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Review Citation - WoS: 74Citation - Scopus: 120Hybrid Blockchain Platforms for the Internet of Things (iot): a Systematic Literature Review(Mdpi, 2022) Alkhateeb, Ahmed; Catal, Cagatay; Kar, Gorkem; Mishra, AlokIn recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study.Article Citation - Scopus: 2The Effects of Paddy Cultivation and Microbiota Members on Arsenic Accumulation in Rice Grain(Mdpi, 2023) Ersoy Omeroglu, Esra; Bayer, Asli; Sudagidan, Mert; Ozalp, Veli Cengiz; Yasa, IhsanAccess to safe food is one of the most important issues. In this context, rice plays a prominent role. Because high levels of arsenic in rice grain are a potential concern for human health, in this study, we determined the amounts of arsenic in water and soil used in the rice development stage, changes in the arsC and mcrA genes using qRT-PCR, and the abundance and diversity (with metabarcoding) of the dominant microbiota. When the rice grain and husk samples were evaluated in terms of arsenic accumulation, the highest values (1.62 ppm) were obtained from areas where groundwater was used as irrigation water, whereas the lowest values (0.21 ppm) occurred in samples from the stream. It was observed that the abundance of the Comamonadaceae family and Limnohabitans genus members was at the highest level in groundwater during grain formation. As rice development progressed, arsenic accumulated in the roots, shoots, and rice grain. Although the highest arsC values were reached in the field where groundwater was used, methane production increased in areas where surface water sources were used. In order to provide arsenic-free rice consumption, the preferred soil, water source, microbiota members, rice type, and anthropogenic inputs for use on agricultural land should be evaluated rigorously.Article Citation - WoS: 3Citation - Scopus: 3Paper-Based Aptasensor Assay for Detection of Food Adulterant Sildenafil(Mdpi, 2024) Kavruk, Murat; Ozalp, Veli CengizSildenafil is used to treat erectile dysfunction and pulmonary arterial hypertension but is often illicitly added to energy drinks and chocolates. This study introduces a lateral flow strip test using aptamers specific to sildenafil for detecting its illegal presence in food. The process involved using graphene oxide SELEX to identify high-affinity aptamers, which were then converted into molecular gate structures on mesoporous silica nanoparticles, creating a unique signaling system. This system was integrated into lateral flow chromatography strips and tested on buffers and chocolate samples containing sildenafil. The method simplifies the lateral flow assay (LFA) for small molecules and provides a tool for signal amplification. The detection limit for these strips was found to be 68.2 nM (31.8 mu g/kg) in spiked food samples.Article Citation - WoS: 10Citation - Scopus: 11Toughening Mechanism Analysis of Recycled Rubber-Based Composites Reinforced With Glass Bubbles, Glass Fibers and Alumina Fibers(Mdpi, 2021) Kabakci, Gamze Cakir; Aslan, Ozgur; Bayraktar, EminRecycling of materials attracts considerable attention around the world due to environmental and economic concerns. Recycled rubber is one of the most commonly used recyclable materials in a number of industries, including automotive and aeronautic because of their low weight and cost efficiency. In this research, devulcanized recycled rubber-based composites are designed with glass bubble microsphere, short glass fiber, aluminum chip and fine gamma alumina fiber (gamma-Al2O3) reinforcements. After the determination of the reinforcements with matrix, bending strength and fracture characteristics of the composite are investigated by three-point bending (3PB) tests. Halpin-Tsai homogenization model is adapted to the rubber-based composites to estimate the moduli of the composites. Furthermore, the relevant toughening mechanisms for the most suitable reinforcements are analyzed and stress intensity factor, K-Ic and critical energy release rate, G(Ic) in mode I are determined by 3PB test with single edge notch specimens. In addition, 3PB tests are simulated by finite element analysis and the results are compared with the experimental results. Microstructural and fracture surfaces analysis are carried out by means of scanning electron microscopy (SEM). Mechanical test results show that the reinforcement with glass bubbles, aluminum oxide ceramic fibers and aluminum chips generally increase the fracture toughness of the composites.Article Citation - WoS: 17Citation - Scopus: 24Iot Platform for Seafood Farmers and Consumers(Mdpi, 2020) Jaeger, Bjorn; Mishra, AlokThere has been a strong growth in aquatic products supported by the global seafood industry. Consumers demand information transparency to support informed decisions and to verify nutrition, food safety, and sustainable operations. Supporting these needs rests on the existence of interoperable Internet of Things (IoT) platforms for traceability that goes beyond the minimum "one up, one down" scheme required by regulators. Seafood farmers, being the source of both food and food-information, are critical to achieving the needed transparency. Traditionally, seafood farmers carry the costs of providing information, while downstream actors reap the benefits, causing limited provision of information. Now, global standards for labelling, data from IoT devices, and the reciprocity of utility from collecting data while sharing them represent great potential for farmers to generate value from traceability systems. To enable this, farmers need an IoT platform integrated with other IoT platforms in the value network. This paper presents a case study of an enterprise-level IoT platform for seafood farmers that satisfies consumers' end-to-end traceability needs while extracting data from requests for information from downstream actors.Article Citation - WoS: 2Citation - Scopus: 4Mobile Application Software Requirements Specification From Consumption Values(Mdpi, 2023) Derawi, Mohammad; Dalveren, Gonca Gokce Menekse; Cagiltay, Nergiz ErcilIn today's society, mobile applications are becoming more popular and providing several advantages. However, users will resist using a product regardless of how well-tested or solid it is if the wrong requirements are implemented. Understanding the factors that influence the purchase of mobile applications can provide useful information for mobile application design and development. Hence, the purpose of this research is to better understand the impact of consumption values on customers in order to identify the software requirements for a mobile application. This study analyzes the possible behavioral changes of similar groups of university students in a five-year period. For this purpose, a questionnaire is administered to engineering faculty students in 2017 (46 females and 66 males) and 2021 (45 females and 90 males) to better understand customer behavioral changes. The findings highlight the significance of conditional value in customer behavior when purchasing mobile applications. Even though the other consumption values were found to have a negligible effect, there is some evidence indicating that the impact of consumption values on different target customer groups may vary considering their gender and familiarity with apps. Further research needs to be conducted to better understand the possible impact of age, cultural differences, education levels, and special considerations such as visually impaired people. Therefore, this study encourages mobile application designers and developers to raise awareness for the effect of consumption values such as conditional value on their customers' mobile application purchasing behaviors. The possible impact of the consumption values needs to be deeply understood, specifically for the target customer groups, and it should be considered in the software requirements specification (SRS), which is one of the important principles that allow software under consideration for development to function. As a result, a better understanding of consumption values will help developers design and develop better applications by specifying software requirements and marketing strategies.Article Citation - WoS: 13Citation - Scopus: 13A Systematic Approach To Optimizing Energy-Efficient Automated Systems With Learning Models for Thermal Comfort Control in Indoor Spaces(Mdpi, 2023) Erisen, SerdarEnergy-efficient automated systems for thermal comfort control in buildings is an emerging research area that has the potential to be considered through a combination of smart solutions. This research aims to explore and optimize energy-efficient automated systems with regard to thermal comfort parameters, energy use, workloads, and their operation for thermal comfort control in indoor spaces. In this research, a systematic approach is deployed, and building information modeling (BIM) software and energy optimization algorithms are applied at first to thermal comfort parameters, such as natural ventilation, to derive the contextual information and compute the building performance of an indoor environment with Internet of Things (IoT) technologies installed. The open-source dataset from the experiment environment is also applied in training and testing unique black box models, which are examined through the users' voting data acquired via the personal comfort systems (PCS), thus revealing the significance of Fanger's approach and the relationship between people and their surroundings in developing the learning models. The contextual information obtained via BIM simulations, the IoT-based data, and the building performance evaluations indicated the critical levels of energy use and the capacities of the thermal comfort control systems. Machine learning models were found to be significant in optimizing the operation of the automated systems, and deep learning models were momentous in understanding and predicting user activities and thermal comfort levels for well-being; this can optimize energy use in smart buildings.Article Citation - WoS: 5Citation - Scopus: 8Clinical Characteristics of Patients With Intraocular Lens Calcification After Pars Plana Vitrectomy(Mdpi, 2023) Bopp, Silvia; Ozdemir, Huseyin Baran; Aktas, Zeynep; Khoramnia, Ramin; Yildirim, Timur M.; Schickhardt, Sonja; Ozdek, SengulAim: To determine the clinical risk factors that may increase the occurrence of intraocular lens (IOL) calcification in patients who had undergone pars plana vitrectomy (PPV). Methods: The medical records of 14 patients who underwent IOL explantation due to clinically significant IOL opacification after PPV were reviewed. The date of primary cataract surgery, technique and implanted IOL characteristics; the time, cause and technique of PPV; tamponade used; additional surgeries; the time of IOL calcification and explantation; and IOL explantation technique were investigated. Results: PPV had been performed as a combined procedure with cataract surgery in eight eyes and solely in six pseudophakic eyes. The IOL material was hydrophilic in six eyes, hydrophilic with a hydrophobic surface in seven eyes and undetermined in one eye. The endotamponades used during primary PPV were C2F6 in eight eyes, C3F8 in one eye, air in two eyes and silicone oil in three eyes. Two of three eyes underwent subsequent silicone oil removal and gas tamponade exchange. Gas in the anterior chamber was detected in six eyes after PPV or silicone oil removal. The mean interval between PPV and IOL opacification was 20.5 +/- 18.6 months. The mean BCVA in logMAR was 0.43 +/- 0.42 after PPV, which significantly decreased to 0.67 +/- 0.68 before IOL explantation for IOL opacification (p = 0.007) and increased to 0.48 +/- 0.59 after the IOL exchange (p = 0.015). Conclusions: PPV with endotamponades in pseudophakic eyes, particularly gas, seems to increase the risk for secondary IOL calcification, especially in hydrophilic IOLs. IOL exchange seems to solve this problem when clinically significant vision loss occurs.Article Citation - WoS: 21Citation - Scopus: 36Deep Learning-Based Computer-Aided Diagnosis (cad): Applications for Medical Image Datasets(Mdpi, 2022) Kadhim, Yezi Ali; Khan, Muhammad Umer; Mishra, AlokComputer-aided diagnosis (CAD) has proved to be an effective and accurate method for diagnostic prediction over the years. This article focuses on the development of an automated CAD system with the intent to perform diagnosis as accurately as possible. Deep learning methods have been able to produce impressive results on medical image datasets. This study employs deep learning methods in conjunction with meta-heuristic algorithms and supervised machine-learning algorithms to perform an accurate diagnosis. Pre-trained convolutional neural networks (CNNs) or auto-encoder are used for feature extraction, whereas feature selection is performed using an ant colony optimization (ACO) algorithm. Ant colony optimization helps to search for the best optimal features while reducing the amount of data. Lastly, diagnosis prediction (classification) is achieved using learnable classifiers. The novel framework for the extraction and selection of features is based on deep learning, auto-encoder, and ACO. The performance of the proposed approach is evaluated using two medical image datasets: chest X-ray (CXR) and magnetic resonance imaging (MRI) for the prediction of the existence of COVID-19 and brain tumors. Accuracy is used as the main measure to compare the performance of the proposed approach with existing state-of-the-art methods. The proposed system achieves an average accuracy of 99.61% and 99.18%, outperforming all other methods in diagnosing the presence of COVID-19 and brain tumors, respectively. Based on the achieved results, it can be claimed that physicians or radiologists can confidently utilize the proposed approach for diagnosing COVID-19 patients and patients with specific brain tumors.Article Citation - WoS: 176Citation - Scopus: 196Interpolative Reich-Rus Type Contractions on Partial Metric Spaces(Mdpi, 2018) Karapinar, Erdal; Agarwal, Ravi; Aydi, HassenBy giving a counter-example, we point out a gap in the paper by Karapinar (Adv. Theory Nonlinear Anal. Its Appl. 2018, 2, 85-87) where the given fixed point may be not unique and we present the corrected version. We also state the celebrated fixed point theorem of Reich-Rus-Ciric in the framework of complete partial metric spaces, by taking the interpolation theory into account. Some examples are provided where the main result in papers by Reich (Can. Math. Bull. 1971, 14, 121-124; Boll. Unione Mat. Ital. 1972, 4, 26-42 and Boll. Unione Mat. Ital. 1971, 4, 1-11.) is not applicable.
