29 research outputs found
Prediction of compressibility parameters of the soils using artificial neural network
No full textThe compression index and recompression index are one of the important compressibility parameters to determine the settlement calculation for fine-grained soil layers. These parameters can be determined by carrying out laboratory oedometer test on undisturbed samples; however, the test is quite time-consuming and expensive. Therefore, many empirical formulas based on regression analysis have been presented to estimate the compressibility parameters using soil index properties. In this paper, an artificial neural network (ANN) model is suggested for prediction of compressibility parameters from basic soil properties. For this purpose, the input parameters are selected as the natural water content, initial void ratio, liquid limit and plasticity index. In this model, two output parameters, including compression index and recompression index, are predicted in a combined network structure. As the result of the study, proposed ANN model is successful for the prediction of the compression index, however the predicted recompression index values are not satisfying compared to the compression index
Enhancing Drone Network Resilience: Investigating Strategies Fori> K/I>-connectivity Restoration
No full textDrones have recently become more popular due to technological improvements that have made them useful in many other industries, including agriculture, emergency services, and military operations. Coordination of communication amongst drones is often required for the efficient performance of missions. With an emphasis on building robust k-connected networks and restoration procedures, this paper investigates the relevance of connection in drone swarms. Specifically, we tackle the k-connectivity restoration problem, which aims to create k-connected networks by moving the drones as little as possible. We propose four novel approaches, including an integer programming model, an integer programming-based heuristic approach, a node converging heuristic, and a cluster moving heuristic. Through extensive measurements taken from various drone networking setups, we provide a comparative analysis of the proposed approaches. Our evaluations reveal that the drone movements produced by the integer programming-based heuristics are nearly the same as the original mathematical formulation, whereas the other heuristics are favorable in terms of execution time.Mustafa Asci, Vahid Khalilpour Akram, Orhan Dagdeviren and Bulent Tavli express their gratitude to The Scientific and Technological Research Council of Turkey (TUBITAK) for providing grant no 121E500.Scientific and Technological Research Council of Turkey (TUBITAK) [121E500
Two population-based optimization algorithms for minimum weight connected dominating set problem
No full textMinimum weight connected dominating set (MWCDS) is a very important NP-Hard problem used in many applications such as backbone formation, data aggregation, routing and scheduling in wireless ad hoc and sensor networks. Population-based approaches are very useful to solve NP-Hard optimization problems. In this study, a hybrid genetic algorithm (HGA) and a population-based iterated greedy (PBIG) algorithm for MWCDS problem are proposed. To the best of our knowledge, the proposed algorithms are the first population-based algorithms to solve MWCDS problem on undirected graphs. HGA is a steady-state procedure which incorporates a greedy heuristic with a genetic search. PBIG algorithm refines the population by partially destroying and greedily reconstructing individual solutions. We compare the performance of the proposed algorithms with other greedy heuristics and brute force methods through extensive simulations. We show that our proposed algorithms perform very well in terms of MWCDS solution quality and CPU time. (C) 2017 Elsevier B.V. All rights reserved.Scientific and Technical Research Council of Turkey (TUBITAK)Turkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [BIDEB 2211-C]The authors would like to thank the anonymous reviewers for their careful reading of the manuscript, insightful comments and suggestions that helped to improve the quality of this paper. We thank to Dr. Aybars Ugur for suggestions and guidance that are very helpful for us. We also thank Scientific and Technical Research Council of Turkey (TUBITAK) for the BIDEB 2211-C program for the PhD scholarship
Genetic ablation of lymphocytes and cytokine signaling in nonobese diabetic mice prevents diet-induced obesity and insulin resistance
No full textFull author list omitted for brevity. For full list of authors see article.
Co-author Sezin Dagdeviren is a doctoral student in the Interdisciplinary Graduate Program in the Graduate School of Biomedical Sciences (GSBS) at UMass Medical School.Obesity is characterized by a dysregulated immune system, which may causally associate with insulin resistance and type 2 diabetes. Despite widespread use of nonobese diabetic (NOD) mice, NOD with severe combined immunodeficiency (scid) mutation (SCID) mice, and SCID bearing a null mutation in the IL-2 common gamma chain receptor (NSG) mice as animal models of human diseases including type 1 diabetes, the underlying metabolic effects of a genetically altered immune system are poorly understood. For this, we performed a comprehensive metabolic characterization of these mice fed chow or after 6 wk of a high-fat diet. We found that NOD mice had approximately 50% less fat mass and were 2-fold more insulin sensitive, as measured by hyperinsulinemic-euglycemic clamp, than C57BL/6 wild-type mice. SCID mice were also more insulin sensitive with increased muscle glucose metabolism and resistant to diet-induced obesity due to increased energy expenditure ( approximately 10%) and physical activity ( approximately 40%) as measured by metabolic cages. NSG mice were completely protected from diet-induced obesity and insulin resistance with significant increases in glucose metabolism in peripheral organs. Our findings demonstrate an important role of genetic background, lymphocytes, and cytokine signaling in diet-induced obesity and insulin resistance.Interdisciplinary Graduate Progra
Histology Laboratory Guide Book
No full textHistology is an important science of visualisation that appeared in medical education consisting many various cells, tissues, organs of human organisms. The histological structure and functional relationships of these various cells, tissues, and organs are considered throughout the book. The purpose of this book is to enable the student to use time efficiently in the laboratory and to understand the histochemical stainings in relation to their cellular biological activity with their functional relationships. Furthermore; the book can prepare them to understand the pathological changes of the diseases. Each laboratory sections with the micrographs and brief explanations, emphasize the basic components of cells, tissues and organs. The micrographs of the slides are from the works of laboratory technicians, research assistants, specialist and academic staffs in Hacettepe University Faculty of Medicine, Department of Histology and Embryology.
In this edition; we would like to emphasize the respect to founder of our department, first female Dean in Turkey Prof.Dr. ilhan Kerse, and special thanks to emeritus academic members of our department Prof.Dr. Ulken Ors, Prof.Dr. Aysel $eftalioglu, Prof.Dr. Esin A§an, Prof.Dr. Atilla Dagdeviren, Assoc.Prof.Dr. Ye§im Ugur, Prof.Dr. Nur Cakar in generating the laboratory slide sources and previous editions of this book
A Pure Genetic Energy-Efficient Backbone Formation Algorithm for Wireless Sensor Networks in Industrial Internet of Things
No full textIndustry 4.0 envisions the utilization of Industrial Internet of Things (IIoT) to increase the efficiency and productivity in industrial automation and manufacturing processes. To interconnect various objects, wireless sensor networks (WSNs) are indispensable technologies located in the communication layer of IIoTs. Backbone formation is an important approach to achieve primitive operations such as data aggregation, routing and scheduling in energy-efficient WSNs. One of the fundamental backbone structures is the connected dominating set (CDS) which has been extensively studied by researchers. Although CDS provides a suitable infrastructure for relaying the sensed data packets, node energies are not primary concern during CDS construction phase. To achieve this, nodes are assigned weight values which are taken as a function of their resident energies. Weighted CDS (WCDS) problem is to find a set of nodes with minimum total weight such that every node in network is in the dominating set or a neighbor of at least one element of the dominating set. WCDS provides an energy-efficient backbone which leads to prolong the network lifetime. WCDS problem is in NP-Hard complexity class, so designing polynomial time heuristics is of utmost importance. To this aim, a pure genetic algorithm is proposed for the minimum WCDS problem on WSNs modeled as undirected graph in this paper. Each member of the population used in the algorithm is represented by a chromosome of bits where each bit indicates the corresponding node is in WCDS or not. Fitness value of a member is calculated by considering its total weight as well as the connectivity status of dominators. We analyze the time complexity of the proposed algorithm and show that our algorithm performs well in terms of total weight of the dominators, count of the dominators and the iteration count with respect to graph size, connection probability and maximum energy. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG
Ferroelectric/piezoelectric flexible mechanical energy harvesters and stretchable epidermal sensors for medical applications
No full textMultifunctional sensing capability, ‘unusual’ formats with flexible/stretchable designs, rugged lightweight construction, and self-powered operation are desired attributes for electronics that directly interface with the human body. The collective results in this dissertation suggest utility in a variety of sensors and energy harvesting components, with lightweight construction, attractive mechanical properties and potential for implementation over large areas, with promising application in unusual bio-integrated electronics, such as self-powered cardiac pacemakers, skin-mounted blood pressure sensors, modulus sensors and skin cancer detection bio-patches. For these and related applications, unusual electronics provide the capability of intimate and conformal integration with a variety of substrates on biological tissues. These systems can be twisted, folded, stretched/flexed and wrapped onto curviliniar surfaces without damage or significant alteration in operation.
In this dissertation, the application of ferroelectric/piezoelectric materials and patterning techniques for ‘unusual’ electronics, with an emphasis on bio-integrated systems were demonstrated. Overall, the results suggest that the various sensor capabilities could be valuable for a range of applications in continuous self-powered health/wellness monitoring and clinical medicine.Item withdrawn by Mark Zulauf ([email protected]) on 2014-12-01T14:42:54Z
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Activation antigens during the proliferative and secretory phases of endometriurn and early-pregnancy decidua
No full textBackground. Clarifying the normal distribution of activation antigens will contribute to database construction studies of monoclonal-antibody-based therapies in endometrial disorders. Methods: In this study, endometrial tissue samples obtained during proliferative and secretory phases and decidual samples of early pregnancies were immunostained by the monoclonal antibodies anti-CD26, anti-CD30, anti-CD70, anti-CD71, and anti-CD98 using the indirect immunoperoxidase method. Results: CD26 is expressed on the glandular epithelium in the endometrium and decidua. Endothelial CD26 is expressed less in the decidua when compared to the endometrium. CD30 is strongly expressed by decidual cells. It is only weakly expressed on endometrial and decidual vessels. Glandular and endothelial CD70 expression is mainly seen in the proliferative phase of the menstrual cycle. Glandular CD71 expression is less in the decidua when compared to the endometrium. Its expression on stromal cells is more in the secretory phase of the menstrual cycle and in early pregnancy deciduae. It is expressed on endometrial vessels but not on decidual vessels. Glandular CD98 is expressed more in the decidua when compared to the endometrium. This antigen exists on endometrial lymphocytes. It is strongly expressed on the endothelium in the endometrium and decidua. Conclusion: It seems that CD26 and CD70 are not involved in the functions of endometrial and decidual stromal cells. CD30 and CD71 are thought to be involved in decidualization. Absence of activation antigens other than CD98 on lymphocytes indicated an antigenic profile for large granular lymphocytes that is different from regular lymphocytes. Copyright (c) 2006 S. Karger AG, Basel
A comprehensive decision framework with interval valued type-2 fuzzy AHP for evaluating all critical success factors of e-learning platforms
No full textThe COVID 19 pandemic not only affected our health and social life in many aspects, but it also changed the classical way of training in classrooms and education preferences of society. As a solution various e-learning platforms were developed and preferred by many educational institutions where the individuals had the opportunity to try the advantages of e-learning platforms. Since the COVID-19 pandemic is neither the first nor the last epidemic, e-learning attracts more attention than ever before and the need for e-learning platforms is expected to be more in the near future. Thus it is necessary to define all critical success factors determining the efficiency of e-learning systems. E-learning platforms have advantages as well as disadvantages and comparisons involve uncertainties and qualitative assessments. A systematic approach should be used to determine the platforms' dimensions, features and weights of critical criteria. The motivation of our study is to determine the weights of all critical success criteria and offer a reliable method for evaluating e-learning platforms. In this study, the interval type-2 fuzzy Analytical Hierarchy Process was utilized to compare critical success factors of e-learning platforms. This is the most comprehensive study considering all critical success factors of e-learning platforms as an Multi Criteria decision Making problem, where 11 criteria and 106 sub-criteria were defined, evaluated and prioritized. This study provides an acceptable rationale for evaluations of e-learning platforms and the results of this study can be used in real-world performance evaluations
