Digital Commons Network^™

Artificial Intelligence And Machine Learning In Cancer Pain: A Systematic Review, Vivian Salama, Brandon Godinich, Yimin Geng, Laia Humbert-Vidan, Laura Maule, Mohamed A Naser, Abdallah S R Mohamed, Clifton D Fuller, Amy C Moreno

Faculty, Staff and Student Publications

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines.

Results: Forty four studies from 2006 to 2023 were included. Nineteen studies used …

3d Gamma Analysis Between Treatment Plans For Nominally Beam-Matched Medical Linear Accelerators Using Pymedphys, Fada Guan, William Donahue, Simon Biggs, Matthew Jennings, Emily Draeger, Yuenan Wang, David J Carlson, Zhe Chen, Dae Yup Han

Faculty, Staff and Student Publications

Beam-matched linear accelerators (linacs) enable flexible patient scheduling and efficient treatment delivery in the event of unexpected machine downtime. The purpose of this study was to test the feasibility of 3D gamma index as an additional metric beyond standard measurement-based comparisons for more efficient evaluation of treatment plans between linacs with nominally matched beam models to ensure safe patient transfer. Seventeen 3D conformal radiotherapy (3DCRT) plans and thirty-six volumetric-modulated radiation therapy (VMAT) plans for different disease sites were selected from the original linac. An in-house script was used to automatically create new plans for the target linac and calculate dose …

Wirelessly Actuated Microfluidic Pump And Valve For Controlled Liquid Delivery In Dental Implants, Yilan Xu, Honglu Lin, Boyang Xiao, Hutomo Tanoto, Joel Berinstein, Simon Young, Xiaoguang Dong

Faculty, Staff and Student Publications

Enabling minimally invasive and precise control of liquid release in dental implants is crucial for therapeutic functions such as delivering antibiotics to prevent biofilm formation, infusing stem cells to promote osseointegration, and administering other biomedicines. However, achieving controllable liquid cargo release in dental implants remains challenging due to the lack of wireless and miniaturized fluidic control mechanisms. Here wireless miniature pumps and valves that allow remote activation of liquid cargo delivery in dental implants, actuated and controlled by external magnetic fields (< 65 mT), are reported. A magnet-screw mechanism in a fluidic channel to function as a piston pump, alongside a flexible magnetic valve designed to open and close the fluidic channel, is proposed. The mechanisms are showcased by storing and releasing of liquid up to 52 µL in a dental implant. The liquid cargos are delivered directly to the implant-bone interface, a region traditionally difficult to access. On-demand liquid delivery is further showed by a metal implant inside both dental phantoms and porcine jawbones. The mechanisms are promising for controllable liquid release after implant placement with minimal invasion, paving the way for implantable devices that enable long-term and targeted delivery of therapeutic agents in various bioengineering applications.

Explainable Artificial Intelligence And Domain Adaptation For Predicting Hiv Infection With Graph Neural Networks, Evan Yu, Jingcheng Du, Yang Xiang, Xinyue Hu, Jingna Feng, John A Schneider, Kayo Fujimoto, Cui Tao

Faculty, Staff and Student Publications

OBJECTIVE: Investigation of explainable deep learning methods for graph neural networks to predict HIV infections with social network information and performing domain adaptation to evaluate model transferability across different datasets.

METHODS: Network data from two cohorts of younger sexual minority men (SMM) from two U.S. cities (Chicago, IL, and Houston, TX) were collected between 2014 and 2016. Feature importance from graph attention network (GAT) models were determined using GNNExplainer. Domain adaptation was performed to examine model transferability from one city dataset to the other dataset, training with 100% of the source dataset with 30% of the target dataset and prediction …

Promotion And Tenure For Medical Physicists Should Be Based On Article Specific Measures And Not On Journal Impact Factor, Samantha Hedrick, Jinzhong Yang, Yi Rong

Faculty, Staff and Student Publications

No abstract provided.

Analytical Solutions For Acoustic Vortex Beam Radiation From Planar And Spherically Focused Circular Pistons, Chirag A Gokani, Michael R Haberman, Mark F Hamilton

Faculty, Staff and Student Publications

Analytical solutions for acoustic vortex beams radiated by sources with uniform circular amplitude distributions are derived in the paraxial approximation. Evaluation of the Fresnel diffraction integral in the far field of an unfocused source and in the focal plane of a focused source leads to solutions in terms of an infinite series of Bessel functions for orbital numbers ℓ>-2. These solutions are reduced to closed forms for 0≤ℓ≤4, which correspond to orbital numbers commonly used in experiments. A scaling law for the vortex ring radius is derived, and its relevance is characterized using ray theory.

Therapeutic Perspectives For Prion Diseases In Humans And Animals, Rebeca Benavente, Rodrigo Morales

Faculty, Staff and Student Publications

No abstract provided.

A Shared Mechanism For Tnp-Atp Recognition By Members Of The P2x Receptor Family, Jin Wang, Motoyuki Hattori, Chang-Zhu Li, Ye Yu, Chang-Run Guo

Faculty, Staff and Student Publications

P2X receptors (P2X1-7) are non-selective cation channels involved in many physiological activities such as synaptic transmission, immunological modulation, and cardiovascular function. These receptors share a conserved mechanism to sense extracellular ATP. TNP-ATP is an ATP derivative acting as a nonselective competitive P2X antagonist. Understanding how it occupies the orthosteric site in the absence of agonism may help reveal the key allostery during P2X gating. However, TNP-ATP/P2X complexes (TNP-ATP/human P2X3 (hP2X3) and TNP-ATP/chicken P2X7 (ckP2X7)) with distinct conformations and different mechanisms of action have been proposed. Whether these represent species and subtype variations or experimental differences remains unclear. Here, we show …

A Multiple X-Ray-Source Array (Mxa) System With A Planar Two-Dimensional Source Distribution For Digital Breast Tomosynthesis, Alejandro Sisniega, Andrew M Hernandez, Shadi A Shakeri, Elizabeth A Morris, John M Boone, Paul R Schwoebel

Faculty, Staff and Student Publications

Background: Digital breast tomosynthesis (DBT) has outpaced digital mammography in clinical adoption in the United States; however, substantial technological limitations remain to image quality in DBT, including undersampling from a one-dimensional (1D) scan geometry, x-ray source motion during acquisition, and patient motion artifacts from long exam times.

Purpose: A thermionic cathode x-ray system employing two-dimensional (2D, planar) multiple x-ray-source arrays (MXA) is proposed to improve DBT image quality.

Methods: A 1D MXA, consisting of a linear array of thermionic cathodes was used to simulate a 2D MXA. The 1D MXA included 11 focal spots separated by a distance of Δd …

Engineering Immunity: Bacterial Delivery Of Cancer Neoantigen Vaccines, Christopher D Johnston, Jennifer A Wargo

Faculty, Staff and Student Publications

In the battle against cancer, researchers are exploring the use of engineered bacteria as living medicines. Redenti and colleagues demonstrate that Escherichia coli Nissle 1917 (EcN) can be engineered to deliver cancer neoantigen payloads, stimulating antigen-specific CD4+ and CD8+ T cells and mediating antitumor immunity in preclinical models of colorectal cancer and melanoma.

Low Intensity Mechanical Signals Promote Proliferation In A Cell-Specific Manner: Tailoring A Non-Drug Strategy To Enhance Biomanufacturing Yields, M Ete Chan, Christopher Ashdown, Lia Strait, Sishir Pasumarthy, Abdullah Hassan, Chanpreet Singh, Gabriel Pagnotti, Omor Khan, Gunes Uzer, Clinton T Rubin

Faculty, Staff and Student Publications

Biomanufacturing relies on living cells to produce biotechnology-based therapeutics, tissue engineering constructs, vaccines, and a vast range of agricultural and industrial products. With the escalating demand for these bio-based products, any process that could improve yields and shorten outcome timelines by accelerating cell proliferation would have a significant impact across the discipline. While these goals are primarily achieved using biological or chemical strategies, harnessing cell mechanosensitivity represents a promising - albeit less studied - physical pathway to promote bioprocessing endpoints, yet identifying which mechanical parameters influence cell activities has remained elusive. We tested the hypothesis that mechanical signals, delivered non-invasively …

A Multi-Institutional Study Of Magnetic Resonance/Ultrasound Fusion-Guided Nanoparticle-Directed Focal Therapy For Prostate Ablation, Steven E Canfield, Arvin K George, Joshua S Jue, Sara C Lewis, Matthew S Davenport, Varaha S Tammisetti, Mahir Maruf, Leonardo D Borregalaes, Yara Kadria-Vili, Jon A Schwartz, Jennifer West, Naomi J Halas, Ardeshir R Rastinehad

Faculty, Staff and Student Publications

Purpose: Focal therapy aims to provide a durable oncologic treatment option for men with prostate cancer (PCa), while preserving their quality of life. Most focal therapy modalities rely on the direct tissue effect, resulting in a possible nontargeted approach to ablation. Here, we report the results of the first human feasibility trial utilizing nanoparticle-directed focal photothermal ablation for PCa.

Materials and methods: A prospective, open-label, single-arm, multicenter study of men with localized PCa in Gleason Grade Group 1 to 3 was conducted. Men received a single infusion of gold nanoparticles (AuroShells), followed by magnetic resonance (MR)/ultrasound (US) fusion-guided laser excitation …

A Near-Wall Methodology For Large-Eddy Simulation Based On Dynamic Hybrid Rans-Les, Michael Tullis, D. Keith Walters

Mechanical Engineering Faculty Publications and Presentations

Attempts to mitigate the computational cost of fully resolved large-eddy simulation (LES) in the near-wall region include both the hybrid Reynolds-averaged Navier–Stokes/LES (HRL) and wall-modeled LES (WMLES) approaches. This paper presents an LES wall treatment method that combines key attributes of the two, in which the boundary layer mesh is sized in the streamwise and spanwise directions comparable to WMLES, and the wall-normal mesh is comparable to a RANS simulation without wall functions. A mixing length model is used to prescribe an eddy viscosity in the near-wall region, with the mixing length scale limited based on local mesh size. The …

Outage Prediction And Detecting Influential Factors Under Extreme Weather Events Using Shap Analysis, Farishta Rahman

Theses and Dissertations

As electricity demands continue to grow and extreme weather events become more frequent, the need for resilient energy infrastructure is increasingly urgent. This thesis addresses three critical areas of power system resilience and monitoring. The first section of the thesis reviews the Texas power blackout in February 2021, which highlighted severe vulnerabilities in energy systems under extreme weather conditions. This analysis investigates the blackout event's causes, including generation capacity limitations and over-reliance on specific energy sources. The second section presents another weather event (ice storms) in North Dakota causing a multi-day blackout. Multiple machine learning approaches are deployed to predict …

Identifying Payable Cluster Distributions For Improved Reservoir Characterization: A Robust Unsupervised Ml Strategy For Rock Typing Of Depositional Facies In Heterogeneous Rocks, Umar Ashraf, Aqsa Anees, Hucai Zhang, Muhammad Ali, Hung Vo Thanh, Yujie Yuan

Research outputs 2022 to 2026

The oil and gas industry relies on accurately predicting profitable clusters in subsurface formations for geophysical reservoir analysis. It is challenging to predict payable clusters in complicated geological settings like the Lower Indus Basin, Pakistan. In complex, high-dimensional heterogeneous geological settings, traditional statistical methods seldom provide correct results. Therefore, this paper introduces a robust unsupervised AI strategy designed to identify and classify profitable zones using self-organizing maps (SOM) and K-means clustering techniques. Results of SOM and K-means clustering provided the reservoir potentials of six depositional facies types (MBSD, DCSD, MBSMD, SSiCL, SMDFM, MBSh) based on cluster distributions. The depositional facies …

Performance Of Optimized Composition Of Epoxy Resin Adhesive Used In High Friction Surface Treatment, Jiapeng Li, Xiao Huan, Shubing Wang, Yanping Sheng, Dahui Xu, Zhanping You

Michigan Tech Publications

The High Friction Surface Treatment (HFST) paving technique is widely employed in engineering practices globally, serving to enhance the long-term anti-skid performance of pavements. However, there still exist significant gaps in research regarding the modified and optimized composition and corresponding content of the polymer binding materials used in HFST, as well as the performance of the pavements laid with modified and optimized blended aggregates during service. To address these issues, this research first modified and optimized the composition ratio of traditional epoxy resin materials. Subsequently, a toughened, high-flow epoxy bonding system suitable for the maintenance of concrete pavements was prepared. …

More Than Semantics? Navigating The "Policy ∗ Design" Concepts' Landscape, Diana Pamela Villa-Alvarez, Adam Wellstead

Michigan Tech Publications

Scholarship from the design discipline and policy sciences has produced rich empirical and theoretical knowledge on the intersection of policy and design. However, using concepts that pair “policy” and “design” in various ways, often with different meanings, can confuse practitioners and scholars alike. This confusion is further exacerbated by the sheer variety of online information, including peer-reviewed articles, books, reports, blogs, courses, and the websites of prominent scholars, practitioners, and policy actors. To address this, we analyze “Policy * Design” concepts from a Google Search Engine scraping tool and, in doing so, identify four distinct approaches: “policy design,” “design for …

Debtor Eligibility Prediction Using Deep Learning With Chatbot-Based Testing, Reski Noviania, Enny Itje Sela, Luther Alexander Latumakulita, Steven R. Sentinuwo

Knowledge Engineering and Data Science

Predicting debtor eligibility is essential for effective risk management and minimizing lousy credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy. It integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non experts to …

Manifold Learning And Undersampling Approaches For Imbalanced Class Sentiment Classification, L.M. Risman Dwi Jumansyah, Agus Mohamad Soleh, Utami Dyah Syafitri

Knowledge Engineering and Data Science

Movie reviews are crucial in determining a film's success by influencing audience decisions. Automating sentiment classification is essential for efficient public opinion analysis. However, it faces challenges such as high-dimensional data and imbalanced class distributions. This study addresses these issues by applying manifold learning techniques, Principal Component Analysis (PCA) and Laplacian Eigenmaps (LE) to reduce data complexity and undersampling strategies (Random Undersampling (RUS) and EasyEnsemble) to balance data and improve predictions for both sentiment classes. On reviews of The Raid 2: Berandal, EasyEnsemble achieved the highest average G-Mean of 0.694 using Term Frequency-Inverse Document Frequency (TF IDF) features with a …

Constructing Qur’An Recitation Classification Using Alexnet Algorithm, Harits Ar Rosyid, Dzulkifli Abdullah, Mohammed S. Alqahtani

Knowledge Engineering and Data Science

The growing demands for accurate and efficient methods in the Qur'an recitation classification highlight the limitations of existing models, particularly in assisting the memorization process. This study aims to address these challenges by implementing the AlexNet Convolutional Neural Network architecture, widely recognized for its effectiveness in image classification, to classify the Qur'an recitations using the Mel Frequency Cepstral Coefficient (MFCC) as the feature extraction method. The research involves several stages, including data collection, preprocessing (audio segmentation by verse), data augmentation, feature extraction, and classification using the AlexNet architecture, followed by performance evaluation. Key results demonstrate that the combination of MFCC …

Deep Learning Approach For Dental Anomalies X-Ray Imaging Using Yolov8, Amelia Ritahani Ismail, Md Salim Sadman Taseen

Knowledge Engineering and Data Science

Dental X-ray imaging is a critical diagnostic tool for identifying various dental anomalies. However, manual interpretation is time-consuming, prone to human error, and requires specialized expertise. Deep learning models, particularly object detection frameworks like YOLO, have demonstrated promising results in automating medical image analysis. This study aims to develop and evaluate a YOLOv8-based deep learning model for automated detection and classification of 14 dental anomaly categories, including Caries, Crowns, Fillings, Implants, and Periapical lesions. The proposed approach addresses limitations in previous YOLO versions by leveraging anchor-free detection and enhanced feature extraction for improved accuracy. The model was trained on a …

Optimal Strategy For Handling Unbalanced Medical Datasets: Performance Evaluation Of K-Nn Algorithm Using Sampling Techniques, Yulita Salim, Aulia Putri Utami, Abdul Rachman Manga, Huzain Azis, Fadhila Tangguh Admojo

Knowledge Engineering and Data Science

This study addresses the critical role of medical image classification in enhancing healthcare effectiveness and tackling the challenges of imbalanced medical datasets. It focuses on optimizing classification performance by integrating Canny edge detection for segmentation and Hu-moment feature extraction and applying oversampling and undersampling techniques. Five diverse medical datasets were utilized, covering Alzheimer’s and Parkinson’s diseases, COVID-19, brain tumours, and lung cancer. The K-Nearest Neighbors (K-NN) algorithm was implemented to enhance classification accuracy, aiming to develop a more robust framework for medical image analysis. The evaluation, conducted using cross-validation, demonstrated notable improvements in key metrics. Specifically, oversampling significantly enhanced lung …

A Hierarchical Density-Based Spatial Clustering Of Applications With Noise (Hdbscan) Approach For Identifying Potential Villages In Buleleng Regency, Dina Nur Amalina, Achmad Fauzan

Knowledge Engineering and Data Science

Buleleng Regency, located in Bali Province, possesses diverse village potential, including agricultural production and tourist attractions. However, this potential has not been fully optimized. Therefore, it is important to enhance village potential by clustering villages based on their specific characteristics to identify and prioritize those requiring special attention. This approach aims to promote equitable village development and reduce poverty levels. This study clusters villages in Buleleng Regency based on their potential using the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) method. The data utilized in this study comprises village potential data obtained from the Buleleng Regency Statistics Office …

Classification Of Anxiety Levels Entering The World Of Work In Final Year Students Using The Neighbor Weighted K-Nearest Neighbor Method, Awang Hendrianto Pratomo, Muhammad Fahmi Adam, Dessyanto Boedi Prasetyo

Knowledge Engineering and Data Science

This study evaluates the accuracy of the Neighbor Weighted K-Nearest Neighbor (NWKNN) method in classifying the anxiety levels of final-year students as they prepare to enter the workforce, particularly in cases of unbalanced data distribution. The system was developed using the prototype method, and NWKNN was applied to classify anxiety levels into low, medium, and high categories. Testing using the Confusion Matrix demonstrated strong performance, achieving an accuracy of 94% based on a dataset of 1009 students, with a 90:10 ratio of training to test data. The results indicate that NWKNN effectively provides classification input values, making it a reliable …

Comparative Analysis Of Bpnn And Lvq For Sundanese Character Recognition, Haviluddin Haviluddin, Herman Santoso Pakpahan, Dinda Izmya Nurpadillah, Hario Jati Setyadi, Medi Taruk, Rayner Alfred

Knowledge Engineering and Data Science

The Sundanese script (Aksara Sunda), an essential part of Sundanese cultural heritage, has been used since the 14th century AD. However, recognizing handwritten Sundanese characters remains challenging due to variations in individual writing styles. This study compares the performance of Backpropagation Neural Network (BPNN) and Learning Vector Quantization (LVQ) for recognizing handwritten Sundanese vowel (Swara) characters. A dataset was collected from 15 individuals, each writing seven Sundanese vowel characters, which were then used for training and testing the recognition models. Experimental results show that BPNN outperforms LVQ, achieving a higher classification accuracy (95.23%), lower Mean Squared Error (MSE), and faster …

Investigating The Effect Of Active Living Wall On Indoor Air Quality And Energy Consumption: Case Study In Uaeu Closed Offices, Atina Mohsen Ghunaim

Theses

Extreme hot climates can significantly affect Indoor Air Quality (IAQ) and ventilation, resulting in higher energy demands. Numerous studies have demonstrated that outdoor greening, such as trees and landscaped areas, can reduce a building’s energy consumption. However, less research has been conducted on the impact of indoor vegetation on energy use and air quality. Indoor vegetation decreases Carbon Dioxide (CO₂) concentration by absorbing CO₂ and releasing oxygen (O₂), while the evapotranspiration process further enhances (IAQ) and reduces energy expenditure. The United Arab Emirates has dry arid climate and summers with high humidity levels, causing individuals to stay longer indoors, which …

Induction Program Practices On New Teachers In Public School In The United Arab Emirates, Reem Mohammed Al Hajri

Theses

This study examines the induction program practices for new teachers in United Arab Emirates (UAE) public schools. It evaluates key components—including orientation, mentoring, professional development, teacher networking, and administrative support— and analyzes their impact on new teachers’ integration, professional growth, and overall satisfaction. Additionally, it investigates how demographic factors such as nationality, educational qualifications, and teaching experience influence teachers’ experiences and perceptions. By employing a cross-sectional survey design, quantitative data were collected from 150 public school teachers across various emirates. Findings revealed that most new teachers held positive perceptions of the induction program domains. Gender comparisons indicated minimal differences, except …

Barriers To The Adoption Of Cross Laminated Timber For Mid-Rise Residential Buildings In Uae, Sabika Nasrim Pilathottathil

Theses

Buildings consume about 40% of the total annual energy produced globally, with significant energy usage across different life cycle stages, including construction and operational phases. The energy associated with the construction of buildings is substantial due to the energy required for the manufacture of materials and products. This underscores the need for stakeholders in the building industry to find construction materials and systems with low embodied energy. In the pursuit of low-energy materials, massive timber products, such as cross-laminated timber (CLT), are gaining popularity in mid-rise buildings around the world. The development of CLT, which boasts excellent load-carrying capabilities, has …

From A Timeline Contact Graph To Close Contact Tracing And Infection Diffusion Intervention, Yipeng Zhang, Zhifeng Bao, Yuchen Li, Baihua Zheng, Xiaoli Wang

Research Collection School Of Computing and Information Systems

This paper proposes a novel graph structure to address the problems of information spreading in a real-world, frequently updating graph, with two main contributions at hand: accurately tracing infection diffusion according to fine-grained user movements and finding vulnerable vertices under the virus immunization scenario to mitigate infection diffusion. Unlike previous work that primarily predicts the long-term epidemic trend at the census level, this study aims to intervene in the short-term at the individual level. Therefore, two downstream tasks are formulated to illustrate practicalities: Epidemic Mitigating in Public Area problem (EMA) and Epidemic Maximized Spread in Public Area problem (ESA), where …

Modeling And Regulating A Ride-Sourcing Market Integrated With Vehicle Rental Services, Dong Mo, Hai Wang, Zeen Cai, W. Y. Szeto, Xiqun (Michael) Chen

Research Collection School Of Computing and Information Systems

With the popularity of on-demand ride services worldwide, ride-sourcing platforms must maintain an adequate fleet size and cope with growing travel demand. Recently, platforms have attempted to provide vehicle rental services to drivers who do not own cars, then recruited them to provide on demand ride services. This helps lower the entry barrier for drivers and offers another profitable business for platforms. From the government's perspective, however, it is challenging to coordinately regulate a ride-sourcing business and vehicle rental business. This paper proposes a bi-level optimization model to investigate how the government regulates the ride-sourcing market integrated with vehicle rental …