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Publication Open Access 3D microprinting of iron platinum nanoparticle-based magnetic mobile microrobots(Wiley, 2021) Giltinan, Joshua; Sridhar, Varun; Bozüyük, Uğur; Sheehan, Devin; Department of Mechanical Engineering; Sitti, Metin; Faculty Member; Department of Mechanical Engineering; School of Medicine; College of Engineering; 297104Wireless magnetic microrobots are envisioned to revolutionize minimally invasive medicine. While many promising medical magnetic microrobots are proposed, the ones using hard magnetic materials are not mostly biocompatible, and the ones using biocompatible soft magnetic nanoparticles are magnetically very weak and, therefore, difficult to actuate. Thus, biocompatible hard magnetic micro/nanomaterials are essential toward easy-to-actuate and clinically viable 3D medical microrobots. To fill such crucial gap, this study proposes ferromagnetic and biocompatible iron platinum (FePt) nanoparticle-based 3D microprinting of microrobots using the two-photon polymerization technique. A modified one-pot synthesis method is presented for producing FePt nanoparticles in large volumes and 3D printing of helical microswimmers made from biocompatible trimethylolpropane ethoxylate triacrylate (PETA) polymer with embedded FePt nanoparticles. The 30 mu m long helical magnetic microswimmers are able to swim at speeds of over five body lengths per second at 200Hz, making them the fastest helical swimmer in the tens of micrometer length scale at the corresponding low-magnitude actuation fields of 5-10mT. It is also experimentally in vitro verified that the synthesized FePt nanoparticles are biocompatible. Thus, such 3D-printed microrobots are biocompatible and easy to actuate toward creating clinically viable future medical microrobots.
Publication Metadata only 3D model retrieval using probability density-based shape descriptors(IEEE Computer Society, 2009) Akgul, Ceyhun Burak; Sankur, Buelent; Schmitt, Francis; Department of Computer Engineering; Yemez, Yücel; Faculty Member; Department of Computer Engineering; College of Engineering; 107907We address content-based retrieval of complete 3D object models by a probabilistic generative description of local shape properties. The proposed shape description framework characterizes a 3D object with sampled multivariate probability density functions of its local surface features. This density-based descriptor can be efficiently computed via kernel density estimation (KDE) coupled with fast Gauss transform. The nonparametric KDE technique allows reliable characterization of a diverse set of shapes and yields descriptors which remain relatively insensitive to small shape perturbations and mesh resolution. Density-based characterization also induces a permutation property which can be used to guarantee invariance at the shape matching stage. As proven by extensive retrieval experiments on several 3D databases, our framework provides state-of-the-art discrimination over a broad and heterogeneous set of shape categories.Publication Metadata only A front tracking method for direct numerical simulation of evaporation process in a multiphase system(Academic Press Inc Elsevier Science, 2017) N/A; N/A; Department of Mechanical Engineering; Irfan, Muhammad; Muradoğlu, Metin; PhD Student; Faculty Member; Department of Mechanical Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 46561A front-tracking method is developed for the direct numerical simulation of evaporation process in a liquid-gas multiphase system. One-field formulation is used to solve the flow, energy and species equations in the framework of the front tracking method, with suitable jump conditions at the interface. Both phases are assumed to be incompressible; however, the divergence-free velocity field condition is modified to account for the phase-change/mass-transfer at the interface. Both temperature and species gradient driven evaporation/phase-change processes are simulated. For the species gradient driven phase change process, the Clausius-Clapeyron equilibrium relation is used to find the vapor mass fraction and subsequently the evaporation mass flux at the interface. A number of benchmark cases are first studied to validate the implementation. The numerical results are found to be in excellent agreement with the analytical solutions for all the studied cases. The methods are then applied to study the evaporation of a static as well as a single and two droplets systems falling in the gravitational field. The methods are demonstrated to be grid convergent and the mass is globally conserved during the phase change process for both the static and moving droplet cases.Publication Metadata only A front-tracking method for computation of interfacial flows with soluble surfactants(Academic Press Inc Elsevier Science, 2008) Tryggvason, Gretar; Department of Mechanical Engineering; Muradoğlu, Metin; Faculty Member; Department of Mechanical Engineering; College of Engineering; 46561A finite-difference/front-tracking method is developed for computations of interfacial flows with soluble surfactants. The method is designed to solve the evolution equations of the interfacial and bulk surfactant concentrations together with the incompressible Navier-Stokes equations using a non-linear equation of state that relates interfacial surface tension to surfactant concentration at the interface. The method is validated for simple test cases and the computational results are found to be in a good agreement with the analytical solutions. The method is then applied to study the cleavage of drop by surfactant-a problem proposed as a model for cytokinesis [H.P. Greenspan, On the dynamics of cell cleavage, J. Theor. Biol. 65(l) (1977) 79; H.P. Greenspan, On fluid-mechanical simulations of cell division and movement, J. Theor. Biol., 70(l) (1978) 125]. Finally the method is used to model the effects of soluble surfactants on the motion of buoyancy-driven bubbles in a circular tube and the results are found to be in a good agreement with available experimental data.Publication Open Access A method for estimating stock-out-based substitution rates by using point-of-sale data(Taylor _ Francis, 2009) Öztürk, Ömer Cem; Department of Business Administration; Tan, Barış; Karabatı, Selçuk; Faculty Member; Faculty Member; Department of Business Administration; College of Administrative Sciences and Economics; 28600; 38819Empirical studies in retailing suggest that stock-out rates are quite high in many product categories. Stock-outs result in demand spillover, or substitution, among items within a product category. Product assortment and inventory management decisions can be improved when the substitution rates are known. In this paper, a method is presented to estimate product substitution rates by using only Point-Of-Sale (POS) data. The approach clusters POS intervals into states where each state corresponds to a specific substitution scenario. Then available POS data for each state is consolidated and the substitution rates are estimated using the consolidated information. An extensive computational analysis of the proposed substitution rate estimation method is provided. The computational analysis and comparisons with an estimation method from the literature show that the proposed estimation method performs satisfactorily with limited information.Publication Metadata only A multitask multiple kernel learning formulation for discriminating early- and late-stage cancers(Oxford University Press (OUP), 2020) N/A; N/A; Department of Industrial Engineering; Rahimi, Arezou; Gönen, Mehmet; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 237468Motivation: Genomic information is increasingly being used in diagnosis, prognosis and treatment of cancer. The severity of the disease is usually measured by the tumor stage. Therefore, identifying pathways playing an important role in progression of the disease stage is of great interest. Given that there are similarities in the underlying mechanisms of different cancers, in addition to the considerable correlation in the genomic data, there is a need for machine learning methods that can take these aspects of genomic data into account. Furthermore, using machine learning for studying multiple cancer cohorts together with a collection of molecular pathways creates an opportunity for knowledge extraction. Results: We studied the problem of discriminating early- and late-stage tumors of several cancers using genomic information while enforcing interpretability on the solutions. To this end, we developed a multitask multiple kernel learning (MTMKL) method with a co-clustering step based on a cutting-plane algorithm to identify the relationships between the input tasks and kernels. We tested our algorithm on 15 cancer cohorts and observed that, in most cases, MTMKL outperforms other algorithms (including random forests, support vector machine and single-task multiple kernel learning) in terms of predictive power. Using the aggregate results from multiple replications, we also derived similarity matrices between cancer cohorts, which are, in many cases, in agreement with available relationships reported in the relevant literature.Publication Open Access A nonuniform spatial rain attenuation model for troposcatter communication links(Institute of Electrical and Electronics Engineers (IEEE), 2015) Dinç, Ergin; Akan, Özgür Barış; PhD Student; Faculty Member; College of EngineeringTroposcatter communication can be used as a communication medium for beyond-line-of-sight (BLOS) links. However, wave propagation in troposphere shows significant dependence on hydro-meteors: especially rain. Therefore, the main motivation of this paper is to develop a rain attenuation model for troposcatter communications, which can model nonuniform multiple rain cells for the first time. At the end, we present simulation results for the amount of rain loss and the distribution of maximum data rates under rain in troposcatter links.Publication Open Access A novel RIS-assisted modulation scheme(Institute of Electrical and Electronics Engineers (IEEE), 2021) Yang, Liang; Meng, Fanxu; Hasna, Mazen O.; Department of Electrical and Electronics Engineering; Başar, Ertuğrul; Faculty Member; Department of Electrical and Electronics Engineering; College of Engineering; 149116In this work, in order to achieve higher spectrum efficiency, we propose a reconfigurable intelligent surface (RIS)-assisted multi-user communication uplink system. Different from previous work in which the RIS only optimizes the phase of the incident users’ signal, we propose the use of the RIS to create a virtual constellation diagram to transmit the data of an additional user. We focus on the two-user case and develop a tight approximation for the probability distribution function (PDF) of the minimum distance between constellation points of both users. Then, based on the proposed statistical distribution, we derive the analytical expressions of the average bit error rate of the considered two users. The letter also shows the trade off between the performance of two users as a function of the proposed phase shift at the RIS.Publication Metadata only A strategy based on protein-protein interface motifs may help in identifying drug off-targets(American Chemical Society (ACS), 2012) Nussinov, Ruth; Department of Chemical and Biological Engineering; Department of Computer Engineering; Keskin, Özlem; Gürsoy, Attila; Ergin, Billur Çelebi; Faculty Member; Faculty Member; Teaching Faculty; Department of Chemical and Biological Engineering; Department of Computer Engineering; The Center for Computational Biology and Bioinformatics (CCBB); College of Engineering; College of Engineering; 26605; 8745; 261792Networks are increasingly used to study the impact of drugs at the systems level. From the algorithmic standpoint, a drug can "attack" nodes or edges of a protein-protein interaction network In this work, we propose a new network strategy, "The Interface Attack", based on protein-protein interfaces. Similar interface architectures can occur between unrelated proteins. Consequently, in principle, a drug that binds to one has a certain probability of binding to others. The interface attack strategy simultaneously removes from the network all interactions that consist of similar interface motifs. This strategy is inspired by network pharmacology and allows inferring potential off-targets. We introduce a network model that we call "Protein Interface and Interaction Network (P2IN)", which is the integration of protein-protein interface structures and protein interaction networks. This interface based, network organization clarifies which protein pairs have structurally similar interfaces and which proteins may compete to bind the same surface region. We built the P2IN with the p53 signaling network and performed network robustness analysis. We show that (1) "hitting" frequent interfaces (a set of edges distributed around the network) might be as destructive as eleminating high degree proteins (hub nodes), (2) frequent interfaces are not always topologically critical elements in the network, and (3) interface attack may reveal functional changes in the system better than the attack of single proteins. In the off target detection case study, we found that drugs blocking the interface between CDK6 and CDKN2D may also affect the interaction between CDK4 and CDKN2D.Publication Metadata only A survey on energy efficiency in P2P systems: file distribution, content streaming, and epidemics(Association for Computing Machinery (ACM), 2015) Brienza, Simone; Masoumzadeh, Seyed Saeid; Hlavacs, Helmut; Anastasi, Giuseppe; Department of Computer Engineering; Department of Computer Engineering; Cebeci, Sena Efsun; Özkasap, Öznur; Researcher; Faculty Member; Department of Computer Engineering; College of Engineering; College of Engineering; N/A; 113507Several Peer-to-Peer (P2P) protocols and applications have been developed to allow file distribution/sharing, video and music streaming, and data and information dissemination. These P2P systems are regularly used by a large number of users, both in desktop and mobile environments, and they generate a remarkable portion of the overall Internet traffic. However, many common P2P protocols and applications were designed neglecting the energy problem. In fact, they often require always-on devices in order to work properly, thus producing significant energy waste. The problem is even more relevant in the mobile context, since the battery lifetime of mobile devices is limited. Therefore, energy efficiency in P2P systems is a highly debated topic in the literature. New P2P approaches-more energy efficient than traditional client/server solutions-have been proposed. In addition, several improvements to existing P2P protocols have been introduced to reduce their energy consumption. In this article, we present a general taxonomy to classify state-of-the-art approaches to the energy problem in P2P systems and applications. Then, we survey themain solutions available in the literature, focusing on three relevant classes of P2P systems and applications: file sharing/distribution, content streaming, and epidemics. Furthermore, we outline open issues and provide future research guidelines for each class of P2P systems.