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Review Citation - WoS: 75Citation - Scopus: 122Hybrid 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.Review Citation - WoS: 5Citation - Scopus: 5Smart Hydrogels in Lab-On (loc) Applications(Elsevier, 2024) Tevlek, Atakan; Cretin, Esin Akbay; Çetin, Esin AkbayLaboratory on-chip (LOC) technology facilitates numerous developments across diverse disciplines, such as medicine, tissue engineering, materials science, biomedical engineering, and biotechnology. Moreover, the potential applications appear boundless when LOC is integrated with intelligent hydrogels. In the literature, however, there are few accounts of the vast array of developments and applications that this combination has spawned. These new systems, which integrate smart hydrogels and LOC and thus significantly advance cuttingedge technology, have been thoroughly examined in this review. The functions of smart hydrogels in LOC applications were described and subsequently the developed intelligent hydrogels were classified as multiresponsive, thermo-responsive, pH-responsive, and stimuli-responsive (light, magnetic, and electric). Following this, details regarding tunable properties for LOC functions were provided, followed by a discussion of the fabrication processes and integration of these intelligent hydrogels into LOC systems, including their benefits and drawbacks. Following that, current literature examples of LOC systems utilizing these intelligent hydrogels for biosensing, 3D culture, tissue engineering, controlled release, personalized medicine, drug delivery, analyte enrichment, and organ-on-a-chip applications were presented. Following the presentation of state-of-the-art information regarding smart hydrogel characterization techniques, present challenges and prospective prospects were discussed.Review Citation - WoS: 3Citation - Scopus: 8Machine Learning for Sustainable Reutilization of Waste Materials as Energy Sources - a Comprehensive Review(Taylor & Francis inc, 2024) Peng, Wei; Sadaghiani, Omid Karimi; Karimi Sadaghiani, OmidThis work reviews Machine Learning applications in the sustainable utilization of waste materials as energy source so that analysis of the past works exposed the lack of reviewing study. To solve it, the origin of waste biomass raw materials is explained, and the application of Machine Learning in this section is scrutinized. After analysis of numerous papers, it is concluded that Machine Learning and Deep Learning are widely utilized in waste biomass production areas to enhance the quality and quantity of production, improve the predictions, diminish the losses, as well as increase storage and transformation conditions. The positive effects and application with the utilized algorithms and other effective information are collected in this work for the first time. According to the statistical analysis, in 20% out of the studies conducted about the application of Machine Learning and Deep Learning in waste biomass raw materials, Artificial Neural Network (ANN) algorithm has been applied. Afterward, the Super Vector Machine (SVM) and Random Forest (RF) are the second and third most-utilized algorithms applied in 15% and 14% of studies. Meanwhile, 27% of studies focused on the applications of Machine Learning and Deep Learning in the Forest wastes.Review Citation - WoS: 10Citation - Scopus: 10Latest Developments in Engineered Skeletal Muscle Tissues for Drug Discovery and Development(Taylor & Francis Ltd, 2023) Ostrovidov, Serge; Ramalingam, Murugan; Bae, Hojae; Orive, Gorka; Fujie, Toshinori; Shi, Xuetao; Kaji, HirokazuIntroductionWith the advances in skeletal muscle tissue engineering, new platforms have arisen with important applications in biology studies, disease modeling, and drug testing. Current developments highlight the quest for engineering skeletal muscle tissues with higher complexity . These new human skeletal muscle tissue models will be powerful tools for drug discovery and development and disease modeling.Areas coveredThe authors review the latest advances in in vitro models of engineered skeletal muscle tissues used for testing drugs with a focus on the use of four main cell culture techniques: Cell cultures in well plates, in microfluidics, in organoids, and in bioprinted constructs. Additional information is provided on the satellite cell niche.Expert opinionIn recent years, more sophisticated in vitro models of skeletal muscle tissues have been fabricated. Important developments have been made in stem cell research and in the engineering of human skeletal muscle tissue. Some platforms have already started to be used for drug testing, notably those based on the parameters of hypertrophy/atrophy and the contractibility of myotubes. More developments are expected through the use of multicellular types and multi-materials as matrices . The validation and use of these models in drug testing should now increase.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, Roohallah; Abbasi Habashi, SoheilaSevere 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.Review Analysis of Different Methods of Suppressing Generator Noise Reaching Indoor Noise(2022) Öz, Hatice Mehtap; Koçyiğit, Filiz Bal; Köse, Ercan; Buluklu, Hatice MehtapThe noise which occurs during the operation of the auxiliary power units is a disturbing factor, although its level varies depending on the environment. This high level of noise in occupational areas can cause not only health but also accidents risks. While silencers are mostly preferred for active noise control, acoustic foam or textile products are preferred according to passive control methods. In this review, the noises produced by the generators were examined and the studies in the literature on suppressing these noises were evaluated by making comparative analyzes. New recommendations have been developed according to the results of the comparative analysis. In case the sampled natural gas generator is closed with 2 mm thick plywood and a steel framed box covered with an acoustic sponge, the sound pressure level is approximately 30 dB in low frequency diesel + electric generators using a silencer according to active control methods. In measurements; It was observed that it decreased from 93.2 to 88.4 dB. While materials such as an acoustic sponge, which are preferred in passive control methods, are open-celled and porous structures, it is advantageous to have sound absorption capacity, but it can be a disadvantage due to its synthetic content.Review Citation - WoS: 26Citation - Scopus: 40Principles of Reverse Electrodialysis and Development of Integrated-Based System for Power Generation and Water Treatment: a Review(Walter de Gruyter Gmbh, 2022) Othman, Nur Hidayati; Kabay, Nalan; Guler, EnverReverse electrodialysis (RED) is among the evolving membrane-based processes available for energy harvesting by mixing water with different salinities. The chemical potential difference causes the movement of cations and anions in opposite directions that can then be transformed into the electrical current at the electrodes by redox reactions. Although several works have shown the possibilities of achieving high power densities through the RED system, the transformation to the industrial-scale stacks remains a challenge particularly in understanding the correlation between ion-exchange membranes (IEMs) and the operating conditions. This work provides an overview of the RED system including its development and modifications of IEM utilized in the RED system. The effects of modified membranes particularly on the psychochemical properties of the membranes and the effects of numerous operating variables are discussed. The prospects of combining the RED system with other technologies such as reverse osmosis, electrodialysis, membrane distillation, heat engine, microbial fuel cell), and flow battery have been summarized based on open-loop and closed-loop configurations. This review attempts to explain the development and prospect of RED technology for salinity gradient power production and further elucidate the integrated RED system as a promising way to harvest energy while reducing the impact of liquid waste disposal on the environment.Review Citation - WoS: 7Citation - Scopus: 10Monkeypox: a Comprehensive Review of Virology, Epidemiology, Transmission, Diagnosis, Prevention, Treatment, and Artificial Intelligence Applications(Shaheed Beheshti University of Medical Sciences and Health Services, 2024) Rahmani,E.; Bayat,Z.; Farrokhi,M.; Karimian,S.; Zahedpasha,R.; Sabzehie,H.; Farrokhi,M.Monkeypox (Mpox), an uncommon zoonotic Orthopoxvirus, is commonly manifested by blisters on the skin and has a mortality rate of approximately 0-10%. Approximately two decades after the cessation of global smallpox vaccination, the number of confirmed cases of Mpox has been growing, making it the most common Orthopoxvirus infection. Therefore, in this narrative review, we aimed to shed light on recent advancements in the pathophysiology, transmission routes, epidemiology, manifestations, diagnosis, prevention, and treatment of Mpox, as well as the application of artificial intelligence (AI) methods for predicting this disease. The clinical manifestations of Mpox, including the onset of symptoms and dermatologic characteristics, are similar to those of the infamous smallpox, but Mpox is clinically milder. Notably, a key difference between smallpox and Mpox is the high prevalence of lymphadenopathy. Human-to-human, animal-to-human, and animal-to-animal transmission are the three main pathways of Mpox spread that must be considered for effective prevention, particularly during outbreaks. PCR testing, as the preferred method for diagnosing Mpox infection, can enhance early detection of new cases and thereby improve infection control measures. JYNNEOS and ACAM2000 are among the vaccines most commonly recommended for the prevention of Mpox. Brincidofovir, Cidofovir, and Tecovirimat are the primary treatments for Mpox cases. Similar to other viral infections, the best approach to managing Mpox is prevention. This can, in part, be achieved through measures such as reducing contact with individuals displaying symptoms, maintaining personal safety, and adhering to practices commonly used to prevent sexually transmitted infections. © This open-access article distributed under the terms of the Creative Commons Attribution NonCommercial 3.0 License (CC BY-NC 3.0).Review Citation - WoS: 13Citation - Scopus: 14Current Surgical Techniques for the Management of Pediatric Glaucoma: a Literature Review(Frontiers Media Sa, 2023) Aktas, Zeynep; Ikiz, Gokcen Deniz Gulpinar; Gulpinar Ikiz, Gokcen Deniz; Surgical Sciences; Surgical SciencesPediatric glaucoma surgery is challenging due to its diverse and complex pathophysiology, altered anterior segment anatomy, greater potential for failure, and complications compared to adult patients. Moreover, numerous challenges are associated with long-term postoperative management. Thus, when dealing with childhood glaucoma, it is important to consider the potential complications in addition to the benefits of each intervention. The purpose of this article is to review recently published literature to shed light on the most recent surgical techniques for the safe and effective treatment of childhood glaucoma. Current literature shows that goniotomy and trabeculotomy are the first choices for the management of primary congenital glaucoma. Although older children with phakic eyes seem to benefit from trabeculectomy with adjunctive mitomycin C, it carries a long-term risk of bleb-related endophthalmitis. Glaucoma drainage devices may be preferred for patients with secondary or refractory glaucoma. However, hypotony or tube-related complications are common and encountered more often in children than in adults. Cyclodestructive procedures are also an option for cases in which filtering surgery has failed, but they can also be used as a temporizing measure to reduce the rate of complications in high-risk patients. However, their outcomes can be unpredictable, in terms of efficiency and complications. Finally, minimally invasive glaucoma surgery (MIGS) as the sole alternative treatment or as an adjunctive surgical procedure is a relatively new path for pediatric patients.Review Benlik Karmaşıklığı ve İyilik Hali: Kısa Bir Değerlendirme(2023) Uslu, Gülçin Akbaş; Akbas, GulcinBenlik karmaşıklığı, benliğin birbiriyle örtüşmeyen yönleri olarak tanımlanır. Linville'in öncü araştırması, benlik karmaşıklığının iyilik halindeki rolüne dikkat çekmiştir. Temel öneri, benlik karmaşıklığının strese karşı bir tampon vazifesi görebileceğidir; benliğin bir yönü tehdit edildiğinde, benliğin zarar görmemiş diğer yönleri bireyin öz benliğini koruyacaktır. Linville'in önermelerini takiben, birçok araştırmacı, benlik karmaşıklığının, depresyon ve duygusal sıkıntı gibi çeşitli iyilik hali çıktıları üzerindeki rolünü test etmiştir. Bu çalışmaların bulguları benlik karmaşıklığının işlevleri açısından karışıktır. Benlik karmaşıklığının olumlu sonuçları olduğunu destekleyen bulgular olsa da bazı araştırmalar benlik karmaşıklığının iyilik hali üzerindeki olumsuz ve külfetli etkisini ortaya koymuştur. Farklı araştırmalar arasındaki tutarsızlık, ölçüm problemleri ve araştırmacıların benlik karmaşıklığını nasıl ele aldığı ile ilgili olabilir. Bu yazı, benlik karmaşıklığıyla ilgili temel bulgularını ele almakta ve benlik karmaşıklığının tampon etkisinin gösterdiği koşulları tartışmaktadır.
