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R-Orani, Covid-19 Tedavisinde Akut Karaciğer Hasarinin Bir Göstergesi Olabilir Mi?
No full textObjective: The aim of the study was to determine whether the
R-ratio could be an indicator in COVID-19 patients.
Material and Method: Alkaline phosphatase (ALP) and alanine
aminotransferase (ALT) were measured in the first blood serum of
all patienAmaç: Çalışmanın amacı, COVID-19 hastalarında R oranının bir
gösterge olup olamayacağını belirlemektir.
Gereç ve Yöntem: Tüm hastaların (n=314) ilk kan serumunda
alkalen fosfataz (ALP) ve alanin aminotransferaz (ALT) ölçülerek
R d
Conceptualizing norm fusion through environmental rights
No full textWhat happens when environmental protection is framed using human rights language? Here, a new type of norm change - norm fusion - is conceptualized. Linking norms derived from different issue areas, it is realized through continuous use of strategic frameEnvironmental Studies; Political ScienceEnvironmental Sciences & Ecology; Government & La
Expertise comparison among product design students: a cross-sectional analysis
Get PDFProduct design expertise has mostly been studied in relation to problem-solving and the act of designing. In this paper, we approach the topic from another perspective and explore the differences in product perception of students from different education levels. We conceptualize product perception as a representation of critical thinking towards designed objects and professional assessment/understanding of artifacts. Our aim is to evaluate how students’ product perception change over the years of undergraduate product design education. Data was collected through students’ written product evaluations of a ball-point pen. 41 first-year, 29 second-year, 33 third-year, and 26 fourth-year undergraduate product design students participated in the study. We analyzed students’ product evaluations through initial and focused coding. Our findings indicate a shift from ordinary to professional sense-making between the second- and third-year students. There are three main points that define the professional sense-making of students: a dependence on subjectivity, the significance attributed to users, and better synthetic capabilities that are built around form, material, manufacturing, and detailing relationships
Correlation structure among countries or industries and its implications for international diversification and portfolio management
Get PDFMulti-objective advisory system for arrhytmia classification
No full textThe study proposes the best electrocardiography (ECG) arrhythmia classification features suited to application needs by using multi-objective approach. The wavelet transform (WT) is successful for ECG classification. Also, the combination of features obtained from different coefficients of different wavelets provides higher performance rate than individual wavelet. However, most of the feature selection algorithm focuses attention on one objective such as accuracy or to the number of features for a real time system. In this study, different solutions were proposed that will increase the classification performance on three different objectives such as positive predictive value (PPV), accuracy and number of selected features. The wavelet type and level that best reflect the 4 different ECG arrhythmia types were searched by using Multi-Objective Evoltionary Algorithm (MOEA). Multilayer perceptron (MLP) was preferred as a fitness function. The non-dominant sequencing genetic algorithm II (NSGA-II) was used and the algorithm ran many times with different seed values. The preferred solutions meeting the preference criteria were examined in detail. The highest accuracy and PPV rate obtained was 97,82% and 94.94%, respectively with 24 features. Moreover, it has been observed that some of the features obtained from an individual wavelet type have a low contribution to the classification performance and some of them can outweigh. To illustrate that the combination of features obtained from different level coefficients of different wavelet types provides more successful discrimination in ECG arrhythmias and to provide feature sets according to the requested metric values are the some of the main contributions of this study
Quality Assurance for Operating Room Illumination through Lean Six Sigma
Get PDFSince every mistake made in the healthcare industry can cause permanent harm or even death, some fundamental requirements should be considered when planning and designing operating rooms. Lighting is one of the most important ergonomic factors, especially for operating rooms. This paper investigates the integration of the lean approach and six sigma in measuring the efficiency of LED technology, which can also be regarded as a factor affecting the operating room efficiency. Measurement System Analysis (MSA) and Gage Control methods were applied to determine measurement variability in operating room illumination measurement process. Repeatability and reproducibility (%R&R) was found 12.89%. After calibration, %R&R value was found 8.21%, which implies that calibration helped reduce variability
Clear-water scour depth prediction in long channel contractions: Application of new hybrid machine learning algorithms
No full textScour depth prediction and its prevention is one of the most important issues in channel and waterway design. However the potential for advanced machine learning (ML) algorithms to provide models of scour depth has yet to be explored. This study provides the first quantification of the predictive power of a range of standalone and hybrid machine learning models. Using previously collected scour depth data from laboratory flume experiments, the performance of five types of recently developed standalone machine learning techniques - the Isotonic Regression (ISOR), Sequential Minimal Optimization (SMO), Iterative Classifier Optimizer (ICO), Locally Weighted learning (LWL) and Least Median of Squares Regression (LMS) - are assessed, along with their hybrid versions with Dagging (DA) and Random Subspace (RS) algorithms. The main findings are five-fold. First, the DA-ICO model had the highest prediction power. Second, the hybrid models had a higher prediction power than standalone models. Third, all algorithms underestimated the maximum scour depth, except DA-ICO which predicted scour depth almost perfectly. Fourth, scour depth was most sensitive to densimetric particle Froude number followed by the non-dimensionalized contraction width, flow depth within the contraction, sediment geometric standard deviation, approach flow velocity and median grain size. Fifth, most of the algorithms performed best when all the input parameters were involved in the building of the model. An important exception was the best performing model that required only four input parameters: densimetric particle Froude number, non-dimensionalized contraction width, flow depth within the contraction and sediment geometric standard deviation. Overall the results revealed that hybrid machine learning algorithms provide more accurate predictions of scour depth than empirical equations and traditional ML-algorithms. In particular, the DA-ICO model not only created the most accurate predictions but also used the fewest easily and readily measured input parameters. Thus this type of model could be of real benefit to practicing engineers required to estimate maximum scour depth when designing in-channel structures
Multi-zone optimisation of high-rise buildings using artificial intelligence for sustainable metropolises. Part 2: Optimisation problems, algorithms, results, and method validation
Get PDFHigh-rise building optimisation is becoming increasingly relevant owing to global population growth and urbanisation trends. Previous studies have demonstrated the potential of high-rise optimisation but have been focused on the use of the parameters of single floors for the entire design; thus, the differences related to the impact of the dense surroundings are not taken into consideration. Part 1 of this study presents a multi-zone optimisation (MUZO) methodology and surrogate models (SMs), which provide a swift and accurate prediction for the entire building design; hence, the SMs can be used for optimisation processes. Owing to the high number of parameters involved in the design process, the optimisation task remains challenging. This paper presents how MUZO can cope with an enormous number of parameters to optimise the entire design of high-rise buildings using three algorithms with an adaptive penalty function. Two design scenarios are considered for quad-grid and diagrid shading devices, glazing type, and building-shape parameters using the setup, and the SMs developed in part 1. The optimisation part of the MUZO methodology reported satisfactory results for spatial daylight autonomy and annual sunlight exposure by meeting the Leadership in Energy and Environmental Design standards in 19 of 20 optimisation problems. To validate the impact of the methodology, optimised designs were compared with 8748 and 5832 typical quad-grid and diagrid scenarios, respectively, using the same design parameters for all floor levels. The findings indicate that the MUZO methodology provides significant improvements in the optimisation of high-rise buildings in dense urban areas
A proposed sustainable and digital collection and classification center model to manage e-waste in emerging economies
No full textPurpose This study aims to propose an electronic waste collection and classification system to enhance social, environmental and economic sustainability by integrating data-driven technologies in emerging economies. Design/methodology/approach GM (1, 1) mComputer Science, Interdisciplinary Applications; Information Science & Library Science; ManagementComputer Science; Information Science & Library Science; Business & Economic