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2024

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Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das Jan 2024

Unsafe Events Detection In Smart Water Meter Infrastructure Via Noise-Resilient Learning, Ayanfeoluwa Oluyomi, Sahar Abedzadeh, Shameek Bhattacharjee, Sajal K. Das

Computer Science Faculty Research & Creative Works

Residential smart water meters (SWMs) collect real-time water consumption data, enabling automated billing and peak period forecasting. The presence of unsafe events is typically detected via deviations from the benign profile of water usage. However, profiling the benign behavior is non-trivial for large-scale SWM networks because once deployed, the collected data already contain those events, biasing the benign profile. To address this challenge, we propose a real-time data-driven unsafe event detection framework for city-scale SWM networks that automatically learns the profile of benign behavior of water usage. Specifically, we first propose an optimal clustering of SWMs based on the recognition …

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Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das Jan 2024

Log Sequence Anomaly Detection Based On Template And Parameter Parsing Via Bert, Xiaolin Chai, Hang Zhang, Jue Zhang, Yan Sun, Sajal K. Das

Computer Science Faculty Research & Creative Works

Logs record various operations and events during system running in text format, which is an essential basis for detecting and identifying potential security threats or system failures and is widely used in system management to ensure security and reliability. Existing log sequence anomaly detection is limited by log parsing and does not consider all key features of logs, which may cause false or missed detection. In this paper, we propose a fast and accurate log parsing method and feed the entire log content into the deep learning network for analysis. To avoid semantic loss during parsing, we replace some variables …

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Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra Jan 2024

Mobilytics: Mobility Analytics Framework For Transferring Semantic Knowledge, Shreya Ghosh, Soumya K. Ghosh, Sajal K. Das, Prasenjit Mitra

Computer Science Faculty Research & Creative Works

The proliferation of sensor-equipped smartphones has led to the generation of vast amounts of GPS data, such as timestamped location points, enabling a range of location-based services. However, deciphering the spatio-temporal dynamics of mobility to understand the underlying motivations behind travel patterns presents a significant challenge. his paper focuses on how individuals' GPS traces (latitude, longitude, timestamp) interpret the connection and correlations among different entities such as people, locations or point-of-interests (POIs), and semantic contexts (trip-purpose). We introduce a mobility analytics framework, named Mobilytics designed to identify trip purposes from individual GPS traces by leveraging a “mobility knowledge graph” (MKG) …

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Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li Jan 2024

Traffic Prediction-Based Vnf Auto-Scaling And Deployment Mechanism For Flexible And Elastic Service Provision, Bo Yi, Jiacheng Wang, Qiang He, Xingwei Wang, Min Huang, Sajal K. Das, Keqin Li

Computer Science Faculty Research & Creative Works

Network Function Virtualization (NFV) provides a flexible way to provision new services by decoupling network functions from hardware and implementing them as Virtual Network Functions (VNFs). However, the rapid development of technologies greatly promotes the explosion of diverse services, which directly results in the exponential increase of heterogeneous traffic. In addition, such a tremendous amount of heterogeneous traffic will generate bursts in a more dynamic and unexpected manner, so it becomes extremely hard to satisfy the customer demands. Aiming at addressing these challenges, this work proposes a positive and elastic VNF deployment mechanism for service provisioning, which introduces three novelties: …

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L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das Jan 2024

L3geocast: Enabling P4-Based Customizable Network-Layer Geocast At The Network Edge, Xindi Hou, Shuai Gao, Ningchun Liu, Fangtao Yao, Hongke Zhang, Sajal K. Das

Computer Science Faculty Research & Creative Works

Geocast is a one-to-many communication paradigm that enables the transmission of data packets to a designated area rather than an IP address. The most common geocast solutions rely on the application-layer Geolocation-to-IP database. But these IP-based approaches cannot cope with the challenges of flexibility and mobility in a granularity-customizable geocast scenario. While some non-IP network-layer (L3) attempts have resulted in low addressing accuracy and poor routing scalability. Besides, the clean-slate design is incompatible with the existing network. To address these issues, this article proposes an innovative network-layer geographic addressing scheme that leverages P4-based Software Defined Networks (SDN) to enable flexible …

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Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das Jan 2024

Federated Graph Anomaly Detection Via Contrastive Self-Supervised Learning, Xiangjie Kong, Wenyi Zhang, Hui Wang, Mingliang Hou, Xin Chen, Xiaoran Yan, Sajal K. Das

Computer Science Faculty Research & Creative Works

Attribute graph anomaly detection aims to identify nodes that significantly deviate from the majority of normal nodes and has received increasing attention due to the ubiquity and complexity of graph-structured data in various real-world scenarios. However, current mainstream anomaly detection methods are primarily designed for centralized settings, which may pose privacy leakage risks in certain sensitive situations. Although federated graph learning offers a promising solution by enabling collaborative model training in distributed systems while preserving data privacy, a practical challenge arises as each client typically possesses a limited amount of graph data. Consequently, naively applying federated graph learning directly to …

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Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio Jan 2024

Real-Time Analysis Of Encrypted Dns Traffic For Threat Detection, Marta Moure-Garrido, Sajal K. Das, Celeste Campo, Carlos Garcia-Rubio

Computer Science Faculty Research & Creative Works

Domain Name System (DNS) tunneling is a well-known cyber-attack that allows data exfiltration - the attackers exploit this tunnel to extract sensitive information from the system. Advanced Persistent Threat (APT) attackers encapsulate malicious traffic in a DNS connection to elude security mechanisms such as Intrusion Detection System (IDS). Although different techniques have been implemented to detect these targeted attacks, their rise induces a threat to Cyber-Physical Systems (CPS). The DNS over HTTPS (DoH) tunnel detection is a challenge because the encrypted data prevents an analysis of DNS traffic content. In this paper, we present a novel detection system that identifies …

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Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das Jan 2024

Secure Location-Based Authenticated Key Establishment Scheme For Maritime Communication, Saurabh Agrawal, Anusha Vangala, Ashok Kumar Das, Neeraj Kumar, Sachin Shetty, Sajal K. Das

Computer Science Faculty Research & Creative Works

Maritime communication helps vessels and ports plan their movements, exchange environmental information, and communicate among themselves. The vessels' movement and changing location are critical to keep them secure from data interception and data tampering by unauthorized parties during transmission. To secure maritime communication, we propose a novel lightweight authentication scheme sensitive to the current ship location. We assess the effectiveness of the proposed protocol in defending against a range of security threats while keeping communication and computation costs low and meeting the desired security and functional requirements of anonymity and untraceability. The detailed security analysis using the widely accepted Scyther …

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Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das Jan 2024

Approximation Algorithm For Connected Submodular Function Maximization Problems, Wenzheng Xu, He Xue, Jing Li, Weifa Liang, Zichuan Xu, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget K, the problem is to find a subset S with K nodes from a graph G so that a given submodular function f (S) on S is maximized while the induced subgraph G[S] by the nodes in S is connected, where the submodular function f can be used to model many practical application problems, such as the number of users within different service areas …

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Quantifying The Impact Of A Constructed Wetland On Downstream Nitrate Concentrations And Loads In The U.S. Midwest, Elliot Anderson, Keith E. Schilling, Craig L. Just, Bong Chul Seo Jan 2024

Quantifying The Impact Of A Constructed Wetland On Downstream Nitrate Concentrations And Loads In The U.S. Midwest, Elliot Anderson, Keith E. Schilling, Craig L. Just, Bong Chul Seo

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Constructed wetlands are standard conservation practices used to reduce nitrate loads in agricultural watersheds. Many studies have examined the efficiency of denitrification in wetlands under various scenarios, but quantifying the watershed-scale impact of wetlands on downstream nitrate levels is rarely done using field observations. In this study, we estimated nitrate removal in a constructed wetland in the headwaters of Mud Creek, a HUC12 watershed in eastern Iowa, from May–September 2022 and May–September 2023 (a ten-month period). We also measured nitrate loads at four successive downstream sites, three along Mud Creek and one below its confluence with the larger Cedar River. …

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Understanding Of Risky Determinants Affecting The Success Of Design-Build Airport Projects Using Computational Clustering Techniques, Ramy Khalef, Islam El-Adaway Jan 2024

Understanding Of Risky Determinants Affecting The Success Of Design-Build Airport Projects Using Computational Clustering Techniques, Ramy Khalef, Islam El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The current delivery practices of airport projects are traditionally outdated, lacking innovative and modern techniques which provide overall better cost, schedule, among other performance metrics. Design-build (DB) is an alternative and viable option for the effective delivery of airport projects. Previous studies have lacked in focusing on studying the determinants that affect the success of DB airport projects. This study aims to fill this gap. The goal of this research is to explore these determinants by computationally analyzing the perception of relevant experts. This goal is achieved by: (1) developing a survey to gather valid data on the likelihood of …

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Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das Jan 2024

Approximation Algorithm And Applications For Connected Submodular Function Maximization Problems, Ziming Wang, Jing Li, He Xue, Wenzheng Xu, Weifa Liang, Zichuan Xu, Jian Peng, Pan Zhou, Xiaohua Jia, Sajal K. Das

Computer Science Faculty Research & Creative Works

In this paper, we study a connected submodular function maximization problem, which arises from many applications including deploying UAV networks to serve users and placing sensors to cover Points of Interest (PoIs). Specifically, given a budget $K$ , the problem is to find a subset $S$ with $K$ nodes from a graph $G$ , so that a given submodular function $f(S)$ on $S$ is maximized and the induced subgraph $G[S]$ by the nodes in $S$ is connected, where the submodular function $f$ can be used to model many practical application problems, such as the number of users within different service …

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A Fuzzy Model And Decision-Support Tool For Assessing And Predicting The Probability Of Bankruptcy Of Construction Companies, Rayan H. Assaad, Ghiwa Assaf, Islam H. El-Adaway Jan 2024

A Fuzzy Model And Decision-Support Tool For Assessing And Predicting The Probability Of Bankruptcy Of Construction Companies, Rayan H. Assaad, Ghiwa Assaf, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction firms face considerable risks that might lead to business bankruptcy. Failed construction companies leave behind unfinished projects, which leads to huge losses to project owners. While previous studies were conducted to understand the factors that contribute to the bankruptcy of construction organizations, little to no research was performed to quantitatively assess the risk of construction business bankruptcy. Hence, this paper addresses this knowledge gap by developing a fuzzy model for predicting the probability of business bankruptcy of construction companies. First, the following six failure warning signs were considered: financial management system, borrowed credit, estimating and job-cost reporting, project management, …

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Risk-Reward Allocation Among Integrated Project Delivery Method Stakeholders: A Gamified Cooperative Data Simulation Approach, Radwa Eissa, Mohamad Abdul Nabi, Islam H. El-Adaway Jan 2024

Risk-Reward Allocation Among Integrated Project Delivery Method Stakeholders: A Gamified Cooperative Data Simulation Approach, Radwa Eissa, Mohamad Abdul Nabi, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Integrated project delivery (IPD) method is associated with numerous benefits, including enhanced project performance, collaboration, and information sharing among project stakeholders. However, the lack of adequate incentive and reward mechanisms is still considered as the main reason for slowing down the adoption of IPD. As such, the goal of this paper is to identify a fair and efficient risk pool distribution among IPD project stakeholders. The adopted methodology included (1) assigning the risks associated with each stakeholder, (2) computing valuations for all possible subset coalitions among IPD project stakeholders that reflect their risk control capabilities as well as their coordination …

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Predicting High-Impact Research In The Construction Engineering And Management Domain Using Computational Machine Learning, Islam H. El-Adaway, Gasser G. Ali, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef Jan 2024

Predicting High-Impact Research In The Construction Engineering And Management Domain Using Computational Machine Learning, Islam H. El-Adaway, Gasser G. Ali, Muaz O. Ahmed, Radwa Eissa, Mohamad Abdul Nabi, Tamima Elbashbishy, Ramy Khalef

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Construction engineering and management (CEM) domain covers multiple evolving sub-disciplines and directions. To understand those directions, numerous research efforts have conducted scientometric analysis of publications to explore their significance and impact. Despite their valuable insights, such research endeavors have been limited to exploratory analyses, whereas no previous attempt has been conducted to predict the future impact of research in CEM. This study fills this knowledge gap by performing a predictive analysis of citation metrics of publications in CEM using machine learning (ML). The developed model was trained on a dataset of 93,868 publications using an XGBoost algorithm. Testing showed that …

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Agent-Based Modeling For Understanding Incentives Associated With Distributed Solar Generation, Gasser Ali, Islam H. El-Adaway Jan 2024

Agent-Based Modeling For Understanding Incentives Associated With Distributed Solar Generation, Gasser Ali, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Distributed solar generation (DSG), such as residential photo-voltaic (PV) solar panels, offers many benefits to consumers and can improve the sustainability and resilience of the electric power infrastructure. As such, many local and national authorities implemented policy incentives, such as rebates and loans with reduced interests, to promote the adoption of DSG. However, the increasing penetration of DSG is creating new obstacles for system operators due to the uncertainty in forecasting electrical power demand. There is a need to investigate how incentives affect DSG adoption. To achieve that goal, a complex system-of-systems (SoS) model is developed using agent-based modeling (ABM). …

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An Agent-Based Cellular Automaton Simulation Model To Study Worker Safety Behavior On Construction Sites: The Impacts Of Different Social Influence Rules, Rayan H. Assaad, Mohamad Abdul Nabi, Ghiwa Assaf, Islam H. El-Adaway Jan 2024

An Agent-Based Cellular Automaton Simulation Model To Study Worker Safety Behavior On Construction Sites: The Impacts Of Different Social Influence Rules, Rayan H. Assaad, Mohamad Abdul Nabi, Ghiwa Assaf, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The unsafe behavior of the construction workforce is one of the leading causes of accidents in the construction industry. It has been shown that construction laborers perceive the safety behavior of their co-workers and adjust their personal safety attitudes accordingly. Thus, there has been an increasing attention to studying the impacts of the social influence and norms on the safety behavior of construction workers. While existing studies examined the factors that influence the safety behavior of the construction workforce, there is little-to-no research that specifically focused on modeling the role of social influence on the safety behavior of the workers. …

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A Contractual Approach To Resolving Disputes Arising From Failure To Achieve Green Certification, Bahaa Chammout, Islam H. El-Adaway Jan 2024

A Contractual Approach To Resolving Disputes Arising From Failure To Achieve Green Certification, Bahaa Chammout, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Green Construction Is An Approach To Reducing The Environmental Impact Of Resource-Intensive Construction. Several Certification Systems Have Been Devised And Are Globally Recognized To Provide Green Building Rating Frameworks. While A Building Can Be Designed To Achieve A Green Rating, The Rating Is Achieved Based On The Completed Facility's Actual Measured Performance. There Had Previously Been Limited Attempts To Give An In-Depth Examination Of The Remedies For Unrealized Green Ratings. As Such, This Paper Addresses This Knowledge Gap Using A Multistep Research Methodology. First, The Authors Analyzed And Classified The Nature Of Damages Resulting From Failed Green Certification Under The …

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Assessing The Impact Of Skilled Labor Shortages On Project Cost Performance, Tamima S. Elbashbishy, Islam H. El-Adaway Jan 2024

Assessing The Impact Of Skilled Labor Shortages On Project Cost Performance, Tamima S. Elbashbishy, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Over The Past Decade, There Has Been A Notable Increase In Skilled Labor Shortages Within The Construction Industry. This Shortage Has Numerous Negative Consequences On Construction Projects, Such As Delays In Completion, Compromised Quality, And Higher Costs. Therefore, The Goal Of This Paper Is To Examine The Extent Of Skilled Labor Shortages Observed Throughout The Industry And Their Impact On The Cost Performance Of Construction Projects. To Achieve This Goal, The Authors Identified 10 Key Construction Trades That Heavily Rely On Skilled Labor. Subsequently, An Industry Survey Was Conducted To Measure The Extent Of Skilled Labor Shortages, As Well As …

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Predicting Construction Costs Under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based On Deepar, Ghiwa Assaf, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi Jan 2024

Predicting Construction Costs Under Uncertain Market Conditions: Probabilistic Forecasting Using Autoregressive Recurrent Networks Based On Deepar, Ghiwa Assaf, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Projects Often Experience Cost Overruns Due To Market Uncertainty And Price Escalations. Traditional Cost Estimation Methods That Rely On Point Estimation Are Incapable Of Providing Prediction Intervals As Well As Probabilistic Assessment. Thus, There Is Need For An Innovative Approach To Predict The Changes And Uncertainties In Construction Material Costs. This Paper Proposes A Novel Stochastic Model To Estimate Construction Material Costs By Applying Probabilistic Forecasting Using Autoregressive Recurrent Networks. First, Price Data Was Collected For Four Different Construction Materials. Second, Data Was Divided Into A Training Set (Pre-COVID-19) And A Testing Test (Post-COVID-19). Third, The State-Of-The-Art DeepAR Algorithm Was …

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Exploring The Influences On Construction Bidding Decisions: Insights From Literature And Industry Experts, Muaz O. Ahmed, Islam H. El-Adaway Jan 2024

Exploring The Influences On Construction Bidding Decisions: Insights From Literature And Industry Experts, Muaz O. Ahmed, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Decision-Making In Construction Bidding Is One Of The Complicated Processes. In Determining Related Decisions, Contractors Weigh Various Bidding Factors To Assess The Expected Benefits Of A Construction Project. Various Studies Have Identified Factors That Impact Construction Bidding-Related Decisions. However, There Is A Lack Of Research That Examines The Alignment Between Industry And Literature As Related To The Perception Of The Importance Of Bidding Factors On Bidding Decisions. This Paper Fills This Area Of Research Need. First, The Authors Performed A Content Analysis Of 124 Construction Bidding-Related Journal Papers, And Accordingly, 43 Bidding Factors Were Identified And Mapped With Analyzed Journal …

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An Accurate-Pricing Estimate Game-Theoretic Model For Determining Price Escalations In Construction Projects During Economic Uncertainties, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi Jan 2024

An Accurate-Pricing Estimate Game-Theoretic Model For Determining Price Escalations In Construction Projects During Economic Uncertainties, Yasser Jezzini, Rayan H. Assaad, Islam H. El-Adaway, Mohamad Abdul Nabi

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Economic Market Uncertainties, Such As Those Experienced During The COVID-19 Pandemic, Can Make Determining Accurate Prices Estimate For Construction Materials A Challenging Task. While Previous Research Focused On The Contractual Aspect Of This Issue By Studying Price Escalation Clauses, There Is Still A Gap In The Literature When It Comes To Proposing An Accurate Pricing Model. Thus, This Study Develops An Accurate Pricing-Estimate Game-Theoretical Model That Can Efficiently And Competitively Account For Escalations In Construction Materials Prices During Uncertain Market Conditions. First, Data On Past Producer Price Indexes (PPIs) Of Different Construction Materials Were Collected. Second, The Percentage Changes In …

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Circular Economy Strategies For Decarbonization Of The Built Environment Value Chain Scope 3 Emissions: A Network Analysis, Radwa Eissa, Islam H. El-Adaway Jan 2024

Circular Economy Strategies For Decarbonization Of The Built Environment Value Chain Scope 3 Emissions: A Network Analysis, Radwa Eissa, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

With The Anticipated Growth In Construction Activities, Minimizing Value Chain Emissions, Including Those Attributed To Activities Beyond An Organization's Direct Control - Aka "Scope 3" Emissions, And Adopting Circular Economy (CE) Practices Have Become Critical To Attaining The 2050 Climate Goals. Extant Literature Lacks A Value-Chain-Comprehensive Set Of CE Strategies On One Hand, And An Analysis Of Their Actual Project Applications On The Other. This Research Aims To Build A Holistic Portfolio Of CE Actionable Strategies That Span Across The Entire Built Environment Value Chain, Analyzing Their Extent Of Implementation And Interlinkages Within The Literature Versus Actual Projects. To This …

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Achieving Project Objectives And Improving Functions: The Benefits Of Ai And Construction Technologies, Fareed Salih, Islam H. El-Adaway Jan 2024

Achieving Project Objectives And Improving Functions: The Benefits Of Ai And Construction Technologies, Fareed Salih, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

The Construction Industry Is Increasingly Incorporating Artificial Intelligence (AI) And Construction Technologies Into Projects. However, It Lags Behind Other Industries In Terms Of Digital Transformation. One Of The Reasons For This Disparity Is The Lack Of Evidence-Based Information On AI And Technologies In Construction Projects. Hence, This Research Aims To Comprehensively Understand The Benefits Of AI Techniques And Construction Technologies And Their Role In Achieving Objectives And Improving Functions. To This End, The Authors Followed A Three-Step Research Methodology. Firstly, An Extensive Literature Review Was Conducted To Identify 4 AI Techniques, 13 Construction Technologies, And 28 Benefits Relevant To Their …

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A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das Jan 2024

A Hypergraph Approach To Deep Learning Based Routing In Software-Defined Vehicular Networks, Ankur Nahar, Nishit Bhardwaj, Debasis Das, Sajal K. Das

Computer Science Faculty Research & Creative Works

Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-Based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-Based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements …

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Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar Jan 2024

Persistent Monitoring Of Insect-Pests On Sticky Traps Through Hierarchical Transfer Learning And Slicing-Aided Hyper Inference, Fateme Fotouhi, Kevin Menke, Aaron Prestholt, Ashish Gupta, Matthew E. Carroll, Hsin Jung Yang, Edwin J. Skidmore, Matthew O'Neal, Nirav Merchant, Sajal K. Das, Peter Kyveryga, Baskar Ganapathysubramanian, Asheesh K. Singh, Arti Singh, Soumik Sarkar

Computer Science Faculty Research & Creative Works

Introduction: Effective monitoring of insect-pests is vital for safeguarding agricultural yields and ensuring food security. Recent advances in computer vision and machine learning have opened up significant possibilities of automated persistent monitoring of insect-pests through reliable detection and counting of insects in setups such as yellow sticky traps. However, this task is fraught with complexities, encompassing challenges such as, laborious dataset annotation, recognizing small insect-pests in low-resolution or distant images, and the intricate variations across insect-pests life stages and species classes. Methods: to tackle these obstacles, this work investigates combining two solutions, Hierarchical Transfer Learning (HTL) and Slicing-Aided Hyper Inference …

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Geochemical Assessment Of Mineral Sequestration Of Carbon Dioxide In The Midcontinent Rift, Alsedik Abousif, David J. Wronkiewicz, Abdelmoniem Masoud Jan 2024

Geochemical Assessment Of Mineral Sequestration Of Carbon Dioxide In The Midcontinent Rift, Alsedik Abousif, David J. Wronkiewicz, Abdelmoniem Masoud

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This Study Examines The Potential Of Midcontinent Rift Rocks To Facilitate Long-Term CO2 Sequestration By Providing The Necessary Ca And Mg For Carbonate Mineralization. Surface Samples Were Collected From The Oronto And Bayfield-Jacobsville Groups Around Lake Superior And Used For Petrography And X-Ray Diffraction To Determine Their Mineral Composition. Also, X-Ray Fluorescence Was Also Used To Assess Their Bulk Chemical Composition. The Samples Were Then Exposed To CO2 And Deionized Water In Teflon-Lined Vessels At 90°C, And The Resulting Leachate Fluids Were Analyzed For The Cation Released During The Testing. SEM Microscopy Was Used To Examine The Samples For Potential …

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Prediction Of Surfactant Performance From Surfactant Huff-Puff In Carbonate Reservoirs Using A Data-Driven Approach And Desktop Application Development, Y. Yao, Mingzhen Wei, Y. Cui, M. Ali, J. Leng, Y. Qiu Jan 2024

Prediction Of Surfactant Performance From Surfactant Huff-Puff In Carbonate Reservoirs Using A Data-Driven Approach And Desktop Application Development, Y. Yao, Mingzhen Wei, Y. Cui, M. Ali, J. Leng, Y. Qiu

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Surfactants have been widely used to alter the wettability of carbonate rocks from oil-wetness to water-wetness and enhanced oil recovery. One of primary methods implemented in filed applications for enhanced oil recovery is surfactant huff-puff. Currently, a large number of surfactant treatments are conducted in laboratories prior to field applications to optimize the design of surfactant huff-puff, test the performance of surfactants, and minimize failure risk. This process is time-consuming since a treatment could last from several days to more than 300 days. Moreover, a fraction of treatments could not improve oil recovery as reported in the literature. In this …

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Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao Jan 2024

Descriptive Statistical Analysis Of Experimental Data For Wettability Alteration With Smart Water Flooding In Carbonate Reservoirs, Muhammad Ali Buriro, Mingzhen Wei, Baojun Bai, Ya Yao

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

Smart water flooding is a promising eco-friendly method for enhancing oil recovery in carbonate reservoirs. The optimal salinity and ionic composition of the injected water play a critical role in the success of this method. This study advances the field by employing machine learning and data analytics to streamline the determination of these critical parameters, which are traditionally reliant on time-intensive laboratory work. The primary objectives are to utilize data analytics to examine how smart water flooding influences wettability modification, identify key parameter ranges that notably alter the contact angle, and formulate guidelines and screening criteria for successful lab design. …

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Lessons Learned From Laboratory Study And Field Application Of Re-Crosslinkable Preformed Particle Gels Rppg For Conformance Control In Mature Oilfields With Conduits/Fractures/Fracture-Like Channels, Baojun Bai, Thomas P. Schuman, David Smith, Tao Song Jan 2024

Lessons Learned From Laboratory Study And Field Application Of Re-Crosslinkable Preformed Particle Gels Rppg For Conformance Control In Mature Oilfields With Conduits/Fractures/Fracture-Like Channels, Baojun Bai, Thomas P. Schuman, David Smith, Tao Song

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

This Paper Surveys the Role of Re-Crosslink able Preformed Particle Gels (RPPG) in Addressing Conformance Challenges within Mature Oilfields. Despite Widespread Preformed Particle Gel (PPG) Application in 15,000+ Wells, their Limitations in Sealing Fractures and Conduits Prevalent in Mature Reservoirs Have Driven the Development of RPPG Formulations. Synthesized in Various Sizes from Micrometer to Millimeter Levels, These Environmentally Friendly RPPGs Are Tailored for Diverse Reservoir Conditions. Findings Showcase the Successful Laboratory-Scale Creation and Upscaling of RPPG Products, Offering Adaptability to Temperatures from 20 to 175°C, Customizable Sizes, Swelling Ratios (5 to 40 Times), and Re-Crosslinking Times Spanning Minutes to Days. …

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