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Patient Safety And Quality Care: Time To Focus On Nonventilator Hospital-Acquired Pneumonia, Karen Giuliano Rn, Dian Baker Phd

Research and Education Celebration 2023 - Nursing Research Posters

No abstract provided.

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Taylor: A Magazine For Taylor University Alumni, Parents And Friends (Summer 2023), Taylor University

The Taylor Magazine (1963-Present)

The Summer 2023 edition of Taylor Magazine, published by Taylor University in Upland, Indiana.

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Improved Logical Reasoning Of Language Models Via Differentiable Symbolic Programming, Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing

Machine Learning Faculty Publications

Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. In this work, we tackle this challenge through the lens of symbolic programming. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning. In contrast to works that rely on hand-crafted logic rules, our differentiable symbolic reasoning framework efficiently learns weighted rules and applies semantic loss to further improve LMs. DSR-LM is scalable, interpretable, and allows easy integration of prior knowledge, thereby supporting extensive symbolic programming to robustly derive …

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Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

It is crucial to automatically construct knowledge graphs (KGs) of diverse new relations to support knowledge discovery and broad applications. Previous KG construction methods, based on either crowdsourcing or text mining, are often limited to a small predefined set of relations due to manual cost or restrictions in text corpus. Recent research proposed to use pretrained language models (LMs) as implicit knowledge bases that accept knowledge queries with prompts. Yet, the implicit knowledge lacks many desirable properties of a full-scale symbolic KG, such as easy access, navigation, editing, and quality assurance. In this paper, we propose a new approach of …

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Phase-Aware Adversarial Defense For Improving Adversarial Robustness, Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Deep neural networks have been found to be vulnerable to adversarial noise. Recent works show that exploring the impact of adversarial noise on intrinsic components of data can help improve adversarial robustness. However, the pattern closely related to human perception has not been deeply studied. In this paper, inspired by the cognitive science, we investigate the interference of adversarial noise from the perspective of image phase, and find ordinarily-trained models lack enough robustness against phase-level perturbations. Motivated by this, we propose a joint adversarial defense method: a phase-level adversarial training mechanism to enhance the adversarial robustness on the phase pattern; …

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Uncovering The Spatio-Temporal Impact Of The Covid-19 Pandemic On Shared E-Scooter Usage: A Spatial Panel Model, Farzana Mehzabin Tuli, Arna Nishita Nithila, Suman Mitra

Civil Engineering Faculty Publications and Presentations

This study examines the spatio-temporal effects of the COVID-19 pandemic on shared e-scooter usage by leveraging two years (2019 and 2020) of daily shared micromobility data from Austin, Texas. We employed a series of random effects spatial-autoregressive model with a spatially autocorrelated error (SAC) to examine the differences and similarities in determinants of e-scooter usage during regular and pandemic periods and to identify factors contributing to the changes in e-scooter use during the Pandemic. Model results provided strong evidence of spatial autocorrelation in the e-scooter trip data and found a spatial negative spillover effect in the 2020 model. The key …

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Swing With Me, Daniel Kyle

IPS/BAS 495 Undergraduate Capstone Projects

This is my Capstone Project for IPS introducing my business plan for Swing with Me, a country swing dance business me and my girlfriend Chelsea are going to start here in Ontario!

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How To Best Retrain A Neural Network If We Added One More Input Variable, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Often, once we have trained a neural network to estimate the value of a quantity y based on the available values of inputs x1, ..., xn, we learn to measure the values of an additional quantity that have some influence on y. In such situations, it is desirable to re-train the neural network, so that it will be able to take this extra value into account. A straightforward idea is to add a new input to the first layer and to update all the weights based on the patterns that include the values of the new input. The problem with …

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Effects Of Preservation By Ethanol On Δ13C And Δ15N Of Three Tissues Of The Critically Endangered European Eel Anguilla Anguilla, Rose M. Boardman, Adrian C. Pinder, Adam T. Piper, Roberts C Gutmann, Rosalind M. Wright, John Robert Britton

More Faculty of Science and Engineering Research

The temporal effects of ethanol preservation on the δ13C and δ15N values of tissues excised from European eel Anguilla anguilla were assessed. Preservation significantly enriched 13C values of fin and mucus but not dorsal muscle. The 13C enrichment occurred in the initial 15 days of preservation and was independent of initial eel mass. Tissue preservation effects on δ15N values were negligible. These tissue-specific isotopic shifts should be considered when ethanol-preserved eel samples are used.

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Optimizing Reliability Of Flow Networks Using Rwga Subject To Total Capacity Constraint, S. Alkhalaf, H. S. Ahmed, M. E. Hussein, M. R. Hassan, A. Mikhaylov, E. Mahmoud

Applied Mathematics & Information Sciences

The study of capacity assignment problem for multistate network is an essential and helps in finding the best set of assigned capacities with maximum system reliability. Analyzing the network structure with respect to the system reliability helps in determining the critical nodes. Sufficient capacity should be assigned for each node in the flow network to keep the network operating normally in the case of failure due to maintenance at one or more nodes. Capacity assignment problem aims to decrease the number of critical nodes by searching for the minimum capacity with considering system reliability constraint, it is known as NP-hard …

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Deep Transfer Learning And Image Segmentation For Fruit Recognition, Ahmad Odat, Mohammad Alodat

Applied Mathematics & Information Sciences

This paper is structured in such a way that you can able to develop the fruit recognition web app from scratch with Machine Learning & Flask. The primary objective of this research is to identify plant products in order to estimate their pricing without having to rely on recollection to enabling retail market administration and enhancing the shopping experience for customers. Methods and techniques used in this research was machine learning, deep learning, data analysis, processing, and interpretation, depending on Experimental Methods, Survey Research, Meta-Analysis, and Data Mining to provide a comprehensive and robust analysis of the data. Convolutional neural …

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Binary Classification With Supervised Machine Learning: A Comparative Analysis, Yassine El Aachab, Mohammed Kaicer, Youness Jouilil

Applied Mathematics & Information Sciences

Over the past decade, Machine Learning has become a practical approach for simulating and examining social issues, notably poverty, education, and health diseases. This study compares the performance of various machine learning methods especially Support Vector Machines (SVM), Decision Trees (DT), and Logistic Regression (LR) in predicting poverty status. For this purpose, the present contribution employs a micro dataset which has been extracted from the National Survey on Household Consumption and Expenditure 2013/2014. Several evaluation metrics such as accuracy, precision, Cohen’s Kappa statistic, F1-score, and recall are used to evaluate the models’ outputs. The R results indicate that the three …

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New Perspectives On Predicting Economic Growth In The Presence Of Multicollinearity And Outliers, A. Ebiwonjumi, R. Chifurira, K. Chinhamu

Applied Mathematics & Information Sciences

Nigeria’s economic growth is a tremendous concern to policymakers, economics and scholars because of the inherent challenges of economic characteristics, consequences and contradictions. The Central Bank of Nigeria has implemented several policies such as tightening of monetary policy rate and heavy borrowing for infrastructural development to stimulate economic growth in the past few years. The prediction of economic growth from economic variables poses a challenge due to collinearity among the economic variables. In this study, we predict Nigerian economic growth using internal and external debt, interest and exchange rate as well as trade openness in the presence of multicollinearity and …

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On A Generalized Fractional Integral And Related Methodological Remarks, Miguel Vivas-Cortez, Juan E. Na ́Poles Valde ́S, Shilpi Jain, Praveen Agarwal

Applied Mathematics & Information Sciences

In this paper, we define a generalized fractional integral of order α which are the natural extension of the newly defined k-fractional conformable integrals and they can be reduced to other fractional integrals. Later, the existence of such k-generalized integrals is proved. Finally, discuses some future possibilities.

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Certain Integral Inequalities Associated With The Strongly Harmonic H−Convex Functions, Miguel Vivas-Cortez, Praveen Agarwal, Jorge E. Herna ́Ndez H., Shaher Momani

Applied Mathematics & Information Sciences

We study the concept of strongly harmonically h-convex functions and some examples and properties of them. Here, we develop few inequalities for this new class of functions, specifically these inequalities are: Hermite-Hadamard and Fejer. In addition, we establish some applications of our results to special media of non zero and non negative real numbers.

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Concerning The Inequality Of Hermite-Hadamard Generalized, Miguel Vivas-Cortez, Juan E. Na ́Poles Valde ́S, Praveen Agarwal, Jorge Eliecer Herna ́Ndez

Applied Mathematics & Information Sciences

n this paper, we present new extensions of the Hermite-Hadamard type generalized inequalities for convex functions, within the framework of a generalized operator integral. Results are general in nature.

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Structure A Public-Key Cryptosystem Based On Hss Algebra, Hiba Shakir Salman, Hassan Rashed Yassein

Applied Mathematics & Information Sciences

NTRU (Number theory research unit) is a public key cryptosystem that uses lattice-based cryptography. It is more resistant to assaults than other commonly used public-key cryptosystems, and its performance has been proven to be much better. This study introduces TRUHIB, a commutative variation of the NTRU, as a novel multidimensional public-key cryptosystem. It operates using a novel mathematical structure that consists of two public keys and five private keys. in a algebra called HSS algebra. This new structure has improved the security and sophistication of the public key cryptosystem.

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Assessing The Waste Management Practices In Stone And Marble Industry In Palestine: Practical Implications, Ahmad Ramahi, Yahya Saleh, Ruaa Binsaddig, Rahaf Ghozzi, Bara’ Abu Baker, Abdelraheem Khateeb

Applied Mathematics & Information Sciences

This paper aims at assessing the current status of solid waste management practices in stone and marble industry (SMI) facilities in Palestine. To this end, mixed qualitative and quantitative approaches have been adopted. Morse specifically, both field visits and semi-structured interviews as well as questionnaires have been employed to gather needed data from a randomly selected sample of Palestinian SMI faculties. Such data include facilities’ demographics, their infrastructure and production inputs, solid and liquid waste estimated quantities as well as the current waste management practice. Statistical analyses show that SMI facilities are highly heterogeneous in their profiles and practices. Besides, …

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Modelling And Optimal Control Strategies Of Corruption Dynamics, Temesgen Duressa Keno, Fekadu Mosisa Legesse

Applied Mathematics & Information Sciences

In this study, we presented a non linear deterministic corruption transmission dynamics using optimal control analysis and cost effective strategies. To begin, we demonstrated that in a given set of initial conditions, the model solution is non-negative and bounded. A basic reproductive number is calculated using the corruption-free equilibrium point via the next generation matrix. The linearization and the Lyapunov function are then used to demonstrate how corruption-free equilibrium is both locally and globally stable. The corruption-free equilibrium point is asymptotically stable both locally and globally if the basic reproduction number is less than one; otherwise, an endemic corruption equilibrium …

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The Double Ara-Formable Transform With Applications, Rania Saadeh, Motasem M. Mustafa

Applied Mathematics & Information Sciences

The main goal of this work is to introduce a new double transform named the double ARA-Formable transform (DARA- FT) we introduce the application of the new double transform to various fundamental functions and master properties. Additionally, we state and prove the convolution and existence theorems. Subsequently, by utilizing these findings, different kinds of partial differential equations (PDEs) are solved. We establish novel methods for resolving families of PDEs. With the latter, one can obtain precise answers to some well-known PDEs, including the telegraph equation, the advection-diffusion equation, the Klein-Gordon equation, and others. The results demonstrate the double ARA-Formable transform …

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Optimal Control Strategy On Mathematical Model For The Dynamics Of Mastitis, Basem Abu Izneid, Eshetu Dadi Gurmu, Legesse Lemecha Obsu, Abdisa Shiferaw Melese, Mohammad Kanan, Ahmad Al-Qerem

Applied Mathematics & Information Sciences

Inthisstudy,anonlinearmathematicalmodelforthetransmissiondynamicsofmastitisdiseasesisformulatedandanalyzed. The local and global stability analysis of mastitis-free equilibrium and endemic equilibrium is obtained using the stability theory of differential equation. It was established that the mastitis-free equilibrium is locally stable if the basic reproduction number is less than unity. The endemic equilibrium, which exists only when the basic reproduction number is greater than unity, is globally asymptotically stable. Sensitivity analysis of the reproduction number suggested that the concentration of bacteria in the environment has a high impact on the dynamics of mastitis. Furthermore, an optimal control problem is formulated by applying Pontryagin’s minimum principle with three control strategies, …

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Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour

Applied Mathematics & Information Sciences

Band-pass filters functioning in the near-infrared (IR) range are desired for laser technology, multi-photon fluorescence, and IR imaging applications. In this study, we have designed four band-pass filters in the near Infrared spectrum (900-1200 nm) by vertically stacking different high and low-index materials. The band-pass filters are modelled by Essential Macleod software with different thicknesses. The layer’s thicknesses were optimized in such a way to provide the negligible reflectance and maximum transmission on the front side. All the simulated band-pass filters exhibit high transmittance, but TiO2/Al2O3 and Ta2O5/Al2O3 outperforms other modelled structure in terms of performance due to the better …

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Ara-Sumudu Method For Solving Volterra Partial Integro-Differential Equations, Ahmad Qazza, Rania Saadeh, Shams A. Ahmed

Applied Mathematics & Information Sciences

The major goal of the study, is investigate solving various kinds of Volterra integral equations via via a new method. This article presents a novel double ARA- Sumudu (ARA-S) transformation . this novel approach is implemented to handle some integral equations and partial integro-differential equations. Fundamental characteristics and results related to double ARA-S transformation are investigated including the existence, inverse derivative and the convolution property. Furthermore, to prove the applicability of the presented transform, we discuss the solution of some examples on integral equations using double ARA-S transformation.

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Prediction Of Dislocation Densities In Pure Metals And Alloys, J. Garey Weaver, Enrique Martinez

Chemistry Summer Research Program

No abstract provided.

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Polymersomes As Chemotherapeutic Agents For Non-Invasive Treatment Of Glioblastoma, Tj Fletcher, Molli Garifo, Jessica Larsen, Jessica Larsen

Chemistry Summer Research Program

No abstract provided.

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Federal Environmental Justice Legislation And Regulations, Nadia B. Ahmad

Faculty Scholarship

No abstract provided.

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Digital Marketing Science, Stephanie Cobian

Title III Professional Development Reports

Marketing involves implementing diverse strategies to promote products and services, strengthen market presence, and attain business success. It combines advertising expertise, sales proficiency, and efficient product delivery. While traditional channels like print, TV, and radio remain relevant, the internet has transformed marketing through digital approaches. Digital marketing utilizes websites, social media, search engines, and applications, enabling interactive communication and incorporating customer feedback. It revolutionizes how businesses engage with consumers in a two-way interaction.

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A Universal Unbiased Method For Classification From Aggregate Observations, Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

Machine Learning Faculty Publications

In conventional supervised classification, true labels are required for individual instances. However, it could be prohibitive to collect the true labels for individual instances, due to privacy concerns or unaffordable annotation costs. This motivates the study on classification from aggregate observations (CFAO), where the supervision is provided to groups of instances, instead of individual instances. CFAO is a generalized learning framework that contains various learning problems, such as multiple-instance learning and learning from label proportions. The goal of this paper is to present a novel universal method of CFAO, which holds an unbiased estimator of the classification risk for arbitrary …

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Coco: A Coupled Contrastive Framework For Unsupervised Domain Adaptive Graph Classification, Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian Sheng Hua, Xiao Luo

Machine Learning Faculty Publications

Although graph neural networks (GNNs) have achieved impressive achievements in graph classification, they often need abundant task-specific labels, which could be extensively costly to acquire. A credible solution is to explore additional labeled graphs to enhance unsupervised learning on the target domain. However, how to apply GNNs to domain adaptation remains unsolved owing to the insufficient exploration of graph topology and the significant domain discrepancy. In this paper, we propose Coupled Contrastive Graph Representation Learning (CoCo), which extracts the topological information from coupled learning branches and reduces the domain discrepancy with coupled contrastive learning. CoCo contains a graph convolutional network …

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Issue Assignment Delay Of Early-View Articles: Impact On Subscribers Through An Aggregator Database, Benjamin L. Hockenberry

Lavery Library Faculty/Staff Publications

Journal articles are often made available prior to assignment to an issue, using labels like “early view,” “advanced online publication,” “ahead of print,” “online first,” or “in press.” While making articles available before an issue can speed up the scholarly communications cycle, a negative effect can be observed when full text is delivered through aggregator databases like EBSCO, ProQuest, or Gale products. This study assessed issue assignment delays across 18 journals which have full text aggregated in EBSCO's CINAHL Ultimate. Results show that articles published first in an early-view state are only available in the aggregator after they have been …

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Full-Text Articles in Entire DC Network

Patient Safety And Quality Care: Time To Focus On Nonventilator Hospital-Acquired Pneumonia, Karen Giuliano Rn, Dian Baker Phd

Research and Education Celebration 2023 - Nursing Research Posters

Taylor: A Magazine For Taylor University Alumni, Parents And Friends (Summer 2023), Taylor University

The Taylor Magazine (1963-Present)

Improved Logical Reasoning Of Language Models Via Differentiable Symbolic Programming, Hanlin Zhang, Jiani Huang, Ziyang Li, Mayur Naik, Eric Xing

Machine Learning Faculty Publications

Bertnet: Harvesting Knowledge Graphs With Arbitrary Relations From Pretrained Language Models, Shibo Hao, Bowen Tan, Kaiwen Tang, Bin Ni, Xiyan Shao, Hengzhe Zhang, Eric P. Xing, Zhiting Hu

Machine Learning Faculty Publications

Phase-Aware Adversarial Defense For Improving Adversarial Robustness, Dawei Zhou, Nannan Wang, Heng Yang, Xinbo Gao, Tongliang Liu

Machine Learning Faculty Publications

Uncovering The Spatio-Temporal Impact Of The Covid-19 Pandemic On Shared E-Scooter Usage: A Spatial Panel Model, Farzana Mehzabin Tuli, Arna Nishita Nithila, Suman Mitra

Civil Engineering Faculty Publications and Presentations

Swing With Me, Daniel Kyle

IPS/BAS 495 Undergraduate Capstone Projects

How To Best Retrain A Neural Network If We Added One More Input Variable, Saeid Tizpaz-Niari, Vladik Kreinovich

Departmental Technical Reports (CS)

Effects Of Preservation By Ethanol On Δ13C And Δ15N Of Three Tissues Of The Critically Endangered European Eel Anguilla Anguilla, Rose M. Boardman, Adrian C. Pinder, Adam T. Piper, Roberts C Gutmann, Rosalind M. Wright, John Robert Britton

More Faculty of Science and Engineering Research

Optimizing Reliability Of Flow Networks Using Rwga Subject To Total Capacity Constraint, S. Alkhalaf, H. S. Ahmed, M. E. Hussein, M. R. Hassan, A. Mikhaylov, E. Mahmoud

Applied Mathematics & Information Sciences

Deep Transfer Learning And Image Segmentation For Fruit Recognition, Ahmad Odat, Mohammad Alodat

Applied Mathematics & Information Sciences

Binary Classification With Supervised Machine Learning: A Comparative Analysis, Yassine El Aachab, Mohammed Kaicer, Youness Jouilil

Applied Mathematics & Information Sciences

New Perspectives On Predicting Economic Growth In The Presence Of Multicollinearity And Outliers, A. Ebiwonjumi, R. Chifurira, K. Chinhamu

Applied Mathematics & Information Sciences

On A Generalized Fractional Integral And Related Methodological Remarks, Miguel Vivas-Cortez, Juan E. Na ́Poles Valde ́S, Shilpi Jain, Praveen Agarwal

Applied Mathematics & Information Sciences

Certain Integral Inequalities Associated With The Strongly Harmonic H−Convex Functions, Miguel Vivas-Cortez, Praveen Agarwal, Jorge E. Herna ́Ndez H., Shaher Momani

Applied Mathematics & Information Sciences

Concerning The Inequality Of Hermite-Hadamard Generalized, Miguel Vivas-Cortez, Juan E. Na ́Poles Valde ́S, Praveen Agarwal, Jorge Eliecer Herna ́Ndez

Applied Mathematics & Information Sciences

Structure A Public-Key Cryptosystem Based On Hss Algebra, Hiba Shakir Salman, Hassan Rashed Yassein

Applied Mathematics & Information Sciences

Assessing The Waste Management Practices In Stone And Marble Industry In Palestine: Practical Implications, Ahmad Ramahi, Yahya Saleh, Ruaa Binsaddig, Rahaf Ghozzi, Bara’ Abu Baker, Abdelraheem Khateeb

Applied Mathematics & Information Sciences

Modelling And Optimal Control Strategies Of Corruption Dynamics, Temesgen Duressa Keno, Fekadu Mosisa Legesse

Applied Mathematics & Information Sciences

The Double Ara-Formable Transform With Applications, Rania Saadeh, Motasem M. Mustafa

Applied Mathematics & Information Sciences

Optimal Control Strategy On Mathematical Model For The Dynamics Of Mastitis, Basem Abu Izneid, Eshetu Dadi Gurmu, Legesse Lemecha Obsu, Abdisa Shiferaw Melese, Mohammad Kanan, Ahmad Al-Qerem

Applied Mathematics & Information Sciences

Numerical Design And Optimization Of Near-Infrared Band- Pass Filter, Hafiza Syeeda Faiza, Ghazi Aman Nowsherwan, Basem A. Abu Izneid, Muhammad Azhar, Saira Riaz, Syed Sajjad Hussain, Saira Ikram, Mohsin Khan, Shahzad Naseem, Mohammad Kanan, Ibrahim M. Mansour

Applied Mathematics & Information Sciences

Ara-Sumudu Method For Solving Volterra Partial Integro-Differential Equations, Ahmad Qazza, Rania Saadeh, Shams A. Ahmed

Applied Mathematics & Information Sciences

Prediction Of Dislocation Densities In Pure Metals And Alloys, J. Garey Weaver, Enrique Martinez

Chemistry Summer Research Program

Polymersomes As Chemotherapeutic Agents For Non-Invasive Treatment Of Glioblastoma, Tj Fletcher, Molli Garifo, Jessica Larsen, Jessica Larsen

Chemistry Summer Research Program

Federal Environmental Justice Legislation And Regulations, Nadia B. Ahmad

Faculty Scholarship

Digital Marketing Science, Stephanie Cobian

Title III Professional Development Reports

A Universal Unbiased Method For Classification From Aggregate Observations, Zixi Wei, Lei Feng, Bo Han, Tongliang Liu, Gang Niu, Xiaofeng Zhu, Heng Tao Shen

Machine Learning Faculty Publications

Coco: A Coupled Contrastive Framework For Unsupervised Domain Adaptive Graph Classification, Nan Yin, Li Shen, Mengzhu Wang, Long Lan, Zeyu Ma, Chong Chen, Xian Sheng Hua, Xiao Luo

Machine Learning Faculty Publications

Issue Assignment Delay Of Early-View Articles: Impact On Subscribers Through An Aggregator Database, Benjamin L. Hockenberry

Lavery Library Faculty/Staff Publications