Digital Commons Network^™

Utilization Of Iron Fillings Solid Waste For Optimum Biodiesel Production, Fady Ihab Bayoumy Eng., Ahmed Osman Ahmed Dr., David W. Rooney Dr., Mai H. Roushdy

Chemical Engineering

This study explores the innovative application of iron filings solid waste, a byproduct from mechanical workshops, as a heterogeneous catalyst in the production of biodiesel from waste cooking oil. Focusing on sustainability and waste valorization, the research presents a dual-benefit approach: addressing the environmental issue of solid waste disposal while contributing to the renewable energy sector. Particle size distribution analysis, X-ray diffraction (XRD), scanning electron microscopy (SEM), X-ray fluorescence (XRF), Thermal analysis (TG-DTA), and FTIR analysis were used to characterize the iron filings. The response surface methodology (RSM) was used to guide a series of experiments that were conducted to …

Modeling And Forecasting Cyanobacterial Harmful Algal Blooms (Cyanohabs) In Lake Pontchartrain, Shekhar Mahat

LSU Master's Theses

Harmful algal blooms (HABs) frequently occur in coastal waters worldwide, adversely affecting economies, human health, aquatic ecosystems, and recreational activities. Lake Pontchartrain, an oligohaline estuary, experienced frequent HABs from 2018 to 2023. While NOAA’s NCCOS and USEPA’s CyAN currently monitor cyanobacteria harmful algal blooms (CyanoHABs) in coastal, freshwater, and riverine systems, there is a notable lack of systems monitoring phycocyanin concentrations in the lake. This thesis utilizes satellite remote sensing environmental stressors to model and predict CyanoHABs in Lake Pontchartrain using machine learning techniques. A key focus of this study is assessing the forecasting capabilities of the random forest method …

Expanding The Horizon: Blockchain Technology Beyond The Bounds Of Cryptocurrency, Hassan Azhar

SMU Data Science Review

Blockchain technology has extended beyond its initial role as the infrastructure for cryptocurrencies to transform various industries with its decentralized and transparent ledger system. This paper examines the broad spectrum of blockchain applications beyond cryptocurrency. It explores its potential to innovate and drive change across finance, supply chain management, healthcare, real estate, and voting systems. We review recent literature, detail specific use cases, and discuss blockchain's challenges and opportunities, aiming to provide a comprehensive overview of its transformative impact. Integrating emerging technologies, scalability, regulatory considerations, and energy consumption are critical challenges to its adoption. Our findings underscore the need for …

The Santa Clara, 2024-05-28, Santa Clara University

The Santa Clara

No abstract provided.

A Data Integration Framework Of Additive Manufacturing Based On Fair Principles, Kristen J. Hernandez, Erika I. Barcelos, Jayvic C. Jimenez, Arafath Nihar, Pawan K. Tripathi, Roger H. French, Laura S. Bruckman

Faculty Scholarship

Abstract: Laser-powder bed fusion (L-PBF) is a popular additive manufacturing (AM) process with rich data sets coming from both in situ and ex situ sources. Data derived from multiple measurement modalities in an AM process capture unique features but often have different encoding methods; the challenge of data registration is not directly intuitive. In this work, we address the challenge of data registration between multiple modalities. Large data spaces must be organized in a machine-compatible method to maximize scientific output. FAIR (findable, accessible, interoperable, and reusable) principles are required to overcome challenges associated with data at various scales. FAIRified data …

Advancing Objective Mobile Device Use Measurement Inchildren Ages 6–11 Through Built-In Device Sensors: A Proof-Of-Concept Study, Olivia L. Finnegan, Robert Glenn Weaver Med, Phd, Hongpeng Yang, James W. White, Srihari Nelakuditi, Zifei Zhong, Rahul Ghosal Ph.D., Yan Tong, Aliye B. Cepni, Elizabeth L. Adams, Sarah Burkart Mph, Ph.D., Michael W. Beets Med, Mph, Phd, Bridget Armstrong Ph.D.

Faculty Publications

Mobile devices (e.g., tablets and smartphones) have been rapidly integrated into the lives of children and have impacted howchildren engage with digital media. The portability of these devices allows for sporadic, on-demand interaction, reducing theaccuracy of self-report estimates of mobile device use. Passive sensing applications objectively monitor time spent on a givendevice but are unable to identify who is using the device, a significant limitation in child screen time research. Behavioralbiometric authentication, using embedded mobile device sensors to continuously authenticate users, could be applied toaddress this limitation. This study examined the preliminary accuracy of machine learning models trained on iPad …

Mobile Robot Path Planning Optimization Problem Using Multi- Objective Genetic Algorithm, Suresh Ks

Theses and Dissertations

Mobile Robot Path Planning problem (MRPPP) is the most prominent research area employed in different real-time environments. The research domain of robotics offers abundant opportunities for researchers in various engineering fields with different dimensions. The automation of physical movements of the robots with intelligence to make dynamic decisions for interacting with the environment opens up a lot of challenges to the research community.

A variety of approaches are employed to solve the Mobile Robot Path Planning Problem (MRPP), which is to derive a feasible collision-free path to reach the destination from the given starting point by avoiding obstacles. The solution …

Synthesis Of Geopolymer Using Coal Fly Ash And Corn Stover, Anu Paneru

Master's Theses

The development of each nation is dependent on its infrastructure, and nations are competing with others in infrastructure, especially in the construction of roadways, since they play a vital role in the economic and social development of the nation. The conventional materials used for road construction are cement and asphalt, which pose significant environmental challenges. This thesis explores the potential of fly ash (FA) and corn stover (CS) in synthesizing geopolymer, as an alternative material for the construction of roads. The study examines the impact of FA and CS percentages and the particle sizes of CS on the compressive strength, …

Botdr-Based Horizontal Deformation Monitoring Of Section Coal Pillars, Chai Jing, Hao Hongru, Yang Jianfeng, Gao Dengyan, Gao Kuiying, Chen Jianhua, Yang Lei, Liu Zeyu

Coal Geology & Exploration

To address potential safety hazards like microcrack initiation, deformation, and failure inside coal pillars, this study monitored the horizontal deformations of coal pillars using the Brillouin optical time-domain reflectometry (BOTDR)—a distributed optical fiber sensing technology. Firstly, this study established a method for converting the axial strain of optical fibers into horizontal deformations based on a theoretical analysis of the failure and bulking characteristics of coal pillars. Secondly, it determined the axial tensile response of metal-based cord-shaped sensing optical fibers by conducting laboratory tests. Then, it corrected the strain coefficient based on the deformation and failure patterns of coal pillars. Finally, …

Construction And Analysis Of A Method For Grading Long-Term Vegetation Carbon Sink In Waste Dumps Of An Open-Pit Coal Mine, Yang Fei, Cui Kuankuan, Zhang Chengye, Li Jun, Wang Jinyang, Du Tianmeng, Zhang Haoran

Coal Geology & Exploration

Vegetation carbon sink serves as a crucial indicator for the ecological assessment of an open-pit coal mine. The accurate inversion and grading of vegetation carbon sink play a significant role in exploring the ecological restoration of coal mines. This study proposed a method for grading vegetation carbon sink of an open-pit coal mine based on the particle swarm optimization (PSO) algorithm. Specifically, using a light use efficiency (LUE) model, this study first determined the spatio-temporal distributions of the 2005‒2020 vegetation carbon sink in the waste dumps of the Shengli No.1 open-pit coal mine through inversion based on Landsat images and …

Development Of Quantum True Random Number Generators And Implementation On Ibm Cloud Lab, Vaishnavi K

Theses and Dissertations

Random numbers are the lifeline of any cryptographic operation in modern computing. Quantum mechanics has the intrinsic ability to generate truly random numbers, making it an ideal alternative for scientific applications that require high-quality randomness. Quantum True Random Number Generators (QTRNGs) can yield real random data to replace random-looking periodic sequences. To construct such a random number generator, this work uses the IBM Q Experience platform called Qiskit.

This research focuses on the development and analysis of quantum-based TRNGs along with their prime characteristics. Qubits and quantum gates are the predominant sources of true randomness on quantum platforms. These inherent …

Combinatorial Studies Of Noble Metal Catalysts For Carbon Utilization, Natalie Leong Page

Theses and Dissertations

Electrocatalysts demonstrate significant promise for sustainable processes like carbon capture and generating recycled carbon fuels which are needed to achieve net zero carbon emissions. However, the commercialization of these processes is limited by several factors, one of which is the high cost of catalysts. The discovery and development of cheaper alternatives has been difficult due to constraints in experimental methodologies, scalability limitations, and a lack of understanding of reaction mechanisms. Within this dissertation is the development of a novel scanning electrochemical system and its implementation for high-throughput data generation from combinatorially sputtered material libraries. Combinatorial Pd-Au alloys were investigated for …

Me-Em Enewsbrief, Mar 2024, Department Of Mechanical Engineering-Engineering Mechanics, Michigan Technological University

Department of Mechanical and Aerospace Engineering eNewsBrief

No abstract provided.

Design And Development Of Contactless Capacitive Coupled Electrodes For Cardiovascular Signal Acquisition, Ramya Lakshmi Vs

Theses and Dissertations

In biosignal acquisition and physiological monitoring, developing advanced electrode technologies is crucial in enhancing signal quality, minimizing interference, and improving overall reliability. One such innovative approach is the utilization of capacitive-coupled electrodes, a cutting-edge solution that addresses some of the challenges associated with traditional electrodes in biosignal recording.

Capacitive-based sensors have become prominent in physiological signal measurement over the past decade. The electric field from the external affects both the human body and the electrode. The primary aim while designing the capacitive electrode is to maximize the signal-to-noise ratio. Hence, we introduce shielding and guarding techniques to improve the signal-to-noise …

A Distributed And Hybrid Ai-Based Security Framework For 5g Real-Time Applications, Ali Ghubaish

McKelvey School of Engineering Graduate Student Theses & Dissertations

This dissertation develops a multifaceted security framework tailored for 5G-enabled real-time Internet of medical things (IoMT) systems to significantly enhance the security infrastructure within healthcare environments. The framework pivots around three core technological advancements: the development of the Light feature Engineering based on the Mean Decrease in Accuracy (LEMDA), the construction of a 5G testbed that serves as a distributed intrusion detection system (IDS), and the implementation of a hybrid deep reinforcement learning (HDRL) method. LEMDA represents a breakthrough in data processing for IoMT systems. By intelligently reducing data complexity, LEMDA enhances the speed and accuracy of threat detection mechanisms, …

Robust Ai-Driven Segmentation Of Glioblastoma T1c And Flair Mri Series And The Low Variability Of The Mrimath© Smart Manual Contouring Platform, Yassine Barhoumi, Abdul Hamid Fattah, Nidhal Carla Bouaynaya, Fanny Moron, Jinsuh Kim, Hassan M. Fathallah-Shaykh, Rouba A. Chahine, Houman Sotoudeh

Henry M. Rowan College of Engineering Departmental Research

Patients diagnosed with glioblastoma multiforme (GBM) continue to face a dire prognosis. Developing accurate and efficient contouring methods is crucial, as they can significantly advance both clinical practice and research. This study evaluates the AI models developed by MRIMath© for GBM T1c and fluid attenuation inversion recovery (FLAIR) images by comparing their contours to those of three neuro-radiologists using a smart manual contouring platform. The mean overall Sørensen–Dice Similarity Coefficient metric score (DSC) for the post-contrast T1 (T1c) AI was 95%, with a 95% confidence interval (CI) of 93% to 96%, closely aligning with the radiologists’ scores. For true positive …

Distributed Conflict Detection And Optimal 4d Trajectory Resolution Leveraging Polynomial Based Methods, Michael Klinefelter, Austin Stone, Joshua Miller, Cameron K. Peterson, John Salmon

Student Works

This paper presents a methodology for distributed conflict detection and resolution of aircraft following time-dependent flight paths. We use parametric fifth-order polynomial splines to define the full, time-based paths of vehicles. This representation can be exploited to rapidly detect conflicts and calculate optimal resolution solutions that minimize deviations from the original path. Conflicts are identified using a Sturm sequencing procedure and resolutions are found using gradient-based optimization techniques. Simulations show the locally optimal resolution of complex multi-vehicle conflicts and large-scale scenarios. Also, a method of fitting the flight path model to data sets is presented and flight path trajectories are …

Analysis Of Inequality In Household Internet Utilization And Policy Implications, Shanisara Chamwong, Thoedsak Chomtohsuwan, Narissara Charoenphandhu

Journal of Demography

This research investigates the pervasive inequality in household internet access and use that contributes to the digital divide. As the internet becomes an integral part of daily life, variations in access and use carry significant implications for social and economic opportunities. A quantitative approach is applied, analyzing data from The National Statistical Office of Thailand to capture a comprehensive understanding of inequality in household internet access and use, as measured by the Gini coefficient. Five aspects of internet access and use are considered as the determinants of inequality, including internet connectivity, internet affordability, internet quality, device availability, and flexibility and …

Nanozyme: Combining Power Of Natural Enzymes And Artificial Catalysis, Peng Du, Lizeng Gao, Jian Jiao, Kelong Fan, Xiyun Yan

Bulletin of Chinese Academy of Sciences (Chinese Version)

Nanozymes represent a novel class of artificial enzymes and biocatalysts, possessing both the physical and chemical properties of nanomaterials along with unique enzyme-like catalytic activities, which breaks the boundary between inorganic materials and organic life. Unlike natural enzymes, traditional enzyme mimics and chemical catalysts, nanozymes exhibit catalytic activity that can be regulated by their nanoscale physical and chemical properties. They are characterized by good stability, high- and lowtemperature resistance, acid and alkali resistance, adjustable activity, and multifunctionality. As a result, nanozymes have garnered widespread attention in the fields of biomedicine, environment treatment, green agriculture, new energy resources, and have begun …

Deep Clustering Of Tabular Data By Weighted Gaussian Distribution Learning, Shourav B. Rabbani, Ivan V. Medri, Manar D. Samad

Computer Science Faculty Research

Deep learning methods are primarily proposed for supervised learning of images or text with limited applications to clustering problems. In contrast, tabular data with heterogeneous features pose unique challenges in representation learning, where deep learning has yet to replace traditional machine learning. This paper addresses these challenges in developing one of the first deep clustering methods for tabular data: Gaussian Cluster Embedding in Autoencoder Latent Space (G-CEALS). G-CEALS is an unsupervised deep clustering framework for learning the parameters of multivariate Gaussian cluster distributions by iteratively updating individual cluster weights. The G-CEALS method presents average rank orderings of 2.9(1.7) and 2.8(1.7) …

Text-To-Sql: A Methodical Review Of Challenges And Models, Ali Buğra Kanburoğlu, Faik Boray Tek

Turkish Journal of Electrical Engineering and Computer Sciences

This survey focuses on Text-to-SQL, automated translation of natural language queries into SQL queries. Initially, we describe the problem and its main challenges. Then, by following the PRISMA systematic review methodology, we survey the existing Text-to-SQL review papers in the literature. We apply the same method to extract proposed Text-to-SQL models and classify them with respect to used evaluation metrics and benchmarks. We highlight the accuracies achieved by various models on Text-to-SQL datasets and discuss execution-guided evaluation strategies. We present insights into model training times and implementations of different models. We also explore the availability of Text-to-SQL datasets in non-English …

Dpafy-Gcaps: Denoising Patch-And-Amplify Gabor Capsule Network For The Recognition Of Gastrointestinal Diseases, Henrietta Adjei Pokuaa, Adeboya Felix Adekoya, Benjamin Asubam Weyori, Owusu Nyarko-Boateng

Turkish Journal of Electrical Engineering and Computer Sciences

Deep learning (DL) models have performed tremendously well in image classification. This good performance can be attributed to the availability of massive data in most domains. However, some domains are known to have few datasets, especially the health sector. This makes it difficult to develop domain-specific high-performing DL algorithms for these fields. The field of health is critical and requires accurate detection of diseases. In the United States Gastrointestinal diseases are prevalent and affect 60 to 70 million people. Ulcerative colitis, polyps, and esophagitis are some gastrointestinal diseases. Colorectal polyps is the third most diagnosed malignancy in the world. This …

Stereo-Image-Based Ground-Line Prediction And Obstacle Detection, Emre Güngör, Ahmet Özmen

Turkish Journal of Electrical Engineering and Computer Sciences

In recent years, vision systems have become essential in the development of advanced driver assistance systems or autonomous vehicles. Although deep learning methods have been the center of focus in recent years to develop fast and reliable obstacle detection solutions, they face difficulties in complex and unknown environments where objects of varying types and shapes are present. In this study, a novel non-AI approach is presented for finding the ground-line and detecting the obstacles in roads using v-disparity data. The main motivation behind the study is that the ground-line estimation errors cause greater deviations at the output. Hence, a novel …

Deep Learning-Based Breast Cancer Diagnosis With Multiview Of Mammography Screening To Reduce False Positive Recall Rate, Meryem Altın Karagöz, Özkan Ufuk Nalbantoğlu, Derviş Karaboğa, Bahriye Akay, Alper Baştürk, Halil Ulutabanca, Serap Doğan, Damla Coşkun, Osman Demi̇r

Turkish Journal of Electrical Engineering and Computer Sciences

Breast cancer is the most prevalent and crucial cancer type that should be diagnosed early to reduce mortality. Therefore, mammography is essential for early diagnosis owing to high-resolution imaging and appropriate visualization. However, the major problem of mammography screening is the high false positive recall rate for breast cancer diagnosis. High false positive recall rates psychologically affect patients, leading to anxiety, depression, and stress. Moreover, false positive recalls increase costs and create an unnecessary expert workload. Thus, this study proposes a deep learning based breast cancer diagnosis model to reduce false positive and false negative rates. The proposed model has …

05.20.2024 Orsp Connect, Liz Williamson

ORED Newsletter

NIH RPPR Update (Research Performance Progress Report)

Assessing The Benefits Of Electrification For The Mackinac Island Ferry From An Environmental And Economic Perspective, Siddharth Gopujkar, Jeremy Worm

Michigan Tech Publications

Ferry electrification has gained attention in the last decade as a potential path to reduce greenhouse gas emissions. This study, conducted by APS LABS at Michigan Technological University for the Mackinac Economic Alliance (MEA) and funded by the Michigan Economic Development Corporation (MEDC), looked at the feasibility and potential benefits of electrification of a particular vessel that is part of a ferry service from Mackinaw City, Michigan, USA, to Mackinac Island, Michigan, USA. The study included a comprehensive analysis of the feasibility of retrofitting the current configuration of the ferry into an all-electric ferry based on the availability of components …

Ri2ap: Robust And Interpretable 2d Anomaly Prediction In Assembly Pipelines, Chathurangi Shyalika, Kaushik Roy, Renjith Prasad, Fadi El Kalach, Yuxin Zi, Priya Mittal, Vignesh Narayanan, Ramy Harik, Amit Sheth

Publications

Predicting anomalies in manufacturing assembly lines is crucial for reducing time and labor costs and improving processes. For instance, in rocket assembly, premature part failures can lead to significant financial losses and labor inefficiencies. With the abundance of sensor data in the Industry 4.0 era, machine learning (ML) offers potential for early anomaly detection. However, current ML methods for anomaly prediction have limitations, with F1 measure scores of only 50% and 66% for prediction and detection, respectively. This is due to challenges like the rarity of anomalous events, scarcity of high-fidelity simulation data (actual data are expensive), and the complex …

Flexible Biosensors For Food Pathogen Detection, Sonatan Biswas, Md Shariful Islam, Fei Jia, Yunteng Cao, Yanbin Li, Changyong Cao

Biological and Agricultural Engineering Faculty Publications and Presentations

Food contamination poses a significant threat to public health, the economy, and human health worldwide, occurring at any stage of the food supply chain, from farm to fork. Efficient and effective real-time monitoring methods for the early identification and rapid detection of pathogen contamination are critical to preventing possible food safety issues. In the past decade, flexible electrochemical biosensors have rapidly expanded in the detection of foodborne pathogens, owing to their ability to function well at biological interfaces that may be soft, intrinsically curvy, irregular, or deformable. The most important features of flexible sensors are their flexibility, multifunctionality, low cost, …

Structural Fiber Mesh Reinforcement Of A Polymeric Heart Valve: Improving Valve Durability And Leaflet Closure Effectiveness, Peter J. Choi, Hugo Zazueta, John A. Acevedo, Philip Park

Civil Engineering Faculty Publications

Objective: Bioprosthetic valves using either porcine or bovine pericardium have been widely used for transcatheter heart valve replacements. However, producing bioprosthetic valves is not a sustainable solution. Acquiring animal tissue is often not readily available and costly and requires time-consuming modifications. Moreover, its primary tissue failure requires reoperation after roughly 15 years. The suggested replacement of porcine and bovine leaflets with biocompatible polymers appears to be an attractive alternative to bioprosthetic valves. In this study, engineered fiber-reinforced polymers were developed, which are less degenerative and offer greater hemocompatibility in relation to mechanical valves.
Methods: Polydimethylsiloxane (PDMS) polymer reduces calcification significantly …

Design Of An Integrated System For The Protection Of Patient Health Information In Medical Images, Manikandan V

Theses and Dissertations

Electronic clinical data such as Patient Health Information (PHI) is stored by adapting the Digital Imaging and Communications in Medicine (DICOM) standard to build an integrated healthcare system where medical images from various sources can be interlinked. The healthcare industry poses a threat from hackers, and the information fetched by hackers provides more money than the other information stolen. This thesis deals with designing and analysing encryption algorithms, key generation mechanisms, and information-hiding schemes for protecting PHI.

Sensitive multimedia information of all forms is encrypted, with a key, before storage and transmission to protect it from illegal use and manipulation …