Digital Commons Network^™

Minimizing The Production Cost Of The Cam-Follower Profile Using Shape Optimization Techniques, Khaled Ahmed, Magdy Samuel, Rania Mostafa

Mansoura Engineering Journal

The study of shape optimization techniques in structural products helps in reducing mechanical stresses and vibrations in related mechanical elements, such as cam follower mechanisms. This, in turn, has a positive impact on cost savings in the manufacturing process and enhances production quality and reliability. The point of view in this research depends on the idea of minimizing the total production cost while ensuring good functional performance of the cam profile during the shape-manufacturing optimization problem-solving process. The investigation of the impact of changing the motion type of a cam, such as Uniform Velocity Motion (UVM) and Simple Harmonic Motion …

The Impact Of Performance Based Funding On Institutional Financial Priorities In Public Two-Year And Four-Year Institutions, Ebony T. Spearman

Master's Theses and Doctoral Dissertations

This dissertation examines the impact of performance-based funding (PBF) policies on institutional financial priorities at 2-and 4-year colleges and universities. PBF is a policy mechanism used by states to incentivize higher education institutions to achieve specific performance outcomes, such as increased graduation and retention rates, by tying a portion of institutional funding to these outcomes. This research aims to determine the efficacy of PBF policies on institutional change and outcomes. More specifically, this research seeks to find the extent to which colleges and universities subjected to PBF policies change their financial priorities and how, if at all, these changes impact …

Using Unsupervised Learning Methods In Extracting Features For Classifying Rice Varieties From Rice Grains Images., Kevin Anthony Martinez

ICT

Rice, a staple food for nearly half of the global population, requires accurate classification of its varieties to ensure food quality, support agricultural trade, and enhance yield optimisation. Traditional manual classification methods are time-intensive and error-prone, prompting this study's exploration of unsupervised learning for feature extraction from rice grain images. The research tested classifiers on 75,000 rice samples across five classes, with 15,000 samples per class.

The study's DCGAN-CNN model achieved the highest classification accuracy of 99.67%. However, the PCA-CNN model underperformed, with only 20% accuracy, due to implementation errors. Recommendations for improvement include optimising model parameters such as learning …

A Comparative Study On Feature Extraction Techniques For The Discrimination Of Frontotemporal Dementia And Alzheimer's Disease With Electroencephalography In Resting-State Adults, Utkarsh Lal, Arjun Vinayak Chikkankod, Luca Longo

Articles

Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% …

A Contribution To The Exploration Of The Chemical Durability, Thermal Stability, And Structural Ability Of Glasses From The Fe2o3-K2o-Moo3-P2o5 Quaternary System, Asmae Er-Rafai, Yasmina Alaoui, Mohamed Laourayed, Mohamed Ounacer, Mouloud El Moudane, Mohammed Sajieddine, Abdelkbir Bellaouchou

Journal of Metals, Materials and Minerals

The relationships between the properties and structure of xFe2O3-(40-x)K2O-10MoO3-50P2O5 glasses have been reported in this paper. Homogeneous glasses are formed for different Fe2O3 contents. The density and molar volume evolution of these glasses shows that Fe2O3 acts as a glass former and strengthens the structural bonds of the glass. Experimental 57Fe Mössbauer spectroscopy graphs suggest the existence of divalent Iron (FeII) and trivalent Iron (FeIII). The boost in chemical durability agrees with the growth in glass transition temperature due to more powerful bonding in the structural network. P-O-Fe bonds replace P-O-P bonds when Fe2O3 is present in the phosphate network.

First-Principles Study On Structural And Electronic Properties Of P3ht-Graphene, Fia Amalia, Ari Dwi Nugraheni, Sholihun Sholihun

Journal of Metals, Materials and Minerals

Based on density functional theory, calculations have been carried out to study the structural and electronic properties of the polymer Poly (3-hexylthiophene) (P3HT) interacting with monolayer graphene as an active layer. (P3HT)1-graphene and (P3HT)2-graphene are optimized on graphene using the generalized-gradient-approximation type of the exchange-correlation functional. Adsorption energy, band gap, and charge transfer are calculated. The calculated adsorption energy shows that all systems have negative adsorption energy, indicating that the reaction is exothermic. Compared to (P3HT)1-graphene, (P3HT)2-graphene has a lower band gap. As for the charge transfer calculation, a negative ΔN indicates that electron transfers from P3HT to graphene.

Nano-Zno Prepared By Using Chaya And Mango Leaves Extract For Photocatalyst Of Methylene Blue, Sri Wahyu Suciyati, Junaidi Junaidi, Rudy Situmeang, Posman Manurung

Journal of Metals, Materials and Minerals

This research aimed to synthesize nano-ZnO from chaya (Cnidoscolus aconitifolius) and mango leaves extract (Mangifera indica) as environmentally friendly photocatalysts. Nano-ZnO was synthesized using green synthesis, with leaves extracts as reducing agents and nanoparticle size stabilizers. The samples were prepared using two methods, namely nano-ZnO-1 and nano-ZnO-2. The results of Fourier transform infrared spectroscopy (FTIR) analysis showed the contribution of metabolite compounds in nano-ZnO synthesis. X-ray diffraction (XRD) and transmission electron microscopy (TEM) showed the crystal size in nano range, with spherical nanorod morphology observed. Ultraviolet-visible diffuse reflectance spectroscopy (UV-Vis DRS) determined the band gap energy …

Development Of Forming Limit Diagrams For Motorcycle Fuel Tank Made From Aa5754-O Under Deep Drawing, Phiraphong Larpprasoetkun, Jidapa Leelaseat, Aeksuwat Nakwattanaset, Surasak Suranuntchai

Journal of Metals, Materials and Minerals

The research studied forming limit curves (FLC) for aluminum alloy 5754-O (AA5754-O) and modified them using two methods. These methods aimed to create FLCs for application in describing the forming behavior of sheet metal material through a deep drawing process simulation in the PAM-STAMP program. The methods are as follows: The Nakajima test combined with a new method, a novel process for grid creation on the specimen: laser marking combined with elastic paints. A mathematical material model using the fracture model along with the yield criteria: YLD2000-2D and hardening law: Swift-Voce. The Keeler-Brazier modified equation was chosen as the fracture …

Bifunctional Cationic Modification Of Modal Fabric For Sustainable In Textile Dyeing With Spent Coffee Grounds, Monthon Nakpathom, Rattanaphol Mongkholrattanasit, Sarute Ummartyotin, Pisutsaran Chitichotpanya, Nattaya Vuthiganond

Journal of Metals, Materials and Minerals

In order to dye cellulose or regenerated cellulose with natural dyes, toxic metal mordants are generally employed. In response to this issue, the novel bifunctional cationic agent was pretreated to modify modal (regenerated cellulose) fabric prior to dyeing to enhance the fiber-dye affinity. The cationization reaction was carried out using a compound containing bicationic sites, N,N'-(oxybis (ethane-2,1-diyl))bis(3-chloro-2-hydroxy-N,N-dimethylpropan-1-aminium. This unique cationic agent is capable of producing nonvolatile amine and has no odor problem. The successful cationic modification was confirmed through FTIR spectroscopy and zeta potential measurement. The cationized process was optimized at a cationic and sodium hydroxide concentration …

Deep Learning Model Compression For Resource-Constrained Environments., Stephen Burke

ICT

This study examines the effects of three Deep Neural Network compression techniques—Quantisation, Pruning, and Weight Sharing/Clustering—on CNN and ANN models trained for image classification tasks. The models were tested on the CIFAR-10 dataset for multiclass classification and a binary classification task using a dataset derived from COCO. The best validation accuracy achieved was 74.7% with a CNN on CIFAR-10 and 53% with the best ANN. On the COCO dataset, a modified CIFAR-10 CNN model achieved 75%. The models were compressed using the three techniques and benchmarked on a ThinkPad laptop and Raspberry Pi 3B+ based on metrics relevant for resource-constrained …

Applying Neural Networks To Predict Factors Affecting Harmful Algal Blooms For Timely Alerting And Implementing Preventive Measures In Ireland's Marine Ecosystem., Nikolai Potapov

ICT

This study applies neural networks to predict harmful algal blooms (HABs) along the Irish coast, addressing ecological, health, and economic risks. Using primary interviews and secondary data on HAB species like Alexandrium and Karenia mikimotoi, the research incorporated Exploratory Data Analysis and tested three neural models: LSTM, Ensemble Stacking LSTM, and CNN-LSTM. Key factors influencing HABs, such as sea surface temperature and euphotic zone depth, were identified.

Results demonstrate the potential of neural networks to improve HAB prediction and monitoring, despite limitations. Future work aims to enhance model accuracy and integrate them into HAB warning systems.

Development And Optimisation Of Convolutional Neural Networks (Cnns) To Predict The Nutrition And Sustainability Scores Of Foods From Crowd Sourced Images., Cormac Mcelhinney

ICT

This research explores the use of Convolutional Neural Networks (CNNs) for the automated classification and profiling of food products based on publicly sourced data. With the vast array of food products available worldwide and the complexity of labelling regulations, food business operators face challenges in ensuring compliance, while regulators struggle to verify adherence. This study addresses the need for efficient and accurate methods for food classification and eco/nutritional profiling. It begins with a comprehensive literature review on the application of CNNs in food product classification, followed by the collection of a large-scale dataset from Open Food Facts. A CNN architecture …

Evaluation And Implementation Of Machine Learning Models To Predict Customer Churn In The Telecommunications Sector., Stephen Hasson

ICT

This research addresses customer churn in the Telecom industry by utilizing Machine Learning (ML) models to predict customers at risk of leaving and provide data-driven retention strategies. The study highlights the effectiveness of ML, particularly in churn prediction, while noting the need for further exploration into the ethical implications of AI, such as potential biases towards vulnerable groups. Using the CRISP-DM framework, the study develops and compares three Supervised Learning (SL) models: Random Forests (RF), LightGBM (LGBM), and XGBoost (XGB), incorporating class resampling techniques to manage data imbalance.

The findings identified five key features as the most significant predictors of …

Data Analysis Of Twitter’S Nasdaq100 Sentiments And Topics As Indicators For News Articles Retrieval: Fine-Tuning Roberta And Rag., Kagan Timur

ICT

This study investigates the combination of sentiment analysis using the VADER lexicon and semantic analysis through Latent Dirichlet Allocation (LDA) to identify real-life events, focusing on Twitter datasets. The research shows that while sentiment analysis alone may be insufficient, combining it with semantic analysis improves the process, particularly for identifying relevant news articles and understanding brand perception on social media. The study also fine-tunes the RoBERTa model for question-answering tasks, yielding significant improvements in the SQuAD evaluation metric. The exact match (EM) score rose dramatically from 2.06% to 62%, and the F1 score improved from 9.41% to 65%. A retrieval …

Responsible Natural Language Processing To Aid Employee Performance Reviews., Grace Rubinger

ICT

This research explores the use of Natural Language Processing (NLP) techniques in assessing evaluators' written appraisals during Employee Performance Reviews (EPRs), aiming to address biases inherent in traditional methods. By integrating Responsible Artificial Intelligence (AI) and foundational Large Language Models (LLMs), the study seeks to enhance the objectivity, fairness, and ethical transparency of performance evaluations. It highlights the potential of AI systems to ensure comprehensive assessments while promoting trust, ethical standards, and employee retention.

The research also aims to advance the field of AI Ethics in practical Human Resources Management (HRM) applications, particularly through NLP-driven tools. These tools are designed …

Legal Matters In Web 3: A Desk Reference, Anup Malani, M. Henderson

Books

No abstract provided.

Hand Tremor Sensing And Characterisation Device, Jackie Le , '24

Senior Theses, Projects, and Awards

Parkinson’s Disease and Essential Tremor are two of the most common tremor-related conditions affecting millions of people globally. Various types of tremors, specifically in the hand, arise as symptoms characteristics to these tremor-related conditions. However, the lack of standardization and certainty between clinical tremor assessments motivates the future of medical devices to provide relevant medical diagnostics. Current literature shows promise for the ability of such devices to revolutionize home healthcare and integrate well into currently existing clinical procedures. This engineering design project aims to design a wearable medical device to detect and computationally characterise various types of tremors in Parkinson’s …

Giddy Guidance: Low-Cost 3d Aircraft With Integrated Autopilot And Stabilization, Mark Lohatepanont , '24

Senior Theses, Projects, and Awards

No abstract provided.

Sticky Charters? The Surprisingly Tepid Embrace Of Officer-Protecting Waivers In Delaware, Jens Frankenreiter, Eric L. Talley

Scholarship@WashULaw

This article investigates the reaction to a much-heralded 2022 legal reform in Delaware that permitted a corporation’s charter to exculpate its officers from monetary exposure for breaching their fiduciary duty of care. To isolate reactions to this statutory reform, we make extensive use of generative AI tools to identify and interpret charter amendments that introduce officer-facing waivers. We find a surprisingly tepid rate of uptake among Delaware corporations through the end of the first post-reform year, notwithstanding widespread predictions that corporate entities would quickly storm the exculpation exits once permitted to do so.

Our study makes two contributions to the …

Do Patents Drive Investment In Software?, James Hicks

Scholarship@WashULaw

In the wake of a quartet of Supreme Court decisions which disrupted decades of settled law, the doctrine of patentable subject matter is in turmoil. Scholars, commentators, and jurists continue to disagree sharply over which kinds of invention should be patentable. In this debate, no technology has been more controversial than software. Advocates of software patents contend that denying protection would stymie innovation in a vital industry; skeptics argue that patents are a poor fit for software, and that the social costs of patents outweigh any plausible benefits. At the core of this disagreement is a basic problem: the debate …

Measuring The Impacts Of Experiential Legal Education, Robert R. Kuehn, Peter A. Joy

Scholarship@WashULaw

Experiential legal education has become an essential component of the law school curriculum, emphasizing "learning by doing" through practical experiences in law clinics, externships, and simulation courses. This pedagogical approach offers law students the critical skills and professional values required for effective and ethical practice. Despite its recognized importance and parallels with other professional disciplines, legal education still requires minimal experiential education compared to other professions. This reluctance likely stems from lingering skepticism by some legal educators about its value, even though many stakeholders, including other educators, students, recent graduates, and other legal professionals, acknowledge its significance.

This Article is …

Arrow's Theorem And Legislative Intent, Nevin Johnson

Campbell Law Review

A number of prominent legal and political theorists have argued, on the basis of Kenneth Arrow's "impossibility" theorem, that the concept of legislative intent is irredeemably problematic. These Arrow-inspired intent skeptics most significantly argue that the presence ofintransitive legislative preferences (cycling) means there is no such thing as the "will of the majority" and hence no such thing as a coherent legislative intent. Legislative outcomes, not being attributable to the will of the majority, are instead the result of things like agenda control. Other theorists defend legislative intent against these challenges, tending to focus on contingent legislative characteristics that make …

Lawyering In The Age Of Artificial Intelligence, Jonathan H. Choi, Monahan Amy, Daniel Schwarcz

Scholarship@WashULaw

We conducted the first randomized controlled trial to study the effect of AI assistance on human legal analysis. We randomly assigned law school students to complete realistic legal tasks either with or without the assistance of GPT-4, tracking how long the students took on each task and blind-grading the results. We found that access to GPT-4 only slightly and inconsistently improved the quality of participants’ legal analysis but induced large and consistent increases in speed. AI assistance improved the quality of output unevenly—where it was useful at all, the lowest-skilled participants saw the largest improvements. On the other hand, AI …

Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …

Advancing Chronic Kidney Disease Prediction Through Machine Learning And Deep Learning With Feature Analysis, Shiddarth Dey Tusar, S. M. Ahad Ali Chowdhury, Md. Jalal Uddin Chowdhury, Rana M. Pir, H. M. Nur A. Alam, Muhammad Rezaur Rahman, Md. Nural Absar Siddiky, Muhammad Enayetur Rahman

Electrical & Computer Engineering Faculty Publications

Chronic Kidney Diesease (CKD) is a significant health issue, ranking as the fourth leading cause of mortality worldwide. The traditional diagnosis and treatment process, reliant on medical experts, is time-consuming. Therefore, thereis an urgent need for more efficient diagnostic methods to improve patient outcomes and reduce mortality rates. In this study, we employ Machine Learning (ML) and Deep Learning (DL) techniques to predict CKD based on important features. Feature analysis was performed using a correlation matrix and the LASSO algo-rithm to identify the most relevant features for model training. We evaluated several ML and DL classifiers, including Logistic Regression (LR), …

Interannual Growth-Climate Relationships Of Western Larch After Wildfire In The Northwest, Junior Burks

Undergraduate Theses, Professional Papers, and Capstone Artifacts

Montane and mixed-conifer forests in the northwestern United States are burning at rates greater than any time in recent decades, due to the combined impacts of global warming and historical and contemporary land use and land management. Western larch (Larix occidentalis) is a tree of high regional significance, exhibiting a variety of traits that make it resistant and resilient to fire. Because seedlings are more sensitive to environmental stressors than adults, the impacts of climate change are expected to be detectable first in juvenile trees. Recent research shows that the natural regeneration of western larch after wildfires has …

Understanding The Effects Of Integrated Movement Interventions On Engagement Outcomes In The High School Ela Classroom, Alexis M. Wigdorski

Undergraduate Theses, Professional Papers, and Capstone Artifacts

No abstract provided.

Supporting Dataset For The Influence Of Tile-Water On Groundwater Nitrate Concentrations Within A Saturated Riparian Buffer Zone, Aminat Tosin Abdulsalam, Eric Wade Peterson

Faculty Publications - Geography, Geology, and the Environment

In the U.S. Midwest, where fertile soils with high water retention are prevalent, the installation of tile drainage networks have become a common practice to drain excess soil water, which enhances plant growth and increases crop productivity. However, tile drainage networks coupled with the use of inorganic fertilizers have significant implications on water quality, contributing to eutrophication, leading to harmful algal blooms, and resulting in hypoxic conditions in surface water bodies. To mitigate excess nutrient exports to surface water bodies, edge-of-field practices have been introduced. One such practice is the saturated riparian buffer (SRB), which utilizes a diversion system to …

Petrologic Relationship Between Lamprophyres, Carbonatites, And Heavy Rare-Earth Element Enriched Breccias At Hicks Dome, Jarek Trela, Jared T. Freiburg, Esteban Gazel, Laurence Nuelle, Anton H. Maria, David H. Malone, John M. Molinarolo

Faculty Publications - Geography, Geology, and the Environment

New petrological, geochemical, and P–T modelling results from igneous samples clarify how carbonatite-lamprophyre magmatism, fluorite and rare earth element (REE) enrichment are petrogenetically related in southern Illinois. P–T modelling reveals that igneous rocks derive from a deep mantle carbonated source, that is consistent with trace element signatures for a fluorine-rich transition zone origin. Major element systematics suggests liquid-immiscibility with lamprophyric melts as the origin for Ca-carbonatites. Heavy REE (HREE) enrichments in Hicks Dome breccias likely formed through preferential partitioning and transport of HREE by brine-melts, exsolved from a deep carbonatite body. Brine-melts redistributed HREEs throughout the …

Transport And Fate Of Nitrate In A Saturated Buffer Zone As Assessed With A Chloride Tracer Test, Alhassan Sahad, Eric W. Peterson

Faculty Publications - Geography, Geology, and the Environment

The Upper Mississippi Basin, which includes Illinois, has highly fertile soils and experiences intensive agricultural practices. Although fertile, the soils do not drain well, resulting in the installation of tile-drainage systems. The practices of tile systems coupled with the application of nitrogen-rich fertilizers have led to the excessive export of nitrate from the agricultural fields into surface waters, contributing to eutrophication and the development of hypoxic zones in aquatic environments. Saturated riparian buffer zones (SRBs) have been proposed as a means to reduce the amount of nitrate discharged from tile-drained waters into streams. Previous works show a reduction in the …