Türk Bilgini Profesör Abdulkadir İnan'ın Hayatı

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Programmable active droplet generation enabled by integrated pneumatic micropumps

A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of the Provost, Dr. Jeffrey Vitter; Vice Chancellor for Research & Graduate Studies, Dr. Steve Warren; Acting KUMC Executive Vice Chancellor, Dr. Steve Stites; and Dr. Paul Terranova, KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml. This journal is The Royal Society of Chemistry 2013. When the author signs the exclusive Licence to Publish for a journal article, he/she retains certain rights that may be exercised without reference to the RSC. He/she may: Reproduce/republish portions of the article (including the abstract) Photocopy the article and distribute such photocopies and distribute copies of the PDF of the article that the RSC makes available to the corresponding author of the article upon publication of the article for personal or professional use only, provided that any such copies are not offered for sale. Persons who receive or access the PDF mentioned above must be notified that this may not be further made available or distributed. Adapt the article and reproduce adaptations of the article for any purpose other than the commercial exploitation of a work similar to the original Reproduce, perform, transmit and otherwise communicate the article to the public in spoken presentations (including those which are accompanied by visual material such as slides, overheads and computer projections)In this work we have investigated the integrated diaphragm micropump as an active fluidic control approach for the on-demand generation of droplets with precisely defined size, frequency and timing. In contrast to valve-actuated devices that only modulate the flow of the dispersed phase being continuously injected, this integrated micropump allows the combination of fluidic transport and modulation to achieve active control of droplet generation. A distinct characteristic of this method compared to the valve modulated droplet formation processes is that it enables independent control of droplet generation frequency by adjusting the pumping frequency and droplet size by flow conditions. We also demonstrated the generation of complex droplet patterns through programming the pumping configurations and the application to multi-volume digital PCR for precise and quantitative detection of genetic targets. Overall, our results suggest that the pump-based droplet microfluidics provide a robust platform for programmable active droplet generation which could facilitate the development of high-performance chemical and biological assays

Electrical Impedance Tomography: A Fair Comparative Study on Deep Learning and Analytic-based Approaches

Electrical Impedance Tomography (EIT) is a powerful imaging technique with diverse applications, e.g., medical diagnosis, industrial monitoring, and environmental studies. The EIT inverse problem is about inferring the internal conductivity distribution of an object from measurements taken on its boundary. It is severely ill-posed, necessitating advanced computational methods for accurate image reconstructions. Recent years have witnessed significant progress, driven by innovations in analytic-based approaches and deep learning. This review explores techniques for solving the EIT inverse problem, focusing on the interplay between contemporary deep learning-based strategies and classical analytic-based methods. Four state-of-the-art deep learning algorithms are rigorously examined, harnessing the representational capabilities of deep neural networks to reconstruct intricate conductivity distributions. In parallel, two analytic-based methods, rooted in mathematical formulations and regularisation techniques, are dissected for their strengths and limitations. These methodologies are evaluated through various numerical experiments, encompassing diverse scenarios that reflect real-world complexities. A suite of performance metrics is employed to assess the efficacy of these methods. These metrics collectively provide a nuanced understanding of the methods' ability to capture essential features and delineate complex conductivity patterns. One novel feature of the study is the incorporation of variable conductivity scenarios, introducing a level of heterogeneity that mimics textured inclusions. This departure from uniform conductivity assumptions mimics realistic scenarios where tissues or materials exhibit spatially varying electrical properties. Exploring how each method responds to such variable conductivity scenarios opens avenues for understanding their robustness and adaptability

Parameter Identification by Deep Learning of a Material Model for Granular Media

Classical physical modelling with associated numerical simulation (model-based), and prognostic methods based on the analysis of large amounts of data (data-driven) are the two most common methods used for the mapping of complex physical processes. In recent years, the efficient combination of these approaches has become increasingly important. Continuum mechanics in the core consists of conservation equations that -- in addition to the always necessary specification of the process conditions -- can be supplemented by phenomenological material models. The latter are an idealized image of the specific material behavior that can be determined experimentally, empirically, and based on a wealth of expert knowledge. The more complex the material, the more difficult the calibration is. This situation forms the starting point for this work's hybrid data-driven and model-based approach for mapping a complex physical process in continuum mechanics. Specifically, we use data generated from a classical physical model by the MESHFREE software to train a Principal Component Analysis-based neural network (PCA-NN) for the task of parameter identification of the material model parameters. The obtained results highlight the potential of deep-learning-based hybrid models for determining parameters, which are the key to characterizing materials occurring naturally, and their use in industrial applications (e.g. the interaction of vehicles with sand).Comment: arXiv admin note: text overlap with arXiv:2212.0313

Structures and programme supports for Creativity, Action, Service in the International Baccalaureate Diploma Programme: An implementation study in Turkey

This qualitative multiple-case study examined the implementation of an experiential learning component of an academic curriculum in six high schools in Turkey. Structures and supports that influenced programme implementation were examined using an implementation framework adapted from Durlak and Dupre. The study describes how the experiential learning programme is implemented. Findings indicate four areas that need ongoing attention: (1) supports for programme coordinators, (2) teacher training, (3) integration with academics and (4) school cultures that better support experiential learning. © 2016, © The Author(s) 2016

Deep Learning Methods for Partial Differential Equations and Related Parameter Identification Problems

Recent years have witnessed a growth in mathematics for deep learning--which seeks a deeper understanding of the concepts of deep learning with mathematics and explores how to make it more robust--and deep learning for mathematics, where deep learning algorithms are used to solve problems in mathematics. The latter has popularised the field of scientific machine learning where deep learning is applied to problems in scientific computing. Specifically, more and more neural network architectures have been developed to solve specific classes of partial differential equations (PDEs). Such methods exploit properties that are inherent to PDEs and thus solve the PDEs better than standard feed-forward neural networks, recurrent neural networks, or convolutional neural networks. This has had a great impact in the area of mathematical modeling where parametric PDEs are widely used to model most natural and physical processes arising in science and engineering. In this work, we review such methods as well as their extensions for parametric studies and for solving the related inverse problems. We equally proceed to show their relevance in some industrial applications

Contour Connection Method for automated identification and classification of landslide deposits

Landslides are a common hazard worldwide that result in major economic, environmental and social impacts. Despite their devastating effects, inventorying existing landslides, often the regions at highest risk of reoccurrence, is challenging, time-consuming, and expensive. Current landslide mapping techniques include field inventorying, photogrammetric approaches, and use of bare-earth (BE) lidar digital terrain models (DTMs) to highlight regions of instability. However, many techniques do not have sufficient resolution, detail, and accuracy for mapping across landscape scale with the exception of using BE DTMs, which can reveal the landscape beneath vegetation and other obstructions, highlighting landslide features, including scarps, deposits, fans and more. Current approaches to landslide inventorying with lidar to create BE DTMs include manual digitizing, statistical or machine learning approaches, and use of alternate sensors (e.g., hyperspectral imaging) with lidar. This paper outlines a novel algorithm to automatically and consistently detect landslide deposits on a landscape scale. The proposed method is named as the Contour Connection Method (CCM) and is primarily based on bare earth lidar data requiring minimal user input such as the landslide scarp and deposit gradients. The CCM algorithm functions by applying contours and nodes to a map, and using vectors connecting the nodes to evaluate gradient and associated landslide features based on the user defined input criteria. Furthermore, in addition to the detection capabilities, CCM also provides an opportunity to be potentially used to classify different landscape features. This is possible because each landslide feature has a distinct set of metadata – specifically, density of connection vectors on each contour – that provides a unique signature for each landslide. In this paper, demonstrations of using CCM are presented by applying the algorithm to the region surrounding the Oso landslide in Washington (March 2014), as well as two 14,000 hectare DTMs in Oregon, which were used as a comparison of CCM and manually delineated landslide deposits. The results show the capability of the CCM with limited data requirements and the agreement with manual delineation but achieving the results at a much faster time