vacuum-furnace. Results revealed that veneering of feldspathic dental porcelain on alumina-added A-W glass ceramic is possible by an interaction between them, with which a diffusion process involving i) seperation of the phases forming the alumina-added A-W glass ceramic, ii) chemical diffusion of elements between alumina-added A-W glass ceramic and feldspathic dental porcelain, and iii) formation of an interface layer, is taking place. The system studied has interfacial characteristics similar to the commercially available dental materials currently used in restorative dentistry. Hence, it may be further processed for potential clinical applications.Article Citation - WoS: 29Citation - Scopus: 31Capacity Loss and Residual Capacity in Weighted k-out-of-n< Systems(Elsevier Sci Ltd, 2015) Eryilmaz, Serkan; Eryılmaz, Serkan; Eryılmaz, Serkan; Industrial Engineering; Industrial EngineeringA binary weighted-k-out-of-n:G system is a system that consists of n binary components, and functions if and only if the total weight of working components is at least k. The performance of such a system is characterized by its total weight/capacity. Therefore, the evaluation of the capacity of the system is of special importance for understanding the behavior of the system over time. This paper is concerned with capacity loss and residual capacity in binary weighted-k-out-of-n:G systems. These measures are potentially useful for the purposes of preventive action. In particular, recursive and non-recursive equations are obtained for the mean capacity loss and mean residual capacity of the binary weighted-k-out-of-n:G system while it is working at a specific time. The mean residual capacity after the failure of the system is also studied. (C) 2014 Elsevier Ltd. All rights reserved.Review Citation - WoS: 194Citation - Scopus: 212Stimulus-Responsive Sequential Release Systems for Drug and Gene Delivery(Elsevier Sci Ltd, 2020) Ahmadi, Sepideh; Rabiee, Navid; Bagherzadeh, Mojtaba; Elmi, Faranak; Fatahi, Yousef; Farjadian, Fatemeh; Hamblin, Michael R.In recent years, a range of studies have been conducted with the aim to design and characterize delivery systems that are able to release multiple therapeutic agents in controlled and programmed temporal sequences, or with spatial resolution inside the body. This sequential release occurs in response to different stimuli, including changes in pH, redox potential, enzyme activity, temperature gradients, light irradiation, and by applying external magnetic and electrical fields. Sequential release (SR)-based delivery systems, are often based on a range of different micro- or nanocarriers and may offer a silver bullet in the battle against various diseases, such as cancer. Their distinctive characteristic is the ability to release one or more drugs (or release drugs along with genes) in a controlled sequence at different times or at different sites. This approach can lengthen gene expression periods, reduce the side effects of drugs, enhance the efficacy of drugs, and induce an anti-proliferative effect on cancer cells due to the synergistic effects of genes and drugs. The key objective of this review is to summarize recent progress in SR-based drug/gene delivery systems for cancer and other diseases. (C) 2020 Elsevier Ltd. All rights reserved.Article Citation - WoS: 44Citation - Scopus: 44Silver-Loaded Tio2 Powders Prepared Through Mechanical Ball Milling(Elsevier Sci Ltd, 2013) Aysin, Basak; Ozturk, Abdullah; Park, JongeeSilver (Ag) was loaded on TiO2 powders through mechanical ball milling. Ag-loading was accomplished by adding 4.6, 9.2, and 13.8 ml of AgNO3 solution to the TiO2 powders during the milling process. The resulting powder was characterized by XRD, XPS, SEM, and EDS. The photocatalytic activity of the silver-loaded powder was evaluated in terms of the degradation of methyl orange (MO) solution under ultraviolet (UV) illumination. XRD patterns were refined using the Rietveld analysis to determine the lattice parameters. XRD analysis suggested that Ag was loaded on TiO2 powders in the form of AgO. X-ray photoelectron spectroscopy and Rietveld analysis revealed that silver did not dope into the crystal structure of TiO2. SEM investigations confirmed that ball milling caused a decrease in the average particle size of the powders. Silver-loading improved the photocatalytic activity of the TiO2 powders. The TiO2 powder ball milled without Ag-loading degraded 46% of the MO solution whereas the ball milled with 13.8 ml AgNO3 solution degraded 96% of the MO solution under 1 h UV irradiation. Moreover, TiO2 powders gained antibacterial property after Ag-loading. (c) 2013 Elsevier Ltd and Techna Group S.r.l. All rights reserved.Article Citation - WoS: 20Citation - Scopus: 21Temperature-Tuned Band Gap Characteristics of Inse Layered Semiconductor Single Crystals(Elsevier Sci Ltd, 2020) Isik, M.; Gasanly, N. M.Layered structured InSe has attracted remarkable attention due to its effective characteristics utilized especially in optoelectronic device technology. This point directs researchers to investigate optical properties of InSe in great detail. The temperature dependent band gap characteristics of InSe and analyses performed on this dependency have been rarely studied in literature. Here, temperature-dependent transmission and room temperature reflection experiments were performed on InSe layered single crystals. The band gap energy was found around 1.22 eV at room temperature and 1.32 eV at 10 K. The temperature-gap energy dependency was analyzed using Varshni and O'Donnell-Chen models to reveal various optical parameters of the crystal. The structural characteristics; crystalline parameters like lattice constants, lattice strain, dislocation density and atomic compositions of InSe were also determined from the analyses of XRD and EDS measurements.Article Citation - WoS: 23Citation - Scopus: 27Inorganic Hole Transport Materials in Perovskite Solar Cells Are Catching Up(Elsevier Sci Ltd, 2023) Sajid, Sajid; Alzahmi, Salem; Ben Salem, Imen; Park, Jongee; Obaidat, Ihab M.; Salem, Imen BenMore research is required to further optimize device efficiency, stability, and reduce the materials cost as perovskite solar cells (PSCs) approach to industrialization. Modulating the optoelectronic features and chemical coupling of the hole transport materials (HTMs) remains a prominent field of study in PSCs due to the significant impact these materials have on the device performance and stability. In order to speed up the commercialization of these cells, it is also important to use cost-effective HTMs in PSCs. InorganicHTMs are superior to other types of HTMs in terms of their advantages in boosting device performance and producing PSCs at a reasonable cost, in addition to their superior charge transport capabilities, desired energy levels, and intrinsic thermal and chemical stability. A detailed overview of inorganicHTMs, including metal oxides, cyanates, phthalocyanines, chalcogenides, nitrides, and carbides, is presented in this review. After briefly discussing the primary physical and optoelectronic characteristics of inorganic-HTMs, the critical functions of the above-mentioned materials as HTMs in PSCs are addressed. This review concludes by offering suggestions for future research that could considerably boost the performance of the PSCs with cost-effective inorganic-HTMs.
