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2024

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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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Quantifying The Role Of Insurance In Tornado-Impacted Community Recovery: A Survey And Simulation-Based Approach, Jie Zhao, Ji Yun Lee, Guirong Grace Yan, Fangjiao Ma Jan 2024

Quantifying The Role Of Insurance In Tornado-Impacted Community Recovery: A Survey And Simulation-Based Approach, Jie Zhao, Ji Yun Lee, Guirong Grace Yan, Fangjiao Ma

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Insurance against disasters plays a critical role in community recovery by providing policyholders with reliable and timely payments for repairing or reconstructing damaged houses. By allowing homeowners to transfer risk, insurance enables homeowners to address house without experiencing significant financial burdens. Although historical events have highlighted the importance of insurance, its quantitative impact on community recovery, particularly in tornado-impacted communities, is understudied. This study focuses on advancing our understanding of whether sufficiently insured houses can have a positive impact on the recovery of tornado-impacted communities (i.e., the main research question). This paper proposes a two-stage simulation framework to quantitatively evaluate …

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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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Distributed Solar Generation: Data Analytics Of The Existing Literature For Guiding Future Prospects, Gasser Ali, Islam H. El-Adaway Jan 2024

Distributed Solar Generation: Data Analytics Of The Existing Literature For Guiding Future Prospects, Gasser Ali, Islam H. El-Adaway

Civil, Architectural and Environmental Engineering Faculty Research & Creative Works

Distributed Solar Generation (DSG) Systems Are Small-Scale Units That Are Located At Or Near End-Consumers Such As Residential Rooftop Photo-Voltaic (PV) Systems. DSG Has Driven A Plethora Of Multidisciplinary Research With Various Interrelated Topics And Perspectives. A Broad Understanding Of Research Directions And Potential Gaps In Knowledge Can Be Elusive. Accordingly, The Goal Of This Paper Is To Investigate The Research Trends Related To DSG And Explore Research Opportunities. This Is Achieved By Performing A Data-Driven Keyword Search And Network Analysis On A Large Dataset Of Publications Related To DSG To Quantify Topics Related To DSG And The Interconnectivity Between …

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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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Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan Jan 2024

Protecting Activity Sensing Data Privacy Using Hierarchical Information Dissociation, Guangjing Wang, Hanqing Guo, Yuanda Wang, Bocheng Chen, Ce Zhou, Qiben Yan

Computer Science Faculty Research & Creative Works

Smartphones and wearable devices have been integrated into our daily lives, offering personalized services. However, many apps become overprivileged as their collected sensing data contains unnecessary sensitive information. For example, mobile sensing data could reveal private attributes (e.g., gender and age) and unintended sensitive features (e.g., hand gestures when entering passwords). To prevent sensitive information leakage, existing methods must obtain private labels and users need to specify privacy policies. However, they only achieve limited control over information disclosure. In this work, we present Hippo to dissociate hierarchical information including private metadata and multi-grained activity information from the sensing data. Hippo …

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A Comprehensive Survey On Pretrained Foundation Models: A History From Bert To Chatgpt, Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun Jan 2024

A Comprehensive Survey On Pretrained Foundation Models: A History From Bert To Chatgpt, Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun

Computer Science Faculty Research & Creative Works

Pretrained Foundation Models (PFMs) are regarded as the foundation for various downstream tasks across different data modalities. A PFM (e.g., BERT, ChatGPT, GPT-4) is trained on large-scale data, providing a solid parameter initialization for a wide range of downstream applications. In contrast to earlier methods that use convolution and recurrent modules for feature extraction, BERT learns bidirectional encoder representations from Transformers, trained on large datasets as contextual language models. Similarly, the Generative Pretrained Transformer (GPT) method employs Transformers as feature extractors and is trained on large datasets using an autoregressive paradigm. Recently, ChatGPT has demonstrated significant success in large language …

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Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes Jan 2024

Interlude: Interactions Between Labeled And Unlabeled Data To Enhance Semi-Supervised Learning, Zhe Huang, Xiaowei Yu, Dajiang Zhu, Michael C. Hughes

Computer Science Faculty Research & Creative Works

Semi-supervised learning (SSL) seeks to enhance task performance by training on both labeled and unlabeled data. Mainstream SSL image classification methods mostly optimize a loss that additively combines a supervised classification objective with a regularization term derived solely from unlabeled data. This formulation often neglects the potential for interaction between labeled and unlabeled images. In this paper, we introduce InterLUDE, a new approach to enhance SSL made of two parts that each benefit from labeled-unlabeled interaction. The first part, embedding fusion, interpolates between labeled and unlabeled embeddings to improve representation learning. The second part is a new loss, grounded in …

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Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu Jan 2024

Core-Periphery Multi-Modality Feature Alignment For Zero-Shot Medical Image Analysis, Xiaowei Yu, Lu Zhang, Zihao Wu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Multi-modality learning, exemplified by the language-image pair pre-trained CLIP model, has demonstrated remarkable performance in enhancing zero-shot capabilities and has gained significant attention recently. However, simply applying language-image pre-trained CLIP to medical image analysis encounters substantial domain shifts, resulting in severe performance degradation due to inherent disparities between natural (non-medical) and medical image characteristics. To address this challenge and uphold or even enhance CLIP's zero-shot capability in medical image analysis, we develop a novel approach, Core-Periphery feature alignment for CLIP (CP-CLIP), to model medical images and corresponding clinical text jointly. To achieve this, we design an auxiliary neural network whose …

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Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu Jan 2024

Enhancing Group-Wise Consistency In 3-Hinge Gyrus Matching Via Anatomical Embedding And Structural Connectivity Optimization, Chao Cao, Xiaowei Yu, Lu Zhang, Tong Chen, Yanjun Lyu, Tianming Liu, Dajiang Zhu

Computer Science Faculty Research & Creative Works

Recently, a novel cortical folding pattern known as the 3-hinge gyrus (3HG) has been identified. 3HGs are defined as the convergence of the gyri coming from three distinct directions on gyral crests. In contrast to cortical regions, 3HGs are defined at a finer scale and they widely exist across different individuals, representing both commonalities and individualities of cortical folding patterns. It is important to note that 3HGs are identified in individual spaces, lacking natural cross-subject correspondences. To address this issue, we have developed a learning-based method to encode anatomical features of 3HGs into a set of embedding vectors that can …

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Eye-Gaze Guided Multi-Modal Alignment For Medical Representation Learning, Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li Jan 2024

Eye-Gaze Guided Multi-Modal Alignment For Medical Representation Learning, Chong Ma, Hanqi Jiang, Wenting Chen, Yiwei Li, Zihao Wu, Xiaowei Yu, Zhengliang Liu, Lei Guo, Dajiang Zhu, Tuo Zhang, Dinggang Shen, Tianming Liu, Xiang Li

Computer Science Faculty Research & Creative Works

In the medical multi-modal frameworks, the alignment of cross-modality features presents a significant challenge. However, existing works have learned features that are implicitly aligned from the data, without considering the explicit relationships in the medical context. This data-reliance may lead to low generalization of the learned alignment relationships. In this work, we propose the Eye-gaze Guided Multi-modal Alignment (EGMA) framework to harness eye-gaze data for better alignment of medical visual and textual features. We explore the natural auxiliary role of radiologists' eye-gaze data in aligning medical images and text and introduce a novel approach by using eye-gaze data, collected synchronously …

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Evaluation Of Novel Preformed Particle Gel System For Conformance Control In Mature Oil Reservoirs, Abdulaziz Almakimi, Ahmed Ben Ali, Ibnelwaleed A. Hussein, Baojun Bai Jan 2024

Evaluation Of Novel Preformed Particle Gel System For Conformance Control In Mature Oil Reservoirs, Abdulaziz Almakimi, Ahmed Ben Ali, Ibnelwaleed A. Hussein, Baojun Bai

Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works

To address challenges associated with excessive water production in mature oil reservoirs, this study introduces a carboxymethyl cellulose (CMC)-based material as a novel preformed particle gel (PPG) designed to plug excessive water pathways and redistribute the subsequent injected water toward unswept zones. Through microwave-assisted grafting copolymerization of CMC with acrylamide (AM), we successfully generated multi-sized dry particles within the range of 250–800 µm. Comprehensive analyses, including Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM), have confirmed the chemical composition and morphology of the resulting carboxymethyl cellulose-grafted crosslinked polyacrylamide (CMC/PAMBA). Swelling kinetics and rheology tests were conducted to confirm the …

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Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu Jan 2024

Adversarial Hidden Link Threats In Meta Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

In the emerging field of Meta Computing, where data collection and integration are essential components, the threat of adversary hidden link attacks poses a significant challenge to web crawlers. In this paper, we investigate the impact of these attacks on data collection by web crawlers, emphasizing their evasion of traditional detection methods. Through empirical evaluation, we uncover vulnerabilities in existing crawler mechanisms, particularly in code inspection, and propose enhancements to mitigate these weaknesses. Our assessment of real-world web pages reveals the prevalence and impact of adversary hidden link attacks, emphasizing the necessity for robust countermeasures. Furthermore, we introduce a mitigation …

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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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Warmonger Attack: A Novel Attack Vector In Serverless Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu Jan 2024

Warmonger Attack: A Novel Attack Vector In Serverless Computing, Junjie Xiong, Mingkui Wei, Zhuo Lu, Yao Liu

Computer Science Faculty Research & Creative Works

We debut the Warmonger attack, a novel attack vector that can cause denial-of-service between a serverless computing platform and an external content server. The Warmonger attack exploits the fact that a serverless computing platform shares the same set of egress IPs among all serverless functions, which belong to different users, to access an external content server. As a result, a malicious user on this platform can purposefully misbehave and cause these egress IPs to be blocked by the content server, resulting in a platform-wide denial of service. To validate the effectiveness of the Warmonger attack, we conducted extensive experiments over …

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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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