17 results
Search Results
Now showing 1 - 10 of 17
Article Citation - WoS: 10Citation - Scopus: 10Higher Rates of Cefiderocol Resistance Among Ndm Producing klebsiella Bloodstream Isolates Applying Eucast Over Clsi Breakpoints(Taylor & Francis Ltd, 2023) Isler, Burcu; Vatansever, Cansel; Ozer, Berna; Cinar, Gule; Aslan, Abdullah Tarik; Falconer, Caitlin; Harris, Patrick N. A.BackgroundCefiderocol is generally active against carbapenem-resistant Klebsiella spp. (CRK) with higher MICs against metallo-beta-lactamase producers. There is a variation in cefiderocol interpretive criteria determined by EUCAST and CLSI. Our objective was to test CRK isolates against cefiderocol and compare cefiderocol susceptibilities using EUCAST and CLSI interpretive criteria.MethodsA unique collection (n = 254) of mainly OXA-48-like- or NDM-producing CRK bloodstream isolates were tested against cefiderocol with disc diffusion (Mast Diagnostics, UK). Beta-lactam resistance genes and multilocus sequence types were identified using bioinformatics analyses on complete bacterial genomes.ResultsMedian cefiderocol inhibition zone diameter was 24 mm (interquartile range [IQR] 24-26 mm) for all isolates and 18 mm (IQR 15-21 mm) for NDM producers. We observed significant variability between cefiderocol susceptibilities using EUCAST and CLSI breakpoints, such that 26% and 2% of all isolates, and 81% and 12% of the NDM producers were resistant to cefiderocol using EUCAST and CLSI interpretive criteria, respectively.ConclusionsCefiderocol resistance rates among NDM producers are high using EUCAST criteria. Breakpoint variability may have significant implications on patient outcomes. Until more clinical outcome data are available, we suggest using EUCAST interpretive criteria for cefiderocol susceptibility testing.Article Citation - WoS: 66Citation - Scopus: 89The Potential Medicinal Value of Plants From Asteraceae Family With Antioxidant Defense Enzymes as Biological Targets(Taylor & Francis Ltd, 2015) Koc, Suheda; Isgor, Belgin S.; Isgor, Yasemin G.; Moghaddam, Naznoosh Shomali; Yildirim, OzlemContext: Plants and most of the plant-derived compounds have long been known for their potential pharmaceutical effects. They are well known to play an important role in the treatment of several diseases from diabetes to various types of cancers. Today most of the clinically effective pharmaceuticals are developed from plant-derived ancestors in the history of medicine. Objective: The aim of this study was to evaluate the free radical scavenging activity and total phenolic and flavonoid contents of methanol, ethanol, and acetone extracts from flowers and leaves of Onopordum acanthium L., Carduus acanthoides L., Cirsium arvense (L.) Scop., and Centaurea solstitialis L., all from the Asteraceae family, for investigating their potential medicinal values of biological targets that are participating in the antioxidant defense system such as catalase (CAT), glutathione S-transferase (GST), and glutathione peroxidase (GPx). Materials and methods: In this study, free radical scavenging activity and total phenolic and flavonoid contents of the plant samples were assayed by DPPH, Folin-Ciocalteu, and aluminum chloride colorimetric methods. Also, the effects of extracts on CAT, GST, and GPx enzyme activities were investigated. Results and discussion: The highest phenolic and flavonoid contents were detected in the acetone extract of C. acanthoides flowers, with 90.305 mg GAE/L and 185.43 mg Q/L values, respectively. The highest DPPH radical scavenging was observed with the methanol leaf extracts of C. arvense with an IC50 value of 366 ng/mL. The maximum GPx and GST enzyme inhibition activities were observed with acetone extracts from the flower of C. solstitialis with IC50 values of 79 and 232 ng/mL, respectively.Review Citation - WoS: 18Citation - Scopus: 24A Literature Review on Mhe Selection Problem: Levels, Contexts, and Approaches(Taylor & Francis Ltd, 2015) Saputro, Thomy Eko; Masudin, Ilyas; Rouyendegh (Babek Erdebilli), Babak DaneshvarThis paper presents a review on selection problem of material handling equipment (MHE) and general equipment used in industry area. The issue on MHE is widely paid attention since MHE has contribution on material, good and product accomplishment. Few methods and softwares have been proposed and developed to select the most appropriate MHE for a complex selection problem. Today's high diverisity of MHE categories and types influence the generation of many possible choices which leads to the complexity. In this paper, a further discussion in terms of MHE and equipment including three major points namely level of selection, the context of selection problem and the approaches are served to highlight the complex MHE selection according to the number of possible choices provided, to analyse the consideration for the problem context, and to reveal the superior method for complex MHE selection. Forty-two papers collected from the past study are presented asscociating each point of the discussion.Article Citation - WoS: 17Citation - Scopus: 17A New Outlier Detection Method Based on Convex Optimization: Application To Diagnosis of Parkinson's Disease(Taylor & Francis Ltd, 2021) Taylan, Pakize; Yerlikaya-Ozkurt, Fatma; Bilgic Ucak, Burcu; Weber, Gerhard-WilhelmNeuroscience is a combination of different scientific disciplines which investigate the nervous system for understanding of the biological basis. Recently, applications to the diagnosis of neurodegenerative diseases like Parkinson's disease have become very promising by considering different statistical regression models. However, well-known statistical regression models may give misleading results for the diagnosis of the neurodegenerative diseases when experimental data contain outlier observations that lie an abnormal distance from the other observation. The main achievements of this study consist of a novel mathematics-supported approach beside statistical regression models to identify and treat the outlier observations without direct elimination for a great and emerging challenge in humankind, such as neurodegenerative diseases. By this approach, a new method named as CMTMSOM is proposed with the contributions of the powerful convex and continuous optimization techniques referred to as conic quadratic programing. This method, based on the mean-shift outlier regression model, is developed by combining robustness of M-estimation and stability of Tikhonov regularization. We apply our method and other parametric models on Parkinson telemonitoring dataset which is a real-world dataset in Neuroscience. Then, we compare these methods by using well-known method-free performance measures. The results indicate that the CMTMSOM method performs better than current parametric models.Article Citation - WoS: 1Citation - Scopus: 1On the Accuracy of an Emitter Localization Method Based on Multipath Exploitation in Realistic Scenarios(Taylor & Francis Ltd, 2022) Al Imran, M. A.; Ank, E.; Dalveren, Y.; Tabakcioglu, M. B.; Kara, A.This study aims to evaluate the accuracy of a method proposed for passive localization of radar emitters around irregular terrains with a single receiver in Electronic Support Measures systems. Previously, the authors targeted only the theoretical development of the localization method. In fact, this could be a serious concern in practice since there is no evidence regarding its accuracy under the real data gathered from realistic scenarios. Therefore, an accurate ray-tracing algorithm is adapted to enable the implementation of the method in practice. Then, realistic scenarios are determined based on the geographic information system map generated to collect high-resolution digital terrain elevation data, as well as realistic localization problems for radar emitters. Next, simulations are performed to test the localization method. Thus, the performance of the method is verified for practical implementation in the electronic warfare context for the first time. Lastly, the performance bounds of the method are discussed.Article Citation - WoS: 22Citation - Scopus: 25Facile synthesis of CsPbBr3/PbSe composite clusters(Taylor & Francis Ltd, 2018) Thang Phan Nguyen; Ozturk, Abdullah; Park, Jongee; Sohn, Woonbae; Tae Hyung Lee; Jang, Ho Won; Kim, Soo YoungIn this work, CsPbBr3 and PbSe nanocomposites were synthesized to protect perovskite material from self-enlargement during reaction. UV absorption and photoluminescence (PL) spectra indicate that the addition of Se into CsPbBr3 quantum dots modified the electronic structure of CsPbBr3, increasing the band gap from 2.38 to 2.48 eV as the Cs:Se ratio increased to 1:3. Thus, the emission color of CsPbBr3 perovskite quantum dots was modified from green to blue by increasing the Se ratio in composites. According to X-ray diffraction patterns, the structure of CsPbBr3 quantum dots changed from cubic to orthorhombic due to the introduction of PbSe at the surface. Transmission electron microscopy and X-ray photoemission spectroscopy confirmed that the atomic distribution in CsPbBr3/PbSe composite clusters is uniform and the composite materials were well formed. The PL intensity of a CsPbBr3/PbSe sample with a 1:1 Cs: Se ratio maintained 50% of its initial intensity after keeping the sample for 81 h in air, while the PL intensity of CsPbBr3 reduced to 20% of its initial intensity. Therefore, it is considered that low amounts of Se could improve the stability of CsPbBr3 quantum dots.Article Citation - WoS: 2Citation - Scopus: 2Estimation in the Partially Nonlinear Model by Continuous Optimization(Taylor & Francis Ltd, 2021) Yerlikaya-Ozkurt, Fatma; Taylan, Pakize; Tez, MujganA useful model for data analysis is the partially nonlinear model where response variable is represented as the sum of a nonparametric and a parametric component. In this study, we propose a new procedure for estimating the parameters in the partially nonlinear models. Therefore, we consider penalized profile nonlinear least square problem where nonparametric components are expressed as a B-spline basis function, and then estimation problem is expressed in terms of conic quadratic programming which is a continuous optimization problem and solved interior point method. An application study is conducted to evaluate the performance of the proposed method by considering some well-known performance measures. The results are compared against parametric nonlinear model.Article Shanghai's High-Rise Buildings: Exploring Space Efficiency, Structural Systems, Forms, Materials and Core Designs(Taylor & Francis Ltd, 2026) Aktas, Kurt Orkun; Aslantamer, Ozlem Nur; Aktas, Gozen Guner; Ilgin, Huseyin EmreThis study examines the architectural and structural design considerations influencing space efficiency in Shanghai's high-rise buildings. Understanding space efficiency is significant because it directly affects land-use intensity, economic returns, and sustainability outcomes. The objective of this study is to quantify space efficiency ratios by analyzing the relationships between core types, function, form, and structural systems, and assess temporal and comparative benchmarks for Shanghai within the global context. The novelty lies in its combined focus on architectural and structural determinants of space efficiency, supported by data on 43 high-rise buildings in Shanghai. Methodologically, this study relies on quantitative analysis of Net Floor Area (NFA), Gross Floor Area (GFA), and core ratios, supplemented with comparative evaluation of building forms, materials, and structural systems. The key findings reveal: (1) average space efficiency at 75% with core-to-GFA ratios of 23%, varying between 52-93% and 5-33% respectively; (2) the dominance of prismatic forms supported by composite outriggered frame systems; (3) a decline in efficiency with increasing building height due to larger service cores. Practically, this research highlights opportunities for stakeholders - including architects, engineers, and policymakers - to adopt lightweight materials, prefabrication techniques, and smart building systems that improve space efficiency in future high-rise developments.Article Citation - WoS: 19Citation - Scopus: 21A Hybrid Approach for Selecting Material Handling Equipment in a Warehouse(Taylor & Francis Ltd, 2016) Saputro, Thomy Eko; Rouyendegh (Babek Erdebilli), Babak DaneshvarWarehouse operations are closely related to material handling activities. Loading, unloading, transporting and picking material constitute a huge part of the activities. In order to handle material properly as well as to contribute value to the material, the operator and the environment, utilizing Material Handling Equipment (MHE) is required. The selection of proper MHEs requires great focus since its consideration is linked to mutli-criteria and multi-objective decision making problems. Here, a hybrid method is proposed to address the MHE selection problem. An approach that integrates the entropy based hierarchical fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and Multi-Objective Mixed Integer Linear Programming (MOMILP) is used for seeking the best alternative. The evaluation of alternatives is performed based on both subjective and objective criteria. Subjective weights are derived from a fuzzy Analytic Hierarchy Process (AHP). To deal with objective criteria, the entropy method is adopted to determine the weights, and the integrated weights are also calculated. The alternatives are rated by using fuzzy TOPSIS. For final execution of the selection, an MOMILP model is developed incorporating two goals, namely to minimize the disadvantage of material handling operation and to minimize the total cost of material handling. The AUGMented E-CONtraint method (AUGMECON) is used to solve the model. A case study is given to illustrate the method. The results show the effectiveness of the hybrid method in complex decision making.Article Citation - WoS: 12Citation - Scopus: 13A two-step machine learning approach to predict S&P 500 bubbles(Taylor & Francis Ltd, 2021) Kabran, Fatma Basoglu; Unlu, Kamil DemirberkIn this paper, we are interested in predicting the bubbles in the S&P 500 stock market with a two-step machine learning approach that employs a real-time bubble detection test and support vector machine (SVM). SVM as a nonparametric binary classification technique is already a widely used method in financial time series forecasting. In the literature, a bubble is often defined as a situation where the asset price exceeds its fundamental value. As one of the early warning signals, prediction of bubbles is vital for policymakers and regulators who are responsible to take preemptive measures against the future crises. Therefore, many attempts have been made to understand the main factors in bubble formation and to predict them in their earlier phases. Our analysis consists of two steps. The first step is to identify the bubbles in the S&P 500 index using a widely recognized right-tailed unit root test. Then, SVM is employed to predict the bubbles by macroeconomic indicators. Also, we compare SVM with different supervised learning algorithms by usingk-fold cross-validation. The experimental results show that the proposed approach with high predictive power could be a favourable alternative in bubble prediction.
