Digital Commons Network^™

Empowering The Future Of Learning: Integrating Fourth Industrial Revolution (4ir) Technology In Enugu State Secondary School Libraries To Enhance Literacy Level Among Students, Cyriacus Sochinwekene Aneke

Library Philosophy and Practice (e-journal)

The study examined the integration of Fourth Industrial Revolution (4IR) technology in Enugu State secondary school libraries to enhance literacy level among students. The research design adopted for this study was Descriptive Survey Design. The population of the study is derived from ten (10) schools from each of the six (6) education zones in Enugu State. The intended population was 1, 044, 890 students and 60 school/teacher librarians, but Taro Yamane's formula resulted in 399 students and 52 school/teacher librarians. Structured questionnaire was used for data collection, and the collected data were analyzed using frequency, mean and standard deviation. The …

Collaborative Fisheries Research Reveals Reserve Size And Age Determine Efficacy Across A Network Of Marine Protected Areas, Shelby L. Ziegler, Rachel O. Brooks, Lyall F. Bellquist, Jennifer E. Caselle, Steven G. Morgan, Timothy J. Mulligan, Benjamin I. Ruttenberg, Brice X. Semmens, Richard M. Starr, Joe Tyburczy, Dean E. Wendt, Andre Buchheister, Jose R.Marin Jarrin, Christina Pasparakis, Salvador J. Jorgensen, Jennifer A. Chiu, Jordan Colby, Connor L. Coscino, Leon Davis

Faculty Research, Scholarly, and Creative Activity

A variety of criteria may influence the efficacy of networks of marine protected areas (MPA) designed to enhance biodiversity conservation and provide fisheries benefits. Meta-analyses have evaluated the influence of MPA attributes on abundance, biomass, and size structure of harvested species, reporting that MPA size, age, depth, and connectivity influence the strength of MPA responses. However, few empirical MPA evaluation studies have used consistent sampling methodology across multiple MPAs and years. Our collaborative fisheries research program systematically sampled 12 no-take or highly protective limited-take MPAs and paired fished reference areas across a network spanning 1100 km of coastline to evaluate …

Achievement Of Target Gain Larger Than Unity In An Inertial Fusion Experiment, H. Abu-Shawareb, R. Acree, P. Adams, J. Adams, B. Addis, R. Aden, P. Adrian, B. B. Afeyan, M. Aggleton, L. Aghaian, A. Aguirre, D. Aikens, J. Akre, F. Albert, M. Albrecht, B. J. Albright, Liam Stanton, Kenneth Wharton, For Full Author List, See Comments Below

Faculty Research, Scholarly, and Creative Activity

On December 5, 2022, an indirect drive fusion implosion on the National Ignition Facility (NIF) achieved a target gain Gtarget of 1.5. This is the first laboratory demonstration of exceeding "scientific breakeven"(or Gtarget>1) where 2.05 MJ of 351 nm laser light produced 3.1 MJ of total fusion yield, a result which significantly exceeds the Lawson criterion for fusion ignition as reported in a previous NIF implosion [H. Abu-Shawareb et al. (Indirect Drive ICF Collaboration), Phys. Rev. Lett. 129, 075001 (2022)PRLTAO0031-900710.1103/PhysRevLett.129.075001]. This achievement is the culmination of more than five decades of research and gives proof that laboratory fusion, based …

Intellectual Property Rights And Copyright Laws In The Regime Of Artificial Intelligence (Ai) In India, Hemavathy C

Library Philosophy and Practice (e-journal)

Artificial Intelligence (AI) has been developing for two decades. The application of AI is budding quickly in business dealings, corporate communication and legal services. AI and Law Forms are increasingly important in the legal arena as they play a significant role in the economy and society. Scientists and policymakers together are facing some of the hardest problems with the advancement of machine learning, cryptology and data protection. This paper is very helpful for policymakers, economists, lawyers and technocrats in the aspect of the ethical use of AI in data protection, privacy, security and social corners turns into very relevant issues …

Bjet_Mcmc: A New Tool To Automatically Fit The Broadband Spectral Energy Distributions Of Blazars, Olivier Hervet, Caitlin A. Johnson, Adrian Youngquist

Faculty Research, Scholarly, and Creative Activity

Multiwavelength observations are now the norm for studying blazars’ various states of activity, classifying them, and determining the possible underlying physical processes driving their emission. Broadband emission models became unavoidable tools for testing emission scenarios and setting the values of physical quantities such as the magnetic field strength, Doppler factor, or shape of the particle distribution of the emission zone(s). We announce here the first public release of a new tool, Bjet_MCMC, that can automatically fit the broadband spectral energy distributions (SEDs) of blazars. The complete code is available on GitHub and allows for testing leptonic synchrotron self-Compton models with …

Long-Term Network Structure Evolution Investigation For Sustainability Improvement: An Empirical Analysis On Global Top Full-Service Carriers, Wendong Yang, Yun Jiang, Yulin Chi, Zhengjia Xu, Wenbin Wei

Faculty Research, Scholarly, and Creative Activity

The continuous and strategic planning of full-service carriers plays a prominent role in transferring and adapting them into resilient full-service carrier network structures. The exploration of full-service carrier network structures using the latest long-term empirical data facilitates enhancing cognitive capabilities in aspects of identifying network development tendencies, readjusting network structures, and supporting determinations of strategic business routes. Aiming at providing sustainable transport network solutions with historical long-term network structure analysis, this paper researches the global top 10 full-service carriers’ air transport networks from 2007 to 2022, applied using social network analysis (SNA). The static metrics from local to path-based perspectives …

Remaining Useful Life Prediction Using Graph Convolutional Attention Networks With Temporal Convolution-Aware Nested Residual Connections, Yupeng Wei, Dazhong Wu, Janis Terpenny

Faculty Research, Scholarly, and Creative Activity

Degradation of engineered systems can result in poor performance and failure. Graph Convolutional Networks (GCNs) have been used to predict the remaining useful life (RUL) of engineered systems by analyzing condition monitoring data. Conventional GCNs typically stack multiple spectral graph convolutional layers, where each layer aggregates condition monitoring data and then projects the aggregated data into another feature space. However, conventional GCNs suffer from two issues. Firstly, repeated aggregation operations affect the temporal correlation of condition monitoring data. Secondly, repeated aggregation and projection operations may generate less significant features, resulting in poor prediction performance. To address these issues, we introduce …

Content Analysis And Applicability Of Zipf’S Law In Technical Writing In The Domain Of Library & Information Science, Pallavi Ramkrushna Dhoke Miss, Mohan Kherde Dr

Library Philosophy and Practice (e-journal)

Through content analysis of articles published in library and information science journals, this work aims to analyse, determine, and apply Zipf's Law in the technical writing of library and information science. DESIDOC Journal of Library and Information Technology, Annals of Library and Information Studies, and Library Herald are the journals that were picked for this research. The examination of the 573 articles that were published in the three mentioned journals during the years 2013 to 2017 is the primary focus of the study. If we assume that an abstract should be between 100 and 200 words in length, then 342 …

Data-Driven Control Of Acoustic Waves Using Movable And Flexible Scatterers, Noam Smilovich

Master's Theses

Partial Differential Equations (PDEs) serve as fundamental tools in scientific and engineering disciplines, modeling phenomena ranging from material design to climate dynamics. Developing robotic systems capable of controlling PDE-governed systems, particularly when these phenomena are only partially observable, has the potential to drive significant technological advancements. This work presents a framework that leverages physics-informed machine learning (ML) for the control of PDEs. At the core of this approach is an agent equipped with sensors that generate a low-dimensional, physics-informed representation of the environment, enabling the derivation of optimal sparse control policies for the agent’s actuators. The focus is on manipulating …

Thermal Runaway Analysis And Cooling Strategies For Nmc 523 Li-Ion Battery Packs Using Pcm, Heat Pipes, And Air Cooling, Vijethvardhan Singh

Master's Theses

The growing demand for electric vehicles (EVs) necessitates efficient thermal management of lithium-ion batteries (LIBs), especially NMC 523 cells. This thesis investigates thermal runaway (TR) mechanisms and evaluates cooling strategies to mitigate overheating risks. Using ANSYS Fluent, the TR of four cells in series was simulated and validated with Jun Wu et al.'s experimental data, achieving an error margin below 10%. Peak temperatures reached 1,126°C in the first cell, with delayed propagation in adjacent cells. Sensitivity analysis of voltage, current, and state of charge (SOC) under varying C-rates was performed using a multi-scale multi-domain (MSMD) battery model. A logarithmic voltage …

Carbon Cycle–Climate Feedbacks In The Post-Paris World, David S. Schimel, Dustin Carroll

Faculty Research, Scholarly, and Creative Activity

The Paris Agreement calls for emissions reductions to limit climate change, but how will the carbon cycle change if it is successful? The land and oceans currently absorb roughly half of anthropogenic emissions, but this fraction will decline in the future. The amount of carbon that can be released before climate is mitigated depends on the amount of carbon the ocean and terrestrial ecosystems can absorb. Policy is based on model projections, but observations and theory suggest that climate effects emerging in today’s climate will increase and carbon cycle tipping points may be crossed. Warming temperatures, drought, and a slowing …

Schools’ Records Management Also Matters: An Information Governance Maturity Model Assessment Of Records Management Practices At Mufakose 4 Secondary School In Harare, Zimbabwe, Patrick Mandizha, Samson Mutsagondo

Library Philosophy and Practice (e-journal)

There has been a tendency to focus on universities and colleges each time researchers look at records management within the educational sector. Primary and secondary schools have many times been left behind, yet schools’ records management also matters. Using the ARMA International’s Information Governance Maturity Model, this study assesses how Mufakose 4 Secondary School in Zimbabwe fared in its records management programme with special focus on the eight principles of the ARMA International’s Information Governance Maturity Model, that is, accountability, transparency, integrity, protection, compliance, availability, retention and disposition. Each of these principles were further assessed on a scale of 1 …

Uncovering Weaknesses In Autonomous Driving: A Formal Approach To Adversarial Scenario Generation And Falsification, Carlos O. Hernandez

Master's Theses

Autonomous vehicles utilize advanced safety features like proactive driving assistance and pre-collision alerts to minimize the risk of accidents. However, evaluating the correct functionality of these systems is complex. First, safety systems are highly sophisticated, integrating software, networking, and hardware components, many of which rely on advanced artificial intelligence and machine learning algorithms. Second, an array of dynamic factors, including numerous actors and physical variables, can influence the performance of safety mechanisms during critical scenarios. Each actor’s unique behavior introduces unpredictability, making it difficult to anticipate future states and outcomes. This thesis presents a comprehensive framework for testing autonomous vehicle …

Assessing Sediment Migration And Salmonid Habitat Recovery Following A Small Dam Removal And A Flood, Mill Creek, California, Usa, Madeline C. Doyle

Master's Theses

In 2021, the lower Mill Creek Dam (~4 m tall, ~8 m wide) was removed from the Mill Creek-San Vicente Creek watershed (18 km2), located in the Santa Cruz Mountains. Topographic surveys and pebble counts were performed at 27 transects four times between 2021 and 2023 to compare changes in channel characteristics and grain size to document the response to a small dam removal and the transition between drought and flood conditions. The goals of this study were to: (1) describe the movement of impounded sediment; (2) assess the success of habitat restoration for two endangered anadromous fish species: coho …

Sdebuddy - Code Documentation Using Large Language Models, Nischay Nagendra

Master's Projects

In this fast developing world of software development, it is crucial to maintain the quality of code and the developers’ productivity. This can be done effectively with good code documentation. SDEBuddy uses the latest generation of Large Language Models (LLMs) and finetuning procedures to create code documentation. In this project, state-of-the-art models such as Llama2 and Llama3 are employed to mimic the behavior of the given code and produce documentation. Such models are tuned for various programming languages and documentation formats using LoRA and QLoRA fine-tuning approaches. These models are evaluated in terms of the BLEU score, ROUGE score and …

Predicting Remaining Useful Life Of Turbofan Engines On Cmapss And N-Cmapss Using Deep Recurrent Neural Networks, Samaikya Tippareddy

Master's Projects

Aircraft engines are susceptible to failure at multiple points over their lifespan and need replacement or repairs. The ability to proactively determine how long an engine will function helps avoid fatalities and build a reliable prognostic system. To accomplish this, predictive models are being developed using various approaches like physics-based and data-driven techniques. Physics-based models need huge computing power for simulations and domain knowledge for understanding and implementing the models. Alternatively, if we have substantial data for prediction, data-driven models can be used. In this research, we use data-driven approach for engine Remaining-Useful-Life (RUL) prediction on the NASA Commercial Modular …

Advancing Phishing Protection: Employing Sophisticated Methods For Precise Url Evaluation, Abbhinav Reddie Nomuiia

Master's Projects

In short, the incidence of phishing - the illegal act of people pretending to be well-known companies to secure personal information - has skyrocketed in the past few years. In 2022 alone, 300,000 consumers in the United States were captured by scammers using phishing techniques, losing in all over $50 million. In the span of two weeks, over 510 million attempts occurred in a variety of sectors, particularly instant messaging platforms, package delivery businesses, and digital currency trading. Since most businesses have recognized that they are prone to these exposure cases, there has been a sixty percent increase in businesses …

Multiclass Lung Disease Classification In Chest X-Ray Images: A Fine-Tuned Hybrid Cnn-Gnn Approach Using Transfer Learning And Feature Extraction, Vinay Vilas Khade

Master's Projects

In clinical practice, it is still difficult to accurately diagnose lung diseases from chest X-ray (CXR) images. In this study, we propose a new hybrid method for identifying several types of lung diseases using CXR images by combining Convolutional Neural Networks (CNNs) with Graph Neural Networks (GNNs). The framework of our proposed methodology takes advantage of CNN’s ability to extract detailed visual features and GNN’s capacity to understand complex relationships between these features, enabling comprehensive analysis in a multi-class classification setting of COVID-19, pneumonia, and normal lung conditions. We used multiple transfer learning models such as DenseNet201, VGG16, VGG19, MobileNetV2, …

Reinforcement Learning And Hidden Markov Models For Simulating And Analyzing Social Engineering Attacks., Bharkavi Sachithanandam

Master's Projects

The advent of the internet has revolutionized communication and connectivity on a global scale. Now every computer is connected to the internet. Although this technological advancement has made human life easier, this has also led to an increase in sophisticated methods of exploitation. Social Engineering is such a prominent threat to the human community. Social engineering attackers manipulate the victim into giving away sensitive details. Understanding the dynamics of social engineering is crucial for developing measures to help individuals and organizations avoid falling prey to these deceptive tactics. Hence it is essential to understand the attackers. Thus gaining insight into …

Ai Generated Text Detection & Source Identification, Anjana Priyatham Tatavarthi

Master's Projects

The detection of AI-generated text by the application of advanced machine learning techniques not only presents a promising approach toward distinguishing human-written content from machine-generated text, but also identifies the source model used for the generation of the text. This helps address the growing concerns about authenticity and accountability in digital communication. The difference between human-generated and AI-generated text lies in the core of several applications, from news media to academic integrity.It also helps in ensuring the transparency and trust in content-driven environments. However, existing models fall short in accurately detecting AI-generated text and identifying the specific AI source due …

Deep-Learning Approaches To Predict Remaining Useful Life Of Hard Disks, Rohan Mohapatra

Master's Projects

On a daily basis, data centers process huge volumes of data using inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life (RUL) of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. We …

Regional Sea Level Rise Prediction In Monterey Bay With Lstms And Vertical Land Motion, Branden Lopez

Master's Projects

Earth system data is vast in volume and variety, and is used to forecast weather,

hurricanes, floods, and sea level. Sea Level Rise (SLR) impacts various sectors, espe- cially ecosystems, food production, industry, population, health, and the availability of

clean water. Because of its broad impact, describing the behavior and forecasting SLR is an important topic. Traditional Machine Learning (ML) models vary in use, but many are not capable of capturing all the non-linear spatial and temporal properties of SLR factors. Deep learning models efficaciously handle complex time series data, noise, and high dimensional spaces, making them a focus of …

Advancing Discourse Analysis In Multiparty Meetings: Comprehensive Classification Of Argument And Relation Types, Vishal Vaitla

Master's Projects

In multi-party meetings, accurately analyzing dialogue is crucial for enhancing communication effectiveness and decision-making. However, the informal and dynamic nature of these discussions presents complex challenges for computational analysis. Dialogues in such settings often include non-standard language, interruptions, and rapid topic changes, making it difficult to extract useful information with conventional text analysis tools. To tackle this challenge, two specific methods were developed:

Argument Classification: We use machine learning models like Gradient Boosting to identify and categorize the main points people make in their discussions. This helps us understand what each person is trying to say, making it easier to …

Breaking The Cycle: Countering Popularity Bias For Diverse Content Discovery, Brandon J. Weaver

Master's Projects

The ways most people consume the media have become very much driven by some pre-set algorithms. It is increasingly important to examine the outcome of these artificial intelligence (AI) models and ensure that any potentially dangerous long-term effects are addressed before they have a significant negative impact in our society. Popularity bias is one of these potentially harmful impacts, which stemmed from the shift from human intelligence to AI, or machine intelligence/machine learning (ML), when one explores the media and receives recommendations (often without requesting). In ML, three key steps usually occur; i.e, pre-processing, in-processing, and post- processing steps. The …

Teaching Children Programming Concepts Through Video Games, Kayla Musleh

Master's Projects

Children have a tendency to lose focus when they are presented with something that does not entertain them or tailor to their personal interests; such as studying [1], [3], [10]. The research performed for this project focuses on studying how much more children can comprehend and focus on learning educational material if they are learning through playing a video game rather than being taught information directly in a typical classroom manner. For this study I created a computer game designed to introduce educational subjects such as mathematics and programming concepts to the child playing the game. By completing the tasks …

Improving The Diversity And Fairness In Job Recommendations Using The Stable Matching Algorithm, Jatin Unecha

Master's Projects

In today’s competitive job market, recommendation systems are essential for con- necting job seekers with suitable opportunities. However, traditional recommendation

models, such as collaborative and content-based filtering face challenges in ensuring diversity and fairness. This results in low diversity and high congestion around popular job listings which refers to the problem where a few jobs are recommended to large numbers of users as well as imbalanced job exposure. These drawbacks lower the satisfaction for job applicants and reduce the employers’ ability to reach a broader pool of candidates. In this research we address these challenges by introducing the Gale-Shapley algorithm …

A Fresh Take: Seasonal Changes In Terrestrial Freshwater Inputs Impact Salt Marsh Hydrology And Vegetation Dynamics, Maya S. Montalvo, Emilio Grande, Anna E. Braswell, Ate Visser, Bhavna Arora, Erin C. Seybold, Corianne Tatariw, John C. Haskins, Charlie A. Endris, Fuller Gerbl, Mong Han Huang, Darya Morozov, Margaret A. Zimmer

Faculty Research, Scholarly, and Creative Activity

Salt marshes exist at the terrestrial-marine interface, providing important ecosystem services such as nutrient cycling and carbon sequestration. Tidal inputs play a dominant role in salt marsh porewater mixing, and terrestrially derived freshwater inputs are increasingly recognized as important sources of water and solutes to intertidal wetlands. However, there remains a critical gap in understanding the role of freshwater inputs on salt marsh hydrology, and how this may impact marsh subsurface salinity and plant productivity. Here, we address this knowledge gap by examining the hydrologic behavior, porewater salinity, and pickleweed (Sarcocornia pacifica also known as Salicornia pacifica) plant productivity along …

Enhancing Medical Chatbots With Image Diagnosis, Swatisri Chavali

Master's Projects

Medical chatbots, at the conjunction of artificial intelligence and healthcare, are the very cornerstone of a transformative force in diagnostic capabilities and communication channels for healthcare professionals. The history of this journey, from early chatbot models to sophisticated systems, is born out of a relentless pursuit of accuracy and contextual understanding. This proposal acknowledges the critical role played by NLTK in raising the interpretability and communicative capabilities of intelligent systems, meeting challenges that arise from varying writing styles and accommodating the standards of the medical field. The integration of NLTK is a linchpin, bridging the gap between sophisticated technological architectures …

Leading From The Middle: Arts Administrators' Beliefs About The Impact Of Arts Education On High-Needs Students, Sofia Fojas

Dissertations

Research on arts education notes the positive impact of arts education on educational outcomes for students, particularly students of color, students in poverty, and students needing additional academic support. Students living in low-income communities receive different educational opportunities than those in high-wealth districts. This disparity is especially true for highly mobile and high-needs students of color. Arts education has made positive differences in student outcomes. This qualitative study examined the beliefs of county arts leads, leaders in the middle, situated between the California Department of Education and their local school districts, and how they can be empowered to be change …

Fake Malware Generation Using Gans As Api Calls, Saieswar Reddy Vaka

Master's Projects

With malware threats on the rise, they have also grown more complicated and subtle. Consequently, incorporating cutting-edge machine learning into cybersecurity defenses has never been more crucial. Nevertheless, building resilient machine-learning models is a significant challenge due to the need for existing diversified and complete malware datasets. This project will relieve this difficulty by employing a Generative

Adversarial Network (GAN) to develop artificial malware samples featuring an Appli- cation Programming Interface (API) call series. While traditional generative modeling

has primarily been limited to image-based fields, we offer an “outside the box” domain – malware signature generation – as an API …