Reiq-Mediated Alarmone Signalling Regulates Growth, Stress-Induced Biofilm Formation And Spore Accumulation In Clostridioides Difficile, Areej Malik, Adenrele Oludiran, Asia Poudel, Orlando Berumen Alvarez, Charles Woodward, Erin B. Purcell

Chemistry & Biochemistry Faculty Publications

The bacterial stringent response (SR) is a conserved transcriptional reprogramming pathway mediated by the nucleotide signalling alarmones, (pp)pGpp. The SR has been implicated in antibiotic survival in Clostridioides difficile, a biofilm- and spore-forming pathogen that causes resilient, highly recurrent C. difficile infections. The role of the SR in other processes and the effectors by which it regulates C. difficile physiology are unknown. C. difficile RelQ is a clostridial alarmone synthetase. Deletion of relQ dysregulates C. difficile growth in unstressed conditions, affects susceptibility to antibiotic and oxidative stressors and drastically reduces biofilm formation. While wild-type C. difficile displays increased biofilm …

Reducing The Uncertainty In Estimating Soil Microbial-Derived Carbon Storage, Han Hu, Chao Qian, Ke Xue, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni, Jixian Ding, Weigen Huang, Jingdong Mao, Rong-Xi Tan, Jizhong Zhou, Thomas W. Crowther, Zhi-Hua Zhou, Jiabao Zhang, Yuting Liang

Chemistry & Biochemistry Faculty Publications

Soil organic carbon (SOC) is the largest carbon pool in terrestrial ecosystems and plays a crucial role in mitigating climate change and enhancing soil productivity. Microbial-derived carbon (MDC) is the main component of the persistent SOC pool. However, current formulas used to estimate the proportional contribution of MDC are plagued by uncertainties due to limited sample sizes and the neglect of bacterial group composition effects. Here, we compiled the comprehensive global dataset and employed machine learning approaches to refine our quantitative understanding of MDC contributions to total carbon storage. Our efforts resulted in a reduction in the relative standard errors …

Urban Flood Extent Segmentation And Evaluation From Real-World Surveillance Camera Images Using Deep Convolutional Neural Network, Yidi Wang, Yawen Shen, Behrouz Salahshour, Mecit Cetin, Khan Iftekharuddin, Navid Tahvildari, Guoping Huang, Devin K. Harris, Kwame Ampofo, Jonathan L. Goodall

Civil & Environmental Engineering Faculty Publications

This study explores the use of Deep Convolutional Neural Network (DCNN) for semantic segmentation of flood images. Imagery datasets of urban flooding were used to train two DCNN-based models, and camera images were used to test the application of the models with real-world data. Validation results show that both models extracted flood extent with a mean F1-score over 0.9. The factors that affected the performance included still water surface with specular reflection, wet road surface, and low illumination. In testing, reduced visibility during a storm and raindrops on surveillance cameras were major problems that affected the segmentation of flood extent. …

Contribution Of High Turbidity To Tidal Dynamics In A Curved Channel In Zhoushan Islands, China, Li Li, Fangzhou Shen, Zhiguo He, Gangfeng Ma, Jiachen Wang, Kailong Huangfu

Civil & Environmental Engineering Faculty Publications

The curved tidal channel, Luotou Deep-water Navigational Channel, is the main channel of the Ningbo Zhoushan Port, which is ranked first in the world. Tidal dynamics in the channel are spatially and temporally asymmetric. In this study, the three-dimensional tidal dynamics in the channel were analyzed using field data and simulated using FVCOM. The results show that the tides in the channel flood/ebb along the northern/southern bank near the bottom/surface layer and these asymmetries are due to the imbalanced Coriolis force, centrifugal force, sea-level gradient, and density gradient. Residual current velocity peaks (0.7 m/s) in the middle of the channel …

Modeling Coupled Driving Behavior During Lane Change: A Multi-Agent Transformer Reinforcement Learning Approach, Hongyu Guo, Mehdi Keyvan-Ekbatani, Kun Xie

Civil & Environmental Engineering Faculty Publications

In a lane change (LC) scenario, the lane change vehicle interacts with surrounding vehicles. The interactions not only affect their driving behaviors but also influence the traffic flow. This study aims to model the coupled behavior of the lane changer and the follower in the target lane during LC. Large-scale real-world connected vehicle (CV) data from the Safety Pilot Model Deployment (SPMD) program are used to extract LCs and study vehicle interactions. A multi-agent Transformer-based deep deterministic policy gradient (MA-TDDPG) method is proposed to model the coupled behaviors during LC. The multi-agent framework can handle the multiple agents’ behaviors with …

Implications Of Alternative Communications And Sensing Technologies For Implementing Variable Speed Limit Control Through Connected Vehicles: Sag Curve As A Case Study, Reza Vatani Nezafat, Mecit Cetin, Elizabeth Williams, George F. List

Civil & Environmental Engineering Faculty Publications

Connected vehicles (CVs) will enable various applications to improve traffic flow. This paper's focus is to investigate how the potential implementation of variable speed limit (VSL) through different types of communication and sensing technologies on CVs makes it possible to mitigate congestion at a sag curve bottleneck. A VSL algorithm is developed and implemented in a simulation environment for controlling the inflow of vehicles to a sag curve to minimize delays and increase throughput. Both vehicle-to-vehicle (V2V) and infrastructure-to-vehicle (I2V) options for CVs are investigated when implementing the VSL control strategy in a simulation environment. Also, for measuring traffic density …

Exploring Hedonic And Utilitarian Aspects Through Perceived Warmth In Human-Designed Vs. Ai-Generated Fashion, Dooyoung Choi, Ha Kyung Lee

Educational Leadership & Workforce Development Faculty Publications

Among various ways in which artificial intelligence (AI) is used in the fashion industry, its utilization in design has sparked public discussion about the potential replacement of human designers by AI. Along with this critical question, it is imminent to examine how consumers would respond to designs by AI. The purpose of this study is to explore consumers’ perceptions toward a fashion product labeled as generated by an AI system, comparing it to the same product labeled as designed by a human designer. Specifically, drawing from existing literature, we examine if the design source affects consumers’ perceptions of a product …

Glance To Count: Learning To Rank With Anchors For Weakly-Supervised Crowd Counting, Zheng Xiong, Liangyu Chai, Wenxi Liu, Yongtuo Liu, Sucheng Ren, Shengfeng He

Research Collection School Of Computing and Information Systems

Crowd image is arguably one of the most laborious data to annotate. In this paper, we devote to reduce the massive demand of densely labeled crowd data, and propose a novel weakly-supervised setting, in which we leverage the binary ranking of two images with highcontrast crowd counts as training guidance. To enable training under this new setting, we convert the crowd count regression problem to a ranking potential prediction problem. In particular, we tailor a Siamese Ranking Network that predicts the potential scores of two images indicating the ordering of the counts. Hence, the ultimate goal is to assign appropriate …

Cooperative Trucks And Drones For Rural Last-Mile Delivery With Steep Roads, Jiuhong Xiao, Ying Li, Zhiguang Cao, Jianhua Xiao

Research Collection School Of Computing and Information Systems

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed …

Affinity Uncertainty-Based Hard Negative Mining In Graph Contrastive Learning, Chaoxi Niu, Guansong Pang, Ling Chen

Research Collection School Of Computing and Information Systems

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this …

Provably Secure Decisions Based On Potentially Malicious Information, Dongxia Wang, Tim Muller, Jun Sun

Research Collection School Of Computing and Information Systems

There are various security-critical decisions routinely made, on the basis of information provided by peers: routing messages, user reports, sensor data, navigational information, blockchain updates, etc. Jury theorems were proposed in sociology to make decisions based on information from peers, which assume peers may be mistaken with some probability. We focus on attackers in a system, which manifest as peers that strategically report fake information to manipulate decision making. We define the property of robustness: a lower bound probability of deciding correctly, regardless of what information attackers provide. When peers are independently selected, we propose an optimal, robust decision mechanism …

Conversational Localization: Indoor Human Localization Through Intelligent Conversation, Sheshadri Smitha, Kotaro Hara

Research Collection School Of Computing and Information Systems

We propose a novel sensorless approach to indoor localization by leveraging natural language conversations with users, which we call conversational localization. To show the feasibility of conversational localization, we develop a proof-of-concept system that guides users to describe their surroundings in a chat and estimates their position based on the information they provide. We devised a modular architecture for our system with four modules. First, we construct an entity database with available image-based floor maps. Second, we enable the dynamic identification and scoring of information provided by users through our utterance processing module. Then, we implement a conversational agent that …

Conceptthread: Visualizing Threaded Concepts In Mooc Videos, Zhiguang Zhou, Li Ye, Lihong Cai, Lei Wang, Yigang Wang, Yongheng Wang, Wei Chen, Yong Wang

Research Collection School Of Computing and Information Systems

Massive Open Online Courses (MOOCs) platforms are becoming increasingly popular in recent years. Online learners need to watch the whole course video on MOOC platforms to learn the underlying new knowledge, which is often tedious and time-consuming due to the lack of a quick overview of the covered knowledge and their structures. In this paper, we propose ConceptThread , a visual analytics approach to effectively show the concepts and the relations among them to facilitate effective online learning. Specifically, given that the majority of MOOC videos contain slides, we first leverage video processing and speech analysis techniques, including shot recognition, …

Segac: Sample Efficient Generalized Actor Critic For The Stochastic On-Time Arrival Problem, Honglian Guo, Zhi He, Wenda Sheng, Zhiguang Cao, Yingjie Zhou, Weinan Gao

Research Collection School Of Computing and Information Systems

This paper studies the problem in transportation networks and introduces a novel reinforcement learning-based algorithm, namely. Different from almost all canonical sota solutions, which are usually computationally expensive and lack generalizability to unforeseen destination nodes, segac offers the following appealing characteristics. segac updates the ego vehicle’s navigation policy in a sample efficient manner, reduces the variance of both value network and policy network during training, and is automatically adaptive to new destinations. Furthermore, the pre-trained segac policy network enables its real-time decision-making ability within seconds, outperforming state-of-the-art sota algorithms in simulations across various transportation networks. We also successfully deploy segac …

Efficient Unsupervised Video Hashing With Contextual Modeling And Structural Controlling, Jingru Duan, Yanbin Hao, Bin Zhu, Lechao Cheng, Pengyuan Zhou, Xiang Wang

Research Collection School Of Computing and Information Systems

The most important effect of the video hashing technique is to support fast retrieval, which is benefiting from the high efficiency of binary calculation. Current video hash approaches are thus mainly targeted at learning compact binary codes to represent video content accurately. However, they may overlook the generation efficiency for hash codes, i.e., designing lightweight neural networks. This paper proposes an method, which is not only for computing compact hash codes but also for designing a lightweight deep model. Specifically, we present an MLP-based model, where the video tensor is split into several groups and multiple axial contexts are explored …

Instant3d: Instant Text-To-3d Generation, Ming Li, Pan Zhou, Jia-Wei Liu, Jussi Keppo, Min Lin, Shuicheng Yan, Xiangyu Xu

Research Collection School Of Computing and Information Systems

Text-to-3D generation has attracted much attention from the computer vision community. Existing methods mainly optimize a neural field from scratch for each text prompt, relying on heavy and repetitive training cost which impedes their practical deployment. In this paper, we propose a novel framework for fast text-to-3D generation, dubbed Instant3D. Once trained, Instant3D is able to create a 3D object for an unseen text prompt in less than one second with a single run of a feedforward network. We achieve this remarkable speed by devising a new network that directly constructs a 3D triplane from a text prompt. The core …

Dynamic Meta-Path Guided Temporal Heterogeneous Graph Neural Networks, Yugang Ji, Chuan Shi, Yuan Fang

Research Collection School Of Computing and Information Systems

Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static …

Privobfnet: A Weakly Supervised Semantic Segmentation Model For Data Protection, Chiat Pin Tay, Vigneshwaran Subbaraju, Thivya Kandappu

Research Collection School Of Computing and Information Systems

The use of social media has made it easy to communicate and share information over the internet. However, it also brings issues such as data privacy leakage, which can be exploited by recipients with malicious intentions to harm the sender. In this paper, we propose a deep neural network that analyzes user’s image for privacy sensitive content and automatically locates sensitive regions for obfuscation. Our approach relies solely on image level annotations and learns to (a) predict an overall privacy score, (b) detect sensitive attributes and (c) demarcate the sensitive regions for obfuscation, in a given input image. We validated …

Pias: Privacy-Preserving Incentive Announcement System Based On Blockchain For Internet Of Vehicles, Yonghua Zhan, Yang Yang, Hongju Cheng, Xiangyang Luo, Zhuangshuang Guan, Robert H. Deng

Research Collection School Of Computing and Information Systems

More vehicles are connecting to the Internet of Things (IoT), transforming Vehicle Ad hoc Networks (VANETs) into the Internet of Vehicles (IoV), providing a more environmentally friendly and safer driving experience. Vehicular announcement networks show promise in vehicular communication applications. However, two major issues arise when establishing such a system. Firstly, user privacy cannot be guaranteed when messages are forwarded anonymously, thus the reliability of these messages is in question. Secondly, users often lack interest in responding to announcements. To address these problems, we introduce a Blockchain-based incentive announcement system called PIAS. This system enables anonymous message commitment in a …

Actionpoint: An App To Combat Cyberbullying By Strengthening Parent-Teen Relationships, Maddie Juarez, Natali Barragan, Deborah Hall, George K. Thiruvathukal, Yasin Silva

Computer Science: Faculty Publications and Other Works

Overview: Urgent need for intervention tools to mitigate increase in cyberbullying • ActionPoint is based on parent-teen relationship research • App includes interactive modules to improve family communication and online behavior to combat cyberbullying effects • To be presented in the IEEE World Forum on Public Safety Technology 2024.

Exploring Women`S Perceptions Of Climate Change Impact On Agriculture, Health & Food Security In Upper Hunza, Gilgit Baltistan, Ambreen, Elina Nizar Ali, Taiba Yar Baig, Fozia Parveen

Institute for Educational Development, Karachi

The Hindu Kush Himalayan (HKH) region, renowned for its towering mountains and major river basins, sustains nearly 1.4 billion people worldwide and is pivotal to global food production. Glacial melt from the HKH region nourishes agriculture, livestock, and horticulture, supporting the livelihoods of mountain communities. However, climate change is accelerating the melting of glaciers, and shifts in wet seasons significantly impact food security in these communities. Sectors such as water resources, agricultural land, and human health, particularly women’s health, are significantly affected by these changes. For centuries, agriculture has been the backbone of Gilgit-Baltistan’s economy, with over 70% of livelihoods …

Dependence Of Energy Transfer On Curvature Similarity In Collisions Involving Curved Shock Fronts, Justin Cassell

Dissertations, Master's Theses and Master's Reports

In high-speed collisions of projectiles, pressure exerted by a resulting shock wave is so high that even solids begin to flow and hydrodynamics becomes relevant. Jetting results from hydrodynamic instability driving the evolution of an interface following shock loading. Our emphasis in this dissertation is mitigation of instabilities via several shocks and, specifically, the transfer of energy from a shock wave to another object. To that end, in 2022 and 2023 while on-site, we adapted Lawrence Livermore National Laboratory (LLNL) to explore the effects of shock front geometry on the transfer of energy during shock collisions via numerous computer simulations. …

Optimizing Php Api Calls With Pagination And Caching, Parsharam Reddy Sudda

Dissertations, Master's Theses and Master's Reports

The Keweenaw Time Traveler (KeTT) project is devoted to mapping the historical and social landscapes of the Keweenaw Peninsula. During the project, it was discovered that the server-side performance needed improvement. To address this issue, the "Optimizing PHP API Calls with Pagination and Caching" initiative was launched. This initiative focused on refining API calls, implementing server caching and pagination, and fortifying security against common vulnerabilities. The project successfully mitigated risks associated with SQL Injection and XSS through meticulous code enhancements while improving error handling. Additionally, the introduction of Scroll-Induced Pagination optimized data delivery, significantly reducing response times, and elevating the …

Origin Of Carbonate Mud Mounds In Southwestern Missouri, Jared Mcavoy

MSU Graduate Theses

Small carbonate mud mounds are found in Lower Mississippian strata of southwestern Missouri. These compare favorably with larger mounds, known as Waulsortian mounds, located in the Meuse River valley of southern Belgium. The origin of Waulsortian and similar mounds is unknown, but they commonly are interpreted as accumulations of mud, where the presence of biological organisms responsible for supposed bio-construction remains uncertain. Alternative hypotheses are that these may have been non-cohesive slump or cohesive slide masses. Multiple techniques, including δ13C and δ18O isotopic analyses and unmanned aerial vehicle photography, were used to determine the most feasible working hypothesis. Determining the …

Thermochronology And Exhumation Dynamics Of Metamorphic Units In The Salmon River Suture Zone, Jonathan J. Cone

MSU Graduate Theses

The Salmon River suture zone in western Idaho records the Jurassic-Cretaceous (160-90 Ma) accretion of amalgamated volcanic arc terranes onto the North American continent. Rocks exposed at the surface record burial to depths of more than 20 kilometers, with unclear drivers for uplift and exhumation. Two competing hypotheses have been proposed to explain the transport of deep crustal rocks to the surface: (1) delamination of a dense lithospheric root resulted in rapid isostatic uplift of the crust and (2) exhumation of crustal blocks along thrust faults. To test these models, I present temperature-time (T-t) paths for mid-crustal metamorphic rocks constructed …

An Exploration Of Misconceptions In Introductory Physics, Christopher Mattthew Wheatley

Graduate Theses, Dissertations, and Problem Reports

The study of student misconceptions about physics concepts has long been an important area of inquiry in physics education research (PER). The research discussed in this dissertation builds upon the developments in PER by exploring the prevalence of consistently held undergraduate student misconceptions in introductory calculus-based physics. This thesis explores the nature of student misconceptions, mistakes, and naive answering patterns in both introductory undergraduate Newtonian mechanics and electromagnetism by applying a network analytic technique called module analysis to student responses to different concept inventories from institutions of various levels of incoming physics preparation. Each study applying these methods also demonstrates …

Quantifying Chemical Erosion In The Lithologically-Heterogenous Appalachian Valley And Ridge, Amelia Jayne Zanoni

Graduate Theses, Dissertations, and Problem Reports

Quantifying chemical erosion in the lithologically-heterogenous Appalachian Valley and Ridge

Amelia J. Zanoni

The interplay between physical and chemical erosion is well understood in landscapes underlain by a uniform rock type, but many regions are underlain by a mixture of rock types with varying erosion resistance and solubility. We used measurements of stream-water chemistry to estimate chemical erosion rates in the Appalachian Valley and Ridge, a region where the mechanisms of lithologic control over topography are poorly understood. Newly acquired stream water samples were collected from 51 locations across the research area during the Fall of 2023. The sampled waters …

Using Ontological Methods To Compare Cybersecurity Maturity Model Certification 2.0 And Cobit 19, Aaron Marshall Ramey

CCE Theses and Dissertations

Cybersecurity frameworks developed by a variety of organizations and implemented by a much larger collection of organizations differ in their focus and application. Whether designed by a private or government organization, the primary goal is to provide a framework to assess and reduce risk. The Department of Defense (DoD) has recently implemented the second version of the Cybersecurity Maturity Model Certification (CMMC 2.0). In some situations, compliance with CMMC 2.0 has already become mandatory for the Defense Industrial Base (DIB). Compliance will soon be required for all Large Businesses (LB) and Small Businesses (SB) within the DIB. While COBIT 19 …

Mesmerizing Moon Mysteries: Unraveling The Compositions Of Irregular Mare Patches (Imps) Using Remote Observations, Nicholas G. Piskurich

Graduate Thesis and Dissertation 2023-2024

Compositional characterization of lunar surface features informs our understanding of the Moon's thermal and magmatic evolution. We investigated the compositions of hypothesized volcanic features known as irregular mare patches (IMPs) and their surroundings to constrain formation mechanisms. We used six datasets to assess the composition of 12 IMPs: 1) Moon Mineralogy Mapper (M³) derived spectral parameters (e.g., band center positions, shapes), 2) Lunar Reconnaissance Orbiter (LRO) Diviner Radiometer Experiment (Diviner) measured Christiansen feature (CF) position, 3) SELENE (Kaguya) Multiband Imager (MI) FeO abundance, 4) Clementine 5-band (Ultraviolet/Visible)-derived FeO abundance, 5) LRO Wide Angle Camera (WAC) TiO₂ abundance, …

Doubly Rotating Coordinates: Wave Functions In Magnetic Resonance Problems, Sunghyun Kim

Graduate Thesis and Dissertation 2023-2024

The nuclear spin response to a rotating field H has been theoretically investigated from the 1930s to the 1950s. Building upon Majorana's probability theory, the behavior of spin 1/2 is well-illustrated in the joint review by Rabi, Ramsey, andSchwinger, and their spin wave function ψ is succinctly restated by Gottfried: ψ(t) = e^-iI_zωt/ℏe^{-i[I_z(ω₀-ω)+I_xω₁]t/ℏ}ψ(0).

However, the complexity involved in evaluating the wave function ψ in terms of probability amplitudes C_m attributed to the noncommutative nature of spin operators [I_x, I …