Digital Commons Network^™

Analyzing The Impact Of Negative Publicity About Employee Turnover On Customer Loyalty: A Case Study Of Amazon., Nyambane Abedinego Nyambwengi

Business ETD Collections

This study examines the effect of negative publicity regarding employee turnover on customer loyalty, focusing on Amazon. Using in-depth interviews and surveys, the research highlights how social media platforms, particularly Facebook, and digital communication have facilitated the spread of negative information about Amazon's high employee turnover. While findings suggest that persistent negative media coverage could erode customer confidence over time, no statistical correlation between high turnover, negative publicity, and customer loyalty was established due to an insufficient sample size. The study recommends robust public relations strategies to protect corporate reputation and contributes to the understanding of how internal organisational issues …

Investigating Drought Response And Paleoclimate Potential Of A New Network Of White Oak Chronologies In Western Kentucky, Usa, Audrey J. Heichelbech, Maegen L. Rochner, Megan Gibson

Undergraduate Research Events

In Kentucky, historic and paleo-climate data are limited, and current understanding of long-term climate change in the state relies on instrumental data spanning only 1895-present. Proxy data are necessary to extend the temporal and spatial span of climate information. One potential proxy source for Kentucky is tree ring data, but currently, only four such datasets are publicly available on the International Tree Ring Data Bank (ITRDB). Archaeological and archival timber sources may help to fill in this gap. In the 1940s, Florence Hawley-Ellis, the first woman dendrochronologist, collected samples of white oak (Quercus alba L.) from four counties in western …

The Attitudes And Practices Of United Arab Emirates Consumers Towards Food Waste: A Nationwide Cross-Sectional Study, Lynne Kennedy, Samir Safi, Tareq M. Osaili, Ala Al Rajabi, Ayesha Alblooshi, Dima Al Jawarneh, Ahmed Al Kaabi, Fakhra Al Rubaei, Maitha Albreiki, Maryam Alfadli, Aseilah Alhefeiti, Moez Al Islam Ezzat Faris, Kholoud Allaham, Sameeha Junaidi, Moien A.B. Khan

All Works

Background: Reducing global food waste is an international environmental, health, and sus-tainability priority. Although significant reductions have been achieved across the food chain, progress by UAE households and consumers remain inadequate. This study seeks to understand the association between consumer attitudes, knowledge, and awareness relating to food waste practice of residents living in the UAE. to help inform policy and action for addressing this national priority. Methods: A cross-sectional study was conducted using a validated semi-structured online questionnaire through stratified sampling (n =1052). The Spearman correlation coefficient was performed to determine the correlations. Two independent regression analysis were used to …

Petrologic And Geochemical Constraints On The Pre-Eruptive Storage Conditions Of Magmas Erupted During The ~12.5 Ka Flare Up Of Medicine Lake Volcano, Ca, Regina M. Khoury

Cal Poly Humboldt theses and projects

The Medicine Lake Volcano (MLV) system in the California Cascades is composed of a large shield volcano with numerous basaltic to rhyolitic vents on and around the flanks, many of which are aligned with N-S trending normal faults. A significant portion of the post-glacial volcanic activity at MLV is comprised of the ~12.5 ka “flare up” eruptions of basalt and basaltic andesite magmas from seven discrete flank vents. Prior work documented surface areas (0.02-198 km²), volumes (0.0001-4.35 km³), and eruption styles (Hawaiian to Strombolian; Donnelly-Nolan, 2010) of the ~12.5 ka deposits. This study uses new geologic …

The Anti-Inflammatory And Antioxidant Effects Of Melissa Officinalis (Lemon Balm)On Paracetamol Induced Hepatotoxicity In Broiler Chickens, Ghada A. Abou Zead, Sara T. Elazab, Azza E. Hassan, Ahmed I. Ateya, Magdy S. Amer

Mansoura Veterinary Medical Journal

background : This study aimed to investigate the hepatoprotective effects of Melissa officinalis (M officinalis) against paracetamol N-acetyl-p-aminophenol (APAP)-induced hepatotoxicity in broiler chickens. METHODS: A total of 48 one-day-old cobb healthy chicks were divided into four groups of (12 chicks). The control group received a basal diet for 21 days, while the APAP group received APAP at 2 g/kg body weight daily from the 15th day onwards and until the termination of the experiment; M officinalis + APAP group was offered a 10 g/kg diet for 2 weeks followed by APAP for one week; and the APAP + M officinalis …

Enhancing Security And Performance In Vehicular Adhoc Networks: A Machine Learning Approach To Combat Adversarial Attacks, Mustafa Abdulfattah Habeeb, Yahya Layth Khaleel, Ahmed Raheem Abdulnabi

Mesopotamian Journal of Computer Science

Integrating Machine Learning (ML) techniques into Vehicular Adhoc Networks (VANETs) provides promising features in autonomous driving and ITS applications. In this paper, DSRC data is used to evaluate the effectiveness of different ML models, including Naive Bayes, Random Forest, KNN, and Gradient Boosting, in normal and adversarial scenarios. Since the dataset is relatively imbalanced, the Synthetic Minority Over-sampling Technique (SMOTE) is employed for sampling, and defensive distillation for improving model resilience to adversarial perturbations. From the results, it is clear that models such as Gradient Boosting and Random Forest show high accuracy in both cases, thus showing the potential of …

Distribution System Losses Allocation Based On Circuit Theory With Distributed Generation, Ahmed S. Abdelkader, Ibrahim I. Mansy, Abdelfattah A. Eladl

Mansoura Engineering Journal

Electricity energy is the most convenient and efficient form of energy transmission and conversion. Nonetheless, losses take place while electrical power is traveling from power stations through transmission and distribution networks to the loads. Despite the high efficiency of electrical transmission and distribution networks, the huge amounts of power moving through these networks make the financial loss due to the lost energy very high and its management becomes a critical consideration for both utility providers and regulatory bodies. The efficient management of losses encompasses two major functions: the first is running the system in a way that minimizes the losses; …

A Decision-Making Approach To Reduce The Risk Of Measurement Uncertainty For Product Size, Mostafa Awadalla Sedeek, Fatma Abdallah Elerian, Ossama Badie Abouelatta, Mona. A. Aboueleaz

Mansoura Engineering Journal

The measurement uncertainty has a significant impact on quality control processes. This is particularly true of standards that are assessed to determine if a product is compliant. This ambiguity introduces two potential risks of decision-making: accepting a non-conventional product (the consumer's risk) or rejecting a conventional product (the producer's risk). This investigation explores the potential for false decisions caused by measurement errors in product assessments. By studying data near the control limits, a greater understanding of the manufacturing process is intended to facilitate informed decision-making, and guide the implementation of effective quality control methods. The investigation highlights the importance of …

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 …

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 …