Physical Sciences and Mathematics Commons^™

Time Series Based Road Traffic Accidents Forecasting Via Sarima And Facebook Prophet Model With Potential Changepoints, Edmund F. Agyemang, Joseph A. Mensah, Eric Ocran, Enock Opoku, Ezekiel N.N. Nortey

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Road traffic accident (RTA) is a critical global public health concern, particularly in developing countries. Analyzing past fatalities and predicting future trends is vital for the development of road safety policies and regulations. The main objective of this study is to assess the effectiveness of univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) and Facebook (FB) Prophet models, with potential change points, in handling time-series road accident data involving seasonal patterns in contrast to other statistical methods employed by key governmental agencies such as Ghana's Motor Transport and Traffic Unit (MTTU). The aforementioned models underwent training with monthly RTA data spanning …

A Low-Power Analog Cell For Implementing Spiking Neural Networks In 65 Nm Cmos, John S. Venker, Luke Vincent, Jeff Dix

Computer Science and Computer Engineering Faculty Publications and Presentations

A Spiking Neural Network (SNN) is realized within a 65 nm CMOS process to demonstrate the feasibility of its constituent cells. Analog hardware neural networks have shown improved energy efficiency in edge computing for real-time-inference applications, such as speech recognition. The proposed network uses a leaky integrate and fire neuron scheme for computation, interleaved with a Spike Timing Dependent Plasticity (STDP) circuit for implementing synaptic-like weights. The low-power, asynchronous analog neurons and synapses are tailored for the VLSI environment needed to effectively make use of hardware SSN systems. To demonstrate functionality, a feedforward Spiking Neural Network composed of two layers, …

Ionically Crosslinked Cellulose Nanocrystals By Metal Nitrates For The Preparation Of Stable Emulsions With Tunable Interface Properties, Joseph Batta-Mpouma, Gurshagan Kandhola, Jin-Woo Kim

Materials Science and Engineering Faculty Publications and Presentations

Biologically extracted cellulose nanocrystals (CNCs) are rod-like and amphiphilic materials with surface-exposed (hydrophilic sites) and hidden (hydrophobic sites) hydroxyl groups. These physicochemical characteristics make CNCs suitable for use as emulsifying agents to stabilize emulsions. Stable oil-in-water emulsions, using sulfated (i.e., – SO₃−) CNCs that were ionically crosslinked with alkaline-earth (i.e., Mg ²⁺) or transition-d-block (i.e., Zn ²⁺) metal cations, were developed without the use of any synthetic surfactants or prior functionalization of pure CNCs with hydrophobic molecules. Various emulsion surface properties such as interfacial tension, surface charge, surface chemistry, as well as rheology were characterized. Ionically …

A Nonparametric Test For Comparing Survival Functions Based On Restricted Distance Correlation, Quinyang Zhang

Mathematical Sciences Faculty Publications and Presentations

In this article, we propose an omnibus test for comparing two survival functions under non-proportional hazards. The test statistic is based on a product-limit estimate of the restricted distance correlation, which is closely related to the L₂ distance between survival curves. The strong consistency is established under mild regularity conditions. Our simulation studies show that the new test has satisfactory power under proportional hazard and various non-proportional hazards settings including delayed treatment effect, diminishing effect, and crossing survival curves; therefore, it can be a competitive alternative to the existing omnibus tests such as Kolmogorov-Smirnov test, Cramer-von Mises test, two-stage …

Interpreting Codebert For Semantic Code Clone Detection, Shamsa Abid, Xuemeng Cai, Lingxiao Jiang

Research Collection School Of Computing and Information Systems

Accurate detection of semantic code clones has many applications in software engineering but is challenging because of lexical, syntactic, or structural dissimilarities in code. CodeBERT, a popular deep neural network based pre-trained code model, can detect code clones with a high accuracy. However, its performance on unseen data is reported to be lower. A challenge is to interpret CodeBERT's clone detection behavior and isolate the causes of mispredictions. In this paper, we evaluate CodeBERT and interpret its clone detection behavior on the SemanticCloneBench dataset focusing on Java and Python clone pairs. We introduce the use of a black-box model interpretation …

Software Composition Analysis For Vulnerability Detection: An Empirical Study On Java Projects, Lida Zhao, Sen Chen, Zhengzi Xu, Lyuye Zhang, Jiahui Wu, Jun Sun, Yang Liu

Research Collection School Of Computing and Information Systems

Software composition analysis (SCA) tools are proposed to detect potential vulnerabilities introduced by open-source software (OSS) imported as third-party libraries (TPL). With the increasing complexity of software functionality, SCA tools may encounter various scenarios during the dependency resolution process, such as diverse formats of artifacts, diverse dependency imports, and diverse dependency specifications. However, there still lacks a comprehensive evaluation of SCA tools for Java that takes into account the above scenarios. This could lead to a confined interpretation of comparisons, improper use of tools, and hinder further improvements of the tools. To fill this gap, we proposed an Evaluation Model …

Deeparc: Modularizing Neural Networks For The Model Maintenance, Xiaoning Ren, Yun Lin, Yinxing Xue, Ruofan Liu, Jun Sun, Zhiyong Feng, Jinsong Dong

Research Collection School Of Computing and Information Systems

Neural networks are an emerging data-driven programming paradigm widely used in many areas. Unlike traditional software systems consisting of decomposable modules, a neural network is usually delivered as a monolithic package, raising challenges for some maintenance tasks such as model restructure and re-adaption. In this work, we propose DeepArc, a novel modularization method for neural networks, to reduce the cost of model maintenance tasks. Specifically, DeepArc decomposes a neural network into several consecutive modules, each of which encapsulates consecutive layers with similar semantics. The network modularization facilitates practical tasks such as refactoring the model to preserve existing features (e.g., model …

Customer Cybersecurity And Supplier Cost Management Strategy, Xu Yang, Peng Liang, Nan Hu, Fujing Xue

Research Collection School Of Computing and Information Systems

In this paper, we explore the spillover effect of customer firms’ data breaches on their upstream supplier firms’ cost management strategies, proxied by cost stickiness. Our primary analyses suggest that data breaches suffered by customer firms are associated with a decrease in cost stickiness among supplier firms. Furthermore, the reductions in supplier cost stickiness are stronger if suppliers are managed by CEOs from national cultural groups with high uncertainty avoidance, low long-term orientations, and/or low individualism. In sum, the findings contribute to both Information Systems (IS) and Operations Management (OM) disciplines in terms of data breach, cost management strategy, and …

On The Effect Of Emotion Identification From Limited Translated Text Samples Using Computational Intelligence, Madiha Tahir, Zahid Halim, Muhmmad Waqas, Shanshan Tu

Research outputs 2022 to 2026

Emotion identification from text data has recently gained focus of the research community. This has multiple utilities in an assortment of domains. Many times, the original text is written in a different language and the end-user translates it to her native language using online utilities. Therefore, this paper presents a framework to detect emotions on translated text data in four different languages. The source language is English, whereas the four target languages include Chinese, French, German, and Spanish. Computational intelligence (CI) techniques are applied to extract features, dimensionality reduction, and classification of data into five basic classes of emotions. Results …

Using Gamification To Foster Student Resilience And Motivation To Learn, And Using Games To Teach Significance Testing Concepts In The Statistics Classroom, Todd Partridge

All Graduate Theses and Dissertations, Fall 2023 to Present

Two studies are outlined in this dissertation.

In the first study, elements of Super Mario Bros. videos games were used to change the way college students in a beginners’ statistics course were graded on their work. This was part of an effort to help students remain optimistic in the face of challenging coursework and even failure on assignments and tests. The study shows that the changes made to the grading structure did help students to keep trying and to use the materials given to them by their professor until they achieved their desired grade in the course, and suggests ways …

Analyzing The Impacts Of Beaver Dam And Beaver Dam Analog Complexes To Stream Ecology Within The Intermountain West, J. Marshall Wolf

All Graduate Theses and Dissertations, Fall 2023 to Present

Streams, rivers, and their floodplains throughout the world are impaired due to human modifications. Recent research has demonstrated that restoration projects prioritizing the proper functioning of ecosystems have better restoration outcomes than projects which focus on restoring form alone. Throughout North America, beaver-mediated restoration is becoming a leading method for improving the functioning of stream ecosystems that are in a degraded state. In areas where beaver are absent or the habitat is too degraded to currently permit their dam building, man-made beaver dam analogs (BDAs) are being used to restore stream habitat with an eye to future beaver recolonization. However, …

An Ensemble Approach For Mapping Snow Water Equivalent In Utah, Logan Schneider

All Graduate Theses and Dissertations, Fall 2023 to Present

Mountain snowpack is an important resource for water management planning in Utah. Snow water equivalent (SWE) is the amount of water contained in a snowpack. A few organizations predict SWE throughout the United States but struggle making accurate predictions in mountainous regions. Weather stations provide accurate measurements of SWE but have limited spatial coverage that hinders the ability to make accurate estimates statewide. This thesis examines the accuracy of current models and proposes using local weather measurements to improve upon national level predictions. An R statistical software package named rsnodas implements this process while allowing the public access to a …

Multi-Objective Water Management In Idaho's Henrys Fork Watershed: Leveraging Reservoir Operation And Groundwater Pathways To Benefit Aquatic Habitat, Christina N. Morrisett

All Graduate Theses and Dissertations, Fall 2023 to Present

Multi-user water management is a challenging arena further complicated by climate change. This research is based in the Henrys Fork, Snake River, Idaho—an agricultural watershed that exemplifies those throughout the semi-arid American West. This dissertation uses an integrated approach that considers groundwater-river relationships, farm-scale decisions and basin-scale outcomes, upstream reservoir operation for downstream aquatic habitat, water rights, and collaborative stakeholder management to identify drought adaptation strategies accordingly.

Chapter 2 uses an interdisciplinary approach to quantify how improvements to irrigation efficiency at the farm-scale (i.e., converting from flood to sprinkler irrigation) can add up to affect hydrology at the landscape-scale and …

Synthesis And Characterization Of Colorimetric And Flurometric Chemosensor For Trivalent Metal Ions, Fazila Haque

Honors Program Theses and Research Projects

There are many biological and environmental uses for trivalent metals (like Fe3+, Al3+, and Cr3+) within our daily lives. Whether these trivalent metals are found in deficiency or excess can cause issues within our environment (global warming) or our bodies (can cause problems with our digestive, cardiovascular, and immune systems). Being able to detect them and understanding their importance becomes crucial in our understanding of the world that surrounds us.
One of the ways we can detect these trivalent metals is through the use of chemosensors. Chemosensors are used to sense analyte (trivalent metal) to produce a signal (fluorescence or …

Global Dataset Of Soil Organic Carbon In Tidal Marshes, Tania L. Maxwell, André S. Rovai, Maria F. Adame, Janine B. Adams, José Álvarez-Rogel, William E. N. Austin, Kim Beasy, Francesco Boscutti, Michael E. Böttcher, Tjeerd J. Bouma, Richard H. Bulmer, Annette Burden, Shannon A. Burke, Saritta Camacho, Doongar R. Chaudhary, Gail L. Chmura, Margareth Copertino, Grace M. Cott, Christopher Craft, John Day, Carmen B. De Los Santos, Lionel Denis, Weixin Ding, Joanna C. Ellison, Carolyn J. E. Lewis, Luise Giani, Maria Gispert, Swanne Gontharet, José A. González-Pérez, M. Nazaret González-Alcaraz, Connor Gorham, Anna E. L. Graversen, Anthony Grey, Roberta Guerra, Qiang He, James R. Holmquist, Alice R. Jones, José A. Juanes, Brian P. Kelleher, Karen E. Kohfeld, Dorte Krause-Jensen, Anna Lafratta, Paul S. Lavery, Edward A. Laws, Carmen Leiva-Dueñas, Pei S. Loh, Catherine E. Lovelock, Carolyn J. Lundquist, Peter I. Macreadie, Inés Mazarrasa, J. Patrick Megonigal, Joao M. Neto, Juliana Nogueira, Michael J. Osland, Jordi F. Pagès, Nipuni Perera, Eva-Maria Pfeiffer, Thomas Pollmann, Jacqueline L. Raw, María Recio, Ana C. Ruiz-Fernández, Sophie K. Russell, John M. Rybczyk, Marek Sammul, Christian Sanders, Rui Santos, Oscar Serrano, Matthias Siewert, Craig Smeaton, Zhaoliang Song, Carmen Trasar-Cepeda, Robert R. Twilley, Marijn Van De Broek, Stefano Vitti, Livia V. Antisari, Baptiste Voltz, Christy N. Wails, Raymond D. Ward, Melissa Ward, Jaxine Wolfe, Renmin Yang, Sebastian Zubrzycki, Emily Landis, Lindsey Smart, Mark Spalding, Thomas A. Worthington

Research outputs 2022 to 2026

Tidal marshes store large amounts of organic carbon in their soils. Field data quantifying soil organic carbon (SOC) stocks provide an important resource for researchers, natural resource managers, and policy-makers working towards the protection, restoration, and valuation of these ecosystems. We collated a global dataset of tidal marsh soil organic carbon (MarSOC) from 99 studies that includes location, soil depth, site name, dry bulk density, SOC, and/or soil organic matter (SOM). The MarSOC dataset includes 17,454 data points from 2,329 unique locations, and 29 countries. We generated a general transfer function for the conversion of SOM to SOC. Using this …

Key Communication Technologies, Applications, Protocols And Future Guides For Iot-Assisted Smart Grid Systems: A Review, Md Ohirul Qays, Iftekhar Ahmad, Ahmed Abu-Siada, Md Liton Hossain, Farhana Yasmin

Research outputs 2022 to 2026

Towards addressing the concerns of conventional power systems including reliability and security, establishing modern Smart Grids (SGs) has been given much attention by the global electric utility applications during the last few years. One of the key advantageous of SGs is its ability for two-way communication and bi-directional power flow that facilitates the inclusion of distributed energy resources, real time monitoring and self-healing systems. As such, the SG employs a large number of digital devices that are installed at various locations to enrich the observability and controllability of the system. This calls for the necessity of employing Internet of Things …

Vegetated Coastal Ecosystems In The Southwestern Atlantic Ocean Are An Unexploited Opportunity For Climate Change Mitigation, Vanessa Hatje, Margareth Copertino, Vinicius F. Patire, Ximena Ovando, Josiah Ogbuka, Beverly J. Johnson, Hilary Kennedy, Pere Masque, Joel C. Creed

Research outputs 2022 to 2026

Vegetated coastal ecosystems (mangroves, seagrasses, and saltmarshes, often called Blue Carbon ecosystems) store large carbon stocks. However, their regional carbon inventories, sequestration rates, and potential as natural climate change mitigation strategies are poorly constrained. Here, we systematically review organic carbon storage and accumulation rates in vegetated coastal ecosystems across the Central and Southwestern Atlantic, extending from Guyana (08.28°N) to Argentina (55.14°S). We estimate that 0.4 Pg organic carbon is stored in the region, which is approximately 2-5% of global carbon stores in coastal vegetated systems, and that they accumulate 0.5 to 3.9 Tg carbon annually. By ecosystem type, mangroves have …

Gut Microbial Communities Of Hybridising Pygmy Angelfishes Reflect Species Boundaries, Megan J. Huggett, Jean-Paul A. Hobbs, Federico Vitelli, Michael Stat, Tane H. Sinclair-Taylor, Michael Bunce, Joseph D. Dibattista

Research outputs 2022 to 2026

Hybridisation and introgression of eukaryotic genomes can generate new species or subsume existing ones, with direct and indirect consequences for biodiversity. An understudied component of these evolutionary forces is their potentially rapid effect on host gut microbiomes, and whether these pliable microcosms may serve as early biological indicators of speciation. We address this hypothesis in a field study of angelfishes (genus Centropyge), which have one of the highest prevalence of hybridisation within coral reef fish. In our study region of the Eastern Indian Ocean, the parent fish species and their hybrids cohabit and display no differences in their diet, behaviour, …

Multi-Scale Mapping Of Australia’S Terrestrial And Blue Carbon Stocks And Their Continental And Bioregional Drivers, Lewis Walden, Oscar Serrano, Mingxi Zhang, Zefang Shen, James Z. Sippo, Lauren T. Bennett, Damien T. Maher, Catherine E. Lovelock, Peter I. Macreadie, Connor Gorham, Anna Lafratta, Paul S. Lavery, Luke Mosley, Gloria M. S. Reithmaier, Jeffrey J. Kelleway, Sabine Dittmann, Fernanda Adame, Carlos M. Duarte, John B. Gallagher, Pawel Waryszak, Paul Carnell, Sabine Kasel, Nina Hinko-Najera, Rakib Hassan, Madeline Goddard, Alice R. Jones, Raphael A. Viscarra Rossel

Research outputs 2022 to 2026

The soil in terrestrial and coastal blue carbon ecosystems is an important carbon sink. National carbon inventories require accurate assessments of soil carbon in these ecosystems to aid conservation, preservation, and nature-based climate change mitigation strategies. Here we harmonise measurements from Australia’s terrestrial and blue carbon ecosystems and apply multi-scale machine learning to derive spatially explicit estimates of soil carbon stocks and the environmental drivers of variation. We find that climate and vegetation are the primary drivers of variation at the continental scale, while ecosystem type, terrain, clay content, mineralogy and nutrients drive subregional variations. We estimate that in the …

A Comprehensive Assessment Of Anthropogenic And Natural Sources And Sinks Of Australasia's Carbon Budget, Yohanna Villalobos, Josep G. Canadell, Elizabeth D. Keller, Peter R. Briggs, Beata Bukosa, Donna L. Giltrap, Ian Harman, Timothy W. Hilton, Miko U. F. Kirschbaum, Ronny Lauerwald, Liyin L. Liang, Taylor Maavara, Sara E. Mikaloff-Fletcher, Peter J. Rayner, Laure Resplandy, Judith Rosentreter, Eva M. Metz, Oscar Serrano, Benjamin Smith

Research outputs 2022 to 2026

Regional carbon budget assessments attribute and track changes in carbon sources and sinks and support the development and monitoring the efficacy of climate policies. We present a comprehensive assessment of the natural and anthropogenic carbon (C-CO2) fluxes for Australasia as a whole, as well as for Australia and New Zealand individually, for the period from 2010 to 2019, using two approaches: bottom-up methods that integrate flux estimates from land-surface models, data-driven models, and inventory estimates; and top-down atmospheric inversions based on satellite and in situ measurements. Our bottom-up decadal assessment suggests that Australasia's net carbon balance was close to carbon …

Substantial Blue Carbon Sequestration In The World’S Largest Seagrass Meadow, Chuancheng Fu, Sofia Frappi, Michelle N. Havlik, Wells Howe, S. David Harris, Elisa Laiolo, Austin J. Gallagher, Pere Masqué, Carlos M. Duarte

Research outputs 2022 to 2026

Seagrass meadows are important sinks for organic carbon and provide co-benefits. However, data on the organic carbon stock in seagrass sediments are scarce for many regions, particularly The Bahamas, which accounts for up to 40.7% of the documented global seagrass area, limiting formulation of blue carbon strategies. Here, we sampled 10 seagrass meadows across an extensive island chain in The Bahamas. We estimate that Bahamas seagrass meadows store 0.42–0.59 Pg organic carbon in the top-meter sediments with an accumulation rate of 2.1–2.9 Tg annually, representing a substantial global blue carbon hotspot. Autochthonous organic carbon in sediments decreased from ~1980 onwards, …

Wastewater Treatment Plants: The Missing Link In Global One-Health Surveillance And Management Of Antibiotic Resistance, Abdolmajid Gholizadeh, Mehdi Khiadani, Maryam Foroughi, Hadi Alizade Siuki, Hadi Mehrfar

Research outputs 2022 to 2026

Introduction: As a global public health crisis, antibiotic resistance (AR) should be monitored and managed under the One-Health concept according to the World Health Organization (WHO), considering the interconnection between humans, animals, and the environment. But this approach often remains focused on human health and rarely on the environment and its compartments, especially wastewater as the main AR receptor. Wastewater treatment plants (WWTPs) not only are not designed for reliving AR but also provide appropriate conditions for enhancing AR through different mechanisms. Methods: By reviewing the research-based statistics on the inclusion of WWTPs in the One-Health/AR program crisis, this paper …

Investigating A Passive Treatment System For The Removal Of Nutrients From Urban Runoff, Jasminn Gray

UNLV Theses, Dissertations, Professional Papers, and Capstones

The Environmental Protection Agency recognizes urban runoff as a major contributor of surface water pollution with nutrients as the second largest cause of surface water impairment in the United States. While a water quality standard of 100 lbs/day total phosphorus load allocation for all nonpoint sources is permitted for the Las Vegas Valley (LVV) under the National Pollutant Discharge Elimination System, currently there is not a counterpart standard for nitrogen. With the continued development of the LVV and the depletion of the quantity of water in Lake Mead due to the ongoing drought, the concentrations of phosphorus and nitrogen species …

Observational Uncertainty For Global Drought-Pluvial Volatility, Yichan Li, Linyin Cheng, Chiyuan Miao, Zhiyong Liu

Geosciences Faculty Publications and Presentations

Droughts and pluvials have occurred in most regions in the past. However, what calls growing attention is the additive effects of these two opposite extreme events occurring in spatial-temporal proximity to one another, sometimes beyond either of the hazards individually. This study examines the likelihood of global drought-pluvial volatility which involves both meteorological drought-to-pluvial (DTP) and pluvial-to-meteorological drought transitions; meanwhile, identifies discrepancies and agreements among the widely used observations for such events, an aspect that remains currently overlooked. Globally, we find that the observation-based data sets including Global Precipitation Climatology Center (GPCC), Climate Research Unit (CRU) and ERA5 reach a …

Uncertainty In Streamflow Measurements Significantly Impacts Estimates Of Downstream Nitrate Export, Shannon L. Speir, C. Nathan Jones, Arial J. Shogren, Carla L. Atkinson

Crop, Soil and Environmental Sciences Faculty Publications and Presentations

Across watershed science, two key variables emerge–streamflow and solute concentration–which serve as the basis for efforts ranging from basic watershed biogeochemistry research to policy decisions surrounding watershed management. However, we rarely account for how error in discharge (Q) impacts estimates of downstream nutrient loading. Here, we examined the impact of uncertainty in streamflow measurements on estimates of downstream nitrate export using publicly available data from the U.S. Geological Survey (USGS). We characterized how uncertainty in stage-discharge relationships impacts annual flux estimates across 70 USGS gages. Our results indicate the interquartile range of relative error in Q was 33% across these …

Discourses Across Periods Of Time, Amanda Raquel Moreno

Theses

This literature review explores the revolutionary effect of generative artificial intelligence (AI) and virtual reality (VR) on digital art history, specifically concentrating on their capacity to enable dialogical exchanges with historical figures and deepen the understanding of artworks. This study considers the current state of research, detecting key methodologies, areas of improvement, and possible challenges and ethical concerns. The example historical figure used in this analysis is the iconic Mexican artist Frida Kahlo. Kahlo’s refusal to correspond to a specific artistic style makes her an ideal subject for generative AI and VR-based investigation, offering fresh insights into her work. The …

Foundations Of Memory Capacity In Models Of Neural Cognition, Chandradeep Chowdhury

Master's Theses

A central problem in neuroscience is to understand how memories are formed as a result of the activities of neurons. Valiant’s neuroidal model attempted to address this question by modeling the brain as a random graph and memories as subgraphs within that graph. However the question of memory capacity within that model has not been explored: how many memories can the brain hold? Valiant introduced the concept of interference between memories as the defining factor for capacity; excessive interference signals the model has reached capacity. Since then, exploration of capacity has been limited, but recent investigations have delved into the …

Influence Of Pavement Conditions On Commercial Motor Vehicle Crashes, Stephen Arhin, Babin Manandhar, Adam Gatiba

Mineta Transportation Institute

Commercial motor vehicle (CMV) safety is a major concern in the United States, including the District of Columbia (DC), where CMVs make up 15% of traffic. This research uses a comprehensive approach, combining statistical analysis and machine learning techniques, to investigate the impact of road pavement conditions on CMV accidents. The study integrates traffic crash data from the Traffic Accident Reporting and Analysis Systems Version 2.0 (TARAS2) database with pavement condition data provided by the District Department of Transportation (DDOT). Data spanning from 2016 to 2020 was collected and analyzed, focusing on CMV routes in DC. The analysis employs binary …

Gr-493 Advancing Non-Invasive Glucose Monitoring Through Integrated Physical Factors And Wavelength Optimization, El Arbi Belfarsi

C-Day Computing Showcase

This work examines the effects of physical factors like skin tone, temperature, thickness, and humidity on the performance of GlucoCheck, a non-invasive glucose monitoring device using IR technology. It delves into how these variables influence light absorption and scattering in the skin, affecting IR image quality in GlucoCheck. The research addresses how skin humidity alters transmittance, and skin temperature and color diversely impact light absorption. These findings underscore the importance of considering these variables to improve glucose level predictions. We propose a data collection strategy using advanced sensors for real-time acquisition of these factors, integrating them into the algorithm for …

Uc-424 Ai Limitations For Web Devlopment, Jacob J. Lalicata, Josh Garske, Thomas Rowlinson, Matthew Madrigal, Muyiwa E. Adewumi

C-Day Computing Showcase

This research project delves into the exploratory journey of using an AI (Artificial Intelligence), specifically ChatGPT, to assist in developing an auction website. Highlighting the iterative process of problem identification, solution finding, and implementation during development, this project aims to furnish insights into leveraging AI capabilities while addressing its limitations. Through this, developers and AI enthusiasts can gain a comprehensive understanding of effective collaboration with AI, addressing common pitfalls, and devising solutions during software development.