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Book Part Citation - Scopus: 2Novel Covid-19 Recognition Framework Based on Conic Functions Classifier(Springer Science and Business Media Deutschland GmbH, 2022) Karim,A.M.; Mishra,A.The new coronavirus has been declared as a global emergency. The first case was officially declared in Wuhan, China, during the end of 2019. Since then, the virus has spread to nearly every continent, and case numbers continue to rise. The scientists and engineers immediately responded to the virus and presented techniques, devices and treatment approaches to fight back and eliminate the virus. Machine learning is a popular scientific tool and is applied to several medical image recognition problems, involving tumour recognition, cancer detection, organ transplantation and COVID-19 diagnosis. It is proved that machine learning presents robust, fast and accurate results in various medical image recognition problems. Generally, machine learning-based frameworks consist of two stages: feature extraction and classification. In the feature extraction, overwhelmingly unsupervised learning techniques are applied to reduce the input data’s size. This step extracts appropriate features by reducing the computational time and increasing the performance of the classifiers. A classifier is the second step that aims to categorise the input. Within the proposed step, the unsupervised part relies on the feature extraction by using local binary patterns (LBP), followed by feature selection relying on factor analysis technique. The LBP is a kind of visual descriptor, mainly applied for image recognition problem. The aim of using LBP is to analyse the input COVID-19 image and extract salient features. Furthermore, factor analysis is a statistical technique applied to define variability among observed variables in less unnoticed variables named factors. The factor analysis applied to the LBP wavelet aims to select sensitive features from input data (LBP output) and reduce the size input. In the last stage, conic functions classifier is applied to classify two sets of data, categorising the extracted features by using LBP and factor analysis as positive or negative COVID-19 cases. The proposed solution aims to diagnose COVID-19 by using LBP and factor analysis, based on conic functions classifier. The conic functions classifier presents remarkable results compared with these popular classifiers and state-of-the-art studies presented in the literature. © 2022, Springer Nature Switzerland AG.Conference Object Citation - Scopus: 1An Investigation of the Effect of Free-Players on Global Cooperative Behavior in a Spatial Prisoner’s Dilemma Game Environment(Springer Science and Business Media Deutschland GmbH, 2025) Efe, B.; Çerkez, E.; Kılıç, H.In this research, we introduced the concept of Free-Player who rejects to play the dictated rational strategy Defect of the original Prisoner’s Dilemma game setup. Then, we investigated whether Free-Players have any impact on the persistent and stable cooperative behavior of the Players in the context of two dimensional spatial Prisoner’s Dilemma game environment. In simulations, two different Player strategy update setups are considered: State-based Majority and Payoff-driven Stochastic. The results for both setups showed that Free-Players have impact on global cooperative behavior of the system. According to the obtained State-based Majority setup results, the increased number of Free-Players has no direct regulative impact on the control of global cooperative behavior of the proposed system. For the Payoff-driven Stochastic strategy update setup, the increased number of Free-Players has an observable regulative impact on the control of global cooperative behavior of the proposed system. However, the net effect of Free-Players on the cooperativeness of the environment was only in the range 0.007 < Net_Coop(α, β) < 0.036 while the attained cooperation ratio results are mostly not sensitive to the initial cooperation ratios. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.Article Differences of Microbial Growth and Biofilm Formation Among Periprosthetic Joint Infection-Causing Species: an Animal Study(Springer Science and Business Media Deutschland GmbH, 2025) Ertan, M.B.; Ayduğan, M.Y.; Evren, E.; İnanç, İ.; Erdemli, E.; Erdemli, B.Purpose: The most frequently used surgical procedures for periprosthetic joint infections (PJIs) are debridement, antibiotics, and implant retention (DAIR), as well as single- or two-stage revision arthroplasty. The choice of surgery is made depending on the full maturation of the biofilm layer. The purpose of this study was to evaluate the biofilm formation and microbial growth using common PJI-causing agents and compare its development on the implant surface. Methods: The in vivo study was performed using 40 Sprague–Dawley rats divided into five groups (n = 8/group): Staphylococcus aureus, Staphylococcus epidermidis, Pseudomonas aeruginosa, Candida albicans, and control. Six standard titanium alloy discs were placed into the subcutaneous air pouches of the interscapular areas of the rats. After the inoculation of microorganisms, disc and soft tissue cultures were collected at 2-week intervals for 6 weeks, and the microbial load and the microscopic appearance of the biofilm were compared. Results: The disc samples from the S. aureus group had the highest infection load at all time points; however, in soft tissue samples, this was only observed at week 4 and 6. Electron microscopic images showed no distinctive differences in the biofilm structures between the groups. Conclusion: S. aureus microbial burden was significantly higher in implant cultures at week 2 compared to other PJI-causing agents examined. These results may explain the higher failure rate seen if the DAIR procedure was performed at < 3–4 weeks after the PJI symptom onset and support the observation that DAIR may not be effective against PJIs caused by S. aureus. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.
