Engineering Commons^™

Reduced Metal Nanocatalysts For Selective Electrochemical Hydrogenation Of Biomass-Derived 5-(Hydroxymethyl)Furfural To 2, 5-Bis(Hydroxymethyl)Furan In Ambient Conditions, Baleeswaraiah Muchharla, Moumita Dikshit, Ujjwal Pokharel, Ravindranath Garimella, Adetayo Adedeji, Kapil Kumar, Wei Cao, Hani Elsayed-Ali, Kishor Kumar Sadasivuni, Naif Abdullah Al-Dhabi, Sandeep Kumar, Bijandra Kumar

Chemistry & Biochemistry Faculty Publications

Selective electrochemical hydrogenation (ECH) of biomass-derived unsaturated organic molecules has enormous potential for sustainable chemical production. However, an efficient catalyst is essential to perform an ECH reaction consisting of superior product selectivity and a higher conversion rate. Here, we examined the ECH performance of reduced metal nanostructures i.e., reduced Ag (rAg) and reduced copper (rCu) prepared via electrochemical or thermal oxidation and electrochemical reduction process, respectively. Surface morphological analysis suggests formation of nanocoral and entangled nanowire structure formation for rAg and rCu catalysts. rCu exhibits slight enhancement in ECH reaction performance in comparison to the pristine Cu. However, the rAg …

A Structured Narrative Prompt For Prompting Narratives From Large Language Models: Sentiment Assessment Of Chatgpt-Generated Narratives And Real Tweets, Christopher J. Lynch, Erik J. Jensen, Virginia Zamponi, Kevin O'Brien, Erika Frydenlund, Ross Gore

VMASC Publications

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent-based models and simulations consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Simulated agents generate large volumes of data and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents' perspectives on key life events by providing natural language narratives. However, these narratives should be factual, transparent, and reproducible. Therefore, we present a structured narrative prompt …

Analyzing Spatio-Temporal Mechanisms Of Land Subsidence In The Parowan Valley, Utah, Usa, Jiawei Li, Ryan G. Smith, Katherine R. Grote

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Parowan Valley, Utah (USA), is an agricultural region experiencing rapid subsidence due to extensive groundwater extraction from aquifers with a significant portion of fine-grained sediments. To analyze the subsidence spatio-temporally, time-series Interferometric Synthetic Aperture Radar (InSAR) of 155 Sentinel-1 C-band scenes were processed. These data showed approximately 30 cm of ground subsidence in Parowan Valley from 2014 to 2020. Because of the high temporal sampling rate of the Sentinel-1 satellite (12-day cycle), it is possible to determine the seasonal changes of ground deformation and relate this to groundwater extraction. To better understand the relationship between ground deformation and groundwater extraction …

Electrical Resistivity Tomography Revealing Possible Breaching Mechanism Of A Late Pleistocene Long-Lasted Gigantic Rockslide Dam In Diexi, China, Lanxin Dai, Xuanmei Fan, Dan Wang, Fanyu Zhang, Ali P. Yunus, Srikrishnan Siva Subramanian, J. David Rogers, Hans Balder Havenith

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Landslide damming is a widespread phenomenon worldwide and significantly affects the evolution of fluvial landscapes. However, it is rarely witnessed from an antiquities perspective, and the case for observing their internal structure is challenging. We attempt to visualize the subsurface structure and understand the likely breaching mechanism of the late Pleistocene Diexi gigantic landslide dam (longevity of ~ 10 ka), using electrical resistivity tomography (ERT) method. Eight ERT measurements on the Diexi dam body revealed high resistivity zones near the periphery and lower resistivity zones in the middle portion of the profiles. Geomorphological mapping based on the LiDAR data determined …

A Novel Technique For The Quantitative Determination Of Wettability Of A Severely Heterogeneous Tight Carbonate Reservoir, Saleh Al-Sayegh, Ralph E. Flori, Waleed Al-Bazzaz, Abdulaziz Abbas, Ali Qubian, Hasan Al-Saedi

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The objective of this study is to accurately measure the wettability contact angle of a cretaceous carbonate reservoir in a vertical well set-up known for as an unconventional tight carbonate oil reservoir. Also, to investigate the relative heterogeneity of these samples using digitally captured images; these images accurately capture natural pore-system in this carbonate rock samples and their wettability performance attributed towards building a vertical depth wettability/heterogeneity model. To capture, measure and model natural tight matrix static contact angle wettability in order to understand their new physics that will advance unconventional tight oil reservoir characterization. Entire vertical well depth reservoir …

Impact Of Composition And Salinity On Swelling And Gel Strength Of Poly (Acrylamide-Co-Acrylic Acid) Preformed Particle Gel, Ahmed Ben Ali, Reem Elaf, Mohammed Saad, Ibnelwaleed A. Hussein, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The effects of various material compositions and reservoir environments on the ultimate strength and swelling kinetics of a commercial preformed particle gel (PPG) have been investigated. This study used different ratios of acrylamide and acrylic acid copolymers with a specific crosslinker concentration. Results have indicated that increasing the acrylic acid proportion enhances the PPGs' ability to swell but weakens their network structure. In contrast, increasing the crosslinker content decreases the swelling ratio and increases the gel strength. The highest equilibrium swelling capacity among the six preformed particle gel samples was obtained for PPG2, which has the highest acrylic acid amount …

The Success Story Of First Ever Polymer Flood Field Pilot To Enhance The Recovery Of Heavy Oils On Alaska's North Slope, Abhijit Dandekar, Baojun Bai, John Barnes, Dave Cercone, Reid Edwards, Samson Ning, Randy Seright, Brent Sheets, Dongmei Wang, Yin Zhang

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The primary goal of the first ever polymer flood field pilot at Milne Point is to validate the use of polymers for heavy oil Enhanced Oil Recovery (EOR) on Alaska North Slope (ANS). The specific objectives are systematic evaluation of advanced technology that integrates polymer flooding, low salinity water flooding, horizontal wells, and numerical simulation based on polymer flood performance data. Accordingly, under the co-sponsorship of the US Department of Energy and Hilcorp Alaska LLC the first ever polymer field pilot commenced on August 28, 2018 in the Schrader Bluff heavy oil reservoir at the Milne Point Unit (MPU) on …

Kinetic Experimental And Modeling Evaluations Of Asphaltene Morphology And Growth Rate Under Varying Temperature And Brine Conditions, Kwamena Ato Quainoo, Abdulmohsin Imqam, Cornelius Borecho Bavoh

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

The utilization of predictive mechanisms to resolve asphaltene precipitation during oil production is a cleaner and less expensive means than the mechanical/chemical remediation techniques currently employed. Existing models lack predictive success due to opposing views on temperature-asphaltene precipitation interactions. In this study, the effect of varying temperatures (40, 50, 60, 70 80 and 90 °C) and brine concentrations (0 – 5 wt.%) on the long-time kinetics of asphaltene precipitations was evaluated. A series of experiments were conducted using the filtration technique and the confocal microscopy to study asphaltene precipitation on a model oil system consisting of asphaltenes, a precipitant, and …

Risk As A Process: A History Informed Hazard Planning Approach Applied To The 2018 Post-Fire Debris Flows, Montecito, California, Anna Serra-Llobet, John Radke, G. Mathias Kondolf, Larry Gurrola, J. David Rogers, Sarah Lindbergh, Johnny Douvinet

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Historical information about floods is not commonly used in the US to inform land use planning decisions. Rather, the current approach to managing floods is based on static maps derived from computer simulations of the area inundated by floods of specified return intervals. These maps provide some information about flood hazard, but they do not reflect the underlying processes involved in creating a flood disaster, which typically include increased exposure due to building on flood-prone land, nor do they account for the greater hazard resulting from wildfire. We developed and applied an approach to analyze how exposure has evolved in …

Transport And Plugging Performance Evaluation Of A Novel Re-Crosslinkable Microgel Used For Conformance Control In Mature Oilfields With Super-Permeable Channels, Adel Alotibi, T. Song, Baojun Bai, Thomas P. Schuman

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Preformed particle gels (PPG) have been widely applied in oilfields to control excessive water production. However, PPG has limited success in treating opening features because the particles can be flushed readily during post-water flooding. We have developed a novel micro-sized Re-crosslinkable PPG (micro-RPPG) to solve the problem. The microgel can re-crosslink to form a bulk gel, avoiding being washed out easily. This paper evaluates the novel microgels' transport and plugging performance through super-permeable channels. Micro-RPPG was synthesized and evaluated for this study. Its storage moduli after fully swelling are approximately 82 Pa. The microgel characterization, self-healing process, transportation behavior, and …

Investigating Pore Body, Pore Throat, Nano-Pore Wettability Preference In Several Unconventional Kuwaiti Carbonate Reservoirs, Saleh Al-Sayegh, Ralph E. Flori, Hussain Alajaj, Waleed Hussien Al-Bazzaz

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This study will investigate measuring the wettability contact angles of native unconventional tight carbonate as well as other unconventional pore system reservoir samples that hosts varied pore shapes and subsequent wettability contact angle distributions in both reservoir matrix and possible natural fractures. Also, the investigation will include validation of the grain/ pore-wall wettability regions and classify the natural wettability preference available inside pores of the rock and their overall wettability performance and recovery efficiency contributions. Further investigation will include modeling pore throat contact angle wettability, and to understand their new physics that will advance reservoir characterization and oil recovery improvement.

Impact Of Hvfr Loading Concentrations On Its Viscoelastic Properties And Proppant Transport Performance, Ghith Biheri, Shari Dunn Norman, Abdulmohsin Imqam

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

In unconventional reservoirs, increasing the low formation conductivities through hydraulic fracturing may substantially increase oil production from wells. This work of improving the fracture conductivity could be conducted by distributing and transporting proppant within the fracture lengths using high fracture fluid concentrations. This research focused on three areas. First, it assessed the fluid rheology including the viscosity and elasticity of a high viscosity friction reducer (HVFR) and linear guar using deionized (DI) water at different fluid concentrations (i.e., 2, 4, and 8 gpt for the HVFR and 15, 25, and 35 ppt for linear guar), and measured the impact of …

Superheating Field In Superconductors With Nanostructured Surfaces, W. P. M. R. Pathirana, A. Gurevich

Physics Faculty Publications

We report calculations of a dc superheating field H_sh in superconductors with nanostructured surfaces. Numerical simulations of the Ginzburg-Landau (GL) equations were performed for a superconductor with an inhomogeneous impurity concentration, a thin superconducting layer on top of another superconductor, and superconductor-insulator-superconductor (S-I-S) multilayers.The superheating field was calculated taking into account the instability of the Meissner state with a nonzero wavelength along the surface, which is essential for realistic values of the GL parameter κ. Simulations were done for the materials parameters of Nb and Nb₃Sn at different values of κ and the mean free paths. We …

Machine-Assisted Discovery Of Integrable Symplectic Mappings, T. Zolkin, Y. Kharkov, S. Nagaitsev

Physics Faculty Publications

We present a new automated method for finding integrable symplectic maps of the plane. These dynamical systems possess a hidden symmetry associated with an existence of conserved quantities, i.e., integrals of motion. The core idea of the algorithm is based on the knowledge that the evolution of an integrable system in the phase space is restricted to a lower-dimensional submanifold. Limiting ourselves to polygon invariants of motion, we analyze the shape of individual trajectories thus successfully distinguishing integrable motion from chaotic cases. For example, our method rediscovers some of the famous McMillan-Suris integrable mappings and ultradiscrete Painlevé equations. In total, …

Graph Based System For Evidential Reasoning, Divyarajsinh Chauhan

Master's Projects

In the modern data driven world, graph editing tools have become very essential as they provide means to understand, visualize and manipulate complex relationships between various datasets. They have especially played a crucial role in the space of evidential reasoning, where it has made a significant impact in the decision making process by developers, analysts and researchers to understand and represent the connection in the data. Existing tools fail to handle huge amounts of data efficiently and also don’t have the features required to handle tasks related to evidential reasoning.To address these gaps, we developed Pygrapher Web UI tool. We …

Pygrapherconnect, Shubham Jain

Master's Projects

The evolving landscape of backend computational systems especially in biomedical research involving heavy data operations which have a gap of not being used properly. It is due to the lack of communication standard between the frontend and backend. This gap presents a problem to researchers who need to use the frontend for visualizing and manipulating their data but also want to do complex analysis. CAPRI a python-based backend system specializing in analyzing Evidential Reasoning data also has the same issue. This project offers a solution PyGrapherConnect module acting as a data conversion layer between CAPRI and PyGrapher, its frontend interface. …

Gesture Recognition With Deep Learning, Chaz Chang

Master's Projects

Gesture recognition is a machine learning and computer vision application where gestures are detected from videos. This project uses pose estimation to find the coordinates of important joints as a preprocessing step before trying to classify the gesture. Machine learning layers such as Convolutional Neural Network and Long Short-Term Memory are used. Various types of machine learning models are trained. The accuracy and f1 score of each model are compared. Feature selection is done by testing with different subsets of features. The results show that pose estimation as a preprocessing step provides good accuracy for gesture recognition. The results also …

Prediction Of 2024 Indian Pm Election Results Using Sentiment Analysis On Twitter Data, Surabhi Gupta

Master's Projects

This sentiments analysis study presents a methodical approach to predict the 2024 Indian Prime Minister Election. Data collected spanning from 2020 to 2023 from Twitter using hashtags such as IndianPMElection2024 and on topics such as the revocation of the special status of Jammu and Kashmir, the Farm Bill, and the Digital India initiative, form the core of this research. We utilized a combination of sentiment extraction tools-namely, the NLP Town's Bidirectional Encoder Representations from Transformers (BERT)-based multilingual uncased sentiment model, Valance Aware Dictionary for Sentiment Reasoning (VADER), and TextBlob. Additionally, we used a well-established machine learning model Naive Bayes, deep …

Mild Cognitive Impairment And Alzheimer’S Disease Detection And Testing Interface (Mci-Addti) Modeller10.4 Integrating Structure-Function Prediction Modules, Grant Galileo Jacobson

Master's Projects

In the population of adult human patients who over express Beta and Tau Amyloids, it is unclear why 40% of them do not have Alzheimer’s Disease (AD), when all patients with AD have an overexpression of Beta and Tau Amyloids. The MCI-AD-DTI project’s epigenetic pipeline is an evolving computation tool that seeks epigenetic-related information related to the observed disparity. The MCI-AD-DTI’s epigenetic pipeline’s ability to identify mutations currently relies solely on PyPDB for verification of its protein functionality evaluation. The assessment process of the industry standard application, Modeller10.4, is independent from the current epigenetic pipeline’s protein evaluation algorithm. Thus, this …

Multimap Implementation In Openjdk, Nishant Yadav

Master's Projects

A key-value pair is an elementary data model in which a unique key is associated with a given value. This association between the key and the value allows for a quick lookup of data based on the key and hence is extensively used in programming languages, NoSQL databases, caches, session management, etc. In Java OpenJDK, this elementary data model is implemented by the interface Map, which allows efficient storage and retrieval of data but can only store a single value against each key. In this project, we have implemented a MultiMap data structure in OpenJDK which allows associating multiple values …

Xai-Driven Cnn For Diabetic Retinopathy Detection, Vikas Shenoy Pete

Master's Projects

Diabetes, a chronic metabolic disorder, poses a significant health threat with potentially severe consequences, including diabetic retinopathy, a leading cause of blindness. In this project, we tackle this threat by developing a Convolutional Neural Network (CNN) to support the diagnosis based on eye images. The aim is early detection and intervention to mitigate the effects of diabetes on eye health. To enhance transparency and interpretability, we incorporate explainable AI techniques. This research not only contributes to the early diagnosis of diabetic eye disease but also advances our understanding of how deep learning models arrive at their decisions, fostering trust and …

Serverless Architecture For Machine Learning, Ikshaku Goswami

Master's Projects

Serverless computing is an area under cloud computing which does not require individual management of cloud infrastructure and services. It is the groundwork behind Function as a Service or FaaS cloud computing technique. FaaS provides a stateless event-driven orchestration of functions and services for applications deployed in the cloud, without having to manage the servers and other infrastructure resources. This event driven architecture is being well utilized to manage different web-applications and services. Machine learning can bring a unique challenge to serverless computing, as it involves high-intensive tasks which requires voluminous data. In such a scenario it becomes essential to …

Uncertainty-Aware And Explainable Artificial Intelligence For Identification Of Human Errors In Nuclear Power Plants, Bhavya Reddy Kotla

Master's Projects

Nuclear Power Plants (NPPs) can face challenges in maintaining standard operations due to a range of issues, including human mistakes, mechanical breakdowns, electrical problems, measurement errors, and external influences. Swift and precise detection of these issues is crucial for stabilizing the NPPs. Identifying such operational anomalies is complex due to the numerous potential scenarios. Additionally, operators need to promptly discern the nature of an incident by tracking various indicators, a process that can be mentally taxing and increase the likelihood of human errors. Inaccurate identification of problems leads to inappropriate corrective actions, adversely affecting the safety and efficiency of NPPs. …

A Natural Language Processing Approach To Malware Classification, Ritik Mehta

Master's Projects

Many different machine learning and deep learning techniques have been successfully employed for malware detection and classification. Examples of popular learning techniques in the malware domain include Hidden Markov Models (HMM), Random Forests (RF), Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Recurrent Neural Networks (RNN) such as Long Short-Term Memory (LSTM) networks. In this research, we consider a hybrid architecture, where HMMs are trained on opcode sequences, and the resulting hidden states of these trained HMMs are used as feature vectors in various classifiers. In this context, extracting the HMM hidden state sequences can be viewed as a …

Image-Based Classification Of Malware Using T-Sne Images, Vincent Stowbunenko

Master's Projects

This Master’s project proposes a novel technique for classifying malware using image-based methods. The approach involves generating t-SNE images from the EMBER dataset, which contains one million samples of both malware and benign files, each represented by over 2,000 features. The t-SNE technique is well-suited for capturing intricate patterns in complex datasets because it effectively maintains the local structure. These t-SNE images are then used as inputs to train two lightweight image classification models, SqueezeNet and MobileNet. Additionally, to provide a benchmark for comparison, a non-image classification model using LightGBM is also explored.

As part of the investigation, the project …

Metagenomic Survey Of Marine 16s Bacterial Communities Off Palmer Station In Antarctica, Daniel Salter

Master's Projects

This project surveys the metagenomic bacterial community composition in marine surface waters off Palmer Station, Western Antarctic Peninsula and correlates findings with temperature and salinity data. Marine bacterial communities play a vital role in nutrient cycling, but data on surface waters in this region are limited. Analyzing fifteen samples of 16S sequencing data from three austral summers, consistent dominance was observed by the classes Alphaproteobacteria, Gammaproteobacteria, and Flavobacteria. Correlation analysis confirmed significant relationships between taxa and environmental conditions. The observed trends suggest varying abilities of phyla to resist and adapt to changing environmental conditions. Notably, Alphaproteobacteria demonstrated adaptability to favorable …

Malware Classification Using Opcode N-Grams And Word Embeddings, Siddhita Joshi

Master's Projects

Malware is a serious risk to any software application whether it is standalone or over the network. In order to protect computer systems, it is essential to detect and classify malware effectively. Modern malware classification research focuses on Machine Learning and Deep Learning techniques to identify advanced malicious software. This project explores malware classification by combining two robust methods: n-grams and word embedding. By extracting opcode n-grams, we make use of sequential nature of malware execution to identify any local patterns within the malware executable.

We use word embedding methods such as Word2Vec, Doc2Vec, and FastText to produce dense vector …

Evaluation Of The Effect Of Walnut Extract On Sp1-Related Pathways, Jihan Yehia

Master's Projects

Walnut extract (WE) has shown promising anti-cancer effects, such as inducing apoptosis and moderating cell cycle progression. A previous study by Dr. Brandon White’s Lab at San Jose State University hypothesizes that WE can downregulate the expression of the pro-tumoral specificity protein 1 (Sp1) in triple negative breast cancer (TNBC). This project builds an RNA-seq pipeline that runs differential gene expression (DGE) analysis to study the effect of WE on TNBC, thereby offering a wider perspective on genes that may be affected by this treatment. The data used in this project originated from Illumina and Nanopore sequencing methods, and DGE …

Efficient Video Qoe Prediction In Intelligent O-Rans, Aditya Kulkarni

Master's Projects

Open Radio Access Network (O-RAN) is a platform developed by a collaboration between wireless operators, infrastructure vendors, and service providers for deploying mobile fronthaul and midhaul networks, built entirely on cloud-native principles. The vision of O-RAN lies in the virtualization of traditional wireless infrastructure components, like Central Units (CU), Radio Units (RU), and Distributed Units (DU). O-RAN decouples the above-mentioned wireless infrastructure components into opensource elements, operating consistently with other elements of different vendors in the network. Quality of Experience (QoE) deals with a user’s subjective measure of satisfaction. RAN Intelligent Controller (RIC) in O-RAN provides flexibility to intelligently program …

Unlearning Hidden Bias Between Refugees : An Initial Empirical Investigation, Akshay Sunil Gurnaney

Master's Projects

The challenges that refugees in various regions encounter are common knowledge. One such challenge is a bias among refugees on ethnocentric grounds. In particular, there are various articles that have pointed out the struggles faced by Syrian refugees in countries like Europe as a result of implicit bias. In fact, the media coverage of Syrian crises and the government responses to the same shed negative light on the refugees themselves. On the contrary, the media coverage of Ukrainian crises is very different with lesser restrictions from the governments.

This research attempts to identify the extent of implicit bias between Ukrainian …