187 research outputs found
sj-png-2-tct-10.1177_15330338231159223 - Supplemental material for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study
No full textSupplemental material, sj-png-2-tct-10.1177_15330338231159223 for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study by Ali Zamanvaziri, Mahboobeh Meshkat, Soroush Alazmani, Sepideh Khaleghi and Mehrdad Hashemi in Technology in Cancer Research & Treatment</p
sj-png-1-tct-10.1177_15330338231159223 - Supplemental material for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study
No full textSupplemental material, sj-png-1-tct-10.1177_15330338231159223 for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study by Ali Zamanvaziri, Mahboobeh Meshkat, Soroush Alazmani, Sepideh Khaleghi and Mehrdad Hashemi in Technology in Cancer Research & Treatment</p
sj-png-3-tct-10.1177_15330338231159223 - Supplemental material for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study
No full textSupplemental material, sj-png-3-tct-10.1177_15330338231159223 for Targeted PEGylated Chitosan Nano-complex for Delivery of Sodium Butyrate to Prostate Cancer: An In Vitro Study by Ali Zamanvaziri, Mahboobeh Meshkat, Soroush Alazmani, Sepideh Khaleghi and Mehrdad Hashemi in Technology in Cancer Research & Treatment</p
Feature engineering for microstructure-property mapping in organic photovoltaics
No full textLinking the highly complex morphology of organic photovoltaic (OPV) thin films to their charge transport properties is critical for achieving high performance material system that serves as a cost-efficient approach for energy harvesting. In this paper, a novel unsupervised feature engineering framework is developed and used to establish reduced-order structure-property linkages for OPV films. This framework takes advantage of digital image processing algorithms to identify the salient material features of OPVs undergoing the charge transport phenomenon. These material states are then used to obtain a low-dimensional representation of OPV microstructures via 2-point spatial correlations and principal component analysis. It is found that in addition to the material PC scores, two distance-based metrics are required to complete the microstructure quantification of complex OPVs. A localized version of the Gaussian process (laGP) is then used to link the material PC scores as well as the two distance-based metrics to the short-circuit current of OPVs. It is demonstrated that the unsupervised feature engineering framework presented in this paper in conjunction with the laGP can lead to high-fidelity and accurate data-driven structure-property linkages for OPV films.This is a pre-print of the article Hashemi, Sepideh, Baskar Ganapathysubramanian, Stephen Casey, Ji Su, and Surya R. Kalidindi. "Feature engineering for microstructure-property mapping in organic photovoltaics." arXiv preprint arXiv:2111.01897v1. (2021). Copyright 2021 The Author(s). CC BY-NC-ND 4.0. Posted with permission
Fast Genetic Algorithm For Feature Selection — A Qualitative Approximation Approach
No full textWe propose a two-stage surrogate-assisted evolutionary approach to address the computational issues arising from using Genetic Algorithm (GA) for feature selection in a wrapper setting for large datasets. The proposed approach involves constructing a lightweight qualitative meta-model by sub-sampling data instances and then using this meta-model to carry out the feature selection task. We define "Approximation Usefulness" to capture the necessary conditions that allow the meta-model to lead the evolutionary computations to the correct maximum of the fitness function. Based on our procedure we create CHCQX a Qualitative approXimations variant of the GA-based algorithm CHC (Cross generational elitist selection, Heterogeneous recombination and Cataclysmic mutation). We show that CHCQX converges faster to feature subset solutions of significantly higher accuracy, particularly for large datasets with over 100K instances. We also demonstrate the applicability of our approach to Swarm Intelligence (SI), with results of PSOQX, a qualitative approximation adaptation of the Particle Swarm Optimization (PSO) method. A GitHub repository with the complete implementation is available2. This paper for the Hot-off-the-Press track at GECCO 2023 summarizes the original work published at [3].References[1] Mohammed Ghaith Altarabichi, Yuantao Fan, Sepideh Pashami, Peyman Sheikholharam Mashhadi, and Sławomir Nowaczyk. 2021. Extracting invariant features for predicting state of health of batteries in hybrid energy buses. In 2021 ieee 8th international conference on data science and advanced analytics (dsaa). IEEE, 1–6.[2] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2021. Surrogate-assisted genetic algorithm for wrapper feature selection. In 2021 IEEE Congress on Evolutionary Computation (CEC). IEEE, 776–785.[3] Mohammed Ghaith Altarabichi, Sławomir Nowaczyk, Sepideh Pashami, and Peyman Sheikholharam Mashhadi. 2023. Fast Genetic Algorithm for feature selection—A qualitative approximation approach. Expert systems with applications 211 (2023), 118528.© 2023 Copyright held by the owner/author(s).</p
Spaces of contestation: the everyday experiences of ten African migrants in Cape Town
No full textIncludes bibliographical references.Xenophobia in South Africa is so overt that it has take a covert form. The 'xenocide' events that took place in 2008 were called xenophobic acts. It is the recurrent denialism of xenophobia on an everyday basis that this project has explored through the narrative accounts of ten African migrants in Cape Town. The lived everyday experiences of ten African migrants have brought forward the central argument of this thesis. From the data, it is evident that as a reponse to everyday pressures of prejudices and xenophobia in social and physical spaces, African migrants have developed mutable, unsettled and vagrant identities in order to cope with everyday low level violence. This argument emerged as four key stressors have been identified as the components of a more substantial explanation of xenophobia in South Africa. The four key components are: the enforcement of identity (national and group), the demarcation of spaces of belonging, the experiences of economic insecurity, and lastly a 'culture of violence' in South Africa. This thesis argues that these four stressors are the result of an on-going active process of xenophobic attitudes
Evaluation of business intelligence maturity level in Iranian banking industry
No full textBusiness intelligence (BI) is a managerial concept which helps managers in the organizations to manage information and make factual decisions. Some have introduced Business Intelligence as a process of turning data into information and then into knowledge. This concept has become a popular trend for businesses interested in adding value to their decision making processes.(Golfarelli et al., 2004) In addition measurement of Business Intelligence readiness/maturity is considered a critical issue. Business intelligence like software development is a process, which expressed in terms of components such as artifacts and workflows. In software engineering, the Capability Maturity Model Integrated (CMMI) developed to define different levels of software process maturity. We draw upon the concepts underlying CMMI to define different maturity levels for a business intelligence process. The study examines the maturity level of Business Intelligence activities as well as the future outlook concerning Business Intelligence in the Iranian banks. The research will also examine key areas of improvement in Business Intelligence operations, benefits gained from Business Intelligence as well as the strength point of Iranian banking industry in using Business Intelligence. Further, a model for business intelligence chose with the factors influencing business intelligence. Then, the questionnaire designed based on CMMI process for testing business intelligence process. Totally 99 valid questionnaire where gathered and by means of factor analysis methods both data and model were evaluated. In addition, the regression tests done in order to test the ability of model. Furthermore, the level of maturity of Iranian banking organization measured and introduced. Finally, the research limitations and some recommendation for further researches offered.Validerat; 20101217 (root
Sublinear algorithms for massive data problems
No full textThesis: Ph. D., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (pages 227-244).In this thesis, we present algorithms and prove lower bounds for fundamental computational problems in the models that address massive data sets. The models include streaming algorithms, sublinear time algorithms, property testing algorithms, sublinear query time algorithms with preprocessing, or computing small summaries for large data. More precisely, we study the following problems. The (Approximate) Nearest Neighbor problem models the task of searching among a large data set of objects. Given a data set of n points in a high dimensional space, its goal is to search for the closest point in the data set to a given query point, in sublinear time, and by suitably preprocessing the data. This problem has numerous applications in image and video databases, information retrieval, clustering, and many others. In these applications, the points model the objects in a large data set, and their closeness measure similarity between the objects. However, for the purpose of many applications, the basic formulation of Nearest Neighbor as described, encounters several challenges which we address in this thesis: we show how to deal with the case where the data is corrupted or incomplete, how to handle multiple related queries, and how to handle a data set of more complex objects rather than simple points. Next, we show a general approach for solving massive data problems. We introduce the notion of Composable Coresets, defined as small summaries of multiple data sets that can be aggregated together to summarize the whole data. We show how to compute such summaries for several clustering problems, and at the same time, demonstrate that no such summaries are possible for other natural problems such as maximum coverage. Finally, we study the Set Cover problem in alternate sublinear models: streaming algorithms (where one makes a small number of passes over the data using small storage), and sublinear time algorithms (where one computes the answer without reading the whole input). We present tight approximation algorithms for the Set Cover problem in both of these models. In this thesis, we introduce theoretical problems and concepts that model computational issues arising in databases, computer vision and other areas. Most of the presented algorithms are simple and practical to implement.by Sepideh Mahabadi.Ph. D
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